names
stringlengths 1
98
| readmes
stringlengths 8
608k
| topics
stringlengths 0
442
| labels
stringclasses 6
values |
|---|---|---|---|
STM32F4-ChibiOS | stm32f4 chibios chibios rt examples running on stm32f4 discovery board required tools gnu arm toolchain https launchpad net gcc arm embedded cross compiler chibios rt https github com mabl chibios real time operating system st link https github com texane stlink programmer notes st flash usage from a project directory st flash write build ch bin 0x08000000 st flash arguments for eclipse write selected resource loc build ch bin 0x08000000 todo i2c code test shell over serial line example adc example dac example | arm chibios rtos stm32f4 | os |
AirSENSE-ESP32-NonOS-Demo | airsense esp32 rtos we are using freertos because nonos is very stupid | os |
|
14-Segment-Display | 14 segment display this is an embedded systems project designed based on the atmega128 micro controller showingthe 14 segment display | os |
|
millionservetech | millionservetech technology related information | server |
|
BIDMach | bidmach is a very fast machine learning library check the latest b a href https github com biddata bidmach wiki benchmarks benchmarks a b the github distribution contains source code only you also need a jdk 8 an installation of nvidia cuda 8 0 if you want to use a gpu and cudnn 5 if you plan to use deep networks for building you need a href https maven apache org docs history html maven 3 x a after doing code git clone code cd to the bidmach directory and build and install the jars with code mvn install code you can then run bidmach with bidmach more details on installing and running are available b a href https github com biddata bidmach wiki installing and running here a b the main project page is b a href http bid2 berkeley edu bid data project here a b documentation is b a href https github com biddata bidmach wiki here in the wiki a b b new b bidmach has a b a href https groups google com forum forum bidmach users group discussion group a b on google groups bidmach is a sister project of bidmat a matrix library which is b a href https github com biddata bidmat also on github a b biddata also has a project for deep reinforcement learning b a href https github com biddata bidmach rl bidmach rl a b contains state of the art implementations of several reinforcement learning algorithms | ai |
|
GEC-Reading-List | gec reading list a grammatical error correction reading list maintained by beijing language and culture university natural language processing group shared task shared task dataset dataset evaluation evaluation smt nmt smt nmt nmt language model pretrain other other algorithm algorithm bea2019 bea labeling tagging na spelling check spell ged ged h2 id shared task shared task h2 dale robert and kilgarriff adam 2011 helping our own the hoo 2011 pilot shared task https dl acm org citation cfm id 2187725 in proceedings of the 13th european workshop on natural language generation dale robert anisimoff ilya and narroway george 2012 hoo 2012 a report on the preposition and determiner error correction shared task https dl acm org citation cfm id 2390390 in proceedings of the seventh workshop on building educational applications using nlp ng hwee tou wu siew mei wu yuanbin hadiwinoto christian and tetreault joel 2013 the conll 2013 shared task on grammatical error correction https www aclweb org anthology w13 3601 in proceedings of the seventeenth conference on computational natural language learning shared task ng hwee tou wu siew mei briscoe ted hadiwinoto christian susanto raymond hendy and bryant christopher 2014 the conll 2014 shared task on grammatical error correction https www aclweb org anthology w14 1701 in proceedings of the eighteenth conference on computational natural language learning shared task christopher bryant mariano felice istein andersen and ted briscoe 2019 building educational applications 2019 shared task grammatical error correction https www aclweb org anthology w19 4406 h2 id dataset dataset h2 mizumoto tomoya komachi mamoru nagata masaaki and matsumoto yuji 2011 mining revision log of language learning sns for automated japanese error correction of second language learners https www aclweb org anthology i11 1017 in proceedings of 5th international joint conference on natural language processing lang 8 dahlmeier daniel ng hwee tou and wu siew mei 2013 building a large annotated corpus of learner english the nus corpus of learner english https www aclweb org anthology w13 1703 in proceedings of the eighth workshop on innovative use of nlp for building educational applications nucle napoles courtney sakaguchi keisuke and tetreault joel 2017 jfleg a fluency corpus and benchmark for grammatical error correction https arxiv org pdf 1702 04066 pdf jfleg yannakoudakis helen briscoe ted and medlock ben 2011 a new dataset and method for automatically grading esol texts https dl acm org citation cfm id 2002496 in proceedings of the 49th annual meeting of the association for computational linguistics human language technologies fce yannakoudakis helen andersen istein e geranpayeh ardeshir briscoe ted and nicholls diane 2018 developing an automated writing placement system for esl learners https www tandfonline com doi abs 10 1080 08957347 2018 1464447 applied measurement in education w i sylviane granger 1998 the computer learner corpus a versatile new source of data for sla research https www researchgate net profile sylviane granger publication 237128463 the computer learner corpus a versatile new source of data for sla research links 0c96051dc1c596def1000000 the computer learner corpus a versatile new source of data for sla research pdf learner english on computer locness napoles c n dejde m tetreault j 2019 enabling robust grammatical error correction in new domains data sets metrics and analyses https transacl org ojs index php tacl article view 1677 transactions of the association for computational linguistics 2019 rozovskaya a roth d 2019 grammar error correction in morphologically rich languages the case of russian https transacl org index php tacl article view 1454 transactions of the association for computational linguistics 2019 flachs s lacroix o yannakoudakis h et al 2020 grammatical error correction in low error density domains a new benchmark and analyses https arxiv org abs 2010 07574 emnlp 2020 h2 id evaluation evaluation h2 dahlmeier daniel and ng hwee tou 2012 better evaluation for grammatical error correction https dl acm org citation cfm id 2382118 in proceedings of the 2012 conference of the north american chapter of the association for computational linguistics human language technologies maxmatch m2 napoles courtney sakaguchi keisuke post matt and tetreault joel 2015 ground truth for grammatical error correction metrics https www aclweb org anthology p15 2097 in proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing volume 2 short papers gleu bryant christopher felice mariano briscoe edward john 2017 automatic annotation and evaluation of error types for grammatical error correction https www aclweb org anthology p17 1074 in proceedings of the 55th annual meeting of the association for computational linguistics volume 1 long papers errant snover matthew bonnie j dorr richard m schwartz linnea micciulla and ralph m weischedel 2005 a study of translation error rate with targeted human annotation https pdfs semanticscholar org a589 9f1ec92af7d01f35225161430116a6eabbea pdf in proceedings of computer science ter h2 id smt smt nmt h2 grundkiewicz roman junczys dowmunt marcinnear 2018 human level performance in grammatical error correction with hybrid machine translation https www aclweb org anthology n18 2046 in proceedings of the 2018 conference of the north american chapter of the association for computational linguistics human language technologies volume 2 short papers chollampatt shamil ng hwee tou 2017 connecting the dots towards human level grammatical error correction https www aclweb org anthology w17 5037 in proceedings of the 12th workshop on innovative use of nlp for building educational applications chollampatt shamil hoang duc tam ng hwee tou 2016 adapting grammatical error correction based on the native language of writers with neural network joint models https www aclweb org anthology d16 1195 in proceedings of the 2016 conference on empirical methods in natural language processing h2 id nmt nmt h2 gotou t nagata r mita m et al 2020 taking the correction difficulty into account in grammatical error correction evaluation https www aclweb org anthology 2020 coling main 188 proceedings of the 28th international conference on computational linguistics hotate k kaneko m komachi m 2020 generating diverse corrections with local beam search for grammatical error correction https www aclweb org anthology 2020 coling main 193 proceedings of the 28th international conference on computational linguistics yoshimura r kaneko m kajiwara t et al 2020 some reference less sub metrics optimized for manual evaluations of grammatical error correction https www aclweb org anthology 2020 coling main 573 proceedings of the 28th international conference on computational linguistics yamashita i katsumata s kaneko m et al 2020 cross lingual transfer learning for grammatical error correction https www aclweb org anthology 2020 coling main 415 proceedings of the 28th international conference on computational linguistics wan z wan x wang w 2020 improving grammatical error correction with data augmentation by editing latent representation https www aclweb org anthology 2020 coling main 200 proceedings of the 28th international conference on computational linguistics hinson c huang h h chen h h 2020 heterogeneous recycle generation for chinese grammatical error correction https www aclweb org anthology 2020 coling main 199 proceedings of the 28th international conference on computational linguistics caines a bentz c knill k et al 2020 grammatical error detection in transcriptions of spoken english https www aclweb org anthology 2020 coling main 195 proceedings of the 28th international conference on computational linguistics zhao wei wang liang shen kewei jia ruoyu liu jingming 2019 improving grammatical error correction via pre training a copy augmented architecture with unlabeled data https arxiv org pdf 1903 00138 pdf junczys dowmunt marcin grundkiewicz roman guha shubha heafield kenneth 2018 approaching neural grammatical error correction as a low resource machine translation task https www aclweb org anthology n18 1055 in proceedings of the 2018 conference of the north american chapter of the association for computational linguistics human language technologies volume 1 long papers chollampatt shamil ng hwee tou 2018 neural quality estimation of grammatical error correction https www aclweb org anthology d18 1274 in proceedings of the 2018 conference on empirical methods in natural language processing chollampatt shamil ng hwee tou 2018 a multilayer convolutional encoder decoder neural network for grammatical error correction https www aaai org ocs index php aaai aaai18 paper view 17308 16137 in thirty second aaai conference on artificial intelligence kasewa sudhanshu stenetorp pontus riedel sebastian 2018 wronging a right generating better errors to improve grammatical error detection https www aclweb org anthology d18 1541 in proceedings of the 2018 conference on empirical methods in natural language processing jared lichtarge christopher alberti shankar kumar noam shazeer niki parmar google ai 2018 weakly supervised grammatical error correction using iterative decoding https arxiv org abs 1811 01710 arxiv adriane boyd 2018 using wikipedia edits in low resource grammatical error correction https www aclweb org anthology w18 6111 in proceedings ofthe 2018 emnlp workshop w nut the 4th workshop on noisy user generated text pages 79 84 brussels keisuke sakaguchi matt post benjamin van durme 2017 grammatical error correction with neural reinforcement learning https www aclweb org anthology i17 2062 in proceedings of the eighth international joint conference on natural language processing volume 2 short papers xie ziang avati anand arivazhagan naveen jurafsky dan ng andrew y 2016 neural language correction with character based attention https arxiv org pdf 1603 09727 pdf arxiv lm in beam search h2 id pretrain language model h2 kenneth heafield 2011 kenlm faster and smaller language model queries https www aclweb org anthology w11 2123 in proceedings of the sixth workshop on statistical machine translation wmt 11 pages 187 197 stroudsburg usa association for computational linguistics kenlm pascal vincent hugo larochelle yoshua bengio and pierre antoine manzagol 2008 corpora generation for grammatical error correction http delivery acm org 10 1145 1400000 1390294 p1096 vincent pdf ip 123 127 77 18 id 1390294 acc active 20service key bf85bba5741fdc6e 2e3079bb709085a683 2e4d4702b0c3e38b35 2e4d4702b0c3e38b35 acm 1568555058 da2bb710b2c14749225dc84cdd8b2136 in proceedings of the 25th international conference on machine learning pages 1096 1103 acm dae jianshu ji qinlong wang kristina toutanova yongengong steven truong and jianfeng gao 2017 a nested attention neural hybrid model for grammatical error correction https www aclweb org anthology p17 1070 pdf in proceedings of the 55th annual meeting of the association for computationallinguistics association for computational linguistics pages 753 762 lm in rerank masahiro kaneko masato mita shun kiyono jun suzuki kentaro inui 2020 encoder decoder models can benefit from pre trained masked language models in grammatical error correction https arxiv org pdf 2005 00987 pdf in proceedings of the 58th annual meeting of the association for computational linguistics pages 4248 4254 july 5 10 2020 c 2020 association for computational linguistics bert fuse hongfei wang michiki kurosawa satoru katsumata and mamoru komachi 2020 chinese grammatical correction using bert based pre trained model https arxiv org pdf 2011 02093 pdf in proceedings of aacl ijcnlp 2020 bert chinese fan yin quanyu long tao meng and kai wei chang 2020 on the robustness of language encoders against grammatical errors https arxiv org pdf 2005 05683 pdf in proceedings of the 58th annual meeting of the association for computational linguistics pages 3386 3403 july 5 10 2020 c 2020 association for computational linguistics bert analysis satoru katsumata mamoru komachi 2020 stronger baselines for grammatical error correction using a pretrained encoder decoder model https arxiv org pdf 2005 11849 pdf in proceedings of aacl ijcnlp 2020 bart mbart h2 id other other h2 lichtarge jared alberti chris kumar shankar shazeer noam parmar niki tong simon 2019 corpora generation for grammatical error correction https arxiv org pdf 1904 05780 pdf arxiv ge tao wei furu zhou ming 2018 fluency boost learning and inference for neural grammatical error correction https www aclweb org anthology p18 1097 in proceedings of the 56th annual meeting of the association for computational linguistics volume 1 long papers bryant christopher briscoe ted 2018 language model based grammatical error correction without annotated training data https www aclweb org anthology w18 0529 in proceedings of the thirteenth workshop on innovative use of nlp for building educational applications rico sennrich barry haddow and alexandra birch 2016 neural machine translation of rare words with subword units https arxiv org pdf 1508 07909 pdf in proceedings of the 54th annual meeting of the association for computational linguistics volume 1 long papers association for computational linguistics berlin germany pages 1715 1725 bpe franz josef och 2003 minimum error rate training in statistical machine translation https www aclweb org anthology p03 1021 pdf in proceedings of the annual meeting of the association for computational linguistics mert h2 id labeling labeling tagging h2 kostiantyn omelianchuk vitaliy atrasevych artem chernodub oleksandr skurzhanskyi 2020 gector grammatical error correction tag not rewrite https arxiv org pdf 2005 12592 pdf in proceedings of the 15th workshop on innovative use of nlp for building educational applications pages 163 170 association for computational linguistics abhijeet awasthi sunita sarawagi rasna goyal sabyasachi ghosh vihari piratla 2019 parallel iterative edit models for local sequence transduction https www aclweb org anthology d19 1435 pdf in proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing pages 4260 4270 hong kong china association for computational linguistics eric malmi sebastian krause sascha rothe daniil mirylenka aliaksei severyn 2019 encode tag realize high precision text editing https www aclweb org anthology d19 1510 pdf in proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing emnlp ijcnlp h2 id algorithm algorithms h2 yen lu chow and richard schwartz 1989 the n best algorithm an efficient procedure for finding top n sentence hypotheses https www aclweb org anthology h89 2027 pdf in proceedings of the workshop on speech and natural language association for computational linguistics 1989 pp 199 202 v h quanmarcello marcello federico mauro cettolo 2005 integrated n best re ranking for spoken language translation https www researchgate net publication 221483809 integrated n best re ranking for spoken language translation in proceedings of the 9th european conference on speech communication and technology lisbon portugal september 4 8 2005 h2 id bea bea2019 competition h2 yoav kantor yoav katz leshem choshen edo cohen karlik naftali liberman assaf toledo amir menczel and noam slonim 2019 learning to combine grammatical error corrections https arxiv org pdf 1906 03897 pdf in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics combining bea sota roman grundkiewicz marcin junczys dowmunt and kenneth heafield 2019 neural grammatical error correction systems with unsupervised pre training on synthetic data https www aclweb org anthology w19 4427 in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 1 yo joong choe jiyeon ham kyubyong park and yeoil yoon 2019 a neural grammatical error correction system built on better pre training and sequential transfer learning https arxiv org pdf 1907 01256 pdf in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 2 ruobing li chuan wang yefei zha yonghong yu shiman guo qiang wang yang liu and hui lin 2019 the laix systems in the bea 2019 gec shared task https www aclweb org anthology w19 4416 in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 3 zheng yuan felix stahlberg marek rei bill byrne and helen yannakoudakis 2019 neural and fst based approaches to grammatical error correction https www aclweb org anthology w19 4424 in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 4 hiroki asano tomoya mizumoto and masato mita 2019 the aip tohoku system at the bea 2019 shared task https www aclweb org anthology w19 4418 in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 9 jakub naplava and milan straka 2019 cuni system for the building educational applications 2019 shared task grammatical error correction https www aclweb org anthology w19 4419 in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 10 liner yang and chencheng wang 2019 the blcu system in the bea 2019 shared task https www aclweb org anthology w19 4421 in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 12 masahiro kaneko kengo hotate satoru katsumata and mamoru komachi 2019 tmu transformer system using bert for re ranking at bea 2019 grammatical error correction on restricted track https www aclweb org anthology w19 4422 in proceedings of the 14th workshop on innovative use of nlp for building educational applications association for computational linguistics no 14 h2 id spell spell checking h2 shaohua zhang haoran huang jicong liu hang li 2020 spelling error correction with soft masked bert https www aclweb org anthology 2020 acl main 82 pdf in proceedings of the 58th annual meeting of the association for computational linguistics xingyi cheng weidi xu kunlong chen shaohua jiang feng wang taifeng wang wei chu yuan qi 2020 spellgcn incorporating phonological and visual similarities into language models for chinese spelling check https www aclweb org anthology 2020 acl main 81 pdf in proceedings of the 58th annual meeting of the association for computational linguistics dingmin wang yi tay li zhong 2019 confusionset guided pointer networks for chinese spelling check https www aclweb org anthology p19 1578 pdf in proceedings of the 57th annual meeting of the association for computational linguistics yuzhong hong xianguo yu neng he nan liu junhui liu 2019 faspell a fast adaptable simple powerful chinese spell checker based on dae decoder paradigm https www aclweb org anthology d19 5522 pdf in proceedings of the 5th workshop on noisy user generated text w nut 2019 dingmin wang yan song jing li jialong han haisong zhang 2018 a hybrid approach to automatic corpus generation for chinese spelling check https www aclweb org anthology d18 1273 pdf in proceedings of the 2018 conference on empirical methods in natural language processing h2 id ged ged system h2 allen schmaltz yoon kim alexander m rush and stuart shieber 2016 sentence level grammatical error identification as sequence to sequence correction https www aclweb org anthology w16 0528 pdf in proceedings of the 11th workshop on innovative use of nlp for building educational applications pages 242 251 san diego california june 16 2016 zhuoran liu yang liu 2016 exploiting unlabeled data for neural grammatical error detection https arxiv org pdf 1611 08987 pdf journal of computer science and technology 32 4 caines a bentz c knill k et al 2020 grammatical error detection in transcriptions of spoken english https www aclweb org anthology 2020 coling main 195 proceedings of the 28th international conference on computational linguistics | ai |
|
blue-app-iota | iota app for ledger hardware wallets license https img shields io github license iota ledger blue app iota svg https github com iota ledger blue app iota blob master license buid status https github com iota ledger blue app iota workflows build badge svg branch master event push https github com iota ledger blue app iota actions codecov https codecov io gh iota ledger blue app iota branch master graph badge svg https codecov io gh iota ledger blue app iota it is strongly recommended to take a few minutes to read this document to make sure you fully understand how iota and the ledger hardware wallet works and how they interact together table of contents iota app for ledger hardware wallets iota app for ledger hardware wallets table of contents table of contents introduction introduction terminology terminology address reuse address reuse iota bundle iota bundle parts of an iota transaction parts of an iota transaction how ledger hardware wallets work how ledger hardware wallets work iota specific considerations for ledger hardware wallets iota specific considerations for ledger hardware wallets iota user facing app functions iota user facing app functions functions functions display display recovery phrase entropy recovery phrase entropy iota security concerns relating to ledger hardware wallets iota security concerns relating to ledger hardware wallets limitations of ledger hardware wallets limitations of ledger hardware wallets faq faq i lost my ledger what should i do now i lost my ledger what should i do now development development load the iota ledger app using docker load the iota ledger app using docker speculos support speculos support specification specification as a developer as a developer introduction iota is a unique cryptocurrency with specific design considerations that must be taken into account this document will attempt to go over how the ledger hardware wallet functions and how to stay safe when using a ledger to store iota terminology seed an iota seed is equivalent to the password for your iota account it can store a near infinite number of addresses private key the private key is used to generate a public key it is also used to prove you own said public key by means of creating a signature for a specific transaction public key also known as public address or just address the public key is the key you would give to somebody else to transfer you funds change tx after sending funds to a 3rd party all remaining funds on the account must be transferred to a new address this is called the change tx or the change address account the iota app stores indexes for 5 seeds these can be used as unique accounts for example one could be a primary or general purpose account and another could be a donations account address reuse iota uses a type of quantum resistance called the winternitz one time signature scheme this means that addresses must not be reused this is because you reveal part of the private key with each transaction as such reusing a spent address can allow attackers to forge fake transactions and steal your balance keep in mind you can receive an infinite number of transactions but as soon as you send from the address you must never use that address again this is handled in iota by utilizing what is called a seed index each index in the seed generates a unique address when creating an iota transaction it uses what s called an atomic bundle this is just a fancy term that means either the entire bundle is accepted or none of it is iota bundle a bundle is just a group of transactions and iota uses both input and output transactions so if bob has 10 iota and wants to send alice 3 iota the bundle could look like this tx1 bob 10 iota tx2 alice 3 iota tx3 bob 7 iota change tx this example highlights how iota handles one time signatures first it takes an input of 10 iota from bob s address it sends 3 of it to alice and it puts the remaining 7 iota on a new address belonging to bob s seed all input transactions require the private key to generate a signature which proves that you are the owner of the funds because bundles are atomic units the network will never accept bob s input tx of 10 iota without also accepting sending 3 to alice and 7 to a new address owned by bob in this example parts of an iota transaction an iota transaction is broken up into 2 halves the first half is generating a transaction bundle and creating signatures for it the second half is selecting 2 other transactions to confirm and performing the proof of work the ledger is only responsible for generating signatures for a specific transaction after that the host machine or anybody else for that matter can take the signatures and broadcast it to the network however the signatures are only valid for the specific transaction bundle promoting an unconfirmed transaction does not require re signing on the ledger how ledger hardware wallets work the ledger hardware wallet works by deterministically generating an iota seed based on your 24 word mnemonic created when setting up the device instead of storing the seed on your computer which could be stolen by an attacker the seed is stored on the ledger device and is never broadcast to the host machine it is connected to instead the host machine must ask the ledger to provide the information such as public keys or signatures when creating transactions the host will generate the necessary information for the transaction bundle and then will send it to the ledger device to be signed the ledger will then use the private keys associated with the input transactions to generate unique signatures and will then transfer only the signatures back to the host machine the host can then use these signatures which are only valid for that specific transaction bundle to broadcast the transaction to the network however as you can see neither the iota seed nor any of the private keys ever leave the device however keep in mind that in iota the signature contains some information about the private key for one specific address see ledger s documentation http ledger readthedocs io to get more info about the inner workings of the ledger hardware wallets iota specific considerations for ledger hardware wallets iota user facing app functions functions display address the wallet can ask the ledger to display the address for a specific index on the seed it only accepts the index and subsequently generates the address itself and thus verifies that the address belongs to the ledger note this does not pertain to displaying addresses in transactions transactions will be denoted as output input or change transactions input and change are verified to belong to the ledger output are not sign transaction the wallet will generate a transaction for the user to approve before the ledger signs it ensure all amounts and addresses are correct before signing these signatures are then sent back to the host machine display for the ledger nano s the sides of the display will have an up or down arrow indicating that you can scroll to a new screen two bars along the top just below the buttons indicates that there is a double press function available generally confirm toggle or back we will be working to ensure this function is always intuitive for the ledger nano s x behavior is the same as the nano s graphically there are no confirmation bars along the top but double pressing the buttons still confirms toggles recovery phrase entropy the 24 word bip39 recovery phrase mnemonic of the hardware wallet represents less information 256 bits of entropy than a 27 tryte iota seed 384 bits of entropy an additional function is used to convert this mnemonic seed and the optional passphrase not your pin number of your choosing into a 512 bit binary seed this happens according to the bip39 standard on the ledger using system calls the extended child key 512 bits of a corresponding bip32 path is then hashed using kerl to derive the final 243 trit seed for iota while having only 256 bits of entropy does not pose a security problem it does not support the full potential of the iota seed thus to use the full entropy supported by iota an additional sufficiently long passphrase is needed on the other hand there are other factors that might have a higher security impact like choosing proper random mnemonics the ledger uses a trng for that matter or selecting a higher security level iota security concerns relating to ledger hardware wallets all warnings on the ledger are there for a reason make sure to read them and refer to this document if there is any confusion don t rush through signing a transaction when generating a transaction for the ledger to sign you will scroll through each transaction before signing off on the bundle the transaction information will come up in order while scrolling from left to right on the ledger nano s s x the first screen will display the tx type output input or change as well as the amount the next screen will display the corresponding address for said transaction this will repeat until all transactions have been displayed then you can select approve or deny all output transactions to 3rd party addresses will say output and below that 1 56 mi for example output being the key word here all input transactions will say input 0 and the final output transaction the change transaction will say change 4 4 being the seed index of the change tx this means the ledger has verified the addresses used for inputs as well as the change tx all belong to the ledger if the input amount perfectly matches the output amount there will be no change transaction if there is no change transaction double check that you are sending the proper amount to the proper address because there is no remainder being sent back to your seed note when transferring from one seed controlled by the ledger device to another seed also controlled by the ledger device it will not display a change tx as such the wallet should first display the address on the new seed that it intends to send it to on the ledger thus verifying it belongs to the ledger and then create the transaction the user would then verify in the transaction that the output tx is in fact going to the address previously displayed on the ledger verify all information in a transaction and never re sign a transaction for other coins you only need to verify the destination address and amount but for iota you must also verify all input transactions if you sign a transaction on the ledger but the transaction is not using all of the funds on a specific address leftover funds on the address are then vulnerable as such make sure the input amount s are what you expect them to be make sure the output destination and amount is what you intend to send and make sure there is a change tx if applicable with all of your remaining funds note after signing a transaction you should verify the transaction was successfully broadcast to the network you should never re sign the same transaction on the ledger if the transaction does not get confirmed you should promote the transaction on the host machine this avoids generating a new signature and weakening the security of the addresses if the transaction was not broadcast to the network and you can t find it in the wallet you should be extremely cautious before proceeding to sign a new transaction if an infected machine is silently storing the signatures without broadcasting them it could steal your funds after re signing if this situation should arise you should consider going to a more trusted machine before re signing a transaction limitations of ledger hardware wallets due to the memory limitations of the ledger nano s s x the transaction bundles have certain restrictions they can only accept a transaction with a maximum bundle size of 10 an output and a change transaction each only require 1 bundle entry however every input transaction requires the same number of bundle entries as the security level being used on the seed thus if using a ledger nano s s or x you could have 1 output 4 inputs security level 2 1 change transaction and this would take up all 10 bundle entries for security level 3 you could only have 1 output 2 inputs 1 change transaction or 1 output 3 inputs without a change transaction security level 2 is the default security level faq i lost my ledger what should i do now hopefully you wrote down your 24 recovery words and your optional passphrase in a safe place if not all your funds are lost if you did the best solution is to buy a new ledger and enter your 24 recovery words and your optional passphrase in the new device br after installation of the iota ledger app all your funds are restored take care to reinitialize your seed index correctly another approach is to use our seed recovery tool which can be found here https github com iota ledger recover iota seed from ledger mnemonics br warning only use this tool in emergencies as exposing your seed defeats the primary purpose of using a hardware wallet development load the iota ledger app using docker the easist way to load the app without needing to download and prepare a development environment uses docker clone this repo ensure that all git submodules are initialized git submodule update init recursive build the app based on the current version for the nano s build sh m nanos connect your ledger to the pc and unlock it load the app build sh l m nanos accept all the messages on the ledger speculos support the speculos simulator can be started by build sh m nanos s after starting speculos provides a website that can be accessed by opening the url http localhost 5000 in a web browser specification see apdu api specification docs specification md as a developer would you like to contribute as a dev please check out our discord channel https discord gg u3qrjzj to contact us | iota hardwarewallet ledger hacktoberfest | server |
sql-challenge | sql challenge sql homework employee database a mystery in two parts data modeling engineering analysis data modeling inspect the csvs and sketch out an erd of the tables data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following list the following details of each employee employee number last name first name sex and salary list first name last name and hire date for employees who were hired in 1986 list the manager of each department with the following information department number department name the manager s employee number last name first name list the department of each employee with the following information employee number last name first name and department name list first name last name and sex for employees whose first name is hercules and last names begin with b list all employees in the sales department including their employee number last name first name and department name list all employees in the sales and development departments including their employee number last name first name and department name in descending order list the frequency count of employee last names i e how many employees share each last name | server |
|
therion | img src https github com kbaylosis therion blob master therion h png raw true width 400 an application development framework designed to create an app that runs over the mobile web and desktop platforms this is based on the following projects create react app https github com facebookincubator create react app create react native app https github com react community create react native app electron https github com electron electron express https expressjs com graphql https graphql org learn how do i get set up creating the project shell git clone git github com kbaylosis therion git project name cd project name install the node modules of each project component cd project name server yarn install cd project name mobile yarn install cd project name web yarn install configure the test database note the base configuration assumes a postgres database other supported databases are mysql mariadb mssql and sqlite see sequelize https sequelize org master manual getting started html for more info run these on the postgres cli create database therion create user test with createdb alter user test with password test grant all on database therion to test if you want to vary the configuration edit the items in server src datastore js development dialect postgres one of mysql mariadb postgres mssql host localhost name therion username test password test running the graphql server shell cd project path server yarn start running the ios mobile client attempts to open your app in the ios simulator if you re on a mac and have it installed shell cd project path mobile yarn run ios running the android mobile client attempts to open your app on a connected android device or emulator requires an installation of android build tools see react native docs https facebook github io react native docs getting started html for detailed setup shell cd project path mobile yarn run android running the web client runs the app in development mode open http localhost 3000 http localhost 3000 to view it in the browser shell cd project path web yarn start running the desktop client shell cd project path web yarn start open another terminal window yarn run desktop debugging logging 1 install the redux devtools https chrome google com webstore detail redux devtools lmhkpmbekcpmknklioeibfkpmmfibljd hl en 2 run the app 3 open the redux devtools menu in chrome find it s icon on the top right corner 4 select open remote devtools | front_end |
|
icit.github.io | icit github io this site builds upon http icsme2016 github io which uses the ncsu bootstrap as a baseline | server |
|
AC3.3 | access code 3 3 mobile development with android welcome to access code schedule schedule md resources resources | front_end |
|
words_counted | wordscounted we are all in the gutter but some of us are looking at the stars oscar wilde wordscounted is a ruby nlp natural language processor wordscounted lets you implement powerful tokensation strategies with a very flexible tokeniser class are you using wordscounted to do something interesting please tell me about it 8 a href http badge fury io rb words counted img src https badge fury io rb words counted 2x png alt gem version height 18 a rubydoc documentation 7 demo visit this website 4 for one example of what you can do with wordscounted features out of the box get the following data from any string or readable file or url token count and unique token count token densities frequencies and lengths char count and average chars per token the longest tokens and their lengths the most frequent tokens and their frequencies a flexible way to exclude tokens from the tokeniser you can pass a string regexp symbol lambda or an array of any combination of those types for powerful tokenisation strategies pass your own regexp rules to the tokeniser if you prefer the default regexp filters special characters but keeps hyphens and apostrophes it also plays nicely with diacritics utf and unicode characters bayr t is treated as bayr t and not bayr t for example opens and reads files pass in a file path or a url instead of a string installation add this line to your application s gemfile gem words counted and then execute bundle or install it yourself as gem install words counted usage pass in a string or a file path and an optional filter and or regexp ruby counter wordscounted count we are all in the gutter but some of us are looking at the stars using a file counter wordscounted from file path or url to my file txt count and from file are convenience methods that take an input tokenise it and return an instance of wordscounted counter initialized with the tokens the wordscounted tokeniser and wordscounted counter classes can be used alone however api wordscounted wordscounted count input options tokenises input and initializes a wordscounted counter object with the resulting tokens ruby counter wordscounted count hello beirut accepts two options exclude and regexp see excluding tokens from the analyser 5 and passing in a custom regexp 6 respectively wordscounted from file path options reads and tokenises a file and initializes a wordscounted counter object with the resulting tokens ruby counter wordscounted from file hello beirut txt accepts the same options as count tokeniser the tokeniser allows you to tokenise text in a variety of ways you can pass in your own rules for tokenisation and apply a powerful filter with any combination of rules as long as they can boil down into a lambda out of the box the tokeniser includes only alpha chars hyphenated tokens and tokens with apostrophes are considered a single token tokenise pattern token regexp exclude nil ruby tokeniser wordscounted tokeniser new hello beirut tokenise with exclude tokeniser wordscounted tokeniser new hello beirut tokenise exclude hello with pattern tokeniser wordscounted tokeniser new i 3 beirut tokenise pattern a z i see excluding tokens from the analyser 5 and passing in a custom regexp 6 for more information counter the wordscounted counter class allows you to collect various statistics from an array of tokens token count returns the token count of a given string ruby counter token count 15 token frequency returns a sorted unstable two dimensional array where each element is a token and its frequency the array is sorted by frequency in descending order ruby counter token frequency the 2 are 2 we 1 all 1 most frequent tokens returns a hash where each key value pair is a token and its frequency ruby counter most frequent tokens are 2 the 2 token lengths returns a sorted unstable two dimentional array where each element contains a token and its length the array is sorted by length in descending order ruby counter token lengths looking 7 gutter 6 stars 5 in 2 longest tokens returns a hash where each key value pair is a token and its length ruby counter longest tokens looking 7 token density precision 2 returns a sorted unstable two dimentional array where each element contains a token and its density as a float rounded to a precision of two the array is sorted by density in descending order it accepts a precision argument which must be a float ruby counter token density are 0 13 the 0 13 but 0 07 we 0 07 char count returns the char count of tokens ruby counter char count 76 average chars per token precision 2 returns the average char count per token rounded to two decimal places accepts a precision argument which defaults to two precision must be a float ruby counter average chars per token 4 uniq token count returns the number of unique tokens ruby counter uniq token count 13 excluding tokens from the tokeniser you can exclude anything you want from the input by passing the exclude option the exclude option accepts a variety of filters and is extremely flexible 1 a space delimited string the filter will normalise the string 2 a regular expression 3 a lambda 4 a symbol that names a predicate method for example odd 5 an array of any combination of the above ruby tokeniser wordscounted tokeniser new magnificent that was magnificent trevor using a string tokeniser tokenise exclude was magnificent that trevor using a regular expression tokeniser tokenise exclude trevor magnificent that was magnificent using a lambda tokeniser tokenise exclude t t length 4 magnificent that magnificent trevor using symbol tokeniser wordscounted tokeniser new hello tokeniser tokenise exclude ascii only using an array tokeniser wordscounted tokeniser new hello tokeniser tokenise exclude ascii only t t length 6 passing in a custom regexp the default regexp accounts for letters hyphenated tokens and apostrophes this means twenty one is treated as one token so is mohamad s ruby p alpha you can pass your own criteria as a ruby regular expression to split your string as desired for example if you wanted to include numbers you can override the regular expression ruby counter wordscounted count numbers 1 2 and 3 pattern p alnum counter tokens numbers 1 2 and 3 opening and reading files use the from file method to open files from file accepts the same options as count the file path can be a url ruby counter wordscounted from file url or path to file text gotchas a hyphen used in leu of an em or en dash will form part of the token this affects the tokeniser algorithm ruby counter wordscounted count how do you do you are well i see counter token frequency do 2 how 1 you 1 you 1 wtf mate are 1 in this example you and you are separate tokens also the tokeniser does not include numbers by default remember that you can pass your own regular expression if the default behaviour does not fit your needs a note on case sensitivity the program will normalise downcase all incoming strings for consistency and filters roadmap ability to open urls ruby def self from url open url and send string here after removing html end contributors see contributors 3 contributing 1 fork it 2 create your feature branch git checkout b my new feature 3 commit your changes git commit am add some feature 4 push to the branch git push origin my new feature 5 create new pull request 2 http www rubydoc info gems words counted 3 https github com abitdodgy words counted graphs contributors 4 http rubywordcount com 5 https github com abitdodgy words counted excluding tokens from the analyser 6 https github com abitdodgy words counted passing in a custom regexp 7 http www rubydoc info gems words counted 8 https github com abitdodgy words counted issues new | natural-language-processing nlp ruby rubynlp wordscounter wordcount word-counter | ai |
industry-machine-learning | machine learning and data science applications in industry finance quant machine learning ml quant com https www ml quant com automated research repository admin have a look at the newly started firmai medium https medium com firmai publication where we have experts of ai in business write about their topics of interest please add your tools and notebooks to this google sheet https docs google com spreadsheets d 1pvdv3r4x3k5d1utkbhmtmju8mjtzslahjzycurgh o4 edit usp sharing or simply add it to this subreddit r datascienceproject https www reddit com r datascienceproject highlight in yellow to get your package added you can also just add it yourself with a pull request p align center img src https github com firmai industry machine learning raw master assets industry png p a curated list of applied machine learning and data science notebooks and libraries accross different industries the code in this repository is in python primarily using jupyter notebooks unless otherwise stated the catalogue is inspired by awesome machine learning r datascienceproject https www reddit com r datascienceproject is a subreddit where you can share all your data science projects caution this is a work in progress please contribute especially if you are a subject expert in any of the industries listed below if you are a analytical computational statistical quantitive researcher analyst in field x or a field x machine learning engineer data scientist modeler programmer then your contribution will be greatly appreciated if you want to contribute to this list please do send me a pull request or contact me dereknow https twitter com dereknow or on linkedin https www linkedin com in snowderek or get in contact on the website firmai https www firmai org also a listed repository should be deprecated if repository s owner explicitly say that this library is not maintained not committed for long time 2 3 years br help needed if there is any contributors out there willing to help first populate and then maintain a python analytics section in any one of the following sub industries please get in contact with me also contact me to add additional industries br accommodation food accommodation agriculture agriculture banking insurance bankfin biotechnological life sciences biotech construction engineering construction education research education emergency relief emergency finance finance manufacturing manufacturing government and public works public healthcare healthcare media publishing media justice law and regulations legal miscellaneous miscellaneous accounting accounting real estate rental leasing realestate utilities utilities wholesale retail wholesale table of contents industry applications markdowntoc depth 4 accommodation food accommodation food accommodation food restaurant accommodation rest accommodation accommodation acc accounting accounting machine learning accounting ml analytics accounting analytics textual analysis accounting text data accounting data research and articles accounting ra websites accounting web courses accounting course agriculture agriculture economics agriculture econ development agriculture dev banking insurance bankfin consumer financial bankfin cf management and operations bankfin mo valuation bankfin value fraud bankfin fraud insurance and risk bankfin ir physical bankfin ph data bankfin data biotechnological life sciences biotech general biotech general sequencing biotech seq chemoinformatics and drug discovery biotech chem genomics biotech gene life sciences biotech life construction engineering construction construction construction const engineering construction eng material science construction mat economics economics general economics general machine learning economics ml computational economics computational education research education student education student school education school emergency relief emergency preventative and reactive emergency prevent crime emergency crime ambulance emergency ambulance disaster management emergency disaster finance finance trading investment finance trade data finance data healthcare healthcare general healthcare general justice law and regulations legal tools legal tools policy and regulatory legal pr judicial legal judicial manufacturing manufacturing general manufacturing general maintenance manufacturing maintenance failure manufacturing fail quality manufacturing quality media publishing media marketing media marketing miscellaneous miscellaneous art miscellaneous art tourism miscellaneous tour physics physics general physics general machine learning physics ml government and public works public social policies public social election analysis public elect disaster management public dis politics public poli charities public charity real estate rental leasing realestate real estate realestate real rental leasing realestate rental utilities utilities electricity utilities elect coal oil gas utilities coal water pollution utilities water transportation utilities transport wholesale retail wholesale wholesale wholesale whole retail wholesale retail markdowntoc ml ds career section for industry machine learning see data science career repo https github com firmai data science career for more platforms 1 triplebyte https triplebyte com a nosq7gm d take a quiz get offers from multiple top tech companies at once now have a machine learning track 1 toptal https www toptal com developers seeking to gain entry into the toptal community are put through a battery of personality and technical tests 1 hired https hired com hired matches employers with qualified candidates through a combination of in house algorithms and online support 1 kaggle https www kaggle com jobs scalable path is a premium talent matching service reviews glassdoor https www glassdoor com index htm best employee narratives indeed https www indeed com best coverage kununu https www kununu com us best well rounded infromation comparably https www comparably com best comparison functionality inhersight https www inhersight com best female friendly perspective a name accommodation a accommodation food a name accommodation food a food robotchef https github com bschreck robo chef refining recipes based on user reviews food amenities https github com ankushr785 food amenities demand prediction predicting the demand for food amenities using neural networks recipe cuisine and rating https github com catherhuang fp3 recipe predict the rating and type of cuisine from a list of ingredients food classification https github com stratospark food 101 keras classification using keras image to recipe https github com murgio food recipe cnn translate an image to a recipe using deep learning calorie estimation https github com jubins deeplearning food image recognition and calorie estimation estimate calories from photos of food fine food reviews https github com architectshwet amazon fine food reviews sentiment analysis on amazon fine food reviews a name accommodation rest a restaurant restaurant violation https github com nd1 dc restaurantviolationforecasting food inspection violation forecasting restaurant success https github com alifier restaurant success model predict whether a restaurant is going to fail predict michelin https github com josephofiowa dc michelin challenge tree master submissions predict the likelihood that restaurant is a michelin restaurant restaurant inspection https github com gzsuyu data analysis nyc restaurant inspection data an inspection analysis to see if cleanliness is related to rating sales https github com ayeright sales forecast lstm restaurant sales forecasting with lstm visitor forecasting https github com anki1909 recruit restaurant visitor forecasting reservation and visitation number prediction restaurant profit https github com everaspiring regressionanalysis restaurant regression analysis competition https github com klin90 missinglink restaurant competitiveness analysis business analysis https github com nvodoor rba restaurant business analysis project location recommendation https github com sanatasy restaurant risk restaurant location recommendation tool and analysis closure rating and recommendation https github com lolonon restaurant analytical solution three prediction tasks using yelp data anti recommender https github com myau5x anti recommender find restaurants you don t want to attend menu analysis https github com bzjin menus deeper analysis of restaurants through their menus menu recommendation https github com rphaneendra menu similarity nlp to recommend restaurants with similar menus food price https gist github com analyticsindiamagazine f9b2ba171a0eef9ad396ce6f1b83bbbc predict food cost automated restaurant report https github com firmai interactive corporate report automated machine learning company report a name accommodation acc a accommodation peer to peer housing https github com rochiecuevas shared accommodations the effect of peer to peer rentals on housing roommate recommendation https github com siddheshacharekar liveright a system for students seeking roommates room allocation https github com nus usp room allocation room allocation process dynamic pricing https github com marcotav hotels hotel dynamic pricing calculations hotel similarity https github com montclair state university info368 assignment 6 compare brands that directly compete hotel reviews https github com eliadproject hotels data science cluster hotel reviews predict prices https github com morenobcn capstone hotels arcpy predict hotel room rates hotels vs airbnb https github com morenobcn hotels vs airbnb proof of concept comparing the two approaches hotel improvement https github com argha48 smarthotels analyse reviews to suggest hotel improvements orders https github com hasan330 order cancellation prediction model order cancellation prediction for hotels fake reviews https github com danielmachinelearning hotelspamdetection identify whether reviews are fake spam reverse image lodging https github com starfoe eye bnb find your preferred lodging by uploading an image a name accounting a accounting a name accounting ml a machine learning chart of account prediction https github com agdgovsg ml coa charging using labeled data to suggest the account name for every transaction accounting anomalies https github com gitihubi deepai blob master gtc 2018 colab ipynb using deep learning frameworks to identify accounting anomalies financial statement anomalies https github com rameshcalamur fin stmt anom detecting anomalies before filing using r useful life prediction firmai http www firmai org documents aged 20debtors predict the useful life of assets using sensor observations and feature engineering ai applied to xbrl https github com niels peter xbrl ai standardized representation of xbrl into ai and machine learning a name accounting analytics a analytics forensic accounting https github com mschermann forensic accounting collection of case studies on forensic accounting using data analysis on the lookout for more data to practise forensic accounting please get in touch https github com mschermann general ledger firmai http www firmai org documents general 20ledger data processing over a general ledger as exported through an accounting system bullet graph firmai http www firmai org documents bullet graph article bullet graph visualisation helpful for tracking sales commission and other performance aged debtors firmai http www firmai org documents aged 20debtors example analysis to invetigate aged debtors automated fs xbrl https github com charleshoffmancpa charleshoffmancpa github io xml language however possibly port analysis into python a name accounting text a textual analysis financial sentiment analysis https github com eriche98 financial statements text analysis sentiment distance and proportion analysis for trading signals extensive nlp https github com tiesdekok python nlp tutorial blob master nlp notebook ipynb comprehensive nlp techniques for accounting research a name accounting data a data parsing and apis edgar https github com tiesdekok uw python camp blob master materials session 5 edgar walkthrough ipynb a walk through in how to obtain edgar data pyedgar https github com gaulinmp pyedgar a library for downloading caching and accessing edgar filings irs http social metrics org sox acessing and parsing irs filings financial corporate http raw rutgers edu corporate 20financial 20data html rutgers corporate financial datasets non financial corporate http raw rutgers edu non financial 20corporate 20data html rutgers non financial corporate dataset pdf parsing https github com danshorstein python4cpas blob master 03 parsing pdf files ar 20aging 20 20working ipynb extracting useful data from pdf documents pdf tabel to excel https github com danshorstein ficpa article how to output an excel file from a pdf a name accounting ra a research and articles understanding accounting analytics http social metrics org accountinganalytics an article that tackles the importance of accounting analytics vlfeat http www vlfeat org vlfeat is an open and portable library of computer vision algorithms which has matlab toolbox a name accounting web a websites rutgers raw http raw rutgers edu good digital accounting research from rutgers a name accounting course a courses computer augmented accounting https www youtube com playlist list plauepkft6dk8tanaq sqzw4lidjhckze2 a video series from rutgers university looking at the use of computation to improve accounting accounting in a digital era https www youtube com playlist list plauepkft6dk8 xun584uqppsg1grykww0 another series by rutgers investigating the effects the digital age will have on accounting a name agriculture a agriculture a name agriculture econ a economics prices https github com deadskull7 agricultural price prediction and visualization on android app agricultural price prediction prices 2 https github com vipul115 statistical time series analysis on agricultural commodity prices agricultural price prediction yield https github com dfs ucu ukrainianagriculture agricultural analysis looking at crop yields in ukraine recovery https github com vicelab slaer strategic land use for agriculture and ecosystem recovery mpr https github com gumballhead mpr mandatory price reporting data from the usda s agricultural marketing service a name agriculture dev a development segmentation https github com chrieke instancesegmentation sentinel2 agricultural field parcel segmentation using satellite images water table https github com jfzhang95 lstm water table depth prediction predicting water table depth in agricultural areas assistant https github com surajmall agriculture assistant tree master models notebooks from agricultural assistant eco evolutionary https github com tecoevo agriculture eco evolutionary dynamics diseases https github com gauravmunjal13 agriculture identification of crop diseases and pests using deep learning framework from the images irrigation and pest prediction https github com divyam3897 agriculture analyse irrigation and predict pest likelihood a name bankfin a banking insurance a name bankfin cv a consumer finance loan acceptance https github com paresh3189 bankruptcy prediction growth modelling classification and time series analysis for loan acceptance predict loan repayment https github com featuretools predict loan repayment predict whether a loan will be repaid using automated feature engineering loan eligibility ranking https github com realradone gyani the loan eligibility predictor system to help the banks check if a customer is eligible for a given loan home credit default firmai http www firmai org documents aggregator each time step takes 30 seconds predict home credit default mortgage analytics https github com abuchowdhury mortgage bank loan analtsics blob master mortgage 20bank 20loan 20analytics ipynb extensive mortgage loan analytics credit approval https github com ibm cloud devfest 2018 data science for banking blob master 02 creditcardapprovalmodel creditcardapprovalmodel ipynb a system for credit card approval loan risk https github com brett777 predict risk predictive model to help to reduce charge offs and losses of loans amortisation schedule firmai http www firmai org documents amortization 20schedule simple amortisation schedule in python for personal use a name bankfin mo a management and operation credit card https github com am aditya artificial intelligence for banking blob master 03 ipy notebooks clv prediction ipynb estimate the clv of credit card customers survival analysis https github com am aditya artificial intelligence for banking blob master 01 code 01 02 clv survival survival analysis py perform a survival analysis of customers next transaction https github com am aditya artificial intelligence for banking blob master 01 code 01 02 clv survival customer nexttransaction prediction py deep learning model to predict the transaction amount and days to next transaction credit card churn https github com am aditya artificial intelligence for banking blob master 01 code 01 02 clv survival customer nexttransaction prediction py predicting credit card customer churn bank of england minutes https github com sekhansen mpc minutes demo blob master information retrieval ipynb textual analysis over bank minutes ceo https github com kaumaron data science tree master ceo compensation analysis of ceo compensation a name bankfin value a valuation zillow prediction https github com eswar3 zillow prediction models zillow valuation prediction as performed on kaggle real estate https github com denadai2 real estate neighborhood prediction predicting real estate prices from the urban environment used car https nbviewer jupyter org github albahnsen practicalmachinelearningclass blob master exercises p1 usedvehiclepriceprediction ipynb used vehicle price prediction a name bankfin fraud a fraud xgboost https github com kspiliop fraud detection fraud detection by tuning xgboost hyper parameters with simulated annealing fraud detection loan in r https github com longtng frauddetectionproject blob master a 20consideration 20point 20of 20 20fraud 20detection 20in 20bank 20loans 20project 20code ipynb fraud detection in bank loans aml finance due diligence https github com michaels72 aml due diligence blob master aml finance dd ipynb search news articles to do finance aml dd credit card fraud https github com am aditya artificial intelligence for banking blob master 03 ipy notebooks fraud detection ipynb detecting credit card fraud a name bankfin ir a insurance and risk car damage detective https github com neokt car damage detective assessing car damage with convolution neural networks for a personal auto claims medical insurance claims https github com roshank1605a04 insurance claim prediction blob master insuranceclaim ipynb predicting medical insurance claims claim denial https github com slegroux claimdenial blob master claim 20denial ipynb predicting insurance claim denial claim fraud https github com rshea3 alpha insurance predictive models to determine which automobile claims are fraudulent claims anomalies https github com dchannah fraudhacker anomaly detection system for medical insurance claims data actuarial sciences r https github com jschelldorfer actuarialdatascience a range of actuarial tools in r bank failure https github com shomona bank failure prediction blob master bank ipynb predicting bank failure risk management https github com andrey lukyanov risk management finance risk engagement course resources var gan https github com hamaadshah market risk gan keras estimate value at risk for market risk management using keras and tensorflow compliance https github com saibiswas bank grievance compliance management blob master the 20main 20file ipynb bank grievance compliance management stress testing https github com apbecker systemic risk blob master generalized ipynb ecb stress testing stress testing techniques https github com kaitai stress testing with jupyter blob master playing 20with 20financial 20data 20and 20python 203 ipynb a notebook with various stress testing exercises reverse stress test https github com arcadynovosyolov reverse stress testing blob master reverse stress testing ipynb given a portfolio and a predefined loss size determine which factors stress scenarios would lead to that loss boe stress test https github com vankatpetr boe stress test blob master boe stress test 5y cummulative imparment charge ipynb stress test results and plotting recovery https github com hkacmaz bankin recovery blob master banking recovery ipynb recovery of money owed quality control https github com mick zhang quality control for banking using lda and lda mallet quality control for banking using lda a name bankfin ph a physical bank note fraud detection https github com apoorv goel bank note authentication using dnn tensorflow classifier and randomforest bank note authentication using dnn tensorflow classifier and randomforest atm surveillance https github com shreyagupta08 infosyshack atm surveillance in banks use case a name biotech a biotechnological life sciences a name biotech general a general programming https github com burkesquires python biologist python programming for biologists introduction dl https colab research google com drive 17e4h5aaoioh5dito7mzg4hpl6z 0fywr a primer on deep learning in genomics pose https github com talmo leap estimating animal poses using dl privacy https github com greenelab sprint gan privacy preserving nns for clinical data sharing population genetics http journals plos org ploscompbiol article id 10 1371 journal pcbi 1004845 dl for population genetic inference bioinformatics course https github com ricket sjtu bioinformatics course materials for computational biology and bioinformatics applied stats https github com waldronlab appstatbio applied statistics for high throughput biology scripts https github com mingzhangyang mybiotools python scripts for biologists molecular nn https github com mitmedialab evolutron a mini framework to build and train neural networks for molecular biology systems biology simulations https github com hallba writingsimulators systems biology practical on writing simulators with f and z3 cell movement https github com jrieke lstm biology lstm to predict biological cell movement deepchem https github com deepchem deepchem democratizing deep learning for drug discovery quantum chemistry materials science and biology a name biotech seq a sequencing dna rna and protein sequencing https github com ehsanasgari deep proteomics anew representation for biological sequences using dl cnn sequencing https github com budach pysster a toolbox for learning motifs from dna rna sequence data using convolutional neural networks nlp sequencing https github com hussius deeplearning biology language transfer learning model for genomics a name biotech chem a chemoinformatics and drug discovery novel molecules https github com hips neural fingerprint a convolutional net that can learn features automating chemical design https github com aspuru guzik group chemical vae generate new molecules for efficient exploration gan drug discovery https github com gablg1 organ a method that combines generative models with reinforcement learning rl https github com marcusolivecrona reinvent generating compounds predicted to be active against a biological target one shot learning https github com deepchem deepchem python library that aims to make the use of machine learning in drug discovery straightforward and convenient a name biotech gene a genomics jupyter genomics https github com ucsd ccbb jupyter genomics collection of computation biology and bioinformatics notebooks variant calling https github com google deepvariant correctly identify variations from the reference genome in an individual s dna gene expression graphs https github com mila iqia gene graph conv using convolutions on an image autoencoding expression https github com greenelab adage extracting relevant patterns from large sets of gene expression data gene expression inference https github com uci cbcl d gex predict the expression of specified target genes from a panel of about 1 000 pre selected landmark genes plant genomics https github com widdowquinn teaching embl plant path genomics presentation and example material for plant and pathogen genomics a name biotech life a life sciences plants disease https github com viritaromero plant diseases classifier app that detects diseases in plants using a deep learning model leaf identification https github com aayushg159 plant leaf identification identification of plants through plant leaves on the basis of their shape color and texture crop analysis https github com openalea eartrack an imaging library to detect and track future position of ears on maize plants seedlings https github com mfsatya plantseedlings classification plant seedlings classification from kaggle competition plant stress https github com planteome ontology of plant stress an ontology containing plant stresses biotic and abiotic animal hierarchy https github com sacul git hierarpy package for calculating animal dominance hierarchies animal identification https github com a7med01 deep learning for animal identification deep learning for animal identification species https github com nomaanahmed bigdata animalspeciesanalysis big data analysis of different species of animals animal vocalisations https github com timsainb avgn a generative network for animal vocalizations evolutionary https github com hardmaru estool evolution strategies tool glaciers https github com oggm oggm edu educational material about glaciers a name construction a construction engineering a name construction con a construction dl architecture https github com carolineh101 deep learning architecture deep learning classifier and image generator for building architecture construction materials https github com damontallen construction materials a course on construction materials bad actor risk model https github com dariusmehri social network bad actor risk tool risk model to improve construction related building safety inspectors https github com dariusmehri tracking inspectors with euclidean distance algorithm determine the assigned inspections corrupt social interactions https github com dariusmehri social network analysis to expose corruption uncover potential corrupt social interactions between an industry member and the staff at the dob risk construction https github com dariusmehri risk screening tool to predict accidents at construction sites identify high risk construction facade risk https github com dariusmehri algorithm for finding buildings with facade risk a risk model to predict unsafe facades staff levels https github com dariusmehri predicting staff levels for front line workers predicting staff levels for front line workers injuries https github com dariusmehri topic modeling and analysis of building related injuries building related injuries topic modelling building violations https github com dariusmehri predictive analysis of building violations predictive analysis of building violations productivity https github com dariusmehri inspection productivity analysis and visualization with tableau productivity analysis and inspection with tableau a name construction eng a engineering structural analysis https github com ritchie46 anastruct 2d structural analysis in python structural engineering https github com buddyd16 structural engineering structural engineering modules nusa https github com jorgedelossantos nusa structural analysis using the finite element method structpy https github com brianchevalier structpy structural analysis library for python based on the direct stiffness method aileron https github com albiboni aileronsimulation structural analysis of the aileron of a boeing 737 vibration https github com vibrationtoolbox vibration toolbox educational vibration programs civil https github com ebrahimraeyat civil collection of civil engineering tools in freecad gestimator https github com manuvarkey gestimator simple civil estimation software fatpack https github com gunnstein fatpack functions and classes for fatigue analysis of data series pysteel https github com yajnab pysteel automated design of different steel structure structural uncertainty https github com davidsteinar structural uncertainty quantifying structural uncertainty with deep learning pymech https github com jellespijker pymech a python module for mechanical engineers aerospace engineering https github com alvaromenduina jupyter notebooks tree master introduction aerospace engineering astrodynamics and statistics interactive quantum chemistry https github com psi4 psi4numpy combining psi4 and numpy for education and development chemical and process engineering https github com cacheme learn various resources pytherm https github com iurisegtovich pytherm applied thermodynamics applied thermodynamics aerogami https github com kshitizkhanal7 aerogami aerodynamics using planes electro geophysics https github com geoscixyz em apps interactive applications for electromagnetics in geophysics graph signal https github com mdeff pygsp tutorial graphsip graph signal processing tutorial mechanical vibrations https github com docvaughan mche485 mechanical vibrations mechanical vibrations at the univsersity of louisiana process dynamics https github com opencheme chbe356 process dynamics and control battery life cycle https github com rdbraatz data driven prediction of battery cycle life before capacity degradation data driven prediction of batter life cycle wind energy https github com dtuwindenergy python4windenergy python for wind energy energy use https github com openeemeter eemeter blob master scripts tutorial ipynb standard methods for calculating normalized metered energy consumption nuclear radiation https github com hitarthishah radiation data analysis how people are affected by radiations emitted by nuclear power plants a name construction mat a material science python materials genomics https github com materialsproject pymatgen robust material analysis code used in a well established project materials mining https github com dchannah materials mining scripts for simulations and analysis of materials emmet https github com materialsproject emmet build databases of material properties megnet https github com materialsvirtuallab megnet graph networks as a ml framework for molecules and crystals atomate https github com hackingmaterials atomate pre built workflows for computational material science bylaws compliance https github com mehranov understandingandpredictingpropertymaintenancefines blob master assignment4 complete ipynb predicting property fines asphalt binder https github com sierraporta asphalt binder construction materials free energy and chemical composition of asphalt binder steel https github com hbutsuak95 quality optimization of steel optimisation of steel awesome materials informatics https github com tilde lab awesome materials informatics curated list of known efforts in materials informatics a name economics a economics a name economics general a general trading economics api https github com tradingeconomics tradingeconomics information for 196 countries development economics https github com jhconning dev ii tree master notebooks development microeconomics are written mostly as interactive jupyter notebooks applied econ fin https github com lnsongxf applied computational economics and finance blob master chapter05 ipynb applied computational economics and finance macroeconomics https github com jlperla econ407 2018 topics in macroeconomics with notebook examples a name economics ml a machine learning econml https github com microsoft econml automated learning and intelligence for causation and economics auctions https github com saisrivatsan deep opt auctions optimal auctions using deep learning a name economics comp a computational quant econ https github com jstac quantecon nyu 2016 quantitative economics course by nyu computational https github com zhentaoshi econ5170 computational methods in economics computational 2 https github com quantecon columbia mini course small course in computational economics econometric theory https github com jstac econometrics tree master notebooks notebooks of a primer on econometric theory a name education a education research a name education student a student student performance https github com roshank1605a04 education process mining mining student performance using machine learning student performance 2 https github com janzaib masood educational data mining student exam performance student performance 3 https github com rohithyogi student performance prediction student achievement in secondary education student performance 4 https github com roshank1605a04 students performance analytics students performance evaluation using feature engineering student intervention https github com eloyekunle student intervention blob master student intervention ipynb building a student intervention system student enrolment https github com arrahman17 learning analytics project student enrolment and performance analysis academic performance https github com janzaib masood educational data mining explore the demographic and family features that have an impact a student s academic performance grade analysis https github com kaumaron data science tree master grade analysis student achievement analysis a name education school a school school choice https github com nprapps school choice data analysis for education s school choice school budgets and priorities https github com tullyvelte schoolperformancedataanalysis helping the school board and mayor make strategic decisions regarding future school budgets and priorities school performance https github com bradleyrobinson school performance data analysis practice using data from data utah gov on school performance school performance 2 https github com vtyeh pandas challenge using pandas to analyze school and student performance within a district school performance 3 https github com benattix philly schools philadelphia school performance school performance 4 https github com adrianakopf njpublicschools nj school performance school closure https github com whugue school closure identify schools at risk for closure by performance and other characteristics school budgets https github com datacamp course resources ml with experts budgets blob master notebooks 1 0 full model ipynb tools and techniques for school budgeting school budgets https github com nymarya school budgets for education tree master notebooks same as a above datacamp pycity https github com jonathanreb budget schoolsanalysis blob master pycityschools starter ipynb school analysis pycity 2 https github com 1davegalloway schooldistrictanalysis school budget vs school results budget nlp https github com jinsonfernandez nlp school budget project nlp classification for budget resources budget nlp 2 https github com divyamadhu school budget prediction further classification exercise budget nlp 3 https github com sushant2811 schoolbudgetdata blob master schoolbudgetdata ipynb budget classification survey analysis https github com kaumaron data science tree master education education survey analysis a name emergency a emergency police a name emergency prevent a preventative and reactive emergency mapping https github com aeronetlab emergency mapping detection of destroyed houses in california emergency room https github com roshetty supporting emergency room decision making with relevant scientific literature supporting em ergency r oom decision making emergency readmission https github com mesgarpour t carer adjusted risk of emergency readmission forest fire https github com leadingindiaai forest fire detection through uav imagery using cnns forest fire detection through uav imagery using cnns emergency response https github com sky t hack or emergency response emergency response analysis emergency transportation https github com bayesimpact bayeshack transportation ems transportation prompt on emergency services emergency dispatch https github com jamesypeng smarter emergency dispatch reducing response times with predictive modeling optimization and automation emergency calls https github com analystiu lict project emergency 911 calls emergency calls analysis project calls data analysis https github com tanoybhattacharya 911 data analysis 911 data analysis emergency response https github com amunategui leak at chemical factory rl chemical factory rl a name emergency crime a crime crime classification https github com datadesk lapd crime classification analysis times analysis of serious assaults misclassified by lapd article tagging https github com chicago justice project article tagging natural language processing of chicago news article crime analysis https github com chrispiemonte crime analysis association rule mining from spatial data for crime analysis chicago crimes https github com search o desc q crime language 3a 22jupyter notebook 22 not 22taxi 22 not 22baseline 22 s stars type repositories exploring public chicago crimes data set in python graph analytics https github com pedrohserrano graph analytics nederlands the hague crimes crime prediction https github com vikram bhati paasbaan crime prediction crime classification analysis prediction in indore city crime prediction https github com tina31726 crime prediction developed predictive models for crime rate crime review https github com felzek crime review data analysis crime review data analysis crime trends https github com benjaminsingleton crime trends the crime trends analysis tool analyses crime trends and surfaces problematic crime conditions crime analytics https github com cmenguy crime analytics analysis of crime data in seattle and san francisco a name emergency ambulance a ambulance ambulance analysis https github com kaiareyes ambulance an investigation of local government area ambulance time variation in victoria site location https github com ankitkariryaa ambulancesitelocation ambulance site locations dispatching https github com dimastoyanov ambulance dispatching applying game theory and discrete event simulation to find optimal solution for ambulance dispatching ambulance allocation https github com scngo sd ambulance allocation time series analysis of ambulance dispatches in the city of san diego response time https github com nonsignificantp ambulance response time an analysis on the improvements of ambulance response time optimal routing https github com aditink emsrouting project to find optimal routing of ambulances in ithaca crash analysis https github com arpitvora maryland crash predicting the probability of accidents on a given segment on a given time a name emergency disaster a disaster management conflict prediction https github com polichinel master thesis notebooks on conflict prediction burglary prediction https github com polichinel master thesis spatio temporal modelling for burglary prediction predicting disease outbreak https github com ab bh disease outbreak prediction blob master disease 20outbreak 20prediction ipynb machine learning implementation based on multiple classifier algorithm implementations road accident prediction https github com leportella federal road accidents prediction on type of victims on federal road accidents in brazil text mining https github com rajaswa disaster management disaster management using text mining twitter and disasters https github com paultopia concrete nlp tutorial blob master nlp notebook ipynb try to correctly predict whether tweets that are about disasters flood risk https github com arijitsaha floodrisk impact of catastrophic flood events fire prediction https github com senkichi the catastrophe coefficient we used 4 different algorithms to predict the likelihood of future fires a name finance a finance a name finance trading a trading and investment for more see financial machine learning https github com firmai financial machine learning for asset management see financial machine learning https github com firmai machine learning asset management deep portfolio https github com dlcolumbia dl forfinance deep learning for finance predict volume of bonds ai trading https github com borisbanushev stockpredictionai blob master readme2 md modern ai trading techniques corporate bonds https github com ishank011 gs quantify bond prediction predicting the buying and selling volume of the corporate bonds simulation https github com chenbowen184 computational finance investigating simulations as part of computational finance industry clustering https github com seanmcowen financeandpython com clusteringindustries project to cluster industries according to financial attributes financial modeling https github com miyainnyc financial modeling tree master codes hft trading and implied volatility modeling trend following http inseaddataanalytics github io inseadanalytics exerciseset2 html a futures trend following portfolio investment strategy financial statement sentiment https github com maydogdu textualanalysis extracting sentiment from financial statements using neural networks applied corporate finance https github com chenbowen184 data science in applied corporate finance studies the empirical behaviors in stock market market crash prediction https github com sarachmax marketcrashes prediction blob master lppl comparasion ipynb predicting market crashes using an lppl model nlp finance papers https github com chenbowen184 research documents curation with nlp curating quantitative finance papers using machine learning arima ltsm hybrid https github com imhgchoi corr prediction arima lstm hybrid hybrid model to predict future price correlation coefficients of two assets basic investments https github com seanmcowen financeandpython com investments basic investment tools in python basic derivatives https github com seanmcowen financeandpython com derivatives basic forward contracts and hedging basic finance https github com seanmcowen financeandpython com basicfinance source code notebooks basic finance applications advanced pricing ml https github com jjakimoto finance ml additional implementation of advances in financial machine learning book options and regression https github com aluo417 financial engineering projects financial engineering project for option pricing techniques quant notebooks https github com longonly quantitative notebooks educational notebooks on quant finance algorithmic trading and investment strategy forecasting challenge https github com bukosabino financial forecasting challenge gresearch financial forecasting challenge by g research hedge fund xgboost https github com firmai after y3vyc29yonyyopk5mjaxos0wns0wmlqwntoymzoymsswmtowmm4kbjiv tab stars a trading algorithm using xgboost research paper trading https github com rawillis98 alpaca a strategy implementation based on a paper using alpaca markets various https github com arcadynovosyolov finance options allocation simulation ml rl nyu https github com joelowj machine learning and reinforcement learning in finance machine learning and reinforcement learning in finance a name finance data a data datastream https github com mbravidor pydsout datastrem from thomson reuters accessible through python alphavantage http twopirllc api wrapper to simplify the process of acquiring free financial data fsa https github com duncangh fsa a project to transfer sec edgar filings financial data to custom financial statement analysis models tradeconnector https github com tradeasystems tradeasystems connector a layer to connect with market data providers employee count sec filings https github com healthgradient sec employee information extraction extraction to get the exact employee count values for companies from sec filings sec parsing https github com healthgradient sec doc info extraction blob master classify sections containing relevant information ipynb nlp to find and extract specific information from long unstructured documents open edgar https github com lexpredict openedgar openedgar openedgar io rating industries http www ratingshistory info histories from multiple agencies converted to csv format personal papers financial machine learning regulation https papers ssrn com sol3 papers cfm abstract id 3371902 predicting restaurant facility closures https papers ssrn com sol3 papers cfm abstract id 3420490 predicting corporate bankruptcies https papers ssrn com sol3 papers cfm abstract id 3420889 predicting earnings surprises https papers ssrn com sol3 papers cfm abstract id 3420722 machine learning in asset management https papers ssrn com sol3 papers cfm abstract id 3420952 a name healtcare a healthcare a name healtcare general a general zepid https github com pzivich zepid epidemiology analysis package python for epidemiologists https github com pzivich python for epidemiologists tutorial to introduce epidemiology analysis in python prescription compliance https github com rjhere prescription compliance prediction an analysis of prescription and medical compliance respiratory disease https github com alistairwallace97 olympian biotech tracking respiratory diseases in olympic athletes bubonic plague https github com callysto curriculum notebooks blob master humanities bubonicplague bubonic plague and sir model ipynb bubonic plague and sir model a name legal a justics law regulations a name legal tools a tools lexpredict https github com lexpredict lexpredict contraxsuite software package and library ai para legal https github com davidawad lobe lobe is the world s first ai paralegal legal entity detection https github com hockeyjudson legal entity detection blob master dataset conv ipynb ner for legal documents legal case summarisation https github com law ai summarization implementation of different summarisation algorithms applied to legal case judgements legal documents google scholar https github com girrajmaheshwari web scrapping blob master google scholar 2bextract 2bcase 2bdocument ipynb using google scholar to extract cases programatically chat bot https github com akarazeev legaltech chat bot and email notifications congress api https github com propublica congress api docs propublica congress api access data generator gdpr https github com toningega data generator dummy data generator for gdpr compliance blackstone https github com iclrandd blackstone spacy pipeline and model for nlp on unstructured legal text a name legal pr a policy and regulatory gdpr scores https github com erickjtorres ai legaldoc hackathon predicting gdpr scores for legal documents driving factors finra https github com siddhantmaharana text analysis on finra docs identify the driving factors that influence the finra arbitration decisions securities bias correction https github com davidsontheath bias corrected estimators blob master bias corrected estimators ipynb bias corrected estimation of price impact in securities litigation public firm to legal decision https github com anshu3769 firmembeddings embed public firms based on their reaction to legal decisions night life regulation https github com kevin mcisaac nightlife australian nightlife and its regulation and policing comments https github com proximadas nlp govt regulations public comments on government regulations clustering https github com philxchen clustering canadian regulations clustering canadian regulations environment https github com ds modules eep 147 regulation of energy and the environment risk https github com vsub21 systemic risk dashboard systematic risk of various financial regulations finra compliance https github com raymond180 finra trace topic modelling on compliance a name legal judicial a judicial applied supreme court prediction https github com davidmasse us supreme court prediction predicting the ideological direction of supreme court decisions ensemble vs unified case based model supreme court topic modeling https github com accelai ai law minicourse tree master supreme court topic modeling multiple steps necessary to implement topic modeling on supreme court decisions judge opinion https github com girrajmaheshwari legal analytics project court misclassification using text mining and machine learning to analyze judges opinions for a particular concern ml law matching https github com whs2k gpo ai a machine learning law match maker bert multi label classification https github com brightmart sentiment analysis fine grain fine grained sentiment analysis from ai some computational ai course https www youtube com channel uc5uhm2j9pbezmwl97z 0hzw video series law mit financial machine learning regulation paper https papers ssrn com sol3 papers cfm abstract id 3371902 a name manufacturing a manufacturing a name manufacturing general a general green manufacturing https github com danila89 kaggle mercedes mercedes benz greener manufacturing competition on kaggle semiconductor manufacturing https github com meena mani secom class imbalance semicondutor manufacturing process line data analysis smart manufacturing https github com usnistgov modelmeth shared work of a modelling methodology bosch manufacturing https github com han yan ds kaggle bosch bosch manufacturing project kaggle a name manufacturing maintenance a maintenance predictive maintenance https github com azure lstms for predictive maintenance 1 predict remaining useful life of aircraft engines predictive maintenance 2 https github com samimust predictive maintenance time to failure ttf or remaining useful life rul manufacturing maintenance https github com m hoff maintsim simulation of maintenance in manufacturing systems a name manufacturing fail a failure predictive analytics https github com ibm iot predictive analytics method for predicting failures in equipment using sensor data detecting defects https github com roshank1605a04 secom detecting defected items anomaly detection for defective semiconductors defect detection https github com jorgehas smart defect inspection smart defect detection for pill manufacturing manufacturing failures https github com aayushmudgal reducing manufacturing failures reducing manufacturing failures manufacturing anomalies https github com mohan mj manufacturing line i4 0 intelligent anomaly detection for manufacturing line a name manufacturing quality a quality quality control https github com buzz11 productionfailures bosh failure of quality control manufacturing quality https github com limberc tianchi imqf intelligent manufacturing quality forecast auto manufacturing https github com trentwoodbury manufacturingauctionregression regression case study project on manufacturing auction sale data a name media a media publishing a name media marketing a marketing video popularity https github com andrei rizoiu hip popularity hip model for predicting the popularity of videos youtube transcriber https github com hathix youtube transcriber automatically transcribe youtube videos marketing analytics https github com byukan marketing data science marketing analytics case studies algorithmic marketing https github com ikatsov algorithmic examples models from introduction to algorithmic marketing book marketing scripts https github com howardntust marketing data science application marketing data science applications social mining https github com mikhailklassen mining the social web 3rd edition tree master notebooks mining the social web a name miscellaneous a miscellaneous a name miscellaneous art a art painting forensics https github com ivan bilan painting forensics analysing paintings to find out their year of creation a name miscellaneous tour a tourism flickr https github com xiaofei6677 tourismflickrminer metadata mining tool for tourism research fashion https github com khanhnamle1994 fashion recommendation a clothing retrieval and visual recommendation model for fashion images a name physics a physics a name physics general a general gamma hadron reconstruction https github com fvisconti gammas machine learning tools used in gamma ray ground based astronomy curriculum https github com callysto curriculum notebooks tree master physics newtonian notebooks interaction networks https github com higgsfield interaction network pytorch interaction networks for learning about objects relations and physics particle physics https github com hep lbdl adversarial jets training generation and analysis code for learning particle physics computational physics https github com ernestyalumni compphys a computational physics repository medical physics https github com robmarkcole useful python for medical physics useful python for medical physics medical physics 2 https github com pymedphys pymedphys a common core python package for medical physics flow physics https github com fpal stanford university floatpy flow physics and aeroacoustics toolbox with python a name physics ml a machine learning physics ml and stats https github com dkirkby machinelearningstatistics machine learning and statistics for physicists high energy https github com arogozhnikov hep ml machine learning for high energy physics high energy gan https github com hep lbdl calogan generative adversarial networks for high energy physics neural networks https github com giggleliu marburg p hysics meets neural networks a name public a government and public works a name public social a social policies triage https github com dssg triage general purpose risk modeling and prediction toolkit for policy and social good problems world bank poverty i https github com worldbank ml classification algorithms poverty tree master notebooks a comparative assessment of machine learning classification algorithms applied to poverty prediction world bank poverty ii https github com avsolatorio world bank pover t tests solution repository for the world bank pover t test competition solution overseas company land ownership overseas company land ownership https github com global witness overseas companies land ownership blob master overseas companies land ownership analysis ipynb identifying foreign ownership in the uk cfpb https github com maydogdu consumerfinancialprotectionbureau blob master cfpb complaints 2017september ipynb consumer finances protection bureau complaints analysis cannabis legalisation effect https github com tslindner effects of cannabis legalization effects of cannabis legalization on crime public credit card https github com dmodjeska barnet transactions blob master barnet transactions analysis ipynb identification of potential fraud for council credit cards data https open barnet gov uk dataset corporate credit card transaction 2016 17 recidivism prediction https github com shayanray glassbox tree master mlpredictor transparency and audibility to recidivism risk assessment household poverty https github com featuretools predict household poverty predict poverty in households in costa rica nlp public policy https github com ancilcrayton nlp public policy an example of an nlp use case in public policy world food production https github com roshank1605a04 world food production comparing top food and feed producers around the globe tax inequality https github com datascienceforgood taxationinequality data project around taxation and inequality in basel stadt sheriff compliance https github com austinbrian sheriffs compliance to ice requests apps detection https github com mengchuanfu suspecious apps detection suspicious app detection for kids social assistance https github com farkhondehm social assistance trending information on social assistance computational social science https github com abjer sds tree master material social data science summer school course liquor and crime https github com bhaveshgoyal safeliquor effect of liquor licenses issued on the crime rate animal placement kennels https github com austinpetsalive distemper outbreak optimising animal placement in shelters staffing wall https github com ryanschaub the u s mexican border wall and staffing a statistical approach independent exploration project on u s mexican border wall worker fatalities https github com zischwartz workerfatalities worker fatalities and catastrophes map from osha data a name public charity a charities census data api https github com johnfwhitesell censuspull blob master census acs5 pull ipynb pull variables from the 5 year american community survey philantropic giving https github com datakind datadive gates92y proj3 form990 work done by numerous datakind volunteers on harnessing form 990 data charity recommender https github com chris manna charity recommender nyc charity collaborative recommender system on an implicit dataset donor identification https github com gouravaich finding donors for charity a machine learning project in which we need to find donors for charity us charities https github com staceb charities in the united states charity exploration and machine learning charity effectiveness https github com laurachen 02 metis web scraping scraping online data about charities to understand effectiveness a name public election a election analysis election analysis https github com 1jinwoo deepwave blob master dr random forest ipynb election analysis and prediction models american election causal https github com akesari12 ls123 data prediction law spring 2019 blob master labs ols 20for 20causal 20inference ols causal inference solution ipynb using anes data with causal inference models campaign finance and election results https github com sfbrigade datasci campaign finance blob master notebooks ml 20campaign 20finance 20and 20election 20results 20example ipynb investigating the relation between campaign finance and subsequent election results voting system https github com nealmcb pr voting methods proportional representation voting methods president vote https github com austinbrian portfolio blob master tax votes president counties ipynb vote by income level analysis a name public politics a politics congressional politics https github com kaumaron data science tree master congressional partisanship house and senate congressional partisanship politico https github com okfn brasil perfil politico a platform for profiling public figures in brazilian politics bots https github com participapy politic bots tools and algorithms to analyze paraguayan tweets in times of election gerrymander tests https github com princetonuniversity gerrymandertests lots of metrics for quantifying gerrymandering sentiment https github com julianmar11 sentimentpoliticalcompass analyse newspapers with respect to their political conviction using entity sentiments of party representatives dl politics https github com muntisa deep politics prediction of spanish political affinity with deep neural nets socialist vs people s party pac money https github com edmundooo more money more problems effects of pac money on us politics power networks https github com abhiagar90 power networks constructing a watchdog for indian corporate and political networks elite https github com philippschmalen project tsds political elite in the us debate analysis https github com kkirchhoff01 debateanalysis program to analyze political debates political affiliation https github com davidjwiner political affiliation prediction political affiliation prediction using twitter metadata political ads https github com philiplbean facebook political ads investigation into facebook political ads and targeting political identity https github com pgromano political identity analysis multi axial political model yt politics https github com kmunger yt descriptive mapping politics on youtube political ideology https github com albertwebson political vector projector unsupervised learning of political ideology by word vector projections a name realestate a real estate rental leasing a name realestate real a real estate finding donuts https github com greteldepaepe findingdonuts finding real estate opportunities by predicting transforming neighbourhoods neighbourhood https github com denadai2 real estate neighborhood prediction predicting real estate prices from the urban environment real estate classification https github com sardhendu propertyclassification classifying the type of property given real estate satellite and street view images recommender https github com hyattsaleh15 realstaterecommender this tools aims to recommend a user the top 5 real estate properties that matches their search house price https github com shreyas3108 house price prediction predicting house prices using linear regression and gbr house price portland https github com girishkuniyal predict housing prices in portland predict housing prices in portland zillow prediction https github com eswar3 zillow prediction models zillow valuation prediction as performed on kaggle real estate https github com denadai2 real estate neighborhood prediction predicting real estate prices from the urban environment a name realestate rent a rental leasing analysing rentals https github com ual rental listings analyzing and visualizing rental listings data interest prediction https github com mratsim apartment interest prediction predict people interest in renting specific nyc apartments housing uni vs non uni https github com 5x12 pwc europe data analytics hackathon the effect on university lodging after the gfc predict household poverty https github com featuretools predict household poverty predict the poverty of households in costa rica using automated feature engineering airbnb public analytics competition http inseaddataanalytics github io inseadanalytics groupprojects airbnbreport2016jan html now strategic management a name utilities a utilities a name utilities elec a electricity electricity price https github com luqmanhakim research on sp wholesale blob master research on sp wholesale plan ipynb electricity price comparison singapore electricity coal correlation https github com richardddli state electricity rates determining the correlation between state electricity rates and coal generation over the past decade electricity capacity https github com datadesk california electricity capacity analysis a los angeles times analysis of california s costly power glut electricity systems https github com pypsa whobs optimal wind hydrogen other battery solar whobs electricity systems for european countries load disaggregation https github com pipette electricity load disaggregation smart meter load disaggregation with hidden markov models price forecasting https github com farwacheema da electricity price forecasting forecasting day ahead electricity prices in the german bidding zone with deep neural networks carbon index https github com gschivley carbon index calculation of electricity co intensity at national state and nerc regions from 2001 present demand forecasting https github com hvantil electricitydemandforecasting electricity demand forecasting for austin electricity consumption https github com un modelling electricity consumption surveys estimating electricity consumption from household surveys household power consumption https github com amirrezaeian individual household electric power consumption data set individual household power consumption lstm electricity french distribution http inseaddataanalytics github io inseadanalytics groupprojects group energy html an analysis of electricity data provided by the french distribution network rte renewable power plants https github com open power system data renewable power plants time series of cumulated installed capacity wind farm flow https github com fused wind fused wake a repository of wind plant flow models connected to fused wind power plant https github com yungchunlu uci power plant the dataset contains 9568 data points collected from a combined cycle power plant over 6 years 2006 2011 a name utilities coal a coal oil gas coal phase out https github com samarthiith de coalphaseout generation adequacy issues with germany s coal phaseout coal prediction https github com jean njoroge coal exploratory tree master notebooks predicting coal production oil gas https github com sdasadia oil natural gas price prediction oil natural gas price prediction using arima neural networks gas formula https github com cep kse natural gas formula calculating potential economic effect of price indexation formula demand prediction https github com victorpena1 natural gas demand prediction natural gas demand prediction consumption forecasting https github com williamadams1 natural gas consumption forecasting natural gas consumption forecasting gas trade https github com bahuisman natgasmodel world model for natural gas trade a name utilities water a water pollution safe water https github com codeforboston safe water predict health based drinking water violations in the united states hydrology data https github com mroberge hydrofunctions a suite of convenience functions for exploring water data in python water observatory https github com sentinel hub water observatory backend monitoring water levels of lakes and reservoirs using satellite imagery water pipelines https github com wassname pipe segmentation using machine learning to find water pipelines in aerial images water modelling https github com awracms awra cms older australian water resource assessment awra community modelling system drought restrictions https github com datadesk california ccscore analysis a los angeles times analysis of water usage after the state eased drought restrictions flood prediction https github com cadrev lstm flood prediction applying lstm on river water level data sewage overflow https github com jesseanddd sewer overflow insights into the sanitary sewage overflow sso this has been removed water accounting https github com johnpfay uswateraccounting assembles water budget data for the us from existing data source air quality prediction https github com txytju air quality prediction predict air quality aq in beijing and london in the next 48 hours a name utilities trans a transportation transdim https github com xinychen transdim creating accurate and efficient solutions for the spatio temporal traffic data imputation and prediction tasks transport recommendation https github com alanconstantine kdd cup 2019 cammtr context aware multi modal transportation recommendation transport data https github com cityoftoronto bdit data sources data and notebooks for toronto transport transport demand https github com pawelmorawiecki traffic jam nairobi predicting demand for public transportation in nairobi demand estimation https github com lemma1 dpfe implementation of dynamic origin destination demand estimation congestion analysis https github com hackoregon transportation congestion analysis transportation systems analysis ts analysis https github com nishanthgampa time series analysis on transportation data time series analysis on transportation data network graph subway https github com fangshulin vulnerability analysis for transportation networks vulnerability analysis for transportation networks have been taken down transportation inefficiencies https github com akpen stockholm 0 1 quantifying the inefficiencies of transportation networks train optimisation https github com crowdai train schedule optimisation challenge starter kit train schedule optimisation traffic prediction https github com mratsim mckinsey smartcities traffic prediction multi attention recurrent neural networks for time series city traffic predict crashes https github com data4democracy crash model crash prediction modelling application that leverages multiple data sources ai supply chain https github com llsourcell ai supply chain supply chain optimisation system transfer learning flight delay https github com cavaunpeu flight delays using variation encoders in keras to predict flight delay replenishment https github com pratishthakapoor retailreplenishement tree master code retail replenishment code for supply chain management a name wholesale a wholesale retail a name wholesale whole a wholesale customer analysis https github com kralmachine wholesalecustomeranalysis wholesale customer analysis distribution https github com semionn jb wholesale distribution analysis jb wholesale distribution analysis clustering https github com prakhardogra921 clustering analysis on customers of a wholesale distributor unsupervised learning techniques are applied on product spending data collected for customers market basket analysis https github com tstreamdoth instacart market basket analysis instacart public dataset to report which products are often shopped together a name wholesale retail a retail retail analysis https github com sarahmestiri online retail case studying online retail dataset and getting insights from it online insights https github com roshank1605a04 online retail transactions of uk analyzing the online transactions in uk retail use case https github com ibm dse cybershop analytics notebooks data for cybershop retail use case dwell time https github com arvindkarir retail customer dwell time and other analysis retail cohort https github com finnqiao cohort online retail cohort analysis | machine-learning jupyter-notebook datascience example python practical-machine-learning data-science firmai | ai |
