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awesome-relation-extraction | awesome relation extraction awesome https awesome re badge svg https awesome re awesome re https user images githubusercontent com 15166794 47858006 62aa7400 de2e 11e8 82d3 165f66aaaec4 png a curated list of awesome resources dedicated to relation extraction inspired by awesome nlp https github com keon awesome nlp and awesome deep vision https github com kjw0612 awesome deep vision contributing please feel free to make pull requests https github com roomylee awesome relation extraction pulls contents research trends and surveys research trends and surveys papers papers supervised approaches supervised approaches distant supervision approaches distant supervision approaches gnn based models gnn based models language models language models encoder representation from transformer encoder representation from transformer decoder representation from transformer decoder representation from transformer knowledge graph based approaches knowledge graph based approaches few shot learning approaches few shot learning approaches datasets datasets videos and lectures videos and lectures systems systems frameworks frameworks research trends and surveys nlp progress relationship extraction https nlpprogress com english relationship extraction html named entity recognition and relation extraction state of the art https www researchgate net profile syed waqar jaffry publication 345315661 named entity recognition and relation extraction state of the art links 603015aaa6fdcc37a83aafd5 named entity recognition and relation extraction state of the art pdf nasar et al 2021 a survey of deep learning methods for relation extraction https arxiv org abs 1705 03645 kumar 2017 a survey on relation extraction https www cs cmu edu nbach papers a survey on relation extraction pdf bach and badaskar 2017 relation extraction a survey https arxiv org abs 1712 05191 pawar et al 2017 a review on entity relation extraction https ieeexplore ieee org stamp stamp jsp arnumber 8269916 zhang et al 2017 review of relation extraction methods what is new out there https citeseerx ist psu edu viewdoc download doi 10 1 1 727 1005 rep rep1 type pdf konstantinova et al 2014 100 best github relation extraction http meta guide com software meta guide 100 best github relation extraction papers supervised approaches cnn based models convolution neural network for relation extraction paper https link springer com chapter 10 1007 978 3 642 53917 6 21 code https github com roomylee cnn relation extraction review https github com roomylee paper review blob master relation extraction convolution 20neural 20network 20for 20relation 20extraction review md chunyang liu wenbo sun wenhan chao and wanxiang che adma 2013 relation classification via convolutional deep neural network paper http www aclweb org anthology c14 1220 code https github com roomylee cnn relation extraction review https github com roomylee paper review blob master relation extraction relation classification via convolutional deep neural network review md daojian zeng kang liu siwei lai guangyou zhou and jun zhao coling 2014 relation extraction perspective from convolutional neural networks paper http www cs nyu edu thien pubs vector15 pdf code https github com roomylee cnn relation extraction review https github com roomylee paper review blob master relation extraction relation extraction perspective from convolutional neural networks review md thien huu nguyen and ralph grishman naacl 2015 classifying relations by ranking with convolutional neural networks paper https arxiv org abs 1504 06580 code https github com pratapbhanu crcnn cicero nogueira dos santos bing xiang and bowen zhou acl 2015 attention based convolutional neural network for semantic relation extraction paper http www aclweb org anthology c16 1238 code https github com nicolay r mlp attention yatian shen and xuanjing huang coling 2016 relation classification via multi level attention cnns paper http aclweb org anthology p16 1123 code https github com lawlietai relation classification via attention model linlin wang zhu cao gerard de melo and zhiyuan liu acl 2016 mit at semeval 2017 task 10 relation extraction with convolutional neural networks paper https aclanthology info pdf s s17 s17 2171 pdf ji young lee franck dernoncourt and peter szolovits semeval 2017 rnn based models relation classification via recurrent neural network paper https arxiv org abs 1508 01006 dongxu zhang and dong wang arxiv 2015 bidirectional long short term memory networks for relation classification paper http www aclweb org anthology y15 1009 shu zhang dequan zheng xinchen hu and ming yang paclic 2015 end to end relation extraction using lstms on sequences and tree structure paper https arxiv org abs 1601 00770 makoto miwa and mohit bansal acl 2016 attention based bidirectional long short term memory networks for relation classification paper http anthology aclweb org p16 2034 code https github com seosangwoo attention based bilstm relation extraction peng zhou wei shi jun tian zhenyu qi bingchen li hongwei hao and bo xu acl 2016 semantic relation classification via hierarchical recurrent neural network with attention paper http www aclweb org anthology c16 1119 minguang xiao and cong liu coling 2016 semantic relation classification via bidirectional lstm networks with entity aware attention using latent entity typing paper https arxiv org abs 1901 08163 code https github com roomylee entity aware relation classification joohong lee sangwoo seo and yong suk choi arxiv 2019 dependency based models semantic compositionality through recursive matrix vector spaces paper http aclweb org anthology d12 1110 code https github com pratapbhanu mvrnn richard socher brody huval christopher d manning and andrew y ng emnlp conll 2012 factor based compositional embedding models paper https www cs cmu edu mgormley papers yu gormley dredze nipsw 2014 pdf mo yu matthw r gormley and mark dredze nips workshop on learning semantics 2014 a dependency based neural network for relation classification paper http www aclweb org anthology p15 2047 yang liu furu wei sujian li heng ji ming zhou and houfeng wang acl 2015 classifying relations via long short term memory networks along shortest dependency path paper https arxiv org abs 1508 03720 code https github com sshanu relation classification xu yan lili mou ge li yunchuan chen hao peng and zhi jin emnlp 2015 semantic relation classification via convolutional neural networks with simple negative sampling paper https www aclweb org anthology d d15 d15 1062 pdf kun xu yansong feng songfang huang and dongyan zhao emnlp 2015 improved relation classification by deep recurrent neural networks with data augmentation paper https arxiv org abs 1601 03651 yan xu ran jia lili mou ge li yunchuan chen yangyang lu and zhi jin coling 2016 bidirectional recurrent convolutional neural network for relation classification paper http www aclweb org anthology p16 1072 rui cai xiaodong zhang and houfeng wang acl 2016 neural relation extraction via inner sentence noise reduction and transfer learning paper https arxiv org abs 1808 06738 tianyi liu xinsong zhang wanhao zhou weijia jia emnlp 2018 gnn based models matching the blanks distributional similarity for relation learning paper https arxiv org abs 1906 03158 livio baldini soares nicholas fitzgerald jeffrey ling tom kwiatkowski acl 2019 relation of the relations a new paradigm of the relation extraction problem paper https arxiv org abs 2006 03719 zhijing jin yongyi yang xipeng qiu zheng zhang emnlp 2020 gdpnet refining latent multi view graph for relation extraction paper https arxiv org abs 2012 06780 pdf code https github com xuefuzhao gdpnet fuzhao xue aixin sun hao zhang eng siong chng aaai 21 recon relation extraction using knowledge graph context in a graph neural network parer https arxiv org abs 2009 08694 pdf code https github com ansonb recon anson bastos abhishek nadgeri kuldeep singh isaiah onando mulang saeedeh shekarpour johannes hoffart manohar kaul www 21 distant supervision approaches distant supervision for relation extraction without labeled data paper https web stanford edu jurafsky mintz pdf review https github com roomylee paper review blob master relation extraction distant supervision for relation extraction without labeled data review md mike mintz steven bills rion snow and dan jurafsky acl 2009 knowledge based weak supervision for information extraction of overlapping relations paper http www aclweb org anthology p11 1055 code http aiweb cs washington edu ai raphaelh mr raphael hoffmann congle zhang xiao ling luke zettlemoyer and daniel s weld acl 2011 multi instance multi label learning for relation extraction paper http www aclweb org anthology d12 1042 code https nlp stanford edu software mimlre shtml mihai surdeanu julie tibshirani ramesh nallapati and christopher d manning emnlp conll 2012 distant supervision for relation extraction via piecewise convolutional neural networks paper http www emnlp2015 org proceedings emnlp pdf emnlp203 pdf review https github com roomylee paper review blob master relation extraction distant supervision for relation extraction via piecewise convolutional neural networks review md code https github com nicolay r sentiment pcnn daojian zeng kang liu yubo chen and jun zhao emnlp 2015 relation extraction with multi instance multi label convolutional neural networks paper https pdfs semanticscholar org 8731 369a707046f3f8dd463d1fd107de31d40a24 pdf review https github com roomylee paper review blob master relation extraction relation extraction with multi instance multi label convolutional neural networks review md code https github com may cnn re tf xiaotian jiang quan wang peng li bin wang coling 2016 incorporating relation paths in neural relation extraction paper http aclweb org anthology d17 1186 review https github com roomylee paper review blob master relation extraction incorporating relation paths in neural relation extraction review md wenyuan zeng yankai lin zhiyuan liu and maosong sun emnlp 2017 neural relation extraction with selective attention over instances paper http www aclweb org anthology p16 1200 code https github com thunlp opennre yankai lin shiqi shen zhiyuan liu huanbo luan and maosong sun acl 2017 learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text paper http www aclweb org anthology k17 1032 code https github com desh2608 crnn relation classification code https github com kwonmha convolutional recurrent neural networks for relation extraction desh raj sunil kumar sahu and ashish anan conll 2017 hierarchical relation extraction with coarse to fine grained attention paper https aclweb org anthology d18 1247 code https github com thunlp hnre xu han pengfei yu zhiyuan liu maosong sun peng li emnlp 2018 reside improving distantly supervised neural relation extraction using side information paper http malllabiisc github io publications papers reside emnlp18 pdf code https github com malllabiisc reside shikhar vashishth rishabh joshi sai suman prayaga chiranjib bhattacharyya and partha talukdar emnlp 2018 distant supervision relation extraction with intra bag and inter bag attentions paper https arxiv org abs 1904 00143 pdf code https github com zhixiuye intra bag and inter bag attentions zhi xiu ye zhen hua ling naacl 2019 language models encoder representation from transformer enriching pre trained language model with entity information for relation classification paper https arxiv org abs 1905 08284 pdf shanchan wu yifan he arxiv 2019 luke deep contextualized entity representations with entity aware self attention paper https www aclweb org anthology 2020 emnlp main 523 code https github com studio ousia luke ikuya yamada akari asai hiroyuki shindo hideaki takeda yuji matsumoto emnlp 2020 spanbert improving pre training by representing and predicting spans paper https arxiv org abs 1907 10529 pdf code https github com facebookresearch spanbert mandar joshi danqi chen yinhan liu daniel s weld luke zettlemoyer and omer levy tacl 2020 transactions of the association for computational linguistics efficient long distance relation extraction with dg spanbert paper https arxiv org abs 2004 03636 jun chen robert hoehndorf mohamed elhoseiny xiangliang zhang decoder representation from transformer improving relation extraction by pretrained language representations paper https arxiv org abs 1906 03088 review https openreview net forum id bjgrxbqp67 code https github com dfki nlp tre christoph alt marc h bner leonhard hennig akbc 19 knowledge graph based approaches kgpool dynamic knowledge graph context selection for relation extraction paper https arxiv org pdf 2106 00459 pdf code https github com nadgeri14 kgpool abhishek nadgeri anson bastos kuldeep singh isaiah onando mulang johannes hoffart saeedeh shekarpour and vijay saraswat acl 2021 findings few shot learning approaches fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation paper https arxiv org abs 1810 10147 website http zhuhao me fewrel code https github com prokil fewrel xu han hao zhu pengfei yu ziyun wang yuan yao zhiyuan liu maosong sun emnlp 2018 miscellaneous jointly extracting relations with class ties via effective deep ranking paper http aclweb org anthology p17 1166 hai ye wenhan chao zhunchen luo and zhoujun li acl 2017 end to end neural relation extraction with global optimization paper http aclweb org anthology d17 1182 meishan zhang yue zhang and guohong fu emnlp 2017 adversarial training for relation extraction paper https people eecs berkeley edu russell papers emnlp17 relation pdf yi wu david bamman and stuart russell emnlp 2017 a neural joint model for entity and relation extraction from biomedical text paper https bmcbioinformatics biomedcentral com articles 10 1186 s12859 017 1609 9 fei li meishan zhang guohong fu and donghong ji bmc bioinformatics 2017 joint extraction of entities and relations using reinforcement learning and deep learning paper https www hindawi com journals cin 2017 7643065 yuntian feng hongjun zhang wenning hao and gang chen journal of computational intelligence and neuroscience 2017 tdeer an efficient translating decoding schema for joint extraction of entities and relations paper https aclanthology org 2021 emnlp main 635 code https github com 4ai tdeer xianming li xiaotian luo chenghao dong daichuan yang beidi luan and zhen he emnlp 2021 back to top contents datasets semeval 2010 task 8 paper http www aclweb org anthology s10 1006 download https docs google com leaf id 0b jqiluggtakmdq5zjzimtutmzq1yy00ywnmlwjlzdytowy1zdmwy2u4yjfk sort name layout list num 50 multi way classification of semantic relations between pairs of nominals new york times nyt corpus paper http www riedelcastro org publications papers riedel10modeling pdf download https catalog ldc upenn edu ldc2008t19 this dataset was generated by aligning freebase relations with the nyt corpus with sentences from the years 2005 2006 used as the training corpus and sentences from 2007 used as the testing corpus fewrel few shot relation classification dataset paper https arxiv org abs 1810 10147 website http zhuhao me fewrel this dataset is a supervised few shot relation classification dataset the corpus is wikipedia and the knowledge base used to annotate the corpus is wikidata tacred the tac relation extraction dataset paper https www aclweb org anthology d17 1004 pdf website https nlp stanford edu projects tacred download https catalog ldc upenn edu ldc2018t24 is a large scale relation extraction dataset with built over newswire and web text from the corpus used in the yearly tac knowledge base population tac kbp challenges ace05 website https catalog ldc upenn edu ldc2006t06 download info https www ldc upenn edu language resources data obtaining this dataset represent texts extracted from a variety of sources broadcast conversation broadcast news newsgroups weblogs the 6 relation types between 7 types on entities acility fac geo politicalentity gpe location loc organization org person per vehicle veh weapon wea semeval 2018 task 7 paper https www aclweb org anthology s18 1111 pdf website https competitions codalab org competitions 17422 download https lipn univ paris13 fr gabor semeval2018task7 the corpus is collected from abstracts and introductions of scientific papers and there are six types of semantic relations in total there are three subtasks of it subtask 1 1 and subtask 1 2 are relation classification on clean and noisy data respectively subtask 2 is the standard relation extraction for state of the art results check out nlpprogress com on relation extraction https nlpprogress com english relationship extraction html back to top contents videos and lectures stanford university cs124 https web stanford edu class cs124 dan jurafsky video week 5 relation extraction and question https www youtube com watch v 5suzf6252 0 list plazqkzp6whwyszpctev4lfgj8lqj5wixk ab channel fromlanguagestoinformation washington university cse517 https courses cs washington edu courses cse517 luke zettlemoyer slide relation extraction 1 https courses cs washington edu courses cse517 13wi slides cse517wi13 relationextraction pdf slide relation extraction 2 https courses cs washington edu courses cse517 13wi slides cse517wi13 relationextractionii pdf new york university csci ga 2590 https cs nyu edu courses spring17 csci ga 2590 001 ralph grishman slide relation extraction rule based approaches https cs nyu edu courses spring17 csci ga 2590 001 dependencypaths pdf michigan university coursera https ai umich edu portfolio natural language processing dragomir r radev video lecture 48 relation extraction https www youtube com watch v tbrlrei 0h8 ab channel artificialintelligence allinone virginia university cs6501 nlp http web cs ucla edu kwchang teaching nlp16 kai wei chang slide lecture 24 relation extraction http web cs ucla edu kwchang teaching nlp16 slides 24 relation pdf back to top contents systems deepdive http deepdive stanford edu stanford relation extractor https nlp stanford edu software relationextractor html back to top contents frameworks opennre github https github com thunlp opennre paper https aclanthology org d19 3029 pdf is an open source and extensible toolkit that provides a unified framework to implement neural models for relation extraction re between named entities it is designed for various scenarios for re including sentence level re bag level re document level re and few shot re it provides various functional re modules based on both tensorflow and pytorch to maintain sufficient modularity and extensibility making it becomes easy to incorporate new models into the framework arekit github https github com nicolay r arekit research applicable paper https arxiv org pdf 2006 13730 pdf is an open source and extensible toolkit focused on data preparation for document level relation extraction organization it complements the opennre functionality as in terms of the latter document level re setting is not widely explored 2 4 paper https aclanthology org d19 3029 pdf the core functionality includes 1 api for document presentation with el entity linking i e object synonymy support for sentence level relations preparation dubbed as contexts 2 api for contexts extraction 3 relations transferring from sentence level onto document level etc it provides neural networks https github com nicolay r arekit tree 0 21 0 rc contrib networks like opennre and bert https github com nicolay r arekit tree 0 21 0 rc contrib bert modules both applicable for sentiment attitude extraction task dere github https github com ims tcl dere paper https aclanthology org d18 2008 is an open source framework for de claritive r elation e xtraction and therefore allows to declare your own task using xml schemas and apply manually implemented models towards it using a provided api the task declaration builds on top of the spans and relations between spans in terms of the latter authors propose frames where every frame yelds of 1 trigger span and 2 n slots where every slot may refer to frame or span the framework poses no theoretical restrictions to the window from which frames are extracted thus this concept may cover sentence level document level and multi document re tasks back to top contents license license https camo githubusercontent com 60561947585c982aee67ed3e3b25388184cc0aa3 687474703a2f2f6d6972726f72732e6372656174697665636f6d6d6f6e732e6f72672f70726573736b69742f627574746f6e732f38387833312f7376672f63632d7a65726f2e737667 https creativecommons org publicdomain zero 1 0 to the extent possible under law joohong lee https roomylee github io has waived all copyright and related or neighboring rights to this work | awesome relation-extraction relation-classification distant-supervision deep-learning natural-language-processing nlp paper machine-learning semeval-2010 new-york-times state-of-the-art acl emnlp naacl aaai nips trends | ai |
socialise-vue | socialise msc in web technologies mobile applications development project app socialise a student hangout app built using ionic and vuejs written in typescript javascript while using firebase on the backend for authentication and data persistence this is a project build for my masters course in web technologies the brief was to build a student hangout style app that offers a social media style service where users are able to post messages and replies to be designed to promote inter departmental interactions additional features could include user authentication notifications and the ability to upload images this is still massively a work in progress x construct structure initial project x add all relevent views with suitable working navigation x implement header footer navigation to work in cojunction with above x setup firebase user authentication and restrict relevant views structure each view fully x hardcode data initially to help structure each view x add ability to get data from firestore x add ability to post data to firestore x add user s ability to post comments add display of username and comment post creation times to quickly start the project and run in a web browser cd socialise vue ionic serve as mentioned above this is still massively a work in progress however see below for a few screenshots demonstrating its development so far main area stories alt text https github com samuelscotts socialise vue blob master images stories png individual story alt text https github com samuelscotts socialise vue blob master images story png adding a story alt text https github com samuelscotts socialise vue blob master images add png login view alt text https github com samuelscotts socialise vue blob master images login png registration view alt text https github com samuelscotts socialise vue blob master images register png | server |
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MCTE_4342_EmbeddedSystemDesign | mcte4342 embedded system design esd this repository is for the weekly projects for esd course webinar video https youtu be nmzaketqdta week 4 gpio contains examples working with leds week 5 analog input contains examples utilising the adc in arduino uno week 6 timer ports contains examples on using timer ports on arduino uno week 7 interrupts contains an example utilising the port interrupt on arduino uno week 8 non volatile memory contains an example utilising the eeprom on the arduino uno week 9 power management contains programs to reduce power consumption for arduino uno week 10 interfacing with motor contains examples for dc motor servo motor and stepper motor week 11 communication with other devices contains a script to communicate to arduino uno via arduino ide week 12 serial communication contains examples on 3 types of serial communication protocols inter integrated circuit i2c serial peripheral interface spi universal asynchronous receiver transmitter uart week 13 interfacing circuits contains notes on interfacing with circuits week 14 design choices contains a comparison table between controllers | os |
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Blockchain-Known-Pools-BCH | blockchain pools bitcoin cash mining known pools tracking tags for https bch btc com stats pool contributions welcome | blockchain |
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ImageAI | imageai v3 0 3 build status https travis ci com olafenwamoses imageai svg branch master https travis ci com olafenwamoses imageai license mit https img shields io badge license mit yellow svg https github com olafenwamoses imageai blob master license pypi version https badge fury io py imageai svg https badge fury io py imageai downloads https pepy tech badge imageai month https pepy tech project imageai downloads https pepy tech badge imageai week https pepy tech project imageai an open source python library built to empower developers to build applications and systems with self contained deep learning and computer vision capabilities using simple and few lines of code if you will like to sponsor this project kindly visit the strong github sponsor page https github com sponsors olafenwamoses strong introducing theiaengine https raw githubusercontent com gen xr theiaengine main logo png we the creators of imageai are glad to announce theiaengine https www genxr co theia engine the next generation computer vision ai api capable of all computer vision tasks in a single api call and available via rest api to all programming languages features include detect 300 objects 220 more objects than imageai provide answers to any content or context questions asked on an image very useful to get information on any object action or information without needing to train a new custom model for every tasks generate scene description and summary convert 2d image to 3d pointcloud and triangular mesh semantic scene mapping of objects walls floors etc stateless face recognition and emotion detection image generation and augmentation from prompt etc visit https www genxr co theia engine https www genxr co theia engine to try the demo and join in the beta testing today logo1 png developed and maintained by moses olafenwa https twitter com olafenwamoses built with simplicity in mind imageai supports a list of state of the art machine learning algorithms for image prediction custom image prediction object detection video detection video object tracking and image predictions trainings imageai currently supports image prediction and training using 4 different machine learning algorithms trained on the imagenet 1000 dataset imageai also supports object detection video detection and object tracking using retinanet yolov3 and tinyyolov3 trained on coco dataset finally imageai allows you to train custom models for performing detection and recognition of new objects eventually imageai will provide support for a wider and more specialized aspects of computer vision new release imageai 3 0 2 what s new pytorch backend tinyyolov3 model training table of contents a href installation white square button installation a a href features white square button features a a href documentation white square button documentation a a href sponsors white square button sponsors a a href sample white square button projects built on imageai a a href real time and high performance implementation white square button high performance implementation a a href recommendation white square button ai practice recommendations a a href contact white square button contact developers a a href citation white square button citation a a href ref white square button references a installation div id installation div to install imageai run the python installation instruction below in the command line download and install https www python org downloads python 3 7 python 3 8 python 3 9 or python 3 10 install dependencies cpu download requirements txt https github com olafenwamoses imageai blob master requirements txt file and install via the command pip install r requirements txt or simply copy and run the command below pip install cython pillow 7 0 0 numpy 1 18 1 opencv python 4 1 2 torch 1 9 0 extra index url https download pytorch org whl cpu torchvision 0 10 0 extra index url https download pytorch org whl cpu pytest 7 1 3 tqdm 4 64 1 scipy 1 7 3 matplotlib 3 4 3 mock 4 0 3 gpu cuda download requirements gpu txt https github com olafenwamoses imageai blob master requirements gpu txt file and install via the command pip install r requirements gpu txt or smiply copy and run the command below pip install cython pillow 7 0 0 numpy 1 18 1 opencv python 4 1 2 torch 1 9 0 extra index url https download pytorch org whl cu102 torchvision 0 10 0 extra index url https download pytorch org whl cu102 pytest 7 1 3 tqdm 4 64 1 scipy 1 7 3 matplotlib 3 4 3 mock 4 0 3 if you plan to train custom ai models download requirements extra txt https github com olafenwamoses imageai blob master requirements extra txt file and install via the command pip install r requirements extra txt or simply copy and run the command below pip install pycocotools git https github com gautamchitnis cocoapi git cocodataset master subdirectory pythonapi then run the command below to install imageai pip install imageai upgrade features div id features div table tr td h2 image classification h2 td tr tr td img src data images 1 jpg h4 imageai provides 4 different algorithms and model types to perform image prediction trained on the imagenet 1000 dataset the 4 algorithms provided for image prediction include mobilenetv2 resnet50 inceptionv3 and densenet121 click the link below to see the full sample codes explanations and best practices guide h4 a href imageai classification get started a td tr table div id features div table tr td h2 object detection h2 td tr tr td img src data images image2new jpg h4 imageai provides very convenient and powerful methods to perform object detection on images and extract each object from the image the object detection class provides support for retinanet yolov3 and tinyyolov3 with options to adjust for state of the art performance or real time processing click the link below to see the full sample codes explanations and best practices guide h4 a href imageai detection get started a td tr table table tr td h2 video object detection analysis h2 td tr tr td img src data images video analysis visualization jpg h4 imageai provides very convenient and powerful methods to perform object detection in videos the video object detection class provided only supports the current state of the art retinanet click the link to see the full videos sample codes explanations and best practices guide h4 a href imageai detection video md get started a td tr table table tr td h2 custom classification model training h2 td tr tr td img src data images idenprof jpg h4 imageai provides classes and methods for you to train a new model that can be used to perform prediction on your own custom objects you can train your custom models using mobilenetv2 resnet50 inceptionv3 and densenet in 5 lines of code click the link below to see the guide to preparing training images sample training codes explanations and best practices h4 a href imageai classification customtraining md get started a td tr table table tr td h2 custom model classification h2 td tr tr td img src data images 4 jpg h4 imageai provides classes and methods for you to run image prediction your own custom objects using your own model trained with imageai model training class you can use your custom models trained with mobilenetv2 resnet50 inceptionv3 and densenet and the json file containing the mapping of the custom object names click the link below to see the guide to sample training codes explanations and best practices guide h4 a href imageai classification customclassification md get started a td tr table table tr td h2 custom detection model training h2 td tr tr td img src data images headsets jpg h4 imageai provides classes and methods for you to train new yolov3 or tinyyolov3 object detection models on your custom dataset this means you can train a model to detect literally any object of interest by providing the images the annotations and training with imageai click the link below to see the guide to sample training codes explanations and best practices guide h4 a href imageai detection custom customdetectiontraining md get started a td tr table table tr td h2 custom object detection h2 td tr tr td img src data images holo2 detected jpg h4 imageai now provides classes and methods for you detect and recognize your own custom objects in images using your own model trained with the detectionmodeltrainer class you can use your custom trained yolov3 or tinyyolov3 model and the json file generated during the training click the link below to see the guide to sample training codes explanations and best practices guide h4 a href imageai detection custom customdetection md get started a td tr table table tr td h2 custom video object detection analysis h2 td tr tr td img src data images customvideodetection gif h4 imageai now provides classes and methods for you detect and recognize your own custom objects in images using your own model trained with the detectionmodeltrainer class you can use your custom trained yolov3 or tinyyolov3 model and the json file generated during the training click the link below to see the guide to sample training codes explanations and best practices guide h4 a href imageai detection custom customvideodetection md get started a td tr table documentation div id documentation div we have provided full documentation for all imageai classes and functions visit the link below documentation english version https imageai readthedocs io https imageai readthedocs io sponsors div id sponsors div real time and high performance implementation div id performance div imageai provides abstracted and convenient implementations of state of the art computer vision technologies all of imageai implementations and code can work on any computer system with moderate cpu capacity however the speed of processing for operations like image prediction object detection and others on cpu is slow and not suitable for real time applications to perform real time computer vision operations with high performance you need to use gpu enabled technologies imageai uses the pytorch backbone for it s computer vision operations pytorch supports both cpus and gpus specifically nvidia gpus you can get one for your pc or get a pc that has one for machine learning and artificial intelligence algorithms implementations projects built on imageai div id sample div ai practice recommendations div id recommendation div for anyone interested in building ai systems and using them for business economic social and research purposes it is critical that the person knows the likely positive negative and unprecedented impacts the use of such technologies will have they must also be aware of approaches and practices recommended by experienced industry experts to ensure every use of ai brings overall benefit to mankind we therefore recommend to everyone that wishes to use imageai and other ai tools and resources to read microsoft s january 2018 publication on ai titled the future computed artificial intelligence and its role in society kindly follow the link below to download the publication https blogs microsoft com blog 2018 01 17 future computed artificial intelligence role society https blogs microsoft com blog 2018 01 17 future computed artificial intelligence role society contact developer div id contact div moses olafenwa email guymodscientist gmail com twitter olafenwamoses https twitter com olafenwamoses medium guymodscientist https medium com guymodscientist facebook moses olafenwa https facebook com moses olafenwa john olafenwa email johnolafenwa gmail com website https john aicommons science https john aicommons science twitter johnolafenwa https twitter com johnolafenwa medium johnolafenwa https medium com johnolafenwa facebook olafenwajohn https facebook com olafenwajohn citation div id citation div you can cite imageai in your projects and research papers via the bibtex entry below misc imageai author moses title imageai an open source python library built to empower developers to build applications and systems with self contained computer vision capabilities url https github com olafenwamoses imageai month mar year 2018 references div id ref div 1 somshubra majumdar densenet implementation of the paper densely connected convolutional networks in keras https github com titu1994 densenet https github com titu1994 densenet 2 broad institute of mit and harvard keras package for deep residual networks https github com broadinstitute keras resnet https github com broadinstitute keras resnet 3 fizyr keras implementation of retinanet object detection https github com fizyr keras retinanet https github com fizyr keras retinanet 4 francois chollet keras code and weights files for popular deeplearning models https github com fchollet deep learning models https github com fchollet deep learning models 5 forrest n et al squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size https arxiv org abs 1602 07360 https arxiv org abs 1602 07360 6 kaiming h et al deep residual learning for image recognition https arxiv org abs 1512 03385 https arxiv org abs 1512 03385 7 szegedy et al rethinking the inception architecture for computer vision https arxiv org abs 1512 00567 https arxiv org abs 1512 00567 8 gao et al densely connected convolutional networks https arxiv org abs 1608 06993 https arxiv org abs 1608 06993 9 tsung yi et al focal loss for dense object detection https arxiv org abs 1708 02002 https arxiv org abs 1708 02002 10 o russakovsky et al imagenet large scale visual recognition challenge https arxiv org abs 1409 0575 https arxiv org abs 1409 0575 11 ty lin et al microsoft coco common objects in context https arxiv org abs 1405 0312 https arxiv org abs 1405 0312 12 moses john olafenwa a collection of images of identifiable professionals https github com olafenwamoses idenprof https github com olafenwamoses idenprof 13 joseph redmon and ali farhadi yolov3 an incremental improvement https arxiv org abs 1804 02767 https arxiv org abs 1804 02767 14 experiencor training and detecting objects with yolo3 https github com experiencor keras yolo3 https github com experiencor keras yolo3 15 mobilenetv2 inverted residuals and linear bottlenecks https arxiv org abs 1801 04381 https arxiv org abs 1801 04381 16 yolov3 in pytorch onnx coreml tflite https github com ultralytics yolov3 https github com ultralytics yolov3 | artificial-intelligence machine-learning prediction image-prediction python python3 offline-capable imageai artificial-neural-networks algorithm image-recognition object-detection squeezenet densenet video inceptionv3 detection gpu ai-practice-recommendations | ai |
DataSciencePython | python data science tutorials this repo contains a curated list of python tutorials for data science nlp and machine learning curated list of r tutorials for data science nlp and machine learning https github com ujjwalkarn datasciencer comprehensive topic wise list of machine learning and deep learning tutorials codes articles and other resources https github com ujjwalkarn machine learning tutorials blob master readme md the python language python 3 in one picture https fossbytes com wp content uploads 2015 09 python 3 in one pic png awesome python https github com vinta awesome python jargon from the functional programming world in simple terms https github com hemanth functional programming jargon dive into python http www diveintopython net index html learn python wiki on reddit https www reddit com r learnpython wiki index learn 90 of python in 90 minutes https www slideshare net mattharrison4 learn 90 highest voted python questions http stackoverflow com questions tagged python sort votes pagesize 50 python basic concepts https github com gumption python for data science blob master 3 python basic concepts ipynb quick reference to python http www dataschool io python quick reference the elements of python style https github com amontalenti elements of python style what does the yield keyword do in python http stackoverflow com questions 231767 what does the yield keyword do in python parsing values from a json file in python http stackoverflow com questions 2835559 parsing values from a json file in python python quora faqs https www quora com topic python programming language 1 time complexity of various operations list dict in current cpython https wiki python org moin timecomplexity scripting in python python scripting tutorial http www dreamsyssoft com python scripting tutorial intro tutorial php scripting with python https www schrodinger com acrobatfile php type supportdocs type2 ident 404 can i use python as a bash replacement http stackoverflow com questions 209470 can i use python as a bash replacement useful online courses learn python codecademy https www codecademy com learn python free interactive course intro to python for data science datacamp https www datacamp com courses intro to python for data science introduction to computer science and programming using python mit https www edx org course introduction computer science mitx 6 00 1x 11 python for everybody https www coursera org learn python python programming essentials https www coursera org learn python programming data science with python data science ipython notebooks https github com donnemartin data science ipython notebooks awesome python data analysis https github com vinta awesome python science and data analysis statistics statistics and data science https github com svaksha pythonidae blob master statistics md an introduction to scientific python and a bit of the maths behind it numpy http www kdnuggets com 2016 06 intro scientific python numpy html data analysis and ipython notebooks https github com kirang89 pycrumbs data analysis python for data science basic concepts https github com gumption python for data science blob master 2 data science basic concepts ipynb pycon india 2015 notes http www analyticsvidhya com blog 2015 10 notes impressions experience excitement pycon india 2015 5 important python data science advancements of 2015 https medium com elgehelge the 5 most important python data science advancements of 2015 a136482da89b sp2c1la9z data exploration with numpy cheat sheet http www analyticsvidhya com blog 2015 07 11 steps perform data analysis pandas python querying craiglist with python http chrisholdgraf com querying craigslist with python imm mid 0d8940 cmp em data na na newsltr 20150916 an introduction to numpy and scipy http www engr ucsb edu shell che210d numpy pdf create nba shot charts http savvastjortjoglou com nba shot sharts html pythor python meets r http nipunbatra github io 2016 01 pythor how do i learn data analysis with python https www quora com how do i learn data analysis with python redirected qid 2464720 what are some interesting things to do with python https www quora com python programming language what are some interesting things to do with python redirected qid 2324227 which is better for data analysis r or python https www quora com which is better for data analysis r or python web scraping in python https github com ujjwalkarn web scraping the guide to learning python for data science http www datasciencecentral com profiles blogs the guide to learning python for data science 2 python for data science a cheat sheet for beginners https www datacamp com community tutorials python data science cheat sheet basics top voted python data science questions http datascience stackexchange com questions tagged python awesome python data visualization https github com vinta awesome python data visualization awesome python map reduce https github com vinta awesome python mapreduce pandas library in python intro to pandas data structures http www gregreda com 2013 10 26 intro to pandas data structures useful pandas cheatsheet https github com pandas dev pandas blob master doc cheatsheet pandas cheat sheet pdf an introduction to scientific python pandas http www datadependence com 2016 05 scientific python pandas 10 minutes to pandas http pandas pydata org pandas docs stable 10min html useful pandas snippets http www swegler com becky blog 2014 08 06 useful pandas snippets timeseries analysis using pandas http nbviewer jupyter org github twiecki financial analysis python tutorial blob master 1 20pandas 20basics ipynb pandas exercises practice your pandas skills https github com guipsamora pandas exercises grouping in pandas http blog yhat com posts grouping pandas html large data work flows using pandas http stackoverflow com questions 14262433 large data work flows using pandas easier data analysis with pandas video series http www dataschool io easier data analysis with pandas pandas basics cheat sheet https www datacamp com community blog python pandas cheat sheet quick operations on a pandas dataframe renaming columns in pandas http stackoverflow com questions 11346283 renaming columns in pandas video https www youtube com watch v 0ubiryfhize list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 5 deleting columns from pandas dataframe http stackoverflow com questions 13411544 delete column from pandas dataframe video https www youtube com watch v gnukks964wq list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 6 adding new column to existing dataframe http stackoverflow com questions 12555323 adding new column to existing dataframe in python pandas add one row in a pandas dataframe http stackoverflow com questions 10715965 add one row in a pandas dataframe changing the order of dataframe columns http stackoverflow com questions 13148429 how to change the order of dataframe columns changing data type of columns http stackoverflow com questions 15891038 pandas change data type of columns video https www youtube com watch v v0awyzvmf54 list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 13 getting a list of the column headers from a dataframe http stackoverflow com questions 19482970 get list from pandas dataframe column headers converting list of dictionaries to dataframe http stackoverflow com questions 20638006 convert list of dictionaries to dataframe getting row count of pandas dataframe http stackoverflow com questions 15943769 how to get row count of pandas dataframe most efficient way to loop through dataframes http stackoverflow com questions 7837722 what is the most efficient way to loop through dataframes with pandas deleting dataframe row based on column value http stackoverflow com questions 18172851 deleting dataframe row in pandas based on column value dropping a list of rows from pandas dataframe http stackoverflow com questions 14661701 how to drop a list of rows from pandas dataframe sorting a dataframe or a single column https www youtube com watch v zy4dof6xsxy list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 7 filtering dataframe rows by column value https www youtube com watch v 2afgpdnn4fm list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 8 filtering dataframe rows