CrossApp | crossapp crossapp c opengl es2 0 ui ios 9 0 sdk android 6 0 sdk mac osx 10 12 sdk c javascript qq 1 345156390 2 70268065 3 63254880 http crossapp 9miao com github https github com 9miao crossapp git gitee https gitee com 9miao crossapp git | front_end |
|
rtos-m0-3 | rtos m0 3 the freertos 202212 00 release updates freertos kernel freertos tcp coremqtt corepkcs11 corehttp corejson aws iot over the air updates ota aws iot device shadow aws iot jobs aws iot device defender backoff algorithm aws iot fleet provisioning coresntp sigv4 and freertos cellular interface libraries to their lts 2 0 versions it also updates coremqtt agent to v1 2 0 to be compatible with coremqtt v2 x x and updates mbedtls to v3 2 1 this release also adds visual studio static library projects for the freertos kernel freertos tcp logging mbedtls corehttp and corepkcs11 with the addition of the static library projects all visual studio projects have been updated to use them additionally all demos dependent on coremqtt have been updated to work with coremqtt v2 x x getting started the freertos org website contains a freertos kernel quick start guide a list of supported devices and compilers the api reference and many other resources getting help you can use your github login to get support from both the freertos community and directly from the primary freertos developers on our active support forum the faq provides another support resource cloning this repository this repo uses git submodules to bring in dependent components note if you download the zip file provided by the github ui you will not get the contents of the submodules the zip file is also not a valid git repository if using windows because this repository and its submodules contain symbolic links set core symlinks to true with the following command git config global core symlinks true in addition to this either enable developer mode or whenever using a git command that writes to the system e g git pull git clone and git submodule update init recursive use a console elevated as administrator so that git can properly create symbolic links for this repository otherwise symbolic links will be written as normal files with the symbolic links paths in them as text this gives more explanation to clone using https git clone https github com freertos freertos git recurse submodules | os |
|
Coldairarrow.Fx.Net.Easyui.GitHub | coldairarrow fx net easyui github web net452 https github com coldairarrow coldairarrow fx net easyui github wiki | front_end |
|
stable-diffusion-react-nextjs-mui-pwa | stable diffusion react nextjs mui pwa pwa web app front end for stable diffusion on react nextjs with material ui copyright c 2022 by gadi cohen dragon wastelands net mit licensed span style font size larger a href https kiri art kiri art a span new project in active development since aug 31st 2022 img align right src docs img cover2 jpg alt super dog web interface to run stable diffusion queries on local pc local installation banana dev https banana dev serverless gpu containers roughly 1 200 requests ymmv local banana dev docker container see docs banana local md docs banana local md others why make this fun stuff more accessible to web developers and friends see the live demo https kiri art run on your own pc for free or deploy if you have a background in web dev dev ops and have wanted to experiment a bit with machine learning latent diffusion ai image generation this is a great project to get involved in doing this in my very limited spare time prs more likely to get responses than issues but try me to develop 1 clone repo 1 yarn install 1 edit env local or just set local vars per below 1 yarn dev note you ll need at least one destination target from the list below where stable diffusion will run destinations targets local docker image recommended pretty easy if you have docker installed see https github com kiri art docker diffusers api local exec if you already have stable diffusion installed locally this will run the python script via node spawn set stable diffusion home to e g home user src stable diffusion works but not as well maintained as the docker based solutions remote bananadev docker container serverless gpu great for local dev if you don t have a supported gpu default for deployments or when node env production i m paying roughly 1 200 requests with default params ymmv follow instructions at https github com kiri art docker diffusers api set banana api key and banana model key env variables set the relevant keys for your deployed models banana model key sd v1 5 by default require registration by default registration i e sign up log in use credits is required in production only you can turn it on in development to test the auth flow or turn it off in production if you re deploying somewhere private bash require registration 1 next public require registration 1 note next public vars are compiled at build time so if you want to deploy to production without requiring registration set it to 0 before building and deploying todo docker image for super easy start vercel clone button i18n we use nextjs s built in i18n routing https nextjs org docs advanced features i18n routing and lingui https lingui js org tutorials setup react html for translations useful commands yarn i18n extract to extract strings send locales messages po to translators resave yarn i18n compile before deploy see also lingui config js lingui config js and locales locales dir refs https github com mui material ui tree master examples nextjs with typescript | materialui mui stable-diffusion stablediffusion | front_end |
chessbot_python | chessbot why create another chessbot the explanation is simple i did not find a free bot i liked online all the bots i saw on internet are parsing the html of the different websites to find the positions but it creates a big limitation if there is a new website or a new html organisation nothing will work on the other hand my bot just looks at the screen and work with it to find the chessboard and the pieces it is much more robust this chess bot can play automatically as white or black on lichess com chess com chess24 com and theoretically any website using drag and drop to move pieces it uses stockfish engine to process moves mss to do fast screenshots pyautogui to move the mouse chess to store and test the moves and opencv to detect the chessboard it has been written only with python about the bot level it beats easily chess com computer on level 8 10 around 2000 elo when taking 1 2 second per move and crushes every human opponent on any time format longer than 1 minute this bot has been developped on ios but all the librairies it is using are compatible on linux and windows too getting started prerequisites stockfish this bot uses stockfish to calculate the next best move here is the procedure to make it work download stockfish for your os https stockfishchess org download the macos stockfish i used is already commited add it to your path with export path path pwd test that stockfish is working well by running the command stockfish in your terminal it should output something like this stockfish 120218 64 by t romstad m costalba j kiiski g linscott python this bot requires python 3 to run using the bot the bot runs very easily go in the folder that contains the source code run the command python3 main py limitations this project is far from perfect yet it has a few limitations because of the computer vision algorithm used to detect the chessboard the square colors should be with plain colors without having wierd textures the gui is still quite basic one small deviation during a game the board moved the user touched the mouse and the bot will not work at all it is not possible to stop the chessbot without closing the window this project has been tested only on a mac please feel free to help me improve it author stanislas heili initial work mygit https github com stanou01260 | python bot chessbot pyautogui opencv-python | ai |
trust-web3-provider | trustweb3provider github release latest semver https img shields io github v release trustwallet trust web3 provider license https img shields io cocoapods l trustweb3provider svg style flat http cocoapods org pods trustweb3provider platform https img shields io cocoapods p trustweb3provider svg style flat http cocoapods org pods trustweb3provider platform https img shields io badge platform android lightgrey svg https jitpack io trustwallet trust web3 provider 0 2 1 trustweb3provider is multi network web3 provider used by trustwallet currently it supports ethereum solana how to identify trust provider if trust provider injected properly istrust will be true javascript window ethereum istrust or window trustwallet solana istrust installation ios trustweb3provider is available through cocoapods and spm locally due to xcode git lfs issue cocoapods add this line to your podfile ruby pod trustweb3provider git https github com trustwallet trust web3 provider branch master swift package manager add this repo as a git submodule then add it this to your package swift swift package name trustweb3provider path local path here is an example project located at ios trustweb3provider xcodeproj to demonstrate how to use this provider android trustweb3provider is available through jitpack https jitpack io jitpack to install it step 1 add jitpack to repositories in your root build gradle file groovy allprojects repositories maven url https jitpack io step 2 add the dependency groovy dependencies implementation com github trustwallet trust web3 provider tag authors vikmeup https github com vikmeup hewigovens https github com hewigovens madcake https github com madcake rsrbk https github com rsrbk license trustweb3provider is available under the mit license see the license file for more info | dapps web3 blockchain swift | blockchain |
C | the algorithms c mainpage the suffix in the above line is required for doxygen to consider this as the index page of the generated documentation site gitpod ready to code https img shields io badge gitpod ready to code blue logo gitpod https gitpod io https github com thealgorithms c codeql ci https github com thealgorithms c actions workflows codeql yml badge svg https github com thealgorithms c actions workflows codeql analysis yml gitter chat https img shields io badge chat gitter ff69b4 svg label chat logo gitter style flat square https gitter im thealgorithms contributions welcome https img shields io static v1 svg label contributions message welcome color 0059b3 style flat square https github com thealgorithms c blob master contributing md github repo size https img shields io github repo size thealgorithms c color red style flat square doxygen ci https github com thealgorithms c workflows doxygen 20ci badge svg https thealgorithms github io c awesome ci https github com thealgorithms c workflows awesome 20ci 20workflow badge svg https github com thealgorithms c actions query workflow 3a 22awesome ci workflow 22 income https img shields io liberapay receives thealgorithms svg logo liberapay https liberapay com thealgorithms discord chat https img shields io discord 808045925556682782 svg logo discord colorb 5865f2 https the algorithms com discord donate https liberapay com assets widgets donate svg https liberapay com thealgorithms donate overview the repository is a collection of open source implementations of a variety of algorithms implemented in c and licensed under gplv3 license https github com thealgorithms c blob master license the algorithms span a variety of topics from computer science mathematics and statistics data science machine learning engineering etc the implementations and their associated documentations are meant to provide a learning resource for educators and students hence one may find more than one implementation for the same objective but using different algorithm strategies and optimizations features the repository provides implementations of various algorithms in one of the most fundamental general purpose languages c https en wikipedia org wiki c programming language well documented source code with detailed explanations provide a valuable resource for educators and students alike each source code is atomic using standard c library libc https en wikipedia org wiki c standard library and no external libraries are required for their compilation and execution thus the fundamentals of the algorithms can be studied in much depth source codes are compiled and tested https github com thealgorithms c actions query workflow 3a 22awesome ci workflow 22 for every commit on the latest versions of two major operating systems viz macos and ubuntu linux using appleclang 14 0 0 and gnu 11 3 0 respectively strict adherence to c11 https en wikipedia org wiki c11 c standard revision standard ensures portability of code to embedded systems as well like esp32 arm cortex etc with little to no changes self checks within programs ensure correct implementations with confidence modular implementations and opensource licensing enable the functions to be utilized conveniently in other applications documentation online documentation https thealgorithms github io c is generated from the repository source codes directly the documentation contains all resources including source code snippets details on execution of the programs diagrammatic representation of program flow and links to external resources where necessary click on files menu https thealgorithms github io c files html to see the list of all the files documented with the code documentation of algorithms in c https thealgorithms github io c by the algorithms contributors https github com thealgorithms c graphs contributors is licensed under cc by sa 4 0 https creativecommons org licenses by sa 4 0 ref chooser v1 br a href https creativecommons org licenses by sa 4 0 img alt creative commons license style height 22px important margin left 3px vertical align text bottom src https mirrors creativecommons org presskit icons cc svg img alt credit must be given to the creator style height 22px important margin left 3px vertical align text bottom src https mirrors creativecommons org presskit icons by svg img alt adaptations must be shared under the same terms style height 22px important margin left 3px vertical align text bottom src https mirrors creativecommons org presskit icons sa svg a contributions as a community developed and maintained repository we welcome new un plagiarized quality contributions please read our contribution guidelines https github com thealgorithms c blob master contributing md | algorithms data-structures datastructures c algorithm-challenges algorithm-competitions education learn-to-code interview-questions interview community-driven mathematics sort educational machine-learning-algorithms machine-learning computer-science search | ai |
pml2-book | probabilistic machine learning advanced topics by kevin murphy this repo is used to store the pdf for a href https probml github io pml book book2 html book 2 a see releases tab on rhs this lets me keep track of downloads and issues in a way which can be tracked separately from a href https probml github io pml book book1 html book 1 a | ai |
|
eliza-rs | eliza rs crates io https img shields io crates v eliza svg https crates io crates eliza documentation https docs rs eliza badge svg https docs rs eliza build status https travis ci org arosspope eliza rs svg branch master https travis ci org arosspope eliza rs this rust binary is an implementation of the early chatbot program eliza the original program was developed from 1964 to 1966 at the mit artificial intelligence laboratory by joseph weizenbaum introduction convo http i imgur com z69mfi8 gif eliza simulates conversation by implementing pattern matching and a substitution methodology that gives users an illusion of understanding on the part of the program directives on how to process input are provided by scripts written originally in mad slip now in json which allow eliza to engage in discourse by following script rules weizenbaum s intention was to demonstrate that the communication between man and machine is superficial the most famous script doctor scripts doctor json simulates a rogerian psychotherapist weizenbaum j 1996 eliza a computer program for the study of natural language communication between man and machine communications of the acm vol 9 issue 1 installation to install this rust binary one can do so from source or from crates io https crates io crates eliza in either case you need to have the rust compiler and cargo installed https rustup rs on your system from crates io installing eliza from crates io is quite simple with cargo bash user foo cargo install eliza from source after forking this project and cloning it to your local machine navigate to the project directory and run bash user foo eliza rs cargo build you may also want to optionally run the unit tests to ensure eliza is behaving as expected bash user foo eliza rs cargo test usage to start an eliza session you must provide the binary with a path to an eliza script this script takes the form of a json file assuming that you have installed from source and wanted to run the famous doctor program the command you would run from the project root would be similar to bash user foo eliza rs cargo run scripts doctor json if instead you installed from crates io then the location of doctor json will be different out of convenience i decided to bundle the doctor json script with the eliza binary on crates io for each user it s location will be slightly different within the crates registry so i would suggest moving it to somewhere more memorable before running bash user foo cp cargo registry src some hash eliza ver scripts doctor json user foo eliza doctor json running https i imgur com runeq7b gif starting eliza with cargo then leaving the session writing your own eliza script the beauty of eliza s design methodology means that the role of the programmer and playwright are separated an important property of eliza is that a script is data it is not part of the program itself hence eliza is not restricted to a particular set of recognition patterns or responses indeed not even to any specific language as such contributors may decide to improve the original doctor json script or completely create their own from scratch a simple example of a pirate script scripts pirate json has been included to show how little is needed to start creating something neat more information on the structure of a script can be found in the documentation for the script module on doc rs https docs rs eliza testing due to the somewhat deterministic nature of eliza you can write unit tests to evaluate script rules for example in tests conversation test rs you could add the following rust test fn your test let mut e eliza new scripts your script json unwrap assert eq bar e respond foo where foo is the users input to eliza and bar is the response it is also important to note that eliza produces logging output to observe these logs during program execution start the binary with the environment variable rust log eliza | eliza nlp weizenbaum linguistics chatbot regex | ai |
iotweb | embedded http and websocket server for uwp net 4 5 the iotweb class library allows you to embed a simple http and websocket server into your c application the library supports both the net 4 5 framework as well as the universal windows platform allowing you to target mono http www mono project com windows desktop and windows 10 iot core https dev windows com en us iot the library is released under the creative commons by sa license http creativecommons org licenses by sa 4 0 this means you can use the library in commercial and non commercial projects as long as you provide attribution a link to this page will be fine and release any changes you make under the same license the full source code is available on github https github com sensaura public iotweb please raise any bug reports or feature requests on the issues page https github com sensaura public iotweb issues for the project you can install the library using nuget https www nuget org packages iotweb be aware that the nuget package will lag behind the source repository only stable updates will be released for help examples and discussions you can join the sensaura slack team https sensaura slack com get your invitation here https docs google com forms d 1ptcu0a5u7ozh136bmpcs3jx0vpocgiwvec2fyyvhnyq viewform and jump in to the iotweb channel the repository includes a simple websocket echo server implementation for net 4 5 https github com sensaura public iotweb tree master webhost 20desktop and the universal windows platform tutorials and examples will also be posted to the project page on sensaura org http sensaura org pages tools iotweb index html as well as the sensaura blog http sensaura org blog index html | server |
|
Okapi | h1 align center p okapi p h1 h3 align center p instruction tuned large language models in multiple languages with reinforcement learning from human feedback p h3 div align center a href https github com nlp uoregon okapi blob main license img alt github src https img shields io badge code 20license apache 2 0 green svg a a href https github com nlp uoregon okapi blob main data license img alt github data src https img shields io badge data 20license cc 20by 20nc 204 0 red svg a div overview this is the repo for the okapi framework that introduces resources and models for instruction tuning for large language models llms with reinforcement learning from human feedback rlhf in multiple languages our framework supports 26 languages including 8 high resource languages 11 medium resource languages and 7 low resource languages p align center img src assets okapi languages png width 450 p okapi resources we provide resources to perform instruction tuning with rlhf for 26 languages including chatgpt prompts multilingual instruction datasets and multilingual response ranking data okapi models we provide rlhf based instruction tuned llms for 26 languages on okapi dataset our models include both bloom based and llama based versions we also provide scripts to interact with our models and fine tune llms with our resources multilingual evaluation benchmark datasets we provide three benchmark datasets for evaluating multilingual large language models llms for 26 languages you can access the full datasets and evaluation scripts here https github com nlp uoregon mlmm evaluation usage and license notices okapi is intended and licensed for research use only the datasets are cc by nc 4 0 allowing only non commercial use and models trained using the dataset should not be used outside of research purposes our technical paper with evaluation results can be found here https arxiv org abs 2307 16039 dataset creation we perform a comprehensive data collection process to prepare necessary data for our multilingual framework okapi in four major steps 1 english instruction generation first we obtain 52k english instructions for tuning llms from alpaca afterward we apply the same self instruct procedure as alpaca to generate 106k additional english instructions resulting in a larger combined dataset of 158k instructions for our rlhf based models in okapi 2 instruction translation we utilize chatgpt to translate our 158k english instructions into 26 target languages for each language the 158k instructions will be divided into data for supervised fine tuning reward modeling and rlhf 3 ranking data production we employ chatgpt to rank multiple response outputs for the same instructions from llms to produce response ranking data for multiple languages we introduce a two turn dialog approach i instructions and responses are first translated into english and ii chatgpt then helps rank the translated data in english 4 evaluation data creation we leverage three datasets in the huggingface open llm leaderboard i e ai2 reasoning challenge arc hellaswag and mmlu to evaluate the multilingual fine tuned llms as these datasets are originally provided for english only we translate them into 26 languages in our framework using chatgpt the evaluation data and scripts can be found here https github com nlp uoregon mlmm evaluation downloading datasets to download the entire dataset you can use the following script bash bash scripts download sh if you only need the data for a specific language you can specify the language code as an argument to the script bash bash scripts download sh lang for example to download the dataset for vietnamese bash scripts download sh vi after downloading our released data can be found in the datasets directory it includes multilingual alpaca 52k the translated data for 52k english instructions in alpaca into 26 languages multilingual ranking data 42k the multilingual response ranking data for 26 languages for each language we provide 42k instructions each of them has 4 ranked responses this data can be used to train reward models for 26 languages multilingual rl tuning 64k the multilingual instruction data for rlhf we provide 62k instructions for each of the 26 languages model using our okapi datasets and the rlhf based instruction tuning technique we introduce multilingual fine tuned llms for 26 languages built upon the 7b versions of llama and bloom the models can be obtained from huggingface here https huggingface co uonlp chat with our models okapi supports interactive chats with the multilingual instruction tuned llms in 26 languages following the following steps for the chats 1 clone our repository git clone https github com nlp uoregon okapi git cd okapi pip install r requirements txt 2 select a model for a language and chat with it for each of the 26 languages both bloom based and llama based models can be selected the full list of the models can be found here https huggingface co uonlp python from chat import pipeline model path uonlp okapi vi bloom p pipeline model path gpu true instruction d ch c u sau sang ti ng vi t translate the following sentence into vietnamese prompt input the city of eugene a great city for the arts and outdoors response p generate instruction instruction prompt input prompt input print response training we also provide scripts to fine tune llms with our instruction data using rlhf covering three major steps supervised fine tuning reward modeling and fine tuning with rlhf use the following steps to fine tune llms set up the environment bash conda create n okapi python 3 9 conda activate okapi pip install r requirements txt training with multiple gpus 1 supervised fine tuning bash bash scripts supervised finetuning sh lang 2 reward modeling bash bash scripts reward modeling sh lang 3 fine tuning with rlhf bash bash scripts rl training sh lang citation if you use the data model or code in this repository please cite bibtex article dac2023okapi title okapi instruction tuned large language models in multiple languages with reinforcement learning from human feedback author dac lai viet and van nguyen chien and ngo nghia trung and nguyen thuat and dernoncourt franck and rossi ryan a and nguyen thien huu journal arxiv e prints pages arxiv 2307 year 2023 | bloom chatbot dataset instruction-tuning language-model large-language-models multilingual nlp question-answering reinforcement-learning reinforcement-learning-from-human-feedback rlhf natural-language-processing llama | ai |
A1-Frontend-Fundamentals-2020 | m dulo front end fundamentals wave bienvenid s si te consideras una persona curiosa de saber c mo funciona la web tienes un computador port til a la mano y est s dispuest a abrir tu mente a un mundo sin l mites este curso es para ti no necesitas tener ning n background t cnico solo aseg rate de contar con una m quina con la que puedas trabajar y de conocer su funcionamiento b sico a nivel de usuario dart objetivo en este curso aprender s las bases del desarrollo front end en el cu l podr s adquirir habilidades que te ayudar n a crear un sitio web est tico desde cero usando los lenguajes bases como html y css adem s de ciertas herramientas que har n que tu flujo de desarrollo sea m s r pido y profesional proyecto en este curso terminar s construyendo el landing page de un negocio esto significa que podr s tener una p gina publicada en internet y accesible a todo el mundo para dar a conocer un negocio o incluso sobre ti mism adem s este sitio ser capaz de permitir a las personas contactarte a trav s de un formulario el cual te enviar un email cada vez que alguien deje sus datos de contacto gear instalaci n qu necesito para empezar a programar para la web el objetivo es que tengas todo lo necesario en tu computador para poder llegar a desgastar los dedos en tu primera sesi n y no invertir tiempo en configurar de tu entorno de desarrollo a continuaci n encontraras la instalaci n https github com beduexpert a1 frontend fundamentals 2020 blob master instalaci n md necesaria para poder iniciar a programar bookmark tabs sesiones 1 git y terminal estructura tu sitio 2 html5 y css3 agregando barra de navegaci n 3 html flexbox dividiendo secciones en columnas 4 css agregando filas y columnas con css grid 5 responsive design adaptando a la vista desde un m vil 6 css frameworks reutilizando componentes visuales 7 preprocesadores de css optimizando la producci n de css 8 email development deploy de un sitio est tico email de bienvenida | front_end |
|
awesome-blockchain-rust | awesome blockchain rust useful components for building blockchains in rust include cryptography distributed p2p consensus etc blockchains blockchains blockchain frameworks blockchain frameworks cross chain cross chain virtual machines virtual machines general purpose consensus general purpose consensus p2p network libraries p2p network libraries cryptography cryptography layer2 layer2 dapps dapps other other contribute contribute license license blockchains aleo https github com aleohq a blockchain with zero knowledge transactions aleph zero https github com aleph zero foundation dag pos snark smart contracts substrate based anoma network https github com anoma pos blockchain with privacy aptos network https github com aptos labs building the safest and most scalable layer 1 blockchain bitcoin cash https github com be cash bitcoin cash a library for creating and parsing bitcoin cash transactions casper https github com casper network casper node a decentralized l1 pos blockchain designed to accelerate enterprise and developer adoption cita https github com cryptape cita a high performance blockchain kernel for enterprise users codechain https github com codechain io codechain programmable multi asset chain concordium https github com concordium privacy centric zk pos chain yet with built in identities and rust smart contracts conflux https github com conflux chain conflux rust the rust implementation of conflux protocol darwinia https github com darwinia network darwinia relay chain of darwinia network can connect to polkadot as parachain in polkadot model dusk network https github com dusk network privacy pos using zk plonk enigma https github com enigmampc enigma core secures the decentralized web multiversx https github com multiversx sharded smart contracts execution platform with a pos consensus mechanism exonum https github com exonum exonum an extensible open source framework for creating private permissioned blockchain applications forest https github com chainsafe forest an implementation of filecoin written in rust fuel https github com fuellabs fuel core rust full node implementation of the fuel protocol gear https github com gear tech gear computational component of polkadot network grin https github com mimblewimble grin minimal implementation of the mimblewimble protocol holochain https github com holochain holochain the core holochain framework written in rust a container and hdk rust library for writing zomes huobi chain https github com huobigroup huobi chain the next generation high performance public chain for financial infrastructure interledger https github com interledger rs interledger rs an easy to use high performance interledger implementation written in rust internet computer protocol icp https github com dfinity ic the world s first blockchain that runs at web speed and can increase its capacity without bound internet of people https github com internet of people iop rs decentralized software stack that provides the building blocks and tools to support a decentralized society libra https github com libra libra global currency and financial infrastructure that empowers billions of people lighthouse https github com sigp lighthouse fast and secure ethereum 2 0 client near https github com nearprotocol nearcore near protocol scalable and usable blockchain nervos ckb https github com nervosnetwork ckb nervos ckb is a public permissionless blockchain the common knowledge layer of nervos network nym https github com nymtech nym selective privacy via a mixnet preventing metadata analysis nomic https github com nomic io nomic nomic is a high performance bitcoin sidechain which is part of the cosmos network mina protocol https github com chainsafe mina rs a rust implementation of the mina succinct blockchain mir protocol https github com mir protocol a succinct blockchain powered by zero knowledge proofs plonk based openethereum https github com openethereum openethereum the ethereum rust client parity bitcoin https github com paritytech parity bitcoin the parity bitcoin client parity ethereum https github com paritytech parity ethereum the fast light and robust evm and wasm client parity zcash https github com paritytech parity zcash rust implementation of zcash protocol polkadot https github com paritytech polkadot polkadot node implementation polymesh https github com polymathnetwork polymesh the polymesh blockchain built on substrate is an identity orientated chain for the issuance lifecycle management and settlement of regulated securities qan https github com qanplatform its alive post quantum blockchain radix https github com radixdlt radixdlt scrypto sharded smart contract defi platform rsnano https github com simpago rsnano node a rust port of nano the eco friendly feeless digital currency setheum https github com setheum labs setheum setheum secure evergreen truthful heterogeneous economically unbiased market is an ethical defi friendly blockchain built on substrate working on achieving mass adoption security scalability affordability inclusivity and ethical defi governance shasper https github com paritytech shasper parity shasper beacon chain implementation using the substrate framework solana https github com solana labs solana blockchain rebuilt for scale stacks 2 0 https github com blockstack stacks blockchain proof of transfer blockchain from blockstack sui network https github com mystenlabs sui a next generation smart contract platform with high throughput low latency and an asset oriented programming model powered by the move programming language tari https github com tari project the tari digital assets protocol tendermint https github com informalsystems tendermint rs tendermint is a high performance blockchain consensus engine for byzantine fault tolerant applications witnet https github com witnet witnet rust open source implementation of witnet decentralized oracle network protocol in rust xx network https github com xx labs xxchain post quantum blockchain mixnet privacy preventing metadata analysis substrate rust go zebra https github com zcashfoundation zebra an ongoing rust implementation of a zcash node zero chain https github com layerxcom zero chain a privacy preserving blockchain on substrate blockchain frameworks substrate https github com paritytech substrate the platform for blockchain innovators slingshot https github com stellar slingshot a new blockchain architecture under active development with a strong focus on scalability privacy and safety tendermint abci https github com tendermint rust abci tendermint abci server written in the rust programming language orga https github com nomic io orga a high performance state machine engine designed for tendermint based blockchain applications anchor https github com coral xyz anchor is a framework for solana s sealevel runtime providing several convenient developer tools for writing smart contracts cross chain atomicdex https github com komodoplatform atomicdex api cross chain and cross protocol p2p orderbook based decentralized exchange and interoperability bridge self custodial comit https github com comit network an open protocol facilitating trustless cross blockchain applications ibc rs https github com cosmos ibc rs rust implementation of the inter blockchain communication ibc protocol hermes https github com informalsystems hermes rust implementation of an inter blockchain communication ibc relayer virtual machines ckb vm https github com nervosnetwork ckb vm risc v virtual machine cosmwasm https github com cosmwasm cosmwasm multi chain smart contract platform built for the cosmos ecosystem evm parity https github com paritytech parity ethereum tree master evmbin parity implementation of evm fuelvm https github com fuellabs fuel vm interpreter for the fuelvm a blazingly fast blockchain virtual machine fvm https github com filecoin project ref fvm the filecoin virtual machine is a hypervisor inspired wasm execution environment that supports multiple runtimes including the evm lunatic https github com lunatic solutions lunatic erlang inspired runtime for webassembly polygon miden https github com maticnetwork miden snark based vm svm https github com spacemeshos svm spacemesh virtual machine wasmi https github com paritytech wasmi webassembly interpreter wasmer https github com wasmerio wasmer a convenient rust wrapper over webassembly backends wasmtime https github com cranestation wasmtime standalone jit style runtime for webassembly using cranelift zinc https github com matter labs zinc zinc zk smart contract language general purpose consensus raft https github com pingcap raft rs raft distributed consensus algorithm implemented in rust honey badger https github com poanetwork hbbft an implementation of the paper honey badger of bft protocols in rust narwhal https github com mystenlabs narwhal the consensus layer used by sui p2p network libraries chamomile https github com placefortea chamomile p2p library support build robust stable connection on p2p distributed network crust https github com maidsafe crust reliable p2p network connections in rust with nat traversal one of the most needed libraries for any server less decentralised projects rust libp2p https github com libp2p rust libp2p the rust implementation of the libp2p networking stack tentacle https github com driftluo tentacle a multiplexed p2p network framework that supports custom protocols p2p nat traversal https github com ustulation p2p nat traversal techniques for p2p communication qp2p https github com maidsafe qp2p peer to peer communications library for rust based on quic protocol sn routing https github com maidsafe sn routing routing specialised storage dht cryptography awesome cryptography rust https github com rust cc awesome cryptography rust dalek cryptography https github com dalek cryptography za https github com adria0 za an experimental rust zksnarks compiler with embeeded bellman bn128 prover openzkp https github com 0xproject openzkp pure rust implementations of zero knowledge proof systems microsoft nova https github com microsoft nova rust recursive snark without trusted setup arkworks https github com arkworks rs an ecosystem for developing and programming with zksnarks layer2 arbitrum s arb os https github com offchainlabs arb os arbos is the operating system that runs an eth layer 2 on an arbitrum chain noir language https github com noir lang noir noir is a domain specific language for snark proving systems aztec eth l2 penumbra https github com penumbra zone penumbra pos network providing privacy to the cosmos ecosystem rust lightning https github com rust bitcoin rust lightning is a bitcoin lightning library written in rust the main crate lightning does not handle networking persistence or any other i o thus it is runtime agnostic but users must implement basic networking logic chain interactions and disk storage zksync https github com matter labs zksync matter labs scaling eth l2 engine secured by zero knowledge proofs dapps serum dex https github com project serum serum dex a decentralized exchange built on solana sewup https github com second state sewup a library to help you build your ethereum webassembly contract with rust and just like develop in a common backend sienna network https github com siennanetwork siennanetwork a privacy first and cross chain decentralized finance platform where you can privately swap lend and convert your tokens into their private equivalent ink athon https github com scio labs inkathon full stack dapp boilerplate for substrate and ink smart contracts other abscissa https github com iqlusioninc abscissa micro framework for cli tools with strong focus on security tesseracts https github com adria0 tesseracts a small block explorer for geth poas written in rust merk https github com nomic io merk high performance merkle key value store written in rust based on rocksdb erc 4337 bundler https github com vid201 aa bundler an ongoing rust implementation of an erc 4337 account abstraction bundler contribute contributions are most welcome github awesome blockchain rust https github com rust in blockchain awesome blockchain rust license creative commons license http i creativecommons org l by 4 0 88x31 png http creativecommons org licenses by 4 0 this work is licensed under a creative commons attribution 4 0 international license http creativecommons org licenses by 4 0 | awesome blockchain rust cryptography p2p consensus | blockchain |
MOBILE-COMMUNICATION-AND-COMPUTING-AND-MOBILE-APPLICATION-DEVELOPMENT-LAB | mobile communication and computing and mobile application development lab csc702 mcc csl702 mad lab semester vii x syllabus https github com amey thakur mobile communication and computing and mobile application development lab blob main syllabus te 20be 20comp 20engg 20cbcgs 20syllabus pdf x mcc reference books https github com amey thakur mobile communication and computing and mobile application development lab tree main reference 20books assignments mcc assignment 1 https github com amey thakur mobile communication and computing and mobile application development lab blob main assignments amey b 50 mcc assignment 1 pdf experiments mobile application development lab virtual lab http vlabs iitkgp ac in fcmc index html mad lab experiment 1 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 1 pdf mad lab experiment 2 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 2 pdf mad lab experiment 3 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 3 pdf mad lab experiment 4 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 4 pdf mad lab experiment 5 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 5 pdf mad lab experiment 6 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 6 pdf mad lab experiment 7 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 7 pdf mad lab experiment 8 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 8 pdf mad lab experiment 9 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 9 pdf mad lab experiment 10 https github com amey thakur mobile communication and computing and mobile application development lab blob main experiments amey b 50 mcc experiment 10 pdf quizzes mcc quiz 1 https github com amey thakur mobile communication and computing and mobile application development lab blob main quizzes quiz 201 20mcc 20 csc702 pdf mcc quiz 2 https github com amey thakur mobile communication and computing and mobile application development lab blob main quizzes quiz 202 20mcc 20 csc702 pdf mcc quiz 3 https github com amey thakur mobile communication and computing and mobile application development lab blob main quizzes quiz 203 20mcc 20 csc702 pdf mcc quiz 4 https github com amey thakur mobile communication and computing and mobile application development lab blob main quizzes quiz 204 20mcc 20 csc702 pdf mcc quiz 5 https github com amey thakur mobile communication and computing and mobile application development lab blob main quizzes quiz 205 20mcc 20 csc702 pdf mcc quiz 6 https github com amey thakur mobile communication and computing and mobile application development lab blob main quizzes quiz 206 20mcc 20 csc702 pdf internal assessment test mcc iat 1 https github com amey thakur mobile communication and computing and mobile application development lab blob main internal 20assessment 20test amey b 50 mcc iat 1 pdf mcc iat 2 https github com amey thakur mobile communication and computing and mobile application development lab blob main internal 20assessment 20test amey b 50 mcc iat 2 pdf semester exam mcc answersheet https github com amey thakur mobile communication and computing and mobile application development lab blob main semester 20exam amey b 50 7278000 mcc pdf question papers previous question papers https github com amey thakur mobile communication and computing and mobile application development lab tree main question 20papers exit survey mcc course exit survey https github com amey thakur mobile communication and computing and mobile application development lab blob main exit 20survey mcc 20course 20exit 20survey pdf mad lab exit survey https github com amey thakur mobile communication and computing and mobile application development lab blob main exit 20survey mcc 20laboratory 20exit 20survey pdf p align center b subject as a part of the 7th semester of engineering university of mumbai b p p align center a href https github com amey thakur achievements engineering style color greenyellow back to engineering p | amey ameythakur engineering computer-engineering computer-science mcc mobile-communications mobile-application-development mobile-communication-networks computing textbooks | front_end |
golearn | golearn img src http talks golang org 2013 advconc gopherhat jpg width 125 br godoc https godoc org github com sjwhitworth golearn status png https godoc org github com sjwhitworth golearn build status https travis ci org sjwhitworth golearn png branch master https travis ci org sjwhitworth golearn br code coverage https codecov io gh sjwhitworth golearn branch master graph badge svg https codecov io gh sjwhitworth golearn support via gittip https rawgithub com twolfson gittip badge 0 2 0 dist gittip png https www gittip com sjwhitworth golearn is a batteries included machine learning library for go simplicity paired with customisability is the goal we are in active development and would love comments from users out in the wild drop us a line on twitter twitter golearn ml http www twitter com golearn ml install see here https github com sjwhitworth golearn wiki installation for installation instructions getting started data are loaded in as instances you can then perform matrix like operations on them and pass them to estimators golearn implements the scikit learn interface of fit predict so you can easily swap out estimators for trial and error golearn also includes helper functions for data like cross validation and train and test splitting go package main import fmt github com sjwhitworth golearn base github com sjwhitworth golearn evaluation github com sjwhitworth golearn knn func main load in a dataset with headers header attributes will be stored think of instances as a data frame structure in r or pandas you can also create instances from scratch rawdata err base parsecsvtoinstances datasets iris csv true if err nil panic err print a pleasant summary of your data fmt println rawdata initialises a new knn classifier cls knn newknnclassifier euclidean linear 2 do a training test split traindata testdata base instancestraintestsplit rawdata 0 50 cls fit traindata calculates the euclidean distance and returns the most popular label predictions err cls predict testdata if err nil panic err prints precision recall metrics confusionmat err evaluation getconfusionmatrix testdata predictions if err nil panic fmt sprintf unable to get confusion matrix s err error fmt println evaluation getsummary confusionmat iris virginica 28 2 56 0 9333 0 9333 0 9333 iris setosa 29 0 59 1 0000 1 0000 1 0000 iris versicolor 27 2 57 0 9310 0 9310 0 9310 overall accuracy 0 9545 examples golearn comes with practical examples dive in and see what is going on bash cd gopath src github com sjwhitworth golearn examples knnclassifier go run knnclassifier iris go bash cd gopath src github com sjwhitworth golearn examples instances go run instances go bash cd gopath src github com sjwhitworth golearn examples trees go run trees go docs english https github com sjwhitworth golearn wiki doc zh cn home md doc zh tw home md join the team please send me a mail at stephenjameswhitworth gmail com | ai |
|
JoppaTest | joppa this project was generated with angular cli https github com angular angular cli version 1 0 6 development server run ng serve for a dev server navigate to http localhost 4200 the app will automatically reload if you change any of the source files code scaffolding run ng generate component component name to generate a new component you can also use ng generate directive pipe service class module build run ng build to build the project the build artifacts will be stored in the dist directory use the prod flag for a production build running unit tests run ng test to execute the unit tests via karma https karma runner github io running end to end tests run ng e2e to execute the end to end tests via protractor http www protractortest org before running the tests make sure you are serving the app via ng serve further help to get more help on the angular cli use ng help or go check out the angular cli readme https github com angular angular cli blob master readme md icons this project uses open iconic to view icons go here https useiconic com open usage example span class oi oi icon name title icon name aria hidden true span | front_end |
|
Databases | databases projects for the databases course at pompeu fabra university for the computer engineering degree | database sql | server |
backpack-react-native | backpack design system for react native backpack is a collection of design resources reusable components and guidelines for creating skyscanner s products android actions build status https github com skyscanner backpack react native workflows android 20ci badge svg https github com skyscanner backpack react native actions ios actions build status https github com skyscanner backpack react native workflows ios 20ci badge svg https github com skyscanner backpack react native actions quick links documentation https skyscanner design changelog changelog md usage installation sh npm install backpack react native save details summary android summary from source our android code is written in kotlin so in order to compile it from source you need to have org jetbrains kotlin kotlin gradle plugin kotlin version in the classpath add the following your root build gradle file groovy buildscript ext kotlin version 1 5 21 dependencies classpath org jetbrains kotlin kotlin gradle plugin kotlin version if you have defined project wide properties in your root build gradle this library will detect the presence of the following properties groovy ext compilesdkversion 30 targetsdkversion 30 minsdkversion 24 1 define the backpack react native project in your settings gradle file groovy include backpack react native project backpack react native projectdir new file rootproject projectdir node modules backpack react native android 2 add bpk appearance as a dependency in your app module build gradle file groovy dependencies implementation project backpack react native pre compiled alternatively the pre compiled version is available on skyscanner s internal artifactory groovy dependencies implementation net skyscanner backpack bpk appearance version details details summary ios summary from source add the following dependencies to your podfile using the path to the npm package as follows ruby pod backpackreactnative path node modules backpack react native ios backpackreactnative pod bvlineargradient path node modules react native linear gradient details third party libs this package depends on react native maps https github com react community react native maps and its native components need to be integrated manually by following their instructions https github com react community react native maps blob master docs installation md configuration details summary android summary 1 add the native packages to the getpackages function in your mainactiviy kotlin override fun getpackages list reactpackage return arrays aslist mainreactpackage mapspackage lineargradientpackage calendarpackage dialogpackage bpksnackbarpackage 2 append uimode to the android configchanges prop of activity in androidmanifest xml example xml activity android name mainactivity android exported true android configchanges keyboard keyboardhidden orientation screensize uimode this ensures the rn code will react to system wide changes to the current appearance icons this method has the advantage of fonts being copied from this module at build time so that the fonts and js are always in sync making upgrades painless edit android app build gradle not android build gradle and add the following apply from node modules backpack react native bpk component icon fonts gradle details details summary ios summary icons the most reliable way to install the file on ios is manually three simple steps are required 1 update the info plist file by adding key uiappfonts key array string bpkicon ttf string array if the entry uiappfonts is already there just add string bpkicon ttf string inside the array like so array existing entries string bpkicon ttf string array 2 in the build phases of your project in the section copy bundle resources add a reference to the bpkicon ttf file path like path to node modules skyscanner bpk svgs dist font bpkicon ttf 3 rebuild the app details contributing to contribute please see contributing md contributing md list of packages bpk appearance lib bpk appearance bpk component alert lib bpk component alert bpk component animate height lib bpk component animate height bpk component badge lib bpk component badge bpk component banner alert lib bpk component banner alert bpk component boilerplate lib bpk component boilerplate bpk component button lib bpk component button bpk component button link lib bpk component button link bpk component calendar lib bpk component calendar bpk component card lib bpk component card bpk component carousel lib bpk component carousel bpk component carousel indicator lib bpk component carousel indicator bpk component chip lib bpk component chip bpk component dialog lib bpk component dialog bpk component flat list lib bpk component flat list bpk component horizontal nav lib bpk component horizontal nav bpk component icon lib bpk component icon bpk component image lib bpk component image bpk component map lib bpk component map bpk component navigation bar lib bpk component navigation bar bpk component nudger lib bpk component nudger bpk component panel lib bpk component panel bpk component phone input lib bpk component phone input bpk component picker lib bpk component picker bpk component progress lib bpk component progress bpk component section list lib bpk component section list bpk component select lib bpk component select bpk component spinner lib bpk component spinner bpk component star rating lib bpk component star rating bpk component switch lib bpk component switch bpk component text lib bpk component text bpk component text input lib bpk component text input bpk component touchable native feedback lib bpk component touchable native feedback bpk component touchable overlay lib bpk component touchable overlay bpk styles lib bpk styles bpk theming lib bpk theming | os |
|
LLMSurvey | llmsurvey a collection of papers and resources related to large language models the organization of papers refers to our survey a survey of large language models https arxiv org abs 2303 18223 paper page https huggingface co datasets huggingface badges raw main paper page sm dark svg https huggingface co papers 2303 18223 please let us know if you find out a mistake or have any suggestions by e mail batmanfly gmail com we suggest ccing another email francis kun zhou 163 com meanwhile in case of any unsuccessful delivery issue if you find our survey useful for your research please cite the following paper article llmsurvey title a survey of large language models author zhao wayne xin and zhou kun and li junyi and tang tianyi and wang xiaolei and hou yupeng and min yingqian and zhang beichen and zhang junjie and dong zican and du yifan and yang chen and chen yushuo and chen zhipeng and jiang jinhao and ren ruiyang and li yifan and tang xinyu and liu zikang and liu peiyu and nie jian yun and wen ji rong year 2023 journal arxiv preprint arxiv 2303 18223 url http arxiv org abs 2303 18223 chinese version to facilitate the reading of our english verison survey we also translate a chinese version assets llm survey chinese pdf for this survey we will continue to update the chinese version chinese version assets chinese version png new the trends of the number of papers related to llms on arxiv here are the trends of the cumulative numbers of arxiv papers that contain the keyphrases language model since june 2018 and large language model since october 2019 respectively arxiv llms assets arxiv llms png the statistics are calculated using exact match by querying the keyphrases in title or abstract by months we set different x axis ranges for the two keyphrases because language models have been explored at an earlier time we label the points corresponding to important landmarks in the research progress of llms a sharp increase occurs after the release of chatgpt the average number of published arxiv papers that contain large language model in title or abstract goes from 0 40 per day to 8 58 per day new technical evolution of gpt series models a brief illustration for the technical evolution of gpt series models we plot this figure mainly based on the papers blog articles and official apis from openai here solid lines denote that there exists an explicit evidence e g the official statement that a new model is developed based on a base model on the evolution path between two models while dashed lines denote a relatively weaker evolution relation gpt series assets gpt series png new evolutionary graph of llama family an evolutionary graph of the research work conducted on llama due to the huge number we cannot include all the llama variants in this figure even much excellent work llama family assets llama 0628 final png to support incremental update we share the source file of this figure and welcome the readers to include the desired models by submitting the pull requests on our github page if you re instrested please request by application new prompts we collect some useful tips for designing prompts that are collected from online notes and experiences from our authors where we also show the related ingredients and principles introduced in section 8 1 prompt examples assets prompts main png please click here prompts readme md to view more detailed information welcome everyone to provide us with more relevant tips in the form of issues https github com rucaibox llmsurvey issues 34 after selection we will regularly update them on github and indicate the source new experiments instruction tuning experiments we will explore the effect of different types of instructions in fine tuning llms i e 7b llama26 as well as examine the usefulness of several instruction improvement strategies instruction tuning table assets instruction tuning table png please click here experiments readme md to view more detailed information ability evaluaition experiments we conduct a fine grained evaluation on the abilities discussed in section 7 1 and section 7 2 for each kind of ability we select representative tasks and datasets for conducting evaluation experiments to examine the corresponding performance of llms ability main assets ability main png please click here experiments readme md to view more detailed information we also call for support of computing power for conducting more comprehensive experiments table of contents llmsurvey llmsurvey chinese version chinese version new the trends of the number of papers related to llms on arxiv new the trends of the number of papers related to llms on arxiv new technical evolution of gpt series models new technical evolution of gpt series models new evolutionary graph of llama family new evolutionary graph of llama family new prompts new prompts new experiments new experiments instruction tuning experiments instruction tuning experiments ability evaluaition experiments ability evaluaition experiments table of contents table of contents timeline of llms timeline of llms list of llms list of llms paper list paper list resources of llms resources of llms publicly available models publicly available models closed source models closed source models commonly used corpora commonly used corpora library resource library resource deep learning frameworks deep learning frameworks pre training pre training data collection data collection architecture architecture mainstream architectures mainstream architectures detailed configuration detailed configuration analysis analysis training algorithms training algorithms pre training on code pre training on code llms for program synthesis llms for program synthesis nlp tasks formatted as code nlp tasks formatted as code adaptation tuning adaptation tuning instruction tuning instruction tuning alignment tuning alignment tuning parameter efficient model adaptation parameter efficient model adaptation memory efficient model adaptation memory efficient model adaptation utilization utilization in context learning icl in context learning icl chain of thought reasoning cot chain of thought reasoning cot planning for complex task solving planning for complex task solving capacity evaluation capacity evaluation the team the team acknowledgments acknowledgments update log update log timeline of llms llms timeline assets llms 0623 final png list of llms table class tg thead tr th class tg nrix align center rowspan 2 category th th class tg baqh align center rowspan 2 model th th class tg 0lax align center rowspan 2 release time th th class tg baqh align center rowspan 2 size b th th class tg 0lax align center rowspan 2 link th tr tr tr thead tbody tr td class tg nrix align center rowspan 25 publicly br accessbile td td class tg baqh align center t5 td td class tg 0lax align center 2019 10 td td class tg baqh align center 11 td td class tg 0lax align center a href https arxiv org abs 1910 10683 paper a td tr tr td class tg baqh align center mt5 td td class tg 0lax align center 2021 03 td td class tg baqh align center 13 td td class tg 0lax align center a href https arxiv org abs 2010 11934 paper a td tr tr td class tg baqh align center pangu td td class tg 0lax align center 2021 05 td td class tg baqh align center 13 td td class tg 0lax align center a href https arxiv org abs 2104 12369 paper a td tr tr td class tg baqh align center cpm 2 td td class tg 0lax align center 2021 05 td td class tg baqh align center 198 td td class tg 0lax align center a href https arxiv org abs 2106 10715 paper a td tr tr td class tg baqh align center t0 td td class tg 0lax align center 2021 10 td td class tg baqh align center 11 td td class tg 0lax align center a href https arxiv org abs 2110 08207 paper a td tr tr td class tg baqh align center gpt neox 20b td td class tg 0lax align center 2022 02 td td class tg baqh align center 20 td td class tg 0lax align center a href https arxiv org abs 2204 06745 paper a td tr tr td class tg baqh align center codegen td td class tg 0lax align center 2022 03 td td class tg baqh align center 16 td td class tg 0lax align center a href https arxiv org abs 2203 13474 paper a td tr tr td class tg baqh align center tk instruct td td class tg 0lax align center 2022 04 td td class tg baqh align center align center 11 td td class tg 0lax align center a href https arxiv org abs 2204 07705 paper a td tr tr td class tg baqh align center ul2 td td class tg 0lax align center 2022 02 td td class tg baqh align center 20 td td class tg 0lax align center a href https arxiv org abs 2205 05131 paper a td tr tr td class tg baqh align center opt td td class tg 0lax align center 2022 05 td td class tg baqh align center 175 td td class tg 0lax align center a href https arxiv org abs 2205 01068 paper a td tr tr td class tg baqh align center yalm td td class tg 0lax align center 2022 06 td td class tg baqh align center 100 td td class tg 0lax align center a href https github com yandex yalm 100b github a td tr tr td class tg baqh align center nllb td td class tg 0lax align center 2022 07 td td class tg baqh align center 55 td td class tg 0lax align center a href https arxiv org abs 2207 04672 paper a td tr tr td class tg baqh align center bloom td td class tg 0lax align center 2022 07 td td class tg baqh align center 176 td td class tg 0lax align center a href https arxiv org abs 2211 05100 paper a td tr tr td class tg baqh align center glm td td class tg 0lax align center 2022 08 td td class tg baqh align center 130 td td class tg 0lax align center a href https arxiv org abs 2210 02414 paper a td tr tr td class tg baqh align center flan t5 td td class tg 0lax align center 2022 10 td td class tg baqh align center 11 td td class tg 0lax align center a href https arxiv org abs 2210 11416 paper a td tr tr td class tg baqh align center mt0 td td class tg 0lax align center 2022 11 td td class tg baqh align center 13 td td class tg 0lax align center a href https arxiv org abs 2211 01786 paper a td tr tr td class tg baqh align center galatica td td class tg 0lax align center align center align center 2022 11 td td class tg baqh align center align center 120 td td class tg 0lax align center a href https arxiv org abs 2211 09085 paper a td tr tr td class tg baqh align center bloomz td td class tg 0lax align center 2022 11 td td class tg baqh align center 176 td td class tg 0lax align center a href https arxiv org abs 2211 01786 paper a td tr tr td class tg baqh align center opt iml td td class tg 0lax align center 2022 12 td td class tg baqh align center 175 td td class tg 0lax align center a href https arxiv org abs 2212 12017 paper a td tr tr td class tg baqh align center pythia td td class tg 0lax align center 2023 01 td td class tg baqh align center 12 td td class tg 0lax align center a href https arxiv org abs 2304 01373 paper a td tr tr td class tg baqh align center llama td td class tg 0lax align center 2023 02 td td class tg baqh align center 65 td td class tg 0lax align center a href https arxiv org abs 2302 13971v1 paper a td tr tr td class tg baqh align center vicuna td td class tg 0lax align center 2023 03 td td class tg baqh align center 13 td td class tg 0lax align center a href https lmsys org blog 2023 03 30 vicuna blog a td tr tr td class tg baqh align center chatglm td td class tg 0lax align center 2023 03 td td class tg baqh align center 6 td td class tg 0lax align center a href https github com thudm chatglm 6b github a td tr tr td class tg baqh align center codegeex td td class tg 0lax align center 2023 03 td td class tg baqh align center 13 td td class tg 0lax align center a href https arxiv org abs 2303 17568 paper a td tr tr td class tg baqh align center koala td td class tg 0lax align center 2023 04 td td class tg baqh align center 13 td td class tg 0lax align center a href https bair berkeley edu blog 2023 04 03 koala blog a td tr tr td class tg nrix align center rowspan 31 closed br source td td class tg baqh align center gshard td td class tg 0lax align center 2020 01 td td class tg baqh align center align center 600 td td class tg 0lax align center a href http arxiv org abs 2006 16668v1 paper a td tr tr td class tg baqh align center gpt 3 td td class tg 0lax align center 2020 05 td td class tg baqh align center 175 td td class tg 0lax align center a href https arxiv org abs 2005 14165 paper a td tr tr td class tg baqh align center lamda td td class tg 0lax align center 2021 05 td td class tg baqh align center 137 td td class tg 0lax align center a href https arxiv org abs 2201 08239 paper a td tr tr td class tg baqh align center hyperclova td td class tg 0lax align center 2021 06 td td class tg baqh align center 82 td td class tg 0lax align center a href https arxiv org abs 2109 04650 paper a td tr tr td class tg baqh align center codex td td class tg 0lax align center 2021 07 td td class tg baqh align center 12 td td class tg 0lax align center a href https arxiv org abs 2107 03374 paper a td tr tr td class tg baqh align center ernie 3 0 td td class tg 0lax align center align center 2021 07 td td class tg baqh align center 10 td td class tg 0lax align center a href https arxiv org abs 2107 02137 paper a td tr tr td class tg baqh align center jurassic 1 td td class tg 0lax align center 2021 08 td td class tg baqh align center 178 td td class tg 0lax align center a href https assets website files com 60fd4503684b466578c0d307 61138924626a6981ee09caf6 jurassic tech paper pdf paper a td tr tr td class tg baqh align center align center flan td td class tg 0lax align center 2021 10 td td class tg baqh align center 137 td td class tg 0lax align center a href https arxiv org abs 2109 01652 paper a td tr tr td class tg baqh align center mt nlg td td class tg 0lax align center 2021 10 td td class tg baqh align center 530 td td class tg 0lax align center a href https arxiv org abs 2201 11990 paper a td tr tr td class tg baqh align center yuan 1 0 td td class tg 0lax align center 2021 10 td td class tg baqh align center 245 td td class tg 0lax align center a href https arxiv org abs 2110 04725 paper a td tr tr td class tg baqh align center anthropic td td class tg 0lax align center 2021 12 td td class tg baqh align center 52 td td class tg 0lax align center a href https arxiv org abs 2112 00861 paper a td tr tr td class tg baqh align center webgpt td td class tg 0lax align center 2021 12 td td class tg baqh align center 175 td td class tg 0lax align center a href https arxiv org abs 2112 09332 paper a td tr tr td class tg baqh align center gopher td td class tg 0lax align center 2021 12 td td class tg baqh align center 280 td td class tg 0lax align center a href http arxiv org abs 2112 11446v2 paper a td tr tr td class tg baqh align center ernie 3 0 titan td td class tg 0lax align center 2021 12 td td class tg baqh align center 260 td td class tg 0lax align center a href https arxiv org abs 2112 12731 paper a td tr tr td class tg baqh align center glam td td class tg 0lax align center 2021 12 td td class tg baqh align center 1200 td td class tg 0lax align center a href https arxiv org abs 2112 06905 paper a td tr tr td class tg baqh align center instructgpt td td class tg 0lax align center 2022 01 td td class tg baqh align center 175 td td class tg 0lax align center a href http arxiv org abs 2203 02155v1 paper a td tr tr td class tg baqh align center alphacode td td class tg 0lax align center 2022 02 td td class tg baqh align center 41 td td class tg 0lax align center a href http arxiv org abs 2203 07814v1 paper a td tr tr td class tg baqh align center chinchilla td td class tg 0lax align center 2022 03 td td class tg baqh align center 70 td td class tg 0lax align center a href https arxiv org abs 2203 15556 paper a td tr tr td class tg baqh align center palm td td class tg 0lax align center 2022 04 td td class tg baqh align center 540 td td class tg 0lax align center a href https arxiv org abs 2204 02311 paper a td tr td class tg baqh align center cohere td td class tg 0lax align center 2022 06 td td class tg baqh align center 54 td td class tg 0lax align center a href https cohere ai homepage a td tr tr td class tg baqh align center alexatm td td class tg 0lax align center 2022 08 td td class tg baqh align center 20 td td class tg 0lax align center a href https arxiv org abs 2208 01448 paper a td tr tr td class tg baqh align center luminous td td class tg 0lax align center 2022 09 td td class tg baqh align center 70 td td class tg 0lax align center a href https docs aleph alpha com docs introduction luminous docs a td tr tr td class tg baqh align center sparrow td td class tg 0lax align center 2022 09 td td class tg baqh align center 70 td td class tg 0lax align center a href http arxiv org abs 2209 14375v1 paper a td tr tr td class tg baqh align center welm td td class tg 0lax align center 2022 09 td td class tg baqh align center 10 td td class tg 0lax align center a href https arxiv org abs 2209 10372 paper a td tr tr td class tg baqh align center u palm td td class tg 0lax align center 2022 10 td td class tg baqh align center 540 td td class tg 0lax align center a href https arxiv org abs 2210 11399 paper a td tr tr td class tg baqh align center flan palm td td class tg 0lax align center 2022 10 td td class tg baqh align center align center 540 td td class tg 0lax align center a href https arxiv org abs 2210 11416 paper a td tr tr td class tg baqh align center flan u palm td td class tg 0lax align center 2022 10 td td class tg baqh align center 540 td td class tg 0lax align center a href https arxiv org abs 2210 11416 paper a td tr tr td class tg baqh align center alpaca td td class tg 0lax align center 2023 03 td td class tg baqh align center 7 td td class tg 0lax align center a href https crfm stanford edu 2023 03 13 alpaca html blog a td tr tr td class tg baqh align center gpt 4 td td class tg 0lax align center 2023 3 td td class tg baqh align center td td class tg 0lax align center a href http arxiv org abs 2303 08774v2 paper a td tr tr td class tg baqh align center pangu td td class tg 0lax align center 2023 3 td td class tg baqh align center 1085 td td class tg 0lax align center a href https arxiv org abs 2303 10845 paper a td tr tbody table paper list resources of llms publicly available models 1 u t5 u exploring the limits of transfer learning with a unified text to text transformer colin raffel et al jmlr 2019 paper https arxiv org abs 1910 10683 checkpoint https huggingface co t5 11b 2 u mt5 u mt5 a massively multilingual pre trained text to text transformer linting xue et al naacl 2021 paper https arxiv org abs 2010 11934 checkpoint https huggingface co google mt5 xxl tree main 3 u pangu u pangu large scale autoregressive pretrained chinese language models with auto parallel computation wei zeng et al arxiv 2021 paper https arxiv org abs 2104 12369 checkpoint https openi pcl ac cn pcl platform intelligence pangu alpha 4 u cpm 2 u cpm 2 large scale cost effective pre trained language models zhengyan zhang et al arxiv 2021 paper https arxiv org abs 2106 10715 checkpoint https github com tsinghuaai cpm 5 u t0 u multitask prompted training enables zero shot task generalization victor sanh et al iclr 2022 paper https arxiv org abs 2110 08207 checkpoint https huggingface co bigscience t0 6 u gpt neox 20b u gpt neox 20b an open source autoregressive language model sid black et al arxiv 2022 paper https arxiv org abs 2204 06745 checkpoint https huggingface co eleutherai gpt neox 20b tree main 7 u codegen u codegen an open large language model for code with multi turn program synthesis erik nijkamp et al arxiv 2022 paper https arxiv org abs 2203 13474 checkpoint https huggingface co salesforce codegen 16b nl 8 u tk instruct u super naturalinstructions generalization via declarative instructions on 1600 nlp tasks yizhong wang et al emnlp 2022 paper https arxiv org abs 2204 07705 checkpoint https huggingface co allenai tk instruct 11b def pos 9 u ul2 u ul2 unifying language learning paradigms yi tay et al arxiv 2022 paper https arxiv org abs 2205 05131 checkpoint https github com google research google research tree master ul2 10 u opt u opt open pre trained transformer language models susan zhang et al arxiv 2022 paper https arxiv org abs 2205 01068 checkpoint https github com facebookresearch metaseq tree main projects opt 11 u nllb u no language left behind scaling human centered machine translation nllb team arxiv 2022 paper https arxiv org abs 2207 04672 checkpoint https github com facebookresearch fairseq tree nllb 12 u bloom u bloom a 176b parameter open access multilingual language model bigscience workshop arxiv 2022 paper https arxiv org abs 2211 05100 checkpoint https huggingface co bigscience bloom 13 u glm u glm 130b an open bilingual pre trained model aohan zeng et al arxiv 2022 paper https arxiv org abs 2210 02414 checkpoint https github com thudm glm 130b 14 u flan t5 u scaling instruction finetuned language models hyung won chung et al arxiv 2022 paper https arxiv org abs 2210 11416 checkpoint https github com google research t5x blob main docs models md flan t5 checkpoints 15 u mt0 bloomz u crosslingual generalization through multitask finetuning niklas muennighoff et al arxiv 2022 paper https arxiv org abs 2211 01786 checkpoint https github com bigscience workshop xmtf 16 u galactica u galactica a large language model for science ross taylor et al arxiv 2022 paper https arxiv org abs 2211 09085 checkpoint https huggingface co facebook galactica 120b 17 u opt iml u opt iml scaling language model instruction meta learning through the lens of generalization srinivasan et al arxiv 2022 paper https arxiv org abs 2212 12017 checkpoint https huggingface co facebook opt iml 30b 18 u codegeex u codegeex a pre trained model for code generation with multilingual evaluations on humaneval x qinkai zheng et al arxiv 2023 paper https arxiv org abs 2303 17568 checkpoint https github com thudm codegeex 19 u pythia u pythia a suite for analyzing large language models across training and scaling stella biderman et al arxiv 2023 paper https arxiv org abs 2304 01373 checkpoint https github com eleutherai pythia 20 u llama u llama open and efficient foundation language models hugo touvron et al arxiv 2023 paper https arxiv org abs 2302 13971v1 checkpoint https github com facebookresearch llama closed source models 1 u gshard u gshard scaling giant models with conditional computation and automatic sharding dmitry lepikhin et al iclr 2021 paper http arxiv org abs 2006 16668v1 2 u gpt 3 u language models are few shot learners tom b brown et al neurips 2020 paper https arxiv org abs 2005 14165 3 u lamda u lamda language models for dialog applications romal thoppilan et al arxiv 2021 paper https arxiv org abs 2201 08239 4 u hyperclova u what changes can large scale language models bring intensive study on hyperclova billions scale korean generative pretrained transformers boseop kim et al emnlp 2021 paper https arxiv org abs 2109 04650 5 u codex u evaluating large language models trained on code mark chen et al arxiv 2021 paper https arxiv org abs 2107 03374 6 u ernie 3 0 u ernie 3 0 large scale knowledge enhanced pre training for language understanding and generation yu sun et al arxiv 2021 paper https arxiv org abs 2107 02137 7 u jurassic 1 u jurassic 1 technical details and evaluation opher lieber et al 2021 paper https assets website files com 60fd4503684b466578c0d307 61138924626a6981ee09caf6 jurassic tech paper pdf 8 u flan u finetuned language models are zero shot learners jason wei et al iclr 2021 paper https arxiv org abs 2109 01652 9 u mt nlg u using deepspeed and megatron to train megatron turing nlg 530b a large scale generative language model shaden smith et al arxiv 2021 paper https arxiv org abs 2201 11990 10 u yuan 1 0 u yuan 1 0 large scale pre trained language model in zero shot and few shot learning shaohua wu et al arxiv 2021 paper https arxiv org abs 2110 04725 11 u anthropic u a general language assistant as a laboratory for alignment amanda askell et al arxiv 2021 paper https arxiv org abs 2112 00861 12 u webgpt u webgpt browser assisted question answering with human feedback reiichiro nakano et al arxiv 2021 paper https arxiv org abs 2112 09332 13 u gopher u scaling language models methods analysis insights from training gopher jack w rae et al arxiv 2021 paper http arxiv org abs 2112 11446v2 14 u ernie 3 0 titan u ernie 3 0 titan exploring larger scale knowledge enhanced pre training for language understanding and generation shuohuan wang et al arxiv 2021 paper https arxiv org abs 2112 12731 15 u glam u glam efficient scaling of language models with mixture of experts nan du et al icml 2022 paper https arxiv org abs 2112 06905 16 u instructgpt u training language models to follow instructions with human feedback long ouyang et al arxiv 2022 paper http arxiv org abs 2203 02155v1 17 u alphacode u competition level code generation with alphacode yujia li et al arxiv 2022 paper http arxiv org abs 2203 07814v1 18 u chinchilla u training compute optimal large language models jordan hoffmann et al arxiv paper https arxiv org abs 2203 15556 19 u palm u palm scaling language modeling with pathways aakanksha chowdhery et al arxiv 2022 paper https arxiv org abs 2204 02311 20 u alexatm u alexatm 20b few shot learning using a large scale multilingual seq2seq model saleh soltan et al arxiv 2022 paper https arxiv org abs 2208 01448 21 u sparrow u improving alignment of dialogue agents via targeted human judgements amelia glaese et al arxiv 2022 paper http arxiv org abs 2209 14375v1 22 u welm u welm a well read pre trained language model for chinese hui su et al arxiv 2022 paper https arxiv org abs 2209 10372 23 u u palm u transcending scaling laws with 0 1 extra compute yi tay et al arxiv 2022 paper https arxiv org abs 2210 11399 24 u flan palm flan u palm u scaling instruction finetuned language models hyung won chung et al arxiv paper https arxiv org abs 2210 11416 25 u gpt 4 u gpt 4 technical report openai arxiv 2023 paper http arxiv org abs 2303 08774v2 26 u pangu u pangu towards trillion parameter language model with sparse heterogeneous computing xiaozhe ren et al arxiv 2023 paper https arxiv org abs 2303 10845 commonly used corpora 1 u bookcorpus u aligning books and movies towards story like visual explanations by watching movies and reading books yukun zhu et al iccv 2015 paper http arxiv org abs 1506 06724v1 source https huggingface co datasets bookcorpus 2 u guntenburg u source https www gutenberg org 3 u commoncrawl u source https commoncrawl org 4 u c4 u exploring the limits of transfer learning with a unified text to text transformer colin raffel et al jmlr 2019 paper http arxiv org abs 1910 10683v3 source https www tensorflow org datasets catalog c4 5 u cc stories r u a simple method for commonsense reasoning trieu h trinh el al arxiv 2018 paper http arxiv org abs 1806 02847v2 source https huggingface co datasets spacemanidol cc stories 6 u cc news u roberta a robustly optimized bert pretraining approach yinhan liu et al arxiv 2019 paper http arxiv org abs 1907 11692v1 source https huggingface co datasets cc news 7 u realnews u defending against neural fake news rowan zellers et al neurips 2019 paper http arxiv org abs 1905 12616v3 source https github com rowanz grover tree master realnews 8 u openwebtext u source https skylion007 github io openwebtextcorpus 9 u pushshift io u the pushshift reddit dataset jason baumgartner et al aaai 2020 paper http arxiv org abs 2001 08435v1 source https files pushshift io reddit 10 u wikipedia u source https dumps wikimedia org 11 u bigquery u source https cloud google com bigquery public data hl zh cn 12 u the pile u the pile an 800gb dataset of diverse text for language modeling leo gao et al arxiv 2021 paper http arxiv org abs 2101 00027v1 source https pile eleuther ai 13 u roots u the bigscience roots corpus a 1 6tb composite multilingual dataset lauren on et al neurips 2022 datasets and benchmarks track paper https arxiv org abs 2303 03915 library resource 1 u transformers u transformers state of the art natural language processing thomas wolf et al emnlp 2020 paper https arxiv org abs 1910 03771 source https huggingface co 2 u deepspeed u deepspeed system optimizations enable training deep learning models with over 100 billion parameters rasley et al kdd 2020 paper https dl acm org doi 10 1145 3394486 3406703 source https github com microsoft deepspeed 3 u megatron lm u megatron lm training multi billion parameter language models using model parallelism mohammad shoeybi et al arxiv 2019 paper https arxiv org abs 1909 08053 source https github com nvidia megatron lm 4 u jax u source https github com google jax 5 u colossal ai u colossal ai a unified deep learning system for large scale parallel training zhengda bian et al arxiv 2021 paper http arxiv org abs 2110 14883v2 source https github com hpcaitech colossalai 6 u bmtrain u source https github com openbmb bmtrain 7 u fastmoe u fastmoe a fast mixture of expert training system jiaao he et al arxiv 2021 paper https arxiv org abs 2103 13262 source https github com laekov fastmoe deep learning frameworks 1 u pytorch u pytorch an imperative style high performance deep learning library adam paszke el al neurips 2019 paper https arxiv org abs 1912 01703 source https pytorch org 2 u tensorflow u tensorflow a system for large scale machine learning mart n abadi et al osdi 2016 paper https arxiv org abs 1605 08695 source https www tensorflow org 3 u mxnet u mxnet a flexible and efficient machine learning library for heterogeneous distributed systems tianqi chen et al arxiv 2015 paper https arxiv org abs 1512 01274 source https github com apache mxnet 4 u paddlepaddle u paddlepaddle an open source deep learning platform from industrial practice yanjun ma et al frontiers of data and domputing 2019 paper http www jfdc cnic cn en abstract abstract2 shtml source https github com paddlepaddle paddle 5 u mindspore u huawei mindspore ai development framework huawei technologies co ltd artificial intelligence technology 2022 paper https link springer com chapter 10 1007 978 981 19 2879 6 5 source https github com mindspore ai mindspore 6 u oneflow u oneflow redesign the distributed deep learning framework from scratch jinhui yuan et al arxiv 2021 paper https arxiv org abs 2110 15032 source https github com oneflow inc oneflow pre training data collection 1 the bigscience roots corpus a 1 6tb composite multilingual dataset lauren on et al neurips 2022 datasets and benchmarks track paper https arxiv org abs 2303 03915 1 deduplicating training data makes language models better katherine lee et al acl 2022 paper https arxiv org abs 2107 06499 1 deduplicating training data mitigates privacy risks in language models nikhil kandpal et al icml 2022 paper https arxiv org abs 2202 06539 1 scaling laws and interpretability of learning from repeated data danny hernandez et al arxiv 2022 paper https arxiv org abs 2205 10487 1 a pretrainer s guide to training data measuring the effects of data age domain coverage quality toxicity shayne longpre et al arxiv 2023 paper https arxiv org abs 2305 13169 architecture mainstream architectures causal decoder 1 language models are few shot learners tom b brown et al neurips 2020 paper http arxiv org abs 2005 14165 1 opt open pre trained transformer language models susan zhang et al arxiv 2022 paper http arxiv org abs 2205 01068 1 bloom a 176b parameter open access multilingual language model teven le scao et al arxiv 2022 paper http arxiv org abs 2211 05100 1 training compute optimal large language models jordan hoffmann et al arxiv 2022 paper http arxiv org abs 2203 15556 1 scaling language models methods analysis insights from training gopher jack w rae et al arxiv 2021 paper http arxiv org abs 2112 11446 1 galactica a large language model for science ross taylor et al arxiv 2022 paper http arxiv org abs 2211 09085 1 palm scaling language modeling with pathways aakanksha chowdhery et al arxiv 2022 paper http arxiv org abs 2204 02311 1 jurassic 1 technical details and evaluation opher lieber et al ai21 labs paper https uploads ssl webflow com 60fd4503684b466578c0d307 61138924626a6981ee09caf6 jurassic tech paper pdf 1 lamda language models for dialog applications romal thoppilan et al arxiv 2022 paper http arxiv org abs 2201 08239 prefix decoder 1 glm 130b an open bilingual pre trained model aohan zeng et al arxiv 2022 paper http arxiv org abs 2210 02414 1 glm general language model pretraining with autoregressive blank infilling zhengxiao du et al acl 2022 paper http arxiv org abs 2103 10360 1 transcending scaling laws with 0 1 extra compute yi tay et al arxiv 2022 paper http arxiv org abs 2210 11399 moe 1 switch transformers scaling to trillion parameter models with simple and efficient sparsity william fedus et al jmlr paper http arxiv org abs 2101 03961 1 unified scaling laws for routed language models aidan clark et al icml 2022 paper http arxiv org abs 2202 01169 ssm 1 pretraining without attention junxiong wang et al arxiv 2022 paper http arxiv org abs 2212 10544 1 efficiently modeling long sequences with structured state spaces albert gu et al iclr 2022 paper http arxiv org abs 2111 00396 1 long range language modeling via gated state spaces harsh mehta et al arxiv 2022 paper http arxiv org abs 2206 13947 1 hungry hungry hippos towards language modeling with state space models daniel y fu et al iclr 2023 paper https arxiv org abs 2212 14052 detailed configuration layer normalization 1 u rmsnorm u root mean square layer normalization biao zhang et al neurips 2019 paper http arxiv org abs 1910 07467 1 u deepnorm u deepnet scaling transformers to 1 000 layers hongyu wang et al arxiv 2022 paper http arxiv org abs 2203 00555 1 u sandwich ln u cogview mastering text to image generation via transformers ming ding et al neirips 2021 paper https arxiv org abs 2105 13290 position encoding 1 u t5 bias u exploring the limits of transfer learning with a unified text to text transformer colin raffel et al jmlr 2019 paper https arxiv org abs 1910 10683 1 u alibi u train short test long attention with linear biases enables input length extrapolation ofir press et al iclr 2022 paper http arxiv org abs 2108 12409 1 u rope u roformer enhanced transformer with rotary position embedding jianlin su et al arxiv 2021 paper http arxiv org abs 2104 09864 1 u xpos u a length extrapolatable transformer yutao sun et al arxiv 2022 paper https arxiv org abs 2212 10554 attention 1 u multi query attention u fast transformer decoding one write head is all you need noam shazeer arxiv 2019 paper https arxiv org abs 1911 02150 1 u flashattention u flashattention fast and memory efficient exact attention with io awareness tri dao et al neurips 2022 paper https arxiv org abs 2205 14135 1 u pagedattention u vllm easy fast and cheap llm serving with pagedattention woosuk kwon et al 2023 paper stay tuned offical website https vllm ai analysis 1 what language model architecture and pretraining objective work best for zero shot generalization thomas wang et al icml 2022 paper http arxiv org abs 2204 05832 1 what language model to train if you have one million gpu hours teven le scao et al findings of emnlp 2022 paper http arxiv org abs 2210 15424 1 examining scaling and transfer of language model architectures for machine translation biao zhang et al icml 2022 paper http arxiv org abs 2202 00528 1 scaling laws vs model architectures how does inductive bias influence scaling yi tay et al arxiv 2022 paper http arxiv org abs 2207 10551 1 do transformer modifications transfer across implementations and applications sharan narang et al emnlp 2021 paper http arxiv org abs 2102 11972 