using multiple criteria https www youtube com watch v ypitfq87qjm list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 9 dropping all non numeric columns from a dataframe https youtu be b r9vuk80dk t 4m31s counting and removing missing values https www youtube com watch v fcmro vzel8 list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 16 selecting multiple rows and columns from a dataframe https www youtube com watch v xvpna7bc8cs list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 19 reducing the size of a dataframe https www youtube com watch v wdydygyn cw list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y index 21 machine learning with python ai ml related list https github com svaksha pythonidae blob master ai md data normalization in python http blog yhat com posts data normalization in python html python machine learning book https github com rasbt python machine learning book table of contents and code notebooks https github com rasbt python machine learning book blob master readme md table of contents and code notebooks machine learning with scikit learn http www dataschool io machine learning with scikit learn machine learning algorithms cheatsheet http www analyticsvidhya com blog 2015 09 full cheatsheet machine learning algorithms how to compute precision recall accuracy and f1 score for the multiclass case with scikit learn http stackoverflow com questions 31421413 how to compute precision recall accuracy and f1 score for the multiclass case one hot encoding for machine learning in python http stackoverflow com questions 17469835 one hot encoding for machine learning building a semi autonomous drone with python http blog yhat com posts autonomous droning with python html awesome python machine learning https github com vinta awesome python machine learning computer vision awesome python computer vision https github com vinta awesome python computer vision scikit learn scikit learn on wikipedia https en wikipedia org wiki scikit learn introduction to machine learning with scikit learn https github com justmarkham scikit learn videos videos http blog kaggle com author kevin markham a gentle introduction to scikit learn a python machine learning library http machinelearningmastery com a gentle introduction to scikit learn a python machine learning library pydata seattle 2015 scikit learn tutorial https github com jakevdp sklearn pydata2015 sklearn scipy2013 https github com jakevdp sklearn scipy2013 sklearn benchmarks a centralized repository to report scikit learn model performance across a variety of parameter settings and data sets https github com rhiever sklearn benchmarks report results of sklearn benchmarks at openml org http www openml org how to get most informative features for scikit learn classifiers http stackoverflow com questions 11116697 how to get most informative features for scikit learn classifiers code example to predict prices of airbnb vacation rentals using scikit learn on spark https github com mapr demos spark sklearn airbnb predict machine learning with scikit learn tutorial http amueller github io sklearn tutorial parallel and large scale machine learning with scikit learn https speakerdeck com ogrisel parallel and large scale machine learning with scikit learn meetup http datasciencelondon org machine learning python scikit learn ipython dsldn data science london kaggle saving classifier to disk in scikit learn http stackoverflow com questions 10592605 save classifier to disk in scikit learn linear regression in python linear regression in python http nbviewer ipython org github justmarkham dat4 blob master notebooks 08 linear regression ipynb blog post http www dataschool io linear regression in python linear regression using scikit learn http scikit learn org stable modules generated sklearn linear model linearregression html a friendly introduction to linear regression using python http www dataschool io linear regression in python linear regression example in python http scipy cookbook readthedocs io items linearregression html regression analysis using python statsmodels package http www turingfinance com regression analysis using python statsmodels and quandl run an ols regression with pandas data frame http stackoverflow com questions 19991445 run an ols regression with pandas data frame logistic regression in python logistic regression with scikit learn http www dataschool io logistic regression in python using scikit learn logistic regression in python http blog yhat com posts logistic regression and python html implementing the softmax function in python http stackoverflow com questions 34968722 softmax function python what is the inverse of regularization strength in logistic regression how should it affect my code http stackoverflow com questions 22851316 what is the inverse of regularization strength in logistic regression how shoul the yhat blog logistic regression in python http blog yhat com posts logistic regression and python html example of logistic regression in python using scikit learn http www dataschool io logistic regression in python using scikit learn tutorial on logistic regression and optimization in python https learningwithdata wordpress com 2015 04 30 tutorial on logistic regression and optimization in python using logistic regression in python for data science http www dummies com how to content using logistic regression in python for data scien html k nearest neighbours in python a good tutorial on implementing k nearest neighbors using scikit learn http scikit learn org stable modules neighbors html is it possible to specify your own distance function using scikit learn k means clustering http stackoverflow com questions 5529625 is it possible to specify your own distance function using scikit learn k means tutorial to implement k nearest neighbors in python from scratch http machinelearningmastery com tutorial to implement k nearest neighbors in python from scratch implementing your own k nearest neighbour algorithm using python https blog cambridgecoding com 2016 01 16 machine learning under the hood writing your own k nearest neighbour algorithm knn python implementation on stackoverflow http stackoverflow com questions 5565935 k nearest neighbour in python knn with big sparse matrices in python http stackoverflow com questions 20333092 knn with big sparse matrices in python sklearn knn usage with a user defined metric http stackoverflow com questions 21052509 sklearn knn usage with a user defined metric neural networks in python implementing a neural network from scratch in python http www wildml com 2015 09 implementing a neural network from scratch code https github com dennybritz nn from scratch a neural network in 11 lines of python http iamtrask github io 2015 07 12 basic python network speeding up your neural network with theano and the gpu http www wildml com 2015 09 speeding up your neural network with theano and the gpu code https github com dennybritz nn theano what is the best neural network library for python https www quora com what is the best neural network library for python recurrent neural net tutorial in python part 1 http www wildml com 2015 09 recurrent neural networks tutorial part 1 introduction to rnns part 2 http www wildml com 2015 09 recurrent neural networks tutorial part 2 implementing a language model rnn with python numpy and theano code https github com dennybritz rnn tutorial rnnlm pybrain modular machine learning library for python http pybrain org neural networks tutorial a pathway to deep learning http www adventuresinmachinelearning com neural networks tutorial decision trees in python how to extract the decision rules from scikit learn decision tree http stackoverflow com questions 20224526 how to extract the decision rules from scikit learn decision tree how do i find which attributes my tree splits on when using scikit learn http stackoverflow com questions 20156951 how do i find which attributes my tree splits on when using scikit learn quora what is a good python library for decision trees https www quora com what is a good python library for decision trees stackoverflow http stackoverflow com questions 3127922 what is a good python library for decision trees building decision trees in python http www onlamp com pub a python 2006 02 09 ai decision trees html page 1 pure python decision trees http kldavenport com pure python decision trees building a decision tree from scratch in python a beginner s tutorial http www patricklamle com tutorials decision 20tree 20python tuto decision 20tree html using python to build and use a simple decision tree classifier https github com gumption python for data science blob master 4 python simple decision tree ipynb decision trees in python with scikit learn and pandas http chrisstrelioff ws sandbox 2015 06 08 decision trees in python with scikit learn and pandas html code for simple decision tree in python https github com gumption python for data science blob master simple decision tree py lesson notebook regression and classification trees http nbviewer jupyter org github justmarkham dat8 blob master notebooks 17 decision trees ipynb discover structure behind data with decision trees http vooban com en tips articles geek stuff discover structure behind data with decision trees random forest with python getting started with random forests titanic competition on kaggle https www kaggle com c titanic details getting started with random forests python sample code https www kaggle com c digit recognizer forums t 2299 getting started python sample code random forest randomforestclassifier vs extratreesclassifier in scikit learn http stackoverflow com questions 22409855 randomforestclassifier vs extratreesclassifier in scikit learn powerful guide to learn random forest http www analyticsvidhya com blog 2015 09 random forest algorithm multiple challenges how are feature importances in randomforestclassifier determined http stackoverflow com questions 15810339 how are feature importances in randomforestclassifier determined random forest interpretation with scikit learn http blog datadive net random forest interpretation with scikit learn random forests in python tutorial http blog yhat com posts random forests in python html unbalanced classification using randomforestclassifier in sklearn http stackoverflow com questions 20082674 unbalanced classification using randomforestclassifier in sklearn random forest with categorical features in sklearn http stackoverflow com questions 24715230 random forest with categorical features in sklearn how to output randomforest classifier from python http stackoverflow com questions 23000693 how to output randomforest classifier from python lesson notebook ensembling bagging and random forests http nbviewer jupyter org github justmarkham dat8 blob master notebooks 18 ensembling ipynb support vector machine in python fastest svm implementation usable in python http stackoverflow com questions 9299346 fastest svm implementation usable in python an example using python bindings for svm library libsvm http stackoverflow com questions 4214868 an example using python bindings for svm library libsvm what is the best svm library usable from python https www quora com what is the best svm library usable from python how does sklearn svm svc s function predict proba work internally http stackoverflow com questions 15111408 how does sklearn svm svcs function predict proba work internally support vector machine in python using libsvm example of features http stackoverflow com questions 30991592 support vector machine in python using libsvm example of features linear svc machine learning svm example with python https pythonprogramming net linear svc example scikit learn svm python understanding support vector machine algorithm from examples along with code http www analyticsvidhya com blog 2015 10 understaing support vector machine example code nlp text mining in python nlp with python oriley book http www nltk org book 1ed python 3 http www nltk org book awesome python nlp https github com vinta awesome python natural language processing awesome python text processing https github com vinta awesome python text processing text analytics intro and tokenization http a4analytics blogspot sg 2015 03 text mining post 1 html nltk book http www nltk org book ch01 html elegant n gram generation in python http locallyoptimal com blog 2013 01 20 elegant n gram generation in python computing n grams using python http stackoverflow com questions 13423919 computing n grams using python n grams explanation 2 applications http stackoverflow com questions 1032288 n grams explanation 2 applications nlp tutorial with python http www datasciencecentral com profiles blogs python nlp tools sentiment analysis with python a comprehensive guide to sentiment analysis https monkeylearn com sentiment analysis twitter sentiment analysis https github com ujjwalkarn twitter sentiment analysis basic sentiment analysis with python http fjavieralba com basic sentiment analysis with python html what is the best way to do sentiment analysis with python https www quora com what is the best way to do sentiment analysis with python 1 how to calculate twitter sentiment using alchemyapi with python http www alchemyapi com developers getting started guide twitter sentiment analysis second try sentiment analysis in python http andybromberg com sentiment analysis python sentiment analysis with python nltk text classification http text processing com demo sentiment codes and explanation sentiment analysis with bag of words http ataspinar com 2016 01 21 sentiment analysis with bag of words sentiment analysis with naive bayes http ataspinar com 2016 02 15 sentiment analysis with the naive bayes classifier pickle convert a python object into a character stream python serialization why pickle http stackoverflow com questions 8968884 python serialization why pickle serializing python objects http www diveinto org python3 serializing html binary files http www diveinto org python3 files html binary what is pickle in python https pythontips com 2013 08 02 what is pickle in python how to cpickle dump and load separate dictionaries to the same file http stackoverflow com questions 11641493 how to cpickle dump and load separate dictionaries to the same file understanding pickling in python http stackoverflow com questions 7501947 understanding pickling in python automl tpot a python tool for automating data science http www randalolson com 2016 05 08 tpot a python tool for automating data science github repo https github com rhiever tpot regex related regexr http regexr com regex101 https regex101 com pythex http pythex org how to use regular expressions regex in microsoft excel both in cell and loops http stackoverflow com questions 22542834 how to use regular expressions regex in microsoft excel both in cell and loops advanced filters excel s amazing alternative to regex http searchengineland com advanced filters excels amazing alternative to regex 143680 shell scripting calling an external command in python http stackoverflow com questions 89228 calling an external command in python running shell command from python and capturing the output http stackoverflow com questions 4760215 running shell command from python and capturing the output can i use python as a bash replacement http stackoverflow com questions 209470 can i use python as a bash replacement python scripts as a replacement for bash utility scripts http www linuxjournal com content python scripts replacement bash utility scripts how to write a shell script using bash shell in ubuntu https www youtube com watch v he 5bpugsag red hat magazine python for bash scripters a well kept secret embed bash in python http stackoverflow com questions 2651874 embed bash in python bash2py a bash to python translator https cs uwaterloo ca ijdavis bash2py final pdf beginners bashscripting https help ubuntu com community beginners bashscripting the beginner s guide to shell scripting the basics http www howtogeek com 67469 the beginners guide to shell scripting the basics linux shell scripting tutorial v1 05r3 a beginner s handbook http www freeos com guides lsst other good lists pycrumbs bits and bytes of python from the internet https github com kirang89 pycrumbs python github projects collect and classify python projects on github https github com checkcheckzz python github projects python reference useful functions tutorials and other python related things https github com rasbt python reference pythonidae curated decibans of scientific programming resources in python https github com svaksha pythonidae | data-science python python-tutorial data-scientists | ai |
blockchain-anchor | blockchain anchor this project is no longer active it has been replaced with https github com tierion btc bridge javascript style guide https cdn rawgit com feross standard master badge svg https github com feross standard npm https img shields io npm l blockchain anchor svg https www npmjs com package blockchain anchor npm https img shields io npm v blockchain anchor svg https www npmjs com package blockchain anchor a javascript library for anchoring data onto the bitcoin blockchain and confirming anchored data on bitcoin and ethereum installation npm install blockchain anchor create blockchainanchor object js const blockchainanchor require blockchain anchor let anchoroptions btcusetestnet true optional use testnet for bitcoin transactions default false service blockcypher optional select a service to use default any blockcyphertoken 521ddb6aa94143a58cab3ee0f65e6280 optional required only when using blockcypher service insightapibase http my server com insight api optional connect to a custom instance of bitcore s insight api when using insightapi service defaults to insight bitpay com public api insightfallback true optional when specifying a custom insightapibase retry with the insight bitpay com public api in the event of failure defaults to false when overriding with insightapibase you must set btcusetestnet to match the state of the server at insightapibase let anchor new blockchainanchor anchoroptions bitcoin anchor data will be included in a transaction s op return output ethereum anchor data will be included in the transaction s data payload this package uses a set of 3rd party apis to read and write data from the bitcoin and ethereum blockchains when working with bitcoin the acceptable values for service are blockcypher insightapi or any if a specific service is chosen then only that service will be used if any is chosen then all services will be used starting with one and moving to the next in the event of failure if you wish to use blockcypher be sure to include a valid blockcypher token currently only blockcypher service is capable of confirming ethereum anchors usage embed btc embed your hex string data into the blockchain and receive a bitcoin transaction id and the raw transaction hex as a response js let privatekeywif 91afbdjd1xj3vbxqg8rksj5bq8iyx1oncc3p5evrksxxkefnjg8 for deriving keypair used in transaction creation let hexdata 05ae04314577b2783b4be98211d1b72476c59e9c413cfb2afa2f0c68e0d93911 the hex data string to be anchored within a transaction let feetotalsatoshi 39000 the total fee paid for this transaction in satoshi let txresult try txresult await anchor btcopreturnasync privatekeywif hexdata feetotalsatoshi console log new transaction id txresult txid console log raw transaction txresult rawtx catch error console error error message confirm btc confirm your hex string data has been embedded into a bitcoin transaction returning true or false js let transactionid 048ac54c4313dc6980cace9fac533d71f8fe5cad881f1271329b98183231a08f the transaction id to inspect for the anchored value let expectedvalue 05ae04314577b2783b4be98211d1b72476c59e9c413cfb2afa2f0c68e0d93911 the hex data string value to verify is anchored within the transaction let confirmed try confirmed await anchor btcconfirmopreturnasync transactionid expectedvalue console log confirmed catch error console error error message confirm btc block header confirm your hex string data equals the merkle root of the given btc block returning true or false js var blockheightorhash 435821 the height of the block to confirm merkle root value a block hash may also be provided instead var expectedvalue 2b10349367c46a91c485abca4f7834454118d631f28996fb2908a0fe8cefa0cd the hex data string value of the expected merkle root value for the block let confirmed try confirmed await anchor btcconfirmblockheaderasync blockheightorhash expectedvalue console log confirmed catch error console error error message get btc transaction stats get basic statistics about a transaction returning an object containing the transaction id block height block hash confirmation count fee paid size and op return value if present js var transactionid b61b35f6f274663c4a1c062174925b97dc705cbfca9bd704e91c7d352f709e9c the transaction id to to get the stats for let txstats try txstats await anchor btcgettxstatsasync transactionid console log transaction id txstats id console log block height txstats blockheight console log block hash txstats blockhash console log block confirmations txstats confirmations console log transaction fee txstats feesatoshi console log transaction size txstats sizebytes console log transaction op return value txstats opreturn catch error console error error message get btc transaction confirmation count find the number of confirmations for a given transaction returning a number js var transactionid b61b35f6f274663c4a1c062174925b97dc705cbfca9bd704e91c7d352f709e9c the transaction id to to get the confirmation count for let count try count await anchor btcgettxconfirmationcountasync transactionid console log count catch error console error error message get btc block stats get basic statistics about a block returning an object containing the block hight block hash merkle root time and transaction ids js var blockheightorhash 435821 the height of the block to retrieve stats for a block hash may also be provided instead let blockstats try blockstats await anchor getbtcblockstatsasync blockheightorhash console log block height blockstats height console log block hash blockstats hash console log block merkle root blockstats merkleroot console log block time blockstats time console log transaction ids blockstats txids catch error console error error message get btc block transaction ids get all the transaction ids in a given block returning an array of transaction id hex strings js var blockheightorhash 435821 the height of the block to confirm merkle root value a block hash may also be provided instead let txarray try txarray await anchor btcgetblocktxidsasync blockheightorhash console log txarray catch error console error error message get btc estimated fee rate get the estimated fee for a transaction to be confirmed within the next 2 blocks returning an integer representing satoshi per byte js let feeratesatperbyte try feeratesatperbyte await anchor btcgetestimatedfeeratesatperbyteasync console log feeratesatperbyte catch error console error error message split btc outputs divides or consolidates your unspent outputs to the number of outputs you set equally distributing btc and returning the transaction id and final output count if the resulting balance per output is less that 10000 satoshi the number of outputs will be decreased until the balance per output exceeds that value js let privatekeywif 91afbdjd1xj3vbxqg8rksj5bq8iyx1oncc3p5evrksxxkefnjg8 for deriving keypair used in transaction creation let maxoutputs 6 the desired number of outputs let feetotalsatoshi 39000 the total fee paid for this transaction in satoshi let splitresult try splitresult await anchor btcsplitoutputsasync privatekeywif maxoutputs feetotalsatoshi console log new transaction id splitresult txid console log output count splitresult count catch error console error error message confirm eth confirm your hex string data has been embedded into an ethereum transaction returning true or false js var transactionid b61b35f6f274663c4a1c062174925b97dc705cbfca9bd704e91c7d352f709e9c the transaction id to inspect for the anchored value var expectedvalue a6ec80e2000000000000000000000000e6a4f92579facb4026096f017243ee839ff72fd1 the hex data string value to verify is anchored within the transaction optionally transactionid and expectedvalue may begin with 0x for all ethereum functions let confirmed try confirmed await anchor ethconfirmdataasync transactionid expectedvalue console log confirmed catch error console error error message | bitcoin nodejs blockchain ethereum api-client | blockchain |
info654sp20 | information technologies at pratt si welcome to 654 we re using github this semester as a tool for distributing files and as a method of helping folks get more acquainted with code editors and open tools etc see below for basic headline info but see the syllabus syllabus section for the formal full course information documents and the assignments assignments folder for the descriptions of materials you will turn in see you in class identifier info 654 01 credits 3 day and time monday 06 30pm 09 20pm location manhattan room 606 instructors ashley blewer you can call me ashley pronouns she hers office hours by appointment course notes tba course wordpress site https commons pratt edu prattsi654sp20 https commons pratt edu prattsi654sp20 invitations and logins to come course hashtag info654 https twitter com search f tweets q 23info654 src typd optional thank you to josh hadro https www pratt edu faculty and staff bio id rxnduwdpzjvlbngwwmrkq1f6wdfmqt09 original designer of this class publishing everything in this repo as cc by see license license md a rel license href http creativecommons org licenses by 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by 4 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by 4 0 creative commons attribution 4 0 international license a | server |
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webdev-setup | webdev setup a simple script to setup a fresh ubuntu box for php web development and some goodies for working with symfony2 its no magic it just uses all install commands from daraff s gist https gist github com 3995789 this setup includes git and gitg memcached apache2 or nginx mysql and sqlite3 graphicsmagick with gmagick for php curl php5 with pear xdebug phing codesniffer phpunit phpunit skeletongenerator java8 for major ide s for php like phpstorm or netbeans vim mc frontend tools like nodejs grunt bower png tools for iconizr to start the installer just run sh installdevenvironment sh during the script it will stop from time to time to ask you for settings after running the script look at the gist and configure the services you just installed things you have to do configure manually look in gist for more detailed description git config global user email name email tld git config global user name full name edit listen port in etc php5 fpm pool d www conf listen 127 0 0 1 9009 comment in etc php5 conf d sqlite ini extension sqlite so add line to etc sysctl conf fs inotify max user watches 524288 apply changes sudo sysctl p restart services for nginx sudo service php5 fpm restart sudo service nginx restart for apache sudo service apache2 restart php settings change this settings in etc php5 cli php ini for all webservers change this settings in etc php5 apache2 php ini if you have installed apache2 change this settings in etc php5 fpm php ini if you have installed nginx and fpm shell memory limit 512m display errors on html errors on post max size 32m upload max filesize 32m default charset utf8 date timezone europe zurich or whatever it is in your country create file etc php5 conf d gmagick ini and add a line extension gmagick so edit etc php5 cli conf d xdebug ini shell xdebug max nesting level 1000 xdebug remote enable on xdebug remote host localhost xdebug remote port 9002 xdebug remote handler dbgp add to bashrc export xdebug config phpstorm reload bash settings source bashrc | front_end |
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perseo-fe | perseo context aware cep fiware processing https nexus lab fiware org static badges chapters processing svg https www fiware org developers catalogue license agplv3 https img shields io github license telefonicaid perseo fe svg license support badge https img shields io badge tag fiware perseo orange svg logo stackoverflow https stackoverflow com questions tagged fiware perseo br quay badge https img shields io badge quay io fiware 2fperseo fe grey logo red 20hat labelcolor ee0000 https quay io repository fiware perseo fe docker badge https img shields io badge docker telefonicaiot 2fperseo fe blue logo docker https hub docker com r telefonicaiot perseo fe br documentation badge https img shields io readthedocs fiware perseo fe svg https fiware perseo fe readthedocs io en latest ci https github com telefonicaid perseo fe workflows ci badge svg https github com telefonicaid perseo fe actions query workflow 3aci coverage status https coveralls io repos github telefonicaid perseo fe badge svg branch master https coveralls io github telefonicaid perseo fe branch master status https nexus lab fiware org static badges statuses perseo svg cii best practices https bestpractices coreinfrastructure org projects 5597 badge https bestpractices coreinfrastructure org projects 5597 overview perseo is a complex event processing cep software designed to be fully ngsi v2 compliant it uses ngsi v2 as the communication protocol for events and thus perseo is able to seamless and jointly work with context brokers the context broker tested with perseo and officially supported is orion context broker https github com telefonicaid fiware orion this project is part of fiware https www fiware org you can find more fiware components in the fiware catalogue https catalogue fiware org books documentation https fiware perseo fe readthedocs io en latest img style height 1em src https quay io static img quay favicon png quay io https quay io repository fiware perseo fe whale docker hub https hub docker com r telefonicaiot perseo fe dart roadmap documentation roadmap md content background background installation installation usage usage api api contributing contributing testing testing more information more information license license background perseo is an esper based http www espertech com esper complex event processing cep software designed to be fully ngsi v2 compliant it follows a straightforward idea listening to events coming from context information to identify patterns described by rules in order to immediately react upon them by triggering actions perseo components docs images perseocomponents png by leveraging on the notifications mechanism http fiware orion readthedocs io en latest user walkthrough apiv2 index html subscriptions clients instruct orion cb to notify perseo of the changes in the entities they care about event api details of this process are explained in the orion subscription part of the user manual user index md orion subscription then rules to the core rule engine can be easily managed using the publicly available wirecloud https github com wirecloud wirecloud operational dashboard or making use of any of the rest clients able to programmaticly use the perseo s rule api these rules will identify patterns that will trigger actions with orion to create or update entities or with other different components or external systems such as web http email smtp or sms smpp servers installation the instructions to install perseo can be found in the deployment guide docs admin deployment md usage information about how to use perseo can be found in the user programmers manual docs user index md api apis and examples of their usage can be found here docs api api md contributing contributions to this project are welcome developers planning to contribute should follow the contribution guidelines docs developer contributing md if you plan to contribute please also read the development documentation docs developer development md testing for performing a basic end to end test you can follow the detailed instructions here docs developer development md testing more information refer to the esper reference documentation http esper espertech com release 8 4 0 reference esper html index html for info on how to use epl as a rule language full perseo documentation docs readme md license perseo fe is licensed under affero general public license gpl version 3 license are there any legal issues with agpl 3 0 is it safe for me to use there is absolutely no problem in using a product licensed under agpl 3 0 issues with gpl or agpl licenses are mostly related with the fact that different people assign different interpretations on the meaning of the term derivate work used in these licenses due to this some people believe that there is a risk in just using software under gpl or agpl licenses even without modifying it for the avoidance of doubt the owners of this software licensed under an agpl 3 0 license wish to make a clarifying public statement as follows please note that software derived as a result of modifying the source code of this software in order to fix a bug or incorporate enhancements is considered a derivative work of the product software that merely uses or aggregates i e links to an otherwise unmodified version of existing software is not considered a derivative work and therefore it does not need to be released as under the same license or even released as open source | front_end |
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atlas-design | atlas logo atlas light svg atlas design system welcome to the atlas design project this repository holds the source code backing the atlas design system our mission the atlas design system strives to empower designers pms and developers to build accessible high quality and consistent experiences at scale across the microsoft skilling web properties atlas powers microsoft learn https learn microsoft com looking for information about the atlas css framework start in css what we do we care deeply about accessibility microsoft learn is one of the very few large grade b websites we are a low level design system that focuses on css and only uses zero dependency js when required for good interactions or accessibility we are compatible with right to left reading direction thanks to our use of logical properties we are themeable with three themes right out of the box light dark and high contrast and we support an unlimited number of themes we provide example markup our website is spartan compared to many design system websites its main purpose is for atomic component documentation and to provide accessible code snippets we have atomics for flexibility and components for consistency we have a vscode extension that provides helpful intellisense and class completion in the ide we publish to npm version and status name status microsoft atlas css microsoft atlas css npm version https badge fury io js 40microsoft 2fatlas css svg https badge fury io js 40microsoft 2fatlas css microsoft atlas js microsoft atlas js npm version https badge fury io js 40microsoft 2fatlas js svg https badge fury io js 40microsoft 2fatlas js site build site https dev azure com ceapex engineering apis build status microsoft atlas design branchname main https dev azure com ceapex engineering build latest definitionid 3602 branchname main release pipeline release https github com microsoft atlas design actions workflows release yml badge svg https github com microsoft atlas design actions workflows release yml pr builds ci https github com microsoft atlas design actions workflows main yml badge svg event push https github com microsoft atlas design actions workflows main yml development ensure git https git scm com is installed ensure that have downloaded and installed a version of nodejs https nodejs org en download releases that supports monorepos it s currently recommended you download nodejs version 18 12 1 and use with npm at a greater version than 8 19 2 alternatively you can install npm with nvm mac https github com nvm sh nvm windows https github com coreybutler nvm windows if contributing code please read about using changesets https github com atlassian changesets and semantic versioning bump types https semver org clone the repostory from the root directory run npm install using atlas css the styles backing the atlas design system are discussed in greater detail in css install atlas css in your project use npm to add microsoft atlas css to your project sh install with npm npm install save microsoft atlas css you may access any file within the css folder using the following unpkg url just add the path to the file relative to the sign or version the end sh https unpkg com browse microsoft atlas css will redirect to latest version https unpkg com browse microsoft atlas css version use this pattern on your page install atlas js in your project behaviors and elements beyond the scope of css are found in the js folder sh install with npm npm install save microsoft atlas js contributing while this project is open source its primary purpose is to serve microsoft web properties through a css first implementation of a design system we do appreciate contributions to our documentation site folder our framework css and its companion scripts js this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments trademarks this project may contain trademarks or logos for projects products or services authorized use of microsoft trademarks or logos is subject to and must follow microsoft s trademark brand guidelines https www microsoft com en us legal intellectualproperty trademarks usage general use of microsoft trademarks or logos in modified versions of this project must not cause confusion or imply microsoft sponsorship any use of third party trademarks or logos are subject to those third party s policies updating dependencies the following commands can be used to update broadly update dependencies note we omit major updates to husky because of major api changes and a general preference for version 4 x sh npm exec package npm check updates workspaces include workspace root npm check updates upgrade reject husky npm exec package npm check updates workspaces include workspace root npm check updates upgrade target minor rm package lock json npm i | css design-system front-end cssframework microsoft-learn | os |
design-system | bc government design system img https img shields io badge lifecycle maturing 007ec6 https github com bcgov repomountie blob master doc lifecycle badges md the design system helps developers and designers build better digital products and services it s a collection of digital resources and tools including a library of reusable ui interface components and design patterns the system makes it easier and faster to build custom b c government websites and applications components are collectively built by the government community meet accessibility standards and are open for input and improvement documentation https developer gov bc ca design system about the design system files in this repository docs project documentation images icons openshift openshift specific files scripts helper scripts templates application templates deployment local development developer workstation requirements setup application specific setup deployment openshift see openshift readme md getting help or reporting an issue to report bugs issues feature requests please file an issue https github com bcdevops opendev template issues how to propose a component if you would like to propose a component to the design system please see our propose a component github issue template propose a new component md guideline please note that this project is released with a contributor code of conduct code of conduct md by participating in this project you agree to abide by its terms license copyright 2016 province of british columbia 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 | citz design-system design-patterns | os |
EmbeddedSystemCourse-2017 | embedded system projects under the supervision of prof pedram | os |
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Deep-Learning-Computer-Vision | deep learning for computer vision courses general info i present my assignment solutions for both 2020 course offerings stanford university cs231n cnns for visual recognition and university of michigan eecs 498 007 598 005 deep learning for computer vision review after reading enormous positive reviews about cs231n i decided to dive in by myself into the course lectures which as expected were great with well presented and explained topics thanks to the instructors that covers a plethora of machine learning deep learning concepts not only computer vision related theoretically via lectures slides and extra reading content and practically via the well designed assignments depending on your ml understanding especially statistics algebra and python programming with numpy package this course can be challenging since the topics are covered from the fundamentals actually from scratch going through all the math behind even me having a mathematical background i found myself struggled with some advanced topics like variational autoencoders which pushed me to review some maths stats formulas that being said most of the course materials aren t that difficult and even so all the putted efforts and spent time are totally worth it you will learn a lot in parallel to cs231n i took also its michigan s updated equivalent eecs 498 007 abbreviated as there is a lot of numbers in the course s title because for cs231n only 2016 and 2017 lectures are available which is a little bit old given the fast progress in ml in general however this concerns only some topics and even that the old lectures are still worthy to watch for eecs 498 007 the 2019 lectures are available they cover more topics like attention 3d video etc and the ones existing in cs231n are updated and some topics are explained in more detail like object detection and vaes this novelty concerns also the assignments the similarity between the two courses is related to the fact that one of cs231n s main instructors precisely justin johnson moved from stanford to michigan in 2019 assignments assignments are the funniest part of the courses they allow practicing most of the learned theoretical concepts that is you will implement vectorized mathematical formulas gradient descent be prepared to spend some hours with a pen and a sheet figuring out how to compute formula gradients neural networks among others cnns and rnns from scratch etc that being said in advanced assignment parts you will also use high level frameworks tensorflow and pytorch assignment questions are in form of jupyter notebooks that call external python files in order to execute properly that is you will mostly implement missing parts in the python files and execute notebook s cells to check the correctness of your implementation however you ll write also some code in the notebooks and respond to inline questions result analysis and theoretical questions for my implementation i solved all from the three cs231n assignments for the questions that use frameworks they ask to pick only one and for that i choosed pytorch that is questions that require framework were implemented with pytorch and not with tensorflow for eecs 498 007 since its assignments are similar to the cs231n ones i solved only those who bring new concepts precisely a4 partially the first two questions about residual networks and attention lstm a5 object detection yolo and faster rcnn and a6 partially the 1st question about vaes for eecs 498 007 there is no choice only pytorch is used which fits perfectly with my choice of using it also in cs231n note that even that my coding solutions are probably correct the cs231n assignments contain inline questions for which i m not sure about their correctness i just responded as well as i know also except for the cs231n first assignment which is less commented for the remaining assignments i tried to comment on my code as richly as i can to make it understandable repository structure the repository file s structure is quite intuitive there are two folders one for each course each one with its sub folders that represent the assignments three for both cs231n and eecs 498 007 note that for each assignment s folder i put a readme which shows covered topics and question descriptions copied from the assignment s website in the rest of this readme i will present a quick access to the assignments files assignment files useful links useful links some obtained results result examples and credits credits courses materials links the table below shows relevant links to both courses materials relevent info cs231n eecs 498 007 official website 2020 http cs231n stanford edu 2020 index html 2017 http cs231n stanford edu 2017 index html 2020 https web eecs umich edu justincj teaching eecs498 fa2020 2019 https web eecs umich edu justincj teaching eecs498 fa2019 lectures playlist 2017 https www youtube com playlist list pl3fw7lu3i5jvhm8ljyj zlfqrf3eo8syv 2016 https www youtube com playlist list plkt2usq6rbvctenovbg1tpcc7oqi31alc 2019 https www youtube com playlist list pl5 tkqafazfbzxjbhtzdvcwe0zbhomg7r syllabus 2020 http cs231n stanford edu 2020 syllabus html 2017 http cs231n stanford edu 2017 syllabus 2020 https web eecs umich edu justincj teaching eecs498 fa2020 schedule html 2019 https web eecs umich edu justincj teaching eecs498 fa2019 schedule html assignment files cs231n convolutional neural networks for visual recognition assignment 1 modified python files k nearest neighbor py cs231n assignment1 cs231n classifiers k nearest neighbor py linear classifier py cs231n assignment1 cs231n classifiers linear classifier py linear svm py cs231n assignment1 cs231n classifiers linear svm py softmax py cs231n assignment1 cs231n classifiers softmax py neural net py cs231n assignment1 cs231n classifiers neural net py question title ipython notebook q1 k nearest neighbor classifier knn ipynb cs231n assignment1 knn ipynb q2 training a support vector machine svm ipynb cs231n assignment1 svm ipynb q3 implement a softmax classifier softmax ipynb cs231n assignment1 softmax ipynb q4 two layer neural network two layer net ipynb cs231n assignment1 two layer net ipynb q5 higher level representations image features features ipynb cs231n assignment1 features ipynb assignment 2 modified python files layers py cs231n assignment2 cs231n layers py optim py cs231n assignment2 cs231n optim py fc net py cs231n assignment2 cs231n classifiers fc net py cnn py cs231n assignment2 cs231n classifiers cnn py question title ipython notebook q1 fully connected neural network fullyconnectednets ipynb cs231n assignment2 fullyconnectednets ipynb q2 batch normalization batchnormalization ipynb cs231n assignment2 batchnormalization ipynb q3 dropout dropout ipynb cs231n assignment2 dropout ipynb q4 convolutional networks convolutionalnetworks ipynb cs231n assignment2 convolutionalnetworks ipynb q5 pytorch tensorflow on cifar 10 pytorch ipynb cs231n assignment2 pytorch ipynb assignment 3 modified python files rnn layers py cs231n assignment3 cs231n rnn layers py rnn py cs231n assignment3 cs231n classifiers rnn py net visualization pytorch py cs231n assignment3 cs231n net visualization pytorch py style transfer pytorch py cs231n assignment3 cs231n style transfer pytorch py gan pytorch py cs231n assignment3 cs231n gan pytorch py question title ipython notebook q1 image captioning with vanilla rnns rnn captioning ipynb cs231n assignment3 rnn captioning ipynb q2 image captioning with lstms lstm captioning ipynb cs231n assignment3 lstm captioning ipynb q3 network visualization networkvisualization pytorch ipynb cs231n assignment3 networkvisualization pytorch ipynb q4 style transfer styletransfer pytorch ipynb cs231n assignment3 styletransfer pytorch ipynb q5 generative adversarial networks generative adversarial networks pytorch ipynb cs231n assignment3 generative adversarial networks pytorch ipynb eecs 498 007 598 005 deep learning for computer vision assignment 4 modified python files pytorch autograd and nn py eecs498 007 a4 pytorch autograd and nn py rnn lstm attention captioning py eecs498 007 a4 rnn lstm attention captioning py network visualization py eecs498 007 a4 network visualization py style transfer py eecs498 007 a4 style transfer py question title ipython notebook q1 pytorch autograd pytorch autograd and nn ipynb eecs498 007 a4 pytorch autograd and nn ipynb q2 image captioning with recurrent neural networks rnn lstm attention captioning ipynb eecs498 007 a4 rnn lstm attention captioning ipynb q3 network visualization network visualization ipynb eecs498 007 a4 network visualization ipynb q4 style transfer style transfer ipynb eecs498 007 a4 style transfer ipynb assignment 5 modified python files single stage detector py eecs498 007 a5 single stage detector py two stage detector py eecs498 007 a5 two stage detector py question title ipython notebook q1 single stage detector single stage detector yolo ipynb eecs498 007 a5 single stage detector yolo ipynb q2 two stage detector two stage detector faster rcnn ipynb eecs498 007 a5 two stage detector faster rcnn ipynb assignment 6 modified python files vae py eecs498 007 a6 vae py gan py eecs498 007 a6 gan py question title ipython notebook q1 variational autoencoder variational autoencoders ipynb eecs498 007 a6 variational autoencoders ipynb q2 generative adversarial networks generative adversarial networks ipynb eecs498 007 a6 generative adversarial networks ipynb useful links the list below provides the most useful external resources that helped me to clarify and understand deeply some ambiguous topics encountered in the lectures note that those are only the most important ones that is completely understanding them will maybe require checking other not mentioned resources convolutional neural networks cnns cnns implementation from scratch in python part 1 https victorzhou com blog intro to cnns part 1 part 2 https victorzhou com blog intro to cnns part 2 a guide to receptive field arithmetic for cnns https medium com mlreview a guide to receptive field arithmetic for convolutional neural networks e0f514068807 normalization layers understanding the backward pass through batch normalization layer https kratzert github io 2016 02 12 understanding the gradient flow through the batch normalization layer html staged computation method deriving the gradient for the backward pass of batch normalization https kevinzakka github io 2016 09 14 batch normalization gradient derivation method group normalization the paper https arxiv org abs 1803 08494 concept and implementation well explained principal component analysis pca principal component analysis pca from scratch https drscotthawley github io blog 2019 12 21 pca from scratch html covariance matrix method statquest pca svd decomposition method part 1 https youtu be fgakzw6k1qq part 2 https youtu be orvgq966yzg in depth principal component analysis https jakevdp github io pythondatasciencehandbook 05 09 principal component analysis html using sklearn package object detection map mean average precision for object detection https jonathan hui medium com map mean average precision for object detection 45c121a31173 object detection for dummies part 1 https lilianweng github io lil log 2017 10 29 object recognition for dummies part 1 html part 2 https lilianweng github io lil log 2017 12 15 object recognition for dummies part 2 html part 3 https lilianweng github io lil log 2017 12 31 object recognition for dummies part 3 html part 4 https lilianweng github io lil log 2018 12 27 object detection part 4 html variational autoencoders vaes kullback leibler kl divergence explained https www countbayesie com blog 2017 5 9 kullback leibler divergence explained variational autoencoders https www jeremyjordan me variational autoencoders variational autoencoder demystified with pytorch implementation https towardsdatascience com variational autoencoder demystified with pytorch implementation 3a06bee395ed generative adversarial networks gans gans from scratch a deep introduction with code in pytorch https medium com ai society gans from scratch 1 a deep introduction with code in pytorch and tensorflow cb03cdcdba0f gan objective function origin explanation https ai stackexchange com a 13038 google deepmind generative adversarial networks https youtu be wfsi2wqufda result examples as mentioned previously assignments are the funniest part of the courses in this section i will provide some interesting obtained results that being said during assignment solving you will encounter other amazing results here i just picked some of them class visualization consists of generating a synthetic image that will maximize some class score illustrations shown below are generated by applying this technique on a pre trained cnn on imagenet dataset you can see the changes on the synthetic image during training for different classes categories even that those images are not understandable because they are supposed to maximize scores not to be pretty you can identify some specific patterns shapes for these particular classes analog clock examples analog clock gif dining table examples dining table gif kit fox examples kit fox gif tarantula examples tarantula gif analog clock dining table kit fox tarantula gans the goal of generative adversarial networks is to generate novel data in our case images that mimic the original data from a dataset illustrations below were generated by training three types of gans on left the most basic one on right the most advanced one on the mnist dataset you can see the changes on the generated images during training from completely noisy to reasonable images that do not exist in the dataset vanilla gan examples vanilla gan gif dcgan examples dcgan gif vanilla gan dcgan style transfer consists of applying a style from an artistic drawing on an input image the illustration below shows the result of applying different styles on two images style transfer examples style transfer png style transfer applied on two images br drawing credits from left to the right the scream https artsandculture google com asset the scream edvard munch eqfdrtfkdtvq1a bicentennial print https artsandculture google com asset bicentennial print roy lichtenstein tahrv5b7p6vh1a horses on the seashore https artsandculture google com asset horses on the seashore wfnr8bqc58ghw head of a clown https artsandculture google com asset head of a clown cqhmqkf7dro76w and the starry night https artsandculture google com asset the starry night vincent van gogh bgeuwdxel93 pg credits i would like to thank everyone involved directly or indirectly for making such a great course freely available for everyone especially the instructors fei fei li https profiles stanford edu fei fei li andrej karpathy https karpathy ai serena yeung http ai stanford edu syyeung and justin johnson https web eecs umich edu justincj | computer-vision visual-recognition machine-learning deep-learning convolutional-neural-networks python numpy pytorch assignment stanford michigan cs231n cs231n-assignment eecs498-007 | ai |
chainer_examples | chainer example code for nlp this repository is out of date and rough i do not guarantee that these code works correctly i am developing a new nmt toolkit nmtkit https github com odashi nmtkit and strongly recommend to use it instead of these samples to train neural translation models this repository contains some neural network examples for natural language processing nlp using chainer framework chainer official http chainer org chainer official github https github com pfnet chainer github making local client before running these scripts making a local python client using pyenv is reccomended like pyenv install 3 5 0 pyenv virtualenv 3 5 0 example pyenv shell example pip install chainer contents machine translation mt s2s encdec py using encoder decoder style recurrent neural network mt s2s attention py using attentional neural network word segmentation tokenization seg ffnn py using feedforward neural network seg rnn py using recurrent neural network language model lm rnn py using recurrent neural network rnnlm contact if you find an issue or have some questions please contact yusuke oda odashi t on twitter faster than other methods yus takara at gmail com | ai |
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Awesome-Efficient-LLM | awesome efficient llm a curated list for efficient large language models knowledge distillation knowledge distillation network pruning network pruning quantization quantization inference acceleration inference acceleration efficient structure design efficient structure design text compression text compression low rank decomposition low rank decomposition hardware hardware survey survey others others updates sep 27 2023 add tag publish https img shields io badge conference neurips 23 blue for papers accepted at neurips 23 sep 6 2023 add a new subdirectory project project to organize those projects that are designed for developing a lightweight llm july 11 2023 in light of the numerous publications that conducts experiments using plms such as bert bart currently a new subdirectory efficient plm efficient plm is created to house papers that are applicable to plms but have yet to be verified for their effectiveness on llms not implying that they are not suitable on llm knowledge distillation title authors introduction links star https img shields io github stars franxyao flant5 cot specialization svg style social label star https github com franxyao flant5 cot specialization publish https img shields io badge conference icml 23 blue br specializing smaller language models towards multi step reasoning https arxiv org abs 2301 12726 br yao fu hao peng litu ou ashish sabharwal tushar khot img width 1002 alt image src figures modelspecialization png github https github com franxyao flant5 cot specialization br paper https arxiv org abs 2301 12726 star https img shields io github stars siyuyuan coscript svg style social label star https github com siyuyuan coscript publish https img shields io badge conference acl 23 20outstanding blue br distilling script knowledge from large language models for constrained language planning https arxiv org abs 2305 05252 br siyu yuan jiangjie chen ziquan fu xuyang ge soham shah charles robert jankowski yanghua xiao deqing yang img width 302 alt image src figures coscript png github https github com siyuyuan coscript br paper https arxiv org abs 2305 05252 publish https img shields io badge conference acl 23 20outstanding blue br scott self consistent chain of thought distillation https arxiv org abs 2305 01879 br peifeng wang zhengyang wang zheng li yifan gao bing yin xiang ren img width 1002 alt image src figures scott png paper https arxiv org abs 2305 01879 star https img shields io github stars eric11eca disco svg style social label star https github com eric11eca disco publish https img shields io badge conference acl 23 blue br disco distilling counterfactuals with large language models https arxiv org abs 2212 10534 br zeming chen qiyue gao antoine bosselut ashish sabharwal kyle richardson img width 1002 alt image src figures disco png github https github com eric11eca disco br paper https arxiv org abs 2212 10534 star https img shields io github stars allenai i2d2 svg style social label 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ruihao gong xiuying wei zhiwei dong jianfei cai bohan zhuang img width 1002 alt image src figures qllm png paper https arxiv org abs 2310 08041 loftq lora fine tuning aware quantization for large language models https arxiv org abs 2310 08659 br yixiao li yifan yu chen liang pengcheng he nikos karampatziakis weizhu chen tuo zhao img width 1002 alt image src figures loftq png paper https arxiv org abs 2310 08659 teq trainable equivalent transformation for quantization of llms https arxiv org abs 2310 10944 br wenhua cheng yiyang cai kaokao lv haihao shen img width 1002 alt image src figures teq png github https github com intel neural compressor br paper https arxiv org abs 2310 10944 bitnet scaling 1 bit transformers for large language models https arxiv org abs 2310 11453 br hongyu wang shuming ma li dong shaohan huang huaijie wang lingxiao ma fan yang ruiping wang yi wu furu wei img width 1002 alt image src https thegenerality com agi assets img bitnet png paper https arxiv org abs 2310 11453 inference acceleration title authors introduction links star https img shields io github stars fminference dejavu svg style social label star https github com fminference dejavu publish https img shields io badge conference icml 23 20oral blue br deja vu contextual sparsity for efficient llms at inference time https openreview net forum id wipihhd00i br zichang liu jue wang tri dao tianyi zhou binhang yuan zhao song anshumali shrivastava ce zhang yuandong tian christopher re beidi chen img width 202 alt image src figures dajevu png github https github com fminference dejavu br paper https openreview net forum id wipihhd00i publish https img shields io badge conference neurips 23 blue br scissorhands exploiting the persistence of importance hypothesis for llm kv cache compression at test time https arxiv org abs 2305 17118 br zichang liu aditya desai fangshuo liao weitao wang victor xie zhaozhuo xu anastasios kyrillidis anshumali shrivastava img width 302 alt image src figures scissorhands png paper https arxiv org abs 2305 17118 publish https img shields io badge conference neurips 23 blue br dynamic context pruning for efficient and interpretable autoregressive transformers https arxiv org abs 2305 15805 br sotiris anagnostidis dario pavllo luca biggio lorenzo noci aurelien lucchi thomas hofmann img width 1602 alt image src figures dcp png paper https arxiv org abs 2305 15805 star https img shields io github stars microsoft llmlingua svg style social label star https github com microsoft llmlingua publish https img shields io badge conference emnlp 23 blue br llmlingua compressing prompts for accelerated inference of large language models https arxiv org abs 2310 05736 br huiqiang jiang qianhui wu chin yew lin yuqing yang lili qiu img width 1002 alt image src https github com microsoft llmlingua blob main images llmlingua framework png github https github com microsoft llmlingua br paper https arxiv org abs 2310 05736 star https img shields io github stars raymin0223 fast robust early exit svg style social label star https github com raymin0223 fast robust early exit publish https img shields io badge conference emnlp 23 blue br fast and robust early exiting framework for autoregressive language models with synchronized parallel decoding https arxiv org abs 2310 05424 br sangmin bae jongwoo ko hwanjun song se young yun img width 1002 alt image src figures free png github https github com raymin0223 fast robust early exit br paper https arxiv org abs 2310 05424 star https img shields io github stars liyucheng09 selective context svg style social label star https github com liyucheng09 selective context publish https img shields io badge conference emnlp 23 blue br compressing context to enhance inference efficiency of large language models https arxiv org abs 2310 06201 br yucheng li bo dong chenghua lin frank guerin img width 1002 alt image src figures selective context png github https github com liyucheng09 selective context br paper https arxiv org abs 2310 06201 publish https img shields io badge conference icml 23 20es 20fomo blue br accelerating llm inference with staged speculative decoding https arxiv org abs 2308 04623 br benjamin spector chris re img width 302 alt image src figures stagedspec png paper https arxiv org abs 2308 04623 inference with reference lossless acceleration of large language models https arxiv org abs 2304 04487 br nan yang tao ge liang wang binxing jiao daxin jiang linjun yang rangan majumder furu wei img width 600 alt image src figures llma png github https github com microsoft lmops tree main llma br paper https arxiv org abs 2304 04487 star https img shields io github stars flexflow flexflow svg style social label star https github com flexflow flexflow tree inference br specinfer accelerating generative llm serving with speculative inference and token tree verification https arxiv org abs 2305 09781 br xupeng miao gabriele oliaro zhihao zhang xinhao cheng zeyu wang rae ying yee wong zhuoming chen daiyaan arfeen reyna abhyankar zhihao jia img width 600 alt image src https github com flexflow flexflow blob inference img overview png github https github com flexflow flexflow tree inference br paper https arxiv org abs 2305 09781 skipdecode autoregressive skip decoding with batching and caching for efficient llm inference https arxiv org abs 2307 02628 br luciano del corro allie del giorno sahaj agarwal bin yu ahmed awadallah subhabrata mukherjee img width 1002 alt image src figures skipdecode png paper https arxiv org abs 2307 02628 skeleton of thought large language models can do parallel decoding https arxiv org abs 2307 15337 br xuefei ning zinan lin zixuan zhou huazhong yang yu wang img width 1002 alt image src figures sot png paper https arxiv org abs 2307 15337 star https img shields io github stars dilab zju self speculative decoding svg style social label star https github com dilab zju self speculative decoding br draft verify lossless large language model acceleration via self speculative decoding https arxiv org abs 2309 08168 br jun zhang jue wang huan li lidan shou ke chen gang chen sharad mehrotra img width 1002 alt image src https github com dilab zju self speculative decoding blob main assets intro png github https github com dilab zju self speculative decoding br paper https arxiv org abs 2309 08168 star https img shields io github stars mit han lab streaming llm svg style social label star https github com mit han lab streaming llm br efficient streaming language models with attention sinks https arxiv org abs 2309 17453 br guangxuan xiao yuandong tian beidi chen song han mike lewis img width 1002 alt image src https github com mit han lab streaming llm blob main figures schemes png github https github com mit han lab streaming llm br paper https arxiv org abs 2309 17453 dynamic prompting might be all you need to repair compressed llms https arxiv org abs 2310 00867 br duc n m hoang minsik cho thomas merth mohammad rastegari zhangyang wang img width 1002 alt image src figures idp png paper https arxiv org abs 2310 00867 model tells you what to discard adaptive kv cache compression for llms https arxiv org abs 2310 01801 br suyu ge yunan zhang liyuan liu minjia zhang jiawei han jianfeng gao img width 1002 alt image src figures fastgen png paper https arxiv org abs 2310 01801 star https img shields io github stars murongyue llm mot cascade svg style social label star https github com murongyue llm mot cascade br large language model cascades with mixture of thoughts representations for cost efficient reasoning https arxiv org abs 2310 03094 br murong yue jie zhao min zhang liang du ziyu yao img width 1002 alt image src figures llm mot cascade png github https github com murongyue llm mot cascade br paper https arxiv org abs 2310 03094 star https img shields io github stars microsoft llmlingua svg style social label star https github com microsoft llmlingua br longllmlingua accelerating and enhancing llms in long context scenarios via prompt compression https arxiv org abs 2310 06839 br huiqiang jiang qianhui wu xufang luo dongsheng li chin yew lin yuqing yang lili qiu img width 1002 alt image src figures longllmlingua png github https github com microsoft llmlingua br paper https arxiv org abs 2310 06839 cachegen fast context loading for language model applications https arxiv org abs 2310 07240 br yuhan liu hanchen li kuntai du jiayi yao yihua cheng yuyang huang shan lu michael maire henry hoffmann ari holtzman ganesh ananthanarayanan junchen jiang img width 1002 alt image src figures cachegen png paper https arxiv org abs 2310 07240 star https img shields io github stars drsy kv compression svg style social label star https github com drsy kv compression publish https img shields io badge conference emnlp 23 blue br context compression for auto regressive transformers with sentinel tokens https arxiv org abs 2310 08152 br siyu ren qi jia kenny q zhu img width 1002 alt image src figures kv compression png github https github com drsy kv compression br paper https arxiv org abs 2310 08152 star https img shields io github stars ielab llm rankers svg style social label star https github com ielab llm rankers br a setwise approach for effective and highly efficient zero shot ranking with large language models https arxiv org abs 2310 09497 br shengyao zhuang honglei zhuang bevan koopman guido zuccon img width 1002 alt image src figures setwise png github https github com ielab llm rankers br paper https arxiv org abs 2310 09497 speed speculative pipelined execution for efficient decoding https arxiv org abs 2310 12072 br coleman hooper sehoon kim hiva mohammadzadeh hasan genc kurt keutzer amir gholami sophia shao img width 1002 alt image src figures speed png paper https arxiv org abs 2310 12072 text compression title authors introduction links publish https img shields io badge conference icml 23 20workshop blue br entropyrank unsupervised keyphrase extraction via side information optimization for language model based text compression https arxiv org abs 2308 13399 br alexander tsvetkov alon kipnis img width 1002 alt image src figures entropyrank png paper https arxiv org abs 2308 13399 llmzip lossless text compression using large language models https arxiv org abs 2306 04050 br chandra shekhara kaushik valmeekam krishna narayanan dileep kalathil jean francois chamberland srinivas shakkottai img width 1002 alt image src figures llmzip png paper https arxiv org abs 2306 04050 unofficial github https github com erika n gptzip star https img shields io github stars princeton nlp autocompressors svg style social label star https github com princeton nlp autocompressors br adapting language models to compress contexts https arxiv org abs 2305 14788 br alexis chevalier alexander wettig anirudh ajith danqi chen img width 202 alt image src figures autocompressor png github https github com princeton nlp autocompressors br paper https arxiv org abs 2305 14788 in context autoencoder for context compression in a large language model https arxiv org abs 2307 06945 br tao ge jing hu xun wang si qing chen furu wei img width 502 alt image src figures icae png paper https arxiv org abs 2307 06945 nugget 2d dynamic contextual compression for scaling decoder only language model https arxiv org abs 2310 02409 br guanghui qin corby rosset ethan c chau nikhil rao benjamin van durme img width 1002 alt image src figures nugget2d png paper https arxiv org abs 2310 02409 low rank decomposition title authors introduction links star https img shields io github stars yxli2123 losparse svg style social label star https github com yxli2123 losparse publish https img shields io badge conference icml 23 blue br losparse structured compression of large language models based on low rank and sparse approximation https arxiv org abs 2306 11222 br yixiao li yifan yu qingru zhang chen liang pengcheng he weizhu chen tuo zhao img width 302 alt image src figures losparse png github https github com yxli2123 losparse br paper https arxiv org abs 2306 11222 star https img shields io github stars pilancilab matrix compressor svg style social label star https github com pilancilab matrix compressor publish https img shields io badge conference neurips 23 blue br matrix compression via randomized low rank and low precision factorization https arxiv org abs 2310 11028 br rajarshi saha varun srivastava mert pilanci img width 1002 alt image src figures lplr png github https github com pilancilab matrix compressor br paper https arxiv org abs 2310 11028 tensorgpt efficient compression of the embedding layer in llms based on the tensor train decomposition https arxiv org abs 2307 00526 br mingxue xu yao lei xu danilo p mandic img width 1002 alt image src figures tt svd png paper https arxiv org abs 2307 00526 lord low rank decomposition of monolingual code llms for one shot compression https arxiv org abs 2309 14021 br ayush kaushal tejas vaidhya irina rish img width 302 alt image src figures lord png paper https arxiv org abs 2309 14021 br project https huggingface co nolanoai hardware publish https img shields io badge conference neurips 22 blue star https img shields io github stars dao ailab flash attention svg style social label star https github com dao ailab flash attention flashattention fast and memory efficient exact attention with io awareness https arxiv org abs 2205 14135 tri dao daniel y fu stefano ermon atri rudra christopher r paper https arxiv org abs 2205 14135 github https github com dao ailab flash attention star https img shields io github stars dao ailab flash attention svg style social label star https github com dao ailab flash attention flashattention 2 faster attention with better parallelism and work partitioning https arxiv org abs 2307 08691 tri dao paper https arxiv org abs 2307 08691 github https github com dao ailab flash attention publish https img shields io badge conference icml 23 blue star https img shields io github stars fminference flexgen svg style social label star https github com fminference flexgen flexgen high throughput generative inference of large language models with a single gpu https arxiv org abs 2303 06865 ying sheng lianmin zheng binhang yuan zhuohan li max ryabinin daniel y fu zhiqiang xie beidi chen clark barrett joseph e gonzalez percy liang christopher r ion stoica ce zhang paper https arxiv org abs 2303 06865 github https github com fminference flexgen publish https img shields io badge conference sosp 202023 blue star https img shields io github stars vllm project vllm svg style social label star https github com vllm project vllm efficient memory management for large language model serving with pagedattention https arxiv org abs 2309 06180 woosuk kwon zhuohan li siyuan zhuang ying sheng lianmin zheng cody hao yu joseph e gonzalez hao zhang ion stoica paper https arxiv org abs 2309 06180 github https github com vllm project vllm edgemoe fast on device inference of moe based large language models https arxiv org abs 2308 14352v1 rongjie yi liwei guo shiyun wei ao zhou shangguang wang mengwei xu paper https arxiv org abs 2308 14352v1 publish https img shields io badge conference iccad 23 blue gpt4aigchip towards next generation ai accelerator design automation via large language models https arxiv org abs 2309 10730 yonggan fu yongan zhang zhongzhi yu sixu li zhifan ye chaojian li cheng wan yingyan lin paper https arxiv org abs 2309 10730 rethinking memory and communication cost for efficient large language model training https arxiv org abs 2310 06003 chan wu hanxiao zhang lin ju jinjing huang youshao xiao zhaoxin huan siyuan li fanzhuang meng lei liang xiaolu zhang jun zhou paper https arxiv org abs 2310 06003 chameleon a heterogeneous and disaggregated accelerator system for retrieval augmented language models https arxiv org abs 2310 09949 wenqi jiang marco zeller roger waleffe torsten hoefler gustavo alonso paper https arxiv org abs 2310 09949 survey a survey on model compression for large language models https arxiv org abs 2308 07633 xunyu zhu jian li yong liu can ma weiping wang paper https arxiv org abs 2308 07633 others cpet effective parameter efficient tuning for compressed large language models https arxiv org abs 2307 07705 weilin zhao yuxiang huang xu han zhiyuan liu zhengyan zhang maosong sun paper https arxiv org abs 2307 07705 star https img shields io github stars liziniu remax svg style social label star https github com liziniu remax remax a simple effective and efficient method for aligning large language models https arxiv org abs 2310 10505 ziniu li tian xu yushun zhang yang yu ruoyu sun zhi quan luo paper https arxiv org abs 2310 10505 github https github com liziniu remax transom an efficient fault tolerant system for training llms https arxiv org abs 2310 10046 baodong wu lei xia qingping li kangyu li xu chen yongqiang guo tieyao xiang yuheng chen shigang li paper https arxiv org abs 2310 10046 | compression knowledge-distillation language-model llm model-quantization pruning-algorithms efficient-llm llm-compression | ai |
CTF-Website-Template-2020 | ctf website template 2020 tldr front end of a capture the flag ctf website ctf website template 2020 is a frontend template made for a simple ctf event you can use it for hosting your own events it consists of the following features home page login page register page challenge quests page 404 not found page feeback page leaderboard page about rules page instructions page machine page info page for containers boxes what is this repo a front end template static html website for ctf capture the flag what it isn t yet fully functional dynamic ctf website backend is being developed in django framework by rutujad182 https github com rutujad182 checkout backend https github com ashawe ctf website template 2020 tree backend branch for this getting started these instructions will get you a copy of the project up and running on your local machine for development and testing purposes installing a step by step series of examples that tell you how to get a development env running just clone the repo git clone https github com ashawe ctf website template 2020 git and let it rip feel free to update and send a pull request built with neon glow https hackerthemes com bootstrap themes demo neon glow free hackerthemes website theme contributing please read contributing md contributing md for details on our code of conduct and the process for submitting pull requests to us authors harsh saglani ashawe https github com ashawe initial work frontend rutuja dhanawade rutujad182 https github com rutujad182 backend see also the list of contributors https github com ashawe ctf website template 2020 graphs contributors who participated in this project license this project is licensed under the gnu gpl v2 license see the license md https github com ashawe ctf website template 2020 blob master license md file for details acknowledgments hats off to following people hackerthemes for neon glow template https hackerthemes com bootstrap themes demo neon glow mary lou https github com crnacura for css glitch effect tutorial https tympanus net codrops 2017 12 21 css glitch effect moseychuk slava https dribbble com moseychuk for inspiring us for the design https dribbble com shots 3443937 cyber conferences website | front-end html html5 css css3 front-end-development frontend frontend-web ctf capture-the-flag ctf-tools ctf-platform ctf-website ctf-web hacktoberfest hackerthemes | front_end |
freeRTOS_workshop | freertos taipei workshop example workshop example code of 1 lab1 freertos workshop 1 https freertos kktix cc events 9aafb3c1 1 slides git 101 http tech jyyan info slides git 101 speaker junyuan yan 1 lab2 lab2 freertos workshop 2 http freertos kktix cc events 9aafb3c1 b4e121 1 slides hardware indroduction and freertos sample https www slideshare net secret nvm9qcrzak551v speaker link chen 1 lab3 lab3 freertos workshop 3 http freertos kktix cc events 9aafb3c1 b4e121 027966 1 slides freertos queue management http tech jyyan info slides freertos queue management speaker junyuan yan 1 lab4 lab4 freertos workshop 4 https freertos kktix cc events 9aafb3c1 b4e121 027966 dd549c 1 freertos softwaer timer https 1drv ms p s amzp4pt1mrrhiapktqy mbr zgma speaker kate huang 1 lab5 lab5 example code freertos workshop 5 1 freertos interrupt management lab5 example code lesson 205 pdf speaker chuangyun lai 1 lab6 lab6 example code freertos workshop 6 http freertos kktix cc events 9aafb3c1 b4e121 027966 1 freertos resource management https www slideshare net ssuser7bffc6 free rtos workshop6 speaker link chen 1 lab7 lab7 example code freertos workshop 7 1 freertos event groups https 1drv ms p s amzp4pt1mrrhiafiw1gv8nlflcon speaker kate huang 1 lab8 lab8 example code freertos workshop 8 1 freertos task notification https www slideshare net ssuser7bffc6 free rtos workshop8 speaker link chen | freertos arduino taipei c-plus-plus eclipse | os |
ameba-gcc-sample-rtos | ameba gcc sample rtos realtek ameba gcc build environment with freertos toolchan arm eabi none gcc usage enerate target axf make | os |
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Data-Engineering-project2 | project summary implement data warehouse for data engineering nanodegree program from udacity objective the purpose of this project is to implement a cloud data warehouse using the following technologies amazon redshift python 3 6 psycopg2 library configparser library in order to install libraries and dependencies use this link https pypi org project psycopg2 business context a music streaming company named sparkify has files in json format stored in an amazon s3 bucket it is needed to put their data on amazon redshift which is the cloud data warehouse service provided by amazon aws how did we tackle the challenge in order to do so we modeled temporary tables named staging tables as well and load the data from json files into them then we modeled a star schema as follows fact table songplays records in event data associated with song plays i e records with page nextsong songplay id start time user id level song id artist id session id location user agent dimension tables users users in the app user id first name last name gender level songs songs in the music database song id title artist id year duration artists artists in music database artist id name location latitude longitude time timestamps of records in songplays broken down into specific units start time hour day week month year weekday project files create table py creates fact and dimension tables described above in the previous topic according to the modeled star schema etl py loads data from s3 into staging tables on redshift and then process the data into final analytics tables sql queries py defines our sql statements it is imported into the two files above dwh cfg contains information about the public s3 bucket which will be accessed you must fill the information about the cluster and the iam role you must create running the project follow the steps below in order to run the project 1 make sure you created an amazon redshift cluster same region as your bucket an iam role and attached to it the right access policy 2 run the create tables py file 3 run the etl py file | cloud |
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huddle-engine | get started in minutes huddle engine for tracking and huddle web applications meteor 1 download and install huddle engine download link not yet available see below how to build and run huddle engine 2 install meteor https www meteor com 3 further details on the huddle javascript api at https github com huddlelamp meteor huddle development of huddle processors c wpf development and for advanced users only 1 install emgucv http www emgu com or direct from sourceforge net http sourceforge net projects emgucv 2 huddle engine was implemented with emgu cv version 2 4 9 alpha 3 copy all dlls from emgu home bin x64 to huddle engine home lib opencv 4 open huddle sln in visual studio 2013 5 set huddle engine as startup project 6 rebuild solution this step might be important if an emgucv exception appears 7 run huddle engine by starting the project in visual studio 2013 run project in debug mode 8 an error indicating that pipeline could not be loaded might occur close exception and ui will appear 9 click open button in tool bar and open huddlelamp hep in huddle engine home pipelines huddlelamp hep 10 run pipeline this section addresses the development a new processor and its integration into the tracking framework it is split into the three parts processor logic processor properties and processor user interface if each part is implemented as described a processor can be integrated into a processing pipeline using the drag and drop user interface of the tracking framework processor logic create a new processor a new processor can be integrated into the tracking framework by deriving it from code baseprocessor code or code rgbprocessor code the code rgbprocessor code is an abstract base class for image processing that handles basic functions such as rendering pre process and post process images into code bitmapsource code objects the code bitmapsource code object can be used later to visualize images apriori and posteriori manipulation the processandview method provides access to the image stream of either an code inputsource code or another processor for more information on the usage of the code baseprocessor code see code mergeprocessor code for the ui a code viewtemplateattribute code with a processor name and a view template name is required the tracking framework uses the template name to search for the corresponding code datatemplate code key within the assembly a data binding between a processor s properties and its code datatemplate code is possible adding properties to a processor class is explained below summary summary viewtemplate myprocessor myviewtemplatename public class myprocessor rgbprocessor public override image rgb byte processandview image rgb byte image manipulation of image goes here return image processor properties add properties to a processor a property is a standard net property and works with any mvvm framework such as mvvm light https mvvmlight codeplex com to enable data binding a property needs to raise property change events see code inotifypropertychanged code region fliphorizontal summary the see cref fliphorizontal property s name summary public const string fliphorizontalpropertyname fliphorizontal private bool fliphorizontal false summary sets and gets the fliphorizontal property changes to that property s value raise the propertychanged event summary public bool fliphorizontal get return fliphorizontal set if fliphorizontal value return raisepropertychanging fliphorizontalpropertyname fliphorizontal value raisepropertychanged fliphorizontalpropertyname endregion important the current code datacontractserializer code uses an opt in method to serialize properties please consult the msdn xml serialization website http msdn microsoft com en us library system runtime serialization datacontractserializer v vs 110 aspx for further information therefore the following is important in order to avoid exception during serialization e g save or load a pipeline add a code ignoredatamemberattribute code to each property that should not be serialized region preprocessimage summary the see cref preprocessimage property s name summary public const string preprocessimagepropertyname preprocessimage private bitmapsource preprocessimage summary sets and gets the preprocessimage property changes to that property s value raise the propertychanged event summary ignoredatamember public bitmapsource preprocessimage get return preprocessimage set if preprocessimage value return raisepropertychanging preprocessimagepropertyname preprocessimage value raisepropertychanged preprocessimagepropertyname endregion processor user interface ui create ui for a processor create a new code resourcedictionary code or use an existing dictionary and add a new code datatemplate code to the dictionary a processor has a code preprocessimage code and a code postprocessimage code property by default each can be bound to an code image code component s source additional properties like the code fliphorizontal code property can be bound in order to make them accessible and manipulable in the ui resourcedictionary xmlns http schemas microsoft com winfx 2006 xaml presentation xmlns x http schemas microsoft com winfx 2006 xaml xmlns util clr namespace tools flockingdevice tracking util xmlns processor clr namespace tools flockingdevice tracking processor datatemplate x key myviewtemplatename datatype processor myprocessor stackpanel stackpanel orientation horizontal margin 5 groupbox header pre processed image image source binding path preprocessimage groupbox groupbox header post processed image image source binding path postprocessimage groupbox stackpanel checkbox content flip horizontal verticalalignment center ischecked binding path fliphorizontal stackpanel datatemplate resourcedictionary notes about the different available notes when started the senz3d node automatically determines the region that the rgb image has within the larger depth image using the uv map the result is then sent a single time further down the pipeline and can be picked up by the basics node as data of type roi the basics node then uses just the region for which rgb and depth information is available the basics node tries to process every input so that s why basics needs some data filtering before the node | ai |
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wav-rtos-sw | wavious rtos sw wavious rtos base software project this project contains all code that runs on the various wavious cores utilizing freertos pre commit this project uses pre commit to ensure that all source files are properly formatted to install pre commit ensure that you have a version of python installed as well as pip with pip installed run pip install pre commit once pre commit is installed via python3 then pre commit needs to be installed for this project run the following to install pre commit pre commit install c wav build pre commit config yaml pre commit will be executed any time git commit command is executed refer to pre commit https pre commit com for more details build configuration when the project is first clone the project needs to be configured using the following command configure board bsp board where bsp board is the targeted supported wavious core since wavious rtos base software project is meant to be used as a submodule for other wavious projects the configure script has been designed such that it can be sourced and expanded for other projects arguments specific to wav rtos sw configuration script are passed when invoking the parent s configure script additionally extra cmake arguments can be passed directly to this projects configure script so that the cmake project is fully configured an example from wavious lpddr sw project invoke rtos configure script with addtional parameters args dconfig target phy config target phy dconfig calibrate pll config cal pll dconfig calibrate zqcal config cal zqcal dconfig calibrate sa config cal sa dconfig dram train config dram train dconfig cal periodic config cal periodic source wav rtos dir configure params args to invoke configure board wavious mcu board argument is passed to wav rtos sw configure script via params argument building with cmake once the environment is configured software can be built using the make command when in the build directory cd build make note it is recommended that you use docker to build this project a docker container can be started by running the following command from the root of this project docker run sh it is important to run the configure command within the docker container fsm vs statemachine fsm provides a dedicated task that all fsms share fsm events are submitted to a queue and processed directly by the fsm task which move the fsm between diferrent states this can be nice to have when the software has many fsms that need to be used but doesn t need tight control of the run loop it can save memory by having all events processed in a single place and dispatched to the appropriate fsm some implementations such as a firmware need better performance and would like tight control of their event loop and context switching statemachine offers a non threaded implementation that can be used for these cases background originally fsm was the only implementation and the need for statemachine was realized later for compatibility reasons the fsm implementation was kept around and a unique non threaded implementation was added | os |