training algorithms 1 megatron lm training multi billion parameter language models using model parallelism mohammad shoeybi et al arxiv 2019 paper http arxiv org abs 1909 08053 1 an efficient 2d method for training super large deep learning models qifan xu et al arxiv 2021 paper http arxiv org abs 2104 05343 1 tesseract parallelize the tensor parallelism efficiently boxiang wang et al icpp 2022 paper http arxiv org abs 2105 14500 1 maximizing parallelism in distributed training for huge neural networks zhengda bian et al arxiv 2021 paper http arxiv org abs 2105 14450 1 gpipe efficient training of giant neural networks using pipeline parallelism yanping huang et al neurips 2019 paper http arxiv org abs 1811 06965 1 pipedream fast and efficient pipeline parallel dnn training aaron harlap et al arxiv 2018 paper http arxiv org abs 1806 03377 1 zero memory optimizations toward training trillion parameter models samyam rajbhandari et al sc 2020 paper http arxiv org abs 1910 02054 1 zero offload democratizing billion scale model training jie ren et al usenix 2021 paper http arxiv org abs 2101 06840 pre training on code llms for program synthesis 1 evaluating large language models trained on code mark chen et al arxiv 2021 paper http arxiv org abs 2107 03374 1 program synthesis with large language models jacob austin et al arxiv 2021 paper http arxiv org abs 2108 07732 1 show your work scratchpads for intermediate computation with language models maxwell nye et al arxiv 2021 paper http arxiv org abs 2112 00114 1 a systematic evaluation of large language models of code frank f xu et al arxiv 2022 paper http arxiv org abs 2202 13169 1 competition level code generation with alphacode yujia li et al science paper http arxiv org abs 2203 07814 1 codegen an open large language model for code with multi turn program synthesis erik nijkamp et al iclr 2023 paper http arxiv org abs 2203 13474 1 incoder a generative model for code infilling and synthesis daniel fried et al iclr 2023 paper http arxiv org abs 2204 05999 1 codet code generation with generated tests bei chen et al iclr 2023 paper http arxiv org abs 2207 10397 1 starcoder may the source be with you raymond li et al arxiv 2023 paper https arxiv org abs 2305 06161 nlp tasks formatted as code 1 language models of code are few shot commonsense learners aman madaan et al emnlp 2022 paper http arxiv org abs 2210 07128 1 autoformalization with large language models yuhuai wu et al neurips 2022 paper http arxiv org abs 2205 12615 adaptation tuning instruction tuning 1 multi task deep neural networks for natural language understanding xiaodong liu et al acl 2019 paper https arxiv org abs 1901 11504 homepage https github com namisan mt dnn 1 exploring the limits of transfer learning with a unified text to text transformer colin raffel et al jmlr 2020 paper https arxiv org abs 1910 10683 checkpoint https github com google research text to text transfer transformer released model checkpoints 1 muppet massive multi task representations with pre finetuning armen aghajanyan et al emnlp 2021 paper https arxiv org abs 2101 11038 checkpoint https huggingface co models other arxiv 2101 11038 1 cross task generalization via natural language crowdsourcing instructions swaroop mishra et al acl 2022 paper https arxiv org abs 2104 08773 collection https instructions apps allenai org data 1 finetuned language models are zero shot learners jason wei et al iclr 2022 paper https arxiv org abs 2109 01652 homepage https github com google research flan 1 multitask prompted training enables zero shot task generalization victor sanh et al iclr 2022 paper https arxiv org abs 2110 08207 checkpoint https huggingface co bigscience t0 how to use 1 promptsource an integrated development environment and repository for natural language prompts stephen h bach et al acl 2022 paper https arxiv org abs 2202 01279 collection https github com bigscience workshop promptsource 1 training language models to follow instructions with human feedback long ouyang et al arxiv 2022 paper https arxiv org abs 2203 02155 1 super naturalinstructions generalization via declarative instructions on 1600 nlp tasks yizhong wang et al emnlp 2022 paper https arxiv org abs 2204 07705 collection https instructions apps allenai org data checkpoint https huggingface co models search tk instruct 1 mvp multi task supervised pre training for natural language generation tianyi tang et al arxiv 2022 paper https arxiv org abs 2206 12131 collection https huggingface co rucaibox checkpoint https huggingface co rucaibox 1 crosslingual generalization through multitask finetuning niklas muennighoff et al arxiv 2022 paper https arxiv org abs 2211 01786 collection https github com bigscience workshop xmtf data checkpoint https github com bigscience workshop xmtf models 1 scaling instruction finetuned language models hyung won chung et al arxiv 2022 paper https arxiv org abs 2210 11416 homepage https github com google research flan 1 unnatural instructions tuning language models with almost no human labor or honovich et al arxiv 2022 paper https arxiv org abs 2212 09689 homepage https github com orhonovich unnatural instructions 1 self instruct aligning language model with self generated instructions yizhong wang et al arxiv 2022 paper https arxiv org abs 2212 10560 homepage https github com yizhongw self instruct 1 opt iml scaling language model instruction meta learning through the lens of generalization srinivasan iyer et al arxiv 2022 paper https arxiv org abs 2212 12017 checkpoint https github com facebookresearch metaseq tree main projects opt iml 1 the flan collection designing data and methods for effective instruction tuning shayne longpre et al arxiv 2023 paper https arxiv org abs 2301 13688 homepage https github com google research flan 1 is prompt all you need no a comprehensive and broader view of instruction learning renze lou et al arxiv 2023 paper https arxiv org abs 2303 10475 1 maybe only 0 5 data is needed a preliminary exploration of low training data instruction tuning hao chen et al arxiv 2023 paper https arxiv org abs 2305 09246 1 lima less is more for alignment chunting zhou arxiv 2023 paper https arxiv org abs 2305 11206 alignment tuning 1 tamer training an agent manually via evaluative reinforcement w bradley knox et al icdl 2008 paper https www cs utexas edu bradknox papers icdl08 knox pdf 1 interactive learning from policy dependent human feedback james macglashan et al icml 2017 paper https arxiv org abs 1701 06049 1 deep reinforcement learning from human preferences paul christiano et al nips 2017 paper https arxiv org abs 1706 03741 1 deep tamer interactive agent shaping in high dimensional state spaces garrett warnell et al aaai 2018 paper https arxiv org abs 1709 10163 1 fine tuning language models from human preferences daniel m ziegler et al arxiv 2019 paper https arxiv org abs 1909 08593 1 learning to summarize from human feedback nisan stiennon et al neurips 2020 paper https arxiv org abs 2009 01325 1 alignment of language agents zachary kenton et al arxiv 2021 paper https arxiv org abs 2103 14659 1 recursively summarizing books with human feedback jeff wu et al arxiv 2021 paper https arxiv org abs 2109 10862 1 a general language assistant as a laboratory for alignment amanda askell et al arxiv 2021 paper https arxiv org abs 2112 00861 1 webgpt browser assisted question answering with human feedback reiichiro nakano et al arxiv 2021 paper https arxiv org abs 2112 09332 1 training language models to follow instructions with human feedback long ouyang et al arxiv 2022 paper https arxiv org abs 2203 02155 1 teaching language models to support answers with verified quotes jacob menick et al arxiv 2022 paper https arxiv org abs 2203 11147 1 training a helpful and harmless assistant with reinforcement learning from human feedback yuntao bai et al arxiv 2022 paper https arxiv org abs 2204 05862 1 dynamic planning in open ended dialogue using reinforcement learning deborah cohen et al arxiv 2022 paper https arxiv org abs 2208 02294 1 red teaming language models to reduce harms methods scaling behaviors and lessons learned deep ganguli et al arxiv 2022 paper https arxiv org abs 2209 07858 1 improving alignment of dialogue agents via targeted human judgements amelia glaese et al arxiv 2022 paper https arxiv org abs 2209 14375 1 is reinforcement learning not for natural language processing benchmarks baselines and building blocks for natural language policy optimization rajkumar ramamurthy et al arxiv 2022 paper https arxiv org abs 2210 01241 1 scaling laws for reward model overoptimization leo gao et al arxiv 2022 paper https arxiv org abs 2210 10760 1 the wisdom of hindsight makes language models better instruction followers tianjun zhang et al arxiv 2023 paper https arxiv org abs 2302 05206 1 raft reward ranked finetuning for generative foundation model alignment hanze dong et al arxiv 2023 paper https arxiv org abs 2304 06767 1 red teaming large language models using chain of utterances for safety alignment rishabh bhardwaj et al arxiv 2023 paper https arxiv org abs 2308 09662 parameter efficient model adaptation 1 parameter efficient transfer learning for nlp neil houlsby et al icml 2019 paper https arxiv org abs 1902 00751 github https github com google research adapter bert 1 mad x an adapter based framework for multi task cross lingual transfer jonas pfeiffer et al emnlp 2020 paper https arxiv org abs 2005 00052 github https github com adapter hub adapter transformers 1 autoprompt eliciting knowledge from language models with automatically generated prompts taylor shin et al emnlp 2020 paper https arxiv org abs 2010 15980 github https ucinlp github io autoprompt 1 prefix tuning optimizing continuous prompts for generation xiang lisa li et al acl 2021 paper https arxiv org abs 2101 00190 github https github com xiangli1999 prefixtuning 1 gpt understands too xiao liu et al arxiv 2021 paper https arxiv org abs 2103 10385 github https github com thudm p tuning 1 the power of scale for parameter efficient prompt tuning brian lester et al emnlp 2021 paper https arxiv org pdf 2104 08691 1 lora low rank adaptation of large language models edward j hu et al arxiv 2021 paper https arxiv org abs 2106 09685 github https github com microsoft lora 1 towards a unified view of parameter efficient transfer learning junxian he et al iclr 2022 paper https arxiv org abs 2110 04366 github https github com jxhe unify parameter efficient tuning 1 p tuning v2 prompt tuning can be comparable to fine tuning universally across scales and tasks xiao liu et al acl 2022 paper https arxiv org abs 2110 07602 github https github com thudm p tuning v2 1 dylora parameter efficient tuning of pre trained models using dynamic search free low rank adaptation mojtaba valipour et al eacl 2023 paper https arxiv org abs 2210 07558 github https github com huawei noah kd nlp tree main dylora 1 parameter efficient fine tuning of large scale pre trained language models ning ding et al nat mach intell paper https www nature com articles s42256 023 00626 4 github https github com thunlp opendelta 1 adaptive budget allocation for parameter efficient fine tuning qingru zhang et al arxiv 2023 paper https arxiv org abs 2303 10512 github https github com qingruzhang adalora 1 llama adapter efficient fine tuning of language models with zero init attention renrui zhang et al arxiv 2023 paper https arxiv org abs 2303 16199 github https github com opengvlab llama adapter 1 llm adapters an adapter family for parameter efficient fine tuning of large language models zhiqiang hu et al arxiv 2023 paper https arxiv org abs 2304 01933 github https github com agi edgerunners llm adapters memory efficient model adaptation 1 a survey of quantization methods for efficient neural network inference amir gholami et al arxiv 2021 paper https arxiv org abs 2103 13630 1 8 bit optimizers via block wise quantization tim dettmers et al arxiv 2021 paper https arxiv org abs 2110 02861 1 compression of generative pre trained language models via quantization chaofan tao et al acl 2022 paper https arxiv org abs 2203 10705 1 zeroquant efficient and affordable post training quantization for large scale transformers zhewei yao et al neurips 2022 paper https arxiv org abs 2206 01861 github https github com microsoft deepspeed 1 llm int8 8 bit matrix multiplication for transformers at scale tim dettmers et al arxiv 2022 paper https arxiv org abs 2208 07339 github https github com timdettmers bitsandbytes 1 gptq accurate post training quantization for generative pre trained transformers elias frantar et al iclr 2023 paper https arxiv org abs 2210 17323 github https github com ist daslab gptq 1 smoothquant accurate and efficient post training quantization for large language models guangxuan xiao et al arxiv 2022 paper https arxiv org abs 2211 10438 github https github com mit han lab smoothquant 1 the case for 4 bit precision k bit inference scaling laws tim dettmers et al arxiv 2022 paper https arxiv org abs 2212 09720 1 zeroquant v2 exploring post training quantization in llms from comprehensive study to low rank compensation zhewei yao et al arxiv 2023 paper https arxiv org abs 2303 08302 1 qlora efficient finetuning of quantized llms tim dettmers et al arxiv 2023 paper https arxiv org abs 2305 14314 github https github com artidoro qlora 1 llm qat data free quantization aware training for large language models zechun liu et al arxiv 2023 paper https arxiv org abs 2305 17888 1 awq activation aware weight quantization for llm compression and acceleration ji lin et al arxiv 2023 paper https arxiv org abs 2306 00978 github https github com mit han lab llm awq utilization in context learning icl 1 an information theoretic approach to prompt engineering without ground truth labels taylor sorensen et al acl 2022 paper https arxiv org abs 2203 11364 2 what makes good in context examples for gpt 3 jiachang liu et al acl 2022 paper https arxiv org abs 2101 06804 3 learning to retrieve prompts for in context learning ohad rubin et al naacl 2022 paper https arxiv org abs 2112 08633 4 diverse demonstrations improve in context compositional generalization itay levy et al arxiv 2022 paper https arxiv org abs 2212 06800 5 demystifying prompts in language models via perplexity estimation hila gonen et al arxiv 2022 paper https arxiv org abs 2212 04037 6 active example selection for in context learning yiming zhang et al emnlp 2022 paper https arxiv org abs 2211 04486 7 self adaptive in context learning zhiyong wu et al arxiv 2022 paper https arxiv org abs 2212 10375 8 fantastically ordered prompts and where to find them overcoming few shot prompt order sensitivity yao lu et al acl 2022 paper https arxiv org abs 2104 08786 9 structured prompting scaling in context learning to 1 000 examples hao yaru et al arxiv 2022 paper https arxiv org abs 2212 06713 10 the unreliability of explanations in few shot prompting for textual reasoning ye xi et al arxiv 2022 paper https arxiv org abs 2205 03401 11 cross task generalization via natural language crowdsourcing instructions swaroop mishra et al acl 2022 paper https arxiv org abs 2104 08773 12 prompt augmented linear probing scaling beyond the limit of few shot in context learner hyunsoo cho et al arxiv 2022 paper https arxiv org abs 2212 10873 13 an explanation of in context learning as implicit bayesian inference s ang michael xie et al iclr 2022 paper https arxiv org abs 2111 02080 14 calibrate before use improving few shot performance of language models zihao zhao et al icml 2021 paper https arxiv org abs 2102 09690 15 data distributional properties drive emergent in context learning in transformers stephanie c y chan et al arxiv 2022 paper https arxiv org abs 2205 05055 16 in context learning and induction heads catherine olsson et al arxiv 2022 paper http arxiv org abs 2209 11895 17 on the effect of pretraining corpora on in context learning by a large scale language model seongjin shin et al naacl 2022 paper https arxiv org abs 2204 13509 18 rethinking the role of demonstrations what makes in context learning work sewon min et al emnlp 2022 paper https arxiv org abs 2202 12837 19 rethinking the role of scale for in context learning an interpretability based case study at 66 billion scale hritik bansal et al arxiv 2022 paper https arxiv org abs 2212 09095 20 transformers as algorithms generalization and implicit model selection in in context learning yingcong li et al arxiv 2023 paper https arxiv org abs 2301 07067 21 transformers learn in context by gradient descent johannes von oswald et al arxiv 2022 paper https arxiv org abs 2212 07677 22 what learning algorithm is in context learning investigations with linear models ekin aky u rek et al arxiv 2022 paper https arxiv org abs 2211 15661 23 a survey for in context learning qingxiu dong et al arxiv 2023 paper https arxiv org abs 2301 00234 24 what in context learning learns in context disentangling task recognition and task learning jane pan et al arxiv 2023 paper https arxiv org abs 2305 09731 25 the learnability of in context learning noam wies et al arxiv 2023 paper https arxiv org abs 2303 07895 26 do prompt based models really understand the meaning of their prompts albert webson et al naacl 2022 paper https aclanthology org 2022 naacl main 167 27 larger language models do in context learning differently jerry wei arxiv 2023 paper https arxiv org abs 2303 03846 28 meta in context learning in large language models julian coda forno arxiv 2023 paper https arxiv org abs 2305 12907 29 symbol tuning improves in context learning in language models jerry wei arxiv 2023 paper https arxiv org abs 2305 08298 chain of thought reasoning cot 1 automatic chain of thought prompting in large language models zhuosheng zhang et al arxiv 2022 paper https arxiv org abs 2210 03493 2 chain of thought prompting elicits reasoning in large language models jason wei et al arxiv 2022 paper https arxiv org abs 2201 11903 3 star self taught reasoner bootstrapping reasoning with reasoning zelikman et al arxiv 2022 paper https arxiv org abs 2203 14465 4 large language models are zero shot reasoners takeshi kojima et al arxiv 2022 paper https arxiv org abs 2205 11916 5 automatic chain of thought prompting in large language models zhuosheng zhang et al arxiv paper http arxiv org abs 2210 03493 6 complexity based prompting for multi step reasoning yao fu et al arxiv 2022 paper https arxiv org abs 2210 00720 7 language models are multilingual chain of thought reasoners freda shi et al arxiv 2022 paper https arxiv org abs 2210 03057 8 rationale augmented ensembles in language models xuezhi wang et al arxiv 2022 paper https arxiv org abs 2207 00747 9 least to most prompting enables complex reasoning in large language models denny zhou et al arxiv 2022 paper https arxiv org abs 2205 10625 10 multimodal chain of thought reasoning in language models zhuosheng zhang et al arxiv 2023 paper https arxiv org abs 2302 00923 11 self consistency improves chain of thought reasoning in language models xuezhi wang et al arxiv 2022 paper https arxiv org abs 2203 11171 12 large language models can self improve jiaxin huang et al arxiv 2022 paper https arxiv org abs 2210 11610 13 training verifiers to solve math word problems karl cobbe et al arxiv 2021 paper https arxiv org abs 2110 14168 14 on the advance of making language models better reasoners yifei li et al arxiv 2022 paper https arxiv org abs 2206 02336 15 large language models are reasoners with self verification yixuan weng et al arxiv 2022 paper https arxiv org abs 2212 09561 16 teaching small language models to reason lucie charlotte magister et al arxiv 2022 paper https arxiv org abs 2212 08410 17 large language models are reasoning teachers namgyu ho et al arxiv 2022 paper https arxiv org abs 2212 10071 18 the unreliability of explanations in few shot prompting for textual reasoning ye xi et al arxiv 2022 paper https arxiv org abs 2205 03401 19 scaling instruction finetuned language models hyung won chung et al arxiv 2022 paper https arxiv org abs 2210 11416 20 solving quantitative reasoning problems with language models aitor lewkowycz et al arxiv 2022 paper https arxiv org abs 2206 14858 21 text and patterns for effective chain of thought it takes two to tango aman madaan et al arxiv 2022 paper https arxiv org abs 2209 07686 22 challenging big bench tasks and whether chain of thought can solve them mirac suzgun et al arxiv 2022 paper http arxiv org abs 2210 09261 23 reasoning with language model prompting a survey shuofei qiao et al arxiv 2022 paper https arxiv org abs 2212 09597 24 towards reasoning in large language models a survey jie huang et al arxiv 2022 paper https arxiv org abs 2212 10403 planning for complex task solving 1 least to most prompting enables complex reasoning in large language models denny zhou et al iclr 2023 paper https openreview net forum id wzh7099tgfm 2 pal program aided language models luyu gao et al icml 2023 paper https openreview net forum id m1fd9z00sj 3 plan and solve prompting improving zero shot chain of thought reasoning by large language models lei wang et al acl 2023 paper https arxiv org abs 2305 04091 4 progprompt generating situated robot task plans using large language models ishika singh et al icra 2022 paper https arxiv org abs 2209 11302 5 tree of thoughts deliberate problem solving with large language models shunyu yao et al arxiv 2023 paper https arxiv org abs 2305 10601 6 voyager an open ended embodied agent with large language models guanzhi wang et al arxiv 2023 paper https arxiv org abs 2305 16291 7 reflexion language agents with verbal reinforcement learning noah shinn et al arxiv 2023 paper https arxiv org abs 2303 11366 8 multimodal procedural planning via dual text image prompting yujie lu et al arxiv 2023 paper https arxiv org abs 2305 01795 9 self planning code generation with large language model xue jiang et al arxiv 2023 paper https arxiv org abs 2303 06689 10 decomposed prompting a modular approach for solving complex tasks tushar khot et al iclr 2023 paper https openreview net forum id nggzqjzary 11 toolformer language models can teach themselves to use tools timo schick et al arxiv 2023 paper https arxiv org abs 2302 04761 12 hugginggpt solving ai tasks with chatgpt and its friends in hugging face yongliang shen et al arxiv 2023 paper https arxiv org abs 2303 17580 13 faithful chain of thought reasoning qing lyu et al arxiv 2023 paper https arxiv org abs 2301 13379 14 llm p empowering large language models with optimal planning proficiency bo liu et al arxiv 2023 paper https arxiv org abs 2304 11477 15 reasoning with language model is planning with world model shibo hao et al arxiv 2023 paper https arxiv org abs 2305 14992 16 generative agents interactive simulacra of human behavior joon sung park et al arxiv 2023 paper https arxiv org abs 2304 03442 17 react synergizing reasoning and acting in language models shunyu yao et al iclr 2023 paper https openreview net forum id we vluyul x 18 chatcot tool augmented chain of thought reasoning on chat based large language models zhipeng chen et al arxiv 2023 paper https arxiv org abs 2305 14323 19 describe explain plan and select interactive planning with large language models enables open world multi task agents zihao wang et al arxiv 2023 paper https arxiv org abs 2302 01560 20 adaplanner adaptive planning from feedback with language models haotian sun et al arxiv 2023 paper https arxiv org abs 2305 16653 capacity evaluation 1 measuring massive multitask language understanding dan hendrycks et al iclr 2021 paper http arxiv org abs 2009 03300v3 2 persistent anti muslim bias in large language models abubakar abid et al aies 2021 paper http arxiv org abs 2101 05783v2 3 understanding the capabilities limitations and societal impact of large language models alex tamkin et al arxiv 2021 paper http arxiv org abs 2102 02503v1 4 behavior benchmark for everyday household activities in virtual interactive and ecological environments sanjana srivastava et al corl 2021 paper http arxiv org abs 2108 03332v1 5 program synthesis with large language models jacob austin et al arxiv 2021 paper http arxiv org abs 2108 07732v1 6 training verifiers to solve math word problems karl cobbe et al arxiv 2021 paper http arxiv org abs 2110 14168v2 7 show your work scratchpads for intermediate computation with language models maxwell i nye et al arxiv 2021 paper http arxiv org abs 2112 00114v1 8 language models as zero shot planners extracting actionable knowledge for embodied agents wenlong huang et al icml 2022 paper http arxiv org abs 2201 07207v2 9 chain of thought prompting elicits reasoning in large language models jason wei et al neurips 2022 paper http arxiv org abs 2201 11903v6 10 training language models to follow instructions with human feedback long ouyang et al arxiv 2022 paper http arxiv org abs 2203 02155v1 11 competition level code generation with alphacode yujia li et al science 2022 paper http arxiv org abs 2203 07814v1 12 do as i can not as i say grounding language in robotic affordances michael ahn et al arxiv 2022 paper http arxiv org abs 2204 01691v2 13 training a helpful and harmless assistant with reinforcement learning from human feedback yuntao bai et al arxiv 2022 paper http arxiv org abs 2204 05862v1 14 autoformalization with large language models yuhuai wu et al neurips 2022 paper http arxiv org abs 2205 12615v1 15 beyond the imitation game quantifying and extrapolating the capabilities of language models aarohi srivastava et al arxiv 2022 paper https arxiv org abs 2206 04615 16 exploring length generalization in large language models cem anil et al neurips 2022 paper http arxiv org abs 2207 04901v2 17 few shot learning with retrieval augmented language models gautier izacard et al arxiv 2022 paper https arxiv org abs 2208 03299 18 limitations of language models in arithmetic and symbolic induction jing qian et al arxiv 2022 paper http arxiv org abs 2208 05051v1 19 code as policies language model programs for embodied control jacky liang et al arxiv 2022 paper http arxiv org abs 2209 07753v3 20 progprompt generating situated robot task plans using large language models ishika singh et al arxiv 2022 paper http arxiv org abs 2209 11302v1 21 law informs code a legal informatics approach to aligning artificial intelligence with humans john j nay et al arxiv 2022 paper http arxiv org abs 2209 13020v13 22 language models are greedy reasoners a systematic formal analysis of chain of thought abulhair saparov et al iclr 2023 paper http arxiv org abs 2210 01240v4 23 language models are multilingual chain of thought reasoners freda shi et al iclr 2023 paper http arxiv org abs 2210 03057v1 24 re3 generating longer stories with recursive reprompting and revision kevin yang et al emnlp 2022 paper http arxiv org abs 2210 06774v3 25 language models of code are few shot commonsense learners aman madaan et al emnlp 2022 paper http arxiv org abs 2210 07128v3 26 challenging big bench tasks and whether chain of thought can solve them mirac suzgun et al arxiv 2022 paper http arxiv org abs 2210 09261v1 27 large language models can self improve jiaxin huang et al arxiv 2022 paper https arxiv org abs 2210 11610 28 draft sketch and prove guiding formal theorem provers with informal proofs albert q jiang et al iclr 2023 paper http arxiv org abs 2210 12283v3 29 holistic evaluation of language models percy liang et al arxiv 2022 paper https arxiv org abs 2211 09110 30 pal program aided language models luyu gao et al arxiv 2022 paper https arxiv org abs 2211 10435 31 legal prompt engineering for multilingual legal judgement prediction dietrich trautmann et al arxiv 2022 paper http arxiv org abs 2212 02199v1 32 how does chatgpt perform on the medical licensing exams the implications of large language models for medical education and knowledge assessment aidan gilson et al medrxiv 2022 paper https www medrxiv org content 10 1101 2022 12 23 22283901v1 33 chatgpt the end of online exam integrity teo susnjak et al arxiv 2022 paper http arxiv org abs 2212 09292v1 34 large language models are reasoners with self verification yixuan weng et al arxiv 2022 paper https arxiv org abs 2212 09561 35 self instruct aligning language model with self generated instructions yizhong wang et al arxiv 2022 paper http arxiv org abs 2212 10560v1 36 chatgpt makes medicine easy to swallow an exploratory case study on simplified radiology reports katharina jeblick et al arxiv 2022 paper http arxiv org abs 2212 14882v1 37 the end of programming matt welsh et al acm 2023 paper https cacm acm org magazines 2023 1 267976 the end of programming fulltext 38 chatgpt goes to law school choi jonathan h et al ssrn 2023 paper https papers ssrn com sol3 papers cfm abstract id 4335905 39 how close is chatgpt to human experts comparison corpus evaluation and detection biyang guo et al arxiv 2023 paper https arxiv org abs 2301 07597v1 40 is chatgpt a good translator a preliminary study wenxiang jiao et al arxiv 2023 paper https arxiv org abs 2301 08745v3 41 could an artificial intelligence agent pass an introductory physics course gerd kortemeyer et al arxiv 2023 paper https arxiv org abs 2301 12127v2 42 mathematical capabilities of chatgpt simon frieder et al arxiv 2023 paper https arxiv org abs 2301 13867v1 43 synthetic prompting generating chain of thought demonstrations for large language models zhihong shao et al arxiv 2023 paper http arxiv org abs 2302 00618v1 44 grounding large language models in interactive environments with online reinforcement learning thomas carta et al arxiv 2023 paper https arxiv org abs 2302 02662v1 45 evaluating chatgpt as an adjunct for radiologic decision making arya yao et al medrxiv 2023 paper https www medrxiv org content 10 1101 2023 02 02 23285399v1 46 theory of mind may have spontaneously emerged in large language models michal kosinski et al arxiv 2023 paper http arxiv org abs 2302 02083v3 47 a categorical archive of chatgpt failures ali borji et al arxiv 2023 paper https arxiv org abs 2302 03494v7 48 a multitask multilingual multimodal evaluation of chatgpt on reasoning hallucination and interactivity yejin bang et al arxiv 2023 paper http arxiv org abs 2302 04023v2 49 toolformer language models can teach themselves to use tools timo schick et al arxiv 2023 paper http arxiv org abs 2302 04761v1 50 is chatgpt a general purpose natural language processing task solver chengwei qin et al arxiv 2023 paper http arxiv org abs 2302 06476v2 51 how good are gpt models at machine translation a comprehensive evaluation hendy amr et al arxiv 2023 paper http arxiv org abs 2302 09210 52 can chatgpt understand too a comparative study on chatgpt and fine tuned bert qihuang zhong et al arxiv 2023 paper https arxiv org abs 2302 10198v2 53 zero shot information extraction via chatting with chatgpt xiang wei et al arxiv 2023 paper https arxiv org abs 2302 10205v1 54 chatgpt jack of all trades master of none jan kocon et al arxiv 2023 paper http arxiv org abs 2302 10724v1 55 on the robustness of chatgpt an adversarial and out of distribution perspective jindong wang et al arxiv 2023 paper https arxiv org abs 2302 12095v4 56 check your facts and try again improving large language models with external knowledge and automated feedback baolin peng et al arxiv 2023 paper http arxiv org abs 2302 12813v3 57 an independent evaluation of chatgpt on mathematical word problems mwp paulo shakarian et al arxiv 2023 paper https arxiv org abs 2302 13814v2 58 how robust is gpt 3 5 to predecessors a comprehensive study on language understanding tasks chen xuanting et al arxiv 2023 paper http arxiv org abs 2303 00293v1 59 the utility of chatgpt for cancer treatment information shen chen et al medrxiv 2023 paper https www medrxiv org content 10 1101 2023 03 16 23287316v1 60 can chatgpt assess human personalities a general evaluation framework haocong rao et al arxiv 2023 paper https arxiv org abs 2303 01248v2 61 will affective computing emerge from foundation models and general ai a first evaluation on chatgpt mostafa m amin et al arxiv 2023 paper https arxiv org abs 2303 03186v1 62 exploring the feasibility of chatgpt for event extraction jun gao et al arxiv 2023 paper https arxiv org abs 2303 03836v2 63 does synthetic data generation of llms help clinical text mining tang ruixiang et al arxiv 2023 paper http arxiv org abs 2303 04360v1 64 consistency analysis of chatgpt myeongjun jang et al arxiv 2023 paper http arxiv org abs 2303 06273v1 65 self planning code generation with large language model shun zhang et al iclr 2023 paper http arxiv org abs 2303 06689v1 66 evaluation of chatgpt as a question answering system for answering complex questions yiming tan et al arxiv 2023 paper https arxiv org abs 2303 07992 67 gpt 4 technical report openai et al openai 2023 paper http arxiv org abs 2303 08774v3 68 a short survey of viewing large language models in legal aspect zhongxiang sun et al arxiv 2023 paper http arxiv org abs 2303 09136v1 69 chatgpt participates in a computer science exam sebastian bordt et al arxiv 2023 paper https arxiv org abs 2303 09461v2 70 a comprehensive capability analysis of gpt 3 and gpt 3 5 series models junjie ye et al arxiv 2023 paper https arxiv org abs 2303 10420v1 71 on the educational impact of chatgpt is artificial intelligence ready to obtain a university degree kamil malinka et al arxiv 2023 paper http arxiv org abs 2303 11146v1 72 sparks of artificial general intelligence early experiments with gpt 4 s ebastien bubeck et al arxiv 2023 paper http arxiv org abs 2303 12712v3 73 is chatgpt a good keyphrase generator a preliminary study mingyang song et al arxiv 2023 paper https arxiv org abs 2303 13001v1 74 capabilities of gpt 4 on medical challenge problems harsha nori et al arxiv 2023 paper https arxiv org abs 2303 13375v1 75 can we trust the evaluation on chatgpt rachith aiyappa et al arxiv 2023 paper https arxiv org abs 2303 12767 76 chatgpt outperforms crowd workers for text annotation tasks fabrizio gilardi et al arxiv 2023 paper http arxiv org abs 2303 15056v1 77 evaluation of chatgpt for nlp based mental health applications bishal lamichhane et al arxiv 2023 paper https arxiv org abs 2303 15727v1 78 chatgpt is a knowledgeable but inexperienced solver an investigation of commonsense problem in large language models bian ning et al arxiv 2023 paper http arxiv org abs 2303 16421v1 79 evaluating gpt 3 5 and gpt 4 models on brazilian university admission exams desnes nunes et al arxiv 2023 paper https arxiv org abs 2303 17003v1 80 humans in humans out on gpt converging toward common sense in both success and failure philipp koralus et al arxiv 2023 paper https arxiv org abs 2303 17276v1 81 yes but can chatgpt identify entities in historical documents carlos emiliano gonz lez gallardo et al arxiv 2023 paper https arxiv org abs 2303 17322v1 82 uncovering chatgpt s capabilities in recommender systems sunhao dai et al arxiv 2023 paper https arxiv org abs 2305 02182 83 editing large language models problems methods and opportunities yunzhi yao et al arxiv 2023 paper https arxiv org abs 2305 13172 84 red teaming chatgpt via jailbreaking bias robustness reliability and toxicity terry yue zhuo et al arxiv 2023 paper https arxiv org abs 2301 12867 85 on robustness of prompt based semantic parsing with large pre trained language model an empirical study on codex terry yue zhuo et al eacl 2023 paper https arxiv org abs 2301 12868 86 a systematic study and comprehensive evaluation of chatgpt on benchmark datasets laskar et al acl 23 paper https arxiv org abs 2305 18486 87 red teaming large language models using chain of utterances for safety alignment rishabh bhardwaj et al arxiv 2023 paper https arxiv org abs 2308 09662 the team here is the list of our student contributors in each section section student contributors the whole paper kun zhou junyi li overview resources of llms yingqian min lead chen yang pretraining yupeng hou lead junjie zhang zican dong yushuo chen adaptaion tuning tianyi tang lead jinhao jiang ruiyang ren zikang liu peiyu liu utilization xiaolei wang lead yifan du xinyu tang capacity evaluation beichen zhang lead zhipeng chen yifan li acknowledgments the authors would like to thank yankai lin and yutao zhu for proofreading this paper since the first release of this paper we have received a number of valuable comments from the readers we sincerely thank the readers who have written to us with constructive suggestions and comments tyler suard damai dai liang ding stella biderman kevin gray jay alammar and yubo feng update log version time update content v1 2023 03 31 the initial version v2 2023 04 09 add the affiliation information br revise figure 1 and table 1 and clarify the br corresponding selection criterion for llms br improve the writing br correct some minor errors v3 2023 04 11 correct the errors for library resources v4 2023 04 12 revise figure 1 and table 1 and clarify the release date of llms v5 2023 04 16 add a new section 2 2 about br the technical evolution of gpt series models v6 2023 04 24 add some new models in table 1 and figure 1 br add the discussion about scaling laws br add some explanations about the br model sizes for emergent abilities section 2 1 br add an illustrative figure for the attention patterns br for different architectures in figure 4 br add the detailed formulas in table 4 v7 2023 04 25 revise some copy errors in figures and tables v8 2023 04 27 add efficient tuning in section 5 3 v9 2023 04 28 revise section 5 3 v10 2023 05 07 revise table 1 table 2 and some minor points v11 br major revision 2023 06 29 section 1 add figure 1 for the trends of published br llm papers in arxiv br section 2 add figure 3 for gpt s evolution and the br corresponding discussion br section 3 add figure 4 for llama family and the br corresponding discussion br section 5 add latest discussion about the synthetic br data formatting of instruction tuning in section 5 1 1 br the empirical analysis for instruction tuning in sec br tion 5 1 4 parameter efficient model adaptation in br section 5 3 and memory efficient adaptation in sec br tion 5 4 br section 6 add latest discussion about the underlying br mechanism of icl 6 1 3 planning for complex task br solving in section 6 3 br section 7 add table 10 for representative datasets for br evaluating advanced abilities of llms and empirical br ability evaluation in section 7 3 2 br section 8 add prompt design br section 9 add the discussions on applications of br llms in finance and scientific research domains | chain-of-thought chatgpt in-context-learning instruction-tuning large-language-models llm llms natural-language-processing pre-trained-language-models pre-training rlhf | ai |
FreeRTOS-Kernel-Community-Supported-Ports | freertos community supported ports this repository contains freertos ports supported by freertos community members follow the steps below to contribute a freertos port to this repository 1 write freertos port for your compiler and architecture 2 optional create a project in the freertos community supported demos repository https github com freertos freertos community supported demos tree main for your hardware for running tests as mentioned here https github com freertos freertos blob main freertos demo thirdparty template readme md 3 optional make sure all the tests pass add the test results in the pull request description 4 add a readme file with the following information 1 how to use this port 2 optional link to the test project created in step 2 3 any other relevant information 5 raise a pull request to merge the port 6 optional raise another pr to merge the test project in the freertos partner supported demos repository https github com freertos freertos partner supported demos tree main license this repository contains multiple directories each individually licensed please see the license file in each directory | os |
|
Project-Cloud-Data-Warehouse--Amazon-S3-to-Redshift-ETL-Pipeline-for-online-streaming-company | instructions 1 business case sparkify company is moving to the cloud to align its processes with the growth of the business it s data is stored in amazon s3 in the form of json log files containing it s users activities as well as it s songs metadata the company needs an etl pipeline developed which will extract data from the json files in s3 and load it into redshift database so that the analytics team can unlock insights into what songs their users are listening to 2 data extraction transformation and loading a python script file etl py has been developed to extract transform and load user activity information from s3 into redshift database 3 execute etl pipeline i open the execute ipynb notebook ii run the first line of code which runs the create table py script to drop any existing tables and create new tables in redshift iii run the second line of code to execute the etl py script which reads data from the json log files and songs metadata in s3 into redshift iv open the test ipynb notebook and run the lines of codes to verify that data has been imported into the database 4 sample songs analysis to get the top 5 most listened to songs you need to execute the line of code below in your test ipynb notebook sql select song id sum duration from songs group by songs song id order by sum duration desc limit 5 5 database design fact table i songplays table play records from the log data columns songplay id bigint identity 0 1 primary key start time timestamp not null foreign key to reference the time table which contains specific date and time units user id int not null level varchar not null song id varchar artist id varchar session id int not null location varchar user agent varchar dimension tables i users record of users in the app columns user id int primary key first name varchar last name varchar gender char level varchar ii songs records of available songs in the music database columns song id varchar primary key title varchar artist id varchar foreign key to reference the artists table based on the artist id year int duration numeric iii artists artists in the music database columns artist id varchar primary key name varchar not null location varchar latitude numeric longitude numeric iv time timestamps of records in songplays broken down into specific units columns start time timestamp primary key hour int not null day int not null week int not null month int not null year int not null weekday int not null | server |
|
mlnotebook | build status https travis ci org beader mlnotebook svg branch master https travis ci org beader mlnotebook https www coursera org course ntumlone latex beader me http beader me markdown gitbook beader qq com mailto beader qq com https www csie ntu edu tw htlin mooc | ai |
|
sentiment | sentiment sentiment analysis using machine learning techniques check info py for the training and testing code a demo of the tool is available here http sentiment vivekn com refer this paper for more information about the algorithms used http arxiv org abs 1305 6143 | ai |
|
Learning-Deep-Learning | paper notes this repository contains my paper reading notes on deep learning and machine learning it is inspired by denny britz https github com dennybritz deeplearning papernotes and daniel takeshi https github com danieltakeshi paper notes a minimalistic webpage generated with github io can be found here https patrick llgc github io learning deep learning about me my name is patrick langechuan liu https www linkedin com in patrick llgc after about a decade of education and research in physics i found my passion in deep learning and autonomous driving currently i am leading the development of perception features at xpeng motors https en xiaopeng com a fast growing autonomous driving company what to read if you are new to deep learning in computer vision and don t know where to start i suggest you spend your first month or so dive deep into this list of papers start first cnn papers md i did so see my notes start first cnn papers notes md and it served me well here is a list of trustworthy sources of papers trusty md in case i ran out of papers to read my review posts by topics i regularly update my blog in toward data science https medium com patrickllgc bev perception in mass production autonomous driving https towardsdatascience com bev perception in mass production autonomous driving c6e3f1e46ae0 challenges of mass production autonomous driving in china https towardsdatascience com challenges of mass production autonomous driving in china 407c7e2dc5d8 vision centric semantic occupancy prediction for autonomous driving https towardsdatascience com vision centric semantic occupancy prediction for autonomous driving 16a46dbd6f65 related paper notes topics topic occupancy network md drivable space in autonomous driving the industry https medium com patrickllgc drivable space in autonomous driving the industry 7a4624b94d41 drivable space in autonomous driving the academia https towardsdatascience com drivable space in autonomous driving a review of academia ef1a6aa4dc15 drivable space in autonomous driving the concept https towardsdatascience com drivable space in autonomous driving the concept df699bb8682f monocular bev perception with transformers in autonomous driving https towardsdatascience com monocular bev perception with transformers in autonomous driving c41e4a893944 related paper notes topics topic transformers bev md illustrated differences between mlp and transformers for tensor reshaping in deep learning https towardsdatascience com illustrated difference between mlp and transformers for tensor reshaping 52569edaf89 monocular 3d lane line detection in autonomous driving https towardsdatascience com monocular 3d lane line detection in autonomous driving 4d7cdfabf3b6 related paper notes topics topic 3d lld md deep learning based object detection in crowded scenes https towardsdatascience com deep learning based object detection in crowded scenes 1c9fddbd7bc4 related paper notes topics topic crowd detection md monocular bird s eye view semantic segmentation for autonomous driving https towardsdatascience com monocular birds eye view semantic segmentation for autonomous driving ee2f771afb59 related paper notes topics topic bev segmentation md deep learning in mapping for autonomous driving https towardsdatascience com deep learning in mapping for autonomous driving 9e33ee951a44 monocular dynamic object slam in autonomous driving https towardsdatascience com monocular dynamic object slam in autonomous driving f12249052bf1 monocular 3d object detection in autonomous driving a review https towardsdatascience com monocular 3d object detection in autonomous driving 2476a3c7f57e self supervised keypoint learning a review https towardsdatascience com self supervised keypoint learning aade18081fc3 single stage instance segmentation a review https towardsdatascience com single stage instance segmentation a review 1eeb66e0cc49 self paced multitask learning a review https towardsdatascience com self paced multitask learning 76c26e9532d0 convolutional neural networks with heterogeneous metadata https towardsdatascience com convolutional neural networks with heterogeneous metadata 2af9241218a9 lifting 2d object detection to 3d in autonomous driving https towardsdatascience com geometric reasoning based cuboid generation in monocular 3d object detection 5ee2996270d1 multimodal regression https towardsdatascience com anchors and multi bin loss for multi modal target regression 647ea1974617 paper reading in 2019 https towardsdatascience com the 200 deep learning papers i read in 2019 7fb7034f05f7 source friends link sk 7628c5be39f876b2c05e43c13d0b48a3 2023 09 1 retnet retentive network a successor to transformer for large language models https arxiv org abs 2307 08621 notes paper notes retnet md msra transformers are rnns fast autoregressive transformers with linear attention https arxiv org abs 2006 16236 notes paper notes transformers are rnns md kbd icml 2020 kbd linear attention roformer enhanced transformer with rotary position embedding https arxiv org abs 2104 09864 aft an attention free transformer https arxiv org abs 2105 14103 notes paper notes aft md apple flashattention fast and memory efficient exact attention with io awareness https arxiv org abs 2205 14135 gpt in 60 lines of numpy https jaykmody com blog gpt from scratch speeding up the gpt kv cache https www dipkumar dev becoming the unbeatable posts gpt kvcache llm parameter counting https kipp ly transformer param count transformer inference arithmetic https kipp ly transformer inference arithmetic kv cache albef align before fuse vision and language representation learning with momentum distillation https arxiv org abs 2107 07651 kbd neurips 2021 kbd junnan li blip bootstrapping language image pre training for unified vision language understanding and generation https arxiv org abs 2201 12086 kbd icml 2022 kbd junnan li blip 2 bootstrapping language image pre training with frozen image encoders and large language models https arxiv org abs 2301 12597 junnan li moo open world object manipulation using pre trained vision language models https arxiv org abs 2303 00905 google robotics end to end visuomotor vc 1 where are we in the search for an artificial visual cortex for embodied intelligence https arxiv org abs 2303 18240 cliport what and where pathways for robotic manipulation https arxiv org abs 2109 12098 kbd corl 2021 kbd nvidia end to end visuomotor gptq accurate post training quantization for generative pre trained transformers https arxiv org abs 2210 17323 kbd iclr 2023 kbd smoothquant accurate and efficient post training quantization for large language models https arxiv org abs 2211 10438 kbd icml 2023 kbd song han llm quant llm int8 8 bit matrix multiplication for transformers at scale https arxiv org abs 2208 07339 kbd neurips 2022 kbd llm quant awq activation aware weight quantization for llm compression and acceleration https arxiv org abs 2306 00978 song han llm quant sapien a simulated part based interactive environment https arxiv org abs 2003 08515 kbd cvpr 2020 kbd film visual reasoning with a general conditioning layer https arxiv org abs 1709 07871 kbd aaai 2018 kbd tokenlearner what can 8 learned tokens do for images and videos https arxiv org abs 2106 11297 kbd neurips 2021 kbd chatgpt for robotics design principles and model abilities https arxiv org abs 2306 17582 microsoft minedojo building open ended embodied agents with internet scale knowledge https arxiv org abs 2206 08853 kbd neurips 2022 kbd outstanding paper award qlora efficient finetuning of quantized llms https arxiv org abs 2305 14314 ovo open vocabulary occupancy https arxiv org abs 2305 16133 code llama open foundation models for code https arxiv org abs 2308 12950 chinchilla training compute optimal large language models https arxiv org abs 2203 15556 deepmind gqa training generalized multi query transformer models from multi head checkpoints https arxiv org abs 2305 13245 roformer enhanced transformer with rotary position embedding https arxiv org abs 2104 09864 rh20t a robotic dataset for learning diverse skills in one shot https arxiv org abs 2307 00595 perceiver actor a multi task transformer for robotic manipulation vima general robot manipulation with multimodal prompts an attention free transformer https arxiv org abs 2105 14103 apple pddl planning with pretrained large language models mit leslie kaelbling 2023 08 3 rt 1 robotics transformer for real world control at scale https arxiv org abs 2212 06817 notes paper notes rt1 md deepmind rt 2 vision language action models transfer web knowledge to robotic control https robotics transformer2 github io assets rt2 pdf notes paper notes rt2 md deepmind end to end visuomotor rwkv reinventing rnns for the transformer era https arxiv org abs 2305 13048 notes paper notes rwkv md 2023 07 6 mile model based imitation learning for urban driving https arxiv org abs 2210 07729 notes paper notes mile md kbd neurips 2022 kbd alex kendall palm e an embodied multimodal language model https arxiv org abs 2303 03378 notes paper notes palm e md google robotics voxposer composable 3d value maps for robotic manipulation with language models https voxposer github io voxposer pdf notes paper notes voxposer md feifei li cap code as policies language model programs for embodied control https arxiv org abs 2209 07753 notes paper notes cap md project https code as policies github io progprompt generating situated robot task plans using large language models https arxiv org abs 2209 11302 kbd icra 2023 kbd tidybot personalized robot assistance with large language models https arxiv org abs 2305 05658 notes paper notes tidybot md project https tidybot cs princeton edu saycan do as i can not as i say grounding language in robotic affordances https arxiv org abs 2204 01691 notes paper notes saycan md project https say can github io 2023 06 5 end to end review by shanghai ai labs https github com opendrivelab end to end autonomous driving pix2seq v2 a unified sequence interface for vision tasks https arxiv org abs 2206 07669 notes paper notes pix2seq v2 md kbd neurips 2022 kbd geoffrey hinton flamingo a visual language model for few shot learning https arxiv org abs 2204 14198 notes paper notes flamingo md kbd neurips 2022 kbd deepmind gato a generalist agent https arxiv org abs 2205 06175 notes paper notes gato md kbd tmlr 2022 kbd deepmind bc sac imitation is not enough robustifying imitation with reinforcement learning for challenging driving scenarios https arxiv org abs 2212 11419 notes paper notes bc sac md kbd neurips 2022 kbd waymo mgail ad hierarchical model based imitation learning for planning in autonomous driving https arxiv org abs 2210 09539 notes paper notes mgail ad md kbd iros 2022 kbd waymo 2023 05 7 surroundocc multi camera 3d occupancy prediction for autonomous driving https arxiv org abs 2303 09551 notes paper notes surroundocc md occupancy network wei yi jiwen lu occ3d a large scale 3d occupancy prediction benchmark for autonomous driving https arxiv org abs 2304 14365 notes paper notes occ3d md occupancy network zhao hang occupancy networks learning 3d reconstruction in function space https arxiv org abs 1812 03828 kbd cvpr 2019 kbd notes paper notes occupancy networks md andreas geiger occformer dual path transformer for vision based 3d semantic occupancy prediction https arxiv org abs 2304 05316 occupancy network phigent pix2seq a language modeling framework for object detection https arxiv org abs 2109 10852 notes paper notes pix2seq md kbd iclr 2022 kbd geoffrey hinton visionllm large language model is also an open ended decoder for vision centric tasks https arxiv org abs 2305 11175 notes paper notes vision llm md jifeng dai hugginggpt solving ai tasks with chatgpt and its friends in hugging face https arxiv org abs 2303 17580 notes paper notes hugging gpt md 2023 04 1 uniad planning oriented autonomous driving https arxiv org abs 2212 10156 notes paper notes uniad md bev e2e hongyang li 2023 03 5 gpt 4 technical report https arxiv org abs 2303 08774 notes paper notes gpt4 md openai gpt openoccupancy a large scale benchmark for surrounding semantic occupancy perception https arxiv org abs 2303 03991 notes paper notes openoccupancy md occupancy network jiwen lu voxformer sparse voxel transformer for camera based 3d semantic scene completion https arxiv org abs 2302 12251 note paper notes voxformer md kbd cvpr 2023 highlight kbd occupancy network nvidia monoscene monocular 3d semantic scene completion https arxiv org abs 2112 00726 kbd cvpr 2022 kbd notes paper notes monoscene md occupancy network single cam corenet coherent 3d scene reconstruction from a single rgb image https arxiv org abs 2004 12989 notes paper notes corenet md kbd eccv 2020 oral kbd 2023 02 4 will we run out of data an analysis of the limits of scaling datasets in machine learning https arxiv org abs 2211 04325 notes paper notes out of data md epoch ai industry report codex evaluating large language models trained on code https arxiv org abs 2107 03374 notes paper notes codex md gpt openai instructgpt training language models to follow instructions with human feedback https arxiv org abs 2203 02155 notes paper notes instructgpt md gpt openai tpvformer tri perspective view for vision based 3d semantic occupancy prediction https arxiv org abs 2302 07817 notes paper notes tpvformer md kbd cvpr 2023 kbd occupancy network jiwen lu 2023 01 2 ppgeo policy pre training for end to end autonomous driving via self supervised geometric modeling https arxiv org abs 2301 01006 notes paper notes ppgeo md kbd iclr 2023 kbd nuplan a closed loop ml based planning benchmark for autonomous vehicles https arxiv org abs 2106 11810 notes paper notes nuplan md 2022 11 1 m2i from factored marginal trajectory prediction to interactive prediction https arxiv org abs 2202 11884 notes paper notes m2i md kbd cvpr 2022 kbd 2022 10 1 delving into the devils of bird s eye view perception a review evaluation and recipe https arxiv org abs 2209 05324 notes paper notes delving bev md pjlab 2022 09 3 vip3d end to end visual trajectory prediction via 3d agent queries https arxiv org abs 2208 01582 notes paper notes vip3d md bev perception prediction hang zhao maptr structured modeling and learning for online vectorized hd map construction https arxiv org abs 2208 14437 notes paper notes maptr md horizon bevnet stopnet scalable trajectory and occupancy prediction for urban autonomous driving https arxiv org abs 2206 00991 kbd icra 2022 kbd motr end to end multiple object tracking with transformer https arxiv org abs 2105 03247 kbd eccv 2022 kbd megvii mot anchor detr query design for transformer based object detection https arxiv org abs 2109 07107 notes paper notes anchor detr md kbd aaai 2022 kbd megvii 2022 08 1 home heatmap output for future motion estimation https arxiv org abs 2105 10968 notes paper notes home md kbd itsc 2021 kbd behavior prediction huawei paris 2022 07 8 persformer 3d lane detection via perspective transformer and the openlane benchmark https arxiv org abs 2203 11089 notes paper notes persformer md bevnet lane line vectormapnet end to end vectorized hd map learning https arxiv org abs 2206 08920 notes paper notes vectormapnet md bevnet lld hang zhao petr position embedding transformation for multi view 3d object detection https arxiv org abs 2203 05625 notes paper notes petr md kbd eccv 2022 kbd bevnet petrv2 a unified framework for 3d perception from multi camera images https arxiv org abs 2206 01256 notes paper notes petrv2 md bevnet megvii m 2bev multi camera joint 3d detection and segmentation with unified birds eye view representation https arxiv org abs 2204 05088 notes paper notes m2bev md bevnet nvidia bevdepth acquisition of reliable depth for multi view 3d object detection https arxiv org abs 2206 10092 notes paper notes bevdepth md bevnet nuscenes sota megvii cvt cross view transformers for real time map view semantic segmentation https arxiv org abs 2205 02833 notes paper notes cvt md kbd cvpr 2022 oral kbd utaustin philipp wayformer motion forecasting via simple efficient attention networks https arxiv org abs 2207 05844 notes paper notes wayformer md behavior prediction waymo 2022 06 3 bevdet4d exploit temporal cues in multi camera 3d object detection https arxiv org abs 2203 17054 notes paper notes bevdet4d md bevnet beverse unified perception and prediction in birds eye view for vision centric autonomous driving https arxiv org abs 2205 09743 notes paper notes beverse md jiwen lu bevnet perception prediction bevfusion multi task multi sensor fusion with unified bird s eye view representation https arxiv org abs 2205 13542 notes paper notes bevfusion md bevnet han song 2022 03 1 bevformer learning bird s eye view representation from multi camera images via spatiotemporal transformers https arxiv org abs 2203 17270 notes paper notes bevformer md kbd eccv 2022 kbd bevnet hongyang li jifeng dai 2022 02 1 tnt target driven trajectory prediction https arxiv org abs 2008 08294 notes paper notes tnt md kbd corl 2020 kbd prediction waymo hang zhao densetnt end to end trajectory prediction from dense goal sets https arxiv org abs 2108 09640 notes paper notes dense tnt md kbd iccv 2021 kbd prediction waymo 1st place winner womd 2022 01 1 manydepth the temporal opportunist self supervised multi frame monocular depth https arxiv org abs 2104 14540 notes paper notes manydepth md kbd cvpr 2021 kbd monodepth niantic dekr bottom up human pose estimation via disentangled keypoint regression https arxiv org abs 2104 02300 notes paper notes dekr md kbd cvpr 2021 kbd 2021 12 5 bn ffn bn leveraging batch normalization for vision transformers https openaccess thecvf com content iccv2021w neurarch papers yao leveraging batch normalization for vision transformers iccvw 2021 paper pdf notes paper notes bn ffn bn md kbd iccvw 2021 kbd bn transformers powernorm rethinking batch normalization in transformers https arxiv org abs 2003 07845 notes paper notes powernorm md kbd icml 2020 kbd bn transformers multipath efficient information fusion and trajectory aggregation for behavior prediction https arxiv org abs 2111 14973 notes paper notes multipath md kbd icra 2022 kbd waymo behavior prediction bevdet high performance multi camera 3d object detection in bird eye view https arxiv org abs 2112 11790 notes paper note bevdet md translating images into maps https arxiv org abs 2110 00966 notes paper notes translating images to maps md kbd icra 2022 kbd bevnet transformers 2021 11 4 detr3d 3d object detection from multi view images via 3d to 2d queries https arxiv org abs 2110 06922 notes paper notes detr3d md kbd corl 2021 kbd bevnet transformers robust cvd robust consistent video depth estimation https arxiv org abs 2012 05901 kbd cvpr 2021 oral kbd website https robust cvd github io mae masked autoencoders are scalable vision learners https arxiv org abs 2111 06377 notes paper notes mae md kaiming he unsupervised learning simmim a simple framework for masked image modeling https arxiv org abs 2111 09886 notes paper notes simmim md msra unsupervised learning mae ibot image bert pre training with online tokenizer https arxiv org abs 2111 07832 2021 10 3 stsu structured bird s eye view traffic scene understanding from onboard images https arxiv org abs 2110 01997 notes paper notes stsu md kbd iccv 2021 kbd bev feat stitching luc van gool panopticbev bird s eye view panoptic segmentation using monocular frontal view images https arxiv org abs 2108 03227 notes paper notes panoptic bev md kbd ral 2022 kbd bevnet vertical horizontal features neat neural attention fields for end to end autonomous driving https arxiv org abs 2109 04456 notes paper notes neat md kbd iccv 2021 kbd supplementary http www cvlibs net publications chitta2021iccv supplementary pdf bevnet 2021 09 11 dd3d is pseudo lidar needed for monocular 3d object detection https arxiv org abs 2108 06417 notes paper notes dd3d md kbd iccv 2021 kbd mono3d toyota efficientdet scalable and efficient object detection https arxiv org abs 1911 09070 notes paper notes efficientdet md kbd cvpr 2020 kbd bifpn tesla ai day pnpnet end to end perception and prediction with tracking in the loop https arxiv org abs 2005 14711 notes paper notes pnpnet md kbd cvpr 2020 kbd uber atg mp3 a unified model to map perceive predict and plan https arxiv org abs 2101 06806 notes paper notes mp3 md kbd cvpr 2021 kbd uber planning bev net assessing social distancing compliance by joint people localization and geometric reasoning http arxiv org abs 2110 04931 notes paper notes bevnet sdca md kbd iccv 2021 kbd bevnet surveillance lidar r cnn an efficient and universal 3d object detector https arxiv org abs 2103 15297 notes paper notes lidar rcnn md kbd cvpr 2021 kbd tusimple naiyan wang corner cases for visual perception in automated driving some guidance on detection approaches https arxiv org abs 2102 05897 notes paper notes corner case vision arxiv md corner cases systematization of corner cases for visual perception in automated driving https ieeexplore ieee org document 9304789 notes paper notes corner case vision iv md kbd iv 2020 kbd corner cases an application driven conceptualization of corner cases for perception in highly automated driving https arxiv org abs 2103 03678 notes paper notes corner case multisensor md kbd iv 2021 kbd corner cases pyva projecting your view attentively monocular road scene layout estimation via cross view transformation https openaccess thecvf com content cvpr2021 html yang projecting your view attentively monocular road scene layout estimation via cvpr 2021 paper html notes paper notes pyva md kbd cvpr 2021 kbd supplementary https openaccess thecvf com content cvpr2021 supplemental yang projecting your view cvpr 2021 supplemental zip bevnet yolof you only look one level feature https arxiv org abs 2103 09460 notes paper notes yolof md kbd cvpr 2021 kbd megvii perceiving humans from monocular 3d localization to social distancing https arxiv org abs 2009 00984 notes paper notes perceiving humans md kbd tits 2021 kbd monoloco pifpaf composite fields for human pose estimation https arxiv org abs 1903 06593 kbd cvpr 2019 kbd bird s eye view panoptic segmentation using monocular frontal view images https arxiv org abs 2108 03227 bevnet transformerfusion monocular rgb scene reconstruction using transformers https arxiv org abs 2107 02191 projecting your view attentively monocular road scene layout estimation via cross view transformation https openaccess thecvf com content cvpr2021 papers yang projecting your view attentively monocular road scene layout estimation via cvpr 2021 paper pdf kbd cvpr 2021 kbd multi modal fusion transformer for end to end autonomous driving https arxiv org abs 2104 09224 kbd cvpr 2021 kbd conditional detr for fast training convergence https arxiv org abs 2108 06152 probabilistic and geometric depth detecting objects in perspective https arxiv org abs 2107 14160 kbd corl 2021 kbd 2021 08 11 egonet exploring intermediate representation for monocular vehicle pose estimation https arxiv org abs 2011 08464 notes paper notes egonet md kbd cvpr 2021 kbd mono3d monoef monocular 3d object detection an extrinsic parameter free approach https arxiv org abs 2106 15796 notes paper notes monoef md kbd cvpr 2021 kbd mono3d gac ground aware monocular 3d object detection for autonomous driving https arxiv org abs 2102 00690 notes paper notes gac md kbd ral 2021 kbd mono3d fcos3d fully convolutional one stage monocular 3d object detection https arxiv org abs 2104 10956 notes paper notes fcos3d md kbd neurips 2020 kbd mono3d sensetime gupnet geometry uncertainty projection network for monocular 3d object detection https arxiv org abs 2107 13774 notes paper notes gupnet md kbd iccv 2021 kbd mono3d wanli ouyang darts differentiable architecture search https arxiv org abs 1806 09055 notes paper notes darts md kbd iclr 2019 kbd vgg author fbnet hardware aware efficient convnet design via differentiable neural architecture search https arxiv org abs 1812 03443 notes paper notes fbnet md kbd cvpr 20219 kbd darts fbnetv2 differentiable neural architecture search for spatial and channel dimensions https arxiv org abs 2004 05565 kbd cvpr 2020 kbd fbnetv3 joint architecture recipe search using predictor pretraining https arxiv org abs 2006 02049 kbd cvpr 2021 kbd perceiver general perception with iterative attention https arxiv org abs 2103 03206 notes paper notes perceiver md kbd icml 2021 kbd transformers multimodal perceiver io a general architecture for structured inputs outputs https arxiv org abs 2107 14795 notes paper notes perceiver io md pillarmotion self supervised pillar motion learning for autonomous driving https arxiv org abs 2104 08683 notes paper notes pillar motion md kbd cvpr 2021 kbd qcraft alan yuille simtrack exploring simple 3d multi object tracking for autonomous driving https arxiv org abs 2108 10312 notes paper notes simtrack md kbd iccv 2019 kbd qcraft alan yuille 2021 07 1 hdmapnet an online hd map construction and evaluation framework https arxiv org abs 2107 06307 notes paper notes hdmapnet md kbd cvpr 2021 workshop kbd youtube video only li auto 2021 06 2 fiery future instance prediction in bird s eye view from surround monocular cameras https arxiv org abs 2104 10490 notes paper notes fiery md kbd iccv 2021 kbd bevnet perception prediction baidu s cnn seg https zhuanlan zhihu com p 35034215 notes paper notes cnn seg md 2021 04 5 rethinking the heatmap regression for bottom up human pose estimation https arxiv org abs 2012 15175 notes paper notes swahr md kbd cvpr 2021 kbd megvii crowdpose efficient crowded scenes pose estimation and a new benchmark https arxiv org abs 1812 00324 kbd cvpr 2019 kbd the overlooked elephant of object detection open set https openaccess thecvf com content wacv 2020 html dhamija the overlooked elephant of object detection open set wacv 2020 paper html kbd wacv 2021 kbd class agnostic object detection https arxiv org abs 2011 14204 kbd wacv 2021 kbd owod towards open world object detection https arxiv org abs 2103 02603 notes paper notes owod md kbd cvpr 2021 oral kbd fsdet frustratingly simple few shot object detection https arxiv org abs 2003 06957 kbd icml 2020 kbd monoflex objects are different flexible monocular 3d object detection https arxiv org abs 2104 02323 notes paper notes monoflex md kbd cvpr 2021 kbd mono3d jiwen lu cropped monodle delving into localization errors for monocular 3d object detection https arxiv org abs 2103 16237 notes paper notes monodle md kbd cvpr 2021 kbd mono3d exploring 2d data augmentation for 3d monocular object detection https arxiv org abs 2104 10786 ocm3d object centric monocular 3d object detection https arxiv org abs 2104 06041 mono3d fsm full surround monodepth from multiple cameras https arxiv org abs 2104 00152 notes paper notes fsm md kbd icra 2021 kbd monodepth xnet 2021 03 4 caddn categorical depth distribution network for monocular 3d object detection https arxiv org abs 2103 01100 notes paper notes caddn md kbd cvpr 2021 oral kbd mono3d bevnet dsnt numerical coordinate regression with convolutional neural networks https arxiv org abs 1801 07372 notes paper notes dsnt md differentiable spatial to numerical transform soft argmax human pose regression by combining indirect part detection and contextual information https arxiv org abs 1710 02322 insta yolo real time instance segmentation https arxiv org abs 2102 06777 notes paper notes insta yolo md kbd icml workshop 2020 kbd single stage instance segmentation centernet2 probabilistic two stage detection https arxiv org abs 2103 07461 notes paper notes centernet2 md centernet two stage 2021 01 7 confluence a robust non iou alternative to non maxima suppression in object detection https arxiv org abs 2012 00257 notes paper notes confluence md nms boxinst high performance instance segmentation with box annotations https arxiv org abs 2012 02310 notes paper notes boxinst md kbd cvpr 2021 kbd chunhua shen tian zhi 3dssd point based 3d single stage object detector https arxiv org abs 2002 10187 notes paper notes 3dssd md kbd cvpr 2020 kbd repvgg making vgg style convnets great again https arxiv org abs 2101 03697 notes paper notes repvgg md megvii xiangyu zhang acnet acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks https arxiv org abs 1908 03930 notes paper notes acnet md kbd iccv 2019 kbd bev feat stitching understanding bird s eye view semantic hd maps using an onboard monocular camera https arxiv org abs 2012 03040 notes paper notes bev feat stitching md bevnet mono3d luc van gool pss object detection made simpler by eliminating heuristic nms https arxiv org abs 2101 11782 notes paper notes pss md transformer detr 2020 12 17 defcn end to end object detection with fully convolutional network https arxiv org abs 2012 03544 notes paper notes defcn md transformer detr onenet end to end one stage object detection by classification cost https arxiv org abs 2012 05780 notes paper notes onenet md transformer detr traffic light mapping localization and state detection for autonomous vehicles http driving stanford edu papers icra2011 pdf notes paper notes tfl stanford md kbd icra 2011 kbd traffic light sebastian thrun towards lifelong feature based mapping in semi static environments https storage googleapis com pub tools public publication data pdf 43966 pdf notes paper notes lifelong feature mapping google md kbd icra 2016 kbd how to keep hd maps for automated driving up to date http www lewissoft com pdf icra2020 1484 pdf notes paper notes keep hd maps updated bmw md kbd icra 2020 kbd bmw generalized focal loss v2 learning reliable localization quality estimation for dense object detection https arxiv org abs 2011 12885 notes paper notes gfocalv2 md kbd cvpr 2021 kbd focal loss visual slam for automated driving exploring the applications of deep learning http openaccess thecvf com content cvpr 2018 workshops papers w9 milz visual slam for cvpr 2018 paper pdf notes paper notes vslam for ad md kbd cvpr 2018 workshop kbd centroid voting object aware centroid voting for monocular 3d object detection https arxiv org abs 2007 09836 notes paper notes centroid voting md kbd iros 2020 kbd mono3d geometry appearance distance monocular 3d object detection in cylindrical images from fisheye cameras https arxiv org abs 2003 03759 notes paper notes mono3d fisheye md gm israel mono3d deepps vision based parking slot detection a dcnn based approach and a large scale benchmark dataset https cslinzhang github io deepps parkingslot pdf kbd tip 2018 kbd parking slot detection ps2 0 dataset psdet efficient and universal parking slot detection https arxiv org abs 2005 05528 notes paper notes psdet md kbd iv 2020 kbd zongmu parking slot detection patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning https arxiv org abs 2001 00138 notes paper notes patdnn md kbd asplos 2020 kbd pruning scaled yolov4 scaling cross stage partial network https arxiv org abs 2011 08036 notes paper notes scaled yolov4 md yolo yolov5 by ultralytics https github com ultralytics yolov5 notes paper notes yolov5 md yolo spatial2channel pp yolo an effective and efficient implementation of object detector https arxiv org abs 2007 12099 notes paper notes pp yolo md yolo paddle paddle baidu pointpainting sequential fusion for 3d object detection https arxiv org pdf 1911 10150 pdf notes paper notes point painting md nuscenece motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps https arxiv org abs 2003 06754 notes paper notes motionnet md kbd cvpr 2020 kbd unseen moving objects bev locating objects without bounding boxes https arxiv org abs 1806 07564 notes paper notes objects without bboxes md kbd cvpr 2019 kbd weighted haussdorf distance nms free 2020 11 18 tsp rethinking transformer based set prediction for object detection https arxiv org abs 2011 10881 notes paper notes tsp md kbd iccv 2021 kbd detr transformers kris kitani sparse r cnn end to end object detection with learnable proposals https arxiv org abs 2011 12450 notes paper notes sparse rcnn md kbd cvpr 2020 kbd detr transformer unsupervised monocular depth learning in dynamic scenes https arxiv org abs 2010 16404 notes paper notes learn depth and motion md kbd corl 2020 kbd learnk improved ver google monet3d towards accurate monocular 3d object localization in real time https arxiv org abs 2006 16007 notes paper notes monet3d md kbd icml 2020 kbd mono3d pairwise relationship argoverse 3d tracking and forecasting with rich maps https arxiv org abs 1911 02620 notes paper notes argoverse md kbd cvpr 2019 kbd hd maps dataset cv lidar the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes https arxiv org abs 1903 01568 notes paper notes h3d md kbd icra 2019 kbd cityscapes 3d dataset and benchmark for 9 dof vehicle detection https arxiv org abs 2006 07864 kbd cvprw 2020 kbd dataset daimler mono3d nyc3dcars a dataset of 3d vehicles in geographic context https www cs cornell edu snavely publications papers nyc3dcars iccv13 pdf kbd iccv 2013 kbd towards fully autonomous driving systems and algorithms https www ri cmu edu wp content uploads 2017 12 levinson iv2011 pdf kbd iv 2011 kbd center3d center based monocular 3d object detection with joint depth understanding https arxiv org abs 2005 13423 notes paper notes center3d md mono3d lid depjoint zoomnet part aware adaptive zooming neural network for 3d object detection https arxiv org abs 2003 00529 kbd aaai 2020 oral kbd mono3d centerfusion center based radar and camera fusion for 3d object detection https arxiv org abs 2011 04841 notes paper notes centerfusion md kbd wacv 2021 kbd early fusion camera radar 3d lanenet anchor free lane detection using a semi local representation https arxiv org abs 2011 01535 notes paper notes 3d lanenet md kbd neurips 2020 workshop kbd gm israel 3d lld lstr end to end lane shape prediction with transformers https arxiv org abs 2011 04233 notes paper notes lstr md kbd wacv 2021 kbd lld transformers pixor real time 3d object detection from point clouds https arxiv org abs 1902 06326 notes paper notes pixor md kbd cvpr 2018 kbd birds eye view hdnet pixor exploiting hd maps for 3d object detection http proceedings mlr press v87 yang18b yang18b pdf notes paper notes pixor md kbd corl 2018 kbd cpndet corner proposal network for anchor free two stage object detection https arxiv org abs 2007 13816 kbd eccv 2020 kbd anchor free two stage mvf end to end multi view fusion for 3d object detection in lidar point clouds https arxiv org abs 1910 06528 notes paper notes mvf md kbd corl 2019 kbd waymo voxelnet 1st author pillar based object detection for autonomous driving https arxiv org abs 2007 10323 notes paper notes pillar od md kbd eccv 2020 kbd training time friendly network for real time object detection https arxiv org abs 1909 00700 kbd aaai 2020 kbd anchor free fast training autonomous driving with deep learning a survey of state of art technologies https arxiv org abs 2006 06091 review of autonomous stack yu huang dense monocular depth estimation in complex dynamic scenes https openaccess thecvf com content cvpr 2016 papers ranftl dense monocular depth cvpr 2016 paper pdf kbd cvpr 2016 kbd probabilistic future prediction for video scene understanding https anthonyhu github io research probabilistic future prediction ab3d a baseline for 3d multi object tracking https arxiv org abs 1907 03961 kbd iros 2020 kbd 3d mot spatial temporal relation networks for multi object tracking https arxiv org abs 1904 11489 kbd iccv 2019 kbd mot feature location over time beyond pixels leveraging geometry and shape cues for online multi object tracking https arxiv org abs 1802 09298 kbd icra 2018 kbd mot iit 3d shape st 3d joint spatial temporal optimization for stereo 3d object tracking https arxiv org abs 2004 09305 kbd cvpr 2020 kbd peilinag li author of vins and s3dot augment your batch improving generalization through instance repetition https openaccess thecvf com content cvpr 2020 papers hoffer augment your batch improving generalization through instance repetition cvpr 2020 paper pdf kbd cvpr 2020 kbd retinatrack online single stage joint detection and tracking https arxiv org abs 2003 13870 kbd cvpr 2020 kbd mot object as hotspots an anchor free 3d object detection approach via firing of hotspots https arxiv org abs 1912 12791 gradient centralization a new optimization technique for deep neural networks https arxiv org abs 2004 01461 kbd eccv 2020 oral kbd depth completion via deep basis fitting https arxiv org abs 1912 10336 kbd wacv 2020 kbd bts from big to small multi scale local planar guidance for monocular depth estimation https arxiv org abs 1907 10326 monodepth supervised the edge of depth explicit constraints between segmentation and depth https arxiv org abs 2004 00171 kbd cvpr 2020 kbd monodepth xiaoming liu on the continuity of rotation representations in neural networks https arxiv org abs 1812 07035 kbd cvpr 2019 kbd rotational representation vdo slam a visual dynamic object aware slam system https arxiv org abs 2005 11052 kbd ijrr 2020 kbd dynamic slam the need for speed https arxiv org abs 2002 08584 pseudo rgb d for self improving monocular slam and depth prediction https arxiv org abs 2004 10681 kbd eccv 2020 kbd traffic light mapping and detection https static googleusercontent com media research google com en pubs archive 37259 pdf notes paper notes tfl mapping google md kbd icra 2011 kbd traffic light google chris urmson traffic light recognition exploiting map and localization at every stage https web yonsei ac kr jksuhr papers traffic 20light 20recognition 20exploiting 20map 20and 20localization 20at 20every 20stage pdf notes paper notes tfl exploting map korea md kbd expert systems 2017 kbd traffic light traffic light recognition using deep learning and prior maps for autonomous cars https arxiv org abs 1906 11886 notes paper notes tfl lidar map building brazil md kbd ijcnn 2019 kbd traffic light espirito santo brazil 2020 10 14 tsm temporal shift module for efficient video understanding https arxiv org abs 1811 08383 notes paper notes tsm md kbd iccv 2019 kbd song han video object detection wod waymo dataset scalability in perception for autonomous driving waymo open dataset https arxiv org abs 1912 04838 notes paper notes wod md kbd cvpr 2020 kbd generalized focal loss learning qualified and distributed bounding boxes for dense object detection https arxiv org abs 2006 04388 notes paper notes gfocal md kbd neurips 2020 kbd classification as regression a ranking based balanced loss function unifying classification and localisation in object detection https arxiv org abs 2009 13592 kbd neurips 2020 spotlight kbd rethinking the value of labels for improving class imbalanced learning https arxiv org abs 2006 07529 kbd neurips 2020 kbd reploss repulsion loss detecting pedestrians in a crowd https arxiv org abs 1711 07752 notes paper notes rep loss md kbd cvpr 2018 kbd crowd detection megvii adaptive nms refining pedestrian detection in a crowd https arxiv org abs 1904 03629 notes paper notes adaptive nms md kbd cvpr 2019 oral kbd crowd detection nms aggloss occlusion aware r cnn detecting pedestrians in a crowd https arxiv org abs 1807 08407 notes paper notes agg loss md kbd eccv 2018 kbd crowd detection crowddet detection in crowded scenes one proposal multiple predictions https arxiv org abs 2003 09163 notes paper notes crowd det md kbd cvpr 2020 oral kbd crowd detection megvii earth mover s distance r2 nms nms by representative region towards crowded pedestrian detection by proposal pairing https arxiv org abs 2003 12729 notes paper notes r2 nms md kbd cvpr 2020 kbd double anchor r cnn for human detection in a crowd https arxiv org abs 1909 09998 notes paper notes double anchor md head body bundle review ap vs mr paper notes ap mr md sku110k precise detection in densely packed scenes https arxiv org abs 1904 00853 notes paper notes sku110k md kbd cvpr 2019 kbd crowd detection no occlusion gossipnet learning non maximum suppression https arxiv org abs 1705 02950 kbd cvpr 2017 kbd tll small scale pedestrian detection based on somatic topology localization and temporal feature aggregation https arxiv org abs 1807 01438 kbd eccv 2018 kbd learning monocular 3d vehicle detection without 3d bounding box labels https arxiv org abs 2010 03506 kbd gcpr 2020 kbd mono3d daniel cremers tum cubifae 3d monocular camera space cubification on autonomous vehicles for auto encoder based 3d object detection https arxiv org abs 2006 04080 notes paper notes cubifae 3d md mono3d depth ae pretraining deformable detr deformable transformers for end to end object detection https arxiv org abs 2010 04159 notes paper notes deformable detr md kbd iclr 2021 kbd jifeng dai detr vit an image is worth 16x16 words transformers for image recognition at scale https arxiv org abs 2010 11929 notes paper notes vit md kbd iclr 2021 kbd byol bootstrap your own latent a new approach to self supervised learning https arxiv org abs 2006 07733 self supervised 2020 09 15 sdflabel autolabeling 3d objects with differentiable rendering of sdf shape priors https arxiv org abs 1911 11288 notes paper notes sdflabel md kbd cvpr 2020 oral kbd tri differentiable rendering densepose dense human pose estimation in the wild https arxiv org abs 1802 00434 notes paper notes densepose md kbd cvpr 2018 oral kbd fair nocs normalized object coordinate space for category level 6d object pose and size estimation https arxiv org abs 1901 02970 kbd cvpr 2019 kbd monodr monocular differentiable rendering for self supervised 3d object detection https arxiv org abs 2009 14524 notes paper notes monodr md kbd eccv 2020 kbd tri mono3d lift splat shoot encoding images from arbitrary camera rigs by implicitly unprojecting to 3d https arxiv org abs 2008 05711 notes paper notes lift splat shoot md kbd eccv 2020 kbd bev net utoronto sanja fidler implicit latent variable model for scene consistent motion forecasting https arxiv org abs 2007 12036 kbd eccv 2020 kbd uber atg rachel urtasun fishing net future inference of semantic heatmaps in grids https arxiv org abs 2006 09917 notes paper notes fishing net md kbd cvprw 2020 kbd bev net mapping zoox vpn cross view semantic segmentation for sensing surroundings https arxiv org abs 1906 03560 notes paper notes vpn md kbd ral 2020 kbd bolei zhou bev net ved monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks https arxiv org abs 1804 02176 notes paper notes ved md kbd icra 2019 kbd bev net cam2bev a sim2real deep learning approach for the transformation of images from multiple vehicle mounted cameras to a semantically segmented image in bird s eye view https arxiv org abs 2005 04078 notes paper notes cam2bev md kbd itsc 2020 kbd bev net learning to look around objects for top view representations of outdoor scenes https arxiv org abs 1803 10870 notes paper notes learning to look around objects md kbd eccv 2018 kbd bev net ucsd manmohan chandraker a parametric top view representation of complex road scenes https arxiv org abs 1812 06152 kbd cvpr 2019 kbd bev net ucsd manmohan chandraker ftm understanding road layout from videos as a whole https arxiv org abs 2007 00822 kbd cvpr 2020 kbd bev net ucsd manmohan chandraker km3d net monocular 3d detection with geometric constraints embedding and semi supervised training https arxiv org abs 2009 00764 notes paper notes km3d net md kbd ral 2021 kbd rtm3d peixuan li instancemotseg real time instance motion segmentation for autonomous driving https arxiv org abs 2008 07008 notes paper notes instance mot seg md kbd iros 2020 kbd motion segmentation mpv nets monocular plan view networks for autonomous driving https arxiv org abs 1905 06937 notes paper notes mpv nets md kbd iros 2019 kbd bev net class balanced loss based on effective number of samples https arxiv org abs 1901 05555 notes paper notes class balanced loss md kbd cvpr 2019 kbd focal loss authors geometric pretraining for monocular depth estimation http lewissoft com pdf icra2020 0035 pdf notes paper notes geometric pretraining md kbd icra 2020 kbd robust traffic light and arrow detection using digital map with spatial prior information for automated driving https www mdpi com 1424 8220 20 4 1181 notes paper notes tfl robust japan md kbd sensors 2020 kbd traffic light 2020 08 26 feature metric loss for self supervised learning of depth and egomotion https arxiv org abs 2007 10603 notes paper notes feature metric md kbd eccv 2020 kbd feature metric local minima monodepth depth vo feat unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction https arxiv org abs 1803 03893 kbd cvpr 2018 kbd feature metric monodepth monoresmatch learning monocular depth estimation infusing traditional stereo knowledge https arxiv org abs 1904 04144 notes paper notes monoresmatch md kbd cvpr 2019 kbd monodepth local minima cheap stereo gt sgdepth self supervised monocular depth estimation solving the dynamic object problem by semantic guidance https arxiv org abs 2007 06936 notes paper notes sgdepth md kbd eccv 2020 kbd moving objects every pixel counts unsupervised geometry learning with holistic 3d motion understanding https arxiv org abs 1806 10556 kbd eccv 2018 kbd dynamic objects rigid and dynamic motion every pixel counts joint learning of geometry and motion with 3d holistic understanding https arxiv org abs 1810 06125 kbd tpami 2018 kbd cc competitive collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation https arxiv org abs 1805 09806 notes paper notes cc md kbd cvpr 2019 kbd objmotionnet self supervised object motion and depth estimation from video https arxiv org abs 1912 04250 notes paper notes obj motion net md kbd cvprw 2020 kbd object motion prediction velocity prediction instance wise depth and motion learning from monocular videos https arxiv org abs 1912 09351 semantics driven unsupervised learning for monocular depth and ego motion estimation https arxiv org abs 2006 04371 self supervised joint learning framework of depth estimation via implicit cues https arxiv org abs 2006 09876 df net unsupervised joint learning of depth and flow using cross task consistency https arxiv org abs 1809 01649 kbd eccv 2018 kbd linenet a zoomable cnn for crowdsourced high definition maps modeling in urban environments https arxiv org abs 1807 05696 mapping road slam road marking based slam with lane level accuracy https www naverlabs com img autonomousdriving intelligence dissertation road slam road 20marking 20based 20slam 20with 20lane level 20accuracy pdf notes paper notes road slam md hd mapping avp slam semantic visual mapping and localization for autonomous vehicles in the parking lot https arxiv org abs 2007 01813 notes paper notes avp slam md kbd iros 2020 kbd huawei hd mapping tong qin vins author autonomous valet parking avp slam late fusion mapping and localization using semantic road marking with centimeter level accuracy in indoor parking lots https ieeexplore ieee org abstract document 8917529 notes paper notes avp slam late fusion md kbd itsc 2019 kbd lane markings based relocalization on highway https ieeexplore ieee org abstract document 8917254 kbd itsc 2019 kbd deeproadmapper extracting road topology from aerial images https openaccess thecvf com content iccv 2017 papers mattyus deeproadmapper extracting road iccv 2017 paper pdf notes paper notes deep road mapper md kbd iccv 2017 kbd uber atg not hd maps roadtracer automatic extraction of road networks from aerial images https openaccess thecvf com content cvpr 2018 papers bastani roadtracer automatic extraction cvpr 2018 paper pdf kbd cvpr 2018 kbd not hd maps polymapper topological map extraction from overhead images https arxiv org abs 1812 01497 notes paper notes polymapper md kbd iccv 2019 kbd mapping polygon not hd maps hran hierarchical recurrent attention networks for structured online maps https openaccess thecvf com content cvpr 2018 papers homayounfar hierarchical recurrent attention cvpr 2018 paper pdf notes paper notes hran md kbd cvpr 2018 kbd hd mapping highway polyline loss chamfer distance deep structured crosswalk end to end deep structured models for drawing crosswalks https openaccess thecvf com content eccv 2018 papers justin liang end to end deep structured eccv 2018 paper pdf notes paper notes deep structured crosswalk md kbd eccv 2018 kbd deepboundaryextractor convolutional recurrent network for road boundary extraction http openaccess thecvf com content cvpr 2019 html liang convolutional recurrent network for road boundary extraction cvpr 2019 paper html notes paper notes deep boundary extractor md kbd cvpr 2019 kbd hd mapping boundary polyline loss dagmapper learning to map by discovering lane topology http openaccess thecvf com content iccv 2019 papers homayounfar dagmapper learning to map by discovering lane topology iccv 2019 paper pdf notes paper notes dagmapper md kbd iccv 2019 kbd hd mapping highway forks and merges polyline loss sparse hd maps exploiting sparse semantic hd maps for self driving vehicle localization https arxiv org abs 1908 03274 notes paper notes sparse hd maps md kbd iros 2019 oral kbd uber atg metadata mapping localization aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks https arxiv org abs 1803 06904 kbd ieee tgrs 2018 kbd monocular localization with vector hd map mlvhm a low cost method for commercial ivs https www mdpi com 1424 8220 20 7 1870 htm kbd sensors 2020 kbd tsinghua 3d hd maps patchnet rethinking pseudo lidar representation https arxiv org abs 2008 04582 notes paper notes patchnet md kbd eccv 2020 kbd sensetime wanli ouyang d4lcn learning depth guided convolutions for monocular 3d object detection https arxiv org abs 1912 04799 notes paper notes d4lcn md kbd cvpr 2020 kbd mono3d mfs learning stereo from single images https arxiv org abs 2008 01484 notes paper notes mfs md kbd eccv 2020 kbd mono for stereo learn stereo matching with mono borderdet border feature for dense object detection https arxiv org abs 2007 11056 kbd eccv 2020 oral kbd megvii scale aware trident networks for object detection https arxiv org abs 1901 01892 kbd iccv 2019 kbd different heads for different scales learning depth from monocular videos using direct methods https arxiv org abs 1712 00175 vid2depth unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints https arxiv org abs 1802 05522 kbd cvpr 2018 kbd google nerf in the wild neural radiance fields for unconstrained photo collections https arxiv org abs 2008 02268 supervising the new with the old learning sfm from sfm http openaccess thecvf com content eccv 2018 papers maria klodt supervising the new eccv 2018 paper pdf notes paper notes learn sfm from sfm md kbd eccv 2018 kbd neural rgb d sensing depth and uncertainty from a video camera https arxiv org abs 1901 02571 kbd cvpr 2019 kbd multi frame monodepth don t forget the past recurrent depth estimation from monocular video https arxiv org abs 2001 02613 multi frame monodepth rnn recurrent neural network for un supervised learning of monocular videovisual odometry and depth https arxiv org abs 1904 07087 multi frame monodepth rnn exploiting temporal consistency for real time video depth estimation https arxiv org abs 1908 03706 kbd iccv 2019 kbd multi frame monodepth rnn indoor sfm net learning of structure and motion from video https arxiv org abs 1704 07804 dynamic object sfm mb net mergeboxes for real time 3d vehicles detection https ieeexplore ieee org document 8500395 notes paper notes mb net md kbd iv 2018 kbd mono3d daimler bs3d beyond bounding boxes using bounding shapes for real time 3d vehicle detection from monocular rgb images https ieeexplore ieee org abstract document 8814036 notes paper notes bs3d md kbd iv 2019 kbd mono3d daimler 3d gck single shot 3d detection of vehicles from monocular rgb images via geometrically constrained keypoints in real time https arxiv org abs 2006 13084 notes paper notes 3d gck md kbd iv 2020 kbd mono3d daimler ur3d distance normalized unified representation for monocular 3d object detection https www ecva net papers eccv 2020 papers eccv html 6559 eccv 2020 paper php notes paper notes ur3d md kbd eccv 2020 kbd mono3d da 3det monocular 3d object detection via feature domain adaptation https www ecva net papers eccv 2020 papers eccv papers 123540018 pdf notes paper notes da 3det md kbd eccv 2020 kbd mono3d rar net reinforced axial refinement network for monocular 3d object detection https www ecva net papers eccv 2020 papers eccv html 2822 eccv 2020 paper php notes paper notes rarnet md kbd eccv 2020 kbd mono3d 2020 07 25 centertrack tracking objects as points https arxiv org abs 2004 01177 notes paper notes centertrack md kbd eccv 2020 spotlight kbd camera based 3d mod mot sota centernet video based object detection philipp kr henb hl centerpoint center based 3d object detection and tracking https arxiv org abs 2006 11275 notes paper notes centerpoint md kbd cvpr 2021 kbd lidar based 3d mod centernet tracktor tracking without bells and whistles https arxiv org abs 1903 05625 notes paper notes tracktor md kbd iccv 2019 kbd tracktor tracktor laura leal taixe tum fairmot a simple baseline for multi object tracking https arxiv org abs 2004 01888 notes paper notes fairmot md deepmot a differentiable framework for training multiple object trackers https arxiv org abs 1906 06618 notes paper notes deepmot md kbd cvpr 2020 kbd trainable hungarian laura leal taixe tum mpntracker learning a neural solver for multiple object tracking https arxiv org abs 1912 07515 kbd cvpr 2020 oral kbd trainable hungarian laura leal taixe tum nuscenes a multimodal dataset for autonomous driving https arxiv org abs 1903 11027 notes paper notes nuscenes md kbd cvpr 2020 kbd dataset point cloud radar cbgs class balanced grouping and sampling for point cloud 3d object detection https arxiv org abs 1908 09492 notes paper notes cbgs md kbd cvprw 2019 kbd megvii lidar wad challenge winner afdet anchor free one stage 3d object detection https arxiv org abs 2006 12671 and competition solution https arxiv org pdf 2006 15505 pdf notes paper notes afdet md kbd cvprw 2020 kbd horizon robotics lidar winning for waymo challenge review of mot and sot notes paper notes mot and sot md crowdhuman a benchmark for detecting human in a crowd https arxiv org abs 1805 00123 notes paper notes crowdhuman md megvii pedestrian dataset widerperson a diverse dataset for dense pedestrian detection in the wild https arxiv org abs 1909 12118 notes paper notes widerperson md kbd tmm 2019 kbd dataset pedestrian tsinghua daimler cyclists a new benchmark for vison based cyclist detection http www gavrila net publications iv16 cyclist benchmark pdf notes paper notes tsinghua daimler cyclist md kbd iv 2016 kbd dataset cyclist detection specialized cyclist detection dataset challenging real world computer vision dataset for cyclist detection using a monocular rgb camera https drive google com drive u 0 folders 1inawrx9nvcchdqzepnbejy4i9aai5mg9 notes paper notes specialized cyclists md kbd iv 2019 kbd extention to kitti pointtrack segment as points for efficient online multi object tracking and segmentation https arxiv org abs 2007 01550 notes paper notes pointtrack md kbd eccv 2020 oral kbd mots pointtrack for effective online multi object tracking and segmentation https arxiv org abs 2007 01549 notes paper notes pointtrack md kbd cvpr 2020 workshop kbd cvpr2020 mots challenge winner pointtrack ranks first on kitti mots spatialembedding instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth https arxiv org abs 1906 11109 notes paper notes spatial embedding md kbd iccv 2019 kbd one stage instance segmentation ba net dense bundle adjustment networks https arxiv org abs 1806 04807 notes paper notes banet md kbd iclr 2019 kbd bundle adjustment multi frame monodepth feature metric deepsfm structure from motion via deep bundle adjustment https arxiv org abs 1912 09697 kbd eccv 2020 oral kbd multi frame monodepth indoor scene cvd consistent video depth estimation https arxiv org abs 2004 15021 notes paper notes cvd md kbd siggraph 2020 kbd multi frame monodepth online finetune deepv2d video to depth with differentiable structure from motion https arxiv org abs 1812 04605 notes paper notes deepv2d md kbd iclr 2020 kbd multi frame monodepth jia deng geonet unsupervised learning of dense depth optical flow and camera pose https arxiv org abs 1803 02276 notes paper notes geonet md kbd cvpr 2018 kbd residual optical flow monodepth rigid and dynamic motion glnet self supervised learning with geometric constraints in monocular video connecting flow depth and camera https arxiv org abs 1907 05820 notes paper notes glnet md kbd iccv 2019 kbd online finetune rigid and dynamic motion depth hints self supervised monocular depth hints https arxiv org abs 1909 09051 notes paper notes depth hints md kbd iccv 2019 kbd monodepth local minima cheap stereo gt monouncertainty on the uncertainty of self supervised monocular depth estimation https arxiv org abs 2005 06209 notes paper notes mono uncertainty md kbd cvpr 2020 kbd depth uncertainty self supervised learning of depth and ego motion with differentiable bundle adjustment https arxiv org abs 1909 13163 notes paper notes ba sfm learner md bundle adjustment xmotors ai multi frame monodepth kinematic 3d object detection in monocular video https arxiv org abs 2007 09548 notes paper notes kinematic mono3d md kbd eccv 2020 kbd multi frame mono3d xiaoming liu velocitynet camera based vehicle velocity estimation from monocular video https arxiv org abs 1802 07094 notes paper notes velocity net md kbd cvpr 2017 workshop kbd monocular velocity estimation cvpr 2017 challenge winner vehicle centric velocitynet end to end learning for inter vehicle distance and relative velocity estimation in adas with a monocular camera https arxiv org abs 2006 04082 notes paper notes vehicle centric velocity net md monocular velocity estimation monocular distance sota 2020 06 20 lego loam lightweight and ground optimized lidar odometry and mapping on variable terrain http personal stevens edu benglot shan englot iros 2018 preprint pdf notes paper notes lego loam md kbd iros 2018 kbd lidar mapping pie a large scale dataset and models for pedestrian intention estimation and trajectory prediction http openaccess thecvf com content iccv 2019 papers rasouli pie a large scale dataset and models for pedestrian intention estimation iccv 2019 paper pdf notes paper notes pie md kbd iccv 2019 kbd jaad are they going to cross a benchmark dataset and baseline for pedestrian crosswalk behavior http openaccess thecvf com content iccv 2017 workshops papers w3 rasouli are they going iccv 2017 paper pdf kbd iccv 2017 kbd pedestrian action anticipation using contextual feature fusion in stacked rnns https bmvc2019 org wp content uploads papers 0283 paper pdf kbd bmvc 2019 kbd is the pedestrian going to cross answering by 2d pose estimation https arxiv org abs 1807 10580 kbd iv 2018 kbd intention recognition of pedestrians and cyclists by 2d pose estimation https arxiv org abs 1910 03858 kbd itsc 2019 kbd skeleton pedestrian cyclist intention attentive single tasking of multiple tasks https arxiv org abs 1904 08918 kbd cvpr 2019 kbd detr end to end object detection with transformers https arxiv org abs 2005 12872 notes paper notes detr md kbd eccv 2020 oral kbd fair transformer attention is all you need https arxiv org abs 1706 03762 notes paper notes transformer md kbd nips 2017 kbd speednet learning the speediness in videos https arxiv org abs 2004 06130 notes paper notes speednet md kbd cvpr 2020 oral kbd monopair monocular 3d object detection using pairwise spatial relationships https arxiv org abs 2003 00504 notes paper notes monopair md kbd cvpr 2020 kbd mono3d pairwise relationship smoke single stage monocular 3d object detection via keypoint estimation https arxiv org abs 2002 10111 notes paper notes smoke md kbd cvprw 2020 kbd mono3d zongmu vehicle re id for surround view camera system https drive google com file d 1e6y8wtharicaehs9cpassgox0aaxcgib view notes paper notes reid surround fisheye md kbd cvprw 2020 kbd tireline vehicle reid zongmu end to end lane marker detection via row wise classification https arxiv org abs 2005 08630 notes paper notes e2e lmd md qualcomm korea lld as cls reliable multilane detection and classification by utilizing cnn as a regression network http openaccess thecvf com content eccvw 2018 papers 11133 chougule reliable multilane detection and classification by utilizing cnn as a eccvw 2018 paper pdf kbd eccv 2018 kbd lld as reg super a novel lane detection system https arxiv org abs 2005 07277 notes paper notes super md learning lightweight lane detection cnns by self attention distillation https arxiv org abs 1908 00821 kbd iccv 2019 kbd stixelnet a deep convolutional network for obstacle detection and road segmentation http www bmva org bmvc 2015 papers paper109 paper109 pdf kbd bmvc 2015 kbd stixelnetv2 real time category based and general obstacle detection for autonomous driving http openaccess thecvf com content iccv 2017 workshops papers w3 garnett real time category based and iccv 2017 paper pdf notes paper notes stixelnetv2 md kbd