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Orion | p align left img width 15 src https dai lids mit edu wp content uploads 2018 06 logo dai highres png alt dai lab i an open source project from data to ai lab at mit i p p align left img width 20 src https dai lids mit edu wp content uploads 2018 08 orion png alt orion p development status https img shields io badge development 20status 2 20 20pre alpha yellow https pypi org search c development status 3a 3a 2 pre alpha python https img shields io badge python 3 6 20 7c 203 7 20 7c 203 8 20 7c 203 9 blue https badge fury io py orion ml pypi shield https img shields io pypi v orion ml svg https pypi python org pypi orion ml tests https github com sintel dev orion workflows run 20tests badge svg https github com sintel dev orion actions query workflow 3a 22run tests 22 branch 3amaster downloads https pepy tech badge orion ml https pepy tech project orion ml binder https mybinder org badge logo svg https mybinder org v2 gh sintel dev orion master filepath tutorials orion a machine learning library for unsupervised time series anomaly detection important links computer website check out the sintel website for more information about the project book documentation quickstarts user and development guides and api reference star tutorials checkout our notebooks octocat repository the link to the github repository of this library scroll license the repository is published under the mit license slack logo community community join our slack workspace for announcements and discussions website https sintel dev documentation https sintel dev github io orion tutorials https github com sintel dev orion tree master tutorials repository https github com sintel dev orion license https github com sintel dev orion blob master license community https join slack com t sintel space shared invite zt q147oimb 4hcphcxpfdam0o9 4pautw slack logo https github com sintel dev orion blob master docs images slack png overview orion is a machine learning library built for unsupervised time series anomaly detection with a given time series data we provide a number of verified ml pipelines a k a orion pipelines that identify rare patterns and flag them for expert review the library makes use of a number of automated machine learning tools developed under data to ai lab at mit https dai lids mit edu read about using an orion pipeline on nyc taxi dataset in a blog series part 1 learn about unsupervised time series anomaly detection https t co yifvm1orwq amp 1 part 2 learn how we use gans to solving the problem https link medium com cgsbd0fevbb part 3 how does one evaluate anomaly detection pipelines https link medium com fqcrfxmevbb docs images tulog part 1 png docs images tulog part 2 png docs images tulog part 3 png notebooks discover orion through colab by launching our notebooks https drive google com drive folders 1facceie1jdsqamjgcmiw5a5xugh13c9q usp sharing quickstart install with pip the easiest and recommended way to install orion is using pip https pip pypa io en stable bash pip install orion ml this will pull and install the latest stable release from pypi https pypi org in the following example we show how to use one of the orion pipelines fit an orion pipeline we will load a demo data for this example python3 from orion data import load signal train data load signal s 1 train train data head which should show a signal with timestamp and value timestamp value 0 1222819200 0 366359 1 1222840800 0 394108 2 1222862400 0 403625 3 1222884000 0 362759 4 1222905600 0 370746 in this example we use aer pipeline and set some hyperparameters in this case training epochs as 5 python3 from orion import orion hyperparameters orion primitives aer aer 1 epochs 5 verbose true orion orion pipeline aer hyperparameters hyperparameters orion fit train data detect anomalies using the fitted pipeline once it is fitted we are ready to use it to detect anomalies in our incoming time series python3 new data load signal s 1 new anomalies orion detect new data warning depending on your system and the exact versions that you might have installed some warnings may be printed these can be safely ignored as they do not interfere with the proper behavior of the pipeline the output of the previous command will be a pandas dataframe containing a table of detected anomalies start end severity 0 1402012800 1403870400 0 122539 leaderboard in every release we run orion benchmark we maintain an up to date leaderboard with the current scoring of the verified pipelines according to the benchmarking procedure we run the benchmark on 12 datasets with their known grounth truth we record the score of the pipelines on each datasets to compute the leaderboard table we showcase the number of wins each pipeline has over the arima pipeline pipeline outperforms arima aer 12 tadgan 7 lstm dynamic thresholding 8 lstm autoencoder 8 dense autoencoder 7 vae 8 matrix profile 9 ganf https arxiv org pdf 2202 07857 pdf 7 azure https azure microsoft com en us products cognitive services anomaly detector 0 you can find the scores of each pipeline on every signal recorded in the details google sheets document https docs google com spreadsheets d 1haydjy bexeobbi65fwg0om5d8kbrarhpk4mvozvmqu edit usp sharing the summarized results can also be browsed in the following summary google sheets document https docs google com spreadsheets d 1zpuwyh8lhdovveujhkygxyny7472hxvczhx6d6pobmg edit usp sharing resources additional resources that might be of interest learn about benchmarking pipelines benchmark md read about pipeline evaluation orion evaluation readme md find out more about tadgan https arxiv org pdf 2009 07769v3 pdf citation if you use aer for your research please consider citing the following paper lawrence wong dongyu liu laure berti equille sarah alnegheimish kalyan veeramachaneni aer auto encoder with regression for time series anomaly detection https arxiv org pdf 2212 13558 pdf inproceedings wong2022aer title aer auto encoder with regression for time series anomaly detection author wong lawrence and liu dongyu and berti equille laure and alnegheimish sarah and veeramachaneni kalyan booktitle 2022 ieee international conference on big data ieee bigdata pages 1152 1161 doi 10 1109 bigdata55660 2022 10020857 organization ieee year 2022 if you use tadgan for your research please consider citing the following paper alexander geiger dongyu liu sarah alnegheimish alfredo cuesta infante kalyan veeramachaneni tadgan time series anomaly detection using generative adversarial networks https arxiv org pdf 2009 07769v3 pdf inproceedings geiger2020tadgan title tadgan time series anomaly detection using generative adversarial networks author geiger alexander and liu dongyu and alnegheimish sarah and cuesta infante alfredo and veeramachaneni kalyan booktitle 2020 ieee international conference on big data ieee bigdata pages 33 43 doi 10 1109 bigdata50022 2020 9378139 organization ieee year 2020 if you use orion which is part of the sintel ecosystem for your research please consider citing the following paper sarah alnegheimish dongyu liu carles sala laure berti equille kalyan veeramachaneni sintel a machine learning framework to extract insights from signals https dl acm org doi pdf 10 1145 3514221 3517910 inproceedings alnegheimish2022sintel title sintel a machine learning framework to extract insights from signals author alnegheimish sarah and liu dongyu and sala carles and berti equille laure and veeramachaneni kalyan booktitle proceedings of the 2022 international conference on management of data pages 1855 1865 numpages 11 publisher association for computing machinery doi 10 1145 3514221 3517910 series sigmod 22 year 2022 | anomaly-detection deep-learning machine-learning time-series benchmarking signals unsupervised-learning orion data-science generative-adversarial-network | ai |
blockchain-reference | blockchain reference readme zh md basic ebook blockchain guide https github com yeasy blockchain guide mastering bitcoin 2th edition cn https github com tianmingyun masterbitcoin2cn or https www 8btc com book 281955 mastering bitcoin 2th edition en https github com bitcoinbook bitcoinbook mastering etherem cn https github com inoutcode ethereum book blochain books https www 8btc com book white paper bitcoin white paper https bitcoin org en bitcoin paper bitcoin white paper cn https bitcoin org files bitcoin paper bitcoin zh cn pdf ethereum white paper cn https github com ethereum wiki wiki 5b e4 b8 ad e6 96 87 5d e4 bb a5 e5 a4 aa e5 9d 8a e7 99 bd e7 9a ae e4 b9 a6 etherem yellow paper https ethereum github io yellowpaper paper pdf hyperledger white paper https docs google com document d 1z4m qwillrehpbvrusj3of8iir gqs zye7w le9gne edit usp sharing hyperledger white paper cn http www 8btc com hyperledger whitepaper libra white paper https www diem com en us white paper china miit blockchain white paper assets 2016 pdf tecent blockchain white paper assets 2017 pdf bcos white paper assets bcos whitepaper pdf baidu blockchain white paper assets v1 0 201809 pdf fisco bcos white paper https github com fisco bcos whitepaper bubi blockchain white paper assets 1 0 2016 pdf corda white paper assets corda 2016 pdf zc blockchain white paper http www zcblockchain com images whitepaper pdf blockchain industry of china 2018 assets 2018 pdf tools bitcoin tests https gobittest appspot com learning resources learning blockchain blog https learnblockchain cn raft demo understandable distributed consensus http thesecretlivesofdata com raft fisco reseach analysis report 2017 assets 2017 pdf security blockchain security blog https blockchain sec com ethereum smart contract best practice https github com consensys smart contract best practices blob master readme zh md security risk of ethereum solidity call https mp weixin qq com s yitzsy4 m64lbuhpo 4xeq blockchain security report 2021 assets 2021 pdf ethereum deployment awesome ethereum https github com bekatom awesome ethereum ethereum deployment guide https yeasy gitbook io blockchain guide 07 ethereum install private ethereum guide https my oschina net u 2349981 blog 865256 private ethereum practice https xiaoping378 github io docs 5 blockchain e4 bb a5 e5 a4 aa e5 9d 8a e7 a7 81 e6 9c 89 e9 93 be e6 90 ad e5 bb ba e5 88 9d e6 ad a5 e5 ae 9e e8 b7 b5 smart contract solidity office docs https solidity readthedocs io solidity office docs cn https solidity cn readthedocs io zh latest solidity learning http www tryblockchain org solidity guide http me tryblockchain org solidity e5 85 a5 e9 97 a8 e7 b3 bb e5 88 97 html smart contract guide http ethfans org posts 101 noob intro smart contract guide https www gitbook com book luren5 dapp develop details build your first ethereum smart contract with solidity tutorial https codeburst io build your first ethereum smart contract with solidity tutorial 94171d6b1c4b smart contract examples erc20 erc721 erc777 https ethereum org zh developers docs standards tokens openzeppelin solidity security smart contract https github com openzeppelin openzeppelin solidity solidity library https github com webankblockchain smartdev contract ethereum development dev resources https ethereum org zh developers ethereum development with go assets ethereum development with go pdf ethereum development with go online https goethereumbook org ethereum api json rpc api https github com ethereum wiki wiki json rpc json rpc management api https geth ethereum org docs rpc server web3 js api https web3js readthedocs io web3 js api cn https web3 tryblockchain org learning resources ethereum blockchain mechanism an interpretation of the ethereum project yellow paper assets ethereum 20blockchain 20mechanism 20 20an 20interpretation 20of 20the 20ethereum 20project 20yellow 20paper jpg cyptozombies create your own crypto collectibles game https cryptozombies io web tools ethereum scan https etherscan io ethereum stats https ethstats net ethereum nodes https ethernodes org ethereum dapps https dapps ethercasts com hyperledger fabric specifications hyperledger fabric 0 6 cn https github com gymgle fabric 0 6 1 preview blob master docs protocol spec zh md fabric latest docs https hyperledger fabric readthedocs io fabric deployment fabric 1 0 muti nodes https github com hainingzhang articles tree master fabric multi nodes fabric 1 0 beta in ubuntu http www cnblogs com studyzy p 6973334 html fabric 0 6 in ubuntu https g2ex github io 2017 06 16 fabric 0 6 demo vedio fabric 0 6 with docker https github com imac cloud blockchain tutorial blob master hyperledger hyperledger docker md fabric development hyperledger fabric 0 6 learning guide https github com ibm blockchain learn chaincode hyperledger fabric 0 6 learning guide cn https github com gymgle learn chaincode blob master readme zh cn md hyperledger fabric source code reading https github com yeasy hyperledger code fabric ibm blockchain 101 https developer ibm com tutorials cl ibm blockchain 101 quick start guide for developers bluemix trs learning resources pbft slice assets pbft pdf bitcoin blockchain slice assets 201707 pdf web tools ibm bluemix blockchain https console ng bluemix net catalog services blockchain some awesome blockchain collections a collection in chinese https github com chaozh awesome blockchain cn a collection to learn blockchain https github com yjjnls awesome blockchain build your own blockchain especially in ruby https github com openblockchains awesome blockchains | bitcoin blockchain ethereum hyperledger | blockchain |
byggern-gr5 | byggern gr5 ttk4155 industrial and embedded computer systems design project | os |
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gatsby-remark-design-system | github license https img shields io github license lekoarts gatsby remark design system svg style flat square https github com lekoarts gatsby remark design system blob master license npm package https img shields io npm v gatsby remark design system svg style flat square https www npmjs org package gatsby remark design system lekoarts homepage https img shields io badge lekoarts homepage blue svg style flat square https www lekoarts de gatsby remark design system create your design system with gatsby in markdown see it live in action https gatsby remark design system netlify com you can also have a look at the example repo https github com lekoarts gatsby remark design system example important note this plugin will no longer be maintained except critical security fixes as all the functionality was ported to mdx and lekoarts gatsby theme specimens https github com lekoarts gatsby themes tree master themes gatsby theme specimens building design systems with mdx is superior to markdown and hence the new theme is way more powerful than the remark version here please give the theme a try scope of this plugin gatsby remark design system inspired by catalog https www catalog style is a plugin that sits on top of gatsby s remark transformer you ll need to setup a gatsby project and install at least the plugins gatsby source filesystem gatsby transformer remark and gatsby plugin sass you then can use the so called specimens in your markdown files to create your design system or styleguide writing content in markdown is easy and so should be creating a design system including this plugin into your project will help you create a design system from scratch in no time and you can also include other plugins e g gatsby remark prismjs to have syntax highlighted code install bash npm install gatsby remark design system how to use note if you re unsure about the instructions take a look at the implementation of the example repo https github com lekoarts gatsby remark design system example js in your gatsby config js plugins resolve gatsby transformer remark options plugins resolve gatsby remark design system options class prefix for all elements of the design system specimens this prefix also needs to be set on wrapper components in your gatsby project default value is grds so if you want you can leave out this option entirely classprefix grds include css the plugin ships with a theme that you can easily include in your gatsby project or you can build your own theme by copying and modyfing the example to load the theme just require its scss e g js layouts index js require gatsby remark design system theme gatsby remark design system theme scss if you just want to change the classprefix or other scss variables but otherwise want to use the preconfigured theme you should overwrite the variables like so scss create a empty scss file that gets imported into your project e g base scss prefix cool primary c93a3c import your other scss files if necessary import the theme import gatsby remark design system theme gatsby remark design system theme scss specimens you can use the specimens by using three backticks followed by the name the content of the specimen then gets defined in the code block name option content audio options autoplay boolean default false loop boolean default false name string span number 1 6 width of the specimen src string the path url to the file needs to be in quotes example audio autoplay false loop false name sound file 1 src sound mp3 span 3 color options color string define the color in hex e g b0f6ff name string example color name smaragd color 939d7b color palette options name string color string each line represents a color first define the name then after a comma the hex value example color palette t400 shabby 448c6c t300 legendary dca114 t200 smoke 6c3b0b download options color string define the background color in hex e g white of the preview box image boolean if true the image will be shown below span number 1 6 width of the specimen src string the path url to the file needs to be in quotes subtitle string the filesize or other information title string width string the width of the preview image default 200px example download color white image true span 3 src logo png subtitle 8kb title avatar social width 250px example options none example example button you can insert your html here button hint options directive green positive note for showing dos warning red warning note for showing don ts neutral neutral note default example hint directive make it so hint neutral hint hint warning nooooooooo not this way typography options size number weight number metrics string weightdesc string usage string each line represents a type you have to define the values in the mentioned order and seperate with example typography 42 700 display 42 line height is 1 1x bold 700 display type is used for visual impact and emphasis 32 400 page title 32 line height is 1 1x normal 400 page title is used to provide hiearchy video options autoplay boolean default false loop boolean default false muted boolean default false name string src string the path url to the file needs to be in quotes example video autoplay false loop false muted false name animation video src https www w3schools com html mov bbb mp4 span 3 | gatsby gatsby-plugin gatsby-remark design system remark | os |
web3.unity | chainsafegaming https user images githubusercontent com 681817 218129249 850c4be0 b64f 4215 a780 7766db8cd75e png img alt discord src https img shields io discord 593655374469660673 svg style for the badge label discord logo discord height 20 https discord gg q6a3ya2 img alt twitter src https img shields io twitter follow espadrine svg style for the badge label twitter color 1da1f2 height 20 https twitter com chainsafeth notices after june 15 2023 all users of the sdk will be required to have their projects registered please ensure that you have a valid project id to avoid any service interruptions by this time if you need help getting a project id we ve put together a tutorial to guide you through the process https docs gaming chainsafe io current project id registration as always we re here to help so feel free to message us in web3unity gaming general or gaming help if you re stuck and need help documentation https docs gaming chainsafe io support need help with web3 unity or found a bug be sure to read the documentation above then review existing issues or create a new one here https github com chainsafe web3 unity issues this is the best way to get help from the chainsafe gaming team need help from the community including questions not related to web3 unity ask in community code support on discord https discord gg q6a3ya2 contributing have an idea for a new feature that would improve web3 unity create a feature request here https github com chainsafe web3 unity issues new assignees labels type 3a feature template feature request md title have a code contribution you d like to make directly make a pull request to this repo join our community say hi on discord https discord gg q6a3ya2 in web3unity gaming general and share your project built with web3 unity in gaming showcase building the code clone the repository and run the setup script you can now open src unitysampleproject in unity and start hacking whenever you make changes to the code in the libraries run the src chainsafe gaming unity publish to unity package script from within the folder it is in this will build and publish the libraries to the upm package s folder thus making any new code accessible inside unity chainsafe security policy reporting a security bug we take all security issues seriously if you believe you have found a security issue within a chainsafe project please notify us immediately if an issue is confirmed we will take all necessary precautions to ensure a statement and patch release is made in a timely manner please email us a description of the flaw and any related information e g reproduction steps version to security at chainsafe dot io mailto security chainsafe io | games blockchain cryptocurrency unity | blockchain |
learning-blockchain | blockchain bitcoin smart contracts blockchain accounting ledger distributed peer to peer chain block transactions transactions block signed hash blockchain images blockchain png id public key value ethereum https www ethereum org openchain https www openchain org hyperledger https www hyperledger org multichain http www multichain com gcoin http g coin org learn html bitcore https github com bitpay bitcore dnschain https github com okturtles dnschain blockstack http blockstack org ibm blockchain http www ibm com blockchain ripple https ripple com steller https www stellar org about mandate eris https erisindustries com tendermint http tendermint com chain of things http www chainofthings com | blockchain transaction fintech ethereum hyperledger | blockchain |
model-my-watershed | model my watershed local development a combination of vagrant 2 2 and ansible 2 8 is used to setup the development environment for this project the project consists of the following virtual machines app services tiler worker the app virtual machine contains an instance of the django application services contains postgresql redis tiler contains windshaft mapnik worker contains celery docker getting started first ensure that you have a set of amazon web services aws credentials with access to azavea s pre processed nlcd data set this setup generally needs to happen on the virtual machine host using the aws cli https aws amazon com cli bash aws configure profile mmw stg you will also need to set the mmw datahub aws credential as your default these are stored in lastpass under the name mmw azavea datahub aws ensure that the aws credentials file has universal read permissions ensure you have the vagrant disksize https github com sprotheroe vagrant disksize plugin installed bash vagrant plugin install vagrant disksize starting with virtualbox 6 1 28 host only networks https www virtualbox org manual ch06 html network hostonly are restricted to 192 168 56 0 21 by default we will need to do the following to override this restriction bash sudo mkdir etc vbox echo 192 168 56 0 21 33 33 0 0 16 sudo tee etc vbox networks conf next use the following command to bring up a local development environment ensuring you have the most recent version of the base box bash vagrant box update vagrant up the application will now be running at http localhost 8000 http localhost 8000 after significant changes you may need to run the following two commands to apply database migrations and rebuild javascript assets bash scripts manage sh migrate scripts bundle sh to load or reload boundary data from an app server run scripts is not mounted by default to the vm you may need to copy the file over bash vagrant upload scripts app vagrant ssh app scripts aws setupdb sh b the same script can be used to load the stream network data bash scripts aws setupdb sh s and all the other data bash scripts aws setupdb sh d scripts aws setupdb sh m scripts aws setupdb sh p scripts aws setupdb sh c scripts aws setupdb sh q note that if you receive out of memory errors while loading the data you may want to increase the ram on your services vm 1512 mb may be all that is necessary see debug messages from the web app server bash scripts debugserver sh watch the javascript and sass files for changes bash scripts bundle sh debug watch when creating new javascript or sass files you may need to stop and restart the bundle script if you add a js dependency and want it to be included in the vendor js bundle you will need to update the js deps array in bundle sh accordingly if changes were made to the one of the vm s configuration or requirements since the last time you provisioned you ll need to reprovision bash vagrant provision vm name after provisioning is complete you can execute django management commands with bash scripts manage sh test note if you get an error that resembles the following try logging into the app virtual machine again for the group permissions changes to take effect envdir fatal unable to switch to directory etc mmw d env access denied ports the vagrant configuration maps the following host ports to services running in the virtual machines service port url django web application 8000 http localhost 8000 http localhost 8000 postgresql 5432 psql h localhost redis 6379 redis cli h localhost 6379 testem 7357 http localhost 7357 http localhost 7357 tiler 4000 http localhost 4000 http localhost 4000 caching to speed up geoprocessing those requests are cached in redis to disable this caching for development purposes set the value of mmw geoprocessing cache to 0 bash vagrant ssh worker c echo 0 sudo tee etc mmw d env mmw geoprocessing cache vagrant ssh worker c sudo service celeryd restart to enable the geoprocessing cache simply set it to 1 and restart the celeryd service in some cases it may be necessary to remove all cached values this can be done with bash vagrant ssh services c redis cli n 1 raw keys 1 geop xargs redis cli n 1 del test mode in order to run the app in test mode which simulates the production static asset bundle reprovision with vagrant env test vagrant provision testing run all the tests bash scripts test sh python to check for python lint bash scripts check sh to run all the tests on the django app bash scripts manage sh test or just for a specific app bash scripts manage sh test apps app name tests more info here https docs djangoproject com en 1 8 topics testing run mapshed tests which require mapshed tables installed in the local database using setupdb sh console scripts manage sh test mapshed javascript to check for javascript lint bash scripts yarn sh run lint when creating new tests or debugging old tests it may be easier to open the testem page which polls for changes to the test bundle and updates the test state dynamically first start the testem process bash scripts testem sh then view the test runner page at http localhost 7357 http localhost 7357 to enable livereload download the browser extension http livereload com extensions and start the livereload server with the following command bash scripts yarn sh run livereload bundling static assets the bundle sh script runs browserify node sass and othe pre processing tasks to generate static assets the vendor bundle is not created until you run this command with the vendor flag this bundle will be very large if combined with debug test bundles are not created unless the tests flag is used in general you should be able to combine vendor tests debug and watch and have it behave as you would expect you can also minify bundles by using the minify flag this operation is not fast and also disables source maps the list flag displays module dependencies and does not actually generate any bundles it doesn t make sense to combine this with watch this flag is for troubleshooting purposes only bundle sh h bundle sh option bundle js and css static assets options watch listen for file changes debug generate source maps minify minify bundles slow disables source maps tests generate test bundles list list browserify dependencies vendor generate vendor bundle and copy assets h help display this help text adding js dependencies to add a new js dependency use console scripts yarn sh add exact dependency this will download the dependency to node modules add to the package json and to yarn lock furthermore it will be pinned to the current version then update the js deps array in bundle sh rebuild the vendor bundle using scripts bundle sh vendor yarn commands can be run using scripts yarn sh | civic-apps-team watersheds hydrology stormwater | front_end |
collaborator-everywhere | this is a burp suite pro extension which augments your in scope proxy traffic by injecting non invasive headers designed to reveal backend systems by causing pingbacks to burp collaborator to use it simply install it and browse the target website findings will be presented in the issues tab you can easily customise injected payloads by editing resources injections for further information please refer to the whitepaper at https portswigger net research cracking the lens targeting https hidden attack surface https portswigger net research cracking the lens targeting https hidden attack surface | os |
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Lane_Detection | lane detection the aim of this project is to develop and implement a computer vision based algorithm such that br 1 all visible lane boundaries are detected for urban highway roads under close to idle weather conditions dependencies br 1 skimage br 2 opencv 2 4 6 br 3 numpy code files br 1 generatebev py code from kitti road dataset to obtain ipm view of an image using a calibrated camera br 2 lane detection py detects multiple lanes taking the ipm image as input br 3 inverse perspective mapping py generic code for obtaining the bev of an image calculates the homography matrix by using the extrinc and intrinsic camera parameters dependencies br 1 c boost 1 7 br 2 cuda toolkit 7 0 br 3 opencv 2 4 6 br relevant code files br 1 generatebev cu cuda acceralated code to get perspective ipm and ipm perspective view of an input image br 2 pre processing cu cuda acceralated code to pre process the input image br 3 rgb2gray cu cuda acceralated code to convert rgb image to grayscale br 4 hough transform cu fast hough transform using cuda br 5 line cpp group identical lanes and eliminates outliers br 6 line fitting cpp using ransac algorithm to fit a line to the detected lane bresenham s algorithm to plot the line pixels br 7 fit poly cpp least squares 2nd degree polynomial fitting for curved lanes br process pipeline br original image input ipm image filtered ipm image thresholded image binary image after selecting roi initial guess for ransac lane detected image after ransac and eliminating false lanes img 24 https github com kky fury lane detection blob master original images img 1 png img 25 https github com kky fury lane detection blob master test images ipm test image 1 png img 19 https github com kky fury lane detection blob master process pipeline filtered image png img 20 https github com kky fury lane detection blob master process pipeline thresholded image png img 21 https github com kky fury lane detection blob master process pipeline binary image after roi png img 22 https github com kky fury lane detection blob master process pipeline initial guess for ransac png img 23 https github com kky fury lane detection blob master lane detected images image 1 png lane detection for test images br orginal image input ipm image kitti ipm image after lane detection perspective view from ipm img 1 https github com kky fury lane detection blob master original images img 10 png img 2 https github com kky fury lane detection blob master test images ipm test image 10 png img 3 https github com kky fury lane detection blob master lane detected images image 10 png img 4 https github com kky fury lane detection blob master lane detected images perspective image 10 png img 5 https github com kky fury lane detection blob master original images img 11 png img 6 https github com kky fury lane detection blob master test images ipm test image 11 png img 7 https github com kky fury lane detection blob master lane detected images image 11 png img 8 https github com kky fury lane detection blob master lane detected images perspective image 11 png img 9 https github com kky fury lane detection blob master original images img 12 png img 10 https github com kky fury lane detection blob master test images ipm test image 12 png img 11 https github com kky fury lane detection blob master lane detected images image 12 png img 12 https github com kky fury lane detection blob master lane detected images perspective image 12 png img 13 https github com kky fury lane detection blob master original images img 13 png img 14 https github com kky fury lane detection blob master test images ipm test image 13 png img 15 https github com kky fury lane detection blob master lane detected images image 20 png img 16 https github com kky fury lane detection blob master lane detected images perspective image 20 png img 17 https github com kky fury lane detection blob master original images img 14 png img 18 https github com kky fury lane detection blob master test images ipm test image 14 png img 19 https github com kky fury lane detection blob master lane detected images image 14 png img 20 https github com kky fury lane detection blob master lane detected images perspective image 14 png img 21 https github com kky fury lane detection blob master original images img 15 png img 22 https github com kky fury lane detection blob master test images ipm test image 15 png img 23 https github com kky fury lane detection blob master lane detected images image 13 png img 24 https github com kky fury lane detection blob master lane detected images perspective image 13 png img 25 https github com kky fury lane detection blob master original images img 16 png img 26 https github com kky fury lane detection blob master test images ipm test image 16 png img 27 https github com kky fury lane detection blob master lane detected images image 16 png img 28 https github com kky fury lane detection blob master lane detected images perspective image 16 png img 29 https github com kky fury lane detection blob master original images img 17 png img 30 https github com kky fury lane detection blob master test images ipm test image 17 png img 31 https github com kky fury lane detection blob master lane detected images image 15 png img 32 https github com kky fury lane detection blob master lane detected images perspective image 15 png img 33 https github com kky fury lane detection blob master original images img 18 png img 34 https github com kky fury lane detection blob master test images ipm test image 18 png img 35 https github com kky fury lane detection blob master lane detected images image 17 png img 36 https github com kky fury lane detection blob master lane detected images perspective image 17 png img 37 https github com kky fury lane detection blob master original images img 19 png img 38 https github com kky fury lane detection blob master test images ipm test image 19 png img 39 https github com kky fury lane detection blob master lane detected images image 18 png img 40 https github com kky fury lane detection blob master lane detected images perspective image 18 png img 41 https github com kky fury lane detection blob master original images img 20 png img 42 https github com kky fury lane detection blob master test images ipm test image 20 png img 43 https github com kky fury lane detection blob master lane detected images image 9 png img 44 https github com kky fury lane detection blob master lane detected images perspective image 19 png img 45 https github com kky fury lane detection blob master original images img 9 png img 46 https github com kky fury lane detection blob master test images ipm test image 9 png img 47 https github com kky fury lane detection blob master lane detected images image 9 png img 48 https github com kky fury lane detection blob master lane detected images perspective image 9 png | lane-detection computer-vision cuda | ai |
RobustAndOptimalControl.jl | robustandoptimalcontrol build status https github com juliacontrol robustandoptimalcontrol jl workflows ci badge svg https github com juliacontrol robustandoptimalcontrol jl actions coverage https codecov io gh juliacontrol robustandoptimalcontrol jl branch master graph badge svg https codecov io gh juliacontrol robustandoptimalcontrol jl https img shields io badge docs latest blue svg https juliacontrol github io robustandoptimalcontrol jl dev a package for analysis and design of robust and optimal linear control systems an extension to controlsystems jl https github com juliacontrol controlsystems jl see examples https github com juliacontrol robustandoptimalcontrol jl tree master examples and documentation https juliacontrol github io robustandoptimalcontrol jl dev installation julia pkg add robustandoptimalcontrol acknowledgement the code for the h design was originally written by marcus greiff mgreiff in the pr https github com juliacontrol controlsystems jl pull 192 | control-systems robust-control optimal-control control-theory automatic-control hinfinity uncertainty-analysis multivariable-control controls control h-infinity | os |
Taxi-Management-System | h1 taxi management system h1 a pbl project based learning project for the course dbe database engineering at kolhapur institute of technology a simple taxi management system start doctoc generated toc please keep comment here to allow auto update don t edit this section instead re run doctoc to update table of contents features features user interface user interface home screen home screen registration registration user registration user registration driver registration driver registration login login admin login admin login user driver login user driver login dashboard dashboard admin dasboard admin dasboard user dashboard user dashboard driver dashboard driver dashboard technical details technical details requirements requirements database database database schema database schema dependencies dependencies how to run how to run application pages application pages our apis our apis end doctoc generated toc please keep comment here to allow auto update features user and driver registration user can book a trip and driver can accept the booking user and driver can view his her booked accepted trips driver can view the available trips along with the distance and fare user can see the driver s details when he she accepts the booking user interface home screen img height auto src documentation ui home jpg hr registration user registration img src documentation ui registration user jpg driver registration img src documentation ui registration driverlicenceinfo jpg hr login admin login img src documentation ui login admin jpg user driver login img src documentation ui login userdriver jpg hr dashboard admin dasboard img src documentation ui admin1 jpg img src documentation ui admin2 jpg img src documentation ui admin3 jpg img src documentation ui admin4 jpg user dashboard img src documentation ui dashboard user jpg driver dashboard img src documentation ui dashboard driver jpg technical details frontend bootstrap html css javascript backend nodejs expressjs database mysql requirements nodejs https nodejs org en mysql https www mysql com below database structure database database database structure database name taxi management system tables user driver admin taxi trip database schema user image https user images githubusercontent com 66154908 144718389 f33644cc db92 4d83 a423 b0d74cbdbaa1 png driver image https user images githubusercontent com 66154908 144718410 7401f1c3 b3fe 479d 9226 2d8a9b7a2e53 png admin image https user images githubusercontent com 66154908 144718433 06abc683 b65e 48e0 836f f31179d2c751 png taxi image https user images githubusercontent com 66154908 144718471 236f7daa 728e 499c b0ec fcaf733f969d png trip image https user images githubusercontent com 66154908 144718450 6b083140 6987 4ae8 9ad5 c32c16ca65c9 png dependencies express mysql nodemon how to run cd root directory sh npm install npm start click on the link to open the application http localhost 1412 application pages home page http localhost 1412 user registration page http localhost 1412 signup user driver registration page http localhost 1412 signup driver login page http localhost 1412 login user dashboard page http localhost 1412 user driver dashboard page http localhost 1412 driver admin login page http localhost 1412 admin login admin dashboard page http localhost 1412 admin our apis registration user api registration user driver api registration driver login api login role session end api session end role credentials api credentials role admin queries user api query user driver api query driver trip api query trip custom api query custom trip booking api trip booking history api trip history role accepting api trip accepting available api trip available | express mysql mysql-database nodejs pbl project | server |
Battle-of-LED | battle of led 1914 made by george khella theo harbers by continuing and or downloading you confirm that you have read and accepted the license license the game is based around world war i each player has a trench of one sandbag row the objective is to reach the other side of the board or kill the opponent destroy opponent s sandbag to create a hole to get through how to run for compiling and running on a linux machine do the following steps 1 make sure you have the drivers avr drivers utils fonts and extras folders and files re clone download the git if not 2 to compile the game this will produce hex o and bin files use the following command make 3 to compile and run the game on the avr make program sound is available with using a piezo buzzer playing the game 1 scrolling title text will appear simply push down the joystick to start the game 2 every player has a trench and 30 bullets more than enough every sandbag has 2 hit points hp each bullet deals 1 damage hp every player has only 1 hp objective is to capture opponent s trench or take them down 3 move by pushing the joystick in the desired direction 4 push to shoot 5 when game is over scrolling text will notify you of the outcome push joystick to return to the menu 6 there is an easter egg find it tis probably the best outcome hint hint reading the code to find the easter egg does not mean you ve found it | os |
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OzJS | layout intro title ozjs a microkernel for modular javascript a toolchain for modern front end a micro framework for growable webapp a id overview a ozjs project ozjs is not yet another script loader but a microkernel that provides sorely missing module mechanism at runtime that means it mainly works at language level not file level use ozma js http ozjs org ozma to process files statically at build time based on the same mechanism for large complex javascript program compatible with the de facto standards amd https github com amdjs amdjs api wiki amd nodejs commonjs http www commonjs org specs modules 1 0 and traditional module pattern http www adequatelygood com 2010 3 javascript module pattern in depth even better it was implemented earlier than the well known requirejs so there are differences between similar apis http ozjs org docs define html in philosophy and approach which bring more value the api and code of oz js are minimalist and stable it won t add new features that aren t truly needed it s absolutely bad practice to meet new requirements through new configuration options or new plugins for a module mechanism provider the ozjs project now focuses on providing bundles of powerful and yet micro framework friendly amd modules a id toolchain a toolchain generator ozjs http ozjs org generator ozjs scaffolding tool for ozjs which offers a packaging workflow integrates toolchain micro framework and many best practices oz js bower grunt mo moui source code https github com dexteryy generator ozjs ozjs app template https github com dexteryy ozjs app template ozmajs http ozjs org ozma intelligent autobuild tool for ozjs unique ability to support transparent dynamic dependence source code https github com dexteryy ozma js grunt ozjs http ozjs org grunt ozjs grunt tasks for oz js and ozma js source code https github com dexteryy grunt ozjs gulp ozjs https github com kebot gulp ozjs gulp tasks for oz js and ozma js karma ozjs http ozjs org karma ozjs a karma plugin adapter for ozjs framework source code https github com dexteryy karma ozjs grunt dispatch http ozjs org grunt dispatch bower mate copy the necessary files of package like bower components directory to your src directory source code https github com dexteryy grunt dispatch grunt furnace http ozjs org grunt furnace transform code from one format to another template amd amd cjs cjs amd source code https github com dexteryy grunt furnace karma furnace preprocessor https github com dexteryy karma furnace preprocessor a karma plugin convert code from one format to another like grunt furnace ozjs adapter http ozjs org adapter mini define require mplementation for old web page transform amd module into traditional module pattern source code https github com dexteryy ozjs blob master contrib adapter js a id framework a micro framework it is time to stop using all in one javascript library or framework which bundle all functionalities and solutions within a single global namespace ozjs project provides plenty of tiny mutually independent single purpose modules to help you build mix and match your own mvc or suchlike framework mo http ozjs org mo a collection of ozjs core modules that form a library called mo modules overview mo lang es5 6 shim and minimum utilities for language enhancement mo domready non plugin implementation of cross browser dom ready event based on ozjs s built in module finish mo browsers standalone jquery browsers supports skin browsers popular in china mo template a lightweight and enhanced micro template implementation and minimum utilities mo network standalone jquery ajax api and enhanced getjson and so on mo easing an easing library supports jquery js standalone module and css timing functions mo mainloop implement and manage single loop for webapp life cycle provide tweening api for both property animation and frame animation canvas or css mo key wrapping api for keyboard events support key sequence multiple key press mo console debulg tool source code https github com dexteryy mo eventmaster http ozjs org eventmaster a simple compact and consistent implementation of a variant of commonjs s promises and events source code https github com dexteryy eventmaster nervjs http ozjs org nervjs a tiny pure event based model wrapper for the mvc or mdv model driven views pattern source code https github com dexteryy nervjs dollarjs http ozjs org dollarjs a jquery compatible and non all in one library which is more zepto than zepto js source code https github com dexteryy dollarjs sovietjs http ozjs org sovietjs standalone ui event delegate implementation source code https github com dexteryy sovietjs urlkit http ozjs org urlkit a lightweight implementation of routing and url manager source code https github com dexteryy urlkit a id ui a ui toolkit moui http ozjs org moui oo based ui behavior modules behind cardkit http ozjs org cardkit s view components modules overview moui control minimal stateful component moui picker compose of control objects moui overlay minimal overlay component moui actionview inherit from overlay compose of picker objects moui modalview inherit from overlay moui growl inherit from overlay moui ranger minimal range component moui util stick stick a dom element to anther from any clock position more http ozjs org moui source code https github com dexteryy moui darkdom http ozjs org darkdom design your own markup languages on a higher level of abstraction than html build responsive cross screen ui components source code https github com dexteryy darkdom cardkit http ozjs org cardkit a mobile ui library provides a series of building blocks to help you build mobile web apps quickly and simply or transfer entire website to mobile first web app for touch devices cardkit building blocks are all use html as configure style like custom elements directive components built on darkdom https github com dexteryy darkdom and moui https github com dexteryy moui source code https github com douban f2e cardkit momo momotion http ozjs org momo a framework and a collection for separate and simple implementation of touch gestures source code https github com dexteryy momo choreojs http ozjs org choreojs an animation library which uses stage and actor as metaphors source code https github com dexteryy choreojs a id start a tutorials getting started with oz js and ozma js http ozjs org docs start html define require http ozjs org docs define html quick start install the scaffolding workflow tool http ozjs org generator ozjs then try the ozjs app http ozjs org generator ozjs app generator a id demo a demo app doubanchou https github com dexteryy doubanchou lottery draft app doubanchou ii pachislot https github com dexteryy pachislot lottery app bughunter https github com dexteryy bughunter multiplayer answer first game or a competition responder system demonstory https github com douban f2e demonstory real time movie acts by web page elements todomvc coming soon in the real world alphatown http alphatown com 2d browser based virtual world douban reader http read douban com reader web browser based e book reader douban s contributor system http read douban com submit online self publishing tool for douban reader bubbler http bubbler labs douban com webapp to explore social music technology code douban s github clone for internal use a id ref a follow ozjs http weibo com ozjs http site douban com 199314 more references d2forum2010 js slide http www slideshare net dexter yy js 6228773 d2forum2011 mvc slide http www slideshare net dexter yy mvc 8554206 license copyright c 2010 2014 dexteryy licensed under the mit license | front_end |