iccv 2017 kbd ds real time single image and video super resolution using an efficient sub pixel convolutional neural network https arxiv org abs 1609 05158 notes paper notes subpixel conv md kbd cvpr 2016 kbd channel to pixel car pose in context accurate pose estimation with ground plane constraints https arxiv org abs 1912 04363 mono3d self mono sf self supervised monocular scene flow estimation https arxiv org abs 2004 04143 notes paper notes self mono sf md kbd cvpr 2020 oral kbd scene flow stereo input mebow monocular estimation of body orientation in the wild https arxiv org abs 2011 13688 notes paper notes mebow md kbd cvpr 2020 kbd vg nms visibility guided nms efficient boosting of amodal object detection in crowded traffic scenes https arxiv org abs 2006 08547 notes paper notes vg nms md kbd neurips 2019 workshop kbd crowded scene nms daimler wysiwyg what you see is what you get exploiting visibility for 3d object detection https arxiv org abs 1912 04986 notes paper notes wysiwyg md kbd cvpr 2020 oral kbd occupancy grid real time panoptic segmentation from dense detections https arxiv org abs 1912 01202 notes paper notes realtime panoptic md kbd cvpr 2020 oral kbd bbox semantic segmentation panoptic segmentation toyota human centric efficiency improvements in image annotation for autonomous driving https drive google com file d 1dy95vfwblkoozzyq8gldd0hez6absdji view notes paper notes human centric annotation md kbd cvprw 2020 kbd efficient annotation surfelgan synthesizing realistic sensor data for autonomous driving https arxiv org abs 2005 03844 notes paper notes surfel gan md kbd cvpr 2020 oral kbd waymo auto data generation surfel lidarsim realistic lidar simulation by leveraging the real world https arxiv org abs 2006 09348 notes paper notes lidarsim md kbd cvpr 2020 oral kbd uber atg auto data generation surfel suma efficient lidar based semantic slam http www ipb uni bonn de wp content papercite data pdf chen2019iros pdf kbd iros 2019 kbd semantic segmentation lidar slam pon pyroccnet predicting semantic map representations from images using pyramid occupancy networks https arxiv org abs 2003 13402 notes paper notes pyroccnet md kbd cvpr 2020 oral kbd bev net oft monolayout amodal scene layout from a single image https arxiv org abs 2002 08394 notes paper notes monolayout md kbd wacv 2020 kbd bev net bev seg bird s eye view semantic segmentation using geometry and semantic point cloud https arxiv org abs 2006 11436 notes paper notes bev seg md kbd cvpr 2020 workshop kbd bev net mapping a geometric approach to obtain a bird s eye view from an image https arxiv org abs 1905 02231 kbd iccvw 2019 kbd mapping geometry andrew zisserman frozendepth learning the depths of moving people by watching frozen people https arxiv org abs 1904 11111 notes paper notes frozen depth md kbd cvpr 2019 oral kbd orb slam a versatile and accurate monocular slam system https arxiv org abs 1502 00956 kbd tro 2015 kbd orb slam2 an open source slam system for monocular stereo and rgb d cameras https arxiv org abs 1610 06475 kbd tro 2016 kbd cubeslam monocular 3d object slam https arxiv org abs 1806 00557 notes paper notes cube slam md kbd tro 2019 kbd dynamic slam orb slam mono3d clustervo clustering moving instances and estimating visual odometry for self and surroundings https arxiv org abs 2003 12980 notes paper notes cluster vo md kbd cvpr 2020 kbd general dynamic slam s3dot stereo vision based semantic 3d object and ego motion tracking for autonomous driving https arxiv org abs 1807 02062 notes paper notes s3dot md kbd eccv 2018 kbd peiliang li multi object monocular slam for dynamic environments https arxiv org abs 2002 03528 notes paper notes multi object mono slam md kbd iv 2020 kbd monolayout authors pwc net cnns for optical flow using pyramid warping and cost volume https arxiv org abs 1709 02371 notes paper notes pwc net md kbd cvpr 2018 oral kbd optical flow liteflownet a lightweight convolutional neural network for optical flow estimation https arxiv org abs 1805 07036 kbd cvpr 2018 kbd optical flow flownet learning optical flow with convolutional networks https www cv foundation org openaccess content iccv 2015 papers dosovitskiy flownet learning optical iccv 2015 paper pdf kbd iccv 2015 kbd optical flow flownet 2 0 evolution of optical flow estimation with deep networks https arxiv org abs 1612 01925 kbd cvpr 2017 kbd optical flow espnetv2 a light weight power efficient and general purpose convolutional neural network https arxiv org abs 1811 11431 kbd cvpr 2019 kbd semantic segmentation lightweight mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes https arxiv org abs 1908 06316 kbd iccv 2019 kbd depth uncertainty 2020 05 19 egocentric vision based future vehicle localization for intelligent driving assistance systems https arxiv org abs 1809 07408 notes paper notes hevi md honda kbd icra 2019 kbd packnet 3d packing for self supervised monocular depth estimation https arxiv org abs 1905 02693 notes paper notes packnet md kbd cvpr 2020 oral kbd scale aware depth packnet sg semantically guided representation learning for self supervised monocular depth https arxiv org abs 2002 12319 notes paper notes packnet sg md kbd iclr 2020 kbd tri infinite depth problem trianflow towards better generalization joint depth pose learning without posenet https arxiv org abs 2004 01314 notes paper notes trianflow md kbd cvpr 2020 kbd scale aware understanding the limitations of cnn based absolute camera pose regression https arxiv org abs 1903 07504 notes paper notes understanding apr md kbd cvpr 2019 kbd drawbacks of posenet mapnet laura leal taixe tum to learn or not to learn visual localization from essential matrices https arxiv org abs 1908 01293 notes paper notes to learn or not md kbd icra 2020 kbd sift 5 pt solver others for vo laura leal taixe tum df vo visual odometry revisited what should be learnt https arxiv org abs 1909 09803 notes paper notes df vo md kbd icra 2020 kbd depth and flow for accurate vo d3vo deep depth deep pose and deep uncertainty for monocular visual odometry https arxiv org abs 2003 01060 notes paper notes d3vo md kbd cvpr 2020 oral kbd daniel cremers tum depth uncertainty network slimming learning efficient convolutional networks through network slimming https arxiv org abs 1708 06519 notes paper notes network slimming md kbd iccv 2017 kbd batchnorm pruning rethinking the smaller norm less informative assumption in channel pruning of convolution layers https arxiv org abs 1802 00124 notes paper notes batchnorm pruning md kbd iclr 2018 kbd direct sparse odometry https arxiv org abs 1607 02565 kbd pami 2018 kbd train in germany test in the usa making 3d object detectors generalize https arxiv org abs 2005 08139 notes paper notes train in germany md kbd cvpr 2020 kbd pseudolidarv3 end to end pseudo lidar for image based 3d object detection https arxiv org abs 2004 03080 notes paper notes pseudo lidar v3 md kbd cvpr 2020 kbd atss bridging the gap between anchor based and anchor free detection via adaptive training sample selection https arxiv org abs 1912 02424 notes paper notes atss md kbd cvpr 2020 oral kbd distance iou loss faster and better learning for bounding box regression https arxiv org abs 1911 08287 kbd aaai 2020 kbd enhancing geometric factors in model learning and inference for object detection and instance segmentation https arxiv org abs 2005 03572 journal version yolov4 optimal speed and accuracy of object detection https arxiv org abs 2004 10934 notes paper notes yolov4 md cbn cross iteration batch normalization https arxiv org abs 2002 05712 notes paper notes cbn md stitcher feedback driven data provider for object detection https arxiv org abs 2004 12432 notes paper notes stitcher md sknet selective kernel networks https arxiv org abs 1903 06586 notes paper notes sknet md kbd cvpr 2019 kbd cbam convolutional block attention module https arxiv org abs 1807 06521 notes paper notes cbam md kbd eccv 2018 kbd resnest split attention networks https arxiv org abs 2004 08955 notes paper notes resnest md 2020 04 14 chauffeurnet learning to drive by imitating the best and synthesizing the worst https arxiv org pdf 1812 03079 pdf notes paper notes chauffeurnet md kbd rss 2019 kbd waymo intentnet learning to predict intention from raw sensor data http www cs toronto edu wenjie papers intentnet corl18 pdf notes paper notes intentnet md kbd corl 2018 kbd uber atg perception and prediction lidar map ror rules of the road predicting driving behavior with a convolutional model of semantic interactions https arxiv org abs 1906 08945 notes paper notes ror md kbd cvpr 2019 kbd zoox multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction https arxiv org abs 1910 05449 notes paper notes multipath md kbd corl 2019 kbd waymo authors from ror and chauffeurnet nmp end to end interpretable neural motion planner http www cs toronto edu wenjie papers cvpr19 nmp pdf notes paper notes nmp md kbd cvpr 2019 oral kbd uber atg multimodal trajectory predictions for autonomous driving using deep convolutional networks https arxiv org abs 1809 10732 notes paper notes multipath uber md kbd icra 2019 kbd henggang cui multimodal uber atg pittsburgh uncertainty aware short term motion prediction of traffic actors for autonomous driving https arxiv org abs 1808 05819 kbd wacv 2020 kbd uber atg pittsburgh jointly learnable behavior and trajectory planning for self driving vehicles https arxiv org abs 1910 04586 kbd iros 2019 oral kbd uber atg behavioral planning motion planning tensormask a foundation for dense object segmentation https arxiv org abs 1903 12174 notes paper notes tensormask md kbd iccv 2019 kbd single stage instance seg blendmask top down meets bottom up for instance segmentation https arxiv org abs 2001 00309 notes paper notes blendmask md kbd cvpr 2020 oral kbd mask encoding for single shot instance segmentation https arxiv org abs 2003 11712 notes paper notes meinst md kbd cvpr 2020 oral kbd single stage instance seg chunhua shen polarmask single shot instance segmentation with polar representation https arxiv org abs 1909 13226 notes paper notes polarmask md kbd cvpr 2020 oral kbd single stage instance seg solo segmenting objects by locations https arxiv org abs 1912 04488 notes paper notes solo md kbd eccv 2020 kbd single stage instance seg chunhua shen solov2 dynamic faster and stronger https arxiv org abs 2003 10152 notes paper notes solov2 md single stage instance seg chunhua shen condinst conditional convolutions for instance segmentation https arxiv org abs 2003 05664 notes paper notes condinst md kbd eccv 2020 oral kbd single stage instance seg chunhua shen centermask single shot instance segmentation with point representation https arxiv org abs 2004 04446 notes paper notes centermask md kbd cvpr 2020 kbd 2020 03 15 vpgnet vanishing point guided network for lane and road marking detection and recognition https arxiv org abs 1710 06288 notes paper notes vpgnet md kbd iccv 2017 kbd which tasks should be learned together in multi task learning https arxiv org abs 1905 07553 notes paper notes task grouping md stanford mtl kbd icml 2020 kbd mgda multi task learning as multi objective optimization https arxiv org abs 1810 04650 kbd neurips 2018 kbd taskonomy disentangling task transfer learning https arxiv org abs 1804 08328 notes paper notes taskonomy md kbd cvpr 2018 kbd rethinking imagenet pre training https arxiv org abs 1811 08883 notes paper notes rethinking pretraining md kbd iccv 2019 kbd kaiming he unsuperpoint end to end unsupervised interest point detector and descriptor https arxiv org abs 1907 04011 notes paper notes unsuperpoint md superpoint kp2d neural outlier rejection for self supervised keypoint learning https arxiv org abs 1912 10615 notes paper notes kp2d md kbd iclr 2020 kbd pointnet kp3d self supervised 3d keypoint learning for ego motion estimation https arxiv org abs 1912 03426 notes paper notes kp3d md kbd corl 2020 kbd toyota superpoint ng ransac neural guided ransac learning where to sample model hypotheses https arxiv org abs 1905 04132 notes paper notes ng ransac md kbd iccv 2019 kbd pointnet learning to find good correspondences https arxiv org abs 1711 05971 notes paper notes learning correspondence md kbd cvpr 2018 oral kbd pointnet refinedmpl refined monocular pseudolidar for 3d object detection in autonomous driving https arxiv org abs 1911 09712 notes paper notes refined mpl md huawei mono3d dsp monocular 3d object detection with decoupled structured polygon estimation and height guided depth estimation https arxiv org abs 2002 01619 notes paper notes dsp md kbd aaai 2020 kbd sensetime mono3d robust lane detection from continuous driving scenes using deep neural networks https arxiv org abs 1903 02193 lld lstm lanenet towards end to end lane detection an instance segmentation approach https arxiv org abs 1802 05591 notes paper notes lanenet md kbd iv 2018 kbd lanenet 3d lanenet end to end 3d multiple lane detection http openaccess thecvf com content iccv 2019 papers garnett 3d lanenet end to end 3d multiple lane detection iccv 2019 paper pdf notes paper notes 3d lanenet md kbd iccv 2019 kbd semi local 3d lane detection and uncertainty estimation https arxiv org abs 2003 05257 notes paper notes semilocal 3d lanenet md gm israel 3d lld gen lanenet a generalized and scalable approach for 3d lane detection https arxiv org abs 2003 10656 notes paper notes gen lanenet md kbd eccv 2020 kbd apollo 3d lld long term on board prediction of people in traffic scenes under uncertainty https arxiv org abs 1711 09026 kbd cvpr 2018 kbd egocentric prediction it s not all about size on the role of data properties in pedestrian detection http openaccess thecvf com content eccvw 2018 papers 11129 rasouli its not all about size on the role of data eccvw 2018 paper pdf kbd eccv 2018 kbd pedestrian 2020 02 12 associative embedding end to end learning for joint detection and grouping https arxiv org abs 1611 05424 notes paper notes associative embedding md kbd nips 2017 kbd pixels to graphs by associative embedding https arxiv org abs 1706 07365 notes paper notes pixels to graphs md kbd nips 2017 kbd social lstm human trajectory prediction in crowded spaces http cvgl stanford edu papers cvpr16 social lstm pdf notes paper notes social lstm md kbd cvpr 2017 kbd online video object detection using association lstm http openaccess thecvf com content iccv 2017 papers lu online video iccv 2017 paper pdf notes paper notes association lstm md single stage recurrent superpoint self supervised interest point detection and description https arxiv org abs 1712 07629 notes paper notes superpoint md kbd cvpr 2018 kbd channel to pixel deep slam magic leap pointrend image segmentation as rendering https arxiv org abs 1912 08193 notes paper notes pointrend md kbd cvpr 2020 oral kbd kaiming he fair multigrid a multigrid method for efficiently training video models https arxiv org abs 1912 00998 notes paper notes multigrid training md kbd cvpr 2020 oral kbd kaiming he fair ghostnet more features from cheap operations https arxiv org abs 1911 11907 notes paper notes ghostnet md kbd cvpr 2020 kbd fixres fixing the train test resolution discrepancy https arxiv org abs 1906 06423 notes paper notes fixres md kbd nips 2019 kbd fair movi 3d towards generalization across depth for monocular 3d object detection https arxiv org abs 1912 08035 notes paper notes movi 3d md kbd eccv 2020 kbd virtual cam viewport mapillary facebook mono3d amodal completion and size constancy in natural scenes https arxiv org abs 1509 08147 notes paper notes amodal completion md kbd iccv 2015 kbd amodal completion moco momentum contrast for unsupervised visual representation learning https arxiv org abs 1911 05722 notes paper notes moco md kbd cvpr 2020 oral kbd fair kaiming he 2020 01 19 double descent reconciling modern machine learning practice and the bias variance trade of https arxiv org abs 1812 11118 notes paper notes double descent md kbd pnas 2019 kbd deep double descent where bigger models and more data hurt https arxiv org abs 1912 02292 notes paper notes deep double descent md visualizing the loss landscape of neural nets https arxiv org abs 1712 09913 kbd nips 2018 kbd the apolloscape open dataset for autonomous driving and its application https arxiv org pdf 1803 06184 pdf kbd cvpr 2018 kbd dataset apollocar3d a large 3d car instance understanding benchmark for autonomous driving https arxiv org abs 1811 12222 notes paper notes apollocar3d md kbd cvpr 2019 kbd part level car parsing and reconstruction from a single street view https arxiv org abs 1811 10837 notes paper notes apollo car parts md baidu 6d vnet end to end 6dof vehicle pose estimation from monocular rgb images http openaccess thecvf com content cvprw 2019 papers autonomous 20driving wu 6d vnet end to end 6 dof vehicle pose estimation from monocular rgb images cvprw 2019 paper pdf notes paper notes 6d vnet md kbd cvpr 2019 kbd rtm3d real time monocular 3d detection from object keypoints for autonomous driving https arxiv org abs 2001 03343 notes paper notes rtm3d md kbd eccv 2020 spotlight kbd dorn deep ordinal regression network for monocular depth estimation https arxiv org abs 1806 02446 notes paper notes dorn md kbd cvpr 2018 kbd monodepth supervised d t detect to track and track to detect https arxiv org abs 1710 03958 notes paper notes detect track md kbd iccv 2017 kbd from feichtenhofer crf net a deep learning based radar and camera sensor fusion architecture for object detection https ieeexplore ieee org abstract document 8916629 notes paper notes crf net md kbd sdf 2019 kbd radar detection rvnet deep sensor fusion of monocular camera and radar for image based obstacle detection in challenging environments https www researchgate net profile vijay john3 publication 335833918 rvnet deep sensor fusion of monocular camera and radar for image based obstacle detection in challenging environments links 5d7f164e92851c87c38b09f1 rvnet deep sensor fusion of monocular camera and radar for image based obstacle detection in challenging environments pdf notes paper notes rvnet md kbd psivt 2019 kbd rrpn radar region proposal network for object detection in autonomous vehicles https arxiv org abs 1905 00526 notes paper notes rrpn radar md kbd icip 2019 kbd rolo spatially supervised recurrent convolutional neural networks for visual object tracking https arxiv org abs 1607 05781 notes paper notes rolo md kbd iscas 2016 kbd recurrent ssd recurrent multi frame single shot detector for video object detection https www merl com publications docs tr2018 137 pdf notes paper notes recurrent ssd md kbd bmvc 2018 kbd mitsubishi recurrent retinanet a video object detection model based on focal loss https doi org 10 1007 978 3 030 04212 7 44 notes paper notes recurrent retinanet md kbd iconip 2018 kbd single stage recurrent actions as moving points https arxiv org abs 2001 04608 notes paper notes moc md not suitable for online the prevention dataset a novel benchmark for prediction of vehicles intentions 10 1109 itsc 2019 8917433 notes paper notes prevention dataset md kbd itsc 2019 kbd dataset cut in semi automatic high accuracy labelling tool for multi modal long range sensor dataset https sci hub tw 10 1109 ivs 2018 8500672 notes paper notes prevention annotation md kbd iv 2018 kbd astyx dataset automotive radar dataset for deep learning based 3d object detection https www astyx com fileadmin redakteur dokumente automotive radar dataset for deep learning based 3d object detection pdf notes paper notes astyx dataset md kbd eurad 2019 kbd astyx astyx camera radar deep learning based 3d object detection for automotive radar and camera https www astyx net fileadmin redakteur dokumente deep learning based 3d object detection for automotive radar and camera pdf notes paper notes astyx radar camera fusion md kbd eurad 2019 kbd astyx 2019 12 12 how do neural networks see depth in single images http openaccess thecvf com content iccv 2019 papers van dijk how do neural networks see depth in single images iccv 2019 paper pdf notes paper notes what monodepth see md kbd iccv 2019 kbd self supervised sparse to dense self supervised depth completion from lidar and monocular camera https arxiv org abs 1807 00275 kbd icra 2019 kbd depth completion dc depth coefficients for depth completion https arxiv org abs 1903 05421 notes paper notes depth coeff md kbd cvpr 2019 kbd xiaoming liu multimodal parse geometry from a line monocular depth estimation with partial laser observation https arxiv org pdf 1611 02174 pdf notes paper notes depth from one line md kbd icra 2017 kbd vo monodepth enhancing self supervised monocular depth estimation with traditional visual odometry https arxiv org abs 1908 03127 notes paper notes vo monodepth md kbd 3dv 2019 kbd sparse to dense probabilistic object detection definition and evaluation https arxiv org abs 1811 10800 notes paper notes pdq md the fishyscapes benchmark measuring blind spots in semantic segmentation https arxiv org abs 1904 03215 notes paper notes fishyscape md kbd iccv 2019 kbd on calibration of modern neural networks https arxiv org abs 1706 04599 notes paper notes calib modern nn md kbd icml 2017 kbd weinberger extreme clicking for efficient object annotation https arxiv org abs 1708 02750 notes paper notes extreme clicking md kbd iccv 2017 kbd radar and camera early fusion for vehicle detection in advanced driver assistance systems https ml4ad github io files papers radar 20and 20camera 20early 20fusion 20for 20vehicle 20detection 20in 20advanced 20driver 20assistance 20systems pdf notes paper notes radar camera qcom md kbd neurips 2019 kbd radar deep active learning for efficient training of a lidar 3d object detector https arxiv org abs 1901 10609 notes paper notes deep active learning lidar md kbd iv 2019 kbd c3dpo canonical 3d pose networks for non rigid structure from motion https arxiv org abs 1909 02533 notes paper notes c3dpo md kbd iccv 2019 kbd yolact real time instance segmentation https arxiv org abs 1904 02689 notes paper notes yolact md kbd iccv 2019 kbd single stage instance seg yolact better real time instance segmentation https arxiv org abs 1912 06218 single stage instance seg 2019 11 20 review of image and feature descriptors paper notes review descriptors md vehicle detection with automotive radar using deep learning on range azimuth doppler tensors http openaccess thecvf com content iccvw 2019 papers cvrsuad major vehicle detection with automotive radar using deep learning on range azimuth doppler iccvw 2019 paper pdf notes paper notes radar fft qcom md kbd iccv 2019 kbd gpp ground plane polling for 6dof pose estimation of objects on the road https arxiv org abs 1811 06666 notes paper notes gpp md kbd iv 2020 kbd ucsd trevidi mono 3dod mvra multi view reprojection architecture for orientation estimation http openaccess thecvf com content iccvw 2019 papers adw choi multi view reprojection architecture for orientation estimation iccvw 2019 paper pdf notes paper notes mvra md kbd iccv 2019 kbd yolov3 an incremental improvement https pjreddie com media files papers yolov3 pdf gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving https arxiv org abs 1904 04620 notes paper notes gaussian yolov3 md kbd iccv 2019 kbd detection with uncertainty bayesian yolov3 uncertainty estimation in one stage object detection https arxiv org abs 1905 10296 notes paper notes bayesian yolov3 md driveu towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection https arxiv org abs 1804 05132 notes paper notes towards safe ad md kbd itsc 2018 kbd driveu leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection https arxiv org abs 1809 05590 notes paper notes towards safe ad2 md kbd iv 2019 kbd driveu can we trust you on calibration of a probabilistic object detector for autonomous driving https arxiv org abs 1909 12358 notes paper notes towards safe ad calib md kbd iros 2019 kbd driveu lasernet an efficient probabilistic 3d object detector for autonomous driving https arxiv org abs 1903 08701 notes paper notes lasernet md kbd cvpr 2019 kbd uncertainty lasernet kl learning an uncertainty aware object detector for autonomous driving https arxiv org abs 1910 11375 notes paper notes lasernet kl md lasernet with kl divergence iounet acquisition of localization confidence for accurate object detection https arxiv org abs 1807 11590 notes paper notes iou net md kbd eccv 2018 kbd giou generalized intersection over union a metric and a loss for bounding box regression https arxiv org abs 1902 09630 notes paper notes giou md kbd cvpr 2019 kbd the lov sz softmax loss a tractable surrogate for the optimization of the intersection over union measure in neural networks https arxiv org abs 1705 08790 kbd cvpr 2018 kbd iou as loss kl loss bounding box regression with uncertainty for accurate object detection https arxiv org abs 1809 08545 notes paper notes kl loss md kbd cvpr 2019 kbd cam convs camera aware multi scale convolutions for single view depth https arxiv org abs 1904 02028 notes paper notes cam conv md kbd cvpr 2019 kbd bayesod a bayesian approach for uncertainty estimation in deep object detectors https arxiv org abs 1903 03838 notes paper notes bayes od md tw smnet deep multitask learning of tele wide stereo matching https arxiv org abs 1906 04463 notes paper notes twsm net md kbd icip 2019 kbd accurate uncertainties for deep learning using calibrated regression https arxiv org abs 1807 00263 notes paper notes dl regression calib md kbd icml 2018 kbd calibrating uncertainties in object localization task https arxiv org abs 1811 11210 notes paper notes 2dod calib md kbd nips 2018 kbd smwa on the over smoothing problem of cnn based disparity estimation http openaccess thecvf com content iccv 2019 papers chen on the over smoothing problem of cnn based disparity estimation iccv 2019 paper pdf notes paper notes smwa md kbd iccv 2019 kbd multimodal depth estimation sparse to dense depth prediction from sparse depth samples and a single image https arxiv org abs 1709 07492 notes paper notes sparse to dense md kbd icra 2018 kbd depth completion 2019 10 18 review of monocular object detection paper notes review mono 3dod md review of 2d 3d contraints in mono 3dod paper notes mono 3dod 2d3d constraints md monogrnet 2 monocular 3d object detection via geometric reasoning on keypoints https arxiv org abs 1905 05618 notes paper notes monogrnet russian md estimates depth from keypoints deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image https arxiv org abs 1703 07570 notes paper notes deep manta md kbd cvpr 2017 kbd ss3d monocular 3d object detection and box fitting trained end to end using intersection over union loss https arxiv org abs 1906 08070 notes paper notes ss3d md rergess distance from images centernet like gs3d an efficient 3d object detection framework for autonomous driving https arxiv org abs 1903 10955 notes paper notes gs3d md kbd cvpr 2019 kbd m3d rpn monocular 3d region proposal network for object detection https arxiv org abs 1907 06038 notes paper notes m3d rpn md kbd iccv 2019 oral kbd 3d anchors cyclists xiaoming liu tlnet triangulation learning network from monocular to stereo 3d object detection https arxiv org abs 1906 01193 notes paper notes tlnet md kbd cvpr 2019 kbd a survey on 3d object detection methods for autonomous driving applications http wrap warwick ac uk 114314 1 wrap survey 3d object detection methods autonomous driving applications arnold 2019 pdf notes paper notes 3dod review md kbd tits 2019 kbd review bev ipm deep learning based vehicle position and orientation estimation via inverse perspective mapping image https ieeexplore ieee org abstract document 8814050 notes paper notes bev od ipm md kbd iv 2019 kbd foresee task aware monocular depth estimation for 3d object detection https arxiv org abs 1909 07701 notes paper notes foresee mono3dod md kbd aaai 2020 oral kbd successor to pseudo lidar mono 3dod sota obj dist learning object specific distance from a monocular image https arxiv org abs 1909 04182 notes paper notes obj dist iccv2019 md kbd iccv 2019 kbd xmotors ai nyu monocular distance disnet a novel method for distance estimation from monocular camera https project inria fr ppniv18 files 2018 10 paper22 pdf notes paper notes disnet md kbd iros 2018 kbd monocular distance birdgan learning 2d to 3d lifting for object detection in 3d for autonomous vehicles https arxiv org abs 1904 08494 notes paper notes birdgan md kbd iros 2019 kbd shift r cnn deep monocular 3d object detection with closed form geometric constraints https arxiv org abs 1905 09970 notes paper notes shift rcnn md kbd icip 2019 kbd 3d rcnn instance level 3d object reconstruction via render and compare http openaccess thecvf com content cvpr 2018 papers kundu 3d rcnn instance level 3d cvpr 2018 paper pdf notes paper notes 3d rcnn md kbd cvpr 2018 kbd deep optics for monocular depth estimation and 3d object detection https arxiv org abs 1904 08601 notes paper notes deep optics md kbd iccv 2019 kbd monoloco monocular 3d pedestrian localization and uncertainty estimation https arxiv org abs 1906 06059 notes paper notes monoloco md kbd iccv 2019 kbd joint monocular 3d vehicle detection and tracking https arxiv org abs 1811 10742 notes paper notes mono 3d tracking md kbd iccv 2019 kbd berkeley deepdrive casgeo 3d bounding box estimation for autonomous vehicles by cascaded geometric constraints and depurated 2d detections using 3d results https arxiv org abs 1909 01867 notes paper notes casgeo md 2019 09 17 slimmable neural networks https arxiv org abs 1812 08928 notes paper notes slimmable networks md kbd iclr 2019 kbd universally slimmable networks and improved training techniques https arxiv org abs 1903 05134 notes paper notes universal slimmable md kbd iccv 2019 kbd autoslim towards one shot architecture search for channel numbers https arxiv org abs 1903 11728 once for all train one network and specialize it for efficient deployment https arxiv org pdf 1908 09791 pdf dota a large scale dataset for object detection in aerial images https vision cornell edu se3 wp content uploads 2018 03 2666 pdf notes paper notes dota md kbd cvpr 2018 kbd rotated bbox roitransformer learning roi transformer for oriented object detection in aerial images http openaccess thecvf com content cvpr 2019 papers ding learning roi transformer for oriented object detection in aerial images cvpr 2019 paper pdf notes paper notes roi transformer md kbd cvpr 2019 kbd rotated bbox rrpn arbitrary oriented scene text detection via rotation proposals https arxiv org abs 1703 01086 kbd tmm 2018 kbd r2cnn rotational region cnn for orientation robust scene text detection https arxiv org abs 1706 09579 rotated bbox ti white paper webinar mmwave radar for automotive and industrial applications https training ti com epd pro rap mmwaveradar adh tr webinar eu notes paper notes ti mmwave radar webinar md ti radar federated learning strategies for improving communication efficiency https arxiv org abs 1610 05492 notes paper notes federated learning comm md kbd nips 2016 kbd sort simple online and realtime tracking https arxiv org abs 1602 00763 notes paper notes sort md kbd icip 2016 kbd deep sort simple online and realtime tracking with a deep association metric https arxiv org abs 1703 07402 notes paper notes deep sort md mt cnn joint face detection and alignment using multi task cascaded convolutional networks https kpzhang93 github io mtcnn face detection alignment notes paper notes mtcnn md kbd spl 2016 kbd real time facial landmark retinaface single stage dense face localisation in the wild https arxiv org abs 1905 00641 notes paper notes retina face md kbd cvpr 2020 kbd joint object and landmark detection sc sfm learner unsupervised scale consistent depth and ego motion learning from monocular video https arxiv org abs 1908 10553 notes paper notes sc sfm learner md kbd nips 2019 kbd siammask fast online object tracking and segmentation a unifying approach https arxiv org abs 1812 05050 kbd cvpr 2019 kbd tracking segmentation label propagation review of k lm n filter https www bzarg com p how a kalman filter works in pictures from tim babb pixar animation notes paper notes kalman filter md r fcn object detection via region based fully convolutional networks https arxiv org abs 1605 06409 notes paper notes rfcn md kbd nips 2016 kbd guided backprop striving for simplicity the all convolutional net https arxiv org pdf 1412 6806 pdf notes paper notes guided backprop md kbd iclr 2015 kbd occlusion net 2d 3d occluded keypoint localization using graph networks http www cs cmu edu mvo index files papers onet 19 pdf notes paper notes occlusion net md kbd cvpr 2019 kbd boxy vehicle detection in large images https boxy dataset com boxy index notes paper notes boxy md kbd iccv 2019 kbd fqnet deep fitting degree scoring network for monocular 3d object detection https arxiv org abs 1904 12681 notes paper notes fqnet md kbd cvpr 2019 kbd mono 3dod jiwen lu 2019 08 18 mono3d monocular 3d object detection for autonomous driving https www cs toronto edu urtasun publications chen etal cvpr16 pdf notes paper notes mono3d md kbd cvpr2016 kbd monodis disentangling monocular 3d object detection https arxiv org abs 1905 12365 notes paper notes monodis md kbd iccv 2019 kbd pseudo lidar e2e monocular 3d object detection with pseudo lidar point cloud https arxiv org abs 1903 09847 notes paper notes pseudo lidar e2e md kbd iccv 2019 kbd pseudo lidar with 2d and 3d consistency loss better than pl and worse than pl sota for pure mono3d monogrnet a geometric reasoning network for monocular 3d object localization https arxiv org abs 1811 10247 notes paper notes monogrnet md kbd aaai 2019 kbd sota of mono3dod mlf monogrnet pseudo lidar mlf multi level fusion based 3d object detection from monocular images http openaccess thecvf com content cvpr 2018 papers xu multi level fusion based cvpr 2018 paper pdf notes paper notes mlf md kbd cvpr 2018 kbd precursor to pseudo lidar roi 10d monocular lifting of 2d detection to 6d pose and metric shape https arxiv org abs 1812 02781 notes paper notes roi10d md kbd cvpr 2019 kbd am3d accurate monocular 3d object detection via color embedded 3d reconstruction for autonomous driving https arxiv org abs 1903 11444 notes paper notes am3d md kbd iccv 2019 kbd similar to pseudo lidar color enhanced mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors https arxiv org abs 1901 03446 notes paper notes mono3d md from stefano soatto kbd aaai 2019 kbd deep metadata fusion for traffic light to lane assignment https ieeexplore ieee org stamp stamp jsp arnumber 8613841 notes paper notes deep lane association md kbd ieee ra l 2019 kbd traffic lights association automatic traffic light to ego vehicle lane association at complex intersections https ieeexplore ieee org document 8569421 kbd itsc 2019 kbd traffic lights association distant vehicle detection using radar and vision https arxiv org abs 1901 10951 notes paper notes distant object radar md kbd icra 2019 kbd radar vision radar tracklets fusion distance estimation of monocular based on vehicle pose information https iopscience iop org article 10 1088 1742 6596 1168 3 032040 pdf notes paper notes distance estimation pose radar md multi task learning using uncertainty to weigh losses for scene geometry and semantics https arxiv org abs 1705 07115 notes paper notes uncertainty multitask md kbd cvpr 2018 kbd alex kendall gradnorm gradient normalization for adaptive loss balancing in deep multitask networks https arxiv org abs 1711 02257 notes paper notes gradnorm md kbd icml 2018 kbd multitask dtp dynamic task prioritization for multitask learning http openaccess thecvf com content eccv 2018 papers michelle guo focus on the eccv 2018 paper pdf notes paper notes dtp md kbd eccv 2018 kbd multitask stanford will this car change the lane turn signal recognition in the frequency domain https ieeexplore ieee org document 6856477 notes paper notes tsl frequency md kbd iv 2014 kbd complex yolo real time 3d object detection on point clouds https arxiv org abs 1803 06199 notes paper notes complex yolo md bev detection only complexer yolo real time 3d object detection and tracking on semantic point clouds https arxiv org abs 1904 07537 kbd cvpr 2019 kbd sensor fusion and tracking an intriguing failing of convolutional neural networks and the coordconv solution https arxiv org abs 1807 03247 notes paper notes coord conv md kbd nips 2018 kbd 2019 07 19 deep parametric continuous convolutional neural networks http openaccess thecvf com content cvpr 2018 papers wang deep parametric continuous cvpr 2018 paper pdf notes paper notes parametric cont conv md kbd cvpr 2018 kbd uber sensor fusion contfuse deep continuous fusion for multi sensor 3d object detection http openaccess thecvf com content eccv 2018 papers ming liang deep continuous fusion eccv 2018 paper pdf notes paper notes contfuse md kbd eccv 2018 kbd uber atg sensor fusion bev fast and furious real time end to end 3d detection tracking and motion forecasting with a single convolutional net http openaccess thecvf com content cvpr 2018 papers luo fast and furious cvpr 2018 paper pdf notes paper notes faf md kbd cvpr 2018 oral kbd lidar only perception and prediction learnk depth from videos in the wild unsupervised monocular depth learning from unknown cameras https arxiv org pdf 1904 04998 pdf notes paper notes learnk md kbd iccv 2019 kbd monocular depth estimation intrinsic estimation sota monodepth unsupervised monocular depth estimation with left right consistency https arxiv org abs 1609 03677 notes paper notes monodepth md kbd cvpr 2017 oral kbd monocular depth estimation stereo for training struct2depth depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos https arxiv org abs 1811 06152 notes paper notes struct2depth md kbd aaai 2019 kbd monocular depth estimation estimating movement of dynamic object infinite depth problem online finetune unsupervised learning of geometry with edge aware depth normal consistency https arxiv org pdf 1711 03665 pdf notes paper notes edge aware depth normal md kbd aaai 2018 kbd monocular depth estimation static assumption surface normal lego learning edge with geometry all at once by watching videos https arxiv org pdf 1803 05648 pdf notes paper notes lego md kbd cvpr 2018 spotlight kbd monocular depth estimation static assumption surface normal object detection and 3d estimation via an fmcw radar using a fully convolutional network https arxiv org abs 1902 05394 notes paper notes radar 3d od fcn md radar rd map od arxiv 201902 a study on radar target detection based on deep neural networks https www researchgate net publication 330748053 a study on radar target detection based on deep neural networks notes paper notes radar target detection tsinghua md radar rd map od 2d car detection in radar data with pointnets https arxiv org abs 1904 08414 notes paper notes radar detection pointnet md from ulm univ radar point cloud od arxiv 201904 learning confidence for out of distribution detection in neural networks https arxiv org abs 1802 04865 notes paper notes learning ood conf md budget to cheat a deep learning approach to traffic lights detection tracking and classification assets papers bosch traffic lights pdf notes paper notes bosch traffic lights md kbd icra 2017 kbd bosch traffic lights how hard can it be estimating the difficulty of visual search in an image https arxiv org abs 1705 08280 notes paper notes how hard can it be md kbd cvpr 2016 kbd deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges https arxiv org pdf 1902 07830 pdf notes paper notes deep fusion review md review from bosch review of monocular 3d object detection https zhuanlan zhihu com p 57029694 blog from deep3dbox 3d bounding box estimation using deep learning and geometry https arxiv org abs 1612 00496 notes paper notes deep3dbox md kbd cvpr 2017 kbd zoox monopsr monocular 3d object detection leveraging accurate proposals and shape reconstruction https arxiv org abs 1904 01690 notes paper notes monopsr md kbd cvpr 2019 kbd oft orthographic feature transform for monocular 3d object detection https arxiv org abs 1811 08188 notes paper notes oft md kbd bmvc 2019 kbd convert camera to bev alex kendall 2019 06 12 mixmatch a holistic approach to semi supervised learning https arxiv org abs 1905 02249 notes paper notes mixmatch md efficientnet rethinking model scaling for convolutional neural networks https arxiv org pdf 1905 11946 pdf notes paper notes efficientnet md kbd icml 2019 kbd what uncertainties do we need in bayesian deep learning for computer vision https arxiv org abs 1703 04977 notes paper notes uncertainty bdl md kbd nips 2017 kbd bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding https arxiv org pdf 1511 02680 pdf notes paper notes bayesian segnet md kbd bmvc 2017 kbd trafficpredict trajectory prediction for heterogeneous traffic agents https arxiv org pdf 1811 02146 pdf notes paper notes trafficpredict md kbd aaai 2019 oral kbd deep depth completion of a single rgb d image https arxiv org pdf 1803 09326 pdf notes paper notes deep depth completion rgbd md kbd cvpr 2018 kbd indoor deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image https arxiv org pdf 1812 00488v2 pdf notes paper notes deeplidar md kbd cvpr 2019 kbd outdoor sfmlearner unsupervised learning of depth and ego motion from video https people eecs berkeley edu tinghuiz projects sfmlearner cvpr17 sfm final pdf notes paper notes sfm learner md kbd cvpr 2017 kbd monodepth2 digging into self supervised monocular depth estimation https arxiv org abs 1806 01260 notes paper notes monodepth2 md kbd iccv 2019 kbd niantic deepsignals predicting intent of drivers through visual signals https arxiv org pdf 1905 01333 pdf notes paper notes deep signals md kbd icra 2019 kbd uber turn signal detection fcos fully convolutional one stage object detection https arxiv org abs 1904 01355 notes paper notes fcos md kbd iccv 2019 kbd chunhua shen pseudo lidar accurate depth for 3d object detection in autonomous driving https arxiv org abs 1906 06310 notes paper notes pseudo lidar md kbd iclr 2020 kbd mmf multi task multi sensor fusion for 3d object detection http www cs toronto edu byang papers mmf pdf notes paper notes mmf md kbd cvpr 2019 kbd uber sensor fusion 2019 05 18 centernet objects as points https arxiv org abs 1904 07850 from extremenet authors notes paper notes centernet md centernet object detection with keypoint triplets https arxiv org abs 1904 08189 notes paper notes centernet cas md object detection based on region decomposition and assembly https arxiv org abs 1901 08225 notes paper notes object detection region decomposition md kbd aaai 2019 kbd the lottery ticket hypothesis finding sparse trainable neural networks https arxiv org abs 1803 03635 notes paper notes lottery ticket hypothesis md kbd iclr 2019 kbd m2det a single shot object detector based on multi level feature pyramid network https arxiv org abs 1811 04533 notes paper notes m2det md kbd aaai 2019 kbd deep radar detector https arxiv org abs 1906 12187 notes paper notes deep radar detector md kbd radarcon 2019 kbd semantic segmentation on radar point clouds https ieeexplore ieee org document 8455344 notes paper notes radar point semantic seg md from daimler ag kbd fusion 2018 kbd pruning filters for efficient convnets https arxiv org pdf 1608 08710 pdf notes paper notes pruning filters md kbd iclr 2017 kbd layer compensated pruning for resource constrained convolutional neural networks https arxiv org pdf 1810 00518 pdf notes paper notes layer compensated pruning md kbd nips 2018 talk kbd legr filter pruning via learned global ranking https arxiv org pdf 1904 12368 pdf notes paper notes legr md kbd cvpr 2020 oral kbd nas fpn learning scalable feature pyramid architecture for object detection https arxiv org pdf 1904 07392 pdf notes paper notes nas fpn md kbd cvpr 2019 kbd autoaugment learning augmentation policies from data https arxiv org abs 1805 09501 notes paper notes autoaugment md kbd cvpr 2019 kbd path aggregation network for instance segmentation https arxiv org pdf 1803 01534 pdf notes paper notes panet md kbd cvpr 2018 kbd channel pruning for accelerating very deep neural networks https arxiv org pdf 1707 06168 pdf kbd iccv 2017 kbd face yihui he notes paper notes channel pruning megvii md amc automl for model compression and acceleration on mobile devices https arxiv org pdf 1802 03494 pdf kbd eccv 2018 kbd song han yihui he mobilenetv3 searching for mobilenetv3 https arxiv org abs 1905 02244 notes paper notes mobilenets v3 md kbd iccv 2019 kbd mnasnet platform aware neural architecture search for mobile https arxiv org pdf 1807 11626 pdf notes paper notes mnasnet md kbd cvpr 2019 kbd rethinking the value of network pruning https arxiv org pdf 1810 05270 pdf kbd iclr 2019 kbd 2019 04 12 mobilenetv2 inverted residuals and linear bottlenecks https arxiv org abs 1801 04381 mobilenets v2 notes paper notes mobilenets v2 md kbd cvpr 2018 kbd a new performance measure and evaluation benchmark for road detection algorithms http www cvlibs net publications fritsch2013itsc pdf notes paper notes kitti lane md kbd itsc 2013 kbd multinet real time joint semantic reasoning for autonomous driving https arxiv org pdf 1612 07695 pdf notes paper notes multinet raquel md optimizing the trade off between single stage and two stage object detectors using image difficulty prediction https arxiv org pdf 1803 08707 pdf very nice illustration of 1 and 2 stage object detection light head r cnn in defense of two stage object detector https arxiv org pdf 1711 07264 pdf notes paper notes lighthead rcnn md from megvii csp high level semantic feature detection a new perspective for pedestrian detection https arxiv org abs 1904 02948 notes paper notes csp pedestrian md kbd cvpr 2019 kbd center and scale prediction anchor free near sota pedestrian review of anchor free methods blog anchor free https zhuanlan zhihu com p 62103812 anchor free https zhuanlan zhihu com p 64563186 my slides on csp https docs google com presentation d 1 dufxv63108bzxunvypioadeikrzw5br9 rop ni0x0 densebox unifying landmark localization with end to end object detection https arxiv org pdf 1509 04874 pdf cornernet detecting objects as paired keypoints https arxiv org pdf 1808 01244 pdf notes paper notes cornernet md kbd eccv 2018 kbd extremenet bottom up object detection by grouping extreme and center points https arxiv org pdf 1901 08043 pdf notes paper notes extremenet md kbd cvpr 2019 kbd fsaf feature selective anchor free module for single shot object detection https arxiv org pdf 1903 00621 pdf notes paper notes fsaf detection md kbd cvpr 2019 kbd foveabox beyond anchor based object detector https arxiv org pdf 1904 03797v1 pdf anchor free notes paper notes foveabox md 2019 03 19 bag of freebies for training object detection neural networks https arxiv org pdf 1902 04103 pdf notes paper notes bag of freebies object detection md mixup beyond empirical risk minimization https arxiv org pdf 1710 09412 pdf notes paper notes mixup md kbd iclr 2018 kbd multi view convolutional neural networks for 3d shape recognition https www cv foundation org openaccess content iccv 2015 papers su multi view convolutional neural iccv 2015 paper pdf mvcnn notes paper notes mvcnn md kbd iccv 2015 kbd 3d shapenets a deep representation for volumetric shapes http 3dshapenets cs princeton edu paper pdf notes paper notes 3d shapenets md kbd cvpr 2015 kbd volumetric and multi view cnns for object classification on 3d data https arxiv org pdf 1604 03265 pdf notes paper notes vol vs mvcnn md kbd cvpr 2016 kbd group normalization https arxiv org pdf 1803 08494 pdf notes paper notes groupnorm md kbd eccv 2018 kbd spatial transformer networks https arxiv org pdf 1506 02025 pdf notes paper notes stn md kbd nips 2015 kbd frustum pointnets for 3d object detection from rgb d data https arxiv org pdf 1711 08488 pdf f pointnet notes paper notes frustum pointnet md kbd cvpr 2018 kbd dynamic graph cnn for learning on point clouds https arxiv org pdf 1801 07829 pdf notes paper notes edgeconv md pointrcnn 3d object proposal generation and detection from point cloud https arxiv org abs 1812 04244 sota for 3d object detection notes paper notes point rcnn md kbd cvpr 2019 kbd mv3d multi view 3d object detection network for autonomous driving https arxiv org abs 1611 07759 notes paper notes mv3d md kbd cvpr 2017 kbd baidu sensor fusion bv proposal avod joint 3d proposal generation and object detection from view aggregation https arxiv org abs 1712 02294 notes paper notes avod md kbd iros 2018 kbd sensor fusion multiview proposal mobilenets efficient convolutional neural networks for mobile vision applications https arxiv org abs 1704 04861 notes paper notes mobilenets md pseudo lidar from visual depth estimation bridging the gafp in 3d object detection for autonomous driving https arxiv org abs 1812 07179 notes paper notes pseudo lidar md kbd cvpr 2019 kbd voxelnet end to end learning for point cloud based 3d object detection https arxiv org pdf 1711 06396 pdf kbd cvpr 2018 kbd apple first end to end point cloud encoding to grid second sparsely embedded convolutional detection https www mdpi com 1424 8220 18 10 3337 pdf kbd sensors 2018 kbd builds on voxelnet pointpillars fast encoders for object detection from point clouds https arxiv org abs 1812 05784 notes paper notes point pillars md kbd cvpr 2019 kbd builds on second are we ready for autonomous driving the kitti vision benchmark suite http www cvlibs net publications geiger2012cvpr pdf notes paper notes kitti md kbd cvpr 2012 kbd vision meets robotics the kitti dataset http ww cvlibs net publications geiger2013ijrr pdf notes paper notes kitti md kbd ijrr 2013 kbd 2019 02 9 quo vadis action recognition a new model and the kinetics dataset https arxiv org pdf 1705 07750 pdf i3d notes paper notes quo vadis i3d md kbd video kbd kbd cvpr 2017 kbd initialization strategies of spatio temporal convolutional neural networks https arxiv org pdf 1503 07274 pdf notes paper notes quo vadis i3d md kbd video kbd detect and track efficient pose estimation in videos https arxiv org pdf 1712 09184 pdf notes paper notes quo vadis i3d md kbd iccv 2017 kbd kbd video kbd deep learning based rib centerline extraction and labeling https arxiv org pdf 1809 07082 notes paper notes rib centerline philips md kbd mi kbd kbd miccai 2018 kbd slowfast networks for video recognition https arxiv org pdf 1812 03982 pdf notes paper notes slowfast md kbd iccv 2019 oral kbd aggregated residual transformations for deep neural networks https arxiv org pdf 1611 05431 pdf resnext notes paper notes resnext md kbd cvpr 2017 kbd beyond the pixel plane sensing and learning in 3d https thegradient pub beyond the pixel plane sensing and learning in 3d blog https zhuanlan zhihu com p 44386618 voxnet a 3d convolutional neural network for real time object recognition https www ri cmu edu pub files 2015 9 voxnet maturana scherer iros15 pdf voxnet notes paper notes voxnet md pointnet deep learning on point sets for 3d classification and segmentation https arxiv org pdf 1612 00593 pdf kbd cvpr 2017 kbd notes paper notes pointnet md pointnet deep hierarchical feature learning on point sets in a metric space https arxiv org pdf 1706 02413 pdf kbd nips 2017 kbd notes paper notes pointnet md review of geometric deep learning from https zhuanlan zhihu com p 36888114 up to cvpr 2018 2019 01 10 dqn human level control through deep reinforcement learning nature dqn paper https storage googleapis com deepmind media dqn dqnnaturepaper pdf notes paper notes nature dqn paper md kbd drl kbd retina u net embarrassingly simple exploitation of segmentation supervision for medical object detection https arxiv org pdf 1811 08661 pdf notes paper notes retina unet md kbd mi kbd panoptic segmentation https arxiv org pdf 1801 00868 pdf notes paper notes panoptic segmentation md kbd panseg kbd panoptic feature pyramid networks https arxiv org pdf 1901 02446 pdf notes paper notes panoptic fpn md kbd panseg kbd attention guided unified network for panoptic segmentation https arxiv org pdf 1812 03904 pdf notes paper notes aunet panoptic md kbd panseg kbd bag of tricks for image classification with convolutional neural networks https arxiv org pdf 1812 01187 pdf notes paper notes bag of tricks cnn md kbd cls kbd deep reinforcement learning for vessel centerline tracing in multi modality 3d volumes https link springer com chapter 10 1007 978 3 030 00937 3 86 notes paper notes drl vessel centerline md kbd drl kbd kbd mi kbd deep reinforcement learning for flappy bird http cs229 stanford edu proj2015 362 report pdf notes paper notes drl flappy md kbd drl kbd long term feature banks for detailed video understanding https arxiv org pdf 1812 05038 pdf notes paper notes long term feat bank md kbd video kbd non local neural networks https arxiv org pdf 1711 07971 pdf notes paper notes non local net md kbd video kbd kbd cvpr 2018 kbd 2018 mask r cnn https arxiv org pdf 1703 06870 pdf cascade r cnn delving into high quality object detection https arxiv org pdf 1712 00726 pdf focal loss for dense object detection https arxiv org pdf 1708 02002 pdf retinanet notes paper notes focal loss md squeeze and excitation networks https arxiv org pdf 1709 01507 senet progressive growing of gans for improved quality stability and variation https arxiv org pdf 1710 10196 pdf deformable convolutional networks https arxiv org abs 1703 06211 kbd iccv 2017 kbd build on r fcn learning region features for object detection https arxiv org pdf 1803 07066 pdf 2017 and before learning notes on deep learning learning notes md list of papers on machine learning list of machine learning papers md notes of literature review on cnn in cv paper notes cnn papers md this is the notes for all the papers in the recommended list here papers and books to start md notes of literature review others misc md notes on how to set up dl ml environment ml dl environment setup md useful setup notes installation log md papers to read here is the list of papers waiting to be read deep learning in general squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving https arxiv org pdf 1612 01051 pdf accurate large minibatch sgd training imagenet in 1 hour https arxiv org pdf 1706 02677 pdf imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness https openreview net forum id bygh9j09kx kbd icml 2019 kbd approximating cnns with bag of local features models works surprisingly well on imagenet https openreview net forum id skfmwhaqyq bagnet blog https blog evjang com 2019 02 bagnet html kbd icml 2019 kbd a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay https arxiv org pdf 1803 09820v2 pdf understanding deep learning requires rethinking generalization https arxiv org pdf 1611 03530 pdf gradient reversal unsupervised domain adaptation by backpropagation https arxiv org abs 1409 7495 kbd icml 2015 kbd self training rethinking pre training and self training https arxiv org abs 2006 06882 kbd neurips 2020 kbd quoc le 2d object detection and segmentation mask scoring r cnn https arxiv org pdf 1903 00241 pdf kbd cvpr 2019 kbd training region based object detectors with online hard example mining https arxiv org pdf 1604 03540 pdf gliding vertex on the horizontal bounding box for multi oriented object detection https arxiv org abs 1911 09358 once incremental few shot object detection https arxiv org abs 2003 04668 kbd cvpr 2020 kbd domain adaptive faster r cnn for object detection in the wild https arxiv org abs 1803 03243 kbd cvpr 2018 kbd foggy cityscapes semantic foggy scene understanding with synthetic data https arxiv org abs 1708 07819 kbd ijcv 2018 kbd foggy cityscapes eccv model adaptation with synthetic and real data for semantic dense foggy scene understanding https arxiv org abs 1808 01265 kbd eccv 2018 kbd dropout sampling for robust object detection in open set conditions https arxiv org abs 1710 06677 kbd icra 2018 kbd niko s nderhauf hybrid task cascade for instance segmentation https arxiv org abs 1901 07518 kbd cvpr 2019 kbd cascaded mask rcnn evaluating merging strategies for sampling based uncertainty techniques in object detection https arxiv org abs 1809 06006 kbd icra 2019 kbd niko s nderhauf a unified panoptic segmentation network https arxiv org pdf 1901 03784 pdf kbd cvpr 2019 kbd kbd panseg kbd model vulnerability to distributional shifts over image transformation sets https arxiv org pdf 1903 11900 pdf cvpr workshop tl dr https www reddit com r machinelearning comments b81uwq r model vulnerability to distributional shifts automatic adaptation of object detectors to new domains using self training https arxiv org pdf 1904 07305 pdf kbd cvpr 2019 kbd find corner case and boost missing labels in object detection http openaccess thecvf com content cvprw 2019 papers weakly 20supervised 20learning 20for 20real world 20computer 20vision 20applications xu missing labels in object detection cvprw 2019 paper pdf kbd cvpr 2019 kbd densebox unifying landmark localization with end to end object detection https arxiv org pdf 1509 04874 pdf circular object detection in polar coordinates for 2d lidar data https www researchgate net publication 309365539 circular object detection in polar coordinates for 2d lidar dataccpr2016 kbd ccpr 2016 kbd lffd a light and fast face detector for edge devices https arxiv org abs 1904 10633 lightweight face detection car detection unitbox an advanced object detection network https arxiv org abs 1608 01471 kbd acm mm 2016 kbd ln iou loss thomas huang fisheye generalized object detection on fisheye cameras for autonomous driving dataset representations and baseline https arxiv org abs 2012 02124 kbd wacv 2021 kbd video understanding learning spatiotemporal features with 3d convolutional networks https arxiv org pdf 1412 0767 pdf c3d kbd video kbd kbd iccv 2015 kbd ava a video dataset of spatio temporally localized atomic visual actions https arxiv org pdf 1705 08421 pdf spatiotemporal residual networks for video action recognition https arxiv org pdf 1611 02155 pdf decouple spatiotemporal kbd nips 2016 kbd learning spatio temporal representation with pseudo 3d residual networks https arxiv org pdf 1711 10305 pdf p3d decouple spatiotemporal kbd iccv 2017 kbd a closer look at spatiotemporal convolutions for action recognition https arxiv org pdf 1711 11248 pdf decouple spatiotemporal kbd cvpr 2018 kbd rethinking spatiotemporal feature learning speed accuracy trade offs in video classification https arxiv org pdf 1712 04851 pdf decouple spatiotemporal kbd eccv 2018 kbd can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet https arxiv org pdf 1711 09577 pdf kbd cvpr 2018 kbd agss vos attention guided single shot video object segmentation http openaccess thecvf com content iccv 2019 papers lin agss vos attention guided single shot video object segmentation iccv 2019 paper pdf kbd iccv 2019 kbd one shot video object segmentation https arxiv org abs 1611 05198 kbd cvpr 2017 kbd looking fast and slow memory guided mobile video object detection https arxiv org abs 1903 10172 kbd cvpr 2018 kbd towards high performance video object detection http openaccess thecvf com content cvpr 2018 papers zhu towards high performance cvpr 2018 paper pdf notes paper notes high performance video od md kbd cvpr 2018 kbd towards high performance video object detection for mobiles https arxiv org abs 1804 05830 notes paper notes high performance video od mobile md temporally distributed networks for fast video semantic segmentation https arxiv org abs 2004 01800 kbd cvpr 2020 kbd efficient video segmentation memory enhanced global local aggregation for video object detection https arxiv org abs 2003 12063 kbd cvpr 2020 kbd efficient video object detection co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation https arxiv org abs 1804 06055 kbd ijcai 2018 oral kbd video skeleton rst modnet real time spatio temporal moving object detection for autonomous driving https arxiv org abs 1912 00438 kbd neurips 2019 workshop kbd long term recurrent convolutional networks for visual recognition and description https arxiv org abs 1411 4389 kbd cvpr 2015 oral kbd temporal segment networks towards good practices for deep action recognition https arxiv org abs 1608 00859 kbd eccv 2016 kbd trn temporal relational reasoning in videos https arxiv org abs 1711 08496 kbd eccv 2018 kbd x3d expanding architectures for efficient video recognition https arxiv org abs 2004 04730 kbd cvpr 2020 oral kbd fair temporal context enhanced detection of heavily occluded pedestrians https openaccess thecvf com content cvpr 2020 html wu temporal context enhanced detection of heavily occluded pedestrians cvpr 2020 paper html kbd cvpr 2020 oral kbd pedestrian video flow guided feature aggregation for video object detection https arxiv org abs 1703 10025 kbd iccv 2017 kbd video object detection 3d human pose estimation in video with temporal convolutions and semi supervised training http openaccess thecvf com content cvpr 2019 papers pavllo 3d human pose estimation in video with temporal convolutions and cvpr 2019 paper pdf kbd cvpr 2019 kbd mono3d pose estimation from video omeganet distilled semantics for comprehensive scene understanding from videos https arxiv org abs 2003 14030 kbd cvpr 2020 kbd object detection in videos with tubelet proposal networks https arxiv org abs 1702 06355 kbd cvpr 2017 kbd video object detection t cnn tubelets with convolutional neural networks for object detection from videos https arxiv org abs 1604 02532 video object detection flow guided feature aggregation for video object detection https arxiv org abs 1703 10025 kbd iccv 2017 kbd jifeng dai pruning and compression efficient deep learning inference based on model compression http openaccess thecvf com content cvpr 2018 workshops papers w33 zhang efficient deep learning cvpr 2018 paper pdf model compression neural network distiller https intellabs github io distiller algo pruning html intel architecture improvements concurrent spatial and channel squeeze excitation in fully convolutional networks https arxiv org pdf 1803 02579 pdf cbam convolutional block attention module https arxiv org pdf 1807 06521 pdf reinforcement learning playing atari with deep reinforcement learning https www cs toronto edu vmnih docs dqn pdf kbd nips 2013 kbd multi scale deep reinforcement learning for real time 3d landmark detection in ct scan http comaniciu net papers multiscaledeepreinforcementlearning pami18 pdf an artificial agent for robust image registration https www aaai org ocs index php aaai aaai17 paper download 14751 14296 3d perception 3d cnn 3d convolutional neural networks for landing zone detection from lidar https www ri cmu edu pub files 2015 3 maturana root pdf generative and discriminative voxel modeling with convolutional neural networks https arxiv org pdf 1608 04236 pdf orientation boosted voxel nets for 3d object recognition https arxiv org pdf 1604 03351 pdf orion bmvc 2017 gift a real time and scalable 3d shape search engine https arxiv org pdf 1604 01879 pdf kbd cvpr 2016 kbd 3d shape segmentation with projective convolutional networks https people cs umass edu kalo papers shapepfcn shapepfcn kbd cvpr 2017 kbd learning local shape descriptors from part correspondences with multi view convolutional networks https arxiv org pdf 1706 04496 pdf open3d a modern library for 3d data processing http www open3d org wordpress wp content paper pdf multimodal deep learning for robust rgb d object recognition https arxiv org pdf 1507 06821 pdf kbd iros 2015 kbd flownet3d learning scene flow in 3d point clouds https arxiv org pdf 1806 01411 pdf kbd cvpr 2019 kbd mining point cloud local structures by kernel correlation and graph pooling https arxiv org pdf 1712 06760 pdf kbd cvpr 2018 kbd neighbors do help deeply exploiting local structures of point clouds pu net point cloud upsampling network https arxiv org pdf 1801 06761 pdf kbd cvpr 2018 kbd recurrent slice networks for 3d segmentation of point clouds https arxiv org pdf 1802 04402 pdf kbd cvpr 2018 kbd splatnet sparse lattice networks for point cloud processing https arxiv org pdf 1802 08275 pdf kbd cvpr 2018 kbd convolutional neural networks on graphs with fast localized spectral filtering https arxiv org pdf 1606 09375 pdf kbd nips 2016 kbd semi supervised classification with graph convolutional networks https arxiv org pdf 1609 02907 pdf kbd iclr 2017 kbd geometric matrix completion with recurrent multi graph neural networks https arxiv org pdf 1704 06803 pdf kbd nips 2017 kbd graph attention networks https arxiv org pdf 1710 10903 pdf kbd iclr 2018 kbd 3d ssd learning hierarchical features from rgb d images for amodal 3d object detection https arxiv org pdf 1711 00238 pdf 3d ssd escape from cells deep kd networks for the recognition of 3d point cloud models https arxiv org pdf 1704 01222 pdf kbd iccv 2017 kbd shape completion using 3d encoder predictor cnns and shape synthesis https arxiv org pdf 1612 00101 pdf kbd cvpr 2017 kbd ipod intensive point based object detector for point cloud https arxiv org pdf 1812 05276 pdf amodal detection of 3d objects inferring 3d bounding boxes from 2d ones in rgb depth images https cis temple edu latecki papers dengcvpr2017 pdf kbd cvpr 2017 kbd 2d driven 3d object detection in rgb d images http openaccess thecvf com content iccv 2017 papers lahoud 2d driven 3d object iccv 2017 paper pdf 3d ssd learning hierarchical features from rgb d images for amodal 3d object detection https arxiv org pdf 1711 00238 pdf associate 3ddet perceptual to conceptual association for 3d point cloud object detection https arxiv org abs 2006 04356 classify occluded object stereo and flow psmnet pyramid stereo matching network https arxiv org pdf 1803 08669 pdf kbd cvpr 2018 kbd stereo r cnn based 3d object detection for autonomous driving https arxiv org pdf 1902 09738 pdf kbd cvpr 2019 kbd deep rigid instance scene flow https people csail mit edu weichium papers cvpr19 dsisf paper pdf kbd cvpr 2019 kbd upgrading optical flow to 3d scene flow through optical expansion http openaccess thecvf com content cvpr 2020 papers yang upgrading optical flow to 3d scene flow through optical expansion cvpr 2020 paper pdf kbd cvpr 2020 kbd learning multi object tracking and segmentation from automatic annotations https arxiv org abs 1912 02096 kbd cvpr 2020 kbd automatic mots annotation traffic light and traffic sign traffic sign detection and classification in the wild https www cv foundation org openaccess content cvpr 2016 papers zhu traffic sign detection and cvpr 2016 paper pdf kbd cvpr 2016 kbd tsinghua tencent traffic signs a hierarchical deep architecture and mini batch selection method for joint traffic sign and light detection https arxiv org abs 1806 07987 kbd ieee crv 2018 kbd u torronto detecting traffic lights by single shot detection https arxiv org abs 1805 02523 kbd itsc 2018 kbd deeptlr a single deep convolutional network for detection and classification of traffic lights https sci hub st 10 1109 ivs 2016 7535408 kbd iv 2016 kbd evaluating state of the art object detector on challenging traffic light data https openaccess thecvf com content cvpr 2017 workshops w9 papers jensen evaluating state of the art object cvpr 2017 paper pdf kbd cvpr 2017 workshop kbd traffic light recognition in varying illumination using deep learning and saliency map https www researchgate net profile vijay john3 publication 265014373 traffic light recognition in varying illumination using deep learning and saliency map links 56aac00408ae8f3865666102 pdf kbd itsc 2014 kbd traffic light traffic light recognition using high definition map features https sci hub st https www sciencedirect com science article abs pii s0921889018301234 kbd ras 2019 kbd vision for looking at traffic lights issues survey and perspectives http cvrr ucsd edu publications 2016 trafficsignalsitstrans2016 pdf kbd tits 2015 kbd datasets and surveys the driveu traffic light dataset introduction and comparison with existing datasets https ieeexplore ieee org document 8460737 kbd icra 2018 kbd the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset https arxiv org abs 1909 01300 vision for looking at traffic lights issues survey and perspectives http cvrr ucsd edu publications 2016 trafficsignalsitstrans2016 pdf traffic light survey ucsd lisa review of graph spectrum theory paper notes graph spectrum md wip 3d deep learning tutorial at cvpr 2017 https www youtube com watch v 8cent 4hwyy notes paper notes 3ddl cvpr2017 md wip a survey on neural architecture search https arxiv org pdf 1905 01392 pdf network pruning tutorial https jacobgil github io deeplearning pruning deep learning blog gnn tutorial at cvpr 2019 https xiaolonw github io graphnn large scale interactive motion forecasting for autonomous driving the waymo open motion dataset https arxiv org abs 2104 10133 waymo prediction dataset panda a gigapixel level human centric video dataset https arxiv org abs 2003 04852 kbd cvpr 2020 kbd woodscape a multi task multi camera fisheye dataset for autonomous driving https openaccess thecvf com content iccv 2019 papers yogamani woodscape a multi task multi camera fisheye dataset for autonomous driving iccv 2019 paper pdf kbd iccv 2019 kbd valeo unsupervised depth estimation sparse and dense data with cnns depth completion and semantic segmentation https arxiv org pdf 1808 00769 pdf kbd 3dv 2018 kbd depth map prediction from a single image using a multi scale deep network https arxiv org pdf 1406 2283 pdf kbd nips 2014 kbd eigen et al learning depth from monocular videos using direct methods https arxiv org abs 1712 00175 kbd cvpr 2018 kbd monocular depth estimation virtual normal enforcing geometric constraints of virtual normal for depth prediction https arxiv org abs 1907 12209 notes paper notes virtual normal md kbd iccv 2019 kbd better generation of pl spatial correspondence with generative adversarial network learning depth from monocular videos http openaccess thecvf com content iccv 2019 papers wu spatial correspondence with generative adversarial network learning depth from monocular iccv 2019 paper pdf kbd iccv 2019 kbd unsupervised collaborative learning of keyframe detection and visual odometry towards monocular deep slam http openaccess thecvf com content iccv 2019 papers sheng unsupervised collaborative learning of keyframe detection and visual odometry towards iccv 2019 paper pdf kbd iccv 2019 kbd visualization of convolutional neural networks for monocular depth estimation https arxiv org abs 1904 03380 kbd iccv 2019 kbd indoor depth fast and accurate recovery of occluding contours in monocular depth estimation https arxiv org abs 1905 08598 kbd iccv 2019 workshop kbd indoor multi loss rebalancing algorithm for monocular depth estimation https www ecva net papers eccv 2020 papers eccv html 2890 eccv 2020 paper php kbd eccv 2020 kbd indoor depth disambiguating monocular depth estimation with a single transient https www ecva net papers eccv 2020 papers eccv html 3668 eccv 2020 paper php kbd eccv 2020 kbd additional laser sensor indoor depth guiding monocular depth estimation using depth attention volume https www ecva net papers eccv 2020 papers eccv html 5491 eccv 2020 paper php kbd eccv 2020 kbd indoor depth improving monocular depth estimation by leveraging structural awareness and complementary datasets https arxiv org abs 2007 11256 kbd eccv 2020 kbd indoor depth cliffnet for monocular depth estimation with hierarchical embedding loss https www ecva net papers eccv 2020 papers eccv html 3365 eccv 2020 paper php kbd eccv 2020 kbd indoor depth lidar pointsift a sift like network module for 3d point cloud semantic segmentation https arxiv org pdf 1807 00652 pdf pointnet alternative backbone vehicle detection from 3d lidar using fully convolutional network https arxiv org pdf 1608 07916 pdf velofcn kbd rss 2016 kbd kpconv flexible and deformable convolution for point clouds https arxiv org abs 1904 08889 from the authors of pointnet pointcnn convolution on x transformed points https arxiv org pdf 1801 07791 pdf kbd nips 2018 kbd l3 net towards learning based lidar localization for autonomous driving https songshiyu01 github io pdf l3net w lu y zhou s song cvpr2019 pdf kbd cvpr 2019 kbd roarnet a robust 3d object detection based on region approximation refinement https arxiv org abs 1811 03818 sensor fusion 3d mono proposal refined in point cloud dels 3d deep localization and segmentation with a 3d semantic map https arxiv org pdf 1805 04949 pdf kbd cvpr 2018 kbd frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection https arxiv org abs 1903 01864 kbd iros 2019 kbd pointrnn point recurrent neural network for moving point cloud processing https arxiv org abs 1910 08287 gated2depth real time dense lidar from gated images https arxiv org abs 1902 04997 kbd iccv 2019 oral kbd a multi sensor fusion system for moving object detection and tracking in urban driving environments http www cs cmu edu youngwoo doc icra 14 sensor fusion pdf kbd icra 2014 kbd pointfusion deep sensor fusion for 3d bounding box estimation https arxiv org abs 1711 10871 kbd cvpr 2018 kbd sensor fusion zoox deep hough voting for 3d object detection in point clouds https arxiv org abs 1904 09664 kbd iccv 2019 kbd charles qi stixelnet a deep convolutional network for obstacle detection and road segmentation http www bmva org bmvc 2015 papers paper109 paper109 pdf polarnet an improved grid representation for online lidar point clouds semantic segmentation https arxiv org abs 2003 14032 kbd cvpr 2020 kbd depth sensing beyond lidar range https arxiv org abs 2004 03048 kbd cvpr 2020 kbd wide baseline stereo with trifocal probabilistic semantic mapping for urban autonomous driving applications https arxiv org abs 2006 04894 kbd iros 2020 kbd lidar mapping randla net efficient semantic segmentation of large scale point clouds https arxiv org abs 1911 11236 kbd cvpr 2020 oral kbd lidar segmentation polarnet an improved grid representation for online lidar point clouds semantic segmentation https arxiv org abs 2003 14032 kbd cvpr 2020 kbd lidar segmentation octsqueeze octree structured entropy model for lidar compression https arxiv org abs 2005 07178 kbd cvpr 2020 oral kbd lidar compression muscle multi sweep compression of lidar using deep entropy models https arxiv org abs 2011 07590 kbd neurips 2020 oral kbd lidar compression egocentric bbox prediction long term on board prediction of people in traffic scenes under uncertainty https arxiv org abs 1711 09026 kbd cvpr 2018 kbd on board bbox prediction unsupervised traffic accident detection in first person videos https arxiv org abs 1903 00618 kbd iros 2019 kbd honda nemo future object localization using noisy ego priors https arxiv org abs 1909 08150 honda robust aleatoric modeling for future vehicle localization http openaccess thecvf com content cvprw 2019 papers precognition hudnell robust aleatoric modeling for future vehicle localization cvprw 2019 paper pdf perspective multiple object forecasting predicting future object locations in diverse environments https arxiv org abs 1909 11944 kbd wacv 2020 kbd perspective bbox pedestrian using panoramic videos for multi person localization and tracking in a 3d panoramic coordinate https arxiv org abs 1911 10535 lane detection end to end lane detection through differentiable least squares fitting https arxiv org abs 1902 00293 kbd iccv 2019 kbd line cnn end to end traffic line detection with line proposal unit https doi org 10 1109 tits 2019 2890870 kbd tits 2019 kbd object like proposals detecting lane and road markings at a distance with perspective transformer layers https arxiv org abs 2003 08550 3d lld ultra fast structure aware deep lane detection https arxiv org abs 2004 11757 kbd eccv 2020 kbd lane detection a novel approach for detecting road based on two stream fusion fully convolutional network https ieeexplore ieee org document 8500551 convert camera to bev fastdraw addressing the long tail of lane detection by adapting a sequential prediction network https arxiv org abs 1905 04354 tracking retinatrack online single stage joint detection and tracking https arxiv org abs 2003 13870 kbd cvpr 2020 kbd computer vision for autonomous vehicles problems datasets and state of the art https arxiv org abs 1704 05519v2 latest update in dec 2019 simultaneous identification and tracking of multiple people using video and imus http openaccess thecvf com content cvprw 2019 papers bmtt henschel simultaneous identification and tracking of multiple people using video and cvprw 2019 paper pdf kbd cvpr 2019 kbd detect and track efficient pose estimation in videos http openaccess thecvf com content cvpr 2018 papers girdhar detect and track efficient pose cvpr 2018 paper pdf tracknet simultaneous object detection and tracking and its application in traffic video analysis https arxiv org abs 1902 01466 video action transformer network https arxiv org abs 1812 02707 kbd cvpr 2019 oral kbd online real time multiple spatiotemporal action localisation and prediction https arxiv org abs 1611 08563 kbd iccv 2017 kbd https zhuanlan zhihu com p 65177442 gnn3dmot graph neural network for 3d multi object tracking with multi feature learning https arxiv org abs 2006 07327 kbd cvpr 2020 oral kbd 3dmot cmu kris kitani chained tracker chaining paired attentive regression results for end to end joint multiple object detection and tracking https arxiv org abs 2007 14557 kbd eccv 2020 spotlight kbd mot tencent towards real time multi object tracking https arxiv org abs 1909 12605 kbd eccv 2020 kbd mot probabilistic 3d multi object tracking for autonomous driving https arxiv org abs 2001 05673 tri keypoints pose and face probabilistic face embeddings https arxiv org abs 1904 09658 kbd iccv 2019 kbd data uncertainty learning in face recognition https arxiv org abs 2003 11339 kbd cvpr 2020 kbd self supervised learning of interpretable keypoints from unlabelled videos http openaccess thecvf com content cvpr 2020 html jakab self supervised learning of interpretable keypoints from unlabelled videos cvpr 2020 paper html kbd cvpr 2020 oral kbd vgg self supervised interpretable discriminator general dl revisiting small batch training for deep neural networks https arxiv org abs 1804 07612 icml2019 workshop adaptive and multitask learning algorithms systems https icml cc conferences 2019 schedulemultitrack event 3504 kbd icml 2019 kbd adaptive scheduling for multi task learning https marcpickett com cl2018 cl 2018 paper 82 pdf kbd nips 2018 kbd nmt polar transformer networks https arxiv org abs 1709 01889 kbd iclr 2018 kbd measuring calibration in deep learning https arxiv org abs 1904 01685 kbd cvpr 2019 kbd sampling free epistemic uncertainty estimation using approximated variance propagation https arxiv org abs 1908 00598 kbd iccv 2019 kbd epistemic uncertainty making convolutional networks shift invariant again https arxiv org abs 1904 11486 kbd icml kbd using self supervised learning can improve model robustness and uncertainty https arxiv org abs 1906 12340 kbd neurips 2019 kbd understanding deep learning requires rethinking generalization https arxiv org abs 1611 03530 kbd iclr 2017 kbd iclr best paper a baseline for detecting misclassified and out of distribution examples in neural networks https arxiv org abs 1610 02136 kbd iclr 2017 kbd nll score as anomaly score unsupervised feature learning via non parametric instance level discrimination http openaccess thecvf com content cvpr 2018 cameraready 0801 pdf kbd cvpr 2018 spotlight kbd stella yu theoretical insights into the optimization landscape of over parameterized shallow neural networks https arxiv org abs 1707 04926 kbd tip 2018 kbd the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning https arxiv org abs 1712 06559 kbd icml 2018 kbd designing network design spaces https arxiv org abs 2003 13678 kbd cvpr 2020 kbd moco2 improved baselines with momentum contrastive learning https arxiv org abs 2003 04297 sgd on neural networks learns functions of increasing complexity https arxiv org abs 1905 11604 kbd nips 2019 kbd sgd learns a linear classifier first pay attention to the activations a modular attention mechanism for fine grained image recognition https arxiv org abs 1907 13075 a mixed classification regression framework for 3d pose estimation from 2d images https arxiv org abs 1805 03225 kbd bmvc 2018 kbd multi bin what s new in place activated batchnorm for memory optimized training of dnns https arxiv org abs 1712 02616 kbd cvpr 2018 kbd optimized batchnorm relu fcnn fourier convolutional neural networks http ecmlpkdd2017 ijs si papers paperid11 pdf fft as cnn visualizing the loss landscape of neural nets https papers nips cc paper 7875 visualizing the loss landscape of neural nets pdf kbd nips 2018 kbd xception deep learning with depthwise separable convolutions https arxiv org pdf 1610 02357 pdf xception multi task learning using uncertainty to weigh losses for scene geometry and semantics https arxiv org abs 1705 07115 uncertainty learning to drive from simulation without real world labels https arxiv org abs 1812 03823 kbd icra 2019 kbd domain adaptation sim2real filter response normalization layer eliminating batch dependence in the training of deep neural networks https arxiv org abs 1911 09737 kbd cvpr 2020 oral kbd switchable whitening for deep representation learning https arxiv org abs 1904 09739 kbd iccv 2019 kbd domain adaptation visual chirality https arxiv org abs 2006 09512 kbd cvpr 2020 oral kbd best paper nominee generalized odin detecting out of distribution image without learning from out of distribution data https arxiv org abs 2002 11297 kbd cvpr 2020 kbd self training with noisy student improves imagenet classification https arxiv org abs 1911 04252 kbd cvpr 2020 kbd distillation keep it simple image statistics matching for domain adaptation https arxiv org abs 2005 12551 kbd cvprw 2020 kbd domain adaptation for 2d mod bbox epipolar transformers https arxiv org abs 2005 04551 kbd cvpr 2020 kbd yihui he scalable uncertainty for computer vision with functional variational inference https arxiv org abs 2003 03396 kbd cvpr 2020 kbd epistemic uncertainty with one fwd pass mono3d 3dop 3d object proposals for accurate object class detection https papers nips cc paper 5644 3d object proposals for accurate object class detection kbd nips 2015 kbd directshape photometric alignment of shape priors for visual vehicle pose and shape estimation https arxiv org abs 1904 10097 eliminating the blind spot adapting 3d object detection and monocular depth estimation to 360 panoramic imagery https arxiv org abs 1808 06253 kbd eccv 2018 kbd monocular 3d object detection and depth estimation towards scene understanding unsupervised monocular depth estimation with semantic aware representation http openaccess thecvf com content cvpr 2019 papers chen towards scene understanding unsupervised monocular depth estimation with semantic aware representation cvpr 2019 paper pdf kbd cvpr 2019 kbd unified conditional decoder ddp dense depth posterior from single image and sparse range https arxiv org abs 1901 10034 kbd cvpr 2019 kbd augmented reality meets computer vision efficient data generation for urban driving scenes https arxiv org abs 1708 01566 kbd ijcv 2018 kbd data augmentation with ar toyota exploring the capabilities and limits of 3d monocular object detection a study on simulation and real world data https arxiv org abs 2005 07424 kbd iits kbd towards scene understanding with detailed 3d object representations https arxiv org abs 1411 5935 kbd ijcv 2014 kbd keypoint 3d bbox annotation deep cuboid detection beyond 2d bounding boxes https arxiv org abs 1611 10010 magic leap viewpoints and keypoints https arxiv org abs 1411 6067 malik lifting object detection datasets into 3d https arxiv org abs 1503 06465 pascal 3d object class detection in the wild https arxiv org abs 1503 05038 keypoint based fast single shot detection and pose estimation https arxiv org abs 1609 05590 kbd 3dv 2016 kbd ssd pose wei liu virtual kitti 2 https arxiv org abs 2001 10773 deep supervision with shape concepts for occlusion aware 3d object parsing https arxiv org abs 1612 02699 kbd cvpr 2017 kbd render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views https arxiv org abs 1505 05641 kbd iccv 2015 oral kbd real time seamless single shot 6d object pose prediction https arxiv org abs 1711 08848 kbd cvpr 2018 kbd practical deep stereo pds toward applications friendly deep stereo matching https arxiv org abs 1806 01677 kbd nips 2018 kbd disparity estimation self supervised sparse to dense self supervised depth completion from lidar and monocular camera https arxiv org abs 1807 00275 kbd icra 2019 kbd learning depth with convolutional spatial propagation network https arxiv org abs 1810 02695 baidu depth from spn kbd eccv 2018 kbd just go with the flow self supervised scene flow estimation https arxiv org abs 1912 00497 kbd cvpr 2020 oral kbd scene flow lidar online depth learning against forgetting in monocular videos http openaccess thecvf com content cvpr 2020 papers zhang online depth learning against forgetting in monocular videos cvpr 2020 paper pdf kbd cvpr 2020 kbd monodepth self supervised deep visual odometry with online adaptation https arxiv org abs 2005 06136 kbd cvpr 2020 oral kbd df vo trianflow meta learning self supervised monocular trained depth estimation using self attention and discrete disparity volume https arxiv org abs 2003 13951 kbd cvpr 2020 kbd online depth learning against forgetting in monocular videos https openaccess thecvf com content cvpr 2020 papers zhang online depth learning against forgetting in monocular videos cvpr 2020 paper pdf kbd cvpr 2020 kbd monodepth online learning sdc depth semantic divide and conquer network for monocular depth estimation https openaccess thecvf com content cvpr 2020 papers wang sdc depth semantic divide and conquer network for monocular depth estimation cvpr 2020 paper pdf kbd cvpr 2020 kbd monodepth semantic inferring distributions over depth from a single image http www contrib andrew cmu edu gengshay wordpress wp content uploads 2018 11 iros monodepth uncertainty pdf kbd tro kbd depth confidence stitching them together novel view synthesis of dynamic scenes with globally coherent depths https arxiv org abs 2004 01294 kbd cvpr 2020 kbd the edge of depth explicit constraints between segmentation and depth https arxiv org abs 2004 00171 kbd cvpr 2020 kbd xiaoming liu multimodal depth bleeding radar perception mv rss multi view radar semantic segmentation https arxiv org abs 2103 16214 kbd iccv 2021 kbd classification of objects in polarimetric radar images using cnns at 77 ghz http sci hub tw 10 1109 apmc 2017 8251453 radar polar cnns for interference mitigation and denoising in automotive radar using real world data https ml4ad github io files papers cnns 20for 20interference 20mitigation 20and 20denoising 20in 20automotive 20radar 20using 20real world 20data pdf kbd neurips 2019 kbd radar road scene understanding by occupancy grid learning from sparse radar clusters using semantic segmentation https arxiv org abs 1904 00415 kbd iccv 2019 kbd radar radarnet exploiting radar for robust perception of dynamic objects https arxiv org abs 2007 14366 kbd eccv 2020 kbd uber atg depth estimation from monocular images and sparse radar data https arxiv org abs 2010 00058 kbd iros 2020 kbd camera radar for monodepth nuscenes rpr radar camera sensor fusion for joint object detection and distance estimation in autonomous vehicles https arxiv org abs 2009 08428 kbd iros 2020 kbd radar proposal refinement warping of radar data into camera image for cross modal supervision in automotive applications https arxiv org abs 2012 12809 slam posenet a convolutional network for real time 6 dof camera relocalization https arxiv org abs 1505 07427 notes paper notes posenet md kbd iccv 2015 kbd posenet2 modelling uncertainty in deep learning for camera relocalization https arxiv org abs 1509 05909 kbd icra 2016 kbd posenet3 geometric loss functions for camera pose regression with deep learning https arxiv org abs 1704 00390 kbd cvpr 2017 kbd essnet convolutional neural network architecture for geometric matching https arxiv org abs 1703 05593 kbd cvpr 2017 kbd nc essnet neighbourhood consensus networks https arxiv org abs 1810 10510 kbd neurips 2018 kbd reinforced feature points optimizing feature detection and description for a high level task https arxiv org abs 1912 00623 kbd cvpr 2020 oral kbd eric brachmann ngransac unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints https arxiv org pdf 1802 05522 pdf kbd cvpr 2018 kbd dynslam robust dense mapping for large scale dynamic environments https siegedog com dynslam dynamic slam andreas geiger kbd icra 2018 kbd gcnv2 efficient correspondence prediction for real time slam https arxiv org abs 1902 11046 kbd lra 2019 kbd superpoint orb slam real time scalable dense surfel mapping real time scalable dense surfel mapping kbd icra 2019 kbd dense reconstruction monodepth dynamic slam the need for speed https arxiv org abs 2002 08584 gslam a general slam framework and benchmark https arxiv org abs 1902 07995 kbd iccv 2019 kbd radar perception seeing around street corners non line of sight detection and tracking in the wild using doppler radar https openaccess thecvf com content cvpr 2020 papers scheiner seeing around street corners non line of sight detection and tracking in the wild using cvpr 2020 paper pdf kbd cvpr 2020 kbd daimler radar rgb attentive fusion for robust object detection in autonomous vehicles https arxiv org abs 2008 13642 kbd icip 2020 kbd spatial attention fusion for obstacle detection using mmwave radar and vision sensor https www mdpi com 1424 8220 20 4 956 kbd sensors 2020 kbd radar camera early fusion reviews and surveys a survey on deep learning for localization and mapping towards the age of spatial machine intelligence https arxiv org abs 2006 12567 monocular depth estimation based on deep learning an overview https arxiv org abs 2003 06620 beyond perception in autonomous driving uncertainty guided multi scale residual learning using a cycle spinning cnn for single image de raining https arxiv org abs 1906 11129 kbd cvpr 2019 kbd learn to combine modalities in multimodal deep learning https arxiv org abs 1805 11730 sensor fusion general dl safe trajectory generation for complex urban environments using spatio temporal semantic corridor https arxiv org abs 1906 09788 kbd lra 2019 kbd motion planning dagger driving policy transfer via modularity and abstraction https arxiv org abs 1804 09364 kbd corl 2018 kbd dagger immitation learning efficient uncertainty aware decision making for automated driving using guided branching https arxiv org abs 2003 02746 kbd icra 2020 kbd motion planning baidu apollo em motion planner https arxiv org abs 1807 08048 calibration of heterogeneous sensor systems https arxiv org pdf 1812 11445 pdf intro sensor fusion for adas from https zhuanlan zhihu com p 40967227 up to cvpr 2018 yuvmultinet real time yuv multi task cnn for autonomous driving https arxiv org pdf 1904 05673 pdf kbd cvpr 2019 kbd real time low power deep fusion of heterogeneous sensor modalities for the advancements of adas to autonomous vehicles http sci hub tw 10 1109 vlsi dat 2018 8373245 temporal coherence for active learning in videos https arxiv org abs 1908 11757 kbd iccvw 2019 kbd active learning temporal coherence r tod real time object detector with minimized end to end delay for autonomous driving https arxiv org abs 2011 06372 kbd rtss 2020 kbd perception system design prediction and planning learning lane graph representations for motion forecasting https arxiv org abs 2007 13732 kbd eccv 2020 kbd uber atg dsdnet deep structured self driving network https arxiv org abs 2008 06041 kbd eccv 2020 kbd uber atg annotation and tooling temporal coherence for active learning in videos https arxiv org abs 1908 11757 kbd iccv 2019 workshop kbd leveraging pre trained 3d object detection models for fast ground truth generation https arxiv org abs 1807 06072 kbd itsc 2018 kbd utoronto autolabeling learning multi object tracking and segmentation from automatic annotations http openaccess thecvf com content cvpr 2020 html porzi learning multi object tracking and segmentation from automatic annotations cvpr 2020 paper html kbd cvpr 2020 kbd autolabeling canonical surface mapping via geometric cycle consistency https arxiv org abs 1907 10043 kbd iccv 2019 kbd tide a general toolbox for identifying object detection errors https arxiv org abs 2008 08115 kbd eccv 2018 kbd tools low level dl self supervised camera self calibration from video https arxiv org abs 2112 03325 tri intrinsic calibration fisheye pinhole early nlp papers a convolutional neural network for modelling sentences https arxiv org pdf 1404 2188 pdf kbd acl 2014 kbd fasttext bag of tricks for efficient text classification https arxiv org pdf 1607 01759 pdf kbd acl 2017 kbd siamese recurrent architectures for learning sentence similarity https www aaai org ocs index php aaai aaai16 paper download 12195 12023 kbd aaai 2016 kbd efficient estimation of word representations in vector space https arxiv org pdf 1301 3781 pdf kbd iclr 2013 kbd neural machine translation by jointly learning to align and translate https arxiv org pdf 1409 0473 pdf kbd iclr 2015 kbd transformers attention is all you need https arxiv org pdf 1706 03762 pdf kbd nips 2017 kbd non dl ad https github com wzhe06 ad papers umap uniform manifold approximation and projection for dimension reduction https arxiv org abs 1802 03426 notes paper notes umap md dimension reduction better than t sne technical debt review notes of classical key points and descriptors paper notes classical keypoints md crf visual slam and visual odometry https link springer com content pdf 10 1007 2fs40903 015 0032 7 pdf orb slam bundle adjustment 3d vision slam vio https zhuanlan zhihu com p 34995102 design patterns https refactoring guru design patterns python to be organized cvpr 2021 and iccv 2021 the pile to be read capturing omni range context for omnidirectional segmentation https arxiv org abs 2103 05687 kbd cvpr 2021 kbd up detr unsupervised pre training for object detection with transformers https arxiv org abs 2011 09094 kbd cvpr 2021 kbd transformers dcl dense label encoding for boundary discontinuity free rotation detection https arxiv org abs 2011 09670 kbd cvpr 2021 kbd 4d panoptic lidar segmentation https arxiv org abs 2102 12472 kbd cvpr 2021 kbd tum canonpose self supervised monocular 3d human pose estimation in the wild https arxiv org abs 2011 14679 kbd cvpr 2021 kbd fast and accurate model scaling https arxiv org abs 2103 06877 kbd cvpr 2021 kbd fair cylinder3d cylindrical and asymmetrical 3d convolution networks for lidar segmentation https arxiv org abs 2011 10033 kbd cvpr 2021 kbd lidar semantic segmentation lidar r cnn an efficient and universal 3d object detector https arxiv org abs 2103 15297 kbd cvpr 2021 kbd tusimple lidar predator registration of 3d point clouds with low overlap https arxiv org abs 2011 13005 kbd cvpr 2021 oral kbd dbb diverse branch block building a convolution as an inception like unit https arxiv org abs 2103 13425 kbd cvpr 2021 kbd repvgg acnet xiaohan ding megvii groomed nms grouped mathematically differentiable nms for monocular 3d object detection https arxiv org abs 2103 17202 kbd cvpr 2021 kbd mono3d ddmp depth conditioned dynamic message propagation for monocular 3d object detection https arxiv org abs 2103 16470 kbd cvpr 2021 kbd mono3d m3dssd monocular 3d single stage object detector https arxiv org abs 2103 13164 kbd cvpr 2021 kbd mono3d monorun monocular 3d object detection by reconstruction and uncertainty propagation https arxiv org abs 2103 12605 kbd cvpr 2021 kbd mono3d hvpr hybrid voxel point representation for single stage 3d object detection https arxiv org abs 2104 00902 kbd cvpr 2021 kbd lidar plume efficient 3d object detection from stereo images https arxiv org abs 2101 06594 yan wang uber atg v2f net explicit decomposition of occluded pedestrian detection https arxiv org abs 2104 03106 crowded pedestrian megvii ip basic in defense of classical image processing fast depth completion on the cpu https arxiv org abs 1802 00036 kbd crv 2018 kbd revisiting feature alignment for one stage object detection https arxiv org abs 1908 01570 cls reg per frame map prediction for continuous performance monitoring of object detection during deployment https arxiv org abs 2009 08650 kbd wacv 2021 kbd safetynet tsd revisiting the sibling head in object detector https arxiv org abs 2003 07540 kbd cvpr 2020 kbd sensetime cls reg 1st place solutions for openimage2019 object detection and instance segmentation https arxiv org abs 2003 07557 sensetime cls reg 1st place openimage2019 enabling spatio temporal aggregation in birds eye view vehicle estimation https cvssp org personal oscarmendez papers pdf sahaicra2021 pdf kbd icra 2021 kbd end to end lane detection through differentiable least squares fitting https arxiv org abs 1902 00293 kbd iccv workshop 2019 kbd revisiting resnets improved training and scaling strategies https arxiv org abs 2103 07579 multi modality cut and paste for 3d object detection https arxiv org abs 2012 12741 ld localization distillation for object detection https arxiv org abs 2102 12252 polytransform deep polygon transformer for instance segmentation https arxiv org abs 1912 02801 kbd cvpr 2020 kbd single stage instance segmentation road the road event awareness dataset for autonomous driving https arxiv org abs 2102 11585 lidarmtl a simple and efficient multi task network for 3d object detection and road understanding https arxiv org abs 2103 04056 lidar mtl high performance large scale image recognition without normalization https arxiv org abs 2102 06171 kbd iclr 2021 kbd ground aware monocular 3d object detection for autonomous driving https arxiv org abs 2102 00690 kbd ra l kbd mono3d demystifying pseudo lidar for monocular 3d object detection https arxiv org abs 2012 05796 mono3d pseudo labeling for scalable 3d object detection https arxiv org abs 2103 02093 waymo lla loss aware label assignment for dense pedestrian detection https arxiv org abs 2101 04307 megvii vectornet encoding hd maps and agent dynamics from vectorized representation https arxiv org abs 2005 04259 kbd cvpr 2020 kbd waymo covernet multimodal behavior prediction using trajectory sets https arxiv org abs 1911 10298 kbd cvpr 2020 kbd prediction nuscenes splitnet divide and co training https arxiv org abs 2011 14660 vovnet an energy and gpu computation efficient backbone network for real time object detection https arxiv org abs 1904 09730 kbd cvpr 2019 workshop kbd isometric neural networks non discriminative data or weak model on the relative importance of data and model resolution https arxiv org abs 1909 03205 kbd iccv 2019 workshop kbd spatial2channel tresnet https arxiv org abs 2003 13630 kbd wacv 2021 kbd spatial2channel distance iou loss faster and better learning for bounding box regression https arxiv org abs 1911 08287 kbd aaai 2020 kbd diou nms regnet designing network design spaces https arxiv org abs 2003 13678 kbd cvpr 2020 kbd fair on network design spaces for visual recognition https arxiv org abs 1905 13214 fair lane endpoint detection and position accuracy evaluation for sensor fusion based vehicle localization on highways https www ncbi nlm nih gov pmc articles pmc6308985 kbd sensors 2018 kbd lane endpoints map matching based cascade landmark detection and vehicle localization https ieeexplore ieee org stamp stamp jsp arnumber 8826538 kbd ieee access 2019 kbd lane endpoints gcnet end to end learning of geometry and context for deep stereo regression https arxiv org abs 1703 04309 kbd iccv 2017 kbd disparity estimation alex kendall cost volume traffic control gesture recognition for autonomous vehicles https arxiv org abs 2007 16072 kbd iros 2020 kbd daimler perceiving 3d human object spatial arrangements from a single image in the wild https arxiv org abs 2007 15649 kbd eccv 2020 kbd orcvio object residual constrained visual inertial odometry https arxiv org abs 2007 15107 dynamic slam very mathematical infofocus 3d object detection for autonomous driving with dynamic information modeling https arxiv org abs 2007 08556 kbd eccv 2020 kbd da4ad end to end deep attention based visual localization for autonomous driving https arxiv org abs 2003 03026 kbd eccv 2020 kbd towards lightweight lane detection by optimizing spatial embedding https arxiv org abs 2008 08311 kbd eccv 2020 workshop kbd lld multi frame to single frame knowledge distillation for 3d object detection https arxiv org abs 2009 11859 kbd eccv 2020 workshop kbd lidar deepim deep iterative matching for 6d pose estimation https openaccess thecvf com content eccv 2018 papers yi li deepim deep iterative eccv 2018 paper pdf kbd eccv 2018 kbd pose estimation monocular depth prediction through continuous 3d loss https arxiv org abs 2003 09763 kbd iros 2020 kbd multi task learning for dense prediction tasks a survey https arxiv org abs 2004 13379 mtl luc van gool dynamic task weighting methods for multi task networks in autonomous driving systems https arxiv org abs 2001 02223 kbd itsc 2020 oral kbd mtl neurall towards a unified model for visual perception in automated driving https arxiv org abs 1902 03589 kbd itsc 2019 oral kbd mtl deep evidential regression https papers nips cc paper 2020 file aab085461de182608ee9f607f3f7d18f paper pdf kbd neurips 2020 kbd one pass aleatoric epistemic uncertainty estimating drivable collision free space from monocular video http www cs toronto edu yaojian freespace pdf kbd wacv 2015 kbd drivable space visualization of convolutional neural networks for monocular depth estimation https arxiv org abs 1904 03380 kbd iccv 2019 kbd monodepth differentiable rendering a survey https arxiv org abs 2006 12057 differentiable rendering tri safenet self supervised monocular depth estimation with semantic aware feature extraction https arxiv org abs 2010 02893 monodepth semantics naver labs toward interactive self annotation for video object bounding box recurrent self learning and hierarchical annotation based framework https openaccess thecvf com content wacv 2020 papers le toward interactive self annotation for video object bounding box recurrent self learning wacv 2020 paper pdf kbd wacv 2020 kbd towards good practice for cnn based monocular depth estimation http openaccess thecvf com content wacv 2020 papers fang towards good practice for cnn based monocular depth estimation wacv 2020 paper pdf kbd wacv 2020 kbd self supervised scene de occlusion https arxiv org abs 2004 02788 kbd cvpr 2020 oral kbd tp lsd tri points based line segment detector https arxiv org abs 2009 05505 data distillation towards omni supervised learning https arxiv org abs 1712 04440 kbd cvpr 2018 kbd kaiming he fair midas towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer https arxiv org abs 1907 01341 monodepth dynamic object synthetic dataset semantics driven unsupervised learning for monocular depth and ego motion estimation https arxiv org abs 2006 04371 monodepth towards lightweight lane detection by optimizing spatial embedding https arxiv org abs 2008 08311 kbd eccv 2020 workshop kbd synthetic to real domain adaptation for lane detection https arxiv org abs 2007 04023 gm israel lld polylanenet lane estimation via deep polynomial regression https arxiv org abs 2004 10924 kbd icpr 2020 kbd polynomial lld learning universal shape dictionary for realtime instance segmentation https arxiv org abs 2012 01050 end to end video instance segmentation with transformers https arxiv org abs 2011 14503 detr transformers score cam score weighted visual explanations for convolutional neural networks https arxiv org abs 1910 01279 kbd cvpr 2020 workshop kbd when and why test time augmentation works https arxiv org abs 2011 11156 footprints and free space from a single color image https arxiv org abs 2004 06376 kbd cvpr 2020 oral kbd parking use