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Designs | designs some cool ui designs for web development note some tweaks may not work with other browser than chrome symbols used cr chrome required to see the effects creations 3d imagelayer https chankruze github io designs 3d imagelayer applenavbar https chankruze github io designs applenavbar daynighttoogle https chankruze github io designs daynighttoogle errors 404 one https chankruze github io designs errors 404 1 two https chankruze github io designs errors 404 2 403 one https chankruze github io designs errors 403 1 two https chankruze github io designs errors 403 2 three https chankruze github io designs errors 403 3 four https chankruze github io designs errors 403 4 five https chankruze github io designs errors 403 5 facebookreactions https chankruze github io designs facebookreactions ghosttext https chankruze github io designs ghosttext glowingbutton https chankruze github io designs glowingbutton greetings https chankruze github io designs greetings imagehover https chankruze github io designs imagehover loginform https chankruze github io designs loginform loginpage https chankruze github io designs loginpage socialbuttons https chankruze github io designs socialbuttons stickysection https chankruze github io designs stickysection textpotrait https chankruze github io designs textpotrait cr textreflection https chankruze github io designs textreflection cr developers chankruze https github com chankruze | css html ui front-end demo design | front_end |
azureiotlabs | azure iot hands on labs labs using azure services to build azure iot end to end solutions connecting real simulated devices to azure iot hub imported script images lab png header image modules module 1 introduction to azure iot hub and connect mxchip duration 60 90 minutes azure iothub lab with mxchip https github com azure samples azureiotlabs blob master iothub readme md module 2 connect pi simulator to iot hub duration 30 minutes azure iothub pi simulator lab https github com azure samples azureiotlabs blob master iothub pisimulator readme md module 3 create azure time series insights and visualize device data duration 60 minutes time series insights lab https github com azure samples azureiotlabs blob master timeseriesinsights readme md module 4 capture device events and send notifications duration 30 minutes azure iothub with event grid lab https github com azure samples azureiotlabs blob master eventgrid readme md module 5 cold path storage duration 60 90 minutes azure date lake store lab https github com azure samples azureiotlabs blob master datalakestore readme md module 6 hot path analytics duration 60 minutes cosmos db lab https github com azure samples azureiotlabs blob master cosmosdb readme md module 7 batch analytics duration 60 minutes azure date lake analytics lab https github com azure samples azureiotlabs blob master datalakeanalytics readme md module 8 load test using device simulator duration 30 minutes device simulator https github com azure samples azureiotlabs blob master devicesimulator readme md module 9 configure and monitor iot devices at scale duration 30 minutes automatic device configuration https github com azure samples azureiotlabs blob master automaticdeviceconfiguration readme md | server |
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Internship254-ALX-Portfolio-Project | internship254 alx portfolio project project for alx full stack software engineering program where i used flask and dynamic database driven database to create a web app that links college students and fresh graduates seeking internship opportunities with companies seeking to recruit interns in kenya | server |
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A-Guide-to-Retrieval-Augmented-LLM | a guide to retrieval augmented llm chatgpt large language model llm hallucinations https machinelearningmastery com a gentle introduction to hallucinations in large language models llm data freshness llm llm llm llm retrieval augmented llm retrieval augmented generation rag llm llm llm llm timemate wechat assets timemate wechat jpeg llm llm retrieval augmented llm llm query information retrieval ir llm llm retrieval augmented llm overall architecture assets retrieval augmented llm overall architecture png openai andrej karpathy build 2023 gpt state of gpt https www youtube com watch v bzqun8y4l2a ab channel microsoftdeveloper chatgpt llm llm andrej retrieval augmented llm in state of gpt assets retrieval augmented llm in state of gpt jpeg google bing retrieval only llm memory only llm llm llm working memory llm context window llm andrej llm ai llm 6 the new language model stack https www sequoiacap com article llm stack perspective 33 ai 88 llm sequoia language model survey assets sequoia language model survey png a16z 6 llm emerging architectures for llm applications https a16z com emerging architectures for llm applications llm contextual data llm llm emerging llm app stack assets emerging llm app stack png llm llm llm llm gb token gpt 3 3000 llama 1 4 llm llm llm llm icml large language models struggle to learn long tail knowledge https arxiv org abs 2211 08411 llm llm llm llm long tail evidence assets llm long tail evidence png llm llm llm context llm long tail retrieval method performance assets llm long tail retrieval method performance png chatgpt llm chatgpt chatgpt llm extracting training data from large language models https arxiv org abs 2012 07805 query gpt 2 llm private training data leak example1 assets llm private training data leak example1 png llm llm llm llm gpt 4 2021 09 gpt 4 ceo gpt 4 jack dorsey ceo of twitter gpt assets ceo of twitter gpt png llm llm llm llm llm llm llm bing chat llm bing chat bing chat screeshot assets bing chat screeshot png llm llm llm context window gpt 4 32k claude 100k https www anthropic com index 100k context windows llm llm 32k llm lost in the middle https arxiv org pdf 2307 03172 pdf llm lost in the middle result assets lost in the middle result png llm llm llm document object metadata nlp nlp llm data ingestion overview assets data ingestion overview png google doc sheet slides calendar drive slack discord github gitlab confluence web api txt csv pdf markdown json llm llm pinecone chunking strategies for llm applications https www pinecone io learn chunking strategies html markdown code latex sentence transformer openai text embedding ada 002 256 512 prompt llm llm 1 2 chunk size 3 n n n n markdown latex python nclass ndef markdown header character level token level langchain https python langchain com docs modules data connection document transformers python from langchain text splitter import charactertextsplitter from langchain text splitter import recursivecharactertextsplitter language text split text splitter recursivecharactertextsplitter set a really small chunk size just to show chunk size 100 chunk overlap 20 length function len add start index true code split python splitter recursivecharactertextsplitter from language language language python chunk size 50 chunk overlap 0 markdown split md splitter recursivecharactertextsplitter from language language language markdown chunk size 60 chunk overlap 0 llm node chunk llamaindex how each index works https gpt index readthedocs io en latest core modules data modules index index guide html list index assets list index webp list query assets list query webp list filter query assets list filter query webp tree index assets tree index webp tree query assets tree query png keyword table index assets keyword table index webp keyword query assets keyword query webp text embedding model vector store index assets vector store index webp vector store query assets vector store query webp text embedding model vector vectors 2 assets vectors 2 svg word2vec glove bert sentence transformers https arxiv org abs 1908 10084 openai text embedding ada 002 https openai com blog new and improved embedding model 8191 instructor https instructor embedding github io bge https github com flagopen flagembedding blob master readme zh md mteb mteb leaderboard https huggingface co spaces mteb leaderboard 2023 08 18 mteb leaderboard 20230816 assets mteb leaderboard 20230816 png numpy do you actually need a vector database https www ethanrosenthal com 2023 04 10 nn vs ann 10 numpy hnswlib https github com nmslib hnswlib numpy hnsw numpy nn search benchmark assets hnsw numpy nn search benchmark png numpy python import numpy as np candidate vecs 2d numpy array of shape n x d query vec 1d numpy array of shape d k number of top k similar vectors sim scores np dot candidate vecs query vec topk indices np argsort sim scores 1 k topk values sim scores topk indices numpy facebook faiss https github com facebookresearch faiss faiss cpu gpu faiss pinecone nearest neighbor indexes for similarity search https www pinecone io learn series faiss vector indexes faiss faiss ann algo comparison assets faiss ann algo comparison png numpy faiss pinecone https www pinecone io vespa https vespa ai weaviate https weaviate io milvus https milvus io chroma https www trychroma com tencent cloud vectordb https cloud tencent com product vdb pinecone what is a vector database https www pinecone io learn vector database vector database pipeline assets vector database pipeline png 1 product quantization lsh hnsw 2 3 python pinecone python install python pinecone client pip install pinecone client import pinecone initialize pinecone client pinecone init api key your api key environment your environment create index pinecone create index quickstart dimension 8 metric euclidean connect to the index index pinecone index quickstart upsert sample data 5 8 dimensional vectors index upsert a 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 b 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 c 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 d 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 e 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 query index query vector 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 top k 3 include values true returns matches id c score 0 0 values 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 id d score 0 0799999237 values 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 id b score 0 0800000429 values 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 namespace delete index pinecone delete index quickstart llm what are the approaches to task decomposition how can task decomposition be approached what are the different methods for task decomposition what are the various approaches to decomposing tasks self ask https ofir io self ask pdf react https arxiv org abs 2210 03629 llm single step diagram assets single step diagram png multi step diagram assets multi step diagram png hyde hyde http boston lti cs cmu edu luyug hyde hyde pdf hypothetical document embeddings llm hyde assets hyde png llm llm llamaindex llm llm prompt prompt prompt prompt prompt llamaindex prompt llm python template f context information is below context str using both the context information and also using your own knowledge answer the question query str if the context isn t helpful you can don t answer the question on your own prompt llm python template f the original question is as follows query str we have provided an existing answer existing answer we have the opportunity to refine the existing answer only if needed with some more context below context str using both the new context and your own knowledege update or repeat the existing answer chatgpt chatgpt chatgpt retrieval plugin https github com openai chatgpt retrieval plugin openai llm chatgpt github chatgpt openai chatgpt retrieval plugin and postgresql on azure https techcommunity microsoft com t5 azure database for postgresql openai chatgpt retrieval plugin and postgresql on azure ba p 3826411 chatgpt retrieval plugin internals diagram assets chatgpt retrieval plugin internals diagram png api upsert 200 token openai upsert file pdf txt docx pptx md upsert query filter top k delete llamaindex langchain llamaindex https gpt index readthedocs io en latest index html llm llamaindex llamaindex api llamaindex llama hub https llamahub ai chatgpt langchain flask docker llamaindex rag using llamaindex assets rag using llamaindex jpeg llamaindex langchain https python langchain com docs get started introduction html llm llm llamaindex llm langchain llm agent langchain retrieval https python langchain com docs modules data connection llamaindex langchain rag pipeline assets langchain rag pipeline jpeg github copilot github copilot https github com features copilot ai github copilot copilot github copilot vscode copilot internals https thakkarparth007 github io copilot explorer posts copilot internals copilot analysis https github com mengjian github copilot analysis copilot github copilot vscode prompt prompt codex context github copilot llm ai github copilpot copilot llm prompt prefix suffix isfimenabled prompt promptelementranges github copilot prompt example assets github copilot prompt example png prompt context prompt github copilot prompt copilot prompt beforecursor aftercursor similarfile importedfile import languagemarker pathmarker 60 1 jaccard if then else for token token jaccard copilot j a b frac a cap b a cup b frac a cap b a b a cap b prompt github copilot prompt components assets github copilot prompt components png prompt copilot prompt php js python rust php php github copilot llm github copilot context copilot jaccard token llm mendable https www mendable ai ai llamaindex langchain mendable mendable mendable screenshot assets mendable screenshot png danswer https github com danswer ai danswer llm docker pandagpt https www pandagpt io fastgpt https fastgpt run llm ai quivr https github com stangirard quivr chatfiles https github com guangzhengli chatfiles llm chatfiles llm llm hello world chatfiles architecture assets chatfiles architecture png 1 chatgpt retrieval plugin https github com openai chatgpt retrieval plugin project 2 hypothetical document embeddings https arxiv org abs 2212 10496 ref mattboegner com paper 3 knowledge retrieval architecture for llm s 2023 https mattboegner com knowledge retrieval architecture for llms blog 4 chunking strategies for llm applications https www pinecone io learn chunking strategies blog 5 langchain document transformers https python langchain com docs modules data connection document transformers doc 6 llamaindex index guide https gpt index readthedocs io en latest core modules data modules index index guide html doc 7 full stack llm bootcamp augmented language models https fullstackdeeplearning com llm bootcamp spring 2023 augmented language models course 8 pinecone vector indexes in faiss https www pinecone io learn series faiss vector indexes blog 9 pinecone what is a vector database https www pinecone io learn vector database blog 10 zero and few shot text retrieval and ranking using large language models https blog reachsumit com posts 2023 03 llm for text ranking blog 11 copilot internals https thakkarparth007 github io copilot explorer posts copilot internals blog 12 copilot analysis https github com mengjian github copilot analysis blog 13 discover llamaindex key components to build qa systems https www youtube com watch v a3iqojhbqhm ab channel llamaindex video 14 billion scale approximate nearest neighbor search https wangzwhu github io home file acmmm t part3 ann pdf slide 15 acl 2023 tutorial retrieval based lm https acl2023 retrieval lm github io slide 16 pinecone why use retrieval instead of larger context https www pinecone io blog why use retrieval instead of larger context blog 17 reta llm https github com ruc gsai yulan ir tree main reta llm project 18 document metadata and local models for better faster retrieval https www youtube com watch v njzb6fm0u8g ab channel llamaindex video | large-language-models llm rag retrieval-augmented retrieval-augmented-generation | ai |
Dstny-LLM | div align center img src colab deployment static logo png width 800 div dstny engage is a powerful omnichannel communications software designed to help you create meaningful and personalized interactions with your customers this repository wraps up the work that was done by interns essam wisam https github com essamwisam and ziad atef https github com ziad atef in their deep learning internship at dstny in 2023 internship objectives build and deploy an anonymization model that given a piece of text in any of english dutch or arabic detects and masks any word that contains sensitive information about the user as exactly defined by gdpr https docs private ai com entities while collecting any data as needed this would allow easy and safer interaction with services that process user inputs to dstny chatbots port a state of the art llm in house without using any apis and use it for question answering while considering other options for the question answering task this would allow refraining from standard qa formats for building a chatbot by making it possible to give an answer to a question given just raw text as the reference we divided this task into porting the llm building an abstractive qa model and building an extractive qa model internship outcomes anonymizer model extensive research and documentation for base models and datasets to use experiment with different models and understand dataset structures collect merge and statistically analyze different sources of data for the three languages synthetic data generation in the three languages and cairene arabic automatic labelling run many experiments training xlm roberta base xlm roberta large and mdebertav3 on the task evaluate the model on unseen data financial analysis building a gui and deployment improving the models by collecting entity specific data and retraining final results our final model had a macro f1 score of 92 5 and was proficient in all of english dutch standard and cairene arabic while classifying between over 27 different entities types of sensitive information anonymizer demo h5 align center english example h5 english demo anonymizer anonymizer 20english gif h5 align center dutch example h5 english demo anonymizer anonymizer 20dutch gif h5 align center standard arabic cairene dialect example h5 english demo anonymizer anonymizer 20arabic gif h5 align center mixed language example h5 english demo anonymizer anonymizer 20mixed 20language gif porting a large language model extensive research on the possible llms taking into account performance supported languages and resource requirements converge on porting 7b and 13b llama 2 with ggml or gptq inference optimization compare performance and inference time to finally choose gptq perform financial analysis to compare deployment to chatgpt as a bonus prepare a simple parameter efficient qlora fine tuning script for the model final results 13b llama 2 with gptq optimization running on nvidia s t4 ready to receive new prompts llama 2 demo demo llama 202 llama 202 20poem gif abstractive question answering model evaluate llama 2 on squadv1 qa dataset for both english and dutch low rogue scores in both e g 80 for english and for dutch 40 consider f1 and em metrics initially as well perform extensive prompt tuning to improve the model in both languages re validate the model until converged on decent results provide a translation wrapper for arabic comparing distilbert with google s api final results 13b llama 2 with gptq optimization with 89 rogue score in english and 65 in dutch abstractive qa demo h5 align center english example h5 english demo qa 20abstractive abstractive 20qa 20english gif h5 align center dutch example h5 english demo qa 20abstractive abstractive 20qa 20dutch gif h5 align center standard arabic h5 english demo qa 20abstractive abstractive 20qa 20arabic gif extractive question answering model extensive documented research for models and datasets experiment with models and understand dataset structures choose final model to train xlm roberta large perform a myriad of experiments on the present datasets squadv2 ar artydiqa converge on final model trained on squad v2 english comparing our narrow model to mdeberta v3 base financial analysis on deployment costs generalizing the model for arbitrarily long documents via semantic search with langchain final results extractive qa model with an f1 of 90 for english 74 for arabic and 64 for dutch extractive qa demo h5 align center english example h5 english demo qa 20narrow narrow 20qa 20english gif h5 align center dutch example h5 english demo qa 20narrow narrow 20qa 20dutch gif h5 align center standard arabic h5 english demo qa 20narrow narrow 20qa 20arabic gif h2 align center thank you h2 | ai |
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nextjs-boilerplate | img src https cheatcode assets s3 amazonaws com cheatcode logo sm svg alt cheatcode next js boilerplate beta front end boilerplate for building web applications based on next js https nextjs org join the discord https discord gg uty4fpy table of contents 0 who is this for who is this for 1 introduction introduction 2 getting started getting started 3 file structure file structure 4 pages components pages components pages pages route pages route pages nested pages nested pages parameter pages parameter pages authenticated amp public pages authenticated public pages base pages base pages sitemap sitemap components components class based vs functional components class based vs functional components forms forms 5 styles styles 6 graphql graphql 7 accounts accounts 8 settings settings 9 faq faq 10 contributing contributing 11 license license who is this for this boilerplate was created first and foremost as a teaching aid used in conjunction with tutorials and courses on cheatcode https cheatcode co a site decidated to teaching you how to build full stack apps with javascript and node js beyond this it s also intended as a starting point for your product or service it s a great fit for developers working on a new startup or an app for an existing business it s important to note this boilerplate is front end only it was designed to work in conjunction with a separate back end or api we offer a node js boilerplate https github com cheatcode nodejs server boilerplate to fill this role for you providing a working graphql server and accounts system that this next js boilerplate is already set up to use learn more about this decision in the faq faq introduction blockquote h4 style margin 0 0 10px back end agnostic h4 p style margin 0 while you can use any back end or api you wish with the boilerplate by default it s wired to work with the a href https github com cheatcode nodejs server boilerplate cheatcode node js boilerplate a p blockquote this boilerplate was created to serve as a starting point for the front end of a web application it leverages next js to handle rendering components routing and bundling of application code on top of this additional features are added to speed up your development process including bootstrap v5 for css styling via styled components w ssr support a fully wired accounts ui with signup login recover password and reset password a fully wired graphql client with pattern for managing mutations and queries on the client an example crud documents feature a global redux store for storing app state an seo component for offering google friendly public facing pages a pattern for managing and accessing environment specific settings easy form validation with helper component alerts system for easy feedback and error reporting getting started to get started clone a copy of the boilerplate from github git clone git https github com cheatcode nextjs boilerplate once the boilerplate is cloned cd into its folder and run npm install to download all of the boilerplate s dependencies cd nextjs boilerplate npm install note you can safely use yarn https yarnpkg com for this step if you prefer next steps once you ve cloned the boilerplate and installed all of its dependencies the next step is to familiarize yourself with the file structure and how it differs slightly from a standard next js project file structure while the boilerplate does primarily rely on the standard file structure of a next js project anchored around the pages directory a few additions have been made the following outlines the full structure of the boilerplate components authenticatedroute index js graphqlerror index js styles js loading index js styles js navigation index js navigationlink index js publicroute index js seo index js userform index js styles js validatedform index js graphql mutations documents gql users gql queries documents gql client js lib users login js loginwithtoken js logout js signup js dates js formaterrorstring js formatgraphqlerror js isclient js pong js store js throttle js validateform js validators js pages documents id edit js index js styles js create js index js styles js login index js styles js recover password index js reset password token js signup index js app js document js error js index js sitemap xml js public settings index js settings development js settings production js styles pong css styles css gitignore credits md license md next config js package lock json package json readme md yarn lock files flagged with a are not included by default but assumed to be added by you later depending on need pages components for components there are two conventions in use the standard pages directory that next js uses for routing and rendering and a boilerplate custom directory components that contains a mix of react components used to build out the boilerplate s user interface pages there are two types of pages in the boilerplate route pages and base pages route pages describe the page components contained in each of the folders inside the pages directory route pages route pages is a generic term used to describe the pages rendered by next js located in the folders within the pages directory these folders and the files they contain map to the current url in the browser behind the scenes next js automatically maps the browser s url to the folder with the corresponding name for you for example if we visit http localhost 5000 documents next js will attempt to render the component file located in the pages documents folder to be clear if we went to http localhost 5000 pizza next js would expect a folder located at pages pizza with an index js file inside the index js part is assumed so it s not necessary to include it in the url i e https localhost 5000 documents index js nested pages this introduces an important convention in next js nested routes and parameters if we look inside the documents folder we ll see the following files create js index js styles js here like we saw above the index js file is accessed via the url http localhost 5000 documents conversely the url http localhost 5000 documents create maps to the create js file while the styles js file here does technically map to the url http localhost 5000 documents styles its intent is not to be a rendered page but instead to hold the styled components https styled components com docs css code for the pages in the pages documents folder this is a boilerplate specific convention not a next js convention parameter pages if we take a look inside of the pages documents folder again we ll notice that there s a nested folder with a strange name id inside of the pages folder in next js whenever we see a file or folder name surrounded by brackets that means that it s a parameterized page for example if we visit http localhost 5000 documents 123 or http localhost 5000 documents 456 our goal is to render the same page template but get different content utilizing this bracketed name convention in next js we can specify when a url has a dynamic section in this case the id of the document or 123 or 456 the purpose behind this is that in some cases we won t know the specific url we re rendering only the template that type of page requires if we look into the pages documents id folder we ll start to see a similar pattern emerge to what we saw earlier here we have edit js index js styles js just like before edit will correspond to the name of a route as will index js and styles js will contain styled components css the difference this time is that they re nested beneath a parameterized page so we can expect to get urls like http localhost 5000 documents 123 edit blockquote h4 style margin 0 0 10px works with standalone pages too h4 p style margin 0 though this example uses a parameterized folder if you look at the code pages reset password code page s folder you ll find that individual pages can server as parameterized routes too this is helpful if you only have a single top level not nested parameterized url p blockquote what s neat about parameterized pages is that you can place any word inbetween the brackets like pizza js or hotdog js that name then will capture the dynamic part of the url in that position and make it accessible inside of the component via the router object returned either by next js s userouter hook or by importing the next js router instance directly like import router from next router const nameofpizza router query pizza or with hooks import userouter from next router const pizza const router userouter const nameofpizza router query pizza note the difference here is purly cosmetic and a matter of preference both methods return the same result authenticated amp public pages if you want to define routes and handle redirects based on a user s logged in or logged out status you can use the authenticatedroute or publicroute hocs higer order components these components are nearly identical with the sole difference being that authenticatedroute ensures that a logged in user is present before rendering the component passed to it conversely the publicroute component ensures that a logged in user is not present before rendering the component passed to it useful for redirecting away from accounts related pages like signup usage of these two components is handled by importing one of the components into an existing page component folder e g in this boilerplate pages login index js and then wrapping it around the export of that component at the bottom of the file javascript import react from react import authenticatedroute from components authenticatedroute const documents documents proptypes export default authenticatedroute documents here at the bottom we export a call to authenticatedroute the recommended way to case this is using camel case passing it our component documents the publicroute hoc works in the exact same fashion javascript import react from react import publicroute from components publicroute const login login proptypes export default publicroute login for both the publicroute and authenticatedroute components as a second argument after the component an options object can be passed currently the pathafterfailure option is the only option supported javascript import react from react import publicroute from components publicroute const login login proptypes export default publicroute login pathafterfailure some authenticated route base pages base pages is a generic term used to describe the pages at the root of the pages directory this includes app js a custom implementation of the app component in next js that includes a login handler for previously logged in users on mount and rendering of global provider components for redux apollo application navigation and the currently rendered page passed to app automatically by the next js router as component this page also includes the import for the boilerplates global styles located in styles styles css document js a custom implementation of the document component in next js that includes the base html template for the boilerplate also includes server side rendering handlers for styled components in the page s getinitialprops method and basic html metadata for seo purposes hint check out the seo component in the components directory for a more detailed helper for rendering seo metadata sitemap blockquote p strong note strong make sure to set the code meta rooturl code value in the settings file to the domain where your app is running the sitemap depends on this value for generating the urls it returns to crawlers this is already configured in code setttings settings development js code but needs to be replicated for each environment you support e g staging production if you want those environments crawlable p blockquote to help with improving the seo of your app the boilerplate includes a sitemap xml js file at pages sitemap xml js this file is technically a page component though its component doesn t render anything instead it piggybacks on the next js getserversideprops method and hijacks the inbound http request converting the response s content type header to be text xml because next js creates routes based on file names in the pages directory by having sitemap xml js there next js treats the sitemap xml part as the route for that page i e http localhost 5000 sitemap xml the boilerplate utilizes this technique combined with setting the content type header to trick browsers into thinking it s opening a xml file on the server by default the sitemap only pulls the top level pages in the boilerplate not any of the dynamic data this is intentional because the example documents query in the boilerplate relies on a logged in user if you d like to see a good way to add dynamic data to the sitemap read this tutorial on generating a dynamic sitemap https cheatcode co tutorials how to generate a dynamic sitemap with next js generating dynamic data for our sitemap components though technically speaking the pages in the boilerplate are react components as their name implies they re intended to be pages rendered by the next js router not standalone components to fill in this gap the boilerplate adds a components directory at the root of the project this folder is designed to contain sub folders with each sub folder representing one component in the app as a naming convention folder names are given the pascal case name of the component this is done to make it easier to spot components in the folder as they re using the same as they do when in use in the application for example the component at components navigation is rendered in the app as navigation and the component at components validatedform is rendered in the app as validatedform class based vs functional components both class based and functional components are in use in the boilerplate this is intentional and an opinionated choice the primary reason for this is tidiness and clarity functional components are great for smaller simpler components that don t have a ton of functionality while the separation provided by class methods is more friendly to large complex components which you choose is up to you we recommend familiarizing yourself with both so you have more flexibility when building your app s front end forms generally speaking one of the more tedious and common parts of any app are forms in the boilerplate forms are kept simple using plain html react jsx flavored of course inputs the only custom forms related feature in the app is the validatedform component the validatedform component in conjunction with the lib validateform js and lib validators js files enables real time form validation the component is used as a convenience method for attaching the validateform js function to a form in a react component validateform js is a custom vanilla javascript library for running validation written custom for the boilerplate the underlying library validateform js uses javascript dom manipulation to render error messages and validate the form s inputs validations can be implemented as you wish but a series of built in validators are included in the lib validators js file which relies on the validator npm package validateform usage usage of the validateform component is straight forward class mycomponent extends react component constructor props super props this state emailaddress password handlesubmit submit logic goes here render const emailaddress password this state return validateform rules emailaddress required true email true password required true minlength 8 messages emailaddress required email address is required email is this email valid password required password is required minlength use at least 8 characters onsubmit this handlesubmit form label email address label input type email name emailaddress value emailaddress onchange event this setstate emailaddress event target value label password label input type password name password value password onchange event this setstate password event target value form validateform here the validateform component takes three props rules set to an object containing properties which describe the validation rules for the form each property represents a single field by its name attribute each property is assigned to an object containing the validation rules for the field messages set to an object cotaning properties which descibre the validation rules for the form each property represents a single field by its name attribute each property is assigned to an object containing the validation rules for the field with its values set to error strings that should be displayed when a rule fails validation onsubmit once validation is passed the function to be called this replaces the onsubmit attribute for the form form tag as a user interacts with the form the validation is run displaying any errors beneath the input and highlighting the input with an error class styles css styles are implemented in two ways in the boilerplate global styles not page or component specific styles are implemented using next js built in css processing these styles are located in the styles directory at the root of the project all of the files here are imported into the styles styles css file and that file is imported in the pages app js component at the page and component level css styles are implemented using styled components https styled components com graphql while the boilerplate is technically data agnostic it doesn t force you to use any specific means for retrieving or manipulating data in your app it does include wiring and examples for using graphql the boilerplate includes wiring for a graphql client using the apollo graphql client library located at graphql client js inside a graphql client is established along with some default settings the url being connected to is set in the environment specific settings file in the settings folder i e settings settings development js contains your development settings settings settings production js contains your production settings from that file the client is exported with the connection established this can be imported directly into a component or other file for executing queries directly if you wish to use the apollo client s usequery or usemutation methods as a convenience the apolloprovider has already been implemented in the pages app js component this is required for usequery and usemutation to work accounts though there is no account system immediately present in the boilerplate it is wired to work with the complimentary cheatcode node js boilerplate https gituhb com cheatcode nodejs server boilerplate which does include a full accounts implementation in the boilerplate pages for each of the authentication stages login signup recover password and reset password and calls to the node js boilerplate s authentication setup implemented as a series of graphql mutations relying on jwt tokens stored as http only secure only cookies in the browser are fully implemented for you while you re welcome to use whatever authentication system you d like e g userbase auth0 etc this is an easy free way to get user accounts set up while using all of cheatcode s tools settings to assist in the management of client side settings things like api keys configuration etc a helper method and pattern are included in the boilerplate in the settings directory of the project this directory contains three files index js a loader file that selects the proper settings file based on the current value of process env node env settings development js a file exporting an object settings that contains the settings for your development environment settings production js an assumed file exporting an object settings that contains the settings for your production environment this file is assumed because it is not committed to your git repository as a matter of security you can change this in the gitignore file at the root of the project if you want to use settings in your project you can import the index js file from your settings directory like this example graphql client js import apolloclient apollolink inmemorycache from apollo client import httplink from apollo link http import settings from settings const client new apolloclient credentials include link apollolink from new httplink uri settings graphql uri credentials include export default client you can customize your settings file however you d like if you change names or locations of settings make sure to update the paths in your source code e g in the graphql client example above settings graphql uri must be defined in order for your client to work faq why is this front end only instead of full stack this was a difficult choice from experience one of the more frustrating situations when you re working with a framework is vendor lock in if when the framework you rely on becomes obsolete or financially or operationally unviable solving this problem generally requires eating the cost of refactoring code to mitigate some of this cost we chose to separate out the server side portion of this boilerplate this requires a little more effort in terms of managing multiple projects today but also puts you in an advantageous situation later if next js falls out of favor at worst you just need to relocate some react components and javascript functions not completely restructure your entire stack in addition to this the decision to separate is also based on scalability and accessibility having both your client and server in one app is convenient for developers but can introduce unnecessary strain on computing resources as your app grows not only that but if you offer multiple front ends a web app an ios app an android app etc it means that you re burdening your web app front end with requests from your other front ends finally this choice was made in an effort to help the developers we teach we want to encourage less dependence on specific stacks frameworks and help developers to focus on their ability to adapt to any javascript based stack separating out the pieces invites just enough discomfort to help push our audience s skillset that much further contributing blockquote h4 style margin 0 0 10px please follow instructions h4 p style margin 0 if you don t follow these instructions your proposal will be closed immediately p blockquote the primary goal of this project is to server as a foundation for tutorials and courses offered on cheatcode https cheatcode co in order to offer a relatively consistent api changes are limited to bug fixes and feature additions as a result limited contributions are accepted to this boilerplate while you re welcome to submit a pull request likelihood of acceptance is limited if you have an idea for something you d like to contribute it s best to submit a feature request issue with a type of proposal in the issues tab of this repo there we can discuss the idea and any long term considerations or changes before we greenlight the implementation license mit copyright 2021 cheatcode permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | front_end |
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DBConstructionCompany | dbconstructioncompany project for the databases course computer engineering university of pisa collaboratively developed with tommaso falaschi description the project wants to develop a database dedicated to a generic construction company this db is a comprehensive system designed to address the multifaceted needs of a modern construction enterprise built on the principles of relational database design it ensures data integrity coherence and scalability catering to the dynamic nature of the construction industry key features project management stores detailed information about each construction project including its timeline budget and resources inventory control monitors the quantity and location of construction materials tools and machinery optimizing procurement and usage human resources manages employee data including roles qualifications work assignments and payroll information geological hazard and calamity monitoring offers tools for the assessment monitoring and recording of potential geological dangers and calamities that may impact construction sites ensuring enhanced safety and risk management measures analytics tools in addition to a suite of pre built operations designed to efficiently query the database for essential information our system has incorporated advanced data analytics modules two such specialized analytics focus predominantly on the maintainability of constructed buildings these analytics provide a comprehensive insight into the long term sustainability and performance of structures by analyzing patterns wear and tear and other key indicators they offer predictive maintenance schedules to proactively address potential issues furthermore in relation to identified risks these analytics also suggest recommended interventions this ensures that not only are buildings maintained at their optimum levels but any potential threats are addressed in a timely and efficient manner greatly enhancing the longevity and safety of the constructions the comprehensive documentation can be found in the files img src https img shields io badge mysqlworkbench darkblue style for the badge logo mysql img src https img shields io badge mysql blue style for the badge logo mysql | server |