footprint driving among flatmobiles bird eye view occupancy grids from a monocular camera for holistic trajectory planning https arxiv org abs 2008 04047 bev only predict footprint rethinking classification and localization for object detection https arxiv org abs 1904 06493 kbd cvpr 2020 kbd monocular 3d object detection with sequential feature association and depth hint augmentation https arxiv org abs 2011 14589 mono3d simple copy paste is a strong data augmentation method for instance segmentation https arxiv org abs 2012 07177 vip deeplab learning visual perception with depth aware video panoptic segmentation https arxiv org abs 2012 05258 mvsnet depth inference for unstructured multi view stereo https arxiv org abs 1804 02505 kbd eccv 2018 kbd recurrent mvsnet for high resolution multi view stereo depth inference https arxiv org abs 1902 10556 kbd cvpr 2019 kbd deep learning mvs vidar same author mvsnet artificial dummies for urban dataset augmentation https arxiv org abs 2012 08274 kbd aaai 2021 kbd detr for pedestrian detection https arxiv org abs 2012 06785 transformer pedestrian detection multi modality cut and paste for 3d object detection https arxiv org abs 2012 12741 sensetime rethinking semantic segmentation from a sequence to sequence perspective with transformers https arxiv org abs 2012 15840 transformer semantic segmenatation transpose towards explainable human pose estimation by transformer https arxiv org abs 2012 14214 transformer pose estimation seesaw loss for long tailed instance segmentation https arxiv org abs 2008 10032 swa object detection https arxiv org abs 2012 12645 stochastic weights averaging swa 3d object detection with pointformer https arxiv org abs 2012 11409 toward transformer based object detection https arxiv org abs 2012 09958 detr like boosting monocular depth estimation with lightweight 3d point fusion https arxiv org abs 2012 10296 dense sfm multi modality cut and paste for 3d object detection https arxiv org abs 2012 12741 vision global localization with semantic segmentation and interest feature points http ras papercept net images temp iros files 1899 pdf transformer interpretability beyond attention visualization https arxiv org abs 2012 09838 transformers scaling semantic segmentation beyond 1k classes on a single gpu https arxiv org abs 2012 07489 detectors detecting objects with recursive feature pyramid and switchable atrous convolution https arxiv org abs 2006 02334 empirical upper bound in object detection and more https arxiv org abs 1911 12451 generalized object detection on fisheye cameras for autonomous driving dataset representations and baseline https arxiv org abs 2012 02124 fisheye senthil yogamani monocular 3d object detection with sequential feature association and depth hint augmentation https arxiv org abs 2011 14589 mono3d sosd net joint semantic object segmentation and depth estimation from monocular images http arxiv org abs 2101 07422 jiwen lu monodepth sparse auxiliary networks for unified monocular depth prediction and completion https arxiv org abs 2103 16690 tri linformer self attention with linear complexity https arxiv org abs 2006 04768 set transformer a framework for attention based permutation invariant neural networks https arxiv org abs 1810 00825 kbd icml 2019 kbd pct point cloud transformer https arxiv org abs 2012 09688 kbd computational visual media 2021 kbd ddt unsupervised object discovery and co localization by deep descriptor transforming https arxiv org abs 1707 06397 kbd ijcai 2017 kbd hierarchical road topology learning for urban map less driving https arxiv org abs 2104 00084 mercedes probabilistic future prediction for video scene understanding https arxiv org abs 2003 06409 kbd eccv 2020 kbd alex kendall detecting 32 pedestrian attributes for autonomous vehicles https arxiv org abs 2012 02647 vru mtl cascaded deep monocular 3d human pose estimation with evolutionary training data https arxiv org abs 2006 07778 kbd cvpr 2020 oral kbd monogeo learning geometry guided depth via projective modeling for monocular 3d object detection https arxiv org abs 2107 13931 mono3d aug3d rpn improving monocular 3d object detection by synthetic images with virtual depth https arxiv org abs 2107 13269 mono3d neighbor vote improving monocular 3d object detection through neighbor distance voting https arxiv org abs 2107 02493 mono3d lite fpn for keypoint based monocular 3d object detection https arxiv org abs 2105 00268 mono3d lidar point cloud guided monocular 3d object detection https arxiv org abs 2104 09035 vision transformers for dense prediction https arxiv org abs 2103 13413 vladlen koltun intel efficient transformers a survey https arxiv org abs 2009 06732 do vision transformers see like convolutional neural networks https arxiv org abs 2108 08810 progressive coordinate transforms for monocular 3d object detection http arxiv org abs 2108 05793 mono3d autoshape real time shape aware monocular 3d object detection https arxiv org abs 2108 11127 kbd iccv 2021 kbd mono3d blazepose on device real time body pose tracking https arxiv org abs 2006 10204 todo socratic models composing zero shot multimodal reasoning with language https arxiv org abs 2204 00598 andy zeng large language models as general pattern machines https arxiv org abs 2307 04721 embodied ai retinagan an object aware approach to sim to real transfer planet learning latent dynamics for planning from pixels https arxiv org abs 1811 04551 kbd icml 2019 kbd dreamer dream to control learning behaviors by latent imagination https arxiv org abs 1912 01603 kbd iclr 2020 oral kbd dreamerv2 mastering atari with discrete world models https arxiv org abs 2010 02193 kbd iclr 2021 kbd world models dreamerv3 mastering diverse domains through world models https arxiv org abs 2301 04104 daydreamer world models for physical robot learning https arxiv org abs 2206 14176 kbd corl 2022 kbd jepa a path towards autonomous machine intelligence https openreview net pdf id bz5a1r kvsf i jepa self supervised learning from images with a joint embedding predictive architecture https arxiv org abs 2301 08243 kbd cvpr 2023 kbd magvit masked generative video transformer https arxiv org abs 2212 05199 kbd cvpr 2023 highlight kbd video prediction align your latents high resolution video synthesis with latent diffusion models https arxiv org abs 2304 08818 kbd cvpr 2023 kbd video prediction runway gen 1 structure and content guided video synthesis with diffusion models https arxiv org abs 2302 03011 language models as zero shot planners extracting actionable knowledge for embodied agents https arxiv org abs 2201 07207 kbd icml 2022 kbd learning to drive from a world on rails https arxiv org abs 2105 00636 kbd iccv 2021 oral kbd philipp kr henb hl learning from all vehicles https arxiv org abs 2203 11934 kbd cvpr 2022 kbd philipp kr henb hl end to end urban driving by imitating a reinforcement learning coach https arxiv org abs 2108 08265 kbd iccv 2021 kbd end to end autonomous driving challenges and frontiers https arxiv org abs 2306 16927 il difficulty model embedding synthetic off policy experience for autonomous driving via zero shot curricula https arxiv org abs 2212 01375 kbd corl 2022 kbd waymo decision transformer reinforcement learning via sequence modeling https arxiv org abs 2106 01345 kbd neurips 2021 kbd llm for planning lid pre trained language models for interactive decision making https arxiv org abs 2202 01771 kbd neurips 2022 kbd llm for planning planning with large language models via corrective re prompting https arxiv org abs 2211 09935 kbd neurips 2022 workshop kbd object as query equipping any 2d object detector with 3d detection ability https arxiv org abs 2301 02364 kbd iccv 2023 kbd tusimple speculative sampling accelerating large language model decoding with speculative sampling https arxiv org abs 2302 01318 accelerated llm deepmind inference with reference lossless acceleration of large language models https arxiv org abs 2304 04487 accelerated llm microsoft epsilon an efficient planning system for automated vehicles in highly interactive environments https arxiv org abs 2108 07993 kbd t ro 2021 kbd efficient uncertainty aware decision making for automated driving using guided branching https arxiv org abs 2003 02746 kbd icra 2020 kbd streampetr exploring object centric temporal modeling for efficient multi view 3d object detection https arxiv org abs 2303 11926 sscnet semantic scene completion from a single depth image https arxiv org abs 1611 08974 kbd cvpr 2017 kbd semantickitti a dataset for semantic scene understanding of lidar sequences https arxiv org abs 1904 01416 kbd iccv 2019 kbd pixpro propagate yourself exploring pixel level consistency for unsupervised visual representation learning https arxiv org abs 2011 10043 self supervised pixel wise contrastive distillation https arxiv org abs 2211 00218 self supervised vicregl self supervised learning of local visual features https arxiv org abs 2210 01571 kbd neurips 2022 kbd imagebind one embedding space to bind them all https arxiv org abs 2305 05665 kbd cvpr 2023 kbd kemp keyframe based hierarchical end to end deep model for long term trajectory prediction https arxiv org abs 2205 04624 kbd icra 2022 kbd planning deep interactive motion prediction and planning playing games with motion prediction models https arxiv org abs 2204 02392 kbd l4dc kbd planning gameformer game theoretic modeling and learning of transformer based interactive prediction and planning for autonomous driving https arxiv org abs 2303 05760 planning lookout diverse multi future prediction and planning for self driving https arxiv org abs 2101 06547 planning raquel dipp differentiable integrated motion prediction and planning with learnable cost function for autonomous driving https arxiv org abs 2207 10422 planning imitation is not enough robustifying imitation with reinforcement learning for challenging driving scenarios https arxiv org abs 2212 11419 planning waymo hierarchical model based imitation learning for planning in autonomous driving https arxiv org abs 2210 09539 kbd iros 2022 kbd planning waymo symphony learning realistic and diverse agents for autonomous driving simulation https arxiv org abs 2205 03195 kbd icra 2022 kbd planning waymo jfp joint future prediction with interactive multi agent modeling for autonomous driving https arxiv org abs 2212 08710 planning waymo maskformer per pixel classification is not all you need for semantic segmentation https arxiv org abs 2107 06278 kbd neurips 2021 kbd 3d semantic scene completion a survey https arxiv org abs 2103 07466 kbd ijcv 2022 kbd detic detecting twenty thousand classes using image level supervision https arxiv org abs 2201 02605 kbd eccv 2022 kbd atlas end to end 3d scene reconstruction from posed images https github com magicleap atlas kbd eccv 2020 kbd transformerfusion monocular rgb scene reconstruction using transformers https arxiv org abs 2107 02191 kbd neurips 2021 kbd simpleoccupancy a simple attempt for 3d occupancy estimation in autonomous driving https arxiv org abs 2303 10076 occupancy network occdepth a depth aware method for 3d semantic scene completion https arxiv org abs 2302 13540 occupancy network stereo fast bev towards real time on vehicle bird s eye view perception https arxiv org abs 2301 07870 kbd neurips 2022 kbd fast bev a fast and strong bird s eye view perception baseline https arxiv org abs 2301 12511 prophnet efficient agent centric motion forecasting with anchor informed proposals https arxiv org abs 2303 12071 kbd cvpr 2023 kbd qcraft prediction motion transformer with global intention localization and local movement refinement https arxiv org abs 2209 13508 kbd neurips 2022 oral kbd p4p conflict aware motion prediction for planning in autonomous driving https arxiv org abs 2211 01634 multipath efficient information fusion and trajectory aggregation for behavior prediction https arxiv org abs 2111 14973 vip3d end to end visual trajectory prediction via 3d agent queries https arxiv org abs 2208 01582 sam segment anything https arxiv org abs 2304 02643 fair geomim towards better 3d knowledge transfer via masked image modeling for multi view 3d understanding https arxiv org abs 2303 11325 motion prediction using trajectory sets and self driving domain knowledge https arxiv org abs 2006 04767 encode road requirement to prediction hivt hierarchical vector transformer for multi agent motion prediction transformer feed forward layers are key value memories https arxiv org abs 2012 14913 kbd emnlp 2021 kbd bev lanedet a simple and effective 3d lane detection baseline https arxiv org abs 2210 06006 kbd cvpr 2023 kbd bevnet exploring recurrent long term temporal fusion for multi view 3d perception https arxiv org abs 2303 05970 bevnet megvii vad vectorized scene representation for efficient autonomous driving https arxiv org abs 2303 12077 horizon a simple attempt for 3d occupancy estimation in autonomous driving https arxiv org abs 2303 10076 bevpoolv2 a cutting edge implementation of bevdet toward deployment https arxiv org abs 2211 17111 bevdet phigent nvradarnet real time radar obstacle and free space detection for autonomous driving http aixpaper com view nvradarnet realtime radar obstacle and free space detection for autonomous driving graspnet 1billion a large scale benchmark for general object grasping https openaccess thecvf com content cvpr 2020 papers fang graspnet 1billion a large scale benchmark for general object grasping cvpr 2020 paper pdf kbd cvpr 2020 kbd cewu lu anygrasp robust and efficient grasp perception in spatial and temporal domains https arxiv org abs 2212 08333 cewu lu point cloud forecasting as a proxy for 4d occupancy forecasting https arxiv org abs 2302 13130 hdgt heterogeneous driving graph transformer for multi agent trajectory prediction via scene encoding https arxiv org abs 2205 09753 mtr motion transformer with global intention localization and local movement refinement https arxiv org abs 2209 13508 kbd neurips 2022 kbd uvtr unifying voxel based representation with transformer for 3d object detection https arxiv org abs 2206 00630 bevfusion megvii bevnet camera lidar don t use large mini batches use local sgd https arxiv org abs 1808 07217 kbd iclr 2020 kbd grokking generalization beyond overfitting on small algorithmic datasets https arxiv org abs 2201 02177 progress measures for grokking via mechanistic interpretability understanding deep learning requires rethinking generalization https arxiv org abs 1611 03530 kbd iclr 2017 kbd unifying grokking and double descent https openreview net forum id jqthmztqwm deep interactive motion prediction and planning playing games with motion prediction models https arxiv org abs 2204 02392 kbd l4dc 2022 kbd interactive prediction and planning for autonomous driving from algorithms to fundamental aspects https escholarship org uc item 0vf4q2x1 phd thesis of wei zhan 2019 lyft1001 one thousand and one hours self driving motion prediction dataset https arxiv org abs 2006 14480 lyft level 5 prediction dataset pcaccumulation dynamic 3d scene analysis by point cloud accumulation https arxiv org abs 2207 12394 kbd eccv 2022 kbd video pretraining vpt learning to act by watching unlabeled online videos https arxiv org abs 2206 11795 kbd neurips 2022 kbd unisim a neural closed loop sensor simulator https openaccess thecvf com content cvpr2023 papers yang unisim a neural closed loop sensor simulator cvpr 2023 paper pdf kbd cvpr 2023 kbd simulation raquel geosim realistic video simulation via geometry aware composition for self driving https openaccess thecvf com content cvpr2021 papers chen geosim realistic video simulation via geometry aware composition for self driving cvpr 2021 paper pdf kbd cvpr 2023 kbd accelerating reinforcement learning for autonomous driving using task agnostic and ego centric motion skills https arxiv org abs 2209 12072 driving skill efficient reinforcement learning for autonomous driving with parameterized skills and priors https arxiv org abs 2305 04412 kbd rss 2023 kbd driving skill il difficulty model embedding synthetic off policy experience for autonomous driving via zero shot curricula https arxiv org abs 2212 01375 kbd corl 2022 kbd waymo neural map prior for autonomous driving https arxiv org abs 2304 08481 kbd cvpr 2023 kbd track anything segment anything meets videos https arxiv org abs 2304 11968 self supervised camera self calibration from video https arxiv org abs 2112 03325 kbd icra 2022 kbd tri calibration real time online video detection with temporal smoothing transformers https arxiv org abs 2209 09236 kbd eccv 2022 kbd convlstm style cross attention nerf supervised deep stereo https arxiv org abs 2303 17603 kbd cvpr 2023 kbd get3d a generative model of high quality 3d textured shapes learned from images https arxiv org abs 2209 11163 kbd neurios 2022 kbd omniobject3d large vocabulary 3d object dataset for realistic perception reconstruction and generation https arxiv org abs 2301 07525 kbd cvpr 2023 kbd ego body pose estimation via ego head pose estimation https arxiv org abs 2212 04636 kbd cvpr 2023 kbd fb occ 3d occupancy prediction based on forward backward view transformation https opendrivelab com e2ead ad23challenge track 3 nvocc pdf linkid 100000205404832 panoocc unified occupancy representation for camera based 3d panoptic segmentation https arxiv org abs 2306 10013 blip 2 bootstrapping language image pre training with frozen image encoders and large language models https arxiv org abs 2301 12597 visual instruction tuning https arxiv org abs 2304 08485 videochat chat centric video understanding https arxiv org abs 2305 06355 tot tree of thoughts deliberate problem solving with large language models https arxiv org abs 2305 10601 notes paper notes tot md cot chain of thought prompting elicits reasoning in large language models https arxiv org abs 2201 11903 cobevt cooperative bird s eye view semantic segmentation with sparse transformers https arxiv org abs 2207 02202 kbd corl 2022 kbd bevformer v2 adapting modern image backbones to bird s eye view recognition via perspective supervision https arxiv org abs 2211 10439 bevnet jifeng dai fast bev towards real time on vehicle bird s eye view perception https ml4ad github io files papers2022 fast bev 20towards 20real time 20on vehicle 20bird s eye 20view 20perception pdf kbd neurips 2022 kbd traj human trajectory forecasting in crowds a deep learning perspective https arxiv org abs 2007 03639 kbd tits 2021 kbd data driven prediction architecture for autonomous driving and its application on apollo platform https arxiv org abs 2006 06715 kbd iv 2020 kbd baidu thomas trajectory heatmap output with learned multi agent sampling https arxiv org abs 2110 06607 kbd iclr 2022 kbd learning lane graph representations for motion forecasting https arxiv org abs 2007 13732 kbd eccv 2020 oral kbd identifying driver interactions via conditional behavior prediction https arxiv org abs 2104 09959 kbd icra 2021 kbd waymo trajectron dynamically feasible trajectory forecasting with heterogeneous data https arxiv org abs 2001 03093 kbd eccv 2020 kbd tpnet trajectory proposal network for motion prediction https arxiv org abs 2004 12255 kbd cvpr 2020 kbd gohome graph oriented heatmap output for future motion estimation https arxiv org abs 2109 01827 pecnet it is not the journey but the destination endpoint conditioned trajectory prediction https arxiv org abs 2004 02025 kbd eccv 2020 oral kbd from goals waypoints paths to long term human trajectory forecasting https arxiv org abs 2012 01526 kbd iccv 2019 kbd precog prediction conditioned on goals in visual multi agent settings https arxiv org abs 1905 01296 kbd iccv 2019 kbd pip planning informed trajectory prediction for autonomous driving https arxiv org abs 2003 11476 kbd eccv 2020 kbd multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction https arxiv org abs 1910 05449 kbd corl 2019 kbd lapred lane aware prediction of multi modal future trajectories of dynamic agents https arxiv org abs 2104 00249 kbd cvpr 2021 kbd prime learning to predict vehicle trajectories with model based planning https arxiv org abs 2103 04027 kbd corl 2021 kbd a flexible and explainable vehicle motion prediction and inference framework combining semi supervised aog and st lstm https dl acm org doi abs 10 1109 tits 2020 3016304 kbd tits 2020 kbd multi modal trajectory prediction of surrounding vehicles with maneuver based lstms https arxiv org abs 1805 05499 kbd iv 2018 kbd trivedi hyper learned hybrid trajectory prediction via factored inference and adaptive sampling https arxiv org abs 2110 02344 kbd icra 2022 kbd trajectory prediction with linguistic representations https arxiv org abs 2110 09741 kbd icra 2022 kbd what if motion prediction for autonomous driving https arxiv org abs 2008 10587 end to end contextual perception and prediction with interaction transformer https arxiv org abs 2008 05927 kbd iros 2020 kbd auxiliary collision loss scene compliant pred safecritic collision aware trajectory prediction https arxiv org abs 1910 06673 kbd bmvc 2019 kbd irl scene compliant pred large scale interactive motion forecasting for autonomous driving the waymo open motion dataset https arxiv org abs 2104 10133 kbd iccv 2021 kbd waymo interaction based trajectory prediction over a hybrid traffic graph https arxiv org abs 2009 12916 kbd iros 2020 kbd joint interaction and trajectory prediction for autonomous driving using graph neural networks https arxiv org abs 1912 07882 kbd neurips 2019 workshop kbd fast risk assessment for autonomous vehicles using learned models of agent futures https arxiv org abs 2005 13458 kbd robotics science and systems 2020 kbd monocular 3d object detection an extrinsic parameter free approach https arxiv org abs 2106 15796 kbd cvpr 2021 kbd pjlab uniformer unified multi view fusion transformer for spatial temporal representation in bird s eye view https arxiv org abs 2207 08536 bevformer bevnet temporal gitnet geometric prior baesd transformation for birds yee view segmentation wbf weighted box fusion ensembling boxes from differnt object detection modules nni auto parameter finding algorithm bevformer improving bevformer for 3d camera only object detection https storage googleapis com waymo uploads files research 3dcam 3dcam bevformer pdf waymo open dataset challenge 1st place in mono3d let 3d ap longitudinal error tolerant 3d average precision for camera only 3d detection https arxiv org abs 2206 07705 waymo open dataset challenge official metric high level interpretation of urban road maps fusing deep learning based pixelwise scene segmentation and digital navigation maps https pdfs semanticscholar org 44ac 01c0d356f22e7ee883f8e4ac2cccf199f68d pdf kbd journal of advanced transportation 2018 kbd a hybrid vision map method for urban road detection https downloads hindawi com journals jat 2017 7090549 pdf kbd journal of advanced transportation 2017 kbd terminology and analysis of map deviations in urban domains towards dependability for hd maps in automated vehicles https www researchgate net profile christopher plachetka publication 348367176 terminology and analysis of map deviations in urban domains towards dependability for hd maps in automated vehicles links 607d523f907dcf667babc06b terminology and analysis of map deviations in urban domains towards dependability for hd maps in automated vehicles pdf kbd iv 2020 kbd time will tell new outlooks and a baseline for temporal multi view 3d object detection https arxiv org abs 2210 02443 conditional detr for fast training convergence https arxiv org abs 2108 06152 kbd iccv 2021 kbd dab detr dynamic anchor boxes are better queries for detr https arxiv org abs 2201 12329 kbd iclr 2022 kbd dn detr accelerate detr training by introducing query denoising https arxiv org abs 2203 01305 kbd cvpr 2022 kbd dino detr with improved denoising anchor boxes for end to end object detection https arxiv org abs 2203 03605 trajectory forecasting from detection with uncertainty aware motion encoding https arxiv org abs 2202 01478 ouyang wanli vision based uneven bev representation learning with polar rasterization and surface estimation https arxiv org abs 2207 01878 bevnet polar mutr3d a multi camera tracking framework via 3d to 2d queries https arxiv org abs 2205 00613 bevnet tracking kbd cvpr 2022 workshop kbd hang zhao st p3 end to end vision based autonomous driving via spatial temporal feature learning https arxiv org abs 2207 07601 kbd eccv 2022 kbd hongyang li gkt efficient and robust 2d to bev representation learning via geometry guided kernel transformer https arxiv org abs 2206 04584 bevnet horizon siamrpn high performance visual tracking with siamese region proposal network https yan junjie github io publication dblp confcvpr li ywzh 18 dblp confcvpr li ywzh 18 pdf kbd cvpr 2018 kbd tplr topology preserving local road network estimation from single onboard camera image https arxiv org abs 2112 10155 kbd cvpr 2022 kbd stsu luc van gool lara latents and rays for multi camera bird s eye view semantic segmentation https arxiv org abs 2206 13294 valeo bevnet polar polardetr polar parametrization for vision based surround view 3d detection https arxiv org abs 2206 10965 bevnet exploring geometric consistency for monocular 3d object detection https arxiv org abs 2104 05858 kbd cvpr 2022 kbd imvoxelnet image to voxels projection for monocular and multi view general purpose 3d object detection https arxiv org abs 2106 01178 kbd wacv 2022 kbd mono3d learning to predict 3d lane shape and camera pose from a single image via geometry constraints https arxiv org abs 2112 15351 kbd aaai 2022 kbd detecting lane and road markings at a distance with perspective transformer layers https ieeexplore ieee org document 9294383 kbd icicn 2021 kbd bevnet lane line unsupervised labeled lane markers using maps https openaccess thecvf com content iccvw 2019 papers cvrsuad behrendt unsupervised labeled lane markers using maps iccvw 2019 paper pdf kbd iccv 2019 workshop kbd bosch 2d lane line m3detr multi representation multi scale mutual relation 3d object detection with transformers https arxiv org abs 2104 11896 lidar detection waymo open dataset kbd wacv 2022 kbd k lane lidar lane dataset and benchmark for urban roads and highways https arxiv org abs 2110 11048 lane line dataset robust monocular 3d lane detection with dual attention https ieeexplore ieee org abstract document 9506296 kbd icip 2021 kbd occlusionfusion occlusion aware motion estimation for real time dynamic 3d reconstruction https arxiv org abs 2203 07977 kbd cvpr 2022 kbd mobilevit light weight general purpose and mobile friendly vision transformer https arxiv org abs 2110 02178 kbd iclr 2022 kbd lightweight transformers xformer lightweight vision transformer with cross feature attention https arxiv org abs 2207 07268 samsung centerformer center based transformer for 3d object detection https arxiv org abs 2209 05588 kbd eccv 2022 oral kbd tusimple lidarmultinet towards a unified multi task network for lidar perception https arxiv org abs 2209 09385 2022 waymo open dataset tusimple mtra 1st place solution for 2022 waymo open dataset challenge motion prediction https storage googleapis com waymo uploads files research motionpred motionprediction mtra pdf waymo open dataset challenge 1st place in motion prediction bevsegformer bird s eye view semantic segmentation from arbitrary camera rigs https arxiv org abs 2203 04050 bevnet panoptic segformer delving deeper into panoptic segmentation with transformers https arxiv org abs 2109 03814 kbd cvpr 2022 kbd nvidia efficiently identifying task groupings for multi task learning https arxiv org abs 2109 04617 kbd neurips 2021 spotlight kbd mtl model soups averaging weights of multiple fine tuned models improves accuracy without increasing inference time https arxiv org abs 2203 05482 google golden backbone the pedestrian next to the lamppost adaptive object graphs for better instantaneous mapping https arxiv org abs 2204 02944 kbd cvpr 2022 kbd gitnet geometric prior based transformation for birds eye view segmentation https arxiv org abs 2204 07733 bevnet baidu futr3d a unified sensor fusion framework for 3d detection https arxiv org abs 2203 10642 hang zhao gitnet geometric prior based transformation for birds eye view segmentation https arxiv org abs 2204 07733 bevnet monoformer towards generalization of self supervised monocular depth estimation with transformers https arxiv org abs 2205 11083 monodepth time3d end to end joint monocular 3d object detection and tracking for autonomous driving https arxiv org abs 2205 14882 cosformer rethinking softmax in attention https arxiv org abs 2202 08791 kbd iclr 2022 kbd stretchbev stretching future instance prediction spatially and temporally https arxiv org abs 2203 13641 bevnet prediction scene representation in bird s eye view from surrounding cameras with transformers https openaccess thecvf com content cvpr2022w wad papers zhao scene representation in birds eye view from surrounding cameras with transformers cvprw 2022 paper pdf bevnet lld kbd cvpr 2022 workshop kbd multi frame self supervised depth with transformers https arxiv org abs 2204 07616 kbd cvpr 2022 kbd it s about time analog clock reading in the wild https arxiv org abs 2111 09162 kbd cvpr 2022 kbd andrew zisserman surrounddepth entangling surrounding views for self supervised multi camera depth estimation https arxiv org abs 2204 03636 kbd corl 2022 kbd jiwen lu once 3dlanes building monocular 3d lane detection https arxiv org abs 2205 00301 kbd cvpr 2022 kbd k lane lidar lane dataset and benchmark for urban roads and highways https arxiv org abs 2110 11048 kbd cvpr 2022 workshop kbd 3d lld multi modal 3d human pose estimation with 2d weak supervision in autonomous driving https arxiv org abs 2112 12141 kbd cvpr 2022 workshop kbd a simple baseline for bev perception without lidar https arxiv org abs 2206 07959 tri bevnet vision radar reconstruct from top view a 3d lane detection approach based on geometry structure prior https openaccess thecvf com content cvpr2022w wad papers li reconstruct from top view a 3d lane detection approach based cvprw 2022 paper pdf kbd cvpr 2022 workshop kbd riddle lidar data compression with range image deep delta encoding https arxiv org abs 2206 01738 kbd cvpr 2022 kbd waymo charles qi occupancy flow fields for motion forecasting in autonomous driving https arxiv org abs 2203 03875 kbd ral 2022 kbd waymo occupancy flow challenge safe local motion planning with self supervised freespace forecasting https openaccess thecvf com content cvpr2021 papers hu safe local motion planning with self supervised freespace forecasting cvpr 2021 paper pdf kbd cvpr 2021 kbd auto labeling https zhuanlan zhihu com p 533907821 k lane lidar lane dataset and benchmark for urban roads and highways https arxiv org abs 2110 11048 letr line segment detection using transformers without edges https arxiv org abs 2101 01909 kbd cvpr 2021 oral kbd hdmapgen a hierarchical graph generative model of high definition maps https openaccess thecvf com content cvpr2021 papers mi hdmapgen a hierarchical graph generative model of high definition maps cvpr 2021 paper pdf kbd cvpr 2021 kbd hd mapping sketchrnn a neural representation of sketch drawings https arxiv org abs 1704 03477 david ha polygen an autoregressive generative model of 3d meshes https arxiv org abs 2002 10880 kbd icml 2020 kbd solq segmenting objects by learning queries https arxiv org abs 2106 02351 kbd neurlps 2021 kbd megvii end to end instance segmentation monovit self supervised monocular depth estimation with a vision transformer https arxiv org abs 2208 03543 kbd 3dv 2022 kbd mvster epipolar transformer for efficient multi view stereo https arxiv org abs 2204 07346 kbd eccv 2022 bd movedepth crafting monocular cues and velocity guidance for self supervised multi frame depth learning https arxiv org abs 2208 09170 mvs monodepth surrounddepth entangling surrounding views for self supervised multi camera depth estimation https arxiv org abs 2204 03636 scene transformer a unified architecture for predicting multiple agent trajectories https arxiv org abs 2106 08417 prediction waymo kbd iclr 2022 kbd ssia monocular depth estimation with self supervised instance adaptation https arxiv org abs 2004 05821 vgg team ttr test time refinement cvd comoda continuous monocular depth adaptation using past experiences https openaccess thecvf com content wacv2021 papers kuznietsov comoda continuous monocular depth adaptation using past experiences wacv 2021 paper pdf kbd wacv 2021 kbd monorec semi supervised dense reconstruction in dynamic environments from a single moving camera https arxiv org abs 2011 11814 kbd cvpr 2021 kbd daniel cremmers plenoxels radiance fields without neural networks https arxiv org abs 2112 05131 lidar with velocity motion distortion correction of point clouds from oscillating scanning lidars https arxiv org abs 2111 09497 livox isee nwd a normalized gaussian wasserstein distance for tiny object detection https arxiv org abs 2110 13389 towards optimal strategies for training self driving perception models in simulation https arxiv org abs 2111 07971 kbd neurips 2021 kbd sanja fidler insta dm learning monocular depth in dynamic scenes via instance aware projection consistency https arxiv org abs 2102 02629 kbd aaai 2021 kbd instance wise depth and motion learning from monocular videos https arxiv org abs 1912 09351 kbd neurips 2020 workshop kbd website https sites google com site seokjucv home instadm nerf representing scenes as neural radiance fields for view synthesis https arxiv org abs 2003 08934 kbd eccv 2020 oral kbd barf bundle adjusting neural radiance fields https arxiv org abs 2104 06405 kbd iccv 2021 oral kbd nerfingmvs guided optimization of neural radiance fields for indoor multi view stereo https arxiv org abs 2109 01129 kbd iccv 2021 oral kbd transfuser multi modal fusion transformer for end to end autonomous driving https arxiv org abs 2104 09224 kbd cvpr 2021 kbd yolino generic single shot polyline detection in real time https arxiv org abs 2103 14420 kbd iccv 2021 workshop kbd lld monorcnn geometry based distance decomposition for monocular 3d object detection https arxiv org abs 2104 03775 kbd iccv 2021 kbd monocinis camera independent monocular 3d object detection using instance segmentation https arxiv org abs 2110 00464 kbd iccv 2021 workshop kbd pv rcnn point voxel feature set abstraction for 3d object detection https arxiv org abs 1912 13192 kbd cvpr 2020 kbd waymo challenge 2nd place geometry based distance decomposition for monocular 3d object detection https arxiv org abs 2104 03775 kbd iccv 2021 kbd mono3d offboard 3d object detection from point cloud sequences https arxiv org abs 2103 05073 kbd cvpr 2021 kbd charles qi freeanchor learning to match anchors for visual object detection https arxiv org abs 1909 02466 kbd neurips 2019 kbd autoassign differentiable label assignment for dense object detection https arxiv org abs 2007 03496 probabilistic anchor assignment with iou prediction for object detection https arxiv org abs 2007 08103 kbd eccv 2020 kbd fovea foveated image magnification for autonomous navigation https arxiv org abs 2108 12102 kbd iccv 2021 kbd argo pifpaf composite fields for human pose estimation https arxiv org abs 1903 06593 kbd cvpr 2019 kbd monocular 3d localization of vehicles in road scenes https avvision xyz iccv21 papers 1 cameraready 01 pdf kbd iccv 2021 workshop kbd mono3d tracking transformerfusion monocular rgb scene reconstruction using transformers https arxiv org abs 2107 02191 conditional detr for fast training convergence https arxiv org abs 2108 06152 anchor detr query design for transformer based detector https arxiv org abs 2109 07107 megvii pgd probabilistic and geometric depth detecting objects in perspective https arxiv org abs 2107 14160 kbd corl 2021 kbd adaptive wing loss for robust face alignment via heatmap regression https arxiv org abs 1904 07399 what makes for end to end object detection https proceedings mlr press v139 sun21b html kbd pmlr 2021 kbd instances as queries https arxiv org abs 2105 01928 kbd iccv 2021 kbd instance segmentation one million scenes for autonomous driving once dataset https arxiv org abs 2106 11037 huawei nvs monodepth improving monocular depth prediction with novel view synthesis https arxiv org abs 2112 12577 kbd 3dv 2021 kbd is 2d heatmap representation even necessary for human pose estimation https arxiv org abs 2107 03332 topology preserving local road network estimation from single onboard camera image https arxiv org abs 2112 10155 bevnet luc van gool | deep-learning paper literature-review machine-learning computer-vision cnn paper-reading paper-review reinforcement-learning medical medical-imaging point-cloud 3d-object-detection 3d-object-recognition | ai |
Real-Time-Embedded-System-Repo | real time embedded system repo labs and project files for real time and embedded systems design course | embedded embedded-systems real-time realtime | os |
Myprojects | myprojects advanced information communications technology | server |
|
starter | graphile starter take it for a spin we re running the starter at https graphile starter herokuapp com feel free to register an account and have a poke around as you see fit note emails are sent from graphile starter so please only enter email addresses you control note every time we merge to master we ship a new version of graphile starter to heroku and entirely wipe the database so your data may not persist if you wish to delete your data before this time you can do so via the delete account feature baked into the starter not for beginners we do not advise that you build your own projects on top of this project until you re comfortable with the various tools it uses node js https nodejs org en docs express https expressjs com en api html postgresql https www postgresql org docs current index html graphql https graphql org learn postgraphile https www graphile org postgraphile introduction graphile worker https github com graphile worker graphile migrate https github com graphile migrate typescript https www typescriptlang org docs react https reactjs org docs getting started html apollo client for react https www apollographql com docs react graphql code generator https github com dotansimha graphql code generator eslint https eslint org prettier https prettier io docs en index html jest https jestjs io cypress https www cypress io etc this is an advanced project with deeply integrated tooling across the full stack the project is called starter because it helps you to start new projects with all these technologies tools and techniques already in place if you re not already familiar with these things then you ll probably find the project overwhelming it is not intended to be your first experience of any of these tools if you re just getting started with postgraphile before you dive into this project make sure you check out the postgraphile required knowledge https www graphile org postgraphile required knowledge and especially the schema design tutorial https www graphile org postgraphile postgresql schema design this repository takes a slightly different approach to schema design than the aforementioned tutorial but it s still an incredibly valuable resource purpose graphile starter is an opinionated quick start project for full stack application development in react node js graphql and postgresql it includes the foundations of a modern web application with a full user registration system organizations e g teams companies etc session management optimized job queue a significant amount of pre configured tooling tests both end to end and more granular and much more it is suitable for building projects both large and small with a focus on productivity you might use it to go from conception to launch of a web app during a hack day as the foundation for client projects at your web agency to build your side hustle without spending lots of time on boilerplate to build a saas project to help fund your open source work however you use it the project can be deployed to many platforms and can be scaled to larger audiences both horizontally and vertically with very few changes please note that this software is not complete free of software defects or free of security issues it is not a finished solution but rather the seed of a solution which you should review customize fix and develop further it is intended that you use a point in time version of this software it is not intended that you can merge updates to this software into your own derivative in an automated fashion sponsors begin crowd funded open source software please donate take this software and use it as the starting point to build your project go make some money and give something back https graphile org sponsor to support us building more tools and kits for the node graphql and postgresql ecosystems we have made this project available under the simple and liberal mit license to give you to a huge amount of freedom in how you use it but this isn t possible without the help of our wonderful sponsors we need more people to join our sponsors so we can continue to bring about awesome projects like this we d love to spend more time on open source building tools that will save you and others even more time and money please sponsor our open source efforts click here to find out more about sponsors and sponsorship https www graphile org sponsor and please give some love to our featured sponsors table tr td align center a href https www the guild dev img src https graphile org images sponsors theguild png width 90 height 90 alt the guild br the guild a td td align center a href https dovetailapp com img src https graphile org images sponsors dovetail png width 90 height 90 alt dovetail br dovetail a td td align center a href https www netflix com img src https graphile org images sponsors netflix png width 90 height 90 alt netflix br netflix a td td align center a href img src https graphile org images sponsors chadf png width 90 height 90 alt chad furman br chad furman a td tr tr td align center a href https stellate co img src https graphile org images sponsors stellate png width 90 height 90 alt stellate br stellate a td td align center a href https www accenture com img src https graphile org images sponsors accenture svg width 90 height 90 alt accenture br accenture a td td align center a href https microteam io img src https graphile org images sponsors micro png width 90 height 90 alt we love micro br we love micro a td tr table em sponsors the entire graphile suite em sponsors end table of contents graphile starter graphile starter take it for a spin take it for a spin not for beginners not for beginners purpose purpose crowd funded open source software crowd funded open source software click here to find out more about sponsors and sponsorship click here to find out more about sponsors and sponsorship table of contents table of contents features features variants variants prerequisites prerequisites local development local development docker development docker development getting started getting started running running making it yours making it yours docker development docker development 1 building the production docker image building the production docker image production build for local mode production build for local mode deploying to heroku deploying to heroku cleanup cleanup custom packages custom packages mit license mit license features graphile starter is a full stack graphql https graphql org learn and react https reactjs org project with server side rendering ssr and routing thanks to next js https nextjs org the backend is a beautiful pairing of node js and postgresql running on express js enabled by postgraphile https www graphile org postgraphile in library mode the frontend uses the antd https ant design design framework to accelerate development the entire stack is written in typescript with auto generated graphql types and operations thanks to graphql code generator https github com dotansimha graphql code generator there are four tenets to graphile starter speedy development batteries included type safety best practices graphile starter is easy to start and everything is pre configured as much as possible speedy development hot reloading easy debugging graphile s idempotent migration system https github com graphile migrate job queue technical decisions md job queue and server middleware ready to use not to mention deep integration with vscode should you use that editor plugin recommendations pre configured settings eslint and prettier integration and debugging profiles batteries included full user system and oauth antd design framework jest and cypress end to end technical decisions md cypress e2e tests testing security email templating and transport pre configured linting and code formatting deployment instructions and more type safety pre configured type checking strongly typed throughout with typescript best practices react graphql postgraphile node jest and cypress best practices see technical decisions md technical decisions md for a more detailed list of features included and the technical decisions behind them variants since this is a highly opinionated starter community members may have slightly different opinions and may choose to maintain forks of this project that apply their own opinions a few of these are listed below if you maintain a fork of this project please make a note at the top of your own readme and add it to this list vue 3 vite ssr variant https github com xvaara graphile starter vite ssr vue3 apollo replaces next js with vue and vite ssr nuxt js variant https github com joeschr graphile starter replaces next js for vue users create react app variant https github com alexk111 graphile starter cra replaces next js for apps without server side rendering remix variant https github com fnimick graphile starter remix or sveltekit replaces next js with remix sveltekit variant https github com fnimick graphile starter remix or sveltekit tree sveltekit replaces next js with sveltekit variants are not officially supported and may become out of date or unmaintained over time if you have issues with variants please submit issues or prs to the projects in question not to this project prerequisites you can either work with this project locally directly on your machine or use a pre configured docker environment we ll differentiate this in the readme with a table like this one local mode or docker mode command for local development or command for docker compose development be careful not to mix and match docker mode vs local mode for development you should make a choice and stick to it developing locally but deploying with production docker is absolutely fine important if you choose the docker mode be sure to read docker readme md docker readme md for users of visual studio code vscode a vscode folder is included with editor settings and debugger settings provided plus a list of recommended extensions should you need it there is also a devcontainer folder which enables you to use vscode s remote containers https code visualstudio com docs remote containers giving you a local like development experience whilst still using docker containers local development requires node js v16 must be installed postgresql v10 server must be available pg dump command must be available or you can remove this functionality vscode is recommended but any editor will do this software has been developed under mac and linux and should work in a bash environment windows users making a project like graphile starter run smoothly on windows can be a challenge joeschr and hips on the graphile discord http discord gg graphile have been working in improving this and they re pretty pleased with the result but you may still get some teething problems prs to fix windows compatibility issues are welcome please keep them small failing that try the docker mode docker development requires docker https docs docker com install docker compose https docs docker com compose install ensure you ve allocated docker at least 4gb of ram significantly more recommended development only production is much more efficient has been tested on windows and linux ubuntu 18 04lts getting started this project is designed to work with yarn if you don t have yarn installed you can install it with npm install g yarn the docker setup already has yarn npm installed and configured to get started please run yarn followed by local mode or docker mode yarn setup or export uid yarn docker setup this command will lead you through the necessary steps and create a env file for you containing your secrets note export uid is really important on linux docker hosts otherwise the files and folders created by docker will end up owned by root which is non optimal we recommend adding export uid to your profile or bashrc or similar so you don t have to remember it do not commit env to version control running you can bring up the stack with local mode or docker mode yarn start or export uid yarn docker start after a short period you should be able to load the application at http localhost 5678 this main command runs a number of tasks uses graphile migrate https github com graphile migrate to watch the migrations current sql file for changes and automatically runs it against your database when it changes watches the typescript source code of the server and compiles it from app src to app dist so node graphile worker etc can run the compiled code directly runs the node server includes postgraphile and next js middleware runs graphile worker to execute your tasks e g sending emails watches your graphql files and your postgraphile schema for changes and generates your typescript react hooks for you automatically leading to strongly typed code with minimal effort runs the jest tests in watch mode automatically re running as the database or test files change note docker compose up server also runs the postgresql server that the system connects to you may also choose to develop locally but use the postgresql server via docker compose up d db then for development you may need a console you can open one with local mode or docker mode bash or export uid yarn docker bash to shut everything down local mode or docker mode ctrl c or export uid yarn docker down making it yours 1 download and extract a zip of the latest release from github https github com graphile starter releases 1 in that folder run git init git add git commit m graphile starter base 1 change the project name in package json 1 change the project settings in app config src index ts 1 replace the readme md file 1 add your own copyright notices to the license md file 1 commit as you usually would 1 show your appreciation with sponsorship https www graphile org sponsor docker development be sure to read docker readme md docker readme md building the production docker image to build the production image use docker build as shown below you should supply the root url build variable which will be baked into the client code so cannot be changed as envvars if you don t then the defaults will apply which likely will not be suitable to build the worker pass target worker instead of the default target server sh docker build file production dockerfile build arg root url http localhost 5678 build arg target server when you run the image you must pass it the relevant environmental variables for example sh docker run rm it init p 5678 5678 e graphile license graphile license e secret secret e jwt secret jwt secret e database visitor database visitor e database url database url e auth database url auth database url e github key github key e github secret github secret docker image id here currently if you miss required envvars weird things will happen we don t currently have environment validation prs welcome note if you are using the production dockerfile to run graphile starter in a docker container on eg kubernetes aws ecs digitalocean app platform or similar and you are trying to connect to amazon rds or digitalocean databases or probably other managed databases make sure to replace data amazon rds ca cert pem with the ca certificate of your own database this file is copied into your docker image during build time and can therefore be referenced in your env variables database url and auth database url database url postgres name password host port dbname ssl true sslrootcert app data amazon rds ca cert pem production build for local mode use yarn run build to generate a production build of the project deploying to heroku prerequisites install the heroku cli https devcenter heroku com articles heroku cli if you are using graphile migrate make sure that you have executed graphile migrate commit to commit all your database changes since we only run committed migrations in production make sure you have customized app config src index ts make sure everything is committed and pushed in git set up a database server we recommend using amazon rds once your database server is running you can use our heroku setup script to automate the setup process this script does the following creates the heroku app adds the redis extension to this heroku app creates the database in the database server creates the relevant roles generating random passwords for them installs some common database extensions sets the heroku config variables adds the heroku app as a git remote named heroku pushes the master branch to heroku to perform your initial build create a copy of heroku setup template and rename the copy to heroku setup then edit it and customize the settings at the top we also recommend reading through the script and customizing it as you see fit particularly if you are using additional extensions that need installing now run the script bash heroku setup hopefully all has gone well if not step through the remaining tasks in the heroku setup script and fix each task as you go we ve designed the script so that if your superuser credentials are wrong or the heroku app already exists you can just edit the settings and try again all other errors will probably need manual intervention verbosity is high so you can track exactly what happened the server should be up and running now be sure to access it over https otherwise you will not be able to run graphql queries but it is not yet capable of sending emails to achieve this you must configure an email transport we have pre configured support for amazon ses once ses is set up your domain is verified and you ve verified any emails you wish to send email to or have had your sending limits removed make sure that the fromemail in app config src index ts is correct and then create an iam role for your postgraphile server here s an iam template for sending emails this is the only permission required for our iam role currently but you may wish to add others later version 2012 10 17 statement effect allow action ses sendrawemail resource generate an access key for this iam role and then tell heroku the access key id and secret heroku config set aws access key id aws secret access key a app name now you can tell heroku to run the worker process as well as the currently running web process heroku ps scale worker 1 a app name when you register an account on the server you should receive a verification email containing a clickable link when you click the link your email will be verified and thanks to graphql subscriptions the previous tab should be updated to reflect that your account is now verified you can also configure your application for social login this works the same as in development except the callback url will be different something like https my heroku app name herokuapp com auth github callback set the github oauth secrets on your heroku app to trigger a restart and enable social login heroku config set github key github secret a app name cleanup to delete the heroku app heroku apps destroy a app name to delete the database roles replace dbname with your database name drop database dbname drop role dbname visitor drop role dbname authenticator drop role dbname custom packages when running yarn setup this command will also invoke yarn workspaces foreach run setup this allows you to add custom setup hooks necessary for your individual packages add a line like the following to your scripts section in your package json setup npm i g some package mit license this is open source software you may use modify and distribute it under the terms of the mit license see graphile starter license md graphile starter license md | starter postgraphile graphql node nodejs apollo-client eslint typescript job-queue postgresql graphile saas saas-boilerplate | os |
template-based-3d-reconstruction | key novelties 27 06 2017 simulated annealing user has two options focal length is known or random at the beginning control variables are set in reconstructor init in the second case user has to specify range of focal length there is no need to specify exact value of the focal length algorithm is parameter less in this case quality is a little bit worse but it s still parameter less in both cases there was implemented additional optimisation step of focal length using heuristic method simulated annealing from now on focal length is adjusted using randomly picked values in adjustable manner not just changed arbitrarly according to displacement of points during reprojection as it was before 19 06 2017 focal adjustment i presented novelty in adapted 3d reconstruction algorithm epfl there are algorithms that optimise extrinsic camera parameters but in the knowledge of authors noone tried to estimate the intrinsic parameters in unconstrained reconstruction step i track displacement of reprojected points in each iteration if the displacement in two successive iterations shows that the point is closer to the middle of the image i assume that focal length is a little bit too long somehow the distance from camera is expressed as a function of focal length for better reprojection in next iteration i adjust the focal length and make it smaller otherwise this parameter is enlarged 23 05 2017 simulated local deformation descriptor robust to out of plane rotations by performing similar approach as in asift article simulated local deformation i used opencv implementation of sift how does it work find keypoints and descriptors on reference frame simulated local deformation add a stack of descriptors for each keypoint using perspective transformations that is why we need chessboard files under variety of rotations detect and describe keypoints on test frame perform matching descriptors from test frame with stac of descriptors from reference image improved matching process profit used ideas robust to in plane rotation algorithm asift http www cmap polytechnique fr yu research asift demo html adapted 3d reconstruction algorithm http cvlab epfl ch files content sites cvlab2 files publications publications 2012 ostlundvf12 pdf howto build build using standard procedure mkdir build cd build cmake make in build create chessboard txt file where are listed all images needed for simulated local deformation if the size of chessboard patter is other than 9x6 change it in void kpmatcher findcorners paste to build folder all files from dataset epfl cam ext cam intr cam tdir controlpointids txt im corners txt mesh pts mesh tri webcam intr world corners txt usage affinedsc path to model png path to frame png detector descriptor ratio1 ratio2 ispointcloudsaved 0 1 results simulated local deformation sift was used for evaluation purposes all matches found all matches found https raw githubusercontent com mbed92 asiftplusplus master pc 056 80 90 sift sift all png displacement of added keypoints displacement of improved keypoints https raw githubusercontent com mbed92 asiftplusplus master pc 056 80 90 sift sift disp png position of added keypoints added matches https raw githubusercontent com mbed92 asiftplusplus master pc 056 80 90 sift sift imp png milestones 09 03 17 adapted epfl code for my own purposes 23 05 17 simulated local deformation stable version 19 06 17 focal adjustment added few minor improvements 27 06 17 focal adjustment using simulated annealing algorithm 05 07 17 code refactoring | ai |
|
STM32FreeRTOS | stm32 freertos library for arduino this is a port of freertos for stm32 as arduino libraries for more information about freertos visit the freertos web site http www freertos org freertos also see the very useful getting started http www freertos org freertos quick start guide html quick start guide page this library provides 2 freertos versions 9 x http www freertos org freertos v9 html 10 x http www freertos org freertos v10 html 10 0 1 https github com stmicroelectronics stm32 mw freertos releases tag v10 0 1 10 2 1 https github com stmicroelectronics stm32 mw freertos releases tag v10 2 1 10 3 1 https github com stmicroelectronics stm32 mw freertos releases tag v10 3 1 each have been modified by st see st readme txt in freertos source these are the same provided with the stm32cube mcu packages http www st com en embedded software stm32cube mcu packages html or thanks to stmicroelectronics github organization https github com stmicroelectronics stm32 mw freertos https github com stmicroelectronics stm32 mw freertos configuration freertos has several configuration options which can be specified from within the freertosconfig h file this library provides a default freertos configuration file named freertosconfig default h user can provide his own freertos configuration file at sketch level by adding his configuration in a file named stm32freertosconfig h or add extra freertos configuration to the default at sketch level by adding an extra configuration in a file named stm32freertosconfig extra h heap allocation schemes are provided by freertos see memory allocation implementations included in the rtos source https www freertos org a00111 html to extend those schemes a thread safe heap allocation using c runtime newlib has been added based on dave nadler work http www nadler com embedded newlibandfreertos html by default the heap usenewlib c is used it can be changed thanks a define in the configuration file define memory allocation implementations to use 1 to 5 for heap 1 5 c 1 for heap usenewlib st c default 1 see heap c since v10 0 1 cmsis rtosv2 can be used instead of default cmsis rtos configuse cmsis rtos v2 has to be defined and set to 1 to use cmsis rtosv2 limitations mpu not supported no cmsis rtosv2 support provided it is provided as example on cortex m0 and cortex m0 all it are disabled between xtaskcreate and vtaskstartscheduler so it is not possible to use it inbetween like serial print this is the reason why in example frliylayland between xtaskcreate and vtaskstartscheduler we use direct printf which will access directly usart without interrupt files configuration stm32freertos h must always be include first it references required include files stm32freertosconfig h if exist at sketch level it contains the freertos configurations stm32freertosconfig extra h if exist at sketch level it contains extra freertos configurations freertosconfig default h contains the default freertos configurations for this stm32 port if stm32freertosconfig h doesn t exist test results using arduino core stm32 https github com stm32duino arduino core stm32 stm32freertos v9 0 x board analogread digitalread blink analogread frblink frblinkprint frjitter frliulayland nucleo f091rc http www st com en evaluation tools nucleo f091rc html passed passed passed passed passed failed nucleo f103rb http www st com en evaluation tools nucleo f103rb html passed passed passed passed passed passed nucleo f303re http www st com en evaluation tools nucleo f303re html passed passed passed passed passed passed nucleo f429zi http www st com en evaluation tools nucleo f429zi html passed passed passed passed passed passed stm32f746g discovery http www st com en evaluation tools 32f746gdiscovery html passed passed passed passed passed passed nucleo l053r8 http www st com en evaluation tools nucleo l053r8 html passed passed passed passed passed failed nucleo l152re http www st com en evaluation tools nucleo l152re html passed passed passed passed passed passed b l475e iot01a http www st com en evaluation tools b l475e iot01a html passed passed passed passed passed passed stm32freertos v10 0 x board analogread digitalread frblinkprint frliulayland frblink cmsis rtosv2 blinky cmsis rtosv2 nucleo f091rc http www st com en evaluation tools nucleo f091rc html passed passed failed passed passed nucleo f103rb http www st com en evaluation tools nucleo f103rb html passed passed passed passed passed nucleo f303re http www st com en evaluation tools nucleo f303re html passed passed passed passed passed nucleo f429zi http www st com en evaluation tools nucleo f429zi html passed passed passed passed passed stm32f746g discovery http www st com en evaluation tools 32f746gdiscovery html passed passed passed passed passed nucleo g071rb https www st com en evaluation tools nucleo g071rb html passed passed failed passed passed nucleo h743zi https www st com en evaluation tools nucleo h743zi html passed passed passed passed passed nucleo l053r8 http www st com en evaluation tools nucleo l053r8 html passed passed failed passed passed nucleo l152re http www st com en evaluation tools nucleo l152re html passed passed passed passed passed b l475e iot01a http www st com en evaluation tools b l475e iot01a html passed passed passed passed passed p nucleo wb55rg https www st com en evaluation tools p nucleo wb55 html passed passed failed passed passed passed with configuse newlib reentrant set to 0 due to small ram stm32freertos v10 2 x board analogread digitalread frblinkprint frliulayland frblink cmsis rtosv2 blinky cmsis rtosv2 nucleo f091rc http www st com en evaluation tools nucleo f091rc html passed passed failed passed passed nucleo f103rb http www st com en evaluation tools nucleo f103rb html passed passed passed passed passed nucleo f303re http www st com en evaluation tools nucleo f303re html passed passed passed passed passed nucleo f411re http www st com en evaluation tools nucleo f411re html passed passed passed passed passed stm32f746g discovery http www st com en evaluation tools 32f746gdiscovery html passed passed passed passed passed nucleo g071rb https www st com en evaluation tools nucleo g071rb html passed passed failed passed passed nucleo g474re https www st com en evaluation tools nucleo g474re html passed passed failed passed passed nucleo h743zi https www st com en evaluation tools nucleo h743zi html passed passed passed passed passed nucleo l053r8 http www st com en evaluation tools nucleo l053r8 html passed passed failed passed passed nucleo l152re http www st com en evaluation tools nucleo l152re html passed passed passed passed passed b l475e iot01a http www st com en evaluation tools b l475e iot01a html passed passed passed passed passed p nucleo wb55rg https www st com en evaluation tools p nucleo wb55 html passed passed failed passed passed passed with configuse newlib reentrant set to 0 due to small ram stm32freertos v10 3 1 board analogread digitalread frblinkprint frliulayland frblink cmsis rtosv2 blinky cmsis rtosv2 nucleo f091rc cortex m0 http www st com en evaluation tools nucleo f091rc html passed passed passed passed passed nucleo g071rb cortex m0 http www st com en evaluation tools nucleo g071rb html passed passed passed passed passed nucleo f103rb cortex m3 http www st com en evaluation tools nucleo f103rb html passed passed passed passed passed nucleo l476rg cortex m4 http www st com en evaluation tools nucleo l476rg html passed passed passed passed passed nucleo h743zi cortex m7 https www st com en evaluation tools nucleo h743zi html passed passed passed passed passed nucleo l552ze q cortex m33 https www st com en evaluation tools nucleo l552ze q html passed passed passed passed passed nucleo u575zi q cortex m33 https www st com en evaluation tools nucleo u575zi q html passed passed passed passed passed | os |
|
XLearning-XDML | br div a href https github com qihoo360 xlearning xdml img width 400 heigth 400 src doc img logo jpg a div license https img shields io badge license apache2 0 blue svg style flat license release version https img shields io badge release 1 0 red svg prs welcome https img shields io badge prs welcome brightgreen svg xdml parameter server xdml xdml 360 xdml hadoop xdml 360 issues pull requests architecture doc img xdml png 360 xdml xdml parameter server 360 1 2 3 4 hadoop 5 1 ps xdml ps doc ps md psclient doc psclient md 2 conf xdml jobconfiguration doc jobconfigure md psconfiguration doc psconfiguration md 3 task xdml ps task pulltask pushtask 4 optimization xdml binaryclassify doc binaryclassify md ffm doc ffmprocessor md 5 ml xdml logisticregression doc logisticregression md logisticregressionwithdcasgd doc logisticregressionwithdcasgd md logisticregressionwithftrl doc logisticregressionwithftrl md logisticregressionwithmomentum doc logisticregressionwithmomentum md fieldwarefactorizationmachine doc fieldawarefactorizationmachine md 6 feature xdml doc featureanalysis md label auc ndcg doc featureprocess md categoryencoder multicategoryencoder numericbuckter numericstandardizer 7 model xdml scope https arxiv org pdf 1602 00133 pdf h2o https www h2o ai spark pipeline model doc model md linearscope multilinearscope ovrlinearscope h2odrf h2ogbm h2oglm h2omlp 8 example xdml example doc example md xdml kudu hazelcast hadoop centos 7 jdk 1 8 maven 3 5 4 scala 2 11 hadoop 2 7 3 spark 2 3 0 sparkling water core 2 3 0 kudu 1 9 hazelcast 3 9 3 kudu xdml kudu kudu kudu kudu https github com apache kudu tree 1 7 0 git clone https github com qihoo360 xlearning xdml mvn clean package dmaven test skip true target xdml 1 0 jar xdml 1 0 jar with dependencies jar xdml 1 0 jar spark kudu xdml 1 0 jar with dependencies jar spark kudu spark xdml learningrate spark xdml job type spark xdml train data path spark xdml train data partitionnum spark xdml model path spark xdml train iter spark xdml train batchsize batch ps spark xdml hz clusternum hazelcast spark xdml table name kudu spark home bin spark submit master yarn cluster class net qihoo xitong xdml example lrtest num executors 50 executor memory 40g executor cores 2 driver memory 4g conf spark xdml table name lrtest conf spark xdml job type train conf spark xdml train data path trainpath conf spark xdml train data partitionnum 50 conf spark xdml hz clusternum 50 conf spark xdml model path modelpath conf spark xdml train iter 5 conf spark xdml train batchsize 10000 conf spark xdml learningrate 0 1 jars xdml 1 0 jar with dependencies jar xdml 1 0 jar with dependencies jar spark home trainpath modelpath spark hdfs hdfs faq xdml doc faq cn md xdml shen yi zhao ru xiang ying hao shi peng gao wu jun li scope scalable composite optimization for learning on spark https arxiv org pdf 1602 00133 pdf aaai 2017 2928 2934 shen yi zhao gong duo zhang ming wei li wu jun li proximal scope for distributed sparse learning https arxiv org pdf 1803 05621 pdf proceedings of the annual conference on neural information processing systems nips 2018 shuxin zheng qi meng taifeng wang wei chen zhi ming ma and tie yan liu asynchronous stochastic gradient descent with delay compensation https arxiv org pdf 1609 08326 pdf icml 2017 mail g xlearning dev 360 cn qq 874050710 qq doc img qq jpg | spark kudu hazelcast parameter-server ai machine-learning distributed hadoop | ai |
Fundamentals-of-Database-Engineering | psql 1 to list all the databases use l 2 for creating a database use create database database name 3 to switch to another database use c database name 4 to list all the database tables use dt 5 to describe the table use d table name alter a table by adding a new column and create 1 million fields alter table transactionspractice add column balance int and you can use this script create a test table create table test data id serial primary key value integer generate a million rows with random integer values insert into test data value select floor random 1000000 1 from generate series 1 1000000 check the number of rows select count from test data update particular rows and their fields update taskfailurereason set description new description where reason id 123 for dropping a column alter table table name drop column column name for making an index create index index name on table name column name some sql funcs and their uses 1 getting second highest number select max salary as secondhighestsalary from employee where salary not in select max salary from employee 2 getting the nth highest number limit is used to limit the number the number of rows offset is used to skip the number given to it reate function getnthhighestsalary n int returns int begin set n n 1 return write your mysql query statement below select distinct salary from employee order by salary desc limit 1 offset n end 3 getting the rank based on a column here dense rank sum avg all are examples of window functions and over is used for them to specify the parameter on which these functions will work select score dense rank over order by score desc as rank from scores another example select d name as department e name as employee e salary from employee e department d where e departmentid d id and departmentid salary in select departmentid max salary from employee group by departmentid if you want how atomicity works if you want to know how atomicity works then you can play with trasactions begin transaction your query goes here commit what are different isolation levels image https github com iamskp99 sql practice assets 42648568 d3a57111 8a41 4a08 84d8 2900484d6068 column based databases what are they and why they are used in olap databases example of how they are stored 1 1001 2 1002 3 1003 4 1004 5 1005 100 1100 shashank 1001 mayank 1002 tushar 1003 ritik 1004 cool 1005 gaurav 1100 they are stored like column value row id now when we fetch try to fetch data for a single column then only needed data will be fetched therefore less i o operations will be needed difference between primary and secondary key all the keys in postgres are secondary key generally the tables are index organized or they are called index organized table in this case a primary key is used for an index based on secondary key we need to mantain an index we can t use clustered based indexing why can t we use just redis instead of bloom filters let s consider a scenario where we want to see if a username exists in db or not then we can use redis to do direct lookup instead of using bloom filters using redis for this purpose provides a precise way to verify whether a username exists or not bloom filters on the other hand might be used in situations where memory efficiency is more critical and where some level of approximation is acceptable for example in scenarios where you have a very large set of usernames and you want to quickly filter out obvious non existent usernames before querying the database a bloom filter could be used keep in mind that using a bloom filter in this case introduces the possibility of false positives meaning the filter might incorrectly indicate that a username exists when it actually doesn t vertical vs horizontal partitioning horizontal partitioning suppose you have a table containing million rows so we can split that table into multiple table on the basis of id and we can save precious query time vertical partitioning large column blob that you can store in a slow access drive in its own table space horizontal partitioning vs sharding hp splits big table into smaller tables in the same database client is not aware agnostic sharding splits big table into multiple tables across multiple database servers this is used for distributed processing but the client is aware that it is hitting different servers this is a big problem pros and cons of partitioning pros 1 improves query performance when accessing a single partition 2 in sequential scan it will come handy 3 it is easier to attach partition 4 archive old data that are barely accessed into cheap storage cons 1 updates that move rows from a partition to another partition are slow or fail sometimes 2 inefficient queries could accidently scan all the partition resulting in slower scans 3 schema changes can be challenging how does sharding deals with atomic transactions sharding is a database partitioning technique used to improve scalability and performance in distributed database systems it involves dividing a large database into smaller more manageable pieces called shards and each shard is stored on a separate database server while sharding can significantly improve performance and distribute the workload it introduces challenges when dealing with atomic transactions as maintaining the integrity of transactions across multiple shards can be complex here s how sharding deals with atomic transactions 1 local transactions in a sharded database each shard operates as an independent database system capable of handling local transactions within its own data these local transactions are atomic within the boundaries of a single shard meaning they ensure data consistency and integrity within that shard 2 distributed transactions when a transaction involves data across multiple shards it becomes a distributed transaction ensuring atomicity in distributed transactions is more challenging because you need to ensure that either all participating shards commit changes successfully or none of them commit any changes at all the all or nothing principle of atomicity 3 two phase commit 2pc one common approach to handling distributed transactions in sharded databases is to use a protocol called the two phase commit 2pc in the first phase the coordinator often the application server or a dedicated transaction manager sends a prepare message to all shards involved in the transaction each shard then checks if it can successfully commit the transaction if all shards can commit they reply with a ready message in the second phase the coordinator sends a commit or abort message to all shards based on the responses received in the first phase if all shards respond with ready the coordinator sends a commit message to all shards and they proceed to commit the transaction if any shard responds with abort or if the coordinator times out waiting for responses the coordinator sends an abort message to all shards and they roll back the transaction 4 challenges and considerations while 2pc provides a way to achieve atomicity in distributed transactions it has some drawbacks such as performance overhead and the potential for blocking if a shard becomes unavailable alternative approaches like 3pc three phase commit and paxos may be considered for more fault tolerant and scalable solutions 5 isolation and consistency sharded databases also need to consider transaction isolation levels e g read committed serializable to ensure consistency across shards implementing these isolation levels in a distributed environment requires careful design and coordination 6 monitoring and management managing distributed transactions in a sharded database system requires robust monitoring and management tools to detect and handle issues like network failures shard unavailability and transaction timeouts in summary sharding can be a powerful technique for improving database scalability but it introduces complexity when dealing with atomic transactions to ensure atomicity in distributed transactions protocols like 2pc are often used but they come with their own challenges and trade offs database administrators and developers must carefully design and manage the distributed transaction process to maintain data integrity and consistency in a sharded environment benefits of cdns a content delivery network cdn is a distributed network of servers strategically located in various geographic locations around the world cdns are designed to optimize the delivery of web content such as text images videos scripts and other digital assets to end users they play a crucial role in improving website and application performance security and reliability here s a breakdown of how cdns work and why they are important 1 content replication and distribution cdns replicate and store copies of your website s content on multiple servers often referred to as edge servers or pops points of presence these servers are strategically placed in various regions or cities closer to end users when a user requests content from your website or application the cdn automatically determines the nearest edge server and serves the content from that server 2 reduced latency by serving content from a nearby edge server cdns reduce the physical distance between the user and the server resulting in lower latency and faster loading times this helps improve the overall user experience by making websites and applications more responsive 3 load balancing cdns distribute incoming traffic across multiple servers balancing the load to prevent any single server from becoming overwhelmed this load balancing ensures that websites and applications remain available and responsive even during traffic spikes or ddos distributed denial of service attacks 4 caching cdns use caching mechanisms to store static content like images css files and javascript files on their servers when a user requests this content the cdn serves it directly from the cache reducing the load on your origin server and further improving load times 5 scalability cdns provide scalability by handling a significant portion of your website s traffic this allows your origin server to focus on processing dynamic content and user specific requests while the cdn takes care of delivering static assets 6 improved security many cdns offer security features to protect against common web threats such as ddos attacks sql injection and xss cross site scripting attacks they can also provide ssl tls encryption for secure data transmission 7 content optimization cdns can optimize content for delivery compressing images and minifying scripts to reduce file sizes this not only improves load times but also reduces bandwidth consumption 8 analytics and reporting cdns often provide analytics and reporting tools that give you insights into how your content is being accessed and how your website is performing across different regions this data can help you make informed decisions about optimizing your website in summary cdns are essential tools for web and application developers and operators to enhance performance reduce latency ensure scalability improve security and optimize content delivery they are widely used by organizations of all sizes to provide a faster more reliable and secure experience for their users | server |
|
OpenCV-4-Computer-Vision-Application-Programming-Cookbook-Fourth-Edition | opencv 4 computer vision application programming cookbook fourth edition a href https www packtpub com application development opencv 4 computer vision application programming cookbook fourth edition utm source github utm medium repository utm campaign 9781789340723 img src https d255esdrn735hr cloudfront net sites default files imagecache ppv4 main book cover b11234 png alt opencv 4 computer vision application programming cookbook fourth edition height 256px align right a this is the code repository for opencv 4 computer vision application programming cookbook fourth edition https www packtpub com application development opencv 4 computer vision application programming cookbook fourth edition utm source github utm medium repository utm campaign 9781789340723 published by packt build complex computer vision applications with opencv and c what is this book about opencv is an image and video processing library used for all types of image and video analysis throughout the book you ll work through recipes that implement a variety of tasks such as facial recognition and detection with 70 self contained tutorials this book examines common pain points and best practices for computer vision cv developers each recipe addresses a specific problem and offers a proven best practice solution with insights into how it works so that you can copy the code and configuration files and modify them to suit your needs this book covers the following exciting features install and create a program using the opencv library segment images into homogenous regions and extract meaningful objects apply image filters to enhance image content exploit image geometry to relay different views of a pictured scene calibrate the camera from different image observations detect people and objects in images using machine learning techniques reconstruct a 3d scene from images explore face detection using deep learning if you feel this book is for you get your copy https www amazon com dp 1789340721 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following compute distance from target color if getdistancetotargetcolor it maxdist itout 255 following is what you need for this book if you re a cv developer or professional who already uses or would like to use opencv for building computer vision software this book is for you you ll also find this book useful if you re a c programmer looking to extend your computer vision skillset by learning opencv with the following software and hardware list you can run all code files present in the book chapter 1 15 software and hardware list no software required os required 1 opencv 4 windows mac os x and linux any 2 vtk windows mac os x and linux any 3 cmake windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https www packtpub com sites default files downloads 9781789340723 colorimages pdf related products mastering opencv 4 with python packt https www packtpub com application development mastering opencv 4 python utm source github utm medium repository utm campaign 9781789344912 amazon https www amazon com dp 1789344913 learn opencv 4 by building projects second edition packt https www packtpub com application development learn opencv 4 building projects second edition utm source github utm medium repository utm campaign 9781789341225 amazon https www amazon com dp 1789341221 get to know the author david mill n escriv was 8 years old when he wrote his first program on an 8086 pc in basic which enabled the 2d plotting of basic equations in 2005 he finished his studies in it with honors through the universitat polit cnica de valencia in human computer interaction supported by computer vision with opencv v0 96 he has worked with blender an open source 3d software project and on its first commercial movie plumiferos as a computer graphics software developer david has more than 10 years experience in it with experience in computer vision computer graphics pattern recognition and machine learning working on different projects and at different start ups and companies he currently works as a researcher in computer vision robert laganiere is a professor at the university of ottawa canada he is also a faculty member of the viva research lab and is the coauthor of several scientific publications and patents in content based video analysis visual surveillance driver assistance object detection and tracking he cofounded visual cortek a video analytics start up which was later acquired by iwatchlife he is also a consultant in computer vision and has assumed the role of chief scientist in a number of start ups companies including cognivue corp iwatchlife and tempo analytics robert has a bachelor of electrical engineering degree from ecole polytechnique in montreal 1987 and m sc and ph d degrees from inrs telecommunications montreal 1996 other books by the authors mastering opencv 3 second edition https www packtpub com application development mastering opencv 3 second edition utm source github utm medium repository utm campaign 9781786467171 opencv 3 computer vision application programming cookbook third edition https www packtpub com application development opencv 3 computer vision application programming cookbook third edition utm source github utm medium repository utm campaign 9781786469717 opencv computer vision application programming cookbook second edition https www packtpub com application development opencv computer vision application programming cookbook second edition utm source github utm medium repository utm campaign 9781782161486 build with opencv https www packtpub com application development build opencv utm source github utm medium repository utm campaign 9781788294522 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781789340723 https packt link free ebook 9781789340723 a p | ai |
|
android-grid-wichterle | gridwichterle for android this app will show grid overlay over whole system which helps you to verify your excellent app design download get it on google play http www android com images brand get it on play logo small png https play google com store apps details id eu inmite android gridwichterle what is the grid good for makes adhering to android design guidelines metrics and grids http developer android com design style metrics grids html easy your app will look aesthetically pleasing designers use grid to create designs use this app to verify design implementation how you should align views http developer android com design media metrics closeup png how to use run the app and see the grid over whole system remove the grid or go to settings via notification screenshot of the dialogs graphics screenshot png customization in settings grid size in dp default is 8dp grid color and transparency fullscreen mode if the grid should start on the very top of the screen how to build the project with maven mvn clean package or clone the project and create project from existing sources in ide why wichterle img src http upload wikimedia org wikipedia commons thumb 4 48 prof ing rtdr otto wichterle jpg 220px prof ing rtdr otto wichterle jpg width 60 align right otto wichterle http en wikipedia org wiki otto wichterle was a famous czech inventor best known for his invention of contact lenses nbsp you can see more details with contact lenses just like with this app you won t miss any misaligned views in your app see our other czech personalities http inmite github io who help with androiddev https plus google com s 23androiddev | os |
|
PDF-QLM | document interaction with ai project overview welcome everyone this project combines ai and document analysis to make interacting with pdfs even better a user friendly connection with large language models llms i ve utilized tools like langchain llm gardio and faiss to simplify the process of asking and answering questions about pdfs the repository contains a complete application that makes pdf question and answering a breeze key features easy pdf interaction gone are the days of reading through entire documents with this you can effortlessly upload your pdfs and perform a multitude of tasks without the need to go through the entire document quick q a this project enables you to pose questions as if you re conversing with a person the system intelligently extracts the right answers from your pdfs eliminating the need for manual searches through lengthy documents note if you utilize a quantized llama model the outcomes might contrast when compared to results achieved using fp16 or fp32 models when working with colab you have the option to employ load int8 true in the configuration file you can try using colab notebook a target blank href https colab research google com drive 1kt1mce o0dpxw 4vykgvyegqbinkmtuq usp sharing img src https colab research google com assets colab badge svg alt open in colab a alt text img pdfqa png embedding loading upon uploading a pdf the project generates document embeddings enhancing the efficiency of search and analysis these embeddings are stored locally ensuring rapid access whenever needed project components model llama2 7b framework langchain frontend gradio sentence embeddings thenlper gte large pdf loader pypdfloader getting started to start using this project follow these steps 1 clone the repository to your local machine 2 install the required dependencies using pip install r requirements 3 run the python app py application and begin interacting with your pdfs using natural language note you have the flexibility to select different sentence embeddings and llm models by just changing configure file | ai |
|
neon-js | p align center img src https raw githubusercontent com cityofzion visual identity develop coz 20branding logo logo 20icon png 20200x178px coz icon darkblue 200x178px png width 125px p h1 align center neon js h1 p align center neon javascript sdk p p align center a href https circleci com gh cityofzion neon js img src https circleci com gh cityofzion neon js svg style svg a p overview this is the js sdk for the neo blockchain platform this project aims to be a lightweight library focused on providing blockchain interactions in the browser it is currently in use by neon https github com cityofzion neon wallet visit the docs https dojo coz io neo3 neon js index html to learn how to use this library getting started installation nodejs bash npm i cityofzion neon js browser through cdn html script src https unpkg com cityofzion neon js usage nodejs js import default as neon from cityofzion neon js const acct neon create account nkuybkogdzzslypbjeetherhmjeznfzszf browser once imported using the script tag the module is available as a global object neon js console log neon var acct neon create account nkuybkogdzzslypbjeetherhmjeznfzszf note for most use cases we recommend neon js do not use neon js and neon core in the same project the classes are not cross package compatible see https github com cityofzion neon js issues 850 contributing please refer to contributing contributing md for development practices setup this repository is a typescript mono repo using lerna please ensure the following is installed node latest lts aka v18 at time of writing lerna is optional and only required for advanced operations sh git clone https github com cityofzion neon js git cd neon js yarn npm run bootstrap npm run build testing sh npm run lint npm run build npm run dist npm run test unit npm run test integration docs we use docusaurus for our docs website the docs are stored in docs while the main website and its configuration is in website sh cd website yarn npm run start license open source mit https github com cityofzion neon js blob master license md main author and maintainer is yak jun xiang https github com snowypowers | blockchain |
|
bmw.cloudadoption.sample-dataengineering | tech talent day 2023 data engineering pre requisite knowledge experience and knowledge working with python basic knowledge of git source control experience in deploying and maintaining data engineering jobs in aws knowledge working with infrastructure as code terraform is required familiarity working with linux is required recommended software the following software is required to be installed on the laptop to participate in the tech talent day python 3 10 8 or above python windows downloads https www python org downloads windows or python macos downloads https www python org downloads macos or download python for linux using the relevant python 3 package manager source control git source control https git scm com downloads download and install the git client from here https git scm com downloads this step is required as all the code and the challenges will be hosted in a git repository install the latest version of the aws cli follow the instructions on the home page aws command line interface https aws amazon com cli ide visual studio code https code visualstudio com download it is recommended that visual studio code or a supported python ide like pycharm the following extensions for visual studio code are recommended python visual studio code extension https marketplace visualstudio com items itemname ms python python provides rich support for the python language hashicorp terraform visual studio code extension https marketplace visualstudio com items itemname hashicorp terraform adds editing features for terraform files such as files such as syntax highlighting intellisense code navigation code formatting module explorer and much more infrastructure as code iac terraform https developer hashicorp com terraform downloads terraform version 1 3 4 or above must be installed getting started a check if your python is installed correctly bash check the version of python installed python version or run the below command if you have not aliased python version on linux python3 version b create a virtual environment so that the application is not influenced by global dependencies the section below shows how the virtual environment can be setup in linux bash create a virtual environment using the command below on linux virtualenv venv start the virtual environment source venv bin activate c install the pre requisites for python using the requirements txt file bash install cookiecutter and other libraries required pip install r requirements txt set a phrase to print export phrase welcome to bmw tech talent day 2023 check if python is working you should see cool picture welcome to bmw tech talent day 2023 python sample py or if python is not aliased python3 sample py check if your cookiecutter is working cookiecutter v d check if your cookiecutter application is working correctly e check if the correct version of terraform is installed bash navigate to the infrastructure folder within the repository and running the following commands cd infrastructure run the terraform version and init command terraform version run the init and the plan command terraform init terraform plan apply the configuration and test terraform apply | cloud |
|
headstart | headstart website url an automated front end setup headstart is an all in one task runner that frees front end developers of the little worries that come along with modern web development if you ever wanted to use tools like grunt http gruntjs com or gulp http gulpjs com but found the configuration too troublesome you will probably like this pre configured setup better npm version npm image npm url npm downloads downloads image downloads url gitter gitter image gitter url documentation getting started getting started url base setup base setup url upgrading ugrading url feedback what did you like what didn t you like did you get stuck somewhere where the docs easy to follow or did you give up at a certain point this is a one man project so some approaches might be personated nevertheless headstart is meant to be used by other people as well so your feedback is very valuable mail me anything at all mailto hello flovan me or add an issue issues url updates for all updates follow headstartio twitter url on twitter changes can be found on the changelog page changelog url website url http headstart io getting started url http headstart io installation base setup url http headstart io base setup changelog url http www headstart io changelog ugrading url http headstart io upgrading guide twitter url https twitter com headstartio issues url https github com flovan headstart issues npm url https npmjs org package headstart npm image https badge fury io js headstart svg travis url https travis ci org flovan headstart travis image https travis ci org flovan headstart svg downloads url https github com flovan headstart downloads image http img shields io npm dm headstart svg david url https david dm org flovan headstart david image https david dm org flovan headstart png theme shields io gitter url https gitter im flovan headstart utm source badge utm medium badge utm campaign pr badge utm content badge gitter image https badges gitter im join 20chat svg | front_end |
|
SpringContactApp | springcontactapp the is part of my spring web mvc framework project work series the project springcontactapp is discussed step by step on my youtube channel its complete step by step and industry standard refer my youttube spring project work series from this link https www youtube com watch v maizwi0fjom index 1 list pllgi5phu9e46amea05e eybg1mebo1ev5 motivation writing small small sample example is different than developing a complete project that s why i planned to develop a small but complete project to cover up the best practices of software development and modular approaches used by experts for production grade enterprise application this project covered many concepts like dao services business logic controller loose coupling modular development unit testing modular view user login logout session handing crud operation connection pool datasource jstl taglib jsp view message error success and many more do not skip or miss any video just follow step by step videos also download the source code from this reposotory springcontactapp good luck vikram thakur software architect http www ezeontech com my blogs http ezeon in blog | front_end |
|
SECourseProject | secourseproject learning system project user can login logout can store data into database and retrieve them | server |
|
AirClient | airclientdatabase airclient database for software engineering project | server |
|
nlp_arabic | nlparabic this gem is intended to contain tools for arabic natural language processing as of version 0 1 this toolkit gem allows you to 1 clean a text using a stop list this stop list was generated using the tf idf score calculated on words from over 900 articles the words selected have also been checked and validated by hand which resulted in a stop list of over 270 words 2 stem a word or a text the stemming algorithm used is the isri arabic stemmer it is described in the following research paper arabic stemming without a root dictionary http ieeexplore ieee org xpl login jsp tp arnumber 1428453 url http 3a 2f 2fieeexplore ieee org 2fiel5 2f9755 2f30835 2f01428453 pdf 3farnumber 3d1428453 this root extraction stemmer is similar to the khoja stemmer but does not use a root dictionnary which can be laborious to maintain also when the root can not be found the isri stemmer would return a normalized form and not the orginial unmodified form overall the isri has been proved to perform equivalently if not better than the khoja installation add this line to your application s gemfile ruby gem nlp arabic and then execute bundle or install it yourself as gem install nlp arabic usage once installed you can use it like this nlparabic clean text will return the text without the stop words nlparabic stem word will return the word stemmed nlparabic stem text text will stem an entire text nlparabic clean and stem text will do both nlparabic wash and stem text will stem the text removing stop words and delimiters from it nlparabic tokenize text text will break the text into an array of words and delimiters each step of the isri algorithm is coded in a separate function so you should be able to find the helper function you may be looking for just by browsing the code development after checking out the repo run bin console for an interactive prompt that will allow you to experiment for now the gem doesn t use any dependencies so you don t need to run bin setup to install this gem onto your local machine run bundle exec rake install to release a new version update the version number in version rb and then run bundle exec rake release to create a git tag for the version push git commits and tags and push the gem file to rubygems org https rubygems org contributing you are more than welcome to contribute to this project please try to respect the ruby style guidelines described here https github com bbatsov ruby style guide the default encoding used is utf 8 1 fork it https github com othmanela nlp arabic fork 2 create your feature branch git checkout b my new feature 3 write unit tests and make sure all of them including the old ones pass 3 commit your changes git commit am add some feature 4 push to the branch git push origin my new feature 5 create a new pull request | ai |
|
2017ee94_db_project | 2017ee94 db project ee436 database engineering final project | server |
|