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udacity-cloud-devops-capstone-project | udacity cloud devops engineering nanodegree capstone project this is my capstone project for the cloud devops engineering nanodegree it contains a create react app https github com facebook create react app containerized with docker and deployed to aws by using an eks cluster | cloud |
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ML_RiskManagement | short course applied machine learning for risk management the use of statistical models in computer algorithms allows computers to make decisions and predictions and to perform tasks that traditionally require human cognitive abilities machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such statistical models and interweaves them with computer algorithms it underpins many modern technologies such as speech recognition internet search bioinformatics and computer vision amazon s recommender system google s driverless car and the most recent imaging systems for cancer diagnosis are all based on machine learning technology this course on machine learning will explain how to build systems that learn and adapt using real world applications some of the topics to be covered include linear regression logistic regression deep neural networks clustering and so forth the course will be project oriented with emphasis placed on writing software implementations of learning algorithms applied to real world problems in particular credit risk collections management and fraud detection instructors dr alejandro correa bahnsen email al bahnsen gmail com twitter albahnsen https twitter com albahnsen github albahnsen http github com albahnsen iv n torroledo email ivan torroledo gmail com github torroledo http github com torroledo requiriments python http www python org version 3 5 numpy http www numpy org the core numerical extensions for linear algebra and multidimensional arrays scipy http www scipy org additional libraries for scientific programming matplotlib http matplotlib sf net excellent plotting and graphing libraries ipython http ipython org with the additional libraries required for the notebook interface pandas http pandas pydata org python version of r dataframe scikit learn http scikit learn org machine learning library a good easy to install option that supports mac windows and linux and that has all of these packages and much more is the anaconda https www continuum io git unfortunatelly out of the scope of this class but please take a look at these tutorials https help github com articles good resources for learning git and github sessions session notebook link exercises 1 introduction to machine learning http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 01 intromachinelearning ipynb 2 introduction to python for data analysis http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 02 intropython numpy scypy pandas ipynb python numpy pandas http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 02 python numpy pandas ipynb 3 linear regression http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 04 linear regression ipynb income prediction rent http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 03 incomeprediction ipynb 4 logistic regression http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 05 logistic regression ipynb credit scoring http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 04 creditscoring ipynb 5 data preparation and model evaluation http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 06 data preparation evaluation ipynb credit scoring v2 http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 05 creditscoring cross validation ipynb 6 unbalance datasets http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 08 unbalanced datasets ipynb fraud detection http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 06 fraud detection sampling ipynb 7 decision trees http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 09 decision trees ipynb fraud detection v2 http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 07 fraud detection dt ipynb 8 ensemble methods bagging http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 10 ensemblemethods bagging ipynb fraud detection v3 http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 08 fraud ensemble bagging ipynb 9 statistical inference http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 09 statisticalinference ipynb 10 cost sensitive classification http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 10 costsensitiveclassification ipynb credit scoring v4 http nbviewer jupyter org github albahnsen ml riskmanagement blob master exercises 10 cs churn ipynb 11 model deployment http nbviewer jupyter org github albahnsen ml riskmanagement blob master notebooks 11 model deployment ipynb | ai |
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Data_egineering_exercise | data egineering exercise this is a little data engineering exercise where i explored web scraping etl pipeline to clean data as well as inserting the cleaned data into a mongodb database i ll go deeper into all of these aspects with time and produce a better and more scalable algorithm also i d like to go a little bit futher by contenarizing the etl application with docker | server |
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keep-up-to-date | how to keep up to date on front end technologies the web is a rapidly evolving universe an important part of our job as front end developers is keeping up to date and staying close to new tools trends and workflows hundreds of blog posts and articles are published every day but there is no way you can read all of them we think you should have a strategy to keep up to date so we have created this recipe if you want to contribute to improve this recipe please you should read our wiki https github com frontend rescue keep up to date wiki made by devs for devs guille paz twitter pazguille http twitter com pazguille web http pazguille me http pazguille me hernan mammana twitter hmammana http twitter com hmammana web http mammana me http mammana me lean linares twitter lean8086 http twitter com lean8086 web http leanlinares me http leanlinares me designer star fer adorneti e mail ferador gmail com mailto ferador gmail com twitter cavemandg https twitter com cavemandg thank you alex sexton twitter slexaxton https twitter com slexaxton web http alexsexton com http alexsexton com angelina fabbro twitter hopefulcyborg https twitter com hopefulcyborg web http realityhacking net http realityhacking net axel rauschmayer twitter rauschma https twitter com rauschma web http rauschma de http rauschma de brad frost twitter brad frost https twitter com brad frost web http bradfrostweb com http bradfrostweb com cody lindley twitter codylindley https twitter com codylindley web http codylindley com http codylindley com lea verou twitter leaverou https twitter com leaverou web http lea verou me http lea verou me nati devalle twitter taly https twitter com taly web http taly me http taly me paul irish twitter paul irish https twitter com paul irish web http paulirish com http paulirish com license the mit license copyright c 2013 pazguille http twitter com pazguille permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | front_end |
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USB2DB15 | usb2db15 supergun adapter ko fi https ko fi com img githubbutton sm svg https ko fi com raph friend a simple atmega328p based usb to db15 adapter designed for use with a supergun or neo geo system p align center img width 680 src https i imgur com wuznxtb png br b usb2db15 with a sega astro city mini arcade stick b br br p 1 features usb input to support most modern hid compatible controllers 1ms polling meaning a low latency and fast response from the usb2db15 open source firmware so everyone on the community can collaborate db15 pinout standard ready for neo geo minigun and has superguns 4 customizable and storable mapping configurations 10 customizable and storable controller profiles status led protection circuit for your beloved controllers 2 technical information the usb2db15 adapter is an open sourced device based on an avr microcontroller it adapts a usb controller for use with a jamma pcb via a supergun or neo geo hardware with controller ports presently a variety of common controllers are supported such as ones that follow the ps3 ps4 dualshock ps5 dualsense and xbox 360 one protocols due to the diversity of controllers available and differing standards there may be some controllers that will not work with the usb2db15 or may not work as intended please be patient during these early stages feel free to contact the developers with additional information and requests for help with your usb2db15 adapter via our communication channels github discord arcade projects forum we are using a modified usb host shield 2 0 library for controller ids that aren t natively supported in regards to hardware there are currently two options a custom pcb that accommodates and integrates an arduino pro mini usb shield mini and other relevant hardware usb2db15 2 0 ver a pre built single pcb that incorporates all the required hardware in a standalone form usb2db15 cs ver same as 2 0 ver but with a focus on pcb consolization in place of the db15 controller connector this board has screw terminals and a 20 pin header which is compatible with a 20 pin brook fighting board cable alternatively you can simply use a breadboard to connect it all together if you are building the open source version for yourself a basic understanding of arduino and its programming environment is required visit arduino cc https arduino cc to help you get started p align center img width 380 src https i imgur com nxcxxz3 png br b usb2db15 pinout standard b br br 3 open source required hardware the usb2db15 uses an atmega328p avr any arduino that utilizes this microcontroller such as the mini pro nano and uno are suitable the custom pcb accommodates an arduino mini pro and usb host shield mini usb host shield mini if using an arduino pro mini nano full sized usb host shield for uno an ftdi programmer is required for the arduino pro mini this is not required if using an arduino nano or uno usb2db15 custom pcb or a breadboard to complete all the connections between the avr and usb host shield db15 female connector p align center img width 680 src https i imgur com uezuorb jpg br b arduino pro mini and usb host shield mini b br br p if utilizing the custom pcb in conjunction with an arduino pro mini and mini usb shield a bom is available from the repositories section within the pcb folder jumper settings sj1 bridge this jumper only to disable 5v protection sj2 bridge this jumper only to enable 5v protection jp2 5v solder point see information below regarding usb vbus due to variances of mini pro pcb s a4 and a5 may be in different locations please pay attention to this p align center img width 680 src https i imgur com syrvobi png br b custom pcb 1 2 for arduino pro mini b br p 4 installation download the a href https github com raphfriend usb host shield 2 0 usb host shield 2 0 library a and install it to the arduino ide open the ino file rfusb to db15 ino and program it to your avr if you have a usb host shield mini you will need to cut the trace after the 2k2 resistor near the usb vbus pin solder a wire from jp2 of the custom pcb to the through hole via labeled 5v as depicted in the picture below p align center img width 460 src https i imgur com vggnspl png p this enables devices that require 5v power to function correctly as by default the usb host shield mini only supplies 3 3v to the vbus pin of the usb connector the remainder is plug and play if you are not using a prefabricated pcb please view the schematic for wiring information 4 1 arduino pro mini usb shield mini see flashing md for instructions flashing md 4 2 arduino uno usb host shield 1 install arduino ide 2 mount the usb host shield to the arduino uno correctly 3 connect the arduino uno to your pc via its usb b cable 4 in the arduino ide navigate to tools board arduino avr boards and select arduino uno 5 in the arduino ide navigate to tools port and choose the arduino device 6 in the arduino ide navigate to tools programmer and select avrisp mkii 7 obtain the library path arduino ide file preference settings sketchbook location and drop the required library usb host shield 20 to the library directory 8 obtain the ino firmware rfusb to db15 ino 9 open the ino file with the arduino ide with the arduino uno connected to your pc and select sketch upload 10 wait for the sketch to be uploaded as indicated in the console window at the bottom of the arduino ide software 5 supported controllers see compatibility md for a detailed list of supported controllers compatibility md 6 software usage 6 1 controller profiles the adapter can store profiles for up to 10 unique controller models controllers of the same make and model will share the profiles each controller has 4 profiles associated with it should you plug in an 11th controller the system will replace the oldest controller you have set up next oldest for the 12th and so on this may cause you to have to set up older controllers again if you resume using them these can be accessed by pressing select coin one of the four directions thus if you wanted the first profile you would press select up the second select right and so on the adapter will remember what profile you were using and automatically switch to it when you use the controller again to change the key bindings of a profile first change to the profile you want to setup while holding select for at least 3 seconds press buttons 1 6 in order then release select to lock in the binding finally release select to lock in the binding xbox one controllers appear to the system as the same make and model and thus use the same profiles even if they are different controllers shell avramce https twitter com avramce has created a 3d printed case https www thingiverse com thing 5170415 that you can print for your usb2db15 how it looks in real life when you put all the parts together usb2db15 should look like this p align center img width 680 src https i imgur com s0fqrp9 jpg br b setup and photo by a href https twitter com focux11 focux a b p p align center img width 680 src https i imgur com m0nqrhr jpg br b setup and photo by a href https twitter com atrac17 atrac17 a b p support this project if you have found usb2db15 useful please consider funding it and making a donation so that we may continue our work by obtaining additional controllers equipment and everything else required to keep this project alive and growing ko fi https ko fi com img githubbutton sm svg https ko fi com raph friend is this product available to buy yes we sell the usb2db15 directly you will receive a premium edition that is slightly different from the open source version but has the same features you can choose from a variety of shell color options and you will get detailed instructions following your purchase there are two versions the standard db15 and the cs ver which is targeted towards jamma pcb consolizations a href https ko fi com s b6c3d01043 access our shop by clicking here or on the images below a p align center a href https ko fi com s b6c3d01043 img width 460 src https i imgur com mu63fkn jpg p p align center a href https ko fi com s b6c3d01043 img width 460 src https i imgur com xsnq3t4 jpg p p align center a href https ko fi com s b6c3d01043 img width 460 src https i imgur com l4fs8wo jpg p p align center a href https ko fi com s b6c3d01043 img width 460 src https i imgur com xef0cex jpg p where can i get more help if i need it via arcade projects forum or rf projects discord server p align center a href https www arcade projects com forums index php thread 14205 open source usb to db15 for supergun adapter img width 460 src https i imgur com 7arl7zn png p p align center a href https discord gg xkcjtph img width 460 src https i imgur com lzjjouj png p legal usb2db15 or any of its maintainers are in no way associated with or endorsed by nintendo sony snk microsoft 8bitdo sega brook mad catz nacon honcam daemonbite buffalo qanba razer retrobit toodles hori or any other company not implicitly indicated all other brands or product names are the property of their respective holders | os |
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LangCode | langcode enable nlep reasoning for llms implementation and ipython toolkit for the paper natural language embedded programs for hybrid language symbolic reasoning https arxiv org pdf 2309 10814 pdf improved interpretability and problem solving ability with natural language embedded programs nlep a hybrid language symbolic reasoning technique multi round interactive smooth programming on colab and jupyter notebook div align center img src images demo png width 280px respresenting the language of thought hongyin luo https luohongyin github io mit hyluo at mit dot edu div nlep natural language embedded programming the auto regressive large language models llms generate natural and programming languages by scoring the next tokens sampling flexible but undeterministic texts the python interpreter reads structured source code and output mostly deterministic results ensuring the mathematical correctness in reasoning and computation based on the given python code nlep is an end to end hybrid language symbolic framework with an llm and a code interpreter llm plans for the reasoning by generating step by step programs code interpreter executes the generated program and prints desired responses no further edition needed for program standard outputs nlep can deal with natural language math symbolic and programming tasks details summary game of 24 example summary instruction what is the correct mathematical expression consisting of the numbers 4 9 10 13 in any order along with the parentheses and arithmetic operations that results in 24 program generated by llm python step 1 import necessary built in libraries import itertools import operator step 2 define necessary functions that generally solve this type of problem def find expression numbers target operations operator add operator sub operator mul operator truediv ops operator add operator sub operator mul operator truediv for permutation in itertools permutations numbers for ops combo in itertools product operations repeat len numbers 1 expression f permutation 0 ops ops combo 0 permutation 1 for i in range 2 len numbers expression f expression ops ops combo i 1 permutation i try if eval expression target return expression replace except zerodivisionerror continue return none step 3 define constant variables for the task numbers 4 9 10 13 target 24 step 4 print an answer in natural language expression find expression numbers target if expression print f the correct mathematical expression that results in 24 is expression else print there is no correct mathematical expression that results in 24 using these numbers result the correct mathematical expression that results in 24 is 9 10 13 4 details details summary question answering example summary instruction how many keystrokes are needed to type the numbers from 1 to 104892 program generated by llm python step 1 import necessary built in libraries no need to import step 2 define necessary functions that generally solve this type of problem def calculate keystrokes number keystrokes 0 for i in range 1 number 1 keystrokes len str i return keystrokes step 3 define constant variables for the task number 104892 step 4 print an answer in natural language keystrokes calculate keystrokes number print f to type the numbers from 1 to number keystrokes keystrokes are needed result to type the numbers from 1 to 104892 518247 keystrokes are needed details details summary pytorch programming example summary instruction build a multi layer perceptron with two hiden states and dropout in pytorch program generated by llm python step 1 import necessary built in libraries import torch import torch nn as nn import torch nn functional as f step 2 define necessary classes that generally solve this type of problem class multilayerperceptron nn module def init self input size hidden size1 hidden size2 num classes dropout rate super multilayerperceptron self init self fc1 nn linear input size hidden size1 self fc2 nn linear hidden size1 hidden size2 self fc3 nn linear hidden size2 num classes self dropout nn dropout dropout rate def forward self x out f relu self fc1 x out self dropout out out f relu self fc2 out out self dropout out out self fc3 out return out step 3 define constant variables for the task input size 784 28x28 pixels for mnist dataset hidden size1 500 hidden size2 100 num classes 10 10 digits for mnist dataset dropout rate 0 5 step 4 instantiate the network net multilayerperceptron input size hidden size1 hidden size2 num classes dropout rate print out the network architecture print net result multilayerperceptron fc1 linear in features 784 out features 500 bias true fc2 linear in features 500 out features 100 bias true fc3 linear in features 100 out features 10 bias true dropout dropout p 0 5 inplace false details details summary multi turn conversation example summary instruction 1 who is the first emperor of the tang dynasty first program generated by llm python step 1 import necessary built in libraries no need to import step 2 define necessary functions that generally solve this type of problem no need to define a function step 3 define constant variables for the task first emperor tang dynasty emperor gaozu step 4 print an answer in natural language print f the first emperor of the tang dynasty is first emperor tang dynasty result 1 the first emperor of the tang dynasty is emperor gaozu instruction 2 tell me more about him second program generated by llm python step 1 import necessary built in libraries no need to import step 2 define necessary functions that generally solve this type of problem no need to define a function step 3 define constant variables for the task first emperor tang dynasty info name emperor gaozu born 566 died 635 reign 618 626 full name li yuan era name wude children 27 including emperor taizong notable events founded the tang dynasty expanded the empire implemented policies promoting economic prosperity and cultural innovation step 4 print an answer in natural language print f emperor gaozu born in the year first emperor tang dynasty info born and died in the year first emperor tang dynasty info died was the first emperor of the tang dynasty his reign from first emperor tang dynasty info reign is known as the wude era his given name was li yuan he had 27 children including his successor emperor taizong during his reign he not only founded the tang dynasty but also expanded the empire and implemented policies that promoted economic prosperity and cultural innovation result 2 emperor gaozu born in the year 566 and died in the year 635 was the first emperor of the tang dynasty his reign from 618 626 is known as the wude era his given name was li yuan he had 27 children including his successor emperor taizong during his reign he not only founded the tang dynasty but also expanded the empire and implemented policies that promoted economic prosperity and cultural innovation details contents supported environments supported environments config api endpoint config api endpoint quick start with colab quick start with colab installation installation define langpy agent define langpy agent code generation generated code to answer any question run generated code run the generated code code completion code completion csv file processing csv file processing chat with python by code generation chat with python through code generation hirarchical instructng by code completion hirarchical instruction following through code completion contact contact supported environments option 1 colab recommended you are already good to go example colab notebook https colab research google com drive 132vl1t3mjlq8ekzmnycgejvbtcwodcdm usp sharing might be helpfu option 2 jupyter notebook 6 x deployment create a conda enviroment and launch jupyter notebook documents conda jupyter md langcode does not work in vscode for now customizing nlep generation api by default we provide an api server that generates nleps without saving users api keys we will continue improving the prompt strategy and the quality of generated nelps however we provide an option for customized servers please refer to langcode server https github com luohongyin langcode tree main server for the server setup we provide complete code and minimal prompt to generate high quality nlep for different tasks use the following command to switch to the preferred server python clp config api endpoint then input your server url in the following format http hostname port items 0 our server timeouts at 30s since the gpt 4 api is not efficient enough but the custom server we provide does not involve this limitation quick start with colab installation install the langcode package in the ipython notebook by runing the following command in a code cell bash pip install langpy notebook as an early release we first deal with the python programming language and release the langpy class we also look forward to langjulia langr langgo etc define langpy agent import the library and define a langpy agent the langpy auto get langpy function automatically identify if the backend is jupyter 6 or colab python from langpy auto import get langpy clp an instance of colablangpy clp get langpy api key your api key platform gpt model gpt 4 the platforms we support are gpt openai and palm google and you can select models on these platforms for example platform palm model text bison 001 uses google s text bison 001 generated code to answer any question add the following code to a code cell and run it to generate a program that answers your question for example the game of 24 task python mode control history reading to enable multi turn programming hist mode all default read all cell history hist mode single only read the current instruction hist mode interger read integer history cells hist mode 0 is equivalent to hist mode single numbers 4 9 10 13 target 24 clp generate f what is the correct mathematical expression consisting of the numbers numbers in any order along with the parentheses and arithmetic operations that results in target hist mode single the generated program would be placed in in a scratch cell colab or the next code cell jupyter notebook 6 run the generated code run the generated code you ll get the answer in natural language img src images game24 png we did not use any game of 24 example to prompt gpt 4 but it still gets the correct answer 4 10 9 13 with only sampling one output this is more efficient than tree of thoughts code completion the langpy agent can also complete code that is not finished yet img src images cpl example png run the code in the upper cell will complete the code in the lower cell getting the following program python step 1 import necessary built in libraries import torch import torch nn as nn step 2 define necessary functions that generally solve this type of problem class mlp nn module def init self super mlp self init self layers nn sequential nn linear 10 5 nn relu nn linear 5 2 def forward self x x self layers x return x step 3 instantiate the defined class mlp mlp step 4 print an answer in natural language print f a multi layer perceptron mlp has been defined it consists of an input layer a hidden layer with 5 neurons activated by relu function and an output layer with 2 neurons the input layer has 10 neurons representing the dimension of the input vectors note that both the code and comment in the generated cell can be edited for another completion enabling more flexible and direct instructions csv file processing as an early release we provide basic csv file processing ability running the following code python clp preview data file name example data csv data type csv a new code cell will be inserted python first three rows of the input file placename placedcid xdate xvalue differencerelativetobasedate2006 max temperature rcp45 ydate yvalue percent person withcoronaryheartdisease xpopulation count person ypopulation count person autauga county al geoid 01001 2050 06 1 15811100000001 2020 6 3 59095 56145 baldwin county al geoid 01003 2050 06 1 073211 2020 5 9 239294 229287 file name example data csv input file open file name running this code snipet you can further analyze the data in the csv file using langcode for example asking langcode to generate code for visualization python hist mode all allows langcode to read previous cells clp generate visualize the correlation of xdata and ydata of alabama in the file hist mode all the generated code and execution results are shown below img src images csv example png chat with python through code generation we currently offer three parameters for langpy generate instruction mode all http optional instruction str the input question request in natural language mode str or int wether or note read previous code and markdown cells mode all read all previous cells mode single just read the instruction in the current cell mode int the number of latest history cells to read http wether allow the generated code to include http requests http optional default langcode decides itself about sending http requests or not http forbid langcode will try to avoid generated http requesting code http force langcode will try to generated code that sends http requests to solve the given question hirarchical instruction following through code completion in the previous example we have shown img src images cpl example png langcode implemented a multi layer perceptron mlp with pytorch using the following code python step 1 import necessary built in libraries import torch import torch nn as nn step 2 define necessary functions that generally solve this type of problem class mlp nn module def init self super mlp self init self layers nn sequential nn linear 10 5 nn relu nn linear 5 2 def forward self x x self layers x return x step 3 instantiate the defined class mlp mlp step 4 print an answer in natural language print f a multi layer perceptron mlp has been defined it consists of an input layer a hidden layer with 5 neurons activated by relu function and an output layer with 2 neurons the input layer has 10 neurons representing the dimension of the input vectors unlike chatgpt that requires user to write a good instruction at once langcode allows adding multiple instructions in different places for one task editting the code for completion can guide model to generate different code for example python step 1 import necessary built in libraries import torch import torch nn as nn step 2 define a multi layer perceptron that has three linear layers and uses tanh activation function the code completion result would be python step 1 import necessary built in libraries import torch import torch nn as nn step 2 define a multi layer perceptron that has three linear layers and uses tanh activation function python program class multilayerperceptron nn module def init self input size hidden size output size super multilayerperceptron self init self layer1 nn linear input size hidden size self layer2 nn linear hidden size hidden size self layer3 nn linear hidden size output size self tanh nn tanh def forward self x x self tanh self layer1 x x self tanh self layer2 x x self layer3 x return x step 3 print an answer in natural language print a multi layer perceptron is a type of artificial neural network it has an input layer one or more hidden layers and an output layer in each layer the inputs are multiplied by weights summed and passed through an activation function in this implementation the activation function is the hyperbolic tangent function tanh besides the comments you can also control the direction of the generated code by modifying the imported libraries for example changing import torch to import tensorflow keras as keras your are encourged to explore more possibilities contact if there is any question feel free to post an issue or contact hongyin luo at hyluo at mit dot edu | colab gpt4 jupyter-notebook llm nlep reasoning | ai |
ITKST42-network-data-classifier | itkst42 network data classifier a network data classifier for unsw nb15 data set this is an university course work for itkst42 information security technology unsw nb15 is a network traffic data set with different categories for normal activities and synthetic attack behaviours find the data set here https www unsw adfa edu au australian centre for cyber security cybersecurity adfa nb15 datasets this project includes a classification model for unsw nb15 data set which i developed using a random forest and feed forward neural network the system uses the random forest that classifies data to normal or malicious data this information is then used to train a neural network to further classify the attack data to different attack categories the results for attack detection were very good with approx 0 88 precision for attacks and nearly 1 0 precision for normal data samples attack categorization had problems in differentiating between attack classes and could mostly classify the attacks to two different classes however it could accurately classify normal network data see the report for more details requirements tested with python 3 6 unsw nb15 https www unsw adfa edu au australian centre for cyber security cybersecurity adfa nb15 datasets dataset dependencies keras earlier than keras 2 theano tensorflow numpy pandas h5py matplotlib at least 8 gb ram and plenty of hdd space how to run run the scripts in this order h2 preprocess py h2 create data sets py h2 fit neural py | server |
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bootstrap | jasny bootstrap http jasny github io bootstrap jasny bootstrap is an extension of the famous bootstrap http getbootstrap com adding the following components button labels http jasny github io bootstrap components buttons labels off canvas navmenu http jasny github io bootstrap components navmenu fixed alerts http jasny github io bootstrap components alerts fixed row link http jasny github io bootstrap components rowlink file input widget http jasny github io bootstrap components fileinput to get started check out http jasny github io bootstrap quick start four quick start options are available download the latest release https github com jasny bootstrap releases download v4 0 0 jasny bootstrap 4 0 0 dist zip clone the repo git clone git github com jasny bootstrap git use cdnjs http cdnjs com libraries jasny bootstrap install with meteor https meteor com meteor add jasny bootstrap read the getting started page http jasny github io bootstrap getting started for information on the framework contents templates and examples and more what s included within the download you ll find the following directories and files logically grouping common assets and providing both compiled and minified variations you ll see something like this jasny bootstrap css jasny bootstrap css jasny bootstrap min css js jasny bootstrap js jasny bootstrap min js we provide compiled css and js jasny bootstrap as well as compiled and minified css and js jasny bootstrap min jasny bootstrap should be loaded after vanilla bootstrap bugs and feature requests have a bug or a feature request please open a new issue https github com jasny bootstrap issues before opening any issue please search for existing issues and read the issue guidelines https github com necolas issue guidelines written by nicolas gallagher https github com necolas you may use this jsfiddle http jsfiddle net jasny k9k5d as a template for your bug reports documentation jasny bootstrap s documentation included in this repo in the root directory is built with jekyll http jekyllrb com and publicly hosted on github pages at http jasny github io bootstrap the docs may also be run locally running documentation locally 1 if necessary install jekyll http jekyllrb com docs installation requires v1 x 2 from the root bootstrap directory run jekyll serve in the command line windows users run chcp 65001 first to change the command prompt s character encoding code page http en wikipedia org wiki windows code page to utf 8 so jekyll runs without errors 3 open http localhost 9001 in your browser and voil learn more about using jekyll by reading its documentation http jekyllrb com docs home documentation for previous releases documentation for v2 3 1 has been made available for the time being at http jasny github io bootstrap 2 3 1 while folks transition to bootstrap 3 previous releases https github com jasny bootstrap releases and their documentation are also available for download compiling css and javascript bootstrap uses grunt http gruntjs com with convenient methods for working with the framework it s how we compile our code run tests and more to use it install the required dependencies as directed and then run some grunt commands install grunt from the command line 1 install grunt cli globally with npm install g grunt cli 2 navigate to the root bootstrap directory then run npm install npm will look at package json package json and automatically install the necessary local dependencies listed there when completed you ll be able to run the various grunt commands provided from the command line unfamiliar with npm don t have node installed that s a okay npm stands for node packaged modules http npmjs org and is a way to manage development dependencies through node js download and install node js http nodejs org download before proceeding available grunt commands build grunt run grunt to run tests locally and compile the css and javascript into dist uses recess http twitter github io recess and uglifyjs http lisperator net uglifyjs only compile css and javascript grunt dist grunt dist creates the dist directory with compiled files uses recess http twitter github io recess and uglifyjs http lisperator net uglifyjs tests grunt test runs jshint http jshint com and qunit http qunitjs com tests headlessly in phantomjs http phantomjs org used for ci watch grunt watch this is a convenience method for watching just less files and automatically building them whenever you save troubleshooting dependencies should you encounter problems with installing dependencies or running grunt commands uninstall all previous dependency versions global and local then rerun npm install contributing please read through our contributing guidelines https github com jasny bootstrap blob master contributing md included are directions for opening issues coding standards and notes on development more over if your pull request contains javascript patches or features you must include relevant unit tests all html and css should conform to the code guide http github com mdo code guide maintained by mark otto http github com mdo editor preferences are available in the editor config editorconfig for easy use in common text editors read more and download plugins at http editorconfig org community keep track of development and community news follow arnolddaniels on twitter http twitter com arnolddaniels have a question that s not a feature request or bug report ask on stackoverflow http stackoverflow com versioning for transparency into our release cycle and in striving to maintain backward compatibility jasny bootstrap is maintained under the semantic versioning guidelines sometimes we screw up but we ll adhere to these rules whenever possible releases will be numbered with the following format major minor patch and constructed with the following guidelines breaking backward compatibility bumps the major while resetting minor and patch new additions without breaking backward compatibility bumps the minor while resetting the patch bug fixes and misc changes bumps only the patch for more information on semver please visit http semver org the major version will follow bootstrap s major version this means backward compatibility will only be broken if bootstrap does so authors arnold daniels twitter com arnolddaniels https twitter com arnolddaniels github com jasny https github com jasny jasny net http jasny net fiestainfo com https www fiestainfo com copyright and license copyright 2013 jasny bv under the apache 2 0 license license | bootstrap hacktoberfest | front_end |
go-freeling | go freeling natural language processing in go this is a partial port of freeling 3 1 http nlp lsi upc edu freeling license is gpl to respect the license model of freeling this is the list of features already implemented text tokenization sentence splitting morphological analysis suffix treatment retokenization of clitic pronouns flexible multiword recognition contraction splitting probabilistic prediction of unknown word categories named entity detection pos tagging chart based shallow parsing named entity classification with an external library mitie https github com mit nlp mitie rule based dependency parsing how to use it pre go build gofreeling go gofreeling pre http server listens on default port 9999 port can be changed in conf gofreeling toml file to process a page http get http localhost 9999 analyzer url copy here an url or use as api endpoint pre http post http localhost 9999 analyzer api content text you want to analyze pre response is a self explaining json usage as package example pre package main import lib models fmt encoding json func main document new documententity analyzer newanalyzer document content hello world output analyzer analyzetext document js output tojson b err json marshal js if err nil panic err fmt println string b pre todo clean code add comments add tests implement wordnet based sense annotation and disambiguation linguistic data to run the server can be download here english only https www dropbox com s fwwvfxp2s7dydet data zip wordnet database to add annotation place it inside data folder http wordnetcode princeton edu 3 0 wndb 3 0 tar gz | ai |
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wasm-postflop | wasm postflop important as of october 2023 i have started developing a poker solver as a business and have decided to suspend development of this open source project see this issue for more information this issue https github com b inary postflop solver issues 46 wasm postflop is a free open source gto solver for texas hold em poker that works on web browsers website https wasm postflop pages dev related repositories desktop application https github com b inary desktop postflop solver engine https github com b inary postflop solver image image png why wasm postflop the gto game theory optimal solver has become an indispensable tool for poker research however unfortunately there is a high barrier to trying out the gto solver the need to purchase expensive commercial software this project aims to overcome this situation by developing a free open source gto solver please note that this project does not intend to replace commercial gto solvers they are great software and it is not easy to create a new one that can compete with them this project intends to make the gto solver more easily accessible to a broader audience features free to use the most important feature anyone can try out the solver for free open source the implementation of the gto solver is complex and is not easy to write down accurately by making the program open source we make it possible for anyone to examine the implementation works on web browsers this feature brings several advantages first it allows for the solver to be easily accessible second it naturally makes the solver a cross platform application finally it sandboxes the solver execution so users do not have to worry about security sufficiently fast slow solvers are not wanted by using webassembly we have reduced the performance penalty of being a web application we also supported multithreading and used a state of the art algorithm discounted cfr discounted cfr https arxiv org abs 1809 04040 comparison we tested wasm postflop desktop postflop v0 2 1 piosolver free 2 0 8 gto v1 5 0 and texassolver v0 2 0 with the 3betpotfast preset of piosolver all in threshold is replaced with 100 in piosolver desktop postflop https github com b inary desktop postflop piosolver free https www piosolver com gto https www gtoplus com texassolver https github com bupticybee texassolver execution time and memory usage we experimented on a windows 10 pc with a ryzen 7 3700x cpu 16 threads piosolver free is limited to 6 threads wasm postflop was executed on google chrome 108 the table below shows that desktop postflop a native port of wasm postflop was the clear winner in terms of execution time wasm postflop was about 2x slower than desktop postflop and pio cfr and gto were between them in terms of memory usage the 16 bit integer mode of wasm postflop and desktop postflop the original pio algorithm and gto achieved almost the same efficiency texassolver another free and open source solver suffered from slow execution times and poor memory efficiency we consider that 2x time overhead compared to desktop postflop is acceptable for casual use however if you do not think so please consider trying desktop postflop which is also free and open source 1 32 bit fp 2 16 bit integer 3 pio cfr 4 original pio algorithm 6 threads solver wasm br 1 wasm br 2 desktop br 1 desktop br 2 pio br 3 pio br 4 gto texas time target 0 5 33 4 s 42 2 s 20 0 s 19 8 s 22 9 s 30 3 s 22 0 s 103 5 s time target 0 3 41 2 s 52 3 s 24 9 s 24 7 s 28 2 s 42 4 s 31 4 s 149 0 s time target 0 1 71 9 s 92 6 s 44 4 s 44 0 s 60 1 s 108 4 s 67 7 s 285 9 s memory usage 1 25 gb 660 mb 1 27 gb 679 mb 1 41 gb 634 mb 705 mb 2 84 gb 16 threads solver wasm br 1 wasm br 2 desktop br 1 desktop br 2 gto texas time target 0 5 21 1 s 26 1 s 12 6 s 12 2 s 13 9 s 67 1 s time target 0 3 26 0 s 32 3 s 15 6 s 15 1 s 19 7 s 95 9 s time target 0 1 45 5 s 57 2 s 27 9 s 27 0 s 41 7 s 182 6 s memory usage 1 25 gb 660 mb 1 27 gb 679 mb 705 mb 2 84 gb results a comparison of the obtained results is as follows target exploitability is set to 0 1 we can see that wasm postflop piosolver and gto return nearly identical results wasm postflop piosolver gto texassolver wasm postflop results comparison wasm png piosolver results comparison pio png gto results comparison gtoplus png texassolver results comparison texas png specific values of bet equity and ev are as follows texassolver returned a different solution which is presumably incorrect either way we cannot verify the correctness because we cannot see the overall ev in texassolver solver wasm pio gto texas bet 55 2 55 19 55 2 63 0 equity 55 3 55 347 55 35 ev 105 1 105 11 105 115 build sh prerequisites rustup install nightly rustup nightly component add rust src rustup target add wasm32 unknown unknown cargo install wasm pack npm install build npm run wasm npm run build serve npm run serve lint format npm run lint npm run format license copyright c 2022 wataru inariba this program is free software you can redistribute it and or modify it under the terms of the gnu affero general public license as published by the free software foundation either version 3 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu affero general public license for more details you should have received a copy of the gnu affero general public license along with this program if not see https www gnu org licenses | poker texas-holdem webassembly game-theory gto solver texas-holdem-poker | front_end |