atomic-docs | atomic docs atomic docs is a front end style guide generator and component manager atomic docs is built in php and sass inspired by brad frost s atomic design principles atomic docs works with sass less and stylus documentation can be found at a href http atomicdocs io atomicdocs io a img src atomic core img demo1 gif alt atomic docs gif set up 1 download atomic docs and add to your local php environment 2 configure your preprocessor to output scss main scss to css main css 3 go to http localhost atomic docs atomic core index php and get started document have full documentation to hand off to other teams internal or external img src atomic core img document png edit in the browser edit your components directly in the browser using the ace js editor img src atomic core img editor gif gif organize organize all your components under categories that you name base modules atoms etc img src atomic core img organize png manage manage all your components move rename and delete with a clean gui interface img src atomic core img manage gif save time does all the wiring of your scss partials never write import partial name again img width 500 src atomic core img helpful png documentation can be found at a href http atomicdocs io atomicdocs io a join the conversation we d love to hear your thoughts and suggestions join us on a href https nick578 typeform com to nwx0ox slack a | front_end |
|
Solar-Powered_Auto-Adjusting_Blinds | solar powered auto adjusting blinds an embedded systems proof of concept design implementation an embedded system designed to automatically open and close window blinds based on the amount of light detected as well as temperature at the window a servo motor is used to pull the blinds drawstrings either direction the system is designed to be self sustaining with the use of a solar panel and rechargeable li ion batteries user enhancements include leds to help distinguish when system is in automatic or manual override mode manual override mode is initialized with infrared codes sent via remote control there is also a lcd screen to display the temperature as a quick means for the user to determine the approximate temp outdoors this system is designed around an arduino mega 2560 microcontroller the programming code was written in c within the arduino ide and included multiple arduino libraries e g servo h liquidcrystal h irremote h the program constantly takes voltage readings from the thermistor and photoresistor and uses those values to compare against the baseline thresholds in determining if the servo should open or close the blinds the servo parameters are degrees from the zero position where 0 degrees would be fully closed and 90 degrees would be fully open a pcb was created to hold all of the passive components and insert into the arduino microcontroller as a stand alone unit where the two could be separated if needed for troubleshooting the pcb included a lithium ion battery charger that was between the solar panel and batteries and then a dc dc booster that took the 3 7 volts from the batteries and boosted the voltage up to 5 volts to feed the whole system overall the system powers up from the solar panel and the batteries and functions as intended it was discovered that the servo motor is continuously clicking crackling internally which indicates that it is drawing amperage even when not being called into action this is pulling needed power from the system in future developments the integration of an npn transistor may help to cease amp draw from the motor when not being called to rotate | os |
|
matheuslaidler.github.io | acervo de ti matheus laidler s project portuguese pt br i projeto pessoal de estudo sobre tecnologia e seguran a da informa o i a ideia do projeto vem de criar um portal sobre o mundo da tecnologia e hacking com finalidade acad mica enquanto eu estudo desenvolvendo meus pr prios materiais e compartilhando os assim acabo permitindo que qualquer entusiasta possa ter acesso e crescer junto comigo nessa jornada de aprendizado este portal foi feito inteiramente como uma fonte de estudo contribu ndo para o meu aprendizado e para os demais interessados em aprender e revisar perfeito para quem busca materiais voltados para a rea de computa o tecnologia e ciberseguran a o foco ser mais voltado para a rea de seguran a da informa o por ser o meu foco profissional e pessoal por m por ser extremamente grande e cheia de requisitos acaba sendo necess rio aprender e saber assuntos que tangenciam outras reas da computa o portanto os conte dos que desenvolvo acabam podendo ser teis para a maioria dos entusiastas calouros e estudantes de computa o em geral projeto feito de um estudante amante para outros estudantes amantes projeto em desenvolvimento tema editado para meus objetivos tema original jekyll theme leaf theme resumo geral das mudan as add categorias retirado imagem do centro da barra de menus e a aba contact modificado cores credits matheus laidler founder of this project site posts theme edit github com matheuslaidler supunkavinda founder of hyvor theme github com supunkavinda | matheus-laidler tecnologia hacking information-security information-technology linux articles portuguese-brazilian cyber-security ctf-writeups supunkavinda | server |
textimager-uima | version https img shields io github license texttechnologylab textimager uima latest https img shields io github v release texttechnologylab textimager uima https jitpack io v texttechnologylab textimager uima svg https jitpack io texttechnologylab textimager uima paper http img shields io badge paper acl anthology b31b1b svg https www aclweb org anthology c c16 c16 2013 pdf conference http img shields io badge conference coling 2016 4b44ce svg https coling2016 anlp jp textimager uima software components for natural language processing based on the apache uima framework and dkpro cite wahed hemati tolga uslu alexander mehler textimager a distributed uima based system for nlp coling demos 2016 59 63 pdf https www aclweb org anthology c c16 c16 2013 pdf bibtex https www aclweb org anthology c c16 c16 2013 bib bibtex xml inproceedings hemati uslu mehler 2016 author hemati wahed and uslu tolga and mehler alexander title textimager a distributed uima based system for nlp booktitle proceedings of the coling 2016 system demonstrations year 2016 location osaka japan organization federated conference on computer science and information systems | ai |
|
system-design-patterns | system design patterns this repo is work in progress topics and resources related to distributed systems system design microservices scalability and performance etc check out my other repositories domain driven hexagon https github com sairyss domain driven hexagon guide on domain driven design software architecture design patterns best practices etc backend best practices https github com sairyss backend best practices best practices tools and guidelines for backend development full stack starter template https github com sairyss fullstack starter template template for full stack applications based on typescript react vite chakraui trpc fastify prisma zod etc system design patterns system design patterns distributed systems integration distributed systems integration synchronous communication synchronous communication asynchronous communication asynchronous communication data serialization data serialization api gateway api gateway scalability scalability performance and availability performance and availability creating redundancy creating redundancy autoscaling autoscaling load balancing load balancing transport layer 4 load balancing transport layer 4 load balancing application layer 7 load balancing application layer 7 load balancing databases databases indexing indexing replication replication partitioning partitioning hot warm and cold data hot warm and cold data database federation database federation denormalization denormalization materialized views materialized views multitenancy multitenancy sql vs nosql sql vs nosql identifiers identifiers connection pooling connection pooling caching caching caching strategies caching strategies distributed caches distributed caches cdns cdns coupling coupling location coupling location coupling broker pattern broker pattern temporal coupling temporal coupling message queues message queues business data and logic coupling business data and logic coupling shared nothing architecture shared nothing architecture selective data replication selective data replication event driven architecture event driven architecture event driven end to end event driven end to end designing around dataflow designing around dataflow consistency consistency different views on data different views on data distributed transactions distributed transactions two phase commit 2pc two phase commit 2pc sagas sagas orchestration orchestration choreography choreography process manager process manager workflow engines workflow engines dual writes problem dual writes problem the outbox pattern the outbox pattern retrying failed steps retrying failed steps idempotent consumer idempotent consumer change data capture change data capture time inconsistencies time inconsistencies lamport timestamps lamport timestamps vector clocks vector clocks conflict free replicated data type crdt conflict free replicated data type crdt consensus algorithms consensus algorithms byzantine generals problem byzantine generals problem raft raft paxos paxos other topics patterns other topicspatterns event sourcing event sourcing cqrs cqrs projections projections actor model actor model api federation api federation chaos engineering chaos engineering distributed tracing distributed tracing bulkhead pattern bulkhead pattern local first software local first software consistent hashing consistent hashing hash ring hash ring peer to peer peer to peer gossip protocol gossip protocol distributed hash table dht distributed hash table dht kademlia kademlia chord chord osi model osi model more resources more resources books books articles articles blogs blogs websites websites github repositories github repositories videos videos distributed systems integration synchronous communication synchronous communication is a type of communication in which the systems involved need to be in contact with each other at the same time in order to exchange messages or information in synchronous communication messages are typically transmitted and processed immediately without any delay it is often used in real time applications where the parties need to exchange messages or information in a timely manner in order to maintain the integrity or consistency of the system by using synchronous communication systems can exchange messages and information in a coordinated and immediate manner which can improve the reliability there are several ways to implement synchronous communication including using remote procedure calls rpcs https en wikipedia org wiki remote procedure call sockets https en wikipedia org wiki network socket or other mechanisms the choice of approach depends on the specific requirements and constraints of the system and on the type and amount of data that needs to be exchanged in synchronous communication a client depends on an answer in this form of communication the client sends a request and waits for a response before continuing examples of a synchronous communication are remote procedure calls rpc https en wikipedia org wiki remote procedure call over http https en wikipedia org wiki hypertext transfer protocol grpc https en wikipedia org wiki grpc apache thrift https en wikipedia org wiki apache thrift etc also rabbitmq supports rpc pattern https www rabbitmq com tutorials tutorial six javascript html synchronous calls can be a good choice for communication between your client like web ui mobile app and your backend server http communication with internal shared common services like email service or a service to process some data rpc or http calls communication with external third party services synchronous calls between distributed systems is not always a good idea it creates coupling system becomes fragile and slow for example in microservices world synchronous communication between microservices can transform them to a distributed monolith try to avoid that as much as possible asynchronous communication asynchronous communication is a type of communication in which the systems involved do not need to be in contact with each other at the same time in order to exchange messages or information in asynchronous communication messages are typically processed at a later time it is often used in distributed systems where the parties involved are located in different places or are operating at different times by using asynchronous communication systems can exchange messages without needing to be in contact with each other at the same time which can improve the flexibility and scalability of the system in short when using async communication client or message sender doesn t wait for a response it just sends a message which is stored until the receiver takes it fire and forget there are several ways to implement asynchronous communication including using message queues event streams or other mechanisms the choice of approach depends on the specific requirements and constraints of the system and on the type and amount of data that needs to be exchanged examples message brokers developed on top of amqp protocol https en wikipedia org wiki advanced message queuing protocol like rabbitmq https www rabbitmq com or event streams like kafka https en wikipedia org wiki apache kafka when changes occur you need some way to reconcile changes across the different systems one solution is eventual consistency and event driven communication based on asynchronous messaging ideally you should try to minimize the communication between the distributed systems but it is not always possible in microservices world prefer asynchronous communication since it is better suited for communication between microservices than synchronous the goal of each microservice is to be autonomous if one of the microservices is down that shouldn t affect other microservices asynchronous communication via messaging can remove dependencies on the availability of other services this topic is discussed more in details below in coupling coupling section references asynchronous message based communication https docs microsoft com en us dotnet architecture microservices architect microservice container applications asynchronous message based communication rabbitmq publish subscribe pattern https www rabbitmq com tutorials tutorial three javascript html microservice architecture with rabbitmq why https larionov pro en articles 2019 msa rabbitmq why data serialization data serialization https en wikipedia org wiki serialization is the process of converting structured data into a format that can be easily stored or transmitted the process of serialization involves translating the data into a specific format such as a string of characters or bytes and then deserialized back to the original format when it is needed data serialization is often used in distributed systems where data needs to be transferred between different nodes or components in the system by serializing the data it can be transmitted over a network or stored in a file and then reconstructed at the other end of the transmission this allows the data to be transferred efficiently and reliably to address the issue of multi language microservices communication a possible solution is to describe our data models in some language agnostic way so we can generate the same models for each language this requires the use of an idl or interface definition language https en wikipedia org wiki interface description language one of the most common formats for that lately is json https en wikipedia org wiki json it is simple and human readable for most systems out there this format is a great choice but when your system is growing there are better choices protocol buffers https en wikipedia org wiki protocol buffers apache avro https en wikipedia org wiki apache avro apache thrift https en wikipedia org wiki apache thrift provide tools for cross platform and language neutral data formats to serialize data those formats are useful for services that communicate over a network or for storing data some pros for using a schema and serializing data include messages are serialized to binary format which is compact binary messages occupy about twice less storage than json provides interoperability with other languages language agnostic provides forward and backward compatible schema for your messages events references why use grpc and thrift for remote procedure calls https dzone com articles why use grpc and thrift for remote procedure calls fromrel true the best serialization strategy for event sourcing https blog softwaremill com the best serialization strategy for event sourcing 9321c299632b api gateway your clients need to communicate to your system somehow here is where api gateway comes in an api gateway is a software component that acts as a bridge between an application and the underlying services or resources that the application uses the api gateway is responsible for routing requests from the application to the appropriate service or resource and for translating the request and response data as needed to enable communication between the application and the service an api gateway is an api management tool that sits between a client and a collection of backend services an api gateway acts as a reverse proxy to accept all application programming interface api calls aggregate the various services required to fulfill them and return the appropriate result small quote from what does an api gateway do https www redhat com en topics api what does an api gateway do text an 20api 20gateway 20is 20an and 20return 20the 20appropriate 20result small in many cases the api gateway is implemented as a layer between the application and the underlying services and is responsible for routing requests from the application to the appropriate service and for translating the request and response data as needed this allows the api gateway to abstract the underlying services from the application and to provide a consistent and standardized interface for the application to access the services you can implement your own api gateway if needed or you could use existing solutions like kong https konghq com kong or nginx https www nginx com api gateway can also handle gateway routing pattern https docs microsoft com en us azure architecture patterns gateway routing can help with routing requests to multiple services using a single endpoint load balancing load balancing to evenly distribute load across your nodes rate limiting https github com sairyss backend best practices rate limiting to protect from ddos https en wikipedia org wiki denial of service attack and brute force https en wikipedia org wiki brute force attack attacks authentication https en wikipedia org wiki authentication and authorization https en wikipedia org wiki authorization to allow only trusted clients to access your apis circuit breaker https microservices io patterns reliability circuit breaker html to prevent an application from repeatedly trying to execute an operation that s likely to fail gateway aggregation pattern https docs microsoft com en us azure architecture patterns gateway aggregation can help to compose data from multiple microservices for your client analytics and monitoring for your api calls and much more references pattern api gateway backends for frontends https microservices io patterns apigateway html building microservices using an api gateway https www nginx com blog building microservices using an api gateway my experiences with api gateways https mahesh mahadevan medium com my experiences with api gateways 8a93ad17c4c4 the api gateway pattern versus the direct client to microservice communication https docs microsoft com en us dotnet architecture microservices architect microservice container applications direct client to microservice communication versus the api gateway pattern gateway offloading pattern https docs microsoft com en us azure architecture patterns gateway offloading scalability scalability is the capability to handle the increased workload by repeatedly applying a cost effective strategy for extending a system s capacity vertical scaling means scaling by adding more power cpu ram storage etc to your existing servers horizontal scaling means adding more servers into your pool of resources below are discussed some problems with scalability and patterns to solve those problems performance and availability components of distributed systems communicate through a network networks are unreliable and slow compared to communication in a single process node ensuring good performance and availability in these conditions is an important aspect of a distributed system performance https en wikipedia org wiki computer performance is the amount of useful work accomplished by a computer system at reasonable time availability https www techtarget com searchdatacenter definition high availability is often quantified by uptime or downtime as a percentage of time the service is available for example around 8 hours of downtime per year may be considered as a 99 9 available system below we will discuss some techniques that can help with system performance and availability creating redundancy redundancy https en wikipedia org wiki redundancy engineering means duplication of critical components like data nodes and running processes etc redundancy can improve flexibility reliability availability scalability and performance of your system failure resistance and recovery below we discuss techniques and patterns for creating redundancy autoscaling autoscaling https en wikipedia org wiki autoscaling is a method used in cloud computing that dynamically adjusts the amount of computational resources in a server based on the load autoscaling allows an application to scale up or down automatically based on pre defined policies and rules in order to maintain optimal performance and cost efficiency in cloud autoscaling the application or service is monitored continuously and the capacity of the underlying cloud infrastructure is automatically adjusted based on the workload and performance of the application for example if the workload increases the autoscaling system can automatically add additional resources such as servers or processors in order to handle the increased demand conversely if the workload decreases the autoscaling system can automatically release or shut down unused resources in order to reduce costs references what is autoscaling how does it work in the cloud simply explained https www scaleyourapp com what is autoscaling how does it work in the cloud simply explained kubernetes autoscaling 3 methods and how to make them great https spot io resources kubernetes autoscaling 3 methods and how to make them great load balancing high traffic websites must serve thousands or even millions of concurrent requests to their clients to scale to these amounts of traffic usually requires adding more servers with multiple instances nodes of a backend application running load balancing https en wikipedia org wiki load balancing computing is a technique for distributing workloads or requests across multiple computing resources such as servers processors or network links in order to improve the performance and availability of a system in a load balanced system requests are distributed evenly across the available resources in order to balance the load and prevent any individual resource from becoming overwhelmed load balancing can provide several benefits such as improved performance and scalability better utilization of resources and increased availability and reliability it is often used in distributed systems such as web applications or networks where it can help to distribute workloads and requests across multiple resources in order to improve the performance and availability of the system there are several approaches to load balancing including hardware based load balancers software based load balancers and cloud based load balancers the choice of approach depends on the specific requirements and constraints of the system and on the type and amount of workload that needs to be distributed load balancers can operate at one of the two levels described below transport layer 4 load balancing layer 4 load balancers operate at the transport level and make their routing decisions based on address information extracted from the first few packets in the tcp stream and do not inspect packet content application layer 7 load balancing level 7 load balancing deals with the actual content of each message enabling the load balancer to make smarter load balancing decisions can apply optimizations and changes to the content such as compression and encryption and make decisions based on the message content layer 7 gives a lot of interesting benefits but is less performant note layers 4 and 7 are a part of osi model osi model load balancing can be software and hardware based software load balancing can be done by an api gateway api gateway or a reverse proxy https en wikipedia org wiki reverse proxy there are multiple algorithms for implementing load balancing round robin least connection hashing based etc references system design load balancing https medium com must know computer science system design load balancing 1c2e7675fc27 what is layer 4 load balancing https www nginx com resources glossary layer 4 load balancing what is layer 7 load balancing https www nginx com resources glossary layer 7 load balancing system design proxies https medium com must know computer science system design proxies ef5f2c2676f2 round robin load balancing definition https avinetworks com glossary round robin load balancing databases database can be a bottleneck of the entire application below are some patterns that can help scale databases indexing database indexing is a technique for organizing and storing data in a database in a way that allows it to be accessed and queried efficiently a database index https en wikipedia org wiki database index is a data structure that is used to store a subset of the data in a database and to provide fast access to the data based on the values in one or more columns when a query is executed the database can use the index to quickly locate and retrieve the data that is needed to satisfy the query without having to scan the entire database this can improve the performance of the query and make it more efficient there are several types of indexes that can be used in a database including primary indexes secondary indexes clustered indexes and non clustered indexes the choice of index type depends on the data in the database and the type of queries that are being executed creating indexes is one of the first things you should consider when you need to improve database read performance to be able to create effective indexes here are a few important things to consider you need to know what index data structure works best for your particular use case most common index data structure is a b tree https en wikipedia org wiki b tree it works well for most cases but in some cases like full text search regex patterns like text you may need something else like gin generalized inverted index https pgpedia info g gin html or trigram matching https www postgresql org docs current pgtrgm html your query may need a full index but in some cases a partial index https www postgresql org docs current indexes partial html can be a better choice when creating an index an order of columns in an index may play a big role on how this index is going to perform it is even possible to make query slower by creating a suboptimal index while indexes can improve read performance they slow down write performance since every time you insert something into a database each index has to be updated too create only minimum amount of indices that you will actually use and remove all unused ones indexing is a complex topic creating effective indexes requires deep understanding of how they work internally check out the references below for more in depth guides references youtube things every developer absolutely positively needs to know about database indexing https youtu be hubezkbfl7e using postgres create index https pganalyze com blog postgres create index clustered and nonclustered indexes described https learn microsoft com en us sql relational databases indexes clustered and nonclustered indexes described view sql server ver16 index advisor for postgres https pganalyze com index advisor a tool for postgresql that can suggest indexes to make querying more efficient replication data replication https en wikipedia org wiki replication computing is when the same data is intentionally stored in more than one node database replication is a technique for copying data from one database to another in order to provide multiple copies of the data for fault tolerance scalability or performance database replication can be used to create a redundant copy of a database or to distribute a database across multiple nodes or locations in database replication data is copied from a source database to one or more target databases the source and target databases can be located on the same server or on different servers the data can be copied asynchronously which means that the source and target databases can operate independently of each other and the data is copied in the background without interrupting the operation of the databases database replication can provide several benefits such as improved availability and fault tolerance better performance and scalability and easier data management and maintenance references read consistency with database replicas https shopify engineering read consistency database replicas partitioning database partitioning https en wikipedia org wiki partition database is a technique for dividing a database into smaller independent parts called partitions the goal of partitioning is to improve the performance and scalability of the database by allowing data to be distributed across multiple partitions and by allowing each partition to be managed and accessed independently of the others when you have large amounts of data that don t fit into one database partitioning is a logical next step to improve scalability there are several types of database partitioning including horizontal partitioning in this type of partitioning rows of a database table are divided into multiple partitions based on the values in one or more columns this allows different partitions to be managed and accessed independently of each other and can improve the performance and scalability of the database vertical partitioning in this type of partitioning columns of a database table are divided into multiple partitions this can help to reduce the size of each partition and can make it easier to manage and access the data in the database range partitioning in this type of partitioning the rows of a database table are divided into multiple partitions based on the range of values in a specific column this can be useful for managing data that has a natural range such as dates or numbers database partitioning is a technique that can improve the performance and scalability of a database by allowing data to be distributed across multiple partitions and managed independently references data partitioning guidance https docs microsoft com en us azure architecture best practices data partitioning sharding pattern https docs microsoft com en us azure architecture patterns sharding hot warm and cold data you can think about data as hot cold or warm hot storage is data that is accessed frequently for example blog posts created in the last couple of days will probably have a lot more attention than old posts warm storage is data that is accessed less frequently for example blog posts that are created more than a week ago but still get some attention from time to time cold storage is data that is rarely accessed for example blog posts created long time ago that are rarely accessed nowadays when deciding what data to store where it s important to consider the access patterns hot data can be stored in a fast and expensive storage ssds warm data can be stored in a cheaper storage hdds and cold data can be stored on the cheapest storage references the differences between cold warm and hot storage https www ctera com company blog differences hot warm cold file storage youtube data partitioning don t let growth slow you down https youtu be zrxjuw7w98 database federation database federation https en wikipedia org wiki federated database system allows multiple databases to function as one abstracting underlying complexity by providing a uniform api that can store or retrieve data from multiple databases using a single query for instance using federated approach you can take data from multiple sources and combine it into a single piece without your clients even knowing that this data comes from multiple sources a simple example can be a functional partitioning when we split databases by some particular function or domain for example imagine we have users wallets and transactions tables in a federated approach this could be 3 different databases this can increase performance since load will be split across 3 databases instead of 1 pros federated databases can be more performant and scalable easier to store massive volumes of data single source of truth for your data meaning there is one place where it is stored using a single schema format unlike in denormalized denormalization databases but it also has its downsides you ll need to decide manually which database to connect to joining data from multiple databases will be much harder more complexity in code and infrastructure consistency is harder to achieve since you lose acid https en wikipedia org wiki acid transactions references what is a data federation https www tibco com reference center what is a data federation scaling up to your first 10 million users https youtu be kkjm4ehyims t 2928 federation discussed briefly starting at 48 48 denormalization denormalization https en wikipedia org wiki denormalization is the process of trying to improve the read performance of a database by sacrificing some write performance by adding redundant copies of data database denormalization is a technique for optimizing the performance of a database by adding redundant data to it in a normalized database https en wikipedia org wiki database normalization data is organized into multiple interconnected tables with each table containing a unique set of data this can improve the integrity and flexibility of the database but can also result in complex and costly queries when denormalizing a database data is copied or derived from one table and added to another in order to reduce the number of table joins or other operations that are needed to retrieve the data this can improve the performance of the database by reducing the amount of work that the database engine needs to do to satisfy a query and can also reduce the amount of data that needs to be transmitted between the database engine and the application database denormalization is often used in data warehousing and business intelligence applications where complex and costly queries are common it can also be used in other types of databases such as operational databases where the performance of the database is critical for example instead of having separate users and wallets tables that must be joined you put them in a single document that doesn t need any joins this way querying database will be faster and scaling this database into multiple partitions will be easier since there is no need to join anymore pros reads are faster since there are fewer joins or none at all since there is no joins it is easier to partition and scale queries can be simpler to write cons writes inserts and updates are more expensive more redundancy requires more storage data can be inconsistent since we have multiple copies of the same data some of those copies may be outdated or in some cases never updated at all which is dangerous and must be handled by ensuring eventual consistency consistency references denormalization in databases https www geeksforgeeks org denormalization in databases building robust distributed systems https kislayverma com software architecture building robust distributed systems denormalization discussed briefly materialized views materialized view https en wikipedia org wiki materialized view is a pre computed data set derived from a query specification and stored for later use materialized view can be just a result of a select statement sql create materialized view my view as select from some table result of this select statement will be stored as a view so every time you query for it there is no need to compute the result again materialized views can improve performance but data may be outdated since refreshing a view can be an expensive operation that is only done periodically for example once a day this is great for some things like daily statistics but not good enough for real time systems references working with materialized views https docs snowflake com en user guide views materialized html text a 20materialized 20view 20is 20a base 20table 20of 20the 20view materialized view pattern https docs microsoft com en us azure architecture patterns materialized view multitenancy multitenant architecture https en wikipedia org wiki multitenancy is a design pattern for software applications that allows a single instance of the application to serve multiple tenants or groups of users in a multitenant architecture the application is designed to isolate the data and resources of each tenant so that each tenant can access and use the application independently of other tenants multitenant architecture is often used in software as a service saas applications where the application is provided to multiple tenants on a subscription basis in this type of architecture the application is designed to support multiple tenants each with their own data and resources and to provide each tenant with access to a specific instance of the application this allows the application to be used by multiple tenants concurrently without requiring each tenant to have their own separate instance of the application there are multiple tenancy models for databases single federated database for all tenants one database per tenant sharded multi tenant database and others which model to choose depends on a scale of your application references multi tenant saas database tenancy patterns https docs microsoft com en us azure azure sql database saas tenancy app design patterns view azuresql sql vs nosql sql or relational databases are good for structured data sql databases are great for transaction oriented systems that need strong consistency consistency because sql databases are acid https en wikipedia org wiki acid compliant relational databases can be very efficient at accessing well structured data as it is placed in predictable memory locations relational databases are usually scaled vertically because in relational databases data is structured and can have connections between tables it is harder to partition scale horizontally relational databases are a better choice as a main database for most projects especially new ones and can be good enough up to a few millions of active users depending on a use case sql databases include mysql postgresql microsoft sql server oracle mariadb and many more cloud providers like aws gcp and azure include their own relational database solutions nosql is a good choice for unstructured non relational data because unstructured data is usually self contained and consists of independent objects with no relations it is easier to shard partition thus it is easier to scale horizontally nosql non relational databases can be a good choice for large scale projects with more than few millions of active users especially when you need to do sharding because nosql with it s denormalized data is easier to shard but since nosql databases lack acid properties nosql are usually eventually consistent and relational structures they are not the best choice as a main database for most projects especially on a low scale also contrary to popular claims that nosql is faster it s not always the case in fact for most of the typical query needs relational databases can be much faster as described by this research performance benchmark postgresql mongodb https info enterprisedb com rs 069 alb 339 images postgresql mongodb benchmark whitepaperfinal pdf but ultimately everything depends on your query patterns nosql databases include mongodb cassandra redis and many more cloud providers like aws gcp and azure include their own non relational database solutions references sql vs nosql what s the best option for your database needs https www thorntech com sql vs nosql youtube how do nosql databases work simply explained https www youtube com watch v 0bukqhoklk8 identifiers identifiers or primary keys uniquely identify rows of data in tables and make it easy to fetch data it s important to know what options you have beyond serial integers and uuid v4 since on top of being unique or globally unique guids https en wikipedia org wiki universally unique identifier in big apps your ids can include encoded timestamp and or other important metadata that can be used for efficient sorting better indexing locality and other things there are plenty of id formats lately like uuidv7 snowflake https en wikipedia org wiki snowflake id ulid https github com ulid spec etc and it s important to know which one is best to use for your database models and also event ids especially when using event sourcing choosing a postgres primary key https supabase com blog choosing a postgres primary key ultimate guide to identifiers https wookieb pl guide identifiers goodbye integers hello uuidv7 https buildkite com blog goodbye integers hello uuids connection pooling opening and maintaining a database connection for each client is expensive establishing tcp connection process creation etc connection pool https en wikipedia org wiki connection pool is a cache of database connections maintained so that the connections can be reused when future requests to the database are required after a connection is created it is placed in the pool and reused again so that a new connection does not have to be established this can significantly improve performance when large amount of clients are connecting to the database at once for example if you are using postgresql popular solutions include pgbouncer https github com pgbouncer pgbouncer and pgcat https github com postgresml pgcat though keep in mind that adding those technologies can come at a cost more info here pgbouncer is useful important and fraught with peril https jpcamara com 2023 04 12 pgbouncer is useful html connection pooling caching caching is a technique for storing frequently accessed data in a temporary storage area known as a cache https en wikipedia org wiki cache computing in order to improve the performance and scalability of a system when data is accessed from a cache it is typically accessed more quickly than if it were retrieved from its original location such as a database or a file system this can improve the performance of the system and reduce the load on its underlying data storage caching is often used in web applications and other systems that need to retrieve data quickly and efficiently for example a web application might use a cache to store the results of common database queries or to store the results of computationally expensive operations this can improve the performance of the web application by allowing it to retrieve data more quickly and can reduce the load on its underlying data storage for caching you can use tools like redis https redis io memcached https memcached org or other key value stores caching strategies there are several caching strategies worth knowing most common are cache aside https docs microsoft com en us azure architecture patterns cache aside lazy loading write through write behind refresh ahead references caching guidance https docs microsoft com en us azure architecture best practices caching database caching strategies using redis https docs aws amazon com whitepapers latest database caching strategies using redis database challenges html architecture patterns caching part 1 https kislayverma com software architecture architecture patterns caching part 1 distributed caches a distributed cache https en wikipedia org wiki distributed cache text in 20computing 2c 20a 20distributed 20cache database 20and 20web 20session 20data is a system for storing and managing data in a distributed environment it is a type of cache which is a temporary storage area for frequently accessed data that is designed to work in a distributed system where multiple nodes or servers are used to store and manage data distributed caches are used to improve the performance and scalability of distributed systems by storing data in a way that allows it to be accessed and updated quickly and efficiently they typically use a distributed hash table https en wikipedia org wiki distributed hash table dht to store data across multiple nodes or servers and to map keys to the locations of their corresponding data this allows data to be accessed and updated in a distributed manner and can improve the performance and scalability of the system references distributed cache system design https medium com system design concepts distributed cache system design 9560f7dd07f2 architecture patterns caching part 2 https kislayverma com software architecture architecture patterns caching part 2 cdns content delivery network https en wikipedia org wiki content delivery network is geographically distributed group of servers which work together to provide fast delivery of internet content cdns work by storing copies of content at multiple locations around the world and by routing users requests to the nearest location that has a copy of the requested content this can reduce the distance that data has to travel and can improve the speed and performance of content delivery a cdn allows for the quick transfer of assets needed for loading internet content including html pages javascript files stylesheets images and videos cdn can increase content availability redundancy improve website load times references what is a cdn https www cloudflare com learning cdn what is a cdn coupling one reason why distributed systems are so hard to design is coupling one of the ways you can scale your system is by reducing it below are some topics and techniques that can help prevent coupling location coupling when a program assumes that something is available at a known location we can call that a location coupling for example when one service knows the location of another service lets say knows its url it is difficult to horizontally scale location coupled systems location can change or there may be multiple locations if there are multiple nodes running also systems can assume that accessing those locations may be fast but usually in distributed systems a remote location can be on a entirely different server in a different country thus a network trip to may be slow location coupling is a problem when considering horizontal scalability because it directly prevents resources from being added dynamically at different locations to break location coupling you can abstract the specifics of accessing another part of the system your application should be agnostic to the called system this can be implemented differently in different scenarios at the network layer we can use dns to mask the specific ip addresses of remote servers load balancers can hide that there are multiple instances of some particular system are running to service high workloads clients can hide the details of sharded database clusters when using microservices you can use message broker described below abstracting the specifics of accessing another part of the system makes application depend only on that abstraction instead of keeping bunch of locations it needs this lets application scale better for example this lets you dynamically add more services and instances to the network when you need it during peak hours without changing anything broker pattern broker pattern https en wikipedia org wiki broker pattern is used to structure distributed systems with decoupled components these components can interact with each other by remote service invocations a broker component is responsible for the coordination of communication among components in application that consists of microservices your services shouldn t know the location of each other instead you can publish your commands messages events to a common place like a message broker and let other services pick it up now all of your services will know about a common location message broker but not about each other prefer asynchronous communication asynchronous communication when using message brokers references broker pattern https medium com lalosaimi broker pattern 297ac3cff6c5 temporal coupling temporal coupling usually happens in synchronously communicated systems a part of a system expects all other parts on which it depends to serve its needs instantaneously if one part fails all its dependent systems will also fail creating a chain of failures the most common approach to breaking temporal coupling is the use of message queues instead of invoking other parts of a system synchronously calling and waiting for the response the caller instead puts a request on a queue and the other system consumes this request when it is available message queues a message queue https en wikipedia org wiki message queue is a component used for inter process communication https en wikipedia org wiki inter process communication in distributed systems typically used for asynchronous service to service communication asynchronous communication messages are stored on the queue until they are processed by a consumer each message should be processed only once by a single consumer idempotent consumer idempotent consumer software examples rabbitmq https www rabbitmq com and kafka https en wikipedia org wiki apache kafka references system design message queues https medium com must know computer science system design message queues 245612428a22 queue based load leveling pattern https docs microsoft com en us azure architecture patterns queue based load leveling business data and logic coupling to be autonomous each component of a system must own its domain data and logic this is called data ownership try not to share business data between components otherwise you may end up with a distributed monolith where every change can impact and break the whole system this is especially relevant in microservices world regardless of the method we use to share data across service boundaries we ll end up with services that are not autonomous autonomous services can evolve independently from each other non autonomous ones cannot they are coupled they won t be able to evolve without breaking each other causing evolution to stop or causing friction at development time deployment time and run time as soon as we allow cross service data sharing for example by allowing services to request data from another service we end up with the worst of two worlds a monolith suddenly turns into a distributed monolith with all distributed system problems on top of a monolithic one exception to this rule sharing ids for example an order microservice may need to know what user made an order in this case you can save a user id in both services but not the entire user object with all its properties some data lets say email address can be needed in multiple services for example authentication service to login your user and a marketing service to send marketing emails in those cases you may need to duplicate emails between those services duplication is better than calling one service from another but keep in mind that when updating email you ll need to guarantee tis update in both services we discuss this further in a section below your components must be independently deployable in a microservices world if deploying one microservice you have to change other microservices this means you have a tightly coupled architecture shared nothing architecture shared nothing architecture https en wikipedia org wiki shared nothing architecture is a distributed computing architecture that consists of multiple separated nodes that don t share resources this architecture reduces coupling scales better simplifies upgrades preventing downtime eliminates single points of failure allowing the overall system to continue operating despite failures in individual nodes etc references shared nothing architecture explained https phoenixnap com kb shared nothing architecture selective data replication selective data replication is creating a copy of the data needed from other remote system external api microservice etc into the database of our system essentially creating a cache of that data that is always available you can subscribe to the events that indicate data updates from other services for example subscribe to productpriceupdatedevent and update your local cache when the price changes alternatively if events are not available you can query an external service periodically to update your cache this approach reduces coupling runtime dependency improve latency and makes system more scalable and reliable even if external source of data is inaccessible you still have this data replicated in your own database but keep in mind that cached data can be stale references querying data across microservices https medium com john freeman querying data across microservices 8d7a4667668a event carried state transfer keep a local cache https youtu be izbebfsg0uy i need data from another service do you really https youtu be anl rgkz ak event driven architecture event driven architecture is a design pattern for software applications that emphasizes the production detection and consumption of events as a core mechanism for communication and coordination in an event driven architecture events are used to represent significant changes or actions that occur within the system and are used to trigger functions or processes that react to those events in an event driven architecture events are typically represented as messages or notifications that are sent by one component of the system to another these messages can be sent asynchronously allowing the components of the system to operate independently and in parallel when a component receives an event message it can trigger a response or action such as updating its internal state performing an operation or sending another event message an event is a broadcast by a software system about something which has happened within its boundary for example when the system successfully create an order it can publish an order created event event driven architecture is a flexible and scalable approach to building software applications and can enable applications to respond quickly and efficiently to changes or actions within the system it is often used in distributed systems microservices architectures and real time applications where it can provide a powerful and efficient mechanism for communication and coordination references why microservices should be event driven autonomy vs authority https www google com search client firefox b d q consistent hashing using events to build evolutionary architectures https kislayverma com software architecture using events to build evolutionary architectures youtube moving to event driven architectures https youtu be h46iquqjf3e youtube my top patterns for event driven architecture https youtu be p5hq6iwn p8 event driven end to end in typical web pages when you load a page and data on the server changes afterwards the browser does not know about this change until you reload the page the state displayed on a page is stale cache that is not updated until you explicitly poll or query for changes more recent protocols have moved beyond simple request response pattern websockets https en wikipedia org wiki websocket provide communication channels which allow your browser to establish a tcp connection to your server your server can push messages to your browser as long as it remains connected this provides an ability for the server to actively inform end users about any state changes on the server recent tools for developing client applications like react redux flux etc already manage client side state by subscribing to a stream of changes that represent responses from a server it would be very natural to extend this model to also allow server to push state changing events to a client this way changes and events could flow end to end from the interaction of one device that triggers an event to the server and event bus to processing this event storing it in a database and back to the ui client that is observing the state changes on another device the idea of subscribing for changes instead of querying opens a bunch of new possibilities for more responsive user interfaces it is already used in some places like online games or messaging apps for typical data centric or crud services there may be not a lot of benefit designing an entire application using this approach but some parts of your application can definitely benefit from it references 6 event driven architecture patterns part 1 https medium com wix engineering 6 event driven architecture patterns part 1 93758b253f47 designing around dataflow some microservices example in regular approach when purchasing a product your microservice can query an exchange rate service to get currency exchange rate in a dataflow approach the code that processes purchases would subscribe to a stream of exchange rate updates ahead of time and record the current rate in a local database whenever it changes when processing a purchase it only needs to query its local database selective data replication selective data replication same as changing a single number in a spreadsheet column will change the output of a formula that depends on this value subscribing to a stream of changes rather than querying the current state when needed brings up closer to a spredsheet like model of computation when some piece of data changes any derived data that depends on it can swiftly be updated small martin kleppman small by combining techniques like event driven architecture event driven architecture and selective data replication selective data replication we can achieve interesting results references designing data intensive applications https dataintensive net chapter designing applications around dataflow consistency distributed systems are made up of parts which interact relatively slowly and unreliably asynchronous networks can duplicate data drop packets reorder messages have delays etc data can be spread across multiple data stores so managing and maintaining consistency in this environment is an important aspect of the system weak consistency after a write there is no guarantee that reads will see it works well in real time use cases such as voice or video chat and realtime multiplayer games etc strong consistency after a write there is a guarantee that reads will se it data is replicated synchronously usually using some kind of transaction https en wikipedia org wiki database transaction eventual consistency after a write readers will eventually see written data usually within seconds and data is replicated asynchronously eventual consistency works best for distributed systems at a large scale familiarize with fallacies of distributed computing https en wikipedia org wiki fallacies of distributed computing before implementing any kind of distributed system added complexities may not be worth it below we will discuss where and why we need consistency how to achieve it and some associated problems references data consistency primer https docs microsoft com en us previous versions msp n p dn589800 v pandp 10 fallacies of distributed systems https architecturenotes co fallacies of distributed systems consistency models in distributed system https xzhu0027 gitbook io blog misc index consistency models in distributed system different views on data sometimes there is no single piece of software that satisfies all your requirements one database may not be enough for all your needs application wants to view data as oltp https en wikipedia org wiki online transaction processing analytics team want to view it as olap https en wikipedia org wiki online transaction processing with high loads you may also need to give users an ability to access data very fast using cache https en wikipedia org wiki cache computing or full text search https en wikipedia org wiki full text search there also may be benefit in creating connections between different data using a graph database https en wikipedia org wiki graph database typically you may need to use relational databases like mysql https www mysql com postgresql https www postgresql org etc document databases like mongodb https www mongodb com couchbase https www couchbase com etc graph databases for data with complex relations like neo4j https neo4j com developer graph database dgraph https dgraph io arangodb https www arangodb com etc for caching you can use redis https redis io or memcached https memcached org search engines like elasticsearch https www elastic co solr https solr apache org etc when there is no single storage solution that satisfies all your needs you may end up composing your services out of multiple software solutions that work together tied by your code same data may end up in multiple different storages to satisfy everyone s needs while querying thus creating a lot of redundancy and data duplication but providing performance benefits and ability to analyze and query data from different perspectives keeping writes to several storage systems in sync is necessary just writing to multiple databases without any additional layer that ensures consistency is a naive approach that can lead to dual write problems and inconsistencies we will discuss techniques and patterns that can help reliably replicate data to multiple storages in the next sections references online analytical processing olap https docs microsoft com en us azure architecture data guide relational data online analytical processing distributed transactions another reason why distributed systems are hard is consistency problems in a typical non distributed application you can use database transactions to achieve strong consistency pretty easily but in a distributed world when databases can be on a different servers simple transactions won t work anymore so we need to find workarounds and with those workarounds we usually can only achieve weak or eventual consistency because strong consistency usually means low performance and coupling which is unacceptable when you distribute two phase commit 2pc two phase commit 2pc https en wikipedia org wiki two phase commit protocol is one of the ways how to achieve strong consistency in a distributed world using 2pc each server participating in a transaction starts a local transaction and they all commit only when other participants finish executing the transaction 2pc can guarantee strong consistency but it is rarely used because it is blocking if the coordinator fails permanently some participants will never resolve their transactions it is slow and affects performance of participating systems it creates coupling between systems references two phase commit https martinfowler com articles patterns of distributed systems two phase commit html sagas in modern distributed systems eventual consistency is preferred because it scales better but how do we achieve it one of the patterns for this is sagas the saga pattern is a design pattern for managing transactions and long running processes in distributed systems in a distributed system a transaction or long running process may involve multiple services or components and may require coordination and communication between those services or components in order to complete successfully the saga pattern provides a way to manage these transactions or processes in a consistent and reliable manner in the saga pattern a transaction or long running process is represented as a series of independent steps called sagas each saga is a separate unit of work that can be executed independently of the others and can be rolled back or compensated if necessary a saga is a sequence of local transactions but unlike 2pc each transaction commits immediately without waiting for other participants to finish this way all transactions will be committed asynchronously eventually each participant must have a compensating mechanism to revert a saga so if one of the participants fails saga should start a revert process to undo all the changes references pattern saga https microservices io patterns data saga html saga distributed transactions https docs microsoft com en us azure architecture reference architectures saga saga compensating transaction pattern https docs microsoft com en us azure architecture patterns compensating transaction orchestration when using orchestration approach sagas are coordinated and managed by a saga manager or orchestrator which is responsible for ensuring that the sagas are executed in the correct order and that any errors or exceptions are handled properly orchestration is a centralized approach parts of the system that are participating in a workflow are controlled by a single entity called orchestrator which is responsible for executing each step in a flow and ensuring consistency of the entire operation orchestrated flows are explicit it works nicely for complex workflows with a lot of steps since it is easy to keep track of an entire flow in one place choreography choreography is a decentralized peer to peer approach unlike orchestration when using choreography there is no central place that coordinates workflows each part of the system is responsible for subscribing to events it needs this way data just flows through the system from one part of the system to another choreography is flexible allows a high degree of independence and autonomy however it poses a challenge because many business activities require a logical explicit chain of events in choreography flow of events is implicit when you have complex workflows with a lot of steps it will be hard to track understand maintain monitor and troubleshoot large scale flow of activities that are happening across the system one event may trigger another one then another one and so on to track the entire workflow you ll have to go multiple places and search for an event handler for each step in this case using an orchestration might be a preferred approach compared to choreography since you will have explicit workflows gathered in one place choreography in some cases may be harder to scale with growing business needs and complexities pub sub model works for simple flows but has some issues when the complexity grows process flows are embedded within the code of multiple applications often there is tight coupling and assumptions around input output service level agreements sla https en wikipedia org wiki service level agreement event schemas etc making it harder to adapt to changing needs you lose sight of the larger scale flow it is hard to track steps and overall status of the workflow to solve some of these problems you could use a workflow engine workflow engines reading all the events in a flow and check if they can be correlated to a tracking flow a lot of systems use both orchestration and choreography depending on the use case you don t necessarily need to choose just one pick the right tools for the job references microservice orchestration vs choreography https softobiz com microservice orchestration vs choreography process manager a process manager https www enterpriseintegrationpatterns com patterns messaging processmanager html is a software component or system that is responsible for managing and coordinating the execution of processes within an application or system a process is a unit of work that is performed by the application or system and a process manager is responsible for managing the execution of processes including starting stopping and monitoring processes and handling any errors or exceptions that may occur during their execution the process manager is an important component of many applications and systems and is responsible for ensuring that processes are executed in a coordinated and efficient manner it can provide several benefits such as improved performance reliability and scalability and can also make it easier to manage and maintain the application or system in many cases the process manager is implemented as a separate component that is responsible for managing the execution of processes within the application or system it may interact with other components such as a message queue or a database in order to coordinate the execution of processes and to store and retrieve data as needed process manager is used to maintain the state of the sequence and determine the next processing step based on intermediate results process manager is very similar to orchestrator in fact a lot of people use the terms like saga and orchestration when in reality they are talking about the process manager process managers are state machines that store some state while sagas usually don t have a state and operate only on data that they receive in events saga vs process manager https blog devarchive net 2015 11 saga vs process manager html m 1 workflow engines workflow engines are workflow management systems https en wikipedia org wiki workflow management system that facilitate orchestration of microservices or data flows like etl https en wikipedia org wiki extract transform load they provide apis to define a set of tasks that need to be executed and gives tooling that can help visualize flows as diagrams and or directed acyclic graph https en wikipedia org wiki directed acyclic graph and handle workflow monitoring logging retries handling failures scheduling etc workflow engines temporal https temporal io open source workflow engine apache airflow https github com apache airflow is a platform created by airbnb to programmatically author schedule and monitor workflows google cloud workflows https cloud google com workflows easily build reliable applications process automation data and machine learning pipelines on google cloud camunda https camunda com the universal process orchestrator prefect https www prefect io python based build run and monitor data pipelines at scale conductor https netflix github io conductor workflow orchestration engine created by netflix that runs in the cloud you can build business process model and notation bpmn https en wikipedia org wiki business process model and notation diagrams for your workflows using tooling provided by https bpmn io references the microservices workflow automation cheat sheet https blog bernd ruecker com the microservice workflow automation cheat sheet fc0a80dc25aa gi b977a74ee087 architecture options to run a workflow engine https blog bernd ruecker com architecture options to run a workflow engine 6c2419902d91 microservices and workflow engines https dzone com refcardz microservices and workflow engines youtube prefect tutorial indestructible python code https youtu be 0icn117e4xo awesome workflow engines https github com meirwah awesome workflow engines dual writes problem a dual write describes the situation when you change data in 2 systems e g a database and apache kafka without an additional layer that ensures data consistency over both services for example javascript await database save user await eventbus publish usercreatedevent something can happen in between those operations a network lag process restart event broker crash etc so the second operation can fail resulting in a loss of data this problem is called dual writes problem dual writes are unsafe and can lead to data inconsistencies this scenario can happen if you have multiple asynchronous operations executing one by one in a single place so you should design your system to execute only one such operation at a time or have some process that guarantees consistency there are multiple techniques to avoid dual writes like outbox pattern and retrying failed steps we describe them in details below references dual writes the unknown cause of data inconsistencies https thorben janssen com dual writes don t fail publishing events event driven architecture consistency https youtu be tcepbob8rry the outbox pattern subscribing to an event from a different part of the system and creating your own view of that event saving it to the database in a way that your system needs is one of the ways we introduce redundancy but saving some data and publishing an event that notifies other parts of the system about what happened can face dual write problems the outbox pattern can help with consistency when writing data to a database and publishing an event to a message broker what you have to do is save your message event as a part of database transaction together with a result of an operation that is being executed for example if you are creating a user and sending an event that notifies other services that a user was created you should save a user object and a message that you want to publish in a single database transaction user goes to a user table and a message goes to an outbox or messages table after that a separate message relay process publishes the events inserted into database to a message broker you can use tools like debezium https debezium io that publish changes from your outbox table to kafka or create a custom process that checks database every x seconds and sends pending events this way we can be sure that no message event is lost and will be published eventually even if a message broker is down or a network is lagging this will guarantee consistency of your data across multiple services and reliability of your application overall note when using event sourcing event sourcing you may not need an outbox pattern since all the changes are stored as events anyway references pattern transactional outbox https microservices io patterns data transactional outbox html the outbox pattern http www kamilgrzybek com design the outbox pattern retrying failed steps another way to prevent dual write problems is by retrying for example create a job that makes sure data is eventually consistent in a case of failure consider this example you need to save some data in your local database and index it in an external api typescript await database save movie await externalapi index movie what if your application crashes in the middle of this operation or if external api is unavailable data will be saved to a local database but not indexed in an external api one of the ways to solve this is to have a periodic job that queries the database every x minutes and retries indexing data that was not indexed you can see this implemented in code in this repo fullstack application example https github com sairyss full stack application example tree master apps api src app modules check out a movie and indexer services movie is saved as indexed false by default and indexer service has a interval decorator meaning that it is executed periodically to make sure that if something failed during indexing it will be retried later meaning it is eventually consistent one important detail when retrying make sure your consumers are idempotent idempotent consumer messages commands and events can be delivered twice or multiple times for example when consuming an event from a message broker an acknowledgement can fail for some reason or event is retried due to a timeout it is important to process those events only once otherwise your data may end up corrupted or some unwanted actions can occur in the system for example you could change your user s credit card twice or send the same email multiple times etc we need some mechanism for event de duplication to prevent processing events twice you should make your event consumers idempotent https en wikipedia org wiki idempotence some actions are by definition idempotent for example updating a username twice with the same name will end up in the same result but some actions are not idempotent like charging money from a wallet one of the most used ways to make consumers idempotent is saving some kind of idempotency token uuid generated by a user or an id of the event in the database in the same atomic transaction used to save changes caused by this event and set a unique constraint on that id this way even if an event message is received multiple times it will be processed only once you can go even further and prevent idempotency and duplication issues from end to end starting from your application s clients you can generate an idempotency token like uuid guid on a client side lets say on a web page send it together with a request and propagate it through your entire system this way even if a client sends a request twice by accident for example by refreshing a web page with filled in form the operation will be executed only once if you have an idempotent consumer references pattern idempotent consumer https microservices io patterns communication style idempotent consumer html idempotent receiver https martinfowler com articles patterns of distributed systems idempotent receiver html idempotency patterns https blog jonathanoliver com idempotency patterns change data capture change data capture cdc https en wikipedia org wiki change data capture is a pattern that tracks changes to data in a database in real time allowing to process it continuously as new database events occur cdc is one of the techniques that can help with data consistency when you deal with multiple storage systems in a typical database when you save something data is first appended to a commit log also called change log write ahead log https www postgresql org docs current wal intro html or transaction log https docs microsoft com en us sql relational databases logs the transaction log sql server view sql server ver15 based on a log data structure https scaling dev storage log this commit log is a source of truth and tables are merely views of the commit log we can utilize that log to duplicate changes from a main database to other databases consistently tools like debezium https debezium io allow publishing database changes to your event bus like kafka https kafka apache org and consumers of those events can insert changes to other databases asynchronously why don t we simply save data to multiple databases at once because this is not safe and leads to dual write problems dual writes problem when data crosses the boundary between different technologies and processes using things like cdc commit log events bus and idempotent consumers idempotent consumer is a reliable and robust solution to keep data consistent references change data capture cdc what it is and how it works https www striim com change data capture cdc what it is and how it works domain events versus change data capture https kislayverma com software architecture domain events versus change data capture time inconsistencies if you need to determine an order of some events the most intuitive way is using timestamps event that has a lower timestamp should be first right wrong clocks are unreliable especially in distributed systems hardware can drift threads os and runtimes can sleep system can synchronize time using ntp https en wikipedia org wiki network time protocol causing time to jump forwards or backwards current time can be resynchronized between different systems etc below we will discuss few patterns to deal with that lamport timestamps lamport timestamp https en wikipedia org wiki lamport timestamp algorithm is a simple logical clock algorithm used to determine the order of events in a distributed computer system it works by assigning a unique sequence number to each event that occurs in the system when two nodes communicate they exchange their sequence numbers and each node updates its own sequence number with the maximum of its current sequence number and the received sequence number this allows each node to keep track of the relative order of events across the entire system and to determine whether one event happened before or after another event references lamport clock https martinfowler com articles patterns of distributed systems lamport clock html the trouble with timestamps https aphyr com posts 299 the trouble with timestamps vector clocks vector clock https en wikipedia org wiki vector clock is a data structure used for determining the partial ordering of events in a distributed system and detecting causality violations a vector clock is a set of timestamp counters one for each node in a distributed system each node increments its own timestamp counter when it performs an event and the vector clock is used to record the current timestamp counters at each node vector clocks are used to determine the relative order of events in a distributed system references vector clocks in distributed systems https www geeksforgeeks org vector clocks in distributed systems vector clocks https sookocheff com post time vector clocks conflict free replicated data type crdt conflict free replicated data type crdt https en wikipedia org wiki conflict free replicated data type is a data structure which can be replicated across multiple computers in a network where the replicas can be updated independently and concurrently without coordination between the replicas and where it is always mathematically possible to resolve inconsistencies that might come up references crdt tech https crdt tech automerge https github com automerge automerge javascript library of data structures based on crdts for building collaborative applications consensus algorithms consensus algorithms are algorithms used to achieve agreement on a single data value among a group of distributed processes or systems these algorithms are designed to enable a group of nodes to come to an agreement on the state of the system despite the presence of faulty nodes or network partitions byzantine generals problem byzantine generals problem https en wikipedia org wiki byzantine fault is a problem in computer science and game theory that is used to describe the challenges of achieving consensus in a distributed system in the presence of faulty nodes raft raft https en wikipedia org wiki raft algorithm is a consensus algorithm used in distributed computing for example in distributed databases raft servers are either leaders or followers if a leader becomes unavailable consensus is achieved via leader election by the followers references the raft consensus algorithm https raft github io raft understandable distributed consensus https thesecretlivesofdata com raft visualization of a raft algorithm paxos paxos https en wikipedia org wiki paxos computer science algorithm is a distributed consensus algorithm that is used to achieve consensus in a distributed system other topics patterns event sourcing event sourcing is a software design pattern in which the state of an application is represented and stored as a sequence of events each event represents a change in the state of the application and the sequence of events represents the current and past states of the application in event sourcing the application maintains a log or stream of events which are stored in a durable append only data store such as a database or a file system each event in the log is immutable uniquely identified and is associated with a specific point in time the application can reconstruct the current state of the application by replaying the events in the log from the beginning to the end in the order in which they occurred event sourcing is often used in applications that need to maintain a high level of reliability or that need to be able to reconstruct the state of the application from historical data references event sourcing pattern https docs microsoft com en us azure architecture patterns event sourcing event immutability and dealing with change https www eventstore com blog event immutability and dealing with change cqrs cqrs command query responsibility segregation is an architectural pattern that separates the responsibilities of querying data from the responsibilities of modifying data in cqrs the two responsibilities are handled by separate components or systems which are designed and optimized independently of each other in a cqrs system the query side is responsible for providing access to data in a read only manner it typically exposes a set of apis or interfaces that allow applications to query the data but does not allow the data to be modified the query side is typically optimized for performance and scalability and may use techniques such as caching or denormalization to improve the performance of queries on the other hand the command side is responsible for modifying the data it exposes a set of apis or interfaces that allow applications to submit commands to modify the data such as creating updating or deleting data the command side is typically optimized for consistency and durability and may use techniques such as transactions or event sourcing to ensure that the data is modified in a consistent and reliable manner cqrs is often used in applications that have complex data modification requirements or that need to support high levels of concurrency and scalability it is a useful technique for separating the responsibilities of querying and modifying data usually when using cqrs we write data to a database that provides fast writing speeds and or consistency guarantees and create a copy of that data in a database that is optimized for reads commands are forwarded to a write database queries are forwarded to a read database this can improve read and write performance of your application your application can be separated into commands and queries using cqs pattern you can read about it and see code examples here commands and queries https github com sairyss domain driven hexagon commands and queries cqrs is a great fit when used together with event sourcing but can also be used without it references cqrs pattern https docs microsoft com en us azure architecture patterns cqrs projections projections are usually used in event sourced systems to create a view from a stream of events by reducing those events to a single value imagine js reduce https developer mozilla org en us docs web javascript reference global objects array reduce function event1 event2 event3 reduce this is essentially a left fold https en wikipedia org wiki fold higher order function operation in the functional world with projections you can create as many views on your data as you want projection can be a simple sql table a document in a nosql database a node in a graph database etc this can satisfy query needs for everyone since you can t really query much from a stream of events usually you would subscribe to a stream of events and change projection state when any event happens a naive approach would be to change projection state in the database directly by executing a query for each event but this will be very slow during replays to make projections performant during replays consider loading a batch of events and changing state in memory first using reducer left fold operation combined with pure functions https en wikipedia org wiki pure function and some patterns like identity map https www sourcecodeexamples net 2018 04 identity map pattern html then execute all queries in batches instead of doing it one by one references projections 1 theory https www eventstore com blog projections 1 theory actor model the actor model https en wikipedia org wiki actor model is a computer science concept of concurrent computation that uses actors as the fundamental agents of computing similarly to objects in oop https en wikipedia org wiki object oriented programming world unlike typical oop objects actors only communicate by messages so sending input and output is done only through message passing not method calling actors can also create other actors thus making an actor tree actor model can be a great addition for distributed concurrent and or real time systems like chats the most popular implementations of actor model include erlang https www erlang org programming language and akka https akka io toolkit for java https www java com en and scala https www scala lang org references how the actor model meets the needs of modern distributed systems https doc akka io docs akka current typed guide actors intro html the actor model in 10 minutes https www brianstorti com the actor model api federation the goal of api federation is to expose resources from multiple parts of the system by providing a unified api with common and consistent schema abstracting and hiding complexities of an underlying system that consist of many parts services databases etc similarly to database federation database federation api federation describes set of practices and tools to strategically think about how to deal with complex apis in organizations that are creating distributed systems usually microservices for example when we have multiple services that expose some related data customer service exposes data related to your customer orders service exposes customers orders shipping service exposes shipping status of an order customer may want to query his orders together with a shipping status and some other details instead of querying all three of those services separately in federated approach this could be only one request that gets unified internally into one response making it easier for clients to use your apis federated apis can be built using unified http crud https en wikipedia org wiki create read update and delete text in 20computer 20programming 2c 20create 2c 20read computer 2dbased 20forms 20and 20reports interfaces or using graphql https graphql org graphs references api federation growing scalable api landscapes https engineering salesforce com api federation growing scalable api landscapes a0f1f0dad506 gi c5efe3b03777 how netflix scales its api with graphql federation https netflixtechblog com how netflix scales its api with graphql federation part 1 ae3557c187e2 introduction to apollo federation https www apollographql com docs federation chaos engineering chaos engineering https en wikipedia org wiki chaos engineering is a practice of experimenting on distributed system usually in production environment to test its resilience by introducing some chaos disabling parts of the systems like services and servers simulating hard drive failures adding artificial latency simulating high traffic etc to test how the system will behave in these conditions distributed systems are inherently chaotic parts of such systems communicate through unreliable networks which means those interactions can cause unpredictable outcomes chaos engineering helps uncover system s weak spots and find unreliable parts to make them more resilient the harder it is to disrupt the steady state the more confidence we have in the behavior of the system here are some tools for chaos engineering chaos monkey https netflix github io chaosmonkey gremlin https www gremlin com chaos mesh https chaos mesh org when practicing chaos engineering it is important to use monitoring https github com sairyss backend best practices monitoring logging https github com sairyss backend best practices logging and tracing tools to have proper observability of the system read more youtube what is chaos engineering https www youtube com watch v r4m0rm3qgxk chaos engineering https www techtarget com searchitoperations definition chaos engineering 5 best chaos engineering tools https harness io blog devops chaos engineering tools chaos engineering and observability with visual metaphors https www infoq com articles chaos engineering observability visual metaphors distributed tracing distributed tracing is a method of observing requests and transactions as they propagate through distributed systems like microservices tracing tools help pinpoint where failures occur what causes poor performance and lets you trace cross process transactions distributed tracing is a must have component for organizations that have complex distributed systems and workflows opentelemetry https opentelemetry io a collection of tools apis and sdks to instrument generate collect and export telemetry data metrics logs and traces references learning distributed tracing 101 https tracing cloudnative101 dev docs index html evolving distributed tracing at uber engineering https eng uber com distributed tracing book mastering distributed tracing https www shkuro com books 2019 mastering distributed tracing bulkhead pattern the bulkhead pattern is a type of application design that is tolerant of failure in a bulkhead architecture elements of an application are isolated into pools so that if one fails the others will continue to function it s named after the sectioned partitions bulkheads of a ship s hull if the hull of a ship is compromised only the damaged section fills with water which prevents the ship from sinking references bulkhead pattern https docs microsoft com en us azure architecture patterns bulkhead local first software local first software is a term used to describe a class of software applications that prioritize working offline and storing data locally rather than relying on remote servers and the cloud this approach emphasizes the importance of data ownership and control and aims to provide users with more reliable and efficient access to their data references local first software https www inkandswitch com local first consistent hashing consistent hashing https en wikipedia org wiki consistent hashing is a way of distributing keys across a cluster of nodes in a way that minimizes the number of keys that need to be moved when nodes are added or removed from the cluster this can improve the performance and scalability of the system as well as making it more resilient to changes in the number of nodes consistent hashing is often used in distributed databases distributed file systems and other distributed systems where data needs to be distributed across multiple nodes it is also used in caching systems to distribute cache keys across multiple cache servers references consistent hashing https medium com system design blog consistent hashing b9134c8a9062 hash ring a hash ring also known as a consistent hashing ring is a data structure that is used to distribute keys or values across a cluster of nodes in a distributed system in a hash ring each node in the cluster is assigned a range of keys or values based on the node s position on the ring the hash ring is constructed using a hash function which maps the keys or values to specific points on the ring when a new key or value is added to the system the hash function is used to determine the position of the key or value on the ring and the key or value is assigned to the node that is responsible for the range of keys or values that includes the position of the key or value hash rings are often used in distributed systems such as distributed databases or distributed caches to distribute keys or values across the nodes in the cluster in a consistent and efficient manner references consistent hash rings explained simply https akshatm svbtle com consistent hash rings theory and implementation peer to peer in peer to peer p2p https en wikipedia org wiki peer to peer individual components are known as peers peers may function both as a client requesting services from other peers and as a server providing services to other peers a peer may act as a client or as a server or as both and it can change its role dynamically with time peer to peer is used in file sharing networks torrents cryptocurrency based products like blockchain bitcoin etc gossip protocol gossip protocol https en wikipedia org wiki gossip protocol is a peer to peer communication mechanism that allows designing highly efficient distributed communication systems p2p references youtube parallel distributed computing gossip protocol https www youtube com watch v qjppjzg44r8 distributed hash table dht distributed hash table https en wikipedia org wiki distributed hash table is a distributed system decentralized data store that provides a lookup service based on key value pairs similar to hash tables https en wikipedia org wiki hash table dht provides an easy way to find information in a large collection of data because all keys are in a consistent format and the entire set of keys can be partitioned in a way that allows fast identification on where the key value pair resides references distributed hash tables architecture and implementation https www usenix org legacy publications library proceedings osdi2000 full papers gribble gribble html node4 html kademlia kademlia https en wikipedia org wiki kademlia is a distributed hash table for decentralized peer to peer computer networks kademlia provides a way for large amount of computers to self organize into a network communicate with other computers on the network and share resources like files and blobs kademlia is used by projects like bittorrent https en wikipedia org wiki bittorrent ipfs https en wikipedia org wiki interplanetary file system ethereum https en wikipedia org wiki ethereum i2p https en wikipedia org wiki i2p and others references distributed hash tables with kademlia https codethechange stanford edu guides guide kademlia html chord chord https en wikipedia org wiki chord peer to peer is a protocol and algorithm for a peer to peer distributed hash table osi model osi stands for open systems interconnection https en wikipedia org wiki osi model the osi model is a conceptual model that defines a common basis for network communication standards for the purpose of systems interconnection osi model defines a logical network and describes computer packet transfer by using various layers of protocols you need to know basics of osi model in order to have a better understanding of some problems in distributed computing for example layer 4 and 7 load balancing it is a 7 layer architecture with each layer having specific functionality to perform 1 physical layer responsible for the physical connection between the devices 2 data link responsible for the node to node delivery of the message 3 network works for the transmission of data from one host to the other located in different networks 4 transport provides services to the application layer and takes services from the network layer 5 session responsible for the establishment of connection maintenance of sessions authentication ensures security 6 presentation data from the application layer is extracted here and manipulated as per the required format to transmit over the network 7 application this layer is implemented by the network applications references osi model layers and protocols in computer network https www guru99 com layers of osi model html text osi 20model 20is 20a 20layered mostly 20implemented 20only 20in 20software more resources books designing data intensive applications the big ideas behind reliable scalable and maintainable systems https www amazon com designing data intensive applications reliable maintainable dp 1449373321 by martin kleppmann must read system design interview https www amazon com system design interview insiders guide ebook dp b08b3fwybx by alex xu articles on building scalable systems https kislayverma com software architecture on scalable software blogs kislay s blog https kislayverma com websites microsoft cloud design patterns https docs microsoft com en us azure architecture patterns index patterns patterns of distributed systems https martinfowler com articles patterns of distributed systems microservices io https microservices io github repositories the system design primer https github com donnemartin system design primer awesome distributed systems https github com theanalyst awesome distributed systems an introduction to distributed systems https github com aphyr distsys class videos scaling up to your first 10 million users https www youtube com watch v kkjm4ehyims developing microservices with aggregates chris richardson https youtu be 7kx3fs0pwwc introduction to aws services https youtu be z3sydtmp3me systems engineering https youtube com playlist list pln8prpmsu08owzdpgnqr7vo2o fuqm fl infoq channel https www youtube com nctv codeopinion channel https www youtube com codeopinion | distributed-systems microservices scalability software-design system-design microservices-architecture software-architecture architecture-pattern design-patterns patterns | os |
FullStack_Info_Lab04 | fullstack info lab04 full stack web development lab 04 introduction to javascript instructions within the javascript file | front_end |
|
restaurant-HA-and-robust-application | system architecture diagram https i ibb co z6yjrjf screenshot 1 png ha and robust restaurant application with docker compose this project aims to develop a highly available and robust restaurant application using docker compose for a software engineering for cloud native applications course the assignment focuses on implementing key features such as persistence fault tolerance and load balancing features 1 containerized services the application consists of four services meals service developed in assignment 1 provides information about meals diet service developed for this assignment handles diet related functionalities database service a pre built service obtained from dockerhub responsible for storing and managing data reverse proxy service another pre built service from dockerhub e g nginx used for routing requests 2 persistent storage both the meals service and the diet service require persistent storage to maintain data integrity across restarts the implementation should ensure that data is stored persistently in the database service 3 fault tolerance docker compose is utilized to handle failures and automatically restart the assignment and diet services upon failure the application should seamlessly continue processing requests as if the failure never occurred ensuring high availability 4 reverse proxy routing to efficiently route requests to the appropriate server a reverse proxy server such as nginx is employed the reverse proxy examines the incoming requests and forwards them to the appropriate service either meals service or diet service 5 load balancing load balancing implemented for the meals service load balancing ensures that incoming requests are evenly distributed among multiple instances of the meals service improving performance and scalability technologies used docker compose container orchestration tool used to manage the deployment and scaling of multiple services docker containerization platform used to package the application and its dependencies into containers nginx a popular reverse proxy server used to route requests to the appropriate services database service a pre built docker image or containerized database service for persistent storage goals create a fault tolerant and highly available application using docker compose ensure persistence for the meals service and the diet service by leveraging a persistent storage mechanism utilize docker compose to automatically restart failed services and maintain uninterrupted request processing implement a reverse proxy server e g nginx for efficient request routing implement load balancing for the meals service to distribute requests evenly by completing this assignment i gain practical experience in building cloud native applications that exhibit high availability fault tolerance and efficient request routing through the use of docker compose reverse proxy servers and persistent storage mechanisms | cloud |