rtems-docker | rtems docker a collection of docker files for the rtems rtos tools and bsp builds simiar to the rtems repositories the branches support different versions of rtems the default or main branch supports the master branches of rtems tools and kernel source the rtems4 11 branch supports rtems 4 11 the rtems5 branch supports the rtems 5 branch of the tools and rtems source as new releases of rtems are made the main will advance and a branch will be made for the release branch rtems6 for example the images depend on each other rtems6 essential is the base image and tools rtems6 arch tools provides the rtems toolchain for that architecture rtems6 arch bsp provides a specific bsp build | os |
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nk-vector | npm version http img shields io npm v nk vector svg style flat https npmjs org package nk vector view this project on npm npm downloads https img shields io npm dm nk vector svg https www npmjs com package nk vector view this project on npm gi i thi u v nk vector l do ra i v c b n nk vec l m t neural network embeddings c t ng t word2vec v c nhi m v gi ng t t c c c m h nh nh ng t hi n t i nh ng n c c u t o n gi n h n r t nhi u th ng qua th vi n nk vector b n c th s d ng m h nh nk vec build b vector theo d li u ri ng c a b n m t c ch n gi n nh t ngo i ra nk vector c n cung c p cho b n m t s t nh n ng thu t to n h u ch d ng gi i quy t c c b i to n nlp v t n g i t i t o ra n trong l c nghi n c u d n l n t i ph ng tin h c c a tr ng thcs thpt nguy n khuy n n ng n n nk l ch vi t t t c a t n tr ng y d u n t i mu n l u l i v n x ng ng c th c c t nh n ng hi n t i t o one hot vector t o windows words cho ph p ng i d ng build b vector theo b d li u ri ng c a h t m t t ng t fast knn knn k d tree build vetor cho c u cho o n v n v n b n t ch t ti ng vi t v ti ng anh l m s ch c u cho ti ng vi t v ti ng anh x a t l p l i h m v c c tham s h m tham s v d l u create one hot file url url save e project data txt e project onehot json trong n y s m c nh l c stopword ti ng anh v c c k t c bi t tr d u create window words file url window size url save e project data txt 5 e project window txt trong n y s m c nh l c stopword ti ng anh v c c k t c bi t tr d u window size ph i l s l train size output url data one hot url data window words url save 512 e project onehot json e project window txt e project data vector json size output l s chi u vector u ra v n ph i nh h n s chi u u onehot vector u v o build vec sentences doc url vecs of words url save xin ch o t t c m i ng i e project data vector json n u url save c d i b ng 0 th m c nh tr v b vector m kh ng l u n u l u th h y nh d ng json vd e project data sentence vector json search word similarity target url vecs of word size result king e project data vector json 15 size result t ng ng v i s l ng t c t ng t t cao nh t n th p c tr v knn target type distance data k 7 8 eculid points 4 xem v d s d ng h m knn b n d i vn segmentation tag document ch o m ng b n n v i t i h y ch c ch n r ng version node c a b n l phi n b n b t y t 10 16 0 tr l n clear sentence vn document ch o m ng b n n v i t i t i y c u ti ng vi t c a b n s c l c t stopword ti ng vi t cho n c c k t c bi t clear sentence en document ch o m ng b n n v i t i t i y c u ti ng anh c a b n s c l c t stopword ti ng anh cho n c c k t c bi t remove duplicate words document ch o ch o m ng b n n v i t i t i y s x a c c t b tr ng l p trong c u v n d ng cho c ti ng anh v vi t fast build chatbot text th i ti t h m nay th n o v y t i y bot s tr v m t trong c c nh n chemistry general asking math good bye hello introduction thanks ask weather unknown sentiment vn text h m nay tr i th t m m t i y s tr v m t trong c c nh n bu n vui b c b nh th ng ch a x c nh c nh v d th k t qu tr v l m t string bu n fix telex text anh thisch awn busn char cas t i y s tr v k t qu l chu i c telex nh v d l anh th ch n b n ch c english or vietnamese text hello h e you t i y s tr v k t qu l m t object g m c c tr ng your text label fix text nh v d l your text hello h e you label english fix text hello how are you c i t 1 install node js http nodejs org 2 run npm i nk vector s d ng javascript let nkv require nk vector v d s d ng h m knn javascript let nkv require nk vector let points 1 2 3 4 5 6 7 8 let nearest nkv knn 7 8 eculid points 4 console log nearest result 7 8 0 5 6 8 3 4 32 1 2 72 gi i th ch k t qu m ng tr v vector trong t p d li u kho ng c ch t vector u v o t i vector n y v d s d ng h m build vec sentences javascript let nkv require nk vector let sentence nkv vn segmentation tag nkv clear sentence vn c n b ng ph ng tr nh h a h c let full sentence for let word in sentence full sentence sentence word replace if full sentence length 0 console log full sentence console log nkv build vec sentences full sentence trim e name project data vec json result c n b ng ph ng tr nh h a h c 0 002338010428122218 0 0 0 00111962700489077 0 0009866701202071657 0 00111962700489077 0 0 00111962700489077 0 0 0 0009866701202071657 0 0 0010865777210490053 0 0 0010865777210490053 0 0 0 0 0 0 0009866701202071657 0 0 0 0 0 0 0 0010865777210490053 0 0 0010865777210490053 0 v d s d ng h m clear sentence vn javascript let nkv require nk vector let clear sentence nkv clear sentence vn ch o m ng c c b n l n tr n tr i y l tr n tr i console log clear sentence result ch o m ng tr i tr i m u th ng b o trong terminal span style color red m u span l i kh ng th ch y ti p c br span style color yellow m u v ng span y ch l th ng b o b nh th ng v n ch y ti p c m t s l i b n c th g p l i kh ng t m th y file stop word c a c b n vi t v anh n u g p l i kh ng t m th y file stop word th h y t m v o d ng l i theo ng d n trong terminal v s a l i th nh br 1 path join dirname src stop word txt cho function clear sentence en 2 path join dirname src stop word vn txt cho function clear sentence vn br ho c m t ng d n t p ch nh x c theo c ch c a b n l i n y c th ng b o cho ng i d ng v i m c m u l i kh ng build c vector cho c u l i n y x y ra khi c c t v ng k t c u t o n n c u n m trong b l c stopword v k t c bi t lo i b trong qu tr nh training n n d n n kh ng c vector c a c c t v ng n y d n n c u n p v o s b r ng v kh ng build c c u br l i n y c th ng b o cho ng i d ng v i m c m u v ng l i c m n c m n m i ng i s d ng nk vector t i s c p nh t th ng xuy n c c thu t to n m i br c m n vunb ph t tri n g i vntk th n th nh s n ph m p d ng code search https code search vni herokuapp com br tr c khi gpt 3 publish key cho m i ng i th m nh v n c th t o ra m t n i search code python theo ng ngh a na n c c example c a gpt 3 nh | sentence-embeddings sentiment-classification knn knn-classification embeddings language-model languages-for-javascript detect-languages nlp-library nlp | ai |
intro-to-nltk | nlp with python code and notebooks for the natural language processing with python course the word crab is an arbitrary symbol fixtures crab jpg https flic kr p o6fhsv using this repository the repository has four parts 1 ipython notebooks for code examples and presentation 2 tools for data ingestion and management 3 fixtures with corpora for the examples 4 exercise code for the nlp tasks discussed about this repository contains code and examples for the nlp with python district data labs course the code is licensed under an mit open source software license the content is held under the copyright of the owner but please contact us if you d like to use it the slides can be found here http www slideshare net benjaminbengfort natural language processing with nltk http www slideshare net benjaminbengfort natural language processing with nltk attribution the image used in this readme hemigrapsus nudus crab on neskowin beach https flic kr p o6fhsv by thomas shahan https www flickr com photos 49580580 n02 is used under a cc by 2 0 https creativecommons org licenses by 2 0 license | ai |
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Hospital-Management-System--Java---MS-Access- | this project is written by me using java ide netbeans 7 3 1 and ms access 2010 as back end for thesis submission of the engineering students in java this project is very helpful for the beginners who wanna learn database programming in java main features 1 nurse wardboy entry 2 doctors entry 3 patient 3 1 registration 3 2 services 3 3 admit to room or ward 3 4 discharge from room or ward 3 5 billing 4 automatic allocation and deallocation of room or beds from ward login information username admin password 12345 in order to run the project you must have to first connect your database to the datasources odbc instructions 1 open control panel 2 click administrative tools 3 open datasources odbc 4 click the add button the select microsoft access driver mdb accdb the click finish 5 on the dialog window fill the data source name field with the name of the database and that is hms db 6 click the select button to browse the database and click ok after that you re done | server |
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blocksec-ctfs | blocksec ctfs a curated list of blockchain security wargames challenges and capture the flag ctf competitions and solution writeups wargames and writeups ethernaut https ethernaut openzeppelin com ethernaut ctf solution https github com 0xichigo ethernaut by 0xichigo ethernaut ctf solutions using ape and vyper https github com 0xjcn ethernaut ctf by 0xjcn ethernaut ctf series all things security https blog dixitaditya com series ethernaut solutions in solidity and foundry by aditya dixit ethernaut solutions by aniket21mathur https github com aniket21mathur ethernaut solutions challenges 0 25 ethernaut solutions by cmichel https cmichel io ethernaut solutions challenges 0 21 ethernaut writeups by macmod https github com macmod ethernaut writeups challenges 0 9 ethernaut solutions by tsauvajon https github com tsauvajon ethernaut challenges 1 11 ethernaut solutions by tinchoabbate https www notonlyowner com the ethernaut ctf writeup by arseny reutov https blog positive com the ethernaut ctf writeup dc3021824abc challenges 0 6 ethernaut lvl 0 walkthrough abis web3 and how to abuse them https medium com hackernoon ethernaut lvl 0 walkthrough abis web3 and how to abuse them d92a8842d71b by nicole zhu ethernaut lvl 1 walkthrough how to abuse the fallback function https medium com hackernoon ethernaut lvl 1 walkthrough how to abuse the fallback function 118057b68b56 by nicole zhu ethernaut lvl 2 fallout walkthrough how simple developer errors become big mistakes https medium com nicolezhu ethernaut lvl 2 walkthrough how simple developer errors become big mistakes b705ff00a62f by nicole zhu ethernaut lvl 3 coin flip walkthrough how to abuse psuedo randomness in smart contracts https medium com nicolezhu ethernaut lvl 3 walkthrough how to abuse psuedo randomness in smart contracts 4cc06bb82570 by nicole zhu ethernaut lvl 4 telephone walkthrough how to abuse tx origin msg sender https medium com nicolezhu ethernaut lvl 4 walkthrough how to abuse tx origin msg sender ef37d6751c8 by nicole zhu ethernaut lvl 5 token walkthrough how to abuse arithmetic underflows and overflows https medium com coinmonks ethernaut lvl 5 walkthrough how to abuse arithmetic underflows and overflows 2c614fa86b74 by nicole zhu ethernaut lvl 6 delegation walkthrough how to abuse the delicate delegatecall https medium com coinmonks ethernaut lvl 6 walkthrough how to abuse the delicate delegatecall 466b26c429e4 by nicole zhu ethernaut lvl 7 force walkthrough how to selfdestruct and create an ether blackhole https medium com coinmonks ethernaut lvl 7 walkthrough how to selfdestruct and create an ether blackhole eb5bb72d2c57 by nicole zhu ethernaut lvl 8 vault walkthrough how to read private variables in contract storage with truffle https medium com coinmonks how to read private variables in contract storage with truffle ethernaut lvl 8 walkthrough b2382741da9f by nicole zhu ethernaut lvl 9 king walkthrough how bad contracts can abuse withdrawals https medium com coinmonks ethernaut lvl 9 king walkthrough how bad contracts can abuse withdrawals db12754f359b by nicole zhu ethernaut lvl 10 re entrancy walkthrough how to abuse execution ordering and reproduce the dao hack https medium com coinmonks ethernaut lvl 10 re entrancy walkthrough how to abuse execution ordering and reproduce the dao 7ec88b912c14 by nicole zhu ethernaut lvl 11 elevator walkthrough how to abuse solidity interfaces and function state modifiers https medium com coinmonks ethernaut lvl 11 elevator walkthrough how to abuse solidity interfaces and function state 41005470121d by nicole zhu ethernaut lvl 12 privacy walkthrough how ethereum optimizes storage to save space and be less gassy https medium com coinmonks ethernaut lvl 12 privacy walkthrough how ethereum optimizes storage to save space and be less c9b01ec6adb6 by nicole zhu ethernaut lvl 13 gatekeeper 1 walkthrough how to calculate smart contract gas consumption and byte masking https medium com coinmonks ethernaut lvl 13 gatekeeper 1 walkthrough how to calculate smart contract gas consumption and eb4b042d3009 by nicole zhu ethernaut lvl 14 gatekeeper 2 walkthrough how contracts initialize and how to do bitwise operations https medium com coinmonks ethernaut lvl 14 gatekeeper 2 walkthrough how contracts initialize and how to do bitwise ddac8ad4f0fd by nicole zhu ethernaut lvl 15 naught coin walkthrough how to abuse erc20 tokens and bad icos https medium com coinmonks ethernaut lvl 15 naught coin walkthrough how to abuse erc20 tokens and bad icos 6668b856a176 by nicole zhu ethernaut lvl 16 preservation walkthrough how to inject malicious contracts with delegatecall https medium com coinmonks ethernaut lvl 16 preservation walkthrough how to inject malicious contracts with delegatecall 81e071f98a12 by nicole zhu ethernaut lvl 17 locked walkthrough how to properly use and abuse structs in solidity https medium com coinmonks ethernaut lvl 17 locked walkthrough how to properly use structs in solidity f9900c8843e2 by nicole zhu ethernaut lvl 18 recovery walkthrough how to retrieve lost contract addresses in 2 ways https medium com coinmonks ethernaut lvl 18 recovery walkthrough how to retrieve lost contract addresses in 2 ways aba54ab167d3 by nicole zhu ethernaut lvl 19 magicnumber walkthrough how to deploy contracts using raw assembly opcodes https medium com coinmonks ethernaut lvl 19 magicnumber walkthrough how to deploy contracts using raw assembly opcodes c50edb0f71a2 by nicole zhu ethernaut challenges solutions by asamartino https github com asamartino ethernautchallenges challenges 0 21 ethernaut challenges solutions by simon https github com styj ethernaut solutions challenges 0 21 ethernaut lvl 22 dex writeups https medium com this post ethernaut 22 dex writeups 55d4bfa8a7fa by thispost ethernaut lvl 23 dex two writeups https dev to nvn ethernaut hacks level 23 dex two 4424 by naveen ethernaut lvl 24 puzzle wallet writeups https medium com appsbylamby ethernaut 24 puzzle wallet walkthrough mastering the proxy pattern cc830dc364ce by lamby ethernaut lvl 25 motorbike writeups https medium com appsbylamby ethernaut 25 motorbikewalkthrough 3e1feeee6a4c by lamby ethernaut lvl 26 doubleentrypoint writeups https www youtube com watch v agnc 917yoy by d squared ethernaut lvl 27 good samaritan walkthrough custom errors in solidity https medium com mcverick ethernaut lvl 27 good samaritan walkthrough custom errors in solidity 17c7e20fb58a by chirag agarwal ethernaut ctf level 0 by d squared https www youtube com watch v magavbrwvbg ethernaut ctf level 1 by d squared https www youtube com watch v i 8ccdajpdq ethernaut ctf level 2 by d squared https www youtube com watch v vgbxxdhowvu ethernaut ctf level 3 by d squared https www youtube com watch v roun1y6lda ethernaut ctf level 4 by d squared https www youtube com watch v i12eo0nkoew ethernaut ctf level 5 by d squared https www youtube com watch v ylkn2r o y ethernaut ctf level 6 by d squared https www youtube com watch v gultdn 0 y ethernaut brownie solutions https github com ptisserand ethernaut brownie by ptisserand ethernaut truffle solutions https github com tinchoabbate ethernaut ctf by tincho ethernaut hardhat solutions https github com mrtoph ethernaut by mrtoph ethernaut x foundry solutions https github com ciaranmcveigh5 ethernaut x foundry by ciaranmcveigh5 ethernaut ctf foundry edition https github com stermi foundry ethernaut by stermi ethernaut x foundry 0x0 hello ethernaut https blog 0xeval me ethernaut x foundry 0x0 hello ethernaut by 0xeval damn vulnerable defi https www damnvulnerabledefi xyz damn vulnerable defi foundry edition https github com nicolasgarcia214 damn vulnerable defi foundry by nicolasgarcia214 damn vulnerable defi foundry edition challenge solutions 1 to 12 on medium com https stermi medium com damn vulnerable defi challenge 1 unstoppable 92bacdefafcc by stermi damnvulneradefi solutions using ape and vyper https github com 0xjcn damn vulnerable defi v3 ctf by 0xjcn damnvulnerabledefi abi smuggling challenge walkthrough plus infographic https medium com mattaereal damnvulnerabledefi abi smuggling challenge walkthrough plus infographic 7098855d49a by at as ereal e n damn vulnerable defi solutions by cmichel https cmichel io damn vulnerable de fi solutions write ups and lessons learned from damn vulnerable defi by damian rusinek https drdr zz medium com write ups and lessons learned from damn vulnerable defi caa95d2678ec damn vulnerable defi setup and challenge 1 walkthrough by iphelix https iphelix medium com damn vulnerable defi setup and challenge 1 walkthrough 1ea16ea09709 damn vulnerable defi challenge 2 walkthrough by iphelix https iphelix medium com damn vulnerable defi challenge 2 walkthrough c2a7eac3374d damn vulnerable defi challenge 3 walkthrough by iphelix https iphelix medium com damn vulnerable defi challenge 3 walkthrough fe8e9c8e36f3 damn vulnerable defi challenge 4 walkthrough by iphelix https iphelix medium com damn vulnerable defi challenge 4 walkthrough 881f7f12f118 damn vulnerable defi challenge 5 walkthrough by iphelix https iphelix medium com damn vulnerable defi challenge 5 walkthrough 2dd516735ad6 damn vulnerable defi challenge 6 walkthrough by iphelix https iphelix medium com damn vulnerable defi challenge 6 walkthrough 63c7584e5240 damn vulnerable defi challenge 7 walkthrough by iphelix https iphelix medium com damn vulnerable defi challenge 7 walkthrough ee9fac3fdcd4 damn vulnerable defi challenge 8 walkthrough by iphelix https iphelix medium com damn vulnerable defi challenge 8 walkthrough 4e0d752d21ab damn vulnerable defi video solutions by smart contract programmer https www youtube com playlist list plo5vpqh6owdxkpthrch6u0imgdd3phlxi damn vulnerable defi v2 part 1 setup and challenges 1 to 4 https ventral digital posts 2021 11 13 damn vulnerable defi v2 part 1 setup and challenges 1 to 4 by patrickd damn vulnerable defi v2 5 the rewarder https ventral digital posts 2021 12 1 damn vulnerable defi v2 5 the rewarder by patrickd damn vulnerable defi v2 6 selfie https ventral digital posts 2022 2 21 damn vulnerable defi v2 6 selfie by patrickd damn vulnerable defi v2 7 compromised https ventral digital posts 2022 2 22 damn vulnerable defi v2 7 compromised by patrickd damn vulnerable defi v2 8 puppet https ventral digital posts 2022 2 23 damn vulnerable defi v2 8 puppet by patrickd damn vulnerable defi v2 9 puppet v2 https ventral digital posts 2022 2 28 damn vulnerable defi v2 9 puppet v2 by patrickd damn vulnerable defi v2 10 free rider https ventral digital posts 2022 3 2 damn vulnerable defi v2 10 free rider by patrickd damn vulnerable defi v2 11 backdoor https ventral digital posts 2022 6 28 damn vulnerable defi v2 11 backdoor by patrickd damn vulnerable defi v2 12 climber https ventral digital posts 2022 6 29 damn vulnerable defi v2 12 climber by patrickd damn vulnerable defi v2 13 junior miners https ventral digital posts 2022 7 2 damn vulnerable defi v2 13 junior miners by patrickd damn vulnerable defi v2 15 abi smuggling https www pranavgarg xyz posts damn vuln defi abi smuggling by pranav garg damn vulnerable defi v3 solutions https www youtube com bluealdereth by blue addler numen ctf 2023 https github com numencyber numenctf 2023 numen cyber ctf writeups https github com minaminao ctf blockchain blob main src numenctf readme md 2023 numen ctf writeup goatfinance https defihacklabs substack com p 2023 numen ctf writeup goatfinance by sunweb3sec 2023 numen ctf writeup asslot counter exist lenderpool wallet https defihacklabs substack com p 2023 numen ctf writeup asslot counter by sunweb3sec 2023 numen ctf writeup hexp https defihacklabs substack com p 2023 numen ctf writeup hexp by sunweb3sec security challenges factory for starknet https starknet challenges vercel app boutiful https blog fe lang org posts bountiful round 2 mevsec ctf https ctf mevsec com mr steal yo crypto https mrstealyocrypto xyz mr steal yo crypto primer hints https degenjungle substack com p mr steal yo crypto wargame by 0xtoshii mr steal yo crypto foundry https github com 0xtoshii mr steal yo crypto ctf foundry by 0xtoshii capture the ether https capturetheether com capture the ether solutions by cmichel https cmichel io capture the ether solutions capture the ether solution https www rphad com en blog 2021 12 30 by rd capture the ether foundry solutions https github com 0xraion capture the ether solutions foundry by 0xraion etherhack https etherhack positive com etherhack contest writeup https blog positive com phdays 8 etherhack contest writeup 794523f01248 security innovation blockchain ctf https blockchain ctf securityinnovation com security innovation blockchain ctf solutions https github com narendrakpatel blockchain ctf solutions by narendra patel cipher shastra https ciphershastra com sherlock writeup https razzor writes medium com sherlock writeup 1fb5521ecd3a by razzor defi hack https defihack xyz defi hack hints hardhat implementation https 0xtoshii medium com defi hack ctf hints local hardhat implementation 7a227ebafa64 by 0xtoshii defi hack solutions discolp https raz0r name writeups defi hack solutions discolp defi hack solutions may the force be with you https raz0r name writeups defi hack solutions may the force be with you gacha lab https gachalab inspex co how hackers can become lucky in nft minting https inspexco medium com how hackers can become lucky in nft minting 822f48d4b917 curta ctf https www curta wtf curta ctf sudoku solution using halmos https twitter com 0xkarmacoma status 1632551527729758208 by karma puzzle 3 https twitter com 0x796 status 1637905603682271232 by convergence boy decently safe defi https decentlysafedefi xyz me mev share ctf 2023 mev share ctf writeups https github com minaminao ctf blockchain tree main src mevsharectf by minaminao challenges and writeups project sekai ctf 2023 project sekai ctf 2023 re remix writeup https github com minaminao ctf blockchain tree main src projectsekaictf2023 by minaminao whitenoise ctf https ctf whitenoise rs doves in the wind solution https github com orenyomtov doves in the wind ctf solution by oren yomtov code is law 1 https github com orenyomtov code is law 1 by oren yomtov code is law 2 https github com orenyomtov code is law 2 by oren yomtov dragonfly ctf https ctf dragonfly xyz dragonfly ctf solutions https twitter com merklejerk status 1658510438512852999 puzzlebox ctf https github com smitrajput dragonfly puzzlebox ctf by smitrajput trust chain ctf https trustchain medium com practice smart contract hacking right now ba3a8c6c537c huff puzzles https github com rareskills huff puzzles by rareskills eko2022 enter the metaverse ctf https www ctfprotocol com tracks eko2022 eko2022 enter the metaverse ctf challenge 1 phoenixtto https stermi medium com eko2022 enter the metaverse ctf challenge 1 phoenixtto 21373b3ec60 by stermi eko2022 enter the metaverse ctf challenge 2 metaverse supermarket https stermi medium com eko2022 enter the metaverse ctf challenge 2 metaverse supermarket 33a2065f9f0b by stermi happynewyearctf https twitter com 0x796 status 1610620331387158531 by convergence boy making of happynewyear ctf puzzle https mirror xyz vicnaum eth rencgns7e0rdnx7h8yt0a9xbs7wfss4950dl8tyr2ky by convergence boy bountiful fe challenge https blog fe lang org posts bountiful break things and get paid solution https twitter com plotchy status 1600894668304105474 by plotchy i am the optimizer solution https www rileyholterhus com writing optimizor by riley holterhus solution https twitter com noahcitron status 1580359718341484544 by noahcitron balsnctf https balsnctf com balsnctf 2022 smart contract challenges writeup https github com minaminao ctf blockchain blob main src balsnctf2022 readme md ctf lending https github com mrtoph ctf lending by mrtoph ethernautdao challenges https twitter com ethernautdao ethernautdao solutions using ape and vyper https github com 0xjcn ethernautdao challenges by 0xjcn list of all challenges and solutions https github com romeroadrian ethernaut dao ctf list of challenges with solutions and writeups from beskay https github com beskay solidity challenges ctf level 1 privatedata writeup https mirror xyz ethernautdao eth mxnauuwrx6h42jubczf 9 tbsp14uh eq3xyen4jf7w by giovannidisiena eth ctf level 2 wallet writeup https stermi medium com ethernautdao ctf wallet solution 7f28bc05c564 by stermi ctf level 3 car market writeup https stermi medium com ethernautdao ctf wallet solution 1793f990c2d5 by stermi ctf level 4 vending machine writeup https stermi medium com ethernautdao ctf vending machine solution b30a74ba4a0f by stermi ctf level 6 hackable contract writeup https stermi xyz blog ethernautdao ctf hackable solution by stermi ctf level 8 vnft writeup https github com beskay solidity challenges blob main writeups vnft md by beskay ctf level 9 etherwallet writeup https github com beskay solidity challenges blob main writeups etherwallet md by beskay ctf level 10 vault writeup https github com beskay solidity challenges blob main writeups vault md by beskay ctf level 11 staking writeup https github com beskay solidity challenges blob main writeups staking md by beskay secdim https secdim com play appsec games and challenges https play secdim com learn git based labs on real world exploits https learn secdim com secureum a maze x https github com eugenioclrc secureum a maze x challenges a maze x ctf walkthrough part i https medium com mattaereal amaze x ctf walkthrough part i cdd3a5292a14 by matias a maze x ctf walkthrough part ii https medium com mattaereal a maze x ctf walkthrough part ii c4d3dcc6f700 by matias a maze x ctf walkthrough part iii https medium com mattaereal a maze x ctf walkthrough part iii dd8997b7b5bf by matias a maze x ctf walkthrough part iv https medium com mattaereal a maze x ctf walkthrough part iv last 407f5de236f8 by matias secureum a maze x stanford https github com eugenioclrc defi security summit stanford secureum a maze x stanford ctf https ventral digital posts 2022 8 27 secureum a maze x stanford ctf by patrickd secureum bootcamp race 13 of the secureum bootcamp epoch https ventral digital posts 2023 1 3 race 13 of the secureum bootcamp epoch by patrickd race 14 of the secureum bootcamp epoch https ventral digital posts 2023 1 30 race 14 of the secureum bootcamp epoch infinity by patrickd sol challenges and solutions https github com massun onibakuchi sol challenge readme hats finance challenge https github com hats finance games solution https hatsfinance medium com capture the flag postmortem bbc1b5afdf2c solution https merkleplant xyz posts solution to hatsfinance ctf 1 hackxyk stablecoin challenge https github com hacxyk hackxyk lab solution https gist github com abhishekvispute b0101938489a8b8dc292e3070c27156e by abhishek vispute solution https twitter com arbazkiraak status 1526554729924509697 s 20 t tgllp77ydzhuf bg5oglna by arbaz hussain immunefi community challenges https github com immunefi team community challenges by immunefi evm puzzles https github com fvictorio evm puzzles by fvictorio learning ethereum virtual machine opcodes with evm puzzles https ventral digital posts 2022 2 24 learning ethereum virtual machine opcodes with evm puzzles by patrickd evm puzzles second wind https ventral digital posts 2022 3 12 evm puzzles second wind by patrickd let s play evm puzzles learning ethereum evm while playing https stermi medium com lets play evm puzzles learning ethereum evm while playing 43a8354a02b3 solving more evm puzzles differently part i https medium com mattaereal solving more evm puzzles differently part i 170f2516b88d by matias solving more evm puzzles differently part ii https medium com mattaereal solving more evm puzzles differently part ii c0bd900d19d8 by matias yet another evm puzzle https medium com mattaereal yet another evm puzzle 787c83b67a75 by matias write ups for yet another evm puzzle1 https coinsbench com write ups for yet another evm puzzle1 d2f9594b9d20 by 0xnorman write up for yet another evm puzzle2 https coinsbench com write up for yet another evm puzzle2 1c769e22ab0b by 0xnorman write up for yet another evm puzzle3 https coinsbench com write up for yet another evm puzzle3 69a4f56f2888 by 0xnorman write up for yet evm puzzle4 https coinsbench com write up for yet evm puzzle4 92381aade135 by 0xnorman write up for yet another evm puzzle5 https coinsbench com write up for yet another evm puzzle5 76033711a562 by 0xnorman more evm puzzles https github com daltyboy11 more evm puzzles readme solutions part 1 https ventral digital posts 2022 5 24 more evm puzzles part 1 by patrickd solutions part 2 https ventral digital posts 2022 5 26 more evm puzzles part 2 by patrickd solana security workshop https workshop neodyme io index html by neodyme solution https github com mrtoph neodyme breakpoint workshop by christoph michel interview contracts https github com halbornsecurity ctfs by halborn pinball challenge https rinkeby etherscan io address 0xffb9205c84d0b209c215212a3cdfc50bf1cfb0e0 code by samczsun solution https twitter com karmacoma eth status 1451625194380939270 by karmacoma solution https twitter com adietrichs status 1452040913140822020 s 20 by adietrichs solution https medium com kanewallmann 71759 an untrustworthy pinball machine d9dcd07882c by kane wallmann nccgroup goat casino https github com nccgroup goatcasino damn vulnerable crypto wallet https gitlab com badbounty dvcw cryptohunt by p4d https github com pumpkingwok ctfgym tree master contracts ctf mainnet impossible by u eththrowaway4 https ropsten etherscan io address 0x0daabce0a1261b582e0d949ebca9dff4c22c88ef code break my contract steal my money challenges break my contract steal my money a solidity riddle https www reddit com r cryptocurrency comments o6fshx break my contract steal my money a solidity riddle break my contract part 1 buffer overflow solution https safecrypto medium com break my 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ctf blockchain blob main src paradigmctf2022 readme md kaiziron solutions https github com kaiziron paradigm ctf 2022 writeup paradigm ctf 2022 write up collection https philogy github io posts paradigm ctf 2022 write up collection glossary paradigm ctf 2022 electric sheep https ventral digital posts 2022 8 22 paradigm ctf 2022 electric sheep by patrickd paradigm ctf 2022 trapdooor trapdoooor https ventral digital posts 2022 8 23 paradigm ctf 2022 trapdooor amp trapdoooor by patrickd 0xmonaco writeup https twitter com brockjelmore status 1562228065446801408 by brock 0xmonaco 1 place writeup https twitter com z0age status 1561685707650990084 by oage 0xmonaco writeup https abdullathedruid github io 0xmonaco html by abdulla 0xmonaco exploit https hackmd io onemanbandplus2 s1ez1ulys by brock elmore unlocking the lockbox2 https dev to zemse unlocking the lockbox2 paradigmctf22 10pn by soham zemse vanity https twitter com danielvf status 1561508004423471104 by daniel von fange merkledrop and 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sherlock ctf 0x0 the standoff digital art competition https nft standoff365 com nft the standoff digital art https habr com ru company pt blog 590301 paradigm ctf https ctf paradigm xyz 2021 official challenges and solutions paradigm ctf 2021 https github com paradigm operations paradigm ctf 2021 community solutions paradigm ctf 2021 solutions https cmichel io paradigm ctf 2021 solutions and github repo https github com mrtoph paradigm ctf by cmichel babyrev and upgrade solutions https hackmd io adietrichs paradigm ctf 2021 by ansgar dietrichs paradigm jop solution on twitch part 1 https www twitch tv videos 906353891 and part 2 https www twitch tv videos 907645638 by ansgar dietrichs paradigm ctf 2021 solutions https github com thegostep paradigm ctf by thegostep babycrypto babysandbox and lockbox writeups https gist github com roynalnaruto 3687e0ab19c22ecbc32f0dcff5790198 by roynalnaruto babycrypto challenge https binarycake ca posts paradigm ctf by team dilicious sam wilson broker challenge https binarycake ca posts paradigm ctf broker by team dilicious sam wilson babyrev challenge https binarycake ca posts paradigm ctf babyrev index html by team dilicious sam wilson bank challenge https smarx com posts 2021 02 writeup of paradigm ctf bank by team dilicious smarx vault challenge https smarx com posts 2021 02 writeup of paradigm ctf vault by team dilicious smarx paradigm ctf solutions https medium com furucombo sharing some paradigm ctf solutions befac01800e3 by furucombo swap challenge https samczsun com paradigm ctf 2021 swap by samczsun solutions https ventral digital posts 2022 8 12 paradigm ctf 2021 smart contract challenges write up 1 by patrickd 0xpoland https 0xpoland dev 2020 0xpoland adventure awaits by iphelix https iphelix medium com 0xpoland adventure awaits 338ffc834d80 anchain ctf https www anchain ai defi detectives 2020 defi detectives chef nomi investigation notes by iphelix https iphelix medium com defi detectives chef nomi investigation notes 9468792b5f29 defi detectives discovering the secrets of the defi ecosystem https anchainai medium com defi detectives discovering the secrets of the defi ecosystem f227e5c8038a congratulations bitcoin bounty hunters the world s first blockchain investigation contest https anchainai medium com congratulations bitcoin bounty hunters the worlds first blockchain investigation contest a271d84fcc05 sharky ctf https ctftime org ctf 439 2020 sharky ctf blockchain level 0 to 4 writeup https medium com zh3r0 sharky ctf blockchain level 0 to 4 writeup 524b728709d0 by nithilan pugal sharky ctf blockchain challenges https imagin vip p 1380 by imagin ciphershastra ctf https ciphershastra com sherlock https ciphershastra com sherlock html minion https ciphershastra com minion html limitless https ciphershastra com limitless html shilpkaar https ciphershastra com shilpkaar html undead https ciphershastra com undead html thirtyfive https ciphershastra com thirtyfive html maya https ciphershastra com maya html gambler https ciphershastra com gambler html donjon ctf https donjon ctf io 2020 ctf 2020 capture the fortress https donjon ledger com capture the fortress ledger donjon ctf rationale and winners https www ledger com blog ledger donjon ctf 2020 challenges and winners chain heist https chainheist com 2019 chain heist and blockchain security at def con 2019 https www synopsys com blogs software security chain heist blockchain security chain heist ctf writeup by iphelix https iphelix medium com chain heist writeup 4f008cd6d346 capture the coin https capturethecoin org 2019 capture the coin at defcon and you could win big https blog coinbase com capture the coin at defcon and you could win big 2de5a616929a how the coinbase security team deployed ctfd to power our first capture the flag contest at defcon 27 https blog coinbase com how the coinbase security team deployed ctfd to power our first capture the flag contest at defcon eeb8da3bf2b0 congratulations capture the coin participants https blog coinbase com congratulations capture the coin participants 2028b2e5d14c capture the coin trivia solutions https blog coinbase com capture the coin trivia solutions 98fd99aadb75 capture the coin blockchain category solutions https blog coinbase com capture the coin blockchain category solutions 9aef880d7e00 capture the coin cryptography category solutions https blog coinbase com capture the coin cryptography category solutions fe94d82165c5 capture the coin ctf write up https medium com arpox capture the coin ctf write up 29bc32bc9546 by arpox consensys dilligence ethereum hacking challenge https medium com consensys diligence consensys diligence ether giveaway 1 4985627b7726 2018 consensys ctf writeup by samczsun https samczsun com consensys ctf writeup code blue polyswarm challenge 2018 polyswarm smart contract hacking challenge writeup https raz0r name writeups polyswarm smart contract hacking challenge writeup by arseny reutov real world ctf acoraida monica challenge 2018 challenge files and solution https gist github com liveoverflow 21c8a505ca176e5bb20bc94eb23acdf1 by liveoverflow ethereum smart contract code review 1 real world ctf 2018 https www youtube com watch v ozqoluvkl1s by liveoverflow jump oriented programming ethereum smart contract 2 real world ctf 2018 https www youtube com watch v rfl3fcnvbjg by liveoverflow ethereum smart contract backdoored using malicious constructor https www youtube com watch v wp enghiyec by liveoverflow authio solidity ctf challenges part 1 function types https ropsten etherscan io address 0x727c1c8d4b190d208f3701f106f7301cb1a32f27 code part 2 safe execution https ropsten etherscan io address 0x023916f968af3fbb21ac10abbe18448c79d609c2 code part 3 honeypot https ropsten etherscan io address 0xdc65b61be773f8be72ded22ac008ad5add045e3c code part 4 read the fine print https ropsten etherscan io address 0x1b359afb0bd86a6c435d178b1fbf8a6fda3ead7d code part 5 mirror madness https etherscan io address 0x7cd03c9f1d2dc95358b1992e9afc857aeaab45d5 solidity ctf part 1 function types https www reddit com r ethdev comments 8td9xn challenge empty the contract of funds solidity ctf part 2 safe execution https medium com authio solidity ctf part 2 safe execution ad6ded20e042 by alexander wade solidity ctf part 3 honeypot https medium com authio solidity ctf part 3 honeypot 8a8b6fecc6a2 by alexander wade solidity ctf part 4 read the fine print https medium com authio solidity ctf part 4 read the fine print 5ad259a5f5bb by alex towle solidity ctf part 5 mirror madness https medium com authio ctf duplication dd32cd4ef690 by paul vienhage zeronights ico hacking contest 2017 zeronights ico hacking contest writeup https blog positive com zeronights ico hacking contest writeup 63afb996f1e3 by arseny reutov quillctf https bit ly quillctf road closed https quillctf super site challenges quillctf challenges road closed confidential hash https quillctf super site challenges quillctf challenges ctf02 vip bank https quillctf super site challenges quillctf challenges vip bank safenft https quillctf super site challenges quillctf challenges bulletproof nft d31eg4t3 https quillctf super site challenges quillctf challenges d31eg4t3 collatz puzzle https quillctf super site challenges quillctf challenges collatz puzzle true xor https quillctf super site challenges quillctf challenges true xor pelusa https quillctf super site challenges quillctf challenges pelusa weth 10 https quillctf super site challenges quillctf challenges weth 10 gate https quillctf super site challenges quillctf challenges gate panda token https quillctf super site challenges quillctf challenges panda token | blockchain |
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lighting-engineering | lighting engineering lighting engineering databases in the environment postgresql this is my term paper i decided to share it here i described in detail the various functions for queries unique tables for the operator administrator were created this database can be easily applied to various sales stores image https user images githubusercontent com 75854478 144716610 edebda03 00f7 4762 b289 d2225a2917af png image https user images githubusercontent com 75854478 144716619 c169e03a 5bde 4b9d a8df 44246ba522d1 png image https user images githubusercontent com 75854478 144716627 b316e072 5fd5 49e6 b7d0 72d65a58d768 png database structure image https user images githubusercontent com 75854478 144716670 994914f0 ee3c 4e88 b892 5e23b8376af0 png as you noticed for convenience i used a one to many relationship | server |
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RingBuffer | ringbuffer a flexible thread safe ring buffer circular buffer designed for embedded systems | os |
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vercel-llm-api | python vercel llm api pypi version https img shields io pypi v vercel llm api svg https pypi org project vercel llm api this is a reverse engineered api wrapper for the vercel ai playground https play vercel ai which allows for free access to many llms including openai s chatgpt cohere s command nightly as well as some open source models table of contents features features limitations limitations installation installation documentation documentation using the client using the client downloading the available models downloading the available models generating text generating text generating chat messages generating chat messages misc misc changing the logging level changing the logging level copyright copyright copyright notice copyright notice table of contents generated with markdown toc http ecotrust canada github io markdown toc features download the available models generate text generate chat messages set custom parameters stream the responses limitations user agent is hardcoded no auth support can t use pro or hobby models installation you can install this library by running the following command pip3 install vercel llm api documentation examples can be found in the examples directory to run these examples simply execute the included python files from your terminal python3 examples generate py using the client to use this library simply import vercel ai and create a vercel ai client instance you can specify a proxy using the proxy keyword argument normal example python import vercel ai client vercel ai client proxied example python import vercel ai client vercel ai client proxy socks5h 193 29 62 48 11003 note that the following examples assume client is the name of your vercel ai client instance downloading the available models the client downloads the available models upon initialization and stores them in client models python print json dumps client models indent 2 anthropic claude instant v1 id anthropic claude instant v1 the model s id provider anthropic the model s provider providerhumanname anthropic the provider s display name makerhumanname anthropic the maker of the model minbillingtier hobby the minimum billing tier needed to use the model parameters a dict of optional parameters that can be passed to the generate function temperature the name of the parameter value 1 the default value for the parameter range 0 1 a range of possible values for the parameter note that since there is no auth yet if a model has the minbillingtier property present it can t be used a list of model ids is also available in client model ids python print json dumps client model ids indent 2 anthropic claude instant v1 locked to hobby tier unusable anthropic claude v1 locked to hobby tier unusable replicate replicate alpaca 7b replicate stability ai stablelm tuned alpha 7b huggingface bigscience bloom huggingface bigscience bloomz huggingface google flan t5 xxl huggingface google flan ul2 huggingface eleutherai gpt neox 20b huggingface openassistant oasst sft 4 pythia 12b epoch 3 5 huggingface bigcode santacoder cohere command medium nightly cohere command xlarge nightly openai gpt 4 locked to pro tier unusable openai code cushman 001 openai code davinci 002 openai gpt 3 5 turbo openai text ada 001 openai text babbage 001 openai text curie 001 openai text davinci 002 openai text davinci 003 a dict of default parameters for each model can be found at client model params python print json dumps client model defaults indent 2 anthropic claude instant v1 temperature 1 maximumlength 200 topp 1 topk 1 presencepenalty 1 frequencypenalty 1 stopsequences n nhuman generating text to generate some text use the client generate function which accepts the following arguments model the id of the model you want to use prompt your prompt params a dict of optional parameters see the previous section for how to find these the function is a generator which returns the newly generated text as a string streamed example python for chunk in client generate openai gpt 3 5 turbo summarize the gnu gpl v3 print chunk end flush true non streamed example python result for chunk in client generate openai gpt 3 5 turbo summarize the gnu gpl v3 result chunk print result generating chat messages to generate chat messages use the client chat function which accepts the following arguments model the id of the model you want to use messages a list of messages the format for this is identical to how you would use the official openai api params a dict of optional parameters see the downloading the available models section for how to find these the function is a generator which returns the newly generated text as a string python messages role system content you are a helpful assistant role user content who won the world series in 2020 role assistant content the los angeles dodgers won the world series in 2020 role user content where was it played for chunk in client chat openai gpt 3 5 turbo messages print chunk end flush true print misc changing the logging level if you want to show the debug messages simply call vercel ai logger setlevel python import vercel ai import logging vercel ai logger setlevel logging info copyright this program is licensed under the gnu gpl v3 https github com ading2210 vercel llm api blob main license all code has been written by me ading2210 https github com ading2210 copyright notice ading2210 vercel llm api a reverse engineered api wrapper for the vercel ai playground copyright c 2023 ading2210 this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license along with this program if not see https www gnu org licenses | chatgpt library openai python vercel | ai |
webserverless | webserverless serverless based web development framework | front_end |
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nltk-book | natural language processing with python run jupyter notebook jupyther notebook | jupyter-notebook nltk bigrams ngrams corpora corpus | ai |
mli-resources | machine learning interpretability mli machine learning algorithms create potentially more accurate models than linear models but any increase in accuracy over more traditional better understood and more easily explainable techniques is not practical for those who must explain their models to regulators or customers for many decades the models created by machine learning algorithms were generally taken to be black boxes however a recent flurry of research has introduced credible techniques for interpreting complex machine learned models materials presented here illustrate applications or adaptations of these techniques for practicing data scientists want to contribute your own content just make a pull request want to use the content in this repo just cite the h2o ai machine learning interpretability team or the original author s as appropriate contents practical mli examples https github com h2oai mli resources practical mli examples installation of examples https github com h2oai mli resources installation of examples dockerfile https github com h2oai mli resources dockerfile manual https github com h2oai mli resources manual additional code examples https github com h2oai mli resources additional code examples testing explanations https github com h2oai mli resources testing explanations webinars videos https github com h2oai mli resources webinarsvideos booklets https github com h2oai mli resources booklets conference presentations https github com h2oai mli resources conference presentations miscellaneous resources https github com h2oai mli resources miscellaneous resources references https github com h2oai mli resources references general https github com h2oai mli resources general techniques https github com h2oai mli resources techniques practical mli examples a dockerfile anaconda py36 h2o xgboost graphviz dockerfile is provided that will construct a container with all necessary dependencies to run the examples here decision tree surrogate models notebooks dt surrogate ipynb lime practical samples variant notebooks lime ipynb loco na variant notebooks loco ipynb partial dependence and individual conditional expectation ice notebooks pdp ice ipynb sensitivity analysis notebooks sensitivity analysis ipynb monotonic models with xgboost notebooks mono xgboost ipynb diabetes data set use case https github com jphall663 diabetes use case br diabetes use case has different dockerfile in seperate repo installation of examples dockerfile a dockerfile is provided to build a docker container with all necessary packages and dependencies this is the easiest way to use these examples if you are on mac os x nix or windows 10 to do so 1 install and start docker https www docker com from a terminal 2 create a directory for the dockerfile br mkdir anaconda py36 h2o xgboost graphviz 3 fetch the dockerfile from the mli resources repo br curl https raw githubusercontent com h2oai mli resources master anaconda py36 h2o xgboost graphviz dockerfile anaconda py36 h2o xgboost graphviz dockerfile 4 build a docker image from the dockefile for this and other docker commands below you may need to use sudo br docker build no cache anaconda py36 h2o xgboost graphviz 5 display docker image ids you are probably interested in the most recently created image br docker images 6 start the docker image and the jupyter notebook server br docker run i t p 8888 8888 image id bin bash c opt conda bin conda install jupyter y quiet opt conda bin jupyter notebook notebook dir mli resources ip port 8888 no browser allow root 7 list docker containers br docker ps 8 copy the sample data into the docker container refer to getdata md data getdata md to obtain datasets needed for notebooks br docker cp path to train csv container id mli resources data train csv 9 navigate to the port jupyter directs you to on your machine it will likely include a token manual install 1 anaconda python 5 1 0 from the anaconda archives https repo continuum io archive 2 java https java com download 3 the latest stable h2o https www h2o ai download python package 4 git https git scm com downloads 5 xgboost https github com dmlc xgboost with python bindings 6 graphviz http www graphviz org anaconda python java git and graphviz must be added to your system path from a terminal 7 clone the mli resources repository with examples br git clone https github com h2oai mli resources git 8 cd mli resources 9 copy the sample data into the mli resources repo directory refer to getdata md data getdata md to obtain datasets needed for notebooks br cp path to train csv data 9 start the jupyter notebook server br jupyter notebook 10 navigate to the port jupyter directs you to on your machine additional code examples the notebooks in this repo have been revamped and refined many times other versions with different and potentially interesting details are available at these locations o reilly media gitlab https content oreilly com oriole interpretable machine learning with python xgboost and h2o github com jphall663 https github com jphall663 interpretable machine learning with python testing explanations one way to test generated explanations for accuracy is with simulated data with known characteristics for instance models trained on totally random data with no relationship between a number of input variables and a prediction target should not give strong weight to any input variable nor generate compelling local explanations or reason codes conversely you can use simulated data with a known signal generating function to test that explanations accurately represent that known function detailed examples of testing explanations with simulated data are available here https github com h2oai mli resources tree master lime shap treeint compare readme md a summary of these results are available here https www oreilly com ideas testing machine learning interpretability techniques webinars videos interpretable machine learning meetup washington dc https www youtube com watch v 3ulegw5hhyk machine learning interpretability with driverless ai https www youtube com watch v 3 gm00kbwew interpretability in conversation with patrick hall and sameer singh http blog fastforwardlabs com 2017 09 11 interpretability webinar html nyc big data science meetup i less technical https www youtube com watch v q8rtrmquqsu nyc big data science meetup ii more technical https www youtube com watch v rcuduzf8 su h2o driverless ai machine learning interpretability software walk through accompanies cheatsheet below https www youtube com watch v 5jsu3curexy h2o world sf 2019 human centered ml https www youtube com watch v dimsemhrndw o reilly media interactive notebooks requires o reilly safari membership enhancing transparency in machine learning models with python and xgboost https www safaribooksonline com oriole enhancing transparency in machine learning models with python and xgboost increase transparency and accountability in your machine learning project with python https www safaribooksonline com oriole increase transparency and accountability in your machine learning project with python explain your predictive models to business stakeholders with lime python and h2o https www safaribooksonline com oriole explain your predictive models to business stakeholders w lime python h2o testing machine learning models for accuracy trustworthiness and stability with python and h2o https www safaribooksonline com oriole testing ml models for accuracy trustworthiness stability with python and h2o booklets an introduction to machine learning interpretability http www oreilly com data free an introduction to machine learning interpretability csp machine learning interpretability with h2o driverless ai booklet http docs h2o ai driverless ai latest stable docs booklets mlibooklet pdf conference presentations practical techniques for interpreting machine learning models 2018 fat conference tutorial https www fatconference org static tutorials hall interpretable18 pdf driverless ai hands on focused on machine learning interpretability h2o world 2017 http video h2o ai watch 9g8trvxufgygkq4freia7z interpretable ai not just for regulators strata nyc 2017 notes strata mli sept 17 pdf jsm 2018 slides https github com jphall663 jsm 2018 slides blob master main pdf odsc 2018 slides https www slideshare net pramitchoudhary model evaluation in the land of deep learning 123891695 h2o world sf 2019 human centered ml https github com jphall663 h2oworld sf 2019 blob master main pdf miscellaneous resources ideas on interpreting machine learning slideshare https www slideshare net 0xdata interpretable machine learning predictive modeling striking a balance between accuracy and interpretability https www oreilly com ideas predictive modeling striking a balance between accuracy and interpretability testing machine learning explanation techniques https www oreilly com ideas testing machine learning interpretability techniques interpreting machine learning models an overview https www kdnuggets com 2017 11 interpreting machine learning models overview html h2o driverless ai mli cheatsheet accompanies walk through video above cheatsheet png alt text cheatsheet png imperfect incomplete but one page blueprint for human friendly machine learning png blueprint png draw io xml blueprint xml alt text blueprint png general references towards a rigorous science of interpretable machine learning https arxiv org pdf 1702 08608 pdf ideas for machine learning interpretability https www oreilly com ideas ideas on interpreting machine learning fairness accountability and transparency in machine learning fat ml scholarship https www fatml org resources relevant scholarship explaining explanations an approach to evaluating interpretability of machine learning https arxiv org pdf 1806 00069 pdf a survey of methods for explaining black box models https arxiv org pdf 1802 01933 pdf trends and trajectories for explainable accountable and intelligible systems an hci research agenda https dl acm org citation cfm id 3174156 interpretable machine learning by christoph molnar https github com christophm interpretable ml book on the art and science of machine learning explanations jsm 2018 proceedings paper https github com jphall663 jsm 2018 paper blob master jsm 2018 paper pdf toward dispelling unhelpful explainable machine learning ml misconceptions preprint wip https github com jphall663 xai misconceptions blob master xai misconceptions pdf the mythos of model interpretability https arxiv org pdf 1606 03490 pdf challenges for transparency https arxiv org pdf 1708 01870 pdf explaining by removing a unified framework for model explanation https arxiv org abs 2011 14878 | machine-learning python jupyter-notebooks interpretability data-science data-mining h2o mli xai fatml transparency accountability fairness xgboost machine-learning-interpretability iml interpretable-machine-learning interpretable-ml interpretable-ai explainable-ml | ai |
PowerShell-IoT | powershell iot build status https ci appveyor com api projects status ipvxu77rxb5ou8gb svg true https ci appveyor com project powershell powershell iot travis https img shields io travis rust lang rust svg logo travis https travis ci com powershell powershell iot powershell gallery https img shields io powershellgallery v microsoft powershell iot svg https www powershellgallery com packages microsoft powershell iot note powershell iot is still in preview a powershell module for interacting with hardware sensors and devices using common protocols gpio i2c spi an ssd1306 displaying hello world from powershell https pbs twimg com media dv8c8y3v4ac7pah jpg small information goals the main goal of this project is to provide a friendly interface for interacting with hardware sensors and devices using powershell that said it was built as close to the metal as possible to keep the library broad enough to cover a range of sensors and devices the hope is that this module will be the foundation for other modules that will expose specific cmdlets for interacting with specific sensors and devices for example a cmdlet stack to turn on a light bulb might be powershell set light on your user types this you make this cmdlet set gpiopin id 4 value high you use this to make that we make this cmdlet our code that makes that to see some examples of modules built on top of powershell iot see the examples folder examples supported platforms supported devices raspberry pi 3 raspberry pi 2 supported operating systems raspbian stretch documentation examples please see our docs folder here docs for an api reference pin layout and other docs for examples checkout our examples folder examples dependencies this project relies on net core iot libraries https github com dotnet iot it is a net library for interacting with raspberry pi s io functionality installation powershell gallery you can grab the latest version of powershell iot by running powershell install module microsoft powershell iot please note that since this module works with hardware higher privileges are required run powershell with sudo or as root user then see the section on running running if you want to write a module that uses powershell iot include it in the requiredmodules field of your module manifest github releases you can also manually download the zipped up module from the releases https github com powershell powershell iot releases then see the section on running running appveyor you can download the latest ci build from our appveyor build here https ci appveyor com project powershell powershell iot go to the latest build click on either of the images then click on the artifacts tab from there you can download a zip of the latest ci build then see the section on running running from source prerequisites powershell core 6 or greater https github com powershell powershell releases net core sdk 2 0 or greater https www microsoft com net download invokebuild https www powershellgallery com packages invokebuild a supported device like a raspberry pi 3 https www raspberrypi org with powershell core 6 on it https github com powershell powershell get powershell building note you can t build on arm devices at this time so you will need to build on another machine and copy the build to the device 1 clone download the repo 2 run build ps1 bootstrap to see if you re missing any tooling 3 run build ps1 to build at this point you ll notice an out folder has been generated in the root of your repo the project is ready to be deployed to your device deploying we have included a helper script move psiotbuild ps1 that will move the powershell iot build over to your device this copy uses psrp over ssh so make sure you re able to connect to your pi this way the microsoft powershell iot module will be copied to your env psmodulepath on your device here s an example powershell move psiotbuild ps1 ip 10 123 123 123 ip address of device you can also easily copy examples in the examples folder over using the withexample flag just give a list of examples you want to copy over and it will move those to you env psmodulepath along with microsoft powershell iot powershell move psiotbuild ps1 ip 10 123 123 123 withexample microsoft powershell iot plant microsoft powershell iot ssd1306 also with the build parameter it will build test package your project before moving it note if you d rather not use the script simply copy the out microsoft powershell iot to your device to get started running start powershell powershell pwsh if you have the microsoft powershell iot module in your psmodulepath powershell import module microsoft powershell iot alternatively just import the psd1 powershell import module path to microsoft powershell iot microsoft powershell iot psd1 at this point you can now mess with the module powershell get command module microsoft powershell iot get gpiopin 2 gets the data from gpio pin 2 testing you can run tests but they require a particular setup here is how you run them powershell build ps1 test the setup required for i2c an adafruit bme280 i2c or spi temperature humidity pressure sensor https www adafruit com product 2652 for gpio bend pins 26 and 22 to touch each other or connect them in some way for spi an adafruit lis3dh triple axis accelerometer https www adafruit com product 2809 we currently have a build agent that will deploy pr code onto a test raspberry pi and run the tests found in the test directory | gpio i2c spi powershell netcore | server |
IT-Quiz | it quiz prepare for quiz competitions on information technology index 1 founders of famous companies websites founders md 2 famous taglines taglines md 3 programming languaes programming languages md 4 android os android md 5 books from famous it personalities books md 6 famous quotes quotes md 7 father of father of md 8 list of firsts first in tech md 9 images of personalities images of personalities md 10 abbreviations abbreviation md feel free to add more topics | information-technology quiz it-quiz | server |
AVR-ATMEGA32-Projects | atmega32 projects drivers modules applications and rtos | os |
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unchained | unchained unchanined is a repo for syncing the talks annotations i produced with notability right now it contains these folders blockchain the whitepaper i annotated note i ll gradually add the whitepaper i read into this repo the annotate i put on whitepapers are pretty personal it just reflects my questions and preferences talks the talks i gave since 2018 i m starting using notability to generate my talks the most recent talk is blockchain jumpstart it contains a lot of details on bitcoin ethereum etc and i ll keep update it as i study more and more blockchain related technology please submit issue if you found anything is wrong or you have any comments upon my thoughts thanks and enjoy license cc by nc nd 4 0 | blockchain bitcoin ethereum hyperledger | blockchain |
multiview_notebooks | binder https mybinder org badge logo svg https mybinder org v2 gh maxcrous multiview notebooks main multiview notebooks this is a collection of educational notebooks on multi view geometry and computer vision subjects covered in these notebooks include camera calibration perspective projection 3d point triangulation quaternions as 3d pose representation perspective n point pnp algorithm levenberg marquardt optimization epipolar geometry relative 2nd cam pose from stereo views w fundamental matrix relative 2nd cam pose from stereo views w homography bundle adjustment structure from motion note notebook 5 is working but not as tidy as the rest yet this notebook covers the faugeras method to infer relative pose from a homography how to run the notebooks can be run in the browser by clicking the binder badge binder https mybinder org badge logo svg https mybinder org v2 gh maxcrous multiview notebooks main if one is interested in running the notebooks locally i highly recommend using docker as there is a dependency on g2opy and ipyvolume which are challenging to install builds the environment docker build t multiview notebooks start a jupyter lab which can be opened in the browser docker run it rm p 8888 8888 multiview notebooks jupyter lab ip 0 0 0 0 port 8888 after starting the jupyter lab the notebooks can be found in the home directory for the source of the dockerfile see this repository https github com maxcrous ipyvolume g2opy notebooks examples of visualizations for more examples see this video on youtube https www youtube com watch v mt9lzvy9g2g p align center img src images triangulation gif width 800 alt triangulation br br br br br img src images pnp gif width 800 alt perspective n point p | ai |
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ros_dt_lane_follower | ros lane detection ros nodes for lane detection based on computer vision algorithms lane detection py scans the image captured by the camera looking for white yellow and red lines and calculates the error in pixel from the middle of the lane it subscribes the image topic topic and publishes the error value on the lane detection topic lane controller py contains the pid controller and sets motors speed it subscribes the lane detection topic and publishes linear and angular velocities of the robot on the follow topic topic | ai |
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jetson-containers | a header for a software project about building containers for ai and machine learning https raw githubusercontent com dusty nv jetson containers docs docs images header jpg machine learning containers for jetson and jetpack l4t pytorch https img shields io github actions workflow status dusty nv jetson containers l4t pytorch jp51 yml label l4t pytorch packages l4t l4t pytorch l4t tensorflow https img shields io github actions workflow status dusty nv jetson containers l4t tensorflow tf2 jp51 yml label l4t tensorflow packages l4t l4t tensorflow l4t ml https img shields io github actions workflow status dusty nv jetson containers l4t ml jp51 yml label l4t ml packages l4t l4t ml l4t diffusion https img shields io github actions workflow status dusty nv jetson containers l4t diffusion jp51 yml label l4t diffusion packages l4t l4t diffusion l4t text generation https img shields io github actions workflow status dusty nv jetson containers l4t text generation jp51 yml label l4t text generation packages l4t l4t text generation modular container build system that provides various ai ml packages packages for nvidia jetson https developer nvidia com embedded computing rocket robot ml pytorch packages pytorch tensorflow packages tensorflow onnxruntime packages onnxruntime deepstream packages deepstream tritonserver packages tritonserver jupyterlab packages jupyterlab stable diffusion packages diffusion stable diffusion webui llm transformers packages llm transformers text generation webui packages llm text generation webui text generation inference packages llm text generation inference llava packages llm llava llama cpp packages llm llama cpp exllama packages llm exllama llamaspeak packages llm llamaspeak awq packages llm awq autogptq packages llm auto gptq minigpt 4 packages llm minigpt4 mlc packages llm mlc langchain packages llm langchain optimum packages llm optimum bitsandbytes packages llm bitsandbytes nemo packages nemo riva packages riva client l4t l4t pytorch packages l4t l4t pytorch l4t tensorflow packages l4t l4t tensorflow l4t ml packages l4t l4t ml l4t diffusion packages l4t l4t diffusion l4t text generation packages l4t l4t text generation vit nanoowl packages vit nanoowl nanosam packages vit nanosam segment anything sam packages vit sam track anything tam packages vit tam cuda cupy packages cupy cuda python packages cuda python pycuda packages pycuda numba packages numba cudf packages rapids cudf cuml packages rapids cuml robotics ros packages ros ros2 packages ros opencv cuda packages opencv realsense packages realsense zed packages zed vectordb nanodb packages vectordb nanodb faiss packages vectordb faiss raft packages rapids raft see the packages packages directory for the full list including pre built container images and ci cd status for jetpack l4t using the included tools you can easily combine packages together for building your own containers want to run ros2 with pytorch and transformers no problem just do the system setup docs setup md and build it on your jetson like this bash build sh name my container ros humble desktop pytorch transformers there are shortcuts for running containers too this will pull or build a l4t pytorch packages l4t l4t pytorch image that s compatible bash run sh autotag l4t pytorch sup run sh docs run md forwards arguments to docker run https docs docker com engine reference commandline run with some defaults added like runtime nvidia mounts a data cache and detects devices sup br sup autotag docs run md autotag finds a container image that s compatible with your version of jetpack l4t either locally pulled from a registry or by building it sup if you look at any package s readme like l4t pytorch packages l4t l4t pytorch it will have detailed instructions for running it s container documentation a href https www jetson ai lab com img align right width 200 height 200 src https nvidia ai iot github io jetson generative ai playground images jon gen ai panels png a package list packages package definitions docs packages md system setup docs setup md building containers docs build md running containers docs run md check out the tutorials at the jetson generative ai lab https www jetson ai lab com getting started refer to the system setup docs setup md page for tips about setting up your docker daemon and memory storage tuning bash sudo apt get update sudo apt get install git python3 pip git clone depth 1 https github com dusty nv jetson containers cd jetson containers pip3 install r requirements txt run sh autotag l4t pytorch or you can manually run a container image https hub docker com r dustynv of your choice without using the helper scripts above bash sudo docker run runtime nvidia it rm network host dustynv l4t pytorch r35 4 1 looking for the old jetson containers see the legacy https github com dusty nv jetson containers tree legacy branch | machine-learning dockerfiles jetson pytorch tensorflow pandas scikit-learn numpy ros-containers ros2-foxy docker containers nvidia | ai |
Node.js-Web-Development-Fifth-Edition | node js web development fifth edition a href https www packtpub com web development node js web development fifth edition utm source github utm medium repository utm campaign 9781838987572 img src https www packtpub com media catalog product cache 4cdce5a811acc0d2926d7f857dceb83b 9 7 9781838987572 original 38 jpeg alt node js web development fifth edition height 256px align right a this is the code repository for node js web development fifth edition https www packtpub com web development node js web development fifth edition utm source github utm medium repository utm campaign 9781838987572 published by packt server side web development made easy with node 14 using practical examples what is this book about node js is the leading choice of server side web development platform enabling developers to use the same tools and paradigms for both server side and client side software this updated fifth edition of node js web development focuses on the new features of node js 14 express 4 x and ecmascript taking you through modern concepts techniques and best practices for using node js this book covers the following exciting features install and use node js 14 and express 4 17 for both web development and deployment implement rest services using the restify framework develop test and deploy microservices using kubernetes and node js get up to speed with using data storage engines such as mysql sqlite3 and mongodb secure your web applications using headless browser testing with puppeteer implement https using let s encrypt and enhance application security with helmet if you feel this book is for you get your copy https www amazon com dp 1838987576 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 if test expression statement upon condition is true following is what you need for this book if you re looking for an alternative to the p languages perl php and python or if you want to get started with server side web development with javascript programming or if you want a deep dive into deploying services to cloud hosting this node js book is for you a rudimentary understanding of javascript and web application development is a must before you get started with this book with the following software and hardware list you can run all code files present in the book chapter 1 14 software and hardware list chapter software required os required 2 node js 14 x windows mac os x and linux any 2 python 3 windows mac os x and linux any 2 c c compiler windows mac os x and linux any 5 express windows mac os x and linux any 6 bootstrap windows mac os x and linux any 7 sqlite3 windows mac os x and linux any 7 mysql windows mac os x and linux any 7 mongodb windows mac os x and linux any 7 sequelize windows mac os x and linux any 9 socket io windows mac os x and linux any 10 multipass windows mac os x and linux any 11 docker windows mac os x and linux any 12 terraform windows mac os x and linux any 13 mocha windows mac os x and linux any 13 puppeteer windows mac os x and linux any related products other books you may enjoy full stack react projects second edition packt https www packtpub com web development full stack react projects second edition utm source github utm medium repository utm campaign 9781839215414 amazon https www amazon com dp 1839215410 webpack 5 up and running packt https www packtpub com web development learn webpack utm source github utm medium repository utm campaign 9781789954401 amazon https www amazon com dp 1789954401 errata page 28 return 2 should be replaced as done 2 get to know the author david herron is a software engineer living in silicon valley who has worked on projects ranging from an x 400 email server to being part of the team that launched the openjdk project to yahoo s node js application hosting platform and a solar array performance monitoring service that took david through several companies until he grew tired of communicating primarily with machines and developed a longing for human communication today david is an independent writer of books and blog posts covering topics related to technology programming electric vehicles and clean energy technologies other books by the authors node web development https www packtpub com web development node web development utm source github utm medium repository utm campaign 9781849515146 node web development second edition https www packtpub com web development node web development second edition utm source github utm medium repository utm campaign 9781782163305 node js web development third edition https www packtpub com web development nodejs web development third edition utm source github utm medium repository utm campaign 9781785881503 node js web development fourth edition https www packtpub com web development nodejs web development fourth edition utm source github utm medium repository utm campaign 9781788626859 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 9781838987572 https packt link free ebook 9781838987572 a p | front_end |
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guix | azure rtos guix azure rtos guix is a professional quality package created to meet the needs of embedded systems developers unlike alternative gui solutions azure rtos guix is small fast and easily ported to virtually any hardware configuration that s capable of supporting graphical output azure rtos guix also delivers exceptional visual appeal and an intuitive and powerful api for application level user interface development azure rtos guix studio provides a complete embedded graphical user interface gui application design environment facilitating the creation and maintenance of all graphical elements in the application s gui azure rtos guix studio automatically generates c code that s compatible with the azure rtos guix library ready to be compiled and run on the target the azure rtos guix studio installer is available here https aka ms azrtos guix installer here are the key features and modules of guix guix key features docs guix features png getting started azure rtos guix as part of azure rtos has been integrated to the semiconductor s sdks and development environment you can develop using the tools of choice from nxp https www nxp com design software embedded software azure rtos for nxp microcontrollers azure rtos renesas https github com renesas azure rtos and microchip https mu microchip com get started simplifying your iot design with azure rtos we also samples https github com azure rtos samples using hero development boards from semiconductors you can build and test with see overview of azure rtos guix https learn microsoft com azure rtos guix overview guix for the high level overview and all documentation and apis can be found in azure rtos guix documentation https learn microsoft com azure rtos guix repository structure and usage directory layout cmake cmakelist files for building the project common core guix files fonts fonts required by guix graphics graphic assets ports architecture and compiler specific files samples sample codes tutorials more sample codes license txt license terms license hardware txt licensed hardware from semiconductors contributing md contribution guidance security md microsoft repo security guidance branches releases the master branch has the most recent code with all new features and bug fixes it does not represent the latest general availability ga release of the library each official release preview or ga will be tagged to mark the commit and push it into the github releases tab e g v6 2 rel when you see xx xx xxxx 6 x or x x in function header this means the file is not officially released yet they will be updated in the next release see example below function release tx initialize low level cortex m23 gnu 6 x author scott larson microsoft corporation description this function is responsible for any low level processor initialization including setting up interrupt vectors setting up a periodic timer interrupt source saving the system stack pointer for use in isr processing later and finding the first available ram memory address for tx application define input none output none calls none called by tx initialize kernel enter threadx entry function release history date name description 09 30 2020 scott larson initial version 6 1 xx xx xxxx scott larson include tx user h resulting in version 6 x component dependencies the main components of azure rtos are each provided in their own repository but there are dependencies between them as shown in the following graph this is important to understand when setting up your builds dependency graph docs deps png you will have to take the dependency graph above into account when building anything other than threadx itself building and using the library instruction for building the guix as static library using arm gnu toolchain and cmake if you are using toolchain and ide from semiconductor you might follow its own instructions to use azure rtos components as explained in the getting started getting started section 1 install the following tools cmake https cmake org download version 3 0 or later arm gnu toolchain for arm none eabi https developer arm com downloads arm gnu toolchain downloads ninja https ninja build org 1 build the threadx library https github com azure rtos threadx building and using the library as the dependency 1 cloning the repo bash git clone https github com azure rtos guix git 1 define the features and addons you need in gx user h and build together with the component source code you can refer to gx user sample h https github com azure rtos guix blob master common inc gx user sample h as an example 1 building as a static library each component of azure rtos comes with a composable cmake based build system that supports many different mcus and host systems integrating any of these components into your device app code is as simple as adding a git submodule and then including it in your build using the cmake add subdirectory while the typical usage pattern is to include guix into your device code source tree to be built linked with your code you can compile this project as a standalone static library to confirm your build is set up correctly an example of building the library for cortex m4 bash cmake bbuild gninja dcmake toolchain file cmake cortex m4 cmake cmake build build professional support professional support plans https azure microsoft com support options are available from microsoft for community support and others see the resources resources section below licensing license terms for using azure rtos are defined in the license txt file of this repo please refer to this file for all definitive licensing information no additional license fees are required for deploying azure rtos on hardware defined in the licensed hardware txt licensed hardware txt file if you are using hardware not listed in the file or having licensing questions in general please contact microsoft directly at https aka ms azrtos license resources the following are references to additional azure rtos resources product introduction and white papers https azure com rtos general technical questions https aka ms qna azure rtos product issues and bugs or feature requests https github com azure rtos guix issues licensing and sales questions https aka ms azrtos license product roadmap and support policy https aka ms azrtos lts blogs and videos http msiotblog com and https aka ms iotshow azure rtos tracex installer https aka ms azrtos tracex installer you can also check previous questions https stackoverflow com questions tagged azure rtos guix or ask new ones on stackoverflow using the azure rtos and guix tags security azure rtos provides oems with components to secure communication and to create code and data isolation using underlying mcu mpu hardware protection mechanisms it is ultimately the responsibility of the device builder to ensure the device fully meets the evolving security requirements associated with its specific use case contribution please follow the instructions provided in the contributing md contributing md for the corresponding repository | azure-rtos embedded gui iot mcu microcontroller rtos | os |
blade | div align center img height 96 alt blade logo src https raw githubusercontent com blade lang blade main blade png div blade programming language build status https github com blade lang blade actions workflows ci yml badge svg https github com blade lang blade actions gitter https badges gitter im blade lang community svg https gitter im blade lang community license https img shields io badge license mit blue svg https github com blade lang blade blob master license coverage status https coveralls io repos github blade lang blade badge svg branch main https coveralls io github blade lang blade branch main version https img shields io badge version 0 0 86 green https github com blade lang blade blade is a modern general purpose programming language focused on enterprise web iot and secure application development blade offers a comprehensive set of tools and libraries out of the box leading to reduced reliance on third party packages blade comes equipped with an integrated package management system simplifying the management of both internal and external dependencies and a self hostable repository server making it ideal for private organizational and personal use its intuitive syntax and gentle learning curve ensure an accessible experience for developers of all skill levels leveraging the best features from javascript python ruby and dart blade provides a familiar and robust ecosystem that enables developers to harness the strengths of these languages effortlessly example the following code implements a simple backend api that runs on port 3000 js import http var server http server 3000 server handle get req res res json req echo listening on port 3000 server listen what s interesting about blade built in package manager and repository server package management is built into the language module system blade also comes with nyssa nyssa is a package manager and self hosted repository server highly suitable for private use zero dependency full stack web development blade comes with a built in web server and a rich set of tools and libraries that support it making it easy to build composable full stack web applications out of the box built in model view template mvt based http web server built in testing framework built in object relation mapping support mdash planned built in support for multiple databases built in web template engine mdash wire built in routing library built in mail library with smtp imap and pop3 support built in device integrations such as support for com ports usb etc mdash planned built in cryptography library built in support for media processing image audio video etc mdash planned and more function promotion a feature of the blade language that makes it easy to reuse any code from an imported module access modifiers unlike javascript and python blade supports access modifiers for variables properties functions classes modules etc decorator functions decorator functions are a set of class methods in blade that makes extending the functionality of existing code super easy easy to extend with c modules blade supports external extensions built in c with a built in extension compiler via nyssa this feature makes it easy to extend language features with c modules showcase of other uses while blade focuses on web and iot it is also great for general software development below are a few showcases of libraries using blade for other impressive stuff jsonrpc https github com mcfriend99 jsonrpc a json rpc library for blade programming language tar https github com mcfriend99 tar pure blade library for creating and extracting tar archives installation to install blade please follow the instructions in the building building md guide usage to start using blade please refer to the tutorial https bladelang com tutorial index html section of the online documentation api documentation api documentation for blade is under active development and can be found at bladelang com https bladelang com standard index html community join the conversation on gitter https gitter im blade lang community submit a feature request https github com blade lang blade issues new labels feature request or bug report https github com blade lang blade issues new labels bug follow us on twitter contributing we need your help to make blade great the blade community is as friendly and welcoming as possible all kinds of contributions like pull requests suggestions typo fixes in documentation feature request bug reports and others are highly appreciated please refer to the contributing contributing md guide for more information license blade is licensed under the 2 clause bsdl license https github com blade lang blade blob master license | programming-language interpreter compiler language blade | front_end |
Project-1-Team-Voyager | link to live page https travisswift github io project 1 team voyager voyager space tracker project 1 team voyager an application that provides updates and information about launches and general information about space technology when i view the calender then i see a list of upcoming space events when i view the webpage i see a picture of the day as the hero image when i scroll down the webpage then i see a feed of current events description team voyager wanted to make a website where a user can come and find the latest in any astro news in the homepage of the website the user will be able to view an apod astro picture of the day this portion will display an image for the user and the user will also be able to interact with by choosing any date from present to past also within the homepage of this website there will be a navigation for the user to be able to choose what information they would like to view some of the features we offer as of present are launching information where the user can view different launches along with a countdown till launch other features are information on the iss space station we also added a live view for the user we also have a detailed list of information about astronauts along with a bio of them and lastly a feature to view past and future astro events contributor cameron stroup https github com cameronstroup travis swift https github com travisswift steffen gonzalez https github com steffen568 xan hamilton https github com tsadiktalmudim paul soliz https github com modifir3 screenshots alt text assets images voyager homepage png | server |
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iot-edge-modbus | archived this repository is archived and no longer being maintained note this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments azure iot edge modbus module ga using this module developers can build azure iot edge solutions with modbus tcp rtu rtu is currently not available in windows environment please use linux host linux container to play with rtu mode connectivity the modbus module is an azure iot edge https github com azure iot edge module capable of reading data from modbus devices and publishing data to the azure iot hub via the edge framework developers can modify the module tailoring to any scenario doc diagram png there are prebuilt modbus tcp module container images ready at here https hub docker com r microsoft azureiotedge modbus tcp for you to quickstart the experience of azure iot edge on your target device or simulated device visit http azure com iotdev to learn more about developing applications for azure iot azure iot edge compatibility current version of the module is targeted for the azure iot edge ga https azure microsoft com en us blog azure iot edge generally available for enterprise grade scaled deployments if you are using v1 version of iot edge https github com azure iot edge tree master v1 previously known as azure iot gateway please use v1 version of this module all materials can be found in v1 https github com azure iot edge modbus tree master v1 folder find more information about azure iot edge at here https docs microsoft com en us azure iot edge how iot edge works target device setup platform compatibility azure iot edge is designed to be used with a broad range of operating system platforms modbus module has been tested on the following platforms windows 10 enterprise version 1709 x64 windows 10 iot core version 1709 x64 linux x64 linux arm32v7 device setup windows 10 desktop https docs microsoft com en us azure iot edge quickstart windows 10 iot core https docs microsoft com en us azure iot edge how to install iot core linux https docs microsoft com en us azure iot edge quickstart linux build environment setup modbus module is a net core 2 1 application which is developed and built based on the guidelines in azure iot edge document please follow this link https docs microsoft com en us azure iot edge tutorial csharp module to setup the build environment basic requirement docker ce net core 2 1 sdk howto build in this section the modbus module we be built as an iot edge module open the project in vs code and open vs code command palette type and run the