|
meta_database_engineering | database engineering this repository serves as a reference for all educational material learned throughout the meta professional database engineer certificate https www coursera org professional certificates meta database engineer the program paves the way to learning the skills and roles performed by data database engineers with a primay focus on the following subjects courses areas of focus 1 introduction to databases concepts and principles that underpin how databases work dml ddl sql command categories data types key types constraints crud operations logical physical database structures diagrams operators handling identifying nulls distinct schema types levels ex physical conceptual logical external erd intro to normalization 2 version control idea behind softward collaboration standard software development workflow basic command line navigation git pull push create init branching merge different branches forking resolving conflicts create accept pull requests 3 database structures and management w mysql relationship mapping w primary and foreign keys grouping filtering subqueries insert update w replace mysql functions string date integer type functions cast ing and mutating types coalesce nullif and more 4 advanced mysql topics automate sql by writing stored procedures automate and use constraint data validation w triggers develop user defined functions database transactions database optimization use commit rollback to manage transactions cte construction to construct chained complex queries prepared statements for faster query execution time and greater security index creation and value of explain statement for query execution 5 programming in python foundational programming skills w basic syntax objects classes methods exception handling file handling namespacing and scoping procedural programming algorithmic complexity big o functional programming oop principles comprehension list set dictionaries method resolution order modules packages libraries testing 6 database clients python application capable of administration of mysql database python code to create populate and manipulate mysql databases tables access and use mysql functions stored procedures w python threaded connections connection pools 7 data modeling types of data models mysql workbench manipulate manage data model a database forward reverse engineer database schema entity relationship diagram tool exploration overview of data warehousing olap oltp database types for analytical analysis against real time data processing create dimensional data model star snowflake schemas data ingestion visualization w tableau connect prepare data create interactive dashboards 8 database engineer capstone build end to end mysql db solution separate repository database engineer capstone https github com craigtrupp db capstone project tree main 9 coding interview preparation tips techniques and what to expect when encountering technical interviews repository reference materials repository items from referenced sections above database structures management grouping grouping mysql structures management grouping data md joins joins mysql structures management joins md filtering filtering mysql structures management filtering data md constraints constraints mysql structures management constraints md mysql functions functions mysql structures management mysql funcs md views views mysql structures management views lab md subqueries subqueries mysql structures management subqueries md replace statement replace mysql structures management replace lab md operators clauses operators clauses mysql structures management operators clasuse endwk1 md dml intro dml mysql structures management chngalterstructure md section challenge section overview challenge mysql structures management mysqldb project markdown and separate sql file within brief project folder advanced mysql topics user defined function stored procedures udf procedure advmysql topics dev funcs md prepared statement cte json column accessing prep cte json advmysql topics optimization techniques md select query optimization optimization indexing advmysql topics select optimization md triggers events w delimiter syntax triggers events advmysql topics triggers md data analysis mocked business request data analysis advmysql topics data analysis mysql md client persona scenario sql technique combinations scenario client persona scenario client persona advmysql topics closing course task danalysis client persona md sql queries business queries advmysql topics closing course task client persona queries sql user defined functions procedures triggers python programming early section items scope scope pythonprogramming wk1 wk2 items scopereview py kwargs kwargs args pythonprogramming wk1 wk2 items kwargs args py file handling file handling pythonprogramming wk1 wk2 items file handling py ordering assessment section review pythonprogramming wk1 wk2 items ordering assessment py procedural functional oop object refresher simpleobject pythonprogramming procedural functional oop wk3 customobj py inheritance pythonprogramming procedural functional oop wk3 more obj passing py recursion ex recursion ex pythonprogramming procedural functional oop wk3 reverse recurse py scoping scoping pythonprogramming procedural functional oop wk3 scoping py assessments employee functions pythonprogramming procedural functional oop wk3 wk3 first assessment py classes pythonprogramming procedural functional oop wk3 abstract pythassmnt py tests modules imports test functions assignment details pythonprogramming import scope wk4 write tests functions readme md functions pythonprogramming import scope wk4 write tests functions spellcheck py assetions pythonprogramming import scope wk4 write tests functions test spellcheck py imports packagess udf assessment details pythonprogramming import scope wk4 assessment i s readme md script pythonprogramming import scope wk4 assessment i s jsongenerator py database clients connection cursors establishing connection database 20clients wk1 connction cursors making 20your 20mysql python 20connection ipynb create cursor access mysql database database 20clients wk1 connction cursors working 20with 20cursors ipynb simple python app sql management database 20clients wk1 connction cursors creating 20a 20table 20structure 20in 20a 20mysql 20database 20using 20python ipynb performing queries create read records database 20clients wk2 performingqueries creating 20and 20reading 20records 20in 20a 20mysql 20database 20using 20python ipynb filtering sorting database 20clients wk2 performingqueries filtering 20and 20sorting 20data 20in 20a 20mysql 20database 20using 20python ipynb joining database 20clients wk2 performingqueries performing 20different 20join 20operations 20in 20mysql 20databases 20using 20python ipynb updating deleting database 20clients wk2 performingqueries updating 20and 20deleting 20records 20in 20a 20mysql 20database 20using 20python ipynb accessing procedures functions date time connection pools access stored procedure database 20clients wk3 advdbclients accessing 20stored 20procedures 20in 20a 20mysql 20database 20using 20python ipynb mysql functions database 20clients wk3 advdbclients utilizing 20mysql 20functions 20with 20python ipynb date time database 20clients wk3 advdbclients working 20with 20date 20and 20time 20functions 20in 20python ipynb connection pools database 20clients wk3 advdbclients working 20with 20connection 20pools ipynb section overview brief project database 20clients wk3 advdbclients little 20lemon 20analysis 20and 20sales 20report ipynb final section projects set db environment database 20clients wk4 finalprojects setting 20db 20environment ipynb procedure creation execution database 20clients wk4 finalprojects implement 20and 20query 20stored 20procedures ipynb analysis sales report database 20clients wk4 finalprojects little 20lemon 20analysis 20and 20sales 20report ipynb data modeling workbench sql generated file datamodeling wk1 wbench mysqlwb model sql table insertion view generation datamodeling wk1 wbench insert n view sql section project materials global super store db erd datamodeling datamodelproject globalsuperstoreschemaerd png global store schema sql datamodeling datamodelproject globalstoreschema sql star sales schema datamodeling datamodelproject star schema sales png star sales schema sql datamodeling datamodelproject star schema sql certificate object data https github com craigtrupp meta database engineering blob main images meta db engineering 1 pdf type application pdf width 700px height 700px embed src https github com craigtrupp meta database engineering blob main images meta db engineering 1 pdf p this browser does not support pdfs please download the pdf to view it a href https github com craigtrupp meta database engineering blob main images meta db engineering 1 pdf download pdf a p embed object returned error this browser does not support pdfs please download the pdf to view it download pdf foodie fi intro images certificate mdb png pdf file images meta db engineering 1 pdf verification link https coursera org verify professional cert jvkwu2tmg5h9 | server |
|
BudgeIt | h6 mobile app development 2 term project luann dias ben viscosi h6 h1 budgeit h1 h3 project abstract h3 p the problem budgeit aims to tackle is the ambiguity of typical banking apps when it comes to tracking your financial goals we intend to streamline the money management process by providing our users with a simple but powerful ui after linking their bank account with plaid our app will provide the user with the most important information at a glance along with various options to dive deeper if they choose to do so additionally it will make creating budgets and sticking to them as simple as possible upon completion budgeit will give the user all the necessary resources to get their finances back in line and keep them there p h3 technical details h3 p budgeit is an ios app built with swift 5 and swiftui it utilizes plaid to retrieve user bank information and firebase to handle backend operations p | front_end |
|
dnschain | dnschain npm version https badge fury io js dnschain svg https npmjs org package dnschain build status https img shields io travis okturtles dnschain master svg label build 20 master https travis ci org okturtles dnschain build status https img shields io travis okturtles dnschain dev svg label build 20 dev https travis ci org okturtles dnschain gitter https img shields io badge gitter join 20chat 20 e2 86 92 brightgreen svg https gitter im okturtles dnschain there is a problem with how the internet works today https is not secure http okturtles com not secure like most secure communications protocols it is susceptible to undetectable public key substitution mitm attacks example apple imessages https www taoeffect com blog 2014 11 update on imessages security netizens do not own their online identities we either borrow them from companies like twitter or rent then from organizations like icann these problems arise out of two core internet protocols dns https en wikipedia org wiki domain name system and x 509 https en wikipedia org wiki x 509 dnschain offers a free and secure decentralized alternative while remaining backwards compatible with traditional dns it compares favorably to the alternatives docs comparison md and provides the following features this extra line is necessary for table to render properly dnschain x 509 pki with certificate transparency ct mitm proof ed internet connections mitm white check mark x secure and simple gpg key distribution gpg white check mark x mitm proof restful api to blockchain api white check mark x free and actually secure free ssl certificates white check mark x stops many denial of service attacks dos white check mark x certificate revocation that actually works rev white check mark x dns based censorship circumvention cens white check mark x prevents domain theft theft seizures white check mark x access blockchain domains like bit p2p nxt eth use white check mark x certificate transparency publicly auditable log of certs white check mark white check mark maybe ct ct https blog okturtles com 2015 03 certificate transparency on blockchains mitm docs what is it md mitmproof gpg docs what is it md gpg free docs what is it md free dos docs what is it md ddos rev docs what is it md revocation cens docs what is it md censorship theft https www techdirt com articles 20141006 02561228743 5000 domains seized based sealed court filing confused domain owners have no idea why shtml use docs how do i use it md api docs what is it md api star see also comparison docs comparison md and security model docs security model md star april 21 2017 comparison of dpki https github com okturtles dnschain issues 180 issuecomment 255641398 to coniks key transparency certificate transparency https blog okturtles com 2017 02 coniks vs key transparency vs certificate transparency vs blockchains documentation book what is it docs what is it md dnschain replaces x 509 pki with the blockchain mitm proof authentication simple and secure gpg key distribution secure mitm proof restful api to blockchains free ssl certificates become possible prevents ddos attacks certificate revocation that actually works dns based censorship circumvention other features testing suite rate limiting and caching book using dnschain docs how do i use it md free public dnschain servers access blockchain domains like okturtles bit registering blockchain domains and identities encrypt communications end to end without relying on untrustworthy third parties unblock censored websites coming soon and more book running your own dnschain server docs how do i run my own md requirements getting started configuration guide setting up a dnschain server with namecoin and powerdns coming soon securing https websites with dnschain book developers docs developers md securing your apps with dnschain contributing to dnschain development adding support for your favorite blockchain running tests community forums https forums okturtles com dnschain https twitter com dnschain okturtles https twitter com okturtles gitter https badges gitter im join chat svg https gitter im okturtles dnschain other resources tv watch okturtles dnschain demo at soups 2014 eff cup https www youtube com watch v 7qlakw8aby4 blockchain university lecture on dnschain https www youtube com watch v gjd5uecekss 2h but you will know kung fu https www youtube com watch v 6vmo3xmnxe4 afterward sf bitcoin meetup securing online communications with the blockchain https www youtube com watch v qy1x3ud8lci sf bitcoin developers meetup deep dive into namecoin and dnschain https www youtube com watch v wuimiy9urta speaker listen p2p connects us podcast on dnschain http letstalkbitcoin com blog post p2p connects us episode four frontier podcast on dnschain dnscrypt mitm attacks more http reelsense tv frontier 101 beyond bitcoin hangouts with bitshares crew on dnschain https soundcloud com beyond bitcoin hangouts beyond bitcoin hangout greg slepak dnschain 2014 10 24 katherine albrecht s privacy focused radio show http www katherinealbrecht com show archives 2014 06 19 page facing up read engadget new web service prevents spies from easily intercepting your data http www engadget com 2014 09 29 okturtles let s talk bitcoin security in decentralized domain name systems http letstalkbitcoin com blog post security in decentralized domain name systems programmableweb can the blockchain replace ssl x 509 https www programmableweb com news can blockchain replace ssl analysis 2015 03 17 an intro to dnschain low trust access to definitive data sources http simondlr com post 94988956673 an intro to dnschain low trust access to how to setup a blockchain dns server with dnschain docs setting up dnschain namecoin powerdns server md the trouble with certificate transparency https blog okturtles com 2014 09 the trouble with certificate transparency introducing the dotdns metatld https blog okturtles com 2014 02 introducing the dotdns metatld dnschain versus docs comparison md have a link let us know https twitter com dnschain contributors approximate chronological order greg slepak https twitter com taoeffect original author and current maintainer simon grondin https github com sgrondin unblock feature dns based censorship circumvention matthieu rakotojaona https otokar looc2011 eu dane tlsa contributions and misc fixes tj fontaine https github com tjfontaine for native dns native dns packet modules and related projects za wilgustus https twitter com zancasdearana for pydnschain https github com okturtles pydnschain contributions cayman nava https github com wemeetagain ethereum support api icann dns and core developer vignesh anand https github com vegetableman front end back end for dnschain admin interface mike ward https twitter com bocamike documentation dionysis zindros https github com dionyziz pydnschain https github com okturtles pydnschain work chara podimata https www linkedin com in charapodimata pydnschain https github com okturtles pydnschain work konstantinos lolos https www linkedin com in kostislolos pydnschain https github com okturtles pydnschain work anton wilhelm https github com toenu23 support for nxt http nxt org cryptocurrency tim uy https github com tofutim ubuntu tutorial michael bumann https twitter com bumi optional cors support your name link of choice here release history blog post for 0 5 release https blog okturtles com 2015 03 dnschain 0 5 released https openname resolver api more 0 5 3 september 5 2015 new features optional cors support from michael bumann https twitter com bumi thanks improvements bumped hiredis to 0 4 1 for latest iojs compat 0 5 2 march 11 2015 improvements includes tests for verifying nxt support added superagent https github com visionmedia superagent for simpler http requests moved dnshandler into blockchain coffee template class prevent favicon ico requests from filling logs improved comparisons md documentation misc code and logging improvements fixes 138 nxt resolver not working 140 prevent non json values in namecoin from returning not found 141 allow arbitrary namecoin keys but enforce icann domain rules for for d 142 120 make it less likely travis will fail book older version notes history md copyright c okturtles foundation licensed under mpl 2 0 license http mozilla org mpl 2 0 | blockchain |
|
rtqt | qt4 framework for ontime rtos this is a port of qt libraries to win32 api compatible rtos for 32 64 bit x86 embedded systems from ontime https www on time com rtos 32 ht logo https www on time com images satlarge jpg ontime logo ported modules qt core qt gui 1 qt network qt script qt xml qt sql 1 qt gui module uses vesa linear buffer initialized by rtos on startup for drawing widgets the performance of this approach might not be perfect comparing to using opengl graphics but it brings a lot of fresh air into the process of building ui for embedded systems please see demo project for examples | os |
|
Cloud-Engineering-Fundamentals | cloud engineering fundamentals a repository dedicated to the learning of cloud engineering specifically aws services this project started while earning my certificate in advanced software development in java at code fellows in seattle and as such also includes links to notes i took during that academic time period a note on depreciation do note that a number of these technologies such as amplify have changed versions since i did projects using them so check the docs carefully when applicable and switch to the most up to date version contributor sharina stubbs about me on linkedin https www linkedin com in sharina stubbs index of topics in this repo for now see all the markdown files listed in the repo eventually i m sure i ll have a formal index | cloud |
|
Awesome-Mobility-Machine-Learning-Contents | awesome mobility machine learning contents hits https hits seeyoufarm com api count incr badge svg url https 3a 2f 2fgithub com 2fzzsza 2fawesome mobility machine learning contents https hits seeyoufarm com machine learning deep learning contents in mobility industry transportation i collected it for the purpose of studying i selected paper with at least 10 citations or latest paper made by seongyun byeon https github com zzsza working at socar korea car sharing company last modified date 21 02 13 contents mobility company list mobility company list tech blog tech blog presentation presentation data data map matching map matching route planning route planning eta eta traffic estimation and forecasting traffic estimation and forecasting dispatching dispatching surge pricing dynamic pricing surge pricing rebalancing problem rebalancing problem graph graph supply and demand forecasting supply and demand forecasting electric vehicle electric vehicle platform platform scheduling optimization scheduling optimization autonomous vehicle autonomous vehicle bike sharing bike sharing challenges challenges mobility company list aotonomous vehicle and mobility acquisition investment teams up network doowon cha img src https www dropbox com s i4ms8nm4r9ld2v1 ec 8a a4 ed 81 ac eb a6 b0 ec 83 b7 202019 05 06 2013 11 26 png raw 1 a map of mobility service in korea doowon cha img src https www dropbox com s 7ytmi5mtz7qicra ec 8a a4 ed 81 ac eb a6 b0 ec 83 b7 202019 05 06 2013 12 46 png raw 1 landscape of mobility industry korean autonomous vehicle industry img src https www dropbox com s 4tohfrlrj0xn2b4 ec 8a a4 ed 81 ac eb a6 b0 ec 83 b7 202019 05 06 2023 11 26 jpg raw 1 tech blog uber engineering blog https eng uber com lyft engineering blog https eng lyft com grab tech https engineering grab com go jek tech https blog gojekengineering com data science home kakao brain only korea https www kakaobrain com blog vcnc tech blog only korea http engineering vcnc co kr presentation didi deep reinforcement learning with applications in transportation https outreach didichuxing com tutorial aaai2019 didi artificial intelligence in transportation https outreach didichuxing com tutorial kdd2018 static ai 20in 20transportation kdd2018 tutorial final pdf data awesome public datasets transportation github https github com awesomedata awesome public datasets transportation google dataset search car sharing https toolbox google com datasetsearch search query car 20sharing docid vjvsk6p67moi 2flmcaaaaaa 3d 3d ride hailing https toolbox google com datasetsearch search query ride 20hailing docid 3fmm4 2bhtq7zkz3ozaaaaaa 3d 3d highway tollgates traffic flow prediction kdd cup 2017 url https tianchi aliyun com competition entrance 231597 information uber movement uber url https movement uber com lang en us nyc taxi data nyc url https www1 nyc gov site tlc about tlc trip record data page next generation simulation federal highway administration url https ops fhwa dot gov trafficanalysistools ngsim htm gaia open dataset didi chuxing trajectory data url https outreach didichuxing com research opendata en bss dataset consumer data research centre url https data cdrc ac uk dataset tags bss awesome transportation network data https github com chkwon awesome transportation network data map matching some map matching algorithms for personal navigation assistants 2000 christopher e white pdf http citeseerx ist psu edu viewdoc download doi 10 1 1 420 2506 rep rep1 type pdf on map matching vehicle tracking data 2005 sotiris brakatsoula et al pdf http citeseerx ist psu edu viewdoc download doi 10 1 1 73 7466 rep rep1 type pdf map matching with travel time constraints 2006 john krumm et al pdf https pdfs semanticscholar org c390 d8b8f2d2ac9ac93bfa3edbad5a29553db612 pdf hidden markov map matching through noise and sparseness 2009 paul newson et al pdf https www microsoft com en us research publication hidden markov map matching noise sparseness map matching for low sampling rate gps trajectories 2009 yin lou et al pdf https users wpi edu yli15 courses ds504fall17 includes mapmatching09 pdf online map matching based on hidden markov model for real time traffic sensing applications 2012 c y goh j dauwels et al pdf http web mit edu jaillet www general map matching itsc2012 final pdf large scale joint map matching of gps traces 2013 yang li et al pdf http www geometrie tugraz at kerber kerber papers lhkzg lsjmmgt 13 pdf map matching with inverse reinforcement learning 2013 t osogami et al pdf https www ijcai org proceedings 13 papers 375 pdf route planning contraction hierarchies faster and simpler hierarchical routing in road networks 2008 r geisberger et al pdf http algo2 iti kit edu schultes hwy contract pdf customizable route planning in road networks 2013 daniel delling et al pdf https www microsoft com en us research wp content uploads 2013 01 crp web 130724 pdf route planning in transportation networks 2015 hannah bast et al pdf https arxiv org pdf 1504 05140 pdf modeling trajectories with recurrent neural networks 2017 h wu et al pdf https www ijcai org proceedings 2017 0430 pdf imagination augmented agents for deep reinforcement learning 2017 t weber et al pdf https arxiv org pdf 1707 06203 pdf learning to navigate in cities without a map 2018 piotr mirowski et al pdf https papers nips cc paper 7509 learning to navigate in cities without a map pdf a unified approach to route planning for shared mobility 2018 yongxin tong et al pdf http www vldb org pvldb vol11 p1633 tong pdf primal pathfinding via reinforcement and imitation multi agent learning 2018 guillaume sartoretti et al pdf https arxiv org abs 1809 03531 eta estimation time arrival traffic estimation and prediction based on real time floating car data 2008 corrado de fabritiis et al pdf http ants iis sinica edu tw 3bkmj9ltewxtsrrvnoknfdxrm3zfwrr 53 traffic 20estimation 20and 20prediction 20based 20on 20real 20time 20floating 20car 20data pdf travel time estimation for urban road networks using low frequency probe vehicle data 2013 erik jenelius et al pdf https people kth se jenelius jrk 2012 pdf travel time estimation of a path using sparse trajectories 2014 yilun wang et al pdf https www microsoft com en us research wp content uploads 2016 02 frp0557 zheng1 pdf learning to estimate the travel time 2018 zheng wang et al didi ai labs pdf http delivery acm org 10 1145 3220000 3219900 p858 wang pdf multi task representation learning for travel time estimation 2018 yaguang li et al didi ai labs pdf https infolab usc edu docsdemos kdd 2018 deep eta pdf when will you arrive estimating travel time based on deep neural networks 2018 dong wang et al pdf http urban computing com pdf travel 20time 20estimation aaai 202018 zheng pdf traffic estimation and forecasting traffic flow theory and control 1968 donald r drew pdf http onlinepubs trb org onlinepubs sr sr165 165 pdf dynamic prediction of traffic volume through kalman filtering theory 1984 okutani et al pdf https www researchgate net publication 23527015 dynamic prediction of traffic volume through kalman filtering theory predicting time series with support vector machines 1991 muller et al pdf https alex smola org papers 1997 mulsmoratschetal97 pdf modeling and forecasting vehicular traffic flow as a seasonal arima process theoretical basis and empirical results 2003 billy m et al pdf https www3 nd edu busiforc handouts arima 20engineering 20article pdf travel time prediction with support vector regression 2004 wu et al pdf http ntur lib ntu edu tw bitstream 246246 200336 1 913 pdf gaussian processes for short term traffic volume forecasting 2010 xie et al pdf https es kaust edu sa documents 2010 xzsc jtrb pdf road traffic prediction with spatio temporal correlations 2011 wanli min et al pdf https pdfs semanticscholar org ac05 02e4433fe8843eedd4ec2c4582e3f2b9cc37 pdf utilizing real world transportation data for accurate traffic prediction 2012 pan et al pdf https infolab usc edu docsdemos icdm2012 pdf a k nearest neighbor locally weighted regression method for short term traffic flow forecasting 2012 li s et al pdf http ir ia ac cn bitstream 173211 5077 1 a 20k nearest 20neighbor 20locally 20weighted 20regression 20method 20for 20short term 20traffic 20flow 20forecasting pdf traffic flow prediction with big data a deep learning approach 2015 lv y et al pdf https ieeenewsandalerts files wordpress com 2015 11 traffic flow prediction with big data a deep learning approach pdf smiler a semi lazy time series prediction system for sensors 2015 zhou et al pdf https www comp nus edu sg atung publication smiler pdf latent space model for road networks to predict time varying traffic 2016 deng d et al pdf http roseyu com papers kdd2016 pdf deep learning a generic approach for extreme condition traffic forecasting 2017 li y et al paer https pdfs semanticscholar org d0c8 bb3e73cf7a2779ec82e26afd987895df8a93 pdf learning traffic as images a deep convolutional neural network for large scale transportation network speed prediction 2017 ma x et al pdf https pdfs semanticscholar org a700 569ffb5be538b61b067d6f4eb7688fb37c63 pdf diffusion convolutional recurrent neural network data driven traffic forecasting 2018 li y et al pdf https openreview net pdf id sjihxgwaz forecasting transportation network speed using deep capsule networks with nested lstm models 2018 ma x et al pdf https arxiv org pdf 1811 04745 pdf short term traffic forecasting modeling and learning spatio temporal relations in transportation networks using graph neural networks 2015 b shahsavari pdf https pdfs semanticscholar org 7acf bd623bf203d9b075c205883c56acc3649140 pdf dispatching design and modeling of real time shared taxi dispatch algorithms 2013 j jun et al pdf https pdfs semanticscholar org 65a1 9bf678e034a9987a5ba9b17c1561afa0c7b8 pdf large scale order dispatch in on demand ride sharing platforms a learning and planning approach 2018 zhe xu et al didi ai labs pdf http delivery acm org 10 1145 3220000 3219824 p905 xu pdf order dispatch in price aware ridesharing 2018 libin zheng et al pdf http www vldb org pvldb vol11 p853 zheng pdf efficient ridesharing order dispatching with mean field multi agent reinforcement learning 2019 minne li et al didi research pdf https arxiv org pdf 1901 11454 pdf dynamic pricing and matching in ride hailing platforms 2018 nikita korolko et al uber technologies pdf http www nkorolko com uber matching pdf deeppool distributed model free algorithm for ride sharing using deep reinforcement learning 2019 abubakr alabbasi et al pdf https arxiv org pdf 1903 03882 pdf deep reinforcement learning for ride sharing dispatching and repositioning 2019 zhiwei qin et al pdf https www ijcai org proceedings 2019 0958 pdf employee ridesharing reinforcement learning and choice modeling 2019 wangcheon yan et al pdf https aisel aisnet org cgi viewcontent cgi article 1395 context amcis2019 surge pricing driver surge pricing 2020 nikhil garg pdf https papers ssrn com sol3 papers cfm abstract id 3390346 vehicle sharing system pricing optimization 2013 a waserhole pdf https tel archives ouvertes fr tel 01176190 document pricing in ride share platforms a queueing theoretic approach 2015 carlos riquelme et al pdf http www columbia edu ww2040 8100f16 riquelme johari banerjee pdf dynamic pricing in ridesharing platforms 2015 pdf https simons berkeley edu sites default files docs 4078 simonsrj pdf video https www youtube com watch v ldlqrsye rq dynamic pricing and matching in ride hailing platforms 2018 nikita korolko et al uber technologies pdf http www nkorolko com uber matching pdf dynamic pricing in shared mobility on demand service 2018 han qiu et al pdf https arxiv org pdf 1802 03559 pdf rebalancing problem framework for automated taxi operation the family model 2016 michal k mmel pdf https www sciencedirect com science article pii s2352146517302089 pdf md5 6eb98ea8d04bb7173130ce9976c1f3f7 pid 1 s2 0 s2352146517302089 main pdf the bike sharing rebalancing problem mathematical formulations and benchmark instances 2014 mauro dell link https www sciencedirect com science article pii s0305048313001187 an exact algorithm for the static rebalancing problem arising in bicycle sharing systems 2015 g erdo an link https www researchgate net publication 275034214 an exact algorithm for the static rebalancing problem arising in bicycle sharing systems rebalancing bike sharing systems a multi source data smart optimization 2016 j liu pdf https www kdd org kdd2016 papers files rfp0553 liuaemb pdf video https www youtube com watch v 5qzhvwmeed4 a heuristic algorithm for a single vehicle static bike sharing rebalancing problem 2016 fabio cruz pdf https arxiv org pdf 1605 00702 pdf rebalancing shared mobility on demand systems a reinforcement learning approach 2017 jian wen et al pdf https www researchgate net publication 323791652 rebalancing shared mobility on demand systems a reinforcement learning approach a dynamic approach to rebalancing bike sharing systems 2018 frederico chiariotti pdf https www ncbi nlm nih gov pmc articles pmc5856052 towards stations level demand prediction for effective rebalancing in bike sharing systems 2018 pierre hulot pdf https publications polymtl ca 3160 1 2018 pierrehulot pdf a rebalancing strategy for the imbalance problem in bike sharing systems 2019 peiyu et al pdf https www mdpi com 1996 1073 12 13 2578 pdf a deep reinforcement learning framework for rebalancing dockless bike sharing systems 2018 pan et al link https arxiv org abs 1802 04592 real world ride hailing vehicle repositioning using deep reinforcement learning 2021 yan jiao et al pdf https arxiv org pdf 2103 04555 pdf graph short term traffic forecasting modeling and learning spatio temporal relations in transportation networks using graph neural networks 2015 b shahsavari pdf https pdfs semanticscholar org 7acf bd623bf203d9b075c205883c56acc3649140 pdf traffic graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting 2018 zhiyong cui pdf https arxiv org pdf 1802 07007 pdf combinatorial optimization with graph convolutional networks and guided tree search 2018 z li pdf http papers nips cc paper 7335 combinatorial optimization with graph convolutional networks and guided tree search pdf optimal transport for structured data with application on graphs 2019 titouan vayer pdf https arxiv org pdf 1805 09114 pdf supply and demand forecasting the simpler the better a unified approach to predicting original taxi demands based on large scale online platforms 2017 tong et al pdf https www tik ee ethz ch file 725d288f4b6c2968056a36576dad868f kdd17 tong pdf supply demand forecasting for a ride hailing system 2017 wang runyi pdf https cloudfront escholarship org dist prd content qt7hr5t5vv qt7hr5t5vv pdf t p6rwuy predicting short term uber demand using spatio temporal modeling a new york city case study 2017 sabiheh sadat et al pdf https arxiv org pdf 1712 02001 pdf deep spatio temporal residual networks for citywide crowd flows prediction 2016 zhang et al pdf https arxiv org pdf 1610 00081 pdf short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach 2017 jintao ke et al pdf https arxiv org pdf 1706 06279 pdf predicting citywide crowd flows using deep spatio temporal residual networks 2017 zhang et al pdf http urban computing com pdf predicting 20citywide 20crowd 20flow ai 20journal zheng pdf deep multi view spatial temporal network for taxi demand prediction 2018 yao et al pdf https www aaai org ocs index php aaai aaai18 paper viewfile 16069 15978 forecasting taxi demands with fully convolutional networks and temporal guided embedding 2018 doyup lee et al kakao brain pdf https openreview net pdf id bygf00duix blog 1 https www kakaobrain com blog 42 blog 2 https www kakaobrain com blog 43 electric vehicle a self learning tlbo based dynamic economic environmental dispatch considering multiple plug in electric vehicle loads 2014 zhile yang et al pdf https link springer com article 10 1007 s40565 014 0087 6 a comprehensive study of key electric vehicle ev components technologies challenges impacts and future direction of development 2017 f un noor et al pdf https www mdpi com 1996 1073 10 8 1217 pdf vor planning of electric vehicle charging infrastructure for urban areas with tight land supply 2018 c guo et al pdf https www mdpi com 1996 1073 11 9 2314 pdf optimal allocation model for ev charging stations coordinating investor and user benefits 2018 youbo lie et al pdf https ieeexplore ieee org stamp stamp jsp arnumber 8371598 platform flexible dynamic task assignment in real time spatial data 2017 yongxin tong et al pdf http www vldb org pvldb vol10 p1334 tong pdf ride hailing networks with strategic drivers the impact of platform control capabilities on performance 2018 philipp et al pdf https www0 gsb columbia edu faculty cmaglaras papers ride hailing 02072018 pdf scheduling optimization constraint programming for scheduling 2004 john et al pdf https pdfs semanticscholar org 01cd b0ceedc32f30d641f26c70acda2d5a9499c5 pdf scheduling problem using genetic algorithm simulated annealing and the effects of parameter values on ga performance 2006 a sadegheih pdf https www sciencedirect com science article pii s0307904x05000521 scheduling part time personnel with availability restrictions and preferences to maximize employee satisfaction 2008 s mohan et al pdf https www sciencedirect com science article pii s0895717708000526 genetic algorithms for shop scheduling problems a survey 2011 frank werner pdf http www mat uab cat alseda masteropt p11 31 pdf scheduling part time and mixed skilled workers to maximize employee satisfaction 2012 m akbari et al pdf https link springer com content pdf 10 1007 2fs00170 012 4032 4 pdf optimization of scheduling and dispatching cars on demand 2014 vu tran pdf https pdfs semanticscholar org 1fd3 61e4ffe328a890bdfa47d440920623939011 pdf vehicle relocation scheduling method for car sharing service system based on markov chain and genetic algorithm 2018 tingying song et al pdf http www iaeng org publication imecs2018 imecs2018 pp985 988 pdf uber driver schedule optimization 2018 ivan zhou blog https towardsdatascience com uber driver schedule optimization 62879ea41658 autonomous vehicle awesome autonomous vehicles github https github com takeitallsource awesome autonomous vehicles deep autonomous driving papers github https github com daviddao deep autonomous driving papers bike sharing bicycle sharing system wikipedia https en wikipedia org wiki bicycle sharing system bike sharing history impacts models of provision and future 2009 paul demaio pdf https www nctr usf edu jpt pdf jpt12 4demaio pdf bicycle sharing schemes enhancing sustainable mobility in urban areas 2011 p midgley et al pdf https sustainabledevelopment un org content dsd resources res pdfs csd 19 background paper8 p midgley bicycle pdf static repositioning in a bike sharing system models and solution approaches 2013 tal raviv et al pdf https link springer com article 10 1007 s13676 012 0017 6 bicycle sharing systems demand 2014 i frade et al pdf https cyberleninka org article n 1177610 pdf incentivizing users for balancing bike sharing systems 2015 a singla et al pdf https www aaai org ocs index php aaai aaai15 paper download 9942 9319 mobility modeling and prediction in bike sharing systems 2016 z yang et al pdf https www microsoft com en us research wp content uploads 2016 07 mobisys16bike pdf a dynamic approach to rebalancing bike sharing systems 2018 frederico chiariotti pdf https www ncbi nlm nih gov pmc articles pmc5856052 challenges flatland challenge multi agent reinforcement learning on trains 2020 link https www aicrowd com challenges flatland challenge road extraction from satellite images 2019 link https www aicrowd com challenges epfl ml road segmentation 2019 lyft 3d object detection for autonomous vehicles 2019 link https www kaggle com c 3d object detection for autonomous vehicles license distributed under the mit license see license https github com zzsza awesome mobility machine learning contents blob master license for more information | ai |
|
testing-practice | introduction to testing introduction this repository contains the starter files for the introduction to testing exercise in part 1 of the exercise you will implement and write unit tests for the sum difference product and quotient functions in part 2 of the exercise you will implement the calculator program from our previous exercise in javascript making use of the functions mentioned above write integration tests for the calculator program please review the accompanying class materials for additional details | humber | front_end |
java-blockchain-example | java blockchain example implementation of basic block chain in java | blockchain |
|
keylime | keylime slack cncf chat https img shields io badge chat cncf 20slack informational https cloud native slack com archives c01are2qutz docs status https readthedocs org projects keylime badge version latest https keylime readthedocs io en latest badge latest keylime docs keylime png raw true title keylime is an open source scalable trust system harnessing tpm technology keylime provides an end to end solution for bootstrapping hardware rooted cryptographic trust for remote machines the provisioning of encrypted payloads and run time system integrity monitoring it also provides a flexible framework for the remote attestation of any given pcr platform configuration register users can create their own customized actions that will trigger when a machine fails its attested measurements keylime s mission is to make tpm technology easily accessible to developers and users alike without the need for a deep understanding of the lower levels of a tpm s operations amongst many scenarios it well suited to tenants who need to remotely attest machines not under their own full control such as a consumer of hybrid cloud or a remote edge iot device in an insecure physical tamper prone location keylime can be driven with a cli application and a set of restful apis keylime consists of three main components the verifier registrar and the agent the verifier continuously verifies the integrity state of the machine that the agent is running on the registrar is a database of all agents registered with keylime and hosts the public keys of the tpm vendors the agent is deployed to the remote machine that is to be measured or provisioned with secrets stored within an encrypted payload released once trust is established rust based keylime agent the verifier registrar and agent are all developed in python and situated in this repository keylime the agent was ported to the rust programming language https www rust lang org the code can be found in the rust keylime repository https github com keylime rust keylime the decision was made to port the agent to rust as rust is a low level performant systems language designed with security as a central tenet by means of the rust compiler s ownership model starting with the 0 1 0 release of the rust based keylime agent this agent is now the official agent important the python version is deprecated and will be removed with the next major version 7 0 0 tpm support keylime supports tpm version 2 0 keylime can be used with a hardware tpm or a software tpm emulator for development testing or demonstration purposes however do not use keylime in production with a tpm emulator a software tpm emulator does not provide a hardware root of trust and dramatically lowers the security benefits of using keylime a hardware tpm should always be used when real secrets and trust is required table of contents installation installation usage usage configuring keylime configuring keylime running keylime running keylime provisioning provisioning request a feature request a feature security vulnerability management policy security vulnerability management policy meeting information project meetings contributing first timers support contributing first timers support testing testing additional reading additional reading disclaimer disclaimer installation to install keylime refer to the instructions found in the documentation https keylime readthedocs io en latest installation html usage configuring keylime keylime puts its configuration in etc keylime conf or usr etc keylime conf it will also take an alternate location for the config in the environment var keylime verifier registrar tenant ca logging config those files are documented with comments and should be self explanatory in most cases running keylime keylime has three major component services that run the registrar verifier and the agent the registrar is a simple https service that accepts tpm public keys it then presents an interface to obtain these public keys for checking quotes the verifier is the most important component in keylime it does initial and periodic checks of system integrity and supports bootstrapping a cryptographic key securely with the agent the verifier uses mutual tls for its control interface by default the verifier will create appropriate tls certificates for itself in var lib keylime cv ca the registrar and tenant will use this as well if you use the generated tls certificates then all the processes need to run as root to allow reading of private key files in var lib keylime the agent is the target of bootstrapping and integrity measurements it puts its stuff into var lib keylime provisioning to kick everything off you need to tell keylime to provision a machine this can be done with the keylime tenant provisioning with keylime tenant the keylime tenant utility can be used to provision your agent as an example the following command tells keylime to provision a new agent at 127 0 0 1 with uuid d432fbb3 d2f1 4a97 9ef7 75bd81c00000 and talk to a verifier at 127 0 0 1 finally it will encrypt a file called filetosend and send it to the agent allowing it to decrypt it only if the configured tpm policy is satisfied keylime tenant c add t 127 0 0 1 v 127 0 0 1 u d432fbb3 d2f1 4a97 9ef7 75bd81c00000 f filetosend to stop keylime from requesting attestations keylime tenant c delete t 127 0 0 1 u d432fbb3 d2f1 4a97 9ef7 75bd81c00000 for additional advanced options for the tenant utility run keylime tenant h documentation on how to create runtime and measured boot policies can be found in the keylime user guide https keylime readthedocs io en latest user guide html systemd service support the directory services includes systemd service files for the verifier agent and registrar you can install the services with the following command sudo services installer sh once installed you can run and inspect the services keylime verifier and keylime registrar via systemctl the rust agent repository also contains a systemd service file for the agent request a feature keylime feature requests are tracked as enhancements in the enhancements repository https github com keylime enhancements the enhancement process has been implemented to provide a way to review and assess the impact s of significant changes to keylime security vulnerability management policy if you have found a security vulnerability in keylime and would like to report first of all thank you please contact us directly at security keylime groups io mailto security keylime groups io for any bug that might impact the security of this project do not use a github issue to report any potential security bugs project meetings we meet on the fourth wednesday each month 15 30 gmt to 16 30 anyone is welcome to join the meeting the meeting is normally announced on cncf chat slack https cloud native slack com archives c01are2qutz meeting agenda are hosted and archived in the meetings repo https github com keylime meetings as github issues contributing first timers support we welcome new contributors to keylime of any form including those of you who maybe new to working in an open source project so if you are new to open source development don t worry there are a myriad of ways you can get involved in our open source project as a start try exploring issues with good first issue https github com keylime keylime labels good 20first 20issue label we understand that the process of creating a pull request pr can be a barrier for new contributors these issues are reserved for new contributors like you if you need any help or advice in making the pr feel free to jump into our chat room https cloud native slack com archives c01are2qutz and ask for help there your contribution is our gift to make our project even more robust check out contributing md https github com keylime keylime blob master contributing md to find out more about how to contribute to our project keylime uses semantic versioning https semver org it is recommended you also read the release md release md file to learn more about it and familiarise yourself with simple of examples of using it testing please see testing md testing md for details additional reading executive summary keylime slides docs old keylime elevator slides pptx https github com keylime keylime raw master docs old keylime elevator slides pptx detailed keylime architecture slides docs old keylime detailed architecture v7 pptx https github com keylime keylime raw master docs old keylime detailed architecture v7 pptx see acsac 2016 paper in doc directory docs old tci acm pdf https github com keylime keylime blob master docs old tci acm pdf and the acsac presentation on keylime docs old llsrc keylime acsac v6 pptx https github com keylime keylime raw master docs old llsrc keylime acsac v6 pptx see the hotcloud 2018 paper docs old hotcloud18 pdf https github com keylime keylime blob master docs old hotcloud18 pdf details about keylime rest api docs old keylime restful api docx https github com keylime keylime raw master docs old keylime 20restful 20api docx demo files demo some pre packaged demos to show off what keylime can do ima stub service https github com keylime rust keylime tree master keylime ima emulator allows you to test ima and keylime on a machine without a tpm service keeps emulated tpm synchronized with ima errata from the acsac paper we discovered a typo in figure 5 of the published acsac paper the final interaction between the tenant and cloud verifier showed an hmac of the node s id using the key k e this should be using k b the paper in this repository and the acsac presentation have been updated to correct this typo the software that runs on the system with the tpm is now called the keylime agent rather than the node we have made this change in the documentation and code the acsac paper will remain as it was published using node disclaimer distribution statement a approved for public release distribution unlimited this material is based upon work supported by the assistant secretary of defense for research and engineering under air force contract no fa8721 05 c 0002 and or fa8702 15 d 0001 any opinions findings conclusions or recommendations expressed in this material are those of the author s and do not necessarily reflect the views of the assistant secretary of defense for research and engineering keylime s license was changed from bsd clause 2 to apache 2 0 the original bsd clause 2 licensed code can be found on the mit github organization https github com mit ll mit keylime | tpm opensource attestation ima cloud virtualization security edge iot | server |
pollinator-pathway-android | the pacific northwest pollinator pathway pnwpp the pnw pollinator pathway app is a robust registration and communication tool for new or existing pathway members features include registering your new pollinator box via the included qr code viewing existing pathway sites and their associated profiles meeting and chatting with members and a live map of the pathway customers stakeholders new incoming members existing members administrators owners volunteers personas matt has a farm and just joined the pollinator pathway amy s a member of the pnw pollinator pathway and her pollinator garden has expanded alex is the president of the pnw pollinator pathway pain points and user needs matt is unsure how to register his site with the pathway amy is having difficulty updating her information for her site alex does not currently have administrative access and control of accounts scenarios matt has a farm and just joined the pollinator pathway he wants to set up his site information with a name description photos and a link to his website amy s pollinator garden has expanded and she can go on the app to edit her information alex wants to have administrative access and control of accounts competitive analysis pollination network who beekeepers and growers what connects beekeepers and growers together for mutual pollination when when growers need pollination or want to contact beekeepers where through the app store why to help fuel pollination efforts in areas where it may lack or promote faster pollination in crops how by using the map growers can find local pollinators and learn more about them to help narrow a choice bumble bee watch who bee enthusiasts conservationists nature enthusiast what helps users identify north american bumble bees and view maps of recent sightings when the user would use this app when identifying bees where users discover this app through conservation programs or the app store why someone would use this app to identify different kinds of bees and to share sightings and to educate how people like that they are helping scientists track bees some reviewers were frustrated with errors when trying to upload a bee sighting pollination id who users are michigan residents who want to help raise awareness about pollinators by joining the university of michigan s pollination project what the app helps users identify pollinators and learn more about their habitats when users would use the app to submit accounts of pollinators they find in their neighborhood where users would find this app through the university of michigan dearborn and their bee hotel program why someone would use this app for educational purposes or in conjunction with the pollination pollinator hotel program how users use the app to identify pollinators user reviews indicate that it has limited use and is intended for members of the pollination program pollination hotels who this app is for members of the pollination program through university of michigan who have received a pollinator hotel what the app tracks hotel locations in southeast michigan and allows users to view the types of pollinators visiting the hotels when users would use this app when tracking pollinators of the hotels users without hotels have limited use and can only view the map and information about each point where users would find this app through the university of michigan s pollination hotel program why someone would use this app if they had a pollinator hotel through the pollination program and wanted to identify pollinators and track pollinators visiting various hotels in the program how users are using this app for conservation and educational purposes reviews indicate that has very limited use strengths weaknesses opportunities threats smart search tool to identify bees limited use include more pollinators hummingbirds butterflies etc established users educational bee focused expand to other areas identification tools are easy to use sighting map glitches or bugs in the app could have more information about how to support pollinator habitats established pollinator hotel program illustrations and user submitted photos pollination hotels is primarily for members of the program not useful for the general public information about pollinator habits and habitats pollinator tracking implementation user stories scale p0 most important p2 least important p0 farmer anna has just received her boxes in the mail and wants to set them up she sets the box with its qr code in its location then scans the qr code to register the box with the pollinator pathway server to add the box to the pathways map p0 alex wants to have administrative access to approve new accounts and photos before they are published on the site she logs into the app and approves and denies new content and edits p1 liesl is a member of the pollinator pathway and wants to see other sites on the pnw pollinator pathway and view their information she uses the app to view other locations on the map and view their profiles p2 bradley is walking by a site on the pnw pollinator pathway and wants to see the site information he opens the app on his phone scans the qr code and views the site s profile and information page walk through high fidelity prototype https www figma com file k91nrq7o2edauwd3bhyxrk final prototype node id 0 3a1 screens registering a new site a href https lh3 googleusercontent com a1uk cxkpgsspqjq4dgvnlax4tbjksfwdf21k9q augd4uhcqtcxrwfyybc0iztylklk54cnhwha628v2jl8 xsdvj gdyf3edxagvnhmwgixxkswpdeoxptpc uxgiwtgsdjia w2400 source screenshot guru img src https lh3 googleusercontent com a1uk cxkpgsspqjq4dgvnlax4tbjksfwdf21k9q augd4uhcqtcxrwfyybc0iztylklk54cnhwha628v2jl8 xsdvj gdyf3edxagvnhmwgixxkswpdeoxptpc uxgiwtgsdjia w440 h159 p k a editing site information a href https lh3 googleusercontent com kozkgvl0fiqxecxw yjdqdkx52lphzzpj76byvih8y2potfzgrpcjg7srcohmacvec3mzv4g j e9r5 uycaqr7w1lvcsxoyiidopngenbkxj4ij4usmcx8oa jgijsxa4m roeu5q w2400 source screenshot guru img src https lh3 googleusercontent com kozkgvl0fiqxecxw yjdqdkx52lphzzpj76byvih8y2potfzgrpcjg7srcohmacvec3mzv4g j e9r5 uycaqr7w1lvcsxoyiidopngenbkxj4ij4usmcx8oa jgijsxa4m roeu5q w576 h159 p k a viewing other sites a href https lh3 googleusercontent com j 7gar8yzdn2ge2j2emibaedhyevj3vh2qbavacm9qn c3cabbtu ewt xva 2 zug29wutiskvfa9ahv0ikpndno7s 7gqdqvhn1 cresmlhopq sw13f7102qhkxq nshi iricq w2400 source screenshot guru img src https lh3 googleusercontent com j 7gar8yzdn2ge2j2emibaedhyevj3vh2qbavacm9qn c3cabbtu ewt xva 2 zug29wutiskvfa9ahv0ikpndno7s 7gqdqvhn1 cresmlhopq sw13f7102qhkxq nshi iricq w188 h159 p k a color primary color 168c8c a href https lh3 googleusercontent com cp7rnfsnjpbuy12jlr4sb8s7zcp7dkd5rxdupr2thjgiengaufwzyzhppah trpvgy5 u7dre2h1uweo7a1avbza4qd6skfedkfhslmve qstr fbckccit0v5zzdzevilbqchhlua w2400 source screenshot guru img src https lh3 googleusercontent com cp7rnfsnjpbuy12jlr4sb8s7zcp7dkd5rxdupr2thjgiengaufwzyzhppah trpvgy5 u7dre2h1uweo7a1avbza4qd6skfedkfhslmve qstr fbckccit0v5zzdzevilbqchhlua w216 h144 p k a secondary color 82bf45 a href https lh3 googleusercontent com jax gnnrf oshbzmm erp9i9uz8c yl0yxl q147icdgupas7wywmbkysawcf3d7whw8ougy2kiadjtfrx2 lmbox45rkof4cxfqbkbhmu2qj9m8bgrzvi2xnh6b1lu2gfxexxmnw w2400 source screenshot guru img src https lh3 googleusercontent com jax gnnrf oshbzmm erp9i9uz8c yl0yxl q147icdgupas7wywmbkysawcf3d7whw8ougy2kiadjtfrx2 lmbox45rkof4cxfqbkbhmu2qj9m8bgrzvi2xnh6b1lu2gfxexxmnw w216 h144 p k a primary text color 000 a href https lh3 googleusercontent com 6awgybskqfnn4x85s5kqulkqyjplh7dusreqqtu1o6xuraurw24h1iegd i6otc9ria2eo7y qtnhty6ooyj ygmjpxca 7407ctrwnfwemiknq swvu81p0fupmk6gadlvawpdoyq w2400 source screenshot guru img src https lh3 googleusercontent com 6awgybskqfnn4x85s5kqulkqyjplh7dusreqqtu1o6xuraurw24h1iegd i6otc9ria2eo7y qtnhty6ooyj ygmjpxca 7407ctrwnfwemiknq swvu81p0fupmk6gadlvawpdoyq w216 h144 p k a secondary text color 0a403a a href https lh3 googleusercontent com 6xbvh7vbl7aldewaew0ziuecc51y3tibochu ohuxcsitrszim 9xtn2pu1g7ozxje6 jd8mmx5irv6bn2pgh2gec5thcfy ztkllnfpk2jyfqptifaedltzghaf1irtgwe ahskca w2400 source screenshot guru img src https lh3 googleusercontent com 6xbvh7vbl7aldewaew0ziuecc51y3tibochu ohuxcsitrszim 9xtn2pu1g7ozxje6 jd8mmx5irv6bn2pgh2gec5thcfy ztkllnfpk2jyfqptifaedltzghaf1irtgwe ahskca w216 h144 p k a app icon logo splash screen a href https lh3 googleusercontent com exec4eo7vng 6hyqjdfivuoors9xh76aprkihgijgm07sjdwq2nxikgaadbwx8o2ulzispzugqonpopc91hbshvhz1jgnpbfqupat7bibve8dndpikeevyvbvfocloq c5e7tvra8q w2400 source screenshot guru img src https lh3 googleusercontent com exec4eo7vng 6hyqjdfivuoors9xh76aprkihgijgm07sjdwq2nxikgaadbwx8o2ulzispzugqonpopc91hbshvhz1jgnpbfqupat7bibve8dndpikeevyvbvfocloq c5e7tvra8q w300 h268 p k a icon google play store a href https lh3 googleusercontent com g0ftqndtdx8a44fcmwsdbvdtm 813pnssdi3rvl87xs zv9si2fsqhzwji6e34qwvjoeapdtzyd29up2ohfkdtabkq15irwo6nmh0c8k5fg7bnuauepqmnixgjynamddt05v5vhpaq w2400 source screenshot guru img src https lh3 googleusercontent com g0ftqndtdx8a44fcmwsdbvdtm 813pnssdi3rvl87xs zv9si2fsqhzwji6e34qwvjoeapdtzyd29up2ohfkdtabkq15irwo6nmh0c8k5fg7bnuauepqmnixgjynamddt05v5vhpaq w600 h315 p k a integration google maps api google vision api google play services api future considerations usability summary based on the usability studies on the high fidelity prototype added a welcome screen to the registration flow with directions for how to register a new site we also fixed spacing and alignment consistency issues we adjusted the navigation by removing buttons on the landing page and utilizing the android bottom navigation we changed less important buttons to outlined instead of filled based on material design and changed the text in some buttons for clarity we also adjusted the background to make text boxes clearer monetization adding a storefront for farmers community members and other users to create listings and sell goods and services creating affiliate opportunities for storefront creating referral bonuses for new members who set up a pollinator kit relevant advertising local honey pollinator tools equipment services measuring engagement new users initially scanning qr code upon receipt and setup of pollinator kit creating a new profile and viewing other pollinator locations member profiles and offerings through the app existing users using the app to communicate with other members viewing other pollinator locations editing profile information as needed and interacting with administrators owners management owners admins volunteers approving all incoming requests by new users to be added to the pathway editing deleting profiles as needed and general maintenance stretch features adding storefront for sale of pollinator related goods and services adding blog style board with new information and announcements adding volunteer opportunities adding contact information add informational pages about various pollinators tools for identifying pollinators found at plantings | front_end |
|
Research_Documents_Curation_with_NLP | research documents curation with nlp contributors bowen chen ao luo yihan luo ho kwan henry zhang description applied finance project from ucla anderson using natural language processing techniques to classify and summarize quantitative finance research papers following the technique proposed by meng s team we implemented a research paper classification engine by using weakly supervised nlp technique in order to build a document classifier the original paper could be found here required packages the following packages are already pre built with anaconda distribution numpy pandas seaborn glob os the following packages are used to complete nlp specific tasks gensim https radimrehurek com gensim used to train word embedding nltk https www nltk org used to preprocess text documents the following packages are used to retrieve and transform the research documents arxiv https arxiv org help api index used to perform batch downloads for research papers in pdf format from arxiv pdf2txt https pypi org project pdf2text used to transform pdf files to txt files the main algorithm is implemented in python 3 except for pdf2txt which used python 2 roadmap 1 download 1000 papers in pdf format from arxiv completed 2 transformed 1000 papers in pdf format to txt format completed 3 train a word embedding using gensim completed 4 generate a large set of pseudo documents completed specifications downloading papers we downloaded 2028 quantitative finance papers from arxiv using the api provided taking around 2 hours to process transform papers we transform the 2028 papers into txt format using pdf2txt 1007 papers were valid the txt files could then be read in as a list of strings under the same directory this process could only be completed in python 2 training word embedding we implemented the word embedding training in gensim topic modeling package in python 3 the embedding was trained on the entire length of 1007 documents with 50 classes chosen empircally prior to feeding into the traning algorithm we preprocessed data in 4 different aspects remove white spaces remove words with length less than 2 remove punctuations remove numbers we did not remove stopwords since we believe this word embedding matrix will be used for generating pseudo documents which will definetely require the presence of these words we also did not perform word stemming stemming the word will not work particularly well in finance since most of the keywords in finance are derivative words already see equity trading fixed income etc performming stemming will distort the original meaning of the word taking equity for example if we perform stemming on equity it will become equal which destroys the oringinal meaning of our keyword | ai |
|
NLP | nlp a course on natural language processing binder https mybinder org badge logo svg https mybinder org v2 gh tiagoft nlp head informa es b sicas tiago tavares tiagoft1 insper edu br sala ti 25 7o andar p1 aulas ter qui 9h45 atendimento quintas 13h30 15h00 sala ti 25 7o andar p1 din mica das aulas cada aula tem um notebook aps ser feita individualmente num server comum do discord duas provas ass ncronas 50 da nota 5 partes do aps 50 da nota condi o de barreira m dia das provas 5 m dia da aps 5 1 express es regulares regex 1 term frequency 1 document frequency 1 modelo lingu stico n gramas 1 part of speech tagging 1 tf idf 1 stemming lematiza o 1 wordnet 1 naive bayes 1 regress o log stica 1 estudo de caso classifica o de textos 1 nmf e lda 1 visualiza o 1 clustering 1 classificadores mlp 1 camadas de embedding 1 geradores de conte do com lstm 1 tradu o com geradores seq2seq 1 aten o e transformers 1 bert 1 gpt 1 classifica o de udio 1 codecs de udio 1 modelos multimodais clip clap | ai |
|
imwt22 | dead lovers society a magazine on tragic love final project information modelling and web technologies fabio vitali university of bologna ma dhdk the project is accessible at the following link https deadloversociety github io imwt22 contributors martina pensalfini mailto martina pensalfini studio unibo it orsola maria borrini mailto orsolamaria borrini studio unibo it manuele veggi mailto manuele veggi studio unibo it | server |
|
OpenEpubReader | openepubreader epub reader app using readium mobile | android hilt jetpack-compose readium-2 | front_end |
Anonymer | anonymer redis https img shields io badge redis 23dd0031 svg style for the badge logo redis logocolor white net https img shields io badge net 5c2d91 style for the badge logo net logocolor white react https img shields io badge react 2320232a svg style for the badge logo react logocolor 2361dafb anonymer is student project done for advanced databases subject at faculty of electronic engineering university of ni contributors student id emilija ojba i 18026 matija peleti 18043 or e anti 17544 a href https github com djoleant internclix graphs contributors img src https contrib rocks image repo djoleant internclix a stack redis asp net webapi react js quickstart bash clone repository git clone https github com djoleant anonymer git cd anonymer server start cd anonymer anonymer dotnet watch run client start cd anonymerapp npm install npm start start redis in docker docker run name redis 6opz p 49153 6379 d redis backend struktura kljuc vrednost next post id globalni id za ispovest next category id globalni id za kategoriju next person id globalni id za person categories all set id jeva svih kategorija category id kategorije posts lista id jeva objava u kategoriji category id kategorije postssorted sortirani skup id jeva objava u kategoriji category id kategorije name naziv kategorije post id objave post objekat te objave na osnovu id ja post id objave comments lista id jeva komentara neke objave post id objave upvotes set id jeva osoba koje su upvoteovale post id objave downvotes set id jeva osoba koje su downvoteovale comment id komentara comment komentar objekat comment id komentara upvotes set id jeva osoba koje su upvoteovale comment id komentara downvotes set id jeva osoba koje su downvoteovale person id osobe username username osobe person id osobe posts lista id jeva objava te osobe entiteti post string text datetime time string author id int upvotes int downvotes string categoryid person string id string username category comment string text int upvotes int downvotes string author id datetime time | dotnet nosql nosql-database react redis | server |
Project-Group-30 | info30005 project repo web information technologies project members amelia lee amelia lee andy low andylow14 david liu megagnarly jocelyn sim jocesim25 sahil tandon stndn0 and sahil on 2nd machine project notes please ensure you have npm version 8 6 0 installed or greater how to update npm https docs npmjs com try the latest stable version of npm ensure you have npm express and mongoose installed commands npm install express npm install mongoose npm install dotenv npm install body parser npm install passport npm install passport local npm install express flash npm install express session npm install bcryptjs npm install save sessionstorage misc notes start the server using npm start stop the server using using ctrl c so that the port is freed up | server |
|
Computer-Vision | computer vision computer vision related scripts | ai |
|
lamden | lamden blockchain lamden is an open source python based blockchain development platform designed to help developers create new blockchain applications and networks quickly and easily with lamden developers can leverage the power of blockchain technology to create secure and decentralized applications features quick and easy setup lamden provides a suite of tools and infrastructure to help developers get started with blockchain development quickly and easily flexible and customizable lamden is designed to be flexible and customizable so developers can tailor it to meet their specific needs and requirements secure and decentralized lamden is built on the principles of security and decentralization so applications developed with lamden are secure and decentralized by default getting started getting started with lamden is easy our team has produced a great documentation which you can check out here https arko lamden io community join the lamden community to connect with other developers and get help and support you can find us on telegram https t me lamdenchat discord https discord com invite 3vb8brmek2 twitter https twitter com lamdentau medium https blog lamden io reddit https www reddit com r lamden contributions lamden is an open source project and contributions are welcome if you re interested in contributing check out our contribution guidelines contributing md to get started | blockchain smart-contracts | blockchain |
eva.js | eva js interactive game engine p align center a href https eva js org target blank rel noopener noreferrer img width 300 src https user images githubusercontent com 4632277 145818886 e0b1ca70 d789 4735 a8fe 411046263aa5 png alt eva js logo a p npm version https img shields io npm v eva eva js npm size https img shields io bundlephobia minzip eva eva js npm download https img shields io npm dm eva eva js english chinese readme cn md introduction eva js is a front end game engine specifically for creating interactive game projects easy to use eva js provides out of box game components for developers to use right away yes it s simple and elegant high performance eva js is powered by efficient runtime and rendering pipeline pixi js http pixijs io which makes it possible to unleash the full potential of your device scalability thanks to the ecs entity component system structure you can expand your needs by highly customizable apis the only limitation is your imagination documentation you can find the eva js documentation on eva js org https eva js org we appreciate your devotion by sending pull requests to this repository https github com eva engine eva engine github io checking out the live example https eva js org playground quick start https eva js org tutorials quickstart start demo https github com eva engine start demo awesome https github com eva engine awesome usage with npm bash npm i eva eva js eva plugin renderer eva plugin renderer img save in browser html script src https unpkg com eva eva js 1 0 4 dist eva min js script example html canvas id canvas canvas javascript import game gameobject resource resource type from eva eva js import renderersystem from eva plugin renderer import img imgsystem from eva plugin renderer img resource addresource name imagename type resource type image src image type png url https gw alicdn com tfs tb1dnzoovb2gk0jszk9xxaegfxa 658 1152 webp preload true const game new game systems new renderersystem canvas document queryselector canvas width 750 height 1000 new imgsystem const image new gameobject image size width 750 height 1319 origin x 0 y 0 position x 0 y 319 anchor x 0 y 0 image addcomponent new img resource imagename game scene addchild image questions for questions and support please use gitter https gitter im eva engine eva js or wechat https weixin qq com to scan this qr code https gw alicdn com imgextra i4 o1cn015w09ux1qd1raxbkyn 6000000001941 2 tps 610 1279 png issues please make sure to read the issue reporting checklist github issue template md before opening an issue issues not conforming to the guidelines may be closed immediately changelog release notes https github com eva engine eva js releases in documentation contribute how to contribute github how to contribute md license the eva js is released under the mit license see license file license | evajs pixijs front-end game-engine famework canvas2d webgl | front_end |
etherchain-light | etherchain light lightweight blockchain explorer for your private ethereum chain etherchain light is an ethereum blockchain explorer built with nodejs express and parity it does not require an external database and retrieves all information on the fly from a backend ethereum node while there are several excellent ethereum blockchain explorers available etherscan ether camp and etherchain they operate on a fixed subset of ethereum networks usually the mainnet and testnet currently there are no network agnostic blockchain explorers available if you want to develop dapps on a private testnet or would like to launch a private consortium network etherchain light will allow you to quickly explore such chains a demo instance connected to the kovan ethereum testnet is available at light etherchain org http light etherchain org an example of a verified contract source can be found at 0x0cf37d2d45427a1380db12c9b352d6f083143817 https light etherchain org account 0x0cf37d2d45427a1380db12c9b352d6f083143817 an example of a transaction where the corresponding solidity function name and parameters have been identified can be found at 0x82da63f3d998415b748111e6f1d11051167fb995fdca990acd3cfd5a8b397c20 https light etherchain org tx 0x82da63f3d998415b748111e6f1d11051167fb995fdca990acd3cfd5a8b397c20 current features browse blocks transactions accounts and contracts view pending transactions display contract internal calls call create suicide upload verify contract sources show solidity function calls parameters for contracts with available source code display the current state of verified contracts named accounts advanced transaction tracing vm traces state diff view failed transactions live backend node status display submit signed transactions to the network support for all bootswatch https bootswatch com skins accounts enumeration signature verification supports ipc and http backend connections responsive layout planned features erc20 token support missing a feature please request it by creating a new issue https github com gobitfly etherchain light issues usage notes this blockchain explorer is intended for private ethereum chains as it does not have a dedicated database all data will be retrived on demand from a backend parity node some of those calls are ressource intensive e g retrieval of the full tx list of an account and do not scale well for acounts with a huge number of transactions we currently develop the explorer using the kovan testnet but it will work with every parity compatible ethereum network configuration the explorer is still under heavy development if you find any problems please create an issue or prepare a pull request getting started setup from source supported os ubuntu 16 04 supported ethereum backend nodes parity geth is currently not supported as it does not allow account and received sent tx enumeration 1 setup a nodejs npm environment 2 install the latest version of the parity ethereum client 3 start parity using the following options parity chain yourchain tracing on fat db on pruning archive 4 clone this repository to your local machine git clone https github com gobitfly etherchain light recursive make sure to include recursive in order to fetch the solc bin git submodule 5 install all dependencies npm install 6 rename config js example into config js and adjust the file to your local environment 7 start the explorer npm start 8 browse to http localhost 3000 setup using docker build then run the container bash docker build t etherchain light docker run p 3000 3000 etherchain light or directly bind the config js file to avoid rebuilding the image bash docker run p 3000 3000 v pwd config js usr src app config js etherchain light setup using docker compose bash docker compose up | ethereum parity web3 blockchain-explorer | blockchain |
documentation | documentation has been folded into the emulsify website repo emulsify design system gitbook assets logo png emulsify is an open source tool for creating design systems with reusable components and clear guidelines for teams emulsify helps organizations scale their design while reducing cost streamlining workflows and improving accessibility emulsify design system contains multiple packages which can be used individually to solve small problems or together to solve big ones see below for some of the popular packages starters starters contain application specific configuration and files for example the drupal starter contains the info yml file that defines the drupal theme s name and other metadata as well as other drupal specific files drupal https github com emulsify ds emulsify drupal emulsify drupal is a full prototyping development environment using storybook https storybook js org as a component library and webpack https webpack js org as a build engine it is also a drupal https www drupal org theme it can be used as a standalone prototyping tool or inside a drupal installation wordpress https github com emulsify ds emulsify wordpress theme in progress not ready for active use systems systems are used to define components and assets emulsify projects opt into using systems via the emulsify cli once a system has been selected for a project the system mandates how components are organized and how required components assets are installed it also gives developers the ability to find and install non required components compound https github com emulsify ds compound compound is the default emulsify system and currently includes a variant for drupal supporting projects emulsify cli https github com emulsify ds emulsify cli this is a command line interface for emulsify with it you can initialize a project and identify a system like compound for your project once initialized you can install individual components from that system into your project as boiler plate code emulsify twig the emulsify twig repositories includes two functions bem and add attributes the bem function provides a simple way to programmatically generate bem classes on elements in your components the add attributes function provides a way to programmatically add any html attribute including classes to elements of your components emulsify twig extensions https github com emulsify ds emulsify twig extensions this repo contains the javascript version of the bem and add attributes extensions for compatibility with twig js which makes the work in storybook emulsify twig https github com emulsify ds emulsify twig this repo is a drupal module that contains the php version of the bem and add attributes extensions for support in a drupal project style guide gatsby theme emulsify https github com emulsify ds gatsby theme emulsify workspace a fully customizable style guide powered by gatsbyjs https www gatsbyjs org the style guide is a gatsby theme and instructions for installation and usage can be found here https github com emulsify ds gatsby theme emulsify workspace example websites repos the examples below were created to demonstrate how multiple properties under the same organization could create properties that implement varied languages twig and react in this case and share things that make sense we created a twig repository for the drupal sites a react repository for the gatsby react site and then a separate scss repository that is shared between all of them this means the organizations styles are defined in one place and each language just needs to ensure their markup meets the organizations expectations no duplicating styles across languages the western arts site is also unique because it uses some of the components from the shared repository but also contains custom components used only by that property this demonstrates how individual properties can utilize the organizational components off the shelf without any custom development but expand their component library to fulfill unique business needs websites western university https github com emulsify ds westernuni the western university site is a drupal site which implements the western up twig and western up scss repos off the shelf western arts https github com emulsify ds westernarts the western arts site is a drupal site with implements some of the western up twig scss components off the shelf but also contains custom components not a part of the western u organizational components western law https github com emulsify ds western law the western law site is a gatsby and react site which uses the western up react scss repos for its components western identity https github com emulsify ds western identity the western identity site is a gatsby powered guidelines site that contains information on branding components and other related documentation component repositories western up scss https github com emulsify ds western up scss the western up scss repository contains all of the styles used by western u properties since both the twig and react repos below are configured to consume scss we only write the styles once and let each language focus on creating high quality markup in their respective languages western up twig https github com emulsify ds western up twig the western up twig repo contains the twig components available to all western u twig based properties western up react https github com emulsify ds western up react the western up react repo contains the react components available to all western u react based properties contributors table tr td align center style word wrap break word width 150 0 height 150 0 a href https github com modulesunraveled img src https avatars githubusercontent com u 1663810 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt brian lewis br sub style font size 14px b brian lewis b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com amazingrando img src https avatars githubusercontent com u 409903 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt randy oest br sub style font size 14px b randy oest b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com cruno91 img src https avatars githubusercontent com u 1760366 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt chris runo br sub style font size 14px b chris runo b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com acouch img src https avatars githubusercontent com u 512243 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt aaron couch br sub style font size 14px b aaron couch b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com thejimbirch img src https avatars githubusercontent com u 5177009 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt jim birch br sub style font size 14px b jim birch b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com stephencapellic img src https avatars githubusercontent com u 13124872 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt stephen musgrave br sub style font size 14px b stephen musgrave b sub a td tr tr td align center style word wrap break word width 150 0 height 150 0 a href https github com acrollet img src https avatars githubusercontent com u 101649 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt adrian rollett br sub style font size 14px b adrian rollett b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com hanoii img src https avatars githubusercontent com u 677879 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt ariel barreiro br sub style font size 14px b ariel barreiro b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com ccjjmartin img src https avatars githubusercontent com u 12279982 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt chris martin br sub style font size 14px b chris martin b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com jfitzsimmons2 img src https avatars githubusercontent com u 1531748 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt joe fitzsimmons br sub style font size 14px b joe fitzsimmons b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com kurttrowbridge img src https avatars githubusercontent com u 848721 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt kurt trowbridge br sub style font size 14px b kurt trowbridge b sub a td td align center style word wrap break word width 150 0 height 150 0 a href https github com gitbook bot img src https avatars githubusercontent com u 31919211 v 4 width 100 style border radius 50 align items center justify content center overflow hidden padding top 10px alt gitbook bot br sub style font size 14px b gitbook bot b sub a td tr table | os |
|
HamoyeStage2 | author notes header of the course include a 1280 640 image course title in sentence case and a concise description in emphasis in your repository settings enable template repository add your 1280 640 social image auto delete head branches add your open source license github uses creative commons attribution 4 0 international introduction to github get started using github in less than an hour author notes start of the course include start button a note about actions minutes and tell the learner why they should take the course each step should be wrapped in details summary with an id set the start details should have open as well do not use quotes on the details tag attributes step0 people use github to build some of the most advanced technologies in the world whether you re visualizing data or building a new game there s a whole community and set of tools on github that can help you do it even better github skills introduction to github course guides you through everything you need to start contributing in less than an hour who is this for new developers new github users and students what you ll learn we ll introduce repositories branches commits and pull requests what you ll build we ll make a short markdown file you can use as your profile readme https docs github com account and profile setting up and managing your github profile customizing your profile managing your profile readme prerequisites none this course is a great introduction for your first day on github how long this course is four steps long and takes less than one hour to complete how to start this course 1 above these instructions right click use this template and open the link in a new tab use this template https user images githubusercontent com 1221423 169618716 fb17528d f332 4fc5 a11a eaa23562665e png 2 in the new tab follow the prompts to create a new repository for owner choose your personal account or an organization to host the repository we recommend creating a public repository private repositories will use actions minutes https docs github com en billing managing billing for github actions about billing for github actions create a new repository https user images githubusercontent com 1221423 169618722 406dc508 add4 4074 83f0 c7a7ad87f6f3 png 3 after your new repository is created wait about 20 seconds then refresh the page follow the step by step instructions in the new repository s readme endstep0 author notes step 1 choose 3 5 steps for your course the first step is always the hardest so pick something easy link to docs github com for further explanations encourage users to open new tabs for steps details id 1 summary h2 step 1 create a branch h2 summary welcome to introduction to github wave what is github github is a collaboration platform that uses git https docs github com get started quickstart github glossary git for versioning github is a popular place to share and contribute to open source https docs github com get started quickstart github glossary open source software br tv video what is github https www youtube com watch v w3jlju7dt5e what is a repository a repository https docs github com get started quickstart github glossary repository is a project containing files and folders a repository tracks versions of files and folders br tv video exploring a repository https www youtube com watch v r8oawrcmlrw what is a branch a branch https docs github com en get started quickstart github glossary branch is a parallel version of your repository by default your repository has one branch named main and it is considered to be the definitive branch you can create additional branches off of main in your repository you can use branches to have different versions of a project at one time on additional branches you can make edits without impacting the main version branches allow you to separate your work from the main branch in other words everyone s work is safe while you contribute br tv video branches https www youtube com watch v xgqmu81g1yy what is a profile readme a profile readme https docs github com account and profile setting up and managing your github profile customizing your profile managing your profile readme is essentially an about me section on your github profile where you can share information about yourself with the community on github com github shows your profile readme at the top of your profile page keyboard activity your first branch 1 open a new browser tab and navigate to this same repository then work on the steps in your second tab while you read the instructions in this tab 2 navigate to the code tab 3 click on the main branch drop down br img alt image showing my first branch entry src images my first branch png 4 in the field enter a name for your branch my first branch 5 click create branch my first branch to create your branch 6 move on to step 2 br note if you made a public repository and want to confirm you correctly set up your first branch wait about 20 seconds then refresh this page the one you re following instructions from github actions https docs github com en actions will automatically close this step and open the next one details author notes step 2 start this step by acknowledging the previous step define terms and link to docs github com details id 2 summary h2 step 2 commit a file h2 summary you created a branch tada creating a branch allows you to edit to your project without changing the main branch now that you have a branch it s time to create a file and make your first commit what is a commit a commit https docs github com pull requests committing changes to your project creating and editing commits about commits is a set of changes to the files and folders in your project a commit exists in a branch keyboard activity your first commit the following steps will guide you through the process of committing a change on github committing a change requires first adding a new file to your new branch 1 on the code tab make sure you re on your new branch my first branch 2 select the add file drop down and click create new file br create new file option images create new file png 3 in the name your file field enter profile md 4 in the edit new file area copy the following content to your file welcome to my github profile img alt profile md file screenshot src images my profile file png 5 for commits you can enter a short commit message that describes what you changes you made this message helps others know what s included in your commit github offers a simple default message but let s change it slightly for practice first enter add profile md in the first text entry field below commit new file then if you want to confirm what your screen should look like expand the dropdown below details summary expand to see the screenshot summary img alt screenshot of adding a new file with a commit message src images commit full screen png details 6 in this lesson we ll ignore the other fields and click commit new file 7 move on to step 3 br note like before you can wait about 20 seconds then refresh this page the one you re following instructions from and github actions https docs github com en actions will automatically close this step and open the next one details author notes step 3 just a historic note the previous version of this step forced the learner to write a pull request description checked that main was the receiving branch and that the file was named correctly details id 3 summary h2 step 3 open a pull request h2 summary nice work making that commit sparkles now that you ve created a commit it s time to share your proposed change through a pull request what is a pull request collaboration happens on a pull request the pull request shows the changes in your branch to other people this pull request is going to keep the changes you just made on your branch and propose applying them to the main branch br tv video introduction to pull requests https youtu be kjr pifldl4 keyboard activity create a pull request you may have noticed after your commit that a message displayed indicating your recent push to your branch and providing a button that says compare pull request screenshot of message and button images compare and pull request png if you want feel free to click compare pull request and then skip to step 6 below if you don t click the button the instructions below walk you through manually setting up the pull request 1 click on the pull requests tab in your repository 2 click new pull request 3 in the base dropdown make sure main is selected 4 select the compare dropdown and click my first branch br img alt screenshot showing both branch selections src images pull request branches png 5 click create pull request 6 enter a title for your pull request add my first file 7 the next field helps you provide a description of the changes you made feel free to add a description of what you ve accomplished so far as a reminder you have created a branch created a file and made a commit br img alt screenshot showing pull request src images pull request description png 8 click create pull request 9 move on to step 4 br note like before you can wait about 20 seconds then refresh this page the one you re following instructions from and github actions https docs github com en actions will automatically close this step and open the next one as a perk you may see evidence of github actions running on the tab with the pull request opened the image below shows a line you might see on your pull request after the action finishes running br img alt screenshot of an example of an actions line src images actions to step 4 png details author notes step 4 just a historic note the previous version of this step required responding to a pull request review before merging the previous version also handled if users accidentally closed without merging details id 4 summary h2 step 4 merge your pull request h2 summary nicely done friend sunglasses you successfully created a pull request you can now merge your pull request what is a merge a merge https docs github com en get started quickstart github glossary merge adds the changes in your pull request and branch into the main branch br tv video understanding the github flow https www youtube com watch v pbi2rz zoxu as noted in the previous step you may have seen evidence of an action running which automatically progresses your instructions to the next step you ll have to wait for it to finish before you can merge your pull request it will be ready when the merge pull request button is green screenshot of green merge pull request button images green merge pull request png keyboard activity merge the pull request 1 click merge pull request 1 click confirm merge 1 once your branch has been merged you don t need it anymore to delete this branch click delete branch br img alt screenshot showing delete branch button src images delete branch png 2 check out the finish step to see what you can learn next br note like before you can wait about 20 seconds then refresh this page the one you re following instructions from and github actions https docs github com en actions will automatically close this step and open the next one details author notes finish review what we learned ask for feedback provide next steps details id x open summary h2 finish h2 summary congratulations friend you ve completed this course and joined the world of developers img src https octodex github com images collabocats jpg alt celebrate width 300 align right here s a recap of your accomplishments you learned about github repositories branches commits and pull requests you created a branch a commit and a pull request you merged a pull request you made your first contribution tada what s next if you d like to make a profile readme use the simplified instructions below or follow the instructions in the managing your profile readme https docs github com account and profile setting up and managing your github profile customizing your profile managing your profile readme article 1 make a new public repository with a name that matches your github username 2 create a file named readme md in it s root the root means not inside any folder in your repository 3 edit the contents of the readme md file 4 if you created a new branch for your file open and merge a pull request on your branch 5 we d love to see your new profile share your profile on social media and tag us 6 lastly we d love to hear what you thought of this course in our discussion board https github com skills github discussions check out these resources to learn more or get involved are you a student check out the student developer pack https education github com pack take another github skills course https github com skills read the github getting started docs https docs github com en get started to find projects to contribute to check out github explore https github com explore details author notes footer add a link to get support github status page code of conduct license link get help post in our discussion board https github com skills github discussions bull review the github status page https www githubstatus com copy 2022 github bull code of conduct https www contributor covenant org version 2 1 code of conduct code of conduct md bull cc by 4 0 license https creativecommons org licenses by 4 0 legalcode | cloud |
|
computer-vision | computer vision a set of libraries and projects which will be used in all computer vision applications documentation http uwarg github io computer vision html system overview p img src http i imgur com zt84szq jpg alt computer vision flowchart width 400px height 530px p photos to use for testing 2014 competition https drive google com open id 0byspwxvmbm4jwghrzgk5uwnqnm8 authuser 0 2015 competition https drive google com open id 0b8ozhzojjmqbfkx4wtloynjqv3dkchfxc0f5c1jku2fhrzrsm2vvu3vzv3jgeulzqu9in00 authuser 1 2016 competition https ece uwaterloo ca warg downloads flightdata competition2016 2017 competition https ece uwaterloo ca warg downloads flightdata competition2017 all flight data https ece uwaterloo ca warg downloads flightdata conops rules 2015 conops rules https drive google com open id 0byspwxvmbm4jajdsemzfb0o2ukk authuser 0 2016 conops rules https drive google com open id 0byspwxvmbm4junhyagn0nw5or1k 2017 conops rules https ece uwaterloo ca warg downloads flightdata competition2017 2017 20conops 20and 20rules 20161123 rev2 pdf 2018 conops rules https www unmannedsystems ca download 2018 student uas competition conops v1 0 24 aug 17 building and installing build system cmake to set up build environment then whatever cmake gives you depends on platform make for linux etc dependencies opencv 3 x https opencv org releases html boost 1 58 http www boost org users download decklink http www blackmagicdesign com support sdks optional zbar http zbar sourceforge net download html note on decklink dependencies the most recent tested sdk is desktop video 10 9 5 sdk when downloaded rename the extracted folder to decklinksdk then copy the entire folder into the usr local include directory such that usr local include decklinksdk linux include exists note on opencv help for installing opencv can be found here https www learnopencv com install opencv3 on ubuntu if installing from source installation instructions if you intend to use windows or mac you will need to run a docker image the docker image is pre configured with the appropriate libraries please refer to the instructions here docker installation http docs uwarg com computer vision for linux 1 install the dependencies listed above this may vary based on your system 2 clone the project 3 run build sh to build and compile the project refer to the detailed instructions here linux installation http docs uwarg com computer vision building the project linux running the program after compiling an executable will be present in the build directory to run it type warg cv testing the program after compiling in the build directory run make test or make test args v for verbose output goal to fill this chart with the right values judges evaluation sheet https drive google com open id 0b8ozhzojjmqbwtbwaxbixzbqcfk this evaluation sheet includes spots to fill with latitudes longitudes centroids areas volumes and qr code values licensing copyright c 2015 2016 waterloo aerial robotics group warg all rights reserved redistribution and use in source and binary forms with or without modification are permitted provided that the following conditions are met 1 redistributions of source code must retain the above copyright notice this list of conditions and the following disclaimer 2 redistributions in binary form must reproduce the above copyright notice this list of conditions and the following disclaimer in the documentation and or other materials provided with the distribution 3 usage of this code must be explicitly referenced to warg and this code cannot be used in any competition against warg 4 neither the name of the warg nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission this software is provided by warg as is and any express or implied warranties including but not limited to the implied warranties of merchantability and fitness for a particular purpose are disclaimed in no event shall warg be liable for any direct indirect incidental special exemplary or consequential damages including but not limited to procurement of substitute goods or services loss of use data or profits or business interruption however caused and on any theory of liability whether in contract strict liability or tort including negligence or otherwise arising in any way out of the use of this software even if advised of the possibility of such damage | computer-vision warg | ai |
ob-opportunity-iOS-Developer-or-Android-Developer-or-Both | job opportunity ios developer or android developer or both job type remote contract experience 5 years location united states visa required us citizen gc gc ead tn h4 responsibilities design and build applications for the ios and android platform ensure the performance quality and responsiveness of applications collaborate with a team to define design and ship new features supporting the entire application lifecycle from concept to design testing release and support troubleshooting and bug fixes for applications to ensure that codes are clean and secure help maintain code quality organization and automatization writing clean and efficient codes for android native and ril applications monitoring the performance of live apps and work on optimizing them at the code level identifying and resolving bottlenecks rectifying bugs and enhancing application performance performing unit and instrumentation tests on code collaborating with cross functional teams to define and design new features staying up to date with new mobile technology trends applications and protocols key requirements degree in computer science engineering or a related field prior experience as an ios developer or an android developer proficient with objective c or swift and cocoa touch experience with ios frameworks such as core data core animation etc experience with offline storage threading and performance tuning familiarity with restful apis to connect ios and android applications to back end services knowledge of other web technologies and ui ux standards understanding of apple s design principles and interface guidelines knowledge of low level c based libraries is preferred experience with performance and memory tuning with tools familiarity with cloud message apis and push notifications knack for benchmarking and optimization proficient understanding of code versioning tools such as git mercurial or svn familiarity with continuous integration | os |
|
moapd2023 | mobile app development bsc 2023 this course gives a fundamental overview of android programming concepts and the best practices for mobile app development description the mobile app development has seen significant growth in the recent past mainly due to the current computational power of modern tablets and mobile phones the development of mobile applications brings a set of different challenges to the developer such as where the application will run hardware specifications and how is the application performance when running os specifications this course provides fundamental knowledge on how to develop android applications using both java and kotlin programming languages and introduces the following topics the android application lifecycle the four different types of android components namely 1 activities 2 services 3 broadcast receivers and 4 content providers the design of user interfaces ui using layouts resources and a set of android ui controls e g textview edittext button checkbox progressbar among others how to share data between android components how to persist data using files and databases and how to manage the internal and external file storages the use of concurrency to improve speed and performance in android applications the development of multimedia applications using the built in camera and audio resources the use of geolocation information to develop location aware android applications the use of device sensors motion position environment and advanced sensors to collect additional information for android applications and the security aspects of android deployment to make an android application safer formal prerequisites the student must be familiar with at least one object oriented programming language such as java highly recommended c c or objective c the student must be able to design implement and test medium sized object oriented programs as covered at swu one of the following courses from the 1st and 2nd semesters of the bsc in software development bswu such as introductory programming with project algorithms and data structure or similar provides these background skills experience with kotlin programming language will be an advantage but is not compulsory knowledge of functional programming is not a requisite for this course intended learning outcomes after the course the student should be able to design and implement a non trivial android application identify the different types of android components their purpose and lifecycle and their role in the mobile application pipeline describe the android os architecture and how to build run and debug a mobile app design mobile applications based on the limitations and resources of mobile platforms design and implement mobile applications using different resources such as communication and multimedia local and remote data persistence location aware applications built in sensors and mobile concurrency plan and execute the deployment of an android application using android studio develop native android applications using the kotlin programming language learning activities the course spans 14 weeks with two hour lectures and two hour exercise sessions per week each week the students will read the weekly reading material develop an individual exercise based on the week s content and solve an optional challenge the students must install the android development environment used in the course on their laptops the installation guides for macos windows and linux will be available on learnit the apps presented in the lectures and those developed in the exercise sessions can be tested on standard android phones older versions ok or using the virtual device available on android studio the following table presents the lectures plane week date lecture 01 31 01 2023 getting started lecture01 02 07 02 2023 introduction to kotlin for mobile development lecture02 03 14 02 2023 app resources and basic user interfaces lecture03 04 21 02 2023 advanced user interfaces lecture04 05 28 02 2023 flexible data view and threads lecture05 06 07 03 2023 android backend with google firebase lecture06 07 14 03 2023 data and file storage lecture07 08 21 03 2023 cloud storage on android lecture08 09 28 03 2023 android location aware lecture09 10 11 04 2023 android multimedia and machine learning lecture10 11 18 04 2023 android sensors lecture11 12 25 04 2023 jetpack compose lecture12 13 02 05 2023 shrink obfuscate and optimize an android app lecture13 14 09 05 2023 guest lecturer 01 marcel barboza from mother gaia studio mandatory activities activities the course includes two mandatory assignments the students will develop a single android application that covers most of the topics presented the application and reports must be handed in and approved to be eligible for the final oral examination feedback students will receive feedback on their reports and solutions and whether they have passed the mandatory activities students who do not pass the mandatory assignment may get a second attempt to resubmit the revised solutions before the final exam details about submission and resubmission will be available on learnit the student will receive the grade na not approved at the ordinary exam if the mandatory activities are not approved and the student will use an exam attempt course literature android programming the big nerd ranch guide 5th edition by bryan sills brian gardner kristin marsicano and chris stewart published jul 25 2022 by big nerd ranch guides isbn 10 0137645546 isbn 13 978 0137645541 kotlin programming the big nerd ranch guide 2nd edition by andrew bailey david greenhalgh and josh skeen published nov 01 2021 by big nerd ranch guides isbn 10 0136891055 isbn 13 978 0136891055 student activity budget estimated distribution of learning activities for the typical student preparation for lectures and exercises 14 lectures 28 exercises 28 assignments 15 exam with preparation 15 ordinary exam exam type b oral exam external 7 point scale exam variation b1h oral exam with time for preparation home exam duration for the preparation preparation time at home 1 week the oral examination will be individual we will publish on learnit after correcting the mandatory assignment a list with consecutive exam slots the exam will take about 20 minutes in total these will be the steps of the exam draw randomly one question from the examiners table i e the main question and write your outline on the whiteboard blackboard the student may bring an a4 paper with the exam plan e g outline in bullet form keywords and one or two figures to look at the paper briefly 30 seconds after drawing the question then the student should put this paper aside and not look at it again during the exam the student will have a maximum of 8 minutes to talk about the selected topic while the student speaks the examiners may ask some questions the examiners will ask additional questions about the topic or other topics within the curriculum including topics on the slides weekly work plans exercises mandatory assignments and the reading material grading and feedback we strongly recommend that the student prepare and plan the content of each course topic the student must start the presentation by giving the examiners a brief overview of the topic and then focus on the more exciting and substantial parts of the questions leaving out trivial subjects be sure to be precise in using the correct terminologies for android development exam duration per student for the oral exam 20 minutes reexam reexam type b oral exam external 7 point scale reexam variation b1h oral exam with time for preparation home reexam duration per student for the oral exam 20 minutes time and data ordinary exam wed 14 jun 2023 09 00 21 00 ordinary exam thu 15 jun 2023 09 00 21 00 ordinary exam fri 16 jun 2023 09 00 21 00 reexam wed 16 aug 2023 13 00 18 00 | front_end |
|
Fibonacci-Numbers-Generator | https github com aj anuj fibonacci numbers generator assets 26610301 449f63bb bddb 4eed 966c 71ef7a600695 documentation this web app for generating the first n fibonacci numbers has been build by using django framework for backend relational database and react js for frontend development this design decision is taken to maintain separation of concerns note 0 is being considered as the first fibonacci number the directory structure is as follows readme md cz biohub env first n fib nums frontend fib nums first n fib nums directory contains the codebase for the backend of the web app frontend fib nums directory contains the codebase for the frontend of the web app design details backend design the backend server for this web app has the following 1 a django model called fibonaccitable this model is the relational database for the web app 2 the fibonacitable has the following schema a primary key column name fib no data type bigintegerfield with db index true b name value data type charfield with max length 100 3 one view called fib nums which caters the http get request from the front end frontend design the frontend is a modern react user interface with two pages one with a simple form to get a input from user and the second to display the respective fibonacci numbers the api call is made when the user clicks on the submit button on the first page implementation details 1 on loading the web app in the browser the user will land on the first page where they can enter a valid integer positive n to generate the first n fibonacci numbers 2 after entering the integer and clicking on the submit button a check will be performed to see if the integer is valid if not a message will be displayed to enter a valid integer 3 if the integer is valid an api call will be made to the backend to get the first n fibonacci numbers 4 in the backend i am using the sqlite3 database which comes with django framework it will be first checked if the first n fibonacci numbers already exist in the database if yes they will be retrieved and a response will be sent back 5 if they don t exist the remaining fibonacci numbers are generated and saved in the database for future use and then the result is sent back 6 in the front end after getting the api response back the data fibonacci numbers is transformed into needed form and displayed on a second page 7 if there is any error during an api call the error is caught and logged which can be seen in the developer tools of the browser requirments 1 please install django in your machine if not installed pip install django 2 please install django cors headers package if not installed pip install django cors headers 3 please install node if not installed brew install node steps to run the project steps to start the backend django server 1 go to the first n fib nums directory and run the following cmd python manage py runserver steps to start the frontend ui 1 go to the frontend fib nums directory and run the following cmd npm start the user interface will open up in the browser 2 you can start generating fibonacci numbers now | server |
|
pagoda | pagoda rapid easy full stack web development starter kit in go go report card https goreportcard com badge github com mikestefanello pagoda https goreportcard com report github com mikestefanello pagoda test https github com mikestefanello pagoda actions workflows test yml badge svg https github com mikestefanello pagoda actions workflows test yml license mit https img shields io badge license mit yellow svg https opensource org licenses mit go reference https pkg go dev badge github com mikestefanello pagoda svg https pkg go dev github com mikestefanello pagoda got https img shields io badge made 20with go 1f425f svg https go dev mentioned in awesome go https awesome re mentioned badge svg https github com avelino awesome go p align center img alt logo src https user images githubusercontent com 552328 147838644 0efac538 a97e 4a46 86a0 41e3abdf9f20 png height 200px p table of contents introduction introduction overview overview foundation foundation backend backend frontend frontend storage storage screenshots screenshots getting started getting started dependencies dependencies start the application start the application running tests running tests clients clients service container service container dependency injection dependency injection test dependencies test dependencies configuration configuration environment overrides environment overrides environments environments database database auto migrations auto migrations separate test database separate test database orm orm entity types entity types new entity type new entity type sessions sessions encryption encryption authentication authentication login logout login logout forgot password forgot password registration registration authenticated user authenticated user middleware middleware email verification email verification routes routes custom middleware custom middleware controller dependencies controller dependencies patterns patterns errors errors testing testing http server http server request request helpers request response helpers goquery goquery controller controller page page flash messaging flash messaging pager pager csrf csrf automatic template parsing automatic template parsing cached responses cached responses cache tags cache tags cache middleware cache middleware data data forms forms submission processing submission processing inline validation inline validation headers headers status code status code metatags metatags url and link generation url and link generation htmx support htmx support rendering the page rendering the page template renderer template renderer custom functions custom functions caching caching hot reload for development hot reload for development file configuration file configuration funcmap funcmap cache cache set data set data get data get data flush data flush data flush tags flush tags tasks tasks queues queues scheduled tasks scheduled tasks worker worker monitoring monitoring static files static files cache control headers cache control headers cache buster cache buster email email https https logging logging roadmap roadmap credits credits introduction overview pagoda is not a framework but rather a base starter kit for rapid easy full stack web development in go aiming to provide much of the functionality you would expect from a complete web framework as well as establishing patterns procedures and structure for your web application built on a solid foundation foundation of well established frameworks and modules pagoda aims to be a starting point for any web application with the benefit over a mega framework in that you have full control over all of the code the ability to easily swap any frameworks or modules in or out no strict patterns or interfaces to follow and no fear of lock in while separate javascript frontends have surged in popularity many prefer the reliability simplicity and speed of a full stack approach with server side rendered html even the popular js frameworks all have ssr options this project aims to highlight that go templates can be powerful and easy to work with and interesting frontend frontend libraries can provide the same modern functionality and behavior without having to write any js at all foundation while many great projects were used to build this all of which are listed in the credits credits section the following provide the foundation of the back and frontend it s important to note that you are not required to use any of these swapping any of them out will be relatively easy backend echo https echo labstack com high performance extensible minimalist go web framework ent https entgo io simple yet powerful orm for modeling and querying data frontend go server side rendered html combined with the projects below enable you to create slick modern uis without writing any javascript or css htmx https htmx org access ajax css transitions websockets and server sent events directly in html using attributes so you can build modern user interfaces with the simplicity and power of hypertext alpine js https alpinejs dev rugged minimal tool for composing behavior directly in your markup think of it like jquery for the modern web plop in a script tag and get going bulma https bulma io provides ready to use frontend components that you can easily combine to build responsive web interfaces no javascript dependencies storage postgresql https www postgresql org the world s most advanced open source relational database redis https redis io in memory data structure store used as a database cache and message broker screenshots inline form validation img src https user images githubusercontent com 552328 147838632 570a3116 1e74 428f 8bfc 523ed309ef06 png alt inline validation switch layout templates user registration img src https user images githubusercontent com 552328 147838633 c1b3e4f6 bbfd 44e1 b0be 884d1a83f8f4 png alt registration alpine js modal htmx ajax request img src https user images githubusercontent com 552328 147838634 4b84c5d5 dc3b 4280 ac12 247ab22184a3 png alt alpine and htmx getting started dependencies ensure the following are installed on your system go https go dev docker https www docker com docker compose https docs docker com compose install start the application after checking out the repository from within the root start the docker containers for the database and cache by executing make up git clone git github com mikestefanello pagoda git cd pagoda make up since this repository is a template and not a go library you do not use go get once that completes you can start the application by executing make run by default you should be able to access the application in your browser at localhost 8000 if you ever want to quickly drop the docker containers and restart them in order to wipe all data execute make reset running tests to run all tests in the application execute make test this ensures that the tests from each package are not run in parallel this is required since many packages contain tests that connect to the test database which is dropped and recreated automatically for each package clients the following make commands are available to make it easy to connect to the database and cache make db connects to the primary database make db test connects to the test database make cache connects to the primary cache make cache test connects to the test cache service container the container is located at pkg services container go and is meant to house all of your application s services and or dependencies it is easily extensible and can be created and initialized in a single call the services currently included in the container are configuration cache database orm web validator authentication mail template renderer tasks a new container can be created and initialized via services newcontainer it can be later shutdown via shutdown dependency injection the container exists to faciliate easy dependency injection both for services within the container as well as areas of your application that require any of these dependencies for example the container is passed to and stored within the controller so that the controller and the route using it have full easy access to all services test dependencies it is common that your tests will require access to dependencies like the database or any of the other services available within the container keeping all services in a container makes it especially easy to initialize everything within your tests you can see an example pattern for doing this here environments configuration the config package provides a flexible extensible way to store all configuration for the application configuration is added to the container as a service making it accessible across most of the application be sure to review and adjust all of the default configuration values provided in config config yaml environment overrides leveraging the functionality of viper https github com spf13 viper to manage configuration all configuration values can be overridden by environment variables the name of the variable is determined by the set prefix and the name of the configuration field in config config yaml in config config go the prefix is set as pagoda via viper setenvprefix pagoda nested fields require an underscore between levels for example yaml cache port 1234 can be overridden by setting an environment variable with the name pagoda cache port environments the configuration value for the current environment config app environment is an important one as it can influence some behavior significantly will be explained in later sections a helper function config switchenvironment is available to make switching the environment easy but this must be executed prior to loading the configuration the common use case for this is to switch the environment to test before tests are executed go func testmain m testing m set the environment to test config switchenvironment config envtest start a new container c services newcontainer run tests exitval m run shutdown the container if err c shutdown err nil panic err os exit exitval database the database currently used is postgresql https www postgresql org but you are free to use whatever you prefer if you plan to continue using ent https entgo io the incredible orm you can check their supported databases here https entgo io docs dialects the database driver and client is provided by pgx github com jackc pgx v4 and included in the container database configuration can be found and managed within the config package auto migrations ent https entgo io provides automatic migrations which are executed on the database whenever the container is created which means they will run when the application starts separate test database since many tests can require a database this application supports a separate database specifically for tests within the config the test database name can be specified at config database testdatabase when a container is created if the environment environments is set to config envtest the database client will connect to the test database instead drop the database recreate it and run migrations so your tests start with a clean ready to go database another benefit is that after the tests execute in a given package you can connect to the test database to audit the data which can be useful for debugging orm as previously mentioned ent https entgo io is the supplied orm it can swapped out but i highly recommend it i don t think there is anything comparable for go at the current time if you re not familiar with ent take a look through their top notch documentation https entgo io docs getting started an ent client is included in the container to provide easy access to the orm throughout the application ent relies on code generation for the entities you create to provide robust type safe data operations everything within the ent package in this repository is generated code for the two entity types listed below with the exception of the schema declaration entity types the two included entity types are user passwordtoken new entity type while you should refer to their documentation https entgo io docs getting started for detailed usage it s helpful to understand how to create an entity type and generate code to make this easier the makefile contains some helpers 1 ensure all ent code is downloaded by executing make ent install 2 create the new entity type by executing make ent new name user where user is the name of the entity type this will generate a file like you can see in ent schema user go though the fields and edges will be left empty 3 populate the fields and optionally the edges which are the relationships to other entity types 4 when done generate all code by executing make ent gen the generated code is extremely flexible and impressive an example to highlight this is one used within this application go entity err c orm passwordtoken query where passwordtoken id tokenid where passwordtoken hasuserwith user id userid where passwordtoken createdatgte expiration only ctx request context this executes a database query to return the password token entity with a given id that belong to a user with a given id and has a created at timestamp field that is greater than or equal to a given time sessions sessions are provided and handled via gorilla sessions https github com gorilla sessions and configured as middleware in the router located at pkg routes router go session data is currently stored in cookies but there are many options https github com gorilla sessions store implementations available if you wish to use something else here s a simple example of loading data from a session and saving new values go func somefunction ctx echo context error sess err session get some session key ctx if err nil return err sess values hello world sess values issomething true return sess save ctx request ctx response encryption session data is encrypted for security purposes the encryption key is stored in configuration configuration at config app encryptionkey while the default is fine for local development it is imperative that you change this value for any live environment otherwise session data can be compromised authentication included are standard authentication features you expect in any web application authentication functionality is bundled as a service within services authclient and added to the container if you wish to handle authentication in a different manner you could swap this client out or modify it as needed authentication currently requires sessions sessions and the session middleware login logout the authclient has methods login and logout to log a user in or out to track a user s authentication state data is stored in the session including the user id and authentication status prior to logging a user in the method checkpassword can be used to determine if a user s password matches the hash stored in the database and on the user entity routes are provided for the user to login and logout at user login and user logout forgot password users can reset their password in a secure manner by issuing a new password token via the method generatepasswordresettoken this creates a new passwordtoken entity in the database belonging to the user the actual token itself however is not stored in the database for security purposes it is only returned via the method so it can be used to build the reset url for the email rather a hash of the token is stored using bcrypt the same package used to hash user passwords the reason for doing this is the same as passwords you do not want to store a plain text value in the database that can be used to access an account tokens have a configurable expiration by default they expire within 1 hour this can be controlled in the config package the expiration of the token is not stored in the database but rather is used only when tokens are loaded for potential usage this allows you to change the expiration duration and affect existing tokens since the actual tokens are not stored in the database the reset url must contain the user and password token id using that getvalidpasswordtoken will load a matching non expired password token entity belonging to the user and use bcrypt to determine if the token in the url matches stored hash of the password token entity once a user claims a valid password token all tokens for that user should be deleted using deletepasswordtokens routes are provided to request a password reset email at user password and to reset your password at user password reset token user password token token registration the actual registration of a user is not handled within the authclient but rather just by creating a user entity when creating a user use hashpassword to create a hash of the user s password which is what will be stored in the database a route is provided for the user to register at user register authenticated user the authclient has two methods available to get either the user entity or the id of the user currently logged in for a given request those methods are getauthenticateduser and getauthenticateduserid middleware registered for all routes is middleware that will load the currently logged in user entity and store it within the request context the middleware is located at middleware loadauthenticateduser and if authenticated the user entity is stored within the context using the key context authenticateduserkey if you wish to require either authentication or non authentication for a given route you can use either middleware requireauthentication or middleware requirenoauthentication email verification most web applications require the user to verify their email address or other form of contact information the user entity has a field verified to indicate if they have verified themself when a user successfully registers an email is sent to them containing a link with a token that will verify their account when visited this route is currently accessible at email verify token and handled by routes verifyemail there is currently no enforcement that a user must be verified in order to access the application if that is something you desire it will have to be added in yourself it was not included because you may want partial access of certain features until the user verifies or no access at all verification tokens are json web tokens https jwt io generated and processed by the jwt https github com golang jwt jwt module the tokens are signed using the encryption key stored in configuration configuration config app encryptionkey it is imperative that you override this value from the default in any live environments otherwise the data can be comprimised jwt was chosen because they are secure tokens that do not have to be stored in the database since the tokens contain all of the data required including built in expirations these were not chosen for password reset tokens because jwt cannot be withdrawn once they are issued which poses a security risk since these tokens do not grant access to an account the ability to withdraw the tokens is not needed by default verification tokens expire 12 hours after they are issued this can be changed in configuration at config app emailverificationtokenexpiration there is currently not a route or form provided to request a new link be sure to review the email email section since actual email sending is not fully implemented to generate a new verification token the authclient has a method generateemailverificationtoken which creates a token for a given email address to verify the token pass it in to validateemailverificationtoken which will return the email address associated with the token and an error if the token is invalid routes the router functionality is provided by echo https echo labstack com guide routing and constructed within via the buildrouter function inside pkg routes router go since the echo instance is a service on the container which is passed in to buildrouter middleware and routes can be added directly to it custom middleware by default a middleware stack is included in the router that makes sense for most web applications be sure to review what has been included and what else is available within echo and the other projects mentioned a middleware package is included which you can easily add to along with the custom middleware provided controller dependencies the controller which is described in a section below serves two purposes for routes 1 it provides base functionality which can be embedded in each route most importantly page rendering described in the controller section below 2 it stores a pointer to the container making all services available within your route while using the controller is not required for your routes it will certainly make development easier see the following section for the proposed pattern patterns these patterns are not required but were designed to make development as easy as possible to declare a new route that will have methods to handle a get and post request for example start with a new struct type that embeds the controller go type home struct controller controller func c home get ctx echo context error func c home post ctx echo context error then create the route and add to the router go home home controller controller newcontroller c g get home get name home g post home post name home post your route will now have all methods available on the controller as well as access to the container it s not required to name the route methods to match the http method it is highly recommended that you provide a name for your routes most methods on the back and frontend leverage the route name and parameters in order to generate urls errors routes can return errors to indicate that something wrong happened ideally the error is of type echo httperror to indicate the intended http response code you can use return echo newhttperror http statusinternalservererror for example if an error of a different type is returned an internal server error is assumed the error handler https echo labstack com guide error handling is set to a provided route pkg routes error go in the buildrouter function that means that if any middleware or route return an error the request gets routed there this route conveniently constructs and renders a page which uses the template templates pages error go the status code is passed to the template so you can easily alter the markup depending on the error type testing since most of your web application logic will live in your routes being able to easily test them is important the following aims to help facilitate that the test setup and helpers reside in pkg routes router test go only a brief example of route tests were provided in order to highlight what is available adding full tests did not seem logical since these routes will most likely be changed or removed in your project http server when the route tests initialize a new container is created which provides full access to all of the services that will be available during normal application execution also provided is a test http server with the router added this means your tests can make requests and expect responses exactly as the application would behave outside of tests you do not need to mock the requests and responses request response helpers with the test http server setup test helpers for making http requests and evaluating responses are made available to reduce the amount of code you need to write see httprequest and httpresponse within pkg routes router test go here is an example how to easily make a request and evaluate the response go func testabout get t testing t doc request t setroute about get assertstatuscode http statusok todoc goquery a helpful included package to test html markup from http responses is goquery https github com puerkitobio goquery this allows you to use jquery style selectors to parse and extract html values attributes and so on in the example above todoc will return a goquery document created from the html response of the test http server here is a simple example of how to use it along with testify https github com stretchr testify for making assertions go h1 doc find h1 title assert len t h1 nodes 1 assert equal t about h1 text controller as previously mentioned the controller acts as a base for your routes though it is optional it stores the container which houses all services dependencies but also a wide array of functionality aimed at allowing you to build complex responses with ease and consistency page the page is the major building block of your controller responses it is a struct type located at pkg controller page go the concept of the page is that it provides a consistent structure for building responses and transmitting data and functionality to the templates all example routes provided construct and render a page it s recommended that you review both the page and the example routes as they try to illustrate all included functionality as you develop your application the page can be easily extended to include whatever data or functions you want to provide to your templates initializing a new page is simple go func c home get ctx echo context error page controller newpage ctx using the echo context the page will be initialized with the following fields populated context the passed in context tourl a function the templates can use to generate a url with a given route name and parameters path the requested url path url the requested url statuscode defaults to 200 pager initialized pager see below requestid the request id if the middleware is being used ishome if the request was for the homepage isauth if the user is authenticated authuser the logged in user entity if one csrf the csrf token if the middleware is being used htmx request data from the htmx headers if htmx made the request see below flash messaging while flash messaging functionality is provided outside of the controller and page within the msg package it s really only used within this context flash messaging requires that sessions sessions and the session middleware are in place since that is where the messages are stored creating messages there are four types of messages and each can be created as follows success msg success ctx echo context message string info msg info ctx echo context message string warning msg warning ctx echo context message string danger msg danger ctx echo context message string the message string can contain html rendering messages when a flash message is retrieved from storage in order to be rendered it is deleted from storage so that it cannot be rendered again the page has a method that can be used to fetch messages for a given type from within the template page getmessages typ msg type this is used rather than the funcmap because the page contains the request context which is required in order to access the session data since the page is the data destined for the templates you can use getmessages success for example to make things easier a template component is already provided located at templates components messages gohtml this will render all messages of all types simply by using template messages either within your page or layout template pager a very basic mechanism is provided to handle and facilitate paging located in pkg controller pager go when a page is initialized so is a pager at page pager if the requested url contains a page query parameter with a numeric value that will be set as the page number in the pager during initialization the items per page amount will be set to the default controlled via constant which has a value of 20 it can be overridden by changing pager itemsperpage but should be done before other values are set in order to not provide incorrect calculations methods include setitems items int set the total amount of items in the entire result set isbeginning determine if the pager is at the beginning of the pages isend determine if the pager is at the end of the pages getoffset get the offset which can be useful is constructing a paged database query there is currently no template yet to easily render a pager csrf by default all non get requests will require a csrf token be provided as a form value this is provided by middleware and can be adjusted or removed in the router the page will contain the csrf token for the given request there is a csrf helper component template which can be used to easily render a hidden form element in your form which will contain the csrf token and the proper element name simply include template csrf within your form automatic template parsing dealing with templates can be quite tedious and annoying so the page aims to make it as simple as possible with the help of the template renderer template renderer to start templates for pages are grouped in the following directories within the templates directory layouts base templates that provide the entire html wrapper layout this template should include a call to template content to render the content of the page pages templates that are specific for a given route page these must contain define content end which will be injected in to the layout template components a shared library of common components that the layout and base template can leverage specifying which templates to render for a given page is as easy as go page name home page layout main that alone will result in the following templates being parsed and executed when the page is rendered 1 layouts main gohtml as the base template 2 pages home gohtml to provide the content template for the layout 3 all template files located within the components directory 4 the entire funcmap funcmap the template renderer template renderer also provides caching and local hot reloading cached responses a page can have cached enabled just by setting page cache enabled to true the controller will automatically handle caching the html output headers and status code cached pages are stored using a key that matches the full request url and middleware cache middleware is used to serve it on matching requests by default the cache expiration time will be set according to the configuration value located at config cache expiration page but it can be set per page at page cache expiration cache tags you can optionally specify cache tags for the page by setting a slice of strings on page cache tags this provides the ability to build in cache invalidation logic in your application driven by events such as entity operations for example you can use the cache client cache on the container to easily flush cache tags flush tags if needed cache middleware cached pages are served via the middleware servecachedpage in the middleware package the cache is bypassed if the requests meet any of the following criteria 1 is not a get request 2 is made by an authenticated user cached pages are looked up for a key that matches the exact full url of the given request data the data field on the page is of type interface and is what allows your route to pass whatever it requires to the templates alongside the page itself forms the form field on the page is similar to the data field in that it s an interface type but it s meant to store a struct that represents a form being rendered on the page an example of this pattern is go type contactform struct email string form email validate required email message string form message validate required submission controller formsubmission then in your page go page controller newpage ctx page form contactform how the form gets populated with values so that your template can render them is covered in the next section submission processing form submission processing is made extremely simple by leveraging functionality provided by echo binding https echo labstack com guide binding validator https github com go playground validator and the formsubmission struct located in pkg controller form go using the example form above these are the steps you would take within the post callback for your route start by storing a pointer to the form in the context so that your get callback can access the form values which will be showed at the end go var form contactform ctx set context formkey form parse the input in the post data to map to the struct so it becomes populated this uses the form struct tags to map form values to the struct fields go if err ctx bind form err nil something went wrong process the submission which uses validator https github com go playground validator to check for validation errors go if err form submission process ctx form err nil something went wrong check if the form submission has any validation errors go if form submission haserrors all good now execute something in the event of a validation error you most likely want to re render the form with the values provided and any error messages since you stored a pointer to the form in the context in the first step you can first have the post handler call the get go if form submission haserrors return c get ctx then in your get handler extract the form from the context so it can be passed to the templates go page controller newpage ctx page form contactform if form ctx get context formkey form nil page form form contactform and finally your template html input id email name email type email class input value form email inline validation the formsubmission makes inline validation easier because it will store all validation errors in a map keyed by the form struct field name it also contains helper methods that your templates can use to provide classes and extract the error messages while validator https github com go playground validator is a great package that is used to validate based on struct tags the downside is that the messaging by default is not very human readable or easy to override within formsubmission seterrormessages the validation errors are converted to more readable messages based on the tag that failed validation only a few tags are provided as an example so be sure to expand on that as needed to provide the inline validation in your template there are two things that need to be done first include a status class on the element so it will highlight green or red based on the validation html input id email name email type email class input form submission getfieldstatusclass email value form email second render the error messages if there are any for a given field go template field errors form submission getfielderrors email headers http headers can be set either via the page or the context go page controller newpage ctx page headers headername header value go ctx response header set headername header value status code the http response status code can be set either via the page or the context go page controller newpage ctx page statuscode http statustoomanyrequests go ctx response status http statustoomanyrequests metatags the page provides the ability to set basic html metatags which can be especially useful if your web application is publicly accessible only fields for the description and keywords are provided but adding additional fields is very easy go page controller newpage ctx page metatags description the page description page metatags keywords string go software a component template is included to render metatags in core gohtml which can be used by adding template metatags to your layout url and link generation generating urls in the templates is made easy if you follow the routing patterns patterns and provide names for your routes echo provides a reverse function to generate a route url with a given route name and optional parameters this function is made accessible to the templates via the page field tourl as an example if you have route such as go profile profile controller ctr e get user profile user profile get name user profile and you want to generate a url in the template you can go call tourl user profile 1 which will generate user profile 1 there is also a helper function provided in the funcmap funcmap to generate links which has the benefit of adding an active class if the link url matches the current path this is especially useful for navigation menus go link call tourl user profile authuser id profile path extra class will generate html a href user profile 1 class is active extra class profile a assuming the current path is user profile 1 otherwise the is active class will be excluded htmx support htmx https htmx org is an awesome javascript library allows you to access ajax css transitions websockets and server sent events directly in html using attributes so you can build modern user interfaces with the simplicity and power of hypertext many examples of its usage are available in the included examples all navigation links use boost https htmx org docs boosting which dynamically replaces the page content with an ajax request providing a spa like experience all forms use either boost https htmx org docs boosting or hx post https htmx org docs triggers to submit via ajax the mock search autocomplete modal uses hx get https htmx org docs targets to fetch search results from the server via ajax and update the ui the mock posts on the homepage dashboard use hx get https htmx org docs targets to fetch and page posts via ajax all of this can be easily accomplished without writing any javascript at all another benefit of htmx https htmx org is that it s completely backend agnostic and does not require any special tools or integrations on the backend but to make things easier included is a small package to read and write http headers https htmx org docs requests that htmx uses to communicate additional information and commands the htmx package contains the headers for the request and response when a page is initialized page htmx request will also be initialized and populated with the headers that htmx provides if htmx made the request this allows you to determine if htmx is making the given request and what exactly it is doing which could be useful both in your route as well as your templates if you need to set any htmx headers in your page response this can be done by altering page htmx response layout template override to facilitate easy partial rendering for htmx requests the page will automatically change your layout template to use htmx gohtml which currently only renders template content this allows you to use an htmx request to only update the content portion of the page rather than the entire html this override only happens if the htmx request being made is not a boost request because boost requests replace the entire body element so there is no need to do a partial render conditional processing rendering since htmx communicates what it is doing with the server you can use the request headers to conditionally process in your route or render in your template if needed if your routes aren t doing multiple things you may not need this but it s worth knowing how flexible you can be a simple example of this go if page htmx request target search you know this request htmx is fetching content just for the search element go if eq htmx request target search render content for the search element end csrf token if csrf csrf protection is enabled the token value will automatically be passed to htmx to be included in all non get requests this is done in the footer template by leveraging htmx events https htmx org reference events rendering the page once your page is fully built rendering it via the embedded controller in your route can be done simply by calling renderpage go func c home get ctx echo context error page controller newpage ctx page layout main page name home return c renderpage ctx page template renderer the template renderer is a service on the container that aims to make template parsing and rendering easy and flexible it is the mechanism that allows the page to do automatic template parsing automatic template parsing the standard html template is still the engine used behind the scenes the code can be found in pkg services template renderer go here is an example of a complex rendering that uses multiple template files as well as an entire directory of template files go buf err c templaterenderer parse group page key home base main files layouts main pages home directories components execute data this will do the following cache caching the parsed template with a group of page and key of home so this parse only happens once set the base template file as main include the templates templates layout main gohtml and templates pages home gohtml include all templates located within the directory templates components include the funcmap funcmap execute the parsed template with data being passed in to the templates using the example from the page rendering rendering the page this is what the controller will execute go buf err c container templaterenderer parse group page key page name base page layout files fmt sprintf layouts s page layout fmt sprintf pages s page name directories components execute page if you have a need to separately parse and cache the templates then later execute you can separate the operations go err c templaterenderer parse group my group key my key base auth files layouts auth pages login directories components store go tpl err c templaterenderer load my group my key buf err tpl execute data custom functions all templates will be parsed with the funcmap funcmap so all of your custom functions as well as the functions provided by sprig https github com masterminds sprig will be available caching parsed templates will be cached within a sync map so the operation will only happen once per cache group and id be careful with your cache group and id parameters to avoid collisions hot reload for development if the current environment environments is set to config envlocal which is the default the cache will be bypassed and templates will be parsed every time they are requested this allows you to have hot reloading without having to restart the application so you can see your html changes in the browser immediately file configuration to make things easier and less repetitive parameters given to the template renderer must not include the templates directory or the template file extensions these are stored as constants within the config package if your project has a need to change either of these simply adjust the templatedir and templateext constants funcmap the funcmap package provides a function map template funcmap which will be included for all templates rendered with the template renderer template renderer aside from a few custom functions sprig https github com masterminds sprig is included which provides over 100 commonly used template functions the full list is available here http masterminds github io sprig to include additional custom functions add to the slice in getfuncmap and define the function in the package it will then become automatically available in all templates cache as previously mentioned redis https redis io was chosen as the cache but it can be easily swapped out for something else go redis https github com go redis redis is used as the underlying client but the container contains a custom client wrapper cacheclient that makes typical cache operations extremely simple this wrapper does expose the go redis client however at cacheclient client in case you have a need for it the cache functionality within the cacheclient is powered by gocache https github com eko gocache which was chosen because it makes interfacing with the cache service much easier and it provides a consistent interface if you were to use a cache backend other than redis the built in usage of the cache is currently only for optional page caching cached responses but it can be used for practically anything see examples below similar to how there is a separate test database separate test database to avoid writing to your primary database when running tests the cache supports a separate database as well for tests within the config the test database number can be specified at config cache testdatabase by default the primary database is 0 and the test database is 1 set data set data with just a key go err c cache set key my key data mydata save ctx set data within a group go err c cache set group my group key my key data mydata save ctx include cache tags go err c cache set key my key tags tag1 tag2 data mydata save ctx include an expiration go err c cache set key my key expiration time hour 2 data mydata save ctx get data go data err c cache get group my group key my key type mytype fetch ctx the type method tells the cache what type of data you stored so it can be cast afterwards with result ok data mytype flush data go err c cache flush group my group key my key execute ctx flush tags this will flush all cache entries that were tagged with the given tags go err c cache flush tags tag1 tag2 execute ctx tasks tasks are operations to be executed in the background either in a queue at a specfic time after a given amount of time or according to a periodic interval like cron some examples of tasks could be long running operations bulk processing cleanup notifications and so on since we re already using redis https redis io as a cache it s available to act as a message broker as well and handle the processing of queued tasks asynq https github com hibiken asynq is the library chosen to interface with redis and handle queueing tasks and processing them asynchronously with workers to make things even easier a custom client taskclient is provided as a service on the container which exposes a simple interface with asynq https github com hibiken asynq for more detailed information about asynq https github com hibiken asynq and it s usage review the wiki https github com hibiken asynq wiki queues all tasks must be placed in to queues in order to be executed by the worker worker you are not required to specify a queue when creating a task as it will be placed in the default queue if one is not provided asynq https github com hibiken asynq supports multiple queues which allows for functionality such as prioritization https github com hibiken asynq wiki queue priority creating a queued task is easy and at the minimum only requires the name of the task go err c tasks new my task save this will add a task to the default queue with a task type of my task the type is used to route the task to the correct worker worker options tasks can be created and queued with various chained options go err c tasks new my task payload taskdata queue critical maxretries 5 timeout 30 time second wait 5 time second retain 2 time hour save in this example this task will be assigned a task type of my task the task worker will be sent taskdata as the payload put in to the critical queue be retried up to 5 times in the event of a failure timeout after 30 seconds of execution wait 5 seconds before execution starts retain the task data in redis for 2 hours after execution completes scheduled tasks tasks can be scheduled to execute at a single point in the future or at a periodic interval these tasks can also use the options highlighted in the previous section to execute a task once at a specific time go err c tasks new my task at time date 2022 time november 10 23 0 0 0 time utc save to execute a periodic task using a cron schedule go err c tasks new my task periodic 10 save to execute a periodic task using a simple syntax go err c tasks new my task periodic every 10m save scheduler a service needs to run in order to add periodic tasks to the queue at the specified intervals when the application is started this scheduler service will also be started in cmd web main go this is done with the following code go go func if err c tasks startscheduler err nil c web logger fatalf scheduler shutdown v err in the event of an application restart periodic tasks must be re registered with the scheduler in order to continue being queued for execution worker the worker is a service that executes the queued tasks using task processors included is a basic implementation of a separate worker service that will listen for and execute tasks being added to the queues if you prefer to move the worker so it runs alongside the web server you can do that though it s recommended to keep these processes separate for performance and scalability reasons the underlying functionality of the worker service is provided by asynq https github com hibiken asynq so it s highly recommended that you review the documentation for that project first starting the worker a make target was added to allow you to start the worker service easily from the root of the repository execute make worker understanding the service the worker service is located in cmd worker main go cmd worker main go and starts with the creation of a new asynq server provided by asynq newserver there are various configuration options available so be sure to review them all prior to starting the service we need to route tasks according to their type to their handlers which will process the tasks this is done by using async servemux much like you would use an http router go mux asynq newservemux mux handle tasks typeexample new tasks exampleprocessor in this example all tasks of type tasks typeexample will be routed to exampleprocessor which is a struct that implements processtask see the included basic example pkg tasks example go finally the service is started with async server run mux monitoring asynq https github com hibiken asynq comes with two options to monitor your queues 1 command line tool https github com hibiken asynq command line tool and 2 web ui https github com hibiken asynqmon static files static files are currently configured in the router pkg routes router go to be served from the static directory if you wish to change the directory alter the constant config staticdir the url prefix for static files is files which is controlled via the config staticprefix constant cache control headers static files are grouped separately so you can apply middleware only to them included is a custom middleware to set cache control headers middleware cachecontrol which has been added to the static files router group the cache max life is controlled by the configuration at config cache expiration staticfile and defaults to 6 months cache buster while it s ideal to use cache control headers on your static files so browsers cache the files you need a way to bust the cache in case the files are changed in order to do this a function is provided in the funcmap funcmap to generate a static file url for a given file that appends a cache buster query this query string is randomly generated and persisted until the application restarts for example to render a file located in static picture png you would use html img src file picture png which would result in html img src files picture png v 9fhe73kaf3 where 9fhe73kaf3 is the randomly generated cache buster email an email client was added as a service to the container but it is just a skeleton without any actual email sending functionality the reason is because there are a lot of ways to send email and most prefer using a saas solution for that that makes it difficult to provide a generic solution that will work for most applications the structure in the client mailclient makes composing emails very easy and you have the option to construct the body using either a simple string or with a template by leveraging the template renderer template renderer the standard library can be used if you wish to send email via smtp and most saas providers have a go package that can be used if you choose to go that direction you must finish the implementation of mailclient send the from address will default to the configuration value at config mail fromaddress this can be overridden per email by calling from on the email and passing in the desired address see below for examples on how to use the client to compose emails sending with a string body go err c mail compose to hello example com subject welcome body thank you for registering send ctx sending with a template body go err c mail compose to hello example com subject welcome template welcome templatedata templatedata send ctx this will use the template located at templates emails welcome gohtml and pass templatedata to it https by default the application will not use https but it can be enabled easily just alter the following configuration config http tls enabled true config http tls certificate full path to the certificate file config http tls key full path to the key file to use let s encrypt follow this guide https echo labstack com cookbook auto tls server logging logging is provided by echo https echo labstack com guide customization logging and is accessible within the echo instance which is located in the web field of the container or within any of the context parameters for example go func c home get ctx echo context error ctx logger info something happened if err someoperation err nil ctx logger errorf the operation failed v err the logger can be swapped out for another as long as it implements echo s logging interface https github com labstack echo blob master log go there are projects that provide this bridge for popular logging packages such as zerolog https github com rs zerolog request id by default echo s request id middleware https echo labstack com middleware request id is enabled on the router but it only adds a request id to the log entry for the http request itself log entries that are created during the course of that request do not contain the request id logrequestid is custom middleware included which adds that request id to all logs created throughout the request roadmap future work includes but is not limited to flexible pager templates expanded htmx examples and integration admin section credits thank you to all of the following amazing projects for making this possible alpinejs https github com alpinejs alpine asynq https github com hibiken asynq bulma https github com jgthms bulma docker https www docker com echo https github com labstack echo echo contrib https github com labstack echo contrib ent https github com ent ent go https go dev gocache https github com eko gocache goquery https github com puerkitobio goquery go redis https github com go redis redis htmx https github com bigskysoftware htmx jwt https github com golang jwt jwt pgx https github com jackc pgx postgresql https www postgresql org redis https redis io sprig https github com masterminds sprig sessions https github com gorilla sessions testify https github com stretchr testify validator https github com go playground validator viper https github com spf13 viper | go golang mvc framework web full-stack starter-kit starter web-development web-framework htmx | front_end |
LLM-Adapters | copyright 2023 the huggingface team all rights reserved licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license h1 align center img src picture jpg width 73 height 114 p llm adapters p h1 h3 align center p llm adapters an adapter family for parameter efficient fine tuning of large language models p h3 llm adapters is an easy to use framework that integrates various adapters into llms and can execute adapter based peft methods of llms for different tasks llm adapter is an extension of huggingface s peft library many thanks for their amazing work please find our paper at this link https arxiv org abs 2304 01933 the framework includes state of the art open access llms llama opt bloom and gpt j as well as widely used adapters such as bottleneck adapters parallel adapters and lora supported adapters 1 lora lora low rank adaptation of large language models https arxiv org pdf 2106 09685 pdf 2 adapterh parameter efficient transfer learning for nlp https arxiv org pdf 1902 00751 pdf 3 adapterp gmad x an adapter based framework for multi task cross lingual transfer https arxiv org pdf 2005 00052 pdf 4 parallel towards a unified view of parameter efficient transfer learning https arxiv org pdf 2110 04366 pdf 5 prefix tuning prefix tuning optimizing continuous prompts for generation https aclanthology org 2021 acl long 353 p tuning v2 prompt tuning can be comparable to fine tuning universally across scales and tasks https arxiv org pdf 2110 07602 pdf 6 p tuning gpt understands too https arxiv org pdf 2103 10385 pdf 7 prompt tuning the power of scale for parameter efficient prompt tuning https arxiv org pdf 2104 08691 pdf latest news 2023 07 16 we released commonsense170k dataset and the the llama 13b parallel model outformances chatgpt on 8 commonsense benchmarks 2023 04 21 we released math10k dataset and the llama 13b adapter checkpoints https drive google com file d 1nquv hn makgxsuoqpjkmpkw5gp8mrlo view usp sharing the llama 13b parallel model achieves 91 of gpt 3 5 performance 2023 04 10 we can support gpt neo and chatglm now 2023 04 04 release code and dataset https github com agi edgerunners llm adapters setup 1 install dependencies bash pip install r requirements txt cd peft pip install e 2 set environment variables or modify the files referencing base model bash files referencing base model export hf checkpoint py export state dict checkpoint py export base model yahma llama 7b hf both finetune py and generate py use base model flag as shown further below 3 if bitsandbytes doesn t work install it from source https github com timdettmers bitsandbytes blob main compile from source md windows users can follow these instructions https github com tloen alpaca lora issues 17 training finetune py this file contains some code related to prompt construction and tokenization in this file specify different adapters and different sets of data so that different models can be trained example usage for multiple gpus bash world size 2 cuda visible devices 0 1 torchrun nproc per node 2 master port 3192 finetune py base model yahma llama 7b hf data path math data json output dir trained models llama lora batch size 16 micro batch size 4 num epochs 3 learning rate 3e 4 cutoff len 256 val set size 120 adapter name lora the math data json file contains preprocessed instruction data from the addsub singleeq multiarith aqua svamp and gsm8k dataset yahma llama 7b hf is a base model llama 7b add lora adapter to this model example usage for single gpus bash cuda visible devices 0 python finetune py base model yahma llama 7b hf data path math data json output dir trained models llama lora batch size 16 micro batch size 4 num epochs 3 learning rate 3e 4 cutoff len 256 val set size 120 adapter name lora moreover you can use use gradient checkpointing to save more gpu memory but it will increase the training time to use the adapterh just add the following arguments bash adapter name bottleneck use the bottleneck adapter refers to adapterh in the result table to use the adapterp just add the following arguments bash adapter name bottleneck use adapterp use the adapterp refers to adapterp in the result table to use parallel adapter just add the following arguments bash adapter name bottleneck use parallel adapter note that in order to facilitate int8 training of large models with parallel adapters we have adopted a technique whereby the parallel adapter layers are incorporated into multi head attention layers and mlp layers in parallel with linear layers it is different from hu et al 2021 https arxiv org pdf 2106 09685 pdf inference generate py this file reads the foundation model from the hugging face model hub and the lora weights from trained models llama lora and runs a gradio interface for inference on a specified input users should treat this as example code for the use of the model and modify it as needed example usage bash cuda visible devices 0 torchrun generate py base model yahma llama 7b hf lora weights trained models llama lora evaluation evaluate py to evaluate the performance of the finetuned model on the arithmetic reasoning tasks you can use the following command bash cuda visible devices 0 python evaluate py model llama 7b specify the base model adapter lora specify the adapter name lora adapterh adapterp parallel scaled parallel dataset svamp specify the test dataset base model yahma llama 7b hf lora weights trained models llama lora resource consumption there is a table of resouce needed for different adapters which contains trainable parameters gpu ram usage and fine tuning time on the arithmetic reasoning dataset math data json hyper parameter setting num epochs 3 lora r 8 lora alpha 16 bottleneck size 256 models llama 13b llama 7b bloom 6 7b gpt j 6b dataset 3 2k math word problems hardware 2 3090 gpus model trainable parameters gpu ram usage fine tuning time llama 7b lora 4 2m 18gb 1h llama 7b adapterh 200m 22gb 1h llama 7b adapterp 200m 22gb 1h llama 7b parallel 200m 22gb 1h finetune result there is the finetune results in different model with six math reasoning datasets which contains multiarith gsm8k addsub aqua singleeq svamp in this tabel as adapterh and adapterp are series adapters and adapterp outperformances adapterh we use adapterp with bottleneck size 256 as series adapter model multiarith gsm8k addsub aqua singleeq svamp average gpt 3 5 83 8 56 4 85 3 38 9 88 1 69 9 70 4 bloomz 7b prefix 68 8 13 8 47 1 12 5 49 4 24 1 36 0 bloomz 7b series 80 7 14 3 72 6 20 5 69 3 38 1 49 3 bloomz 7b parallel 85 8 18 5 77 7 18 9 74 8 36 4 52 0 bloomz 7b lora 82 8 17 4 72 4 21 3 69 9 41 0 50 8 gpt j 6b prefix 74 5 16 0 65 6 14 7 61 4 31 0 43 9 gpt j 6b series 91 7 19 5 85 8 15 0 81 7 43 6 56 2 gpt j 6b parallel 92 2 18 9 83 8 17 9 80 7 41 1 55 8 gpt j 6b lora 90 7 23 0 84 1 16 1 84 1 46 0 57 3 llama 7b prefix 63 2 24 4 57 0 14 2 55 3 38 1 42 0 llama 7b series 92 8 33 3 80 0 15 0 83 5 52 3 59 5 llama 7b parallel 94 5 35 3 86 6 18 1 86 0 49 6 61 7 llama 7b lora 95 0 37 5 83 3 18 9 84 4 52 1 61 9 llama 13b prefix 72 2 31 1 56 0 15 7 62 8 41 4 46 5 llama 13b series 93 0 44 0 80 5 22 0 87 6 50 8 63 0 llama 13b parallel 94 3 43 3 83 0 20 5 89 6 55 7 64 4 llama 13b lora 94 8 47 5 87 3 18 5 89 8 54 6 65 4 there is the finetune results in different model with eight commonsense reasoning datasets model boolq piqa siqa hellaswag winogrande arc e arc c obqa average chatgpt 73 1 85 4 68 5 78 5 66 1 89 8 79 9 74 8 77 0 bloomz 7b prefix 45 6 53 7 46 3 26 7 49 5 52 1 39 7 44 3 44 7 bloomz 7b series 65 4 70 4 73 6 53 4 69 3 72 3 55 9 68 0 66 0 bloomz 7b parallel 64 1 71 5 72 1 52 9 67 0 70 5 54 7 69 6 65 3 bloomz 7b lora 65 9 75 3 74 5 57 3 72 5 74 6 57 8 73 4 68 9 gpt j 6b prefix 63 1 66 9 68 7 34 4 64 5 64 4 46 8 59 0 58 5 gpt j 6b series 62 1 63 5 72 3 30 6 68 0 63 9 48 1 63 8 59 0 gpt j 6b parallel 62 2 69 7 70 0 41 7 65 0 60 2 44 6 58 2 59 0 gpt j 6b lora 62 4 68 6 49 5 43 1 57 3 43 4 31 0 46 6 50 2 llama 7b prefix 64 3 76 8 73 9 42 1 72 1 72 9 54 0 60 6 64 6 llama 7b series 63 0 79 2 76 3 67 9 75 7 74 5 57 1 72 4 70 8 llama 7b parallel 67 9 76 4 78 8 69 8 78 9 73 7 57 3 75 2 72 3 llama 7b lora 68 9 80 7 77 4 78 1 78 8 77 8 61 3 74 8 74 7 llama 13b prefix 65 3 75 4 72 1 55 2 68 6 79 5 62 9 68 0 68 4 llama 13b series 71 8 83 0 79 2 88 1 82 4 82 5 67 3 81 8 79 5 llama 13b parallel 72 5 84 8 79 8 92 1 84 7 84 2 71 2 82 4 81 5 llama 13b lora 72 1 83 5 80 5 90 5 83 7 82 8 68 3 82 4 80 5 adapter support matrix this metrix shows whether different models can use lora adapterh adapterp parallel and scaled parallel adapters adapter lora adapterh adapterp parallel prefix tuning p tuning prompt tuning llama bloom gpt j opt gpt 2 developing developing developing gpt neo gpt neox 20b developing developing developing chatglm todo list x add adapterh x add adapterp x add parallel adapter support more llms support multiple adapter support adapter composition support adapter fusion star star history star history chart https api star history com svg repos agi edgerunners llm adapters type date https star history com agi edgerunners llm adapters date citing img src picture jpg width 14px height 14px llm adapter if you use img src picture jpg width 14px height 14px llm adapters in your publication please cite it by using the following bibtex entry bibtex article hu2023llm title llm adapters an adapter family for parameter efficient fine tuning of large language models author hu zhiqiang and lan yihuai and wang lei and xu wanyu and lim ee peng and lee roy ka wei and bing lidong and poria soujanya journal arxiv preprint arxiv 2304 01933 year 2023 acknowledgement this repo benefits from peft https github com huggingface peft adapter transformer https github com adapter hub adapter transformers alpaca lora https github com tloen alpaca lora thanks for their wonderful works additionally we thank dong shan and dream ai https dream ai create for the exceptional logo design which has added immense value to our project | adapters fine-tuning large-language-models parameter-efficient | ai |
computer-vision-algorithms | about custom implementations of common machine learning algorithms purely for training purposes and not intended for productive use files all files can simply be executed with python filename py i e no command line arguments computer vision binary dilation erosion py dilation erosion opening and closing for binary images canny py canny edge detector crosscorrelation convolution py implementation of crosscorrelation and convolution to apply filters to images eigenfaces py apply a pca from sklearn to human faces plot the principal components aka eigenfaces gauss py gauss filter for smoothing of images gradients py calculate x and y derivatives of an image using symmetric backward and forward gradient techniques graph image segmentation py split an image to segments using a simple graph based technique with internal and external subgraph differences harris py calculate the harris edge detector score of pixels in an image histogram equalization py normalize the intensity histogram of an image using histogram stretching and cumulative histogram equalization hough py find a line in a noisy image using a hough transformation mean shift segmentation py split an image to segments using mean shift clustering otsu py binarize an image using otsu s method prewitt py apply a prewitt filter to an image to calculate the x y gradients rank order py apply rank order filters to an image for non binary erosion dilation closing opening median filtering and morphological edge detection sift py simplified implementation of the sift keypoint locator does not contain the descriptor nor sophisticated keypoint filtering using hessian und principal curvatures also does not resize scales with higher sigmas sobel py apply a sobel filter to an image for smoothed gradient calculation template matching py find an example template image in a larger image classifiers gaussian mixture 1d em py train a mixture model of 1d gaussians using the em algorithm requirements python 2 7 scipy numpy sklearn scikit image matplotlib | ai |
|
DepressionDetectionNLP | detecting human emotions using natural language processing the purpose of this project was to develop an application that could be used to detect depression or suicidal ideation in user submitted social media posts how to use the app to run the application simply enter the following code into your terminal git clone https github com allenfp depressiondetectionnlp once the repository is downloaded enter the following command into your command line python app py once the application is running go to the web address shown on the terminal thats it performing analysis there are two ways to use the app one is using the website s enter comment here and pressing submit the other way is using the app s api for instructions on using the api click this link https github com allenfp depressiondetectionnlp blob master api documentation md tools used data collection python praw requests and pandas analysis python pyspark vader sentiment analysis and pandas visualization python flask wordcloud wtforms bootstrap html css methodology the dataset used in this project was constructed by extracting user submitted posts from the r suicidewatch and r casualconversation subreddits found on reddit com data was extracted from reddit using the reddit api and praw the python reddit api wrapper the reddit api limits calls to only the 1000 most recent posts on any subreddit to work around this limitation we performed api calls periodically over a 10 day period for both subreddits posts were aggregated and checked for duplicates in the end our dataset was comprised of 3412 reddit submissions these submissions came out to 549 008 words 32 288 lines 2 873 664 characters or about 705 pages of single spaced 12 point times new roman text the data taken from reddit was classified according to which subreddit and had vader sentiment analysis performed on each submission a training set of data was created separately from the test set the training set of data was selectively pruned to eliminate exceedingly positive r suicidewatch submissions and exceedingly negative r casualconveration submissions this was done to create higher divergence in the training data after the pruning process was completed the data was fitted and transformed using a spark machine learning pipeline that created features based on tf idf analysis and the negative sentiment vader sentiment analysis and was vectorized using the newly transformed data a naive bayes multinomial classifier model is trained this model was used to predict whether a given submission from the test set was posted in r suicidewatch or r casualconversation accuracy was determined to be 90 2 disclaimer this website is a project designed to predict emotional sentiments using machine learning this is in no way a clinical tool nor should it be used as a replacement for professional or clinical advice if you are in a state of emergency please call 911 or and seek professional support immediately flowchart alt text https github com allenfp depressiondetectionnlp blob master wordmap and flowchart depression 20detecting 20nlp 20model png website alt text https github com allenfp depressiondetectionnlp blob master depressionnlpwebsite png | ai |
|
RPi2-FreeRTOS | rpi2 freertos latest stable 9 0 0 freertos port on raspberry pi tested on raspbery pi 2b with uart bootloader by david welch https github com dwelch67 raspberrypi this port is based on https github com forty tw0 raspberrypi freertos and https github com tonyhccdev raspberrypi freertos | os |
|
Hybrid-Mobile-Development-with-Ionic | hybrid mobile development with ionic this is the code repository for hybrid mobile development with ionic https www packtpub com application development hybrid mobile development ionic utm source github utm medium repository utm content 9781785286056 published by packt it contains all the supporting project files necessary to work through the book from start to finish about the book this book will help you to develop a complete professional and quality mobile application with ionic framework you will start the journey by learning to configure customize and migrate ionic 1x to 3x then you will move on to ionic 3 components and see how you can customize them according to your applications you will also implement various native plugins and integrate them with ionic and ionic cloud services to use them optimally in your application by this time you will be able to create a full fledged e commerce application next you will master authorization authentication and security techniques in ionic 3 to ensure that your application and data are secure further you will integrate the backend services such as firebase and the cordova ibeacon plugin in your application lastly you will be looking into progressive web applications and its support with ionic with a demonstration of an offline first application instructions and navigation all of the code is organized into folders the commands and instructions will look like the following angular ionic 1 angular module wedding controllers controller loginctrl function scope categoryservice controller function and di of categoryservice angular 4 ionic 3 import component from angular core import navcontroller from ionic angular component templateurl build pages catalog categories html export class categorypage di of navcontroller for navigation constructor private navctrl navcontroller this nav navctrl related products beginning ionic hybrid application development video https www packtpub com web development beginning ionic hybrid application development video utm source github utm medium repository utm content 9781785284465 hybrid cloud management with red hat cloudforms https www packtpub com virtualization and cloud hybrid cloud management red hat cloudforms utm source github utm medium repository utm content 9781785283574 getting started with ionic https www packtpub com application development getting started ionic utm source github utm medium repository utm content 9781784390570 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781785286056 https packt link free ebook 9781785286056 a p | front_end |
|
infinity-war-spacy | reliving avengers infinity war with spacy and natural language processing overview this repo contains the scripts used in my latest experiment titled reliving avengers infinity war with spacy and natural language processing available at this link reliving avengers infinity war with spacy and natural language processing https towardsdatascience com reliving avengers infinity war with spacy and natural language processing 2abcb48e4ba1 using spacy https spacy io an nlp python open source library designed to help us process and understand volumes of text i analyzed the script of the movie to investigate the following concepts overall top 10 verbs nouns adverbs and adjectives from the film top verbs and nouns spoke by a particular character top 30 named entities from the film the similarity between the lines spoken by each character pair e g the similarity between thor s and thanos lines tools used python spacy repo content besides the scripts the repo contains the full movie script raw script txt the script without comments scenes descriptions and the subjects cleaned script txt and the cleaned script but with the subjects cleaned script subject txt moreover the plots directory contains all the plots that show the top nouns adverbs adjetives verbs and entities per character thanks to manuel romero https github com mrm8488 for writing the jupyter notebook | nlp spacy machine-learning avengers | ai |
EmbeddedSystems_SystemsApproachToGameDesign_TimerExamples | embeddedsystems systemsapproachtogamedesign timerexamples sample code for timers design in the lecture about systems approach to games design in embedded systems lecture | os |
|
ts-askit | raw askit ts askit typescript ci https github com katsumiok ts askit actions workflows typescript ci yml badge svg https github com katsumiok ts askit actions workflows typescript ci yml arxiv https img shields io badge arxiv 2308 15645 b31b1b svg https arxiv org abs 2308 15645 overview askit is a language plugin for typescript that enables you to leverage the capabilities of a large language model llm such as gpt 4 directly within your programming environment no complex apis needed askit s extensive range of applications includes translation paraphrasing sentiment analysis math problem solving code generation and many more built upon the openai api https beta openai com askit provides a user friendly interface for incorporating llms into your applications you can use askit not only in typescript but also in javascript and python for integrating askit with javascript please refer to the corresponding javascript section use askit with javascript if python is your preferred language you can learn more about how to utilize askit by visiting our dedicated askit pyaskit page https github com katsumiok pyaskit key features x type guided output control get a response in the specified type no need to specify the output format in the prompt no need to parse the response to extract the desired output type guided output control demonstration https katsumiok github io ts askit images type guided gif x template based function definition define functions using a prompt template template based function definition demonstration https katsumiok github io ts askit images func def gif x code generation generate functions from the unified interface see code generation with askit code generation with askit for more details x programming by example pbe define functions using examples see programming by example with askit programming by example with askit for more details installation before starting ensure that node js http nodejs org and npm https npmjs com are installed on your system then execute the following command bash npm install ts askit this package relies on ts patch to install ts patch run bash npx ts patch install add the following snippet to your tsconfig json json compileroptions plugins transform ts askit transform this modification allows the typescript compiler to support type parameters for the ask and define apis in askit the ts patch package is crucial for unleashing the full potential of askit as it extends the typescript compiler to fully integrate askit s type system while askit can be used without ts patch this integration offers a more feature rich experience before using askit you need to set your openai api key as an environment variable openai api key bash export openai api key your openai api key your openai api key is a string that looks like this sk your key you can find your api key in the openai dashboard https platform openai com account api keys you can also specify the model name as an environment variable askit model bash export askit model model name model name is the name of the model you want to use the latest askit is tested with gpt 4 and gpt 3 5 turbo 16k you can find the list of available models in the openai api documentation https platform openai com docs models api usage here are some introductory examples ts import ask from ts askit ask string paraphrase hello world then result console log result in this example ask is an api function that allows your program to pose queries to a large language model llm the type parameter represents the expected output type from the llm here the output type is string the prompt is passed as an argument in natural language describing the task for the llm to perform ask is asynchronous returning a promise of the specified output type the code snippet above should print something like this greetings universe for a prompt with parameters you can use the define api as follows ts import define from ts askit const paraphrase define string paraphrase text paraphrase text hello world then result console log result define is an api function that allows you to define a custom function its type parameter indicates the output type of the llm and consequently the return value of the function the function receives a string template as an argument serving as the llm s task prompt the template can include parameters enclosed in double curly braces in the example above text is a parameter within the template and it can be any valid javascript identifier once the function is defined it can be invoked like any other function this function accepts an object as an argument which maps to the template parameters values in this case text maps to the string hello world code generation with askit askit is adept at generating code from natural language descriptions here s an example ts import define from ts askit const sort define number numbers number sort numbers in ascending order this example showcases a function that sorts an array of numbers in ascending order utilizing the define api to instruct the llm to define the function while efficient in concept this method may seem computationally heavy as each function call requires a new llm task to streamline this process we can leverage the llm to generate the sorting function s code rather than resorting to the llm for every sorting task this optimizes the function without requiring any changes in its implementation thanks to askit s code generation capabilities the code for the aforementioned function can be generated in three steps 1 first compile the code using the typescript compiler tsc the askit analyzer scans the code and generates a jsonl file containing details about the define and ask api calls 2 next run the ts askit command to generate the function s code bash npx askgen jsonl file 3 finally recompile the code with the typescript compiler tsc this time the define and ask api calls are replaced by the references and calls to the newly generated function respectively thanks to askit s auto replacement feature programming by example with askit askit allows you to leverage the power of programming by example pbe pbe simplifies the programming process by enabling you to define functionality through examples rather than hard coded logic the following example illustrates this by showing you how to add two binary numbers using pbe with askit ts import define example from ts askit const trainingexamples example input x 1 y 0 output 1 input x 1 y 1 output 10 input x 101 y 11 output 1000 input x 1001 y 110 output 1111 input x 1111 y 1 output 10000 const testexamples input x 0 y 1 output 1 input x 10 y 0 output 10 input x 110 y 10 output 1000 const addinbase2 define string x string y string add x and y trainingexamples testexamples async function doit console log await addinbase2 x 101 y 11 doit in this example we define a function addinbase2 that takes two binary numbers represented as strings and adds them the define function is invoked with a prompt and two arrays of examples training examples and test examples the training examples are reflected in the prompt in a few shot learning manner on the other hand the test examples are used to validate the generated function s correctness test examples are not required if you don t generate code for the function the result is a powerful feature allowing you to instruct the llm to perform complex operations like binary addition using nothing but examples this approach enables you to develop complex functionality rapidly and with less explicit logic once the function addinbase2 is defined you can call it with binary number strings to perform addition in base 2 as with traditional function calls askit s ask operation returns a promise that resolves with the computed result leverage askit in javascript type guided output control in javascript javascript developers can fully exploit the potential of type guided output control offered by askit just like its sibling typescript javascript incorporates the api method ask to achieve this the function ask takes two parameters a type and a prompt here is an array of examples demonstrating its usage js const ai require ts askit const t require ts askit types ai ask t number what is the third prime number then answer console log answer ai ask t string what is the month number of january then answer console log answer ai ask t array t number what are the month numbers in the second quarter const monthtype t type name t string number t number ai ask monthtype what is the month number of october then answer console log answer ai ask t array monthtype what are the months in the second quarter then answer console log answer in the code snippet above the ask function is invoked with a type and a prompt the type parameter serves the purpose of informing askit about the format and structure of the desired output this becomes extremely handy when you re dealing with complex data structures template based function definition in javascript with askit javascript developers have the ability to define functions using easy to understand templates the define method is the hero behind the scenes here as it helps create functions based on the task template provided once created these functions can be called with any object that provides values for the placeholders in the template here s an example of how it s done js const ai require ts askit const t require ts askit types let f ai define t string translate text into language f text hello language french then answer console log answer in the code above the define method is employed to establish a function f using a task template translate text into language the function f is then invoked with an object that provides values for text and language diverse supported types in javascript the ts askit types module is a treasure trove of types that you can utilize to guide askit s output here s a table to help you quickly grasp these types type description type example value example numbertype numeric type t number 123 stringtype string type t string hello world booleantype boolean type t boolean true literaltype literal value type t literal 123 123 arraytype array type t array t number 1 2 3 uniontype union type multiple possible values t union t literal yes t literal no yes or no interfacetype interface dictionary type t type a t number b t number a 1 b 2 codetype code type t code python def hello world print hello world each type has specific properties that askit uses to comprehend the task at hand and properly format the output code generation in javascript the road ahead as of the time of writing the code generation feature is exclusively available in typescript however efforts are in full swing to extend this powerful feature to the realm of javascript if your requirements call for the use of code generation in the interim we recommend using typescript until further updates contributing for details on our code of conduct and the process for submitting pull requests please refer to contributing md contributing md license this project is licensed under the mit license for more information see the license license file bibtex misc okuda2023askit title askit unified programming interface for programming with large language models author katsumi okuda and saman amarasinghe year 2023 eprint 2308 15645 archiveprefix arxiv primaryclass cs pl endraw | gpt gpt-3 gpt-4 llm openai openai-api javascript typescript wrapper parser prompt prompt-engineering prompt-toolkit | ai |
Helicopter-Embedded-System-Project | ence361 helicopter project git repository for monday morning lab group 86 this repository contains the source files for the helicopter control project note this helicopter was tested on an emulator rather than real rig due to test rigs breaking getting started before starting download ti code composer via ti website download the tivaware api for tm4c123g installing repository clone repo onto a local directory of choice using git clone https eng git canterbury ac nz ence361 2023 group 86 git create the code composer project as an empty project in the new project wizard set the target as the tiva tm4c123gh6pm set the debug target as the stellaris in circuit debug interface import the source files from repo and add the api library building for devboard click the build project hammer icon to build files click the debug bug icon to build and run code in debug mode then press the play button set breakpoints etc to flash the code without running in debug mode click the flash code icon authors arabella cryer acr151 uclive ac nz amber waymouth awa155 uclive ac nz acknowledgements phil bones author of buttons and circle buffer files mdp46 code for orbitoled display | os |
|
The-Complete-Rust-Programming-Reference-Guide | github issues https img shields io github issues packtpublishing the complete rust programming reference guide svg https github com packtpublishing the complete rust programming reference guide issues github forks https img shields io github forks packtpublishing the complete rust programming reference guide svg https github com packtpublishing the complete rust programming reference guide network github stars https img shields io github stars packtpublishing the complete rust programming reference guide svg https github com packtpublishing the complete rust programming reference guide stargazers prs welcome https img shields io badge prs welcome brightgreen svg https github com packtpublishing the complete rust programming reference guide pulls the complete rust programming reference guide this learning path is your easy reference to mastering rust programming it begins with an introduction to rust data structures and algorithms and covers the entire spectrum including memory safety type system concurrency and other features of rust 2018 what you will learn design and implement complex data structures in rust create and use well tested and reusable components with rust understand the basics of multithreaded programming and advanced algorithm design explore application profiling based on benchmarking and testing study and apply best practices and strategies in error handling create efficient web applications with the actix web framework use diesel for type safe database interactions in your web application hardware requirements for an optimal student experience we recommend the following hardware configuration processor 2 6 ghz or higher multi core processor memory 8gb ram hard disk 30gb or more an internet connection software requirements you ll also need the following software installed microsoft s visual studio code https code visualstudio com arguably one of the best rust code editors rust support for visual studio code via a plugin https github com rust lang rls vscode rust language server rls found at https github com rust lang rls vscode installed via rustup https rustup rs debugging support using the lldb frontend plugin https github com vadimcn vscode lldb for visual studio code download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781838828103 https packt link free ebook 9781838828103 a p | rust metaprogramming dynamic-programming greedy-algorithms | os |
MyInstagram | myinstagram myinstagram is a database design inspired by instagram and implemented using nosql databases namely mongodb document oriented and cassandradb column oriented what s here myinstagram use cases description https github com srimyinstagram myinstagram blob main myinstagram 20use 20cases 20description pdf this document contains all the use cases that were identified during the requirements gathering phase entities and their attributes https github com srimyinstagram myinstagram blob main entities 20and 20their 20attributes pdf this document contains all the entities and their respective attributes er myinstagram https github com srimyinstagram myinstagram blob main er 20 20myinstagram pdf this document contains a simple er diagram of the database designed myinstagram query list https github com srimyinstagram myinstagram blob main myinstagram 20query 20list pdf this document lists down all the queries that are required to be executed in order to perform the use cases described in the myinstagram use cases description document cassandradb https github com srimyinstagram myinstagram tree main cassandradb this folder contains all the documents related to the implementation of the myinstagram database using cassandradb mongodb https github com srimyinstagram myinstagram tree main mongodb this folder contains all the documents related to the implementation of the myinstagram database using mongodb more server implementation of the myinstagram database using cassandradb is here https github com srimyinstagram myinstagram cassandradb backend server implementation of the myinstagram database using mongodb is here https github com srimyinstagram myinstagram mongodb backend authors mihir doshi mihir786687 gmail com sanket jain sanketjain07032000 gmail com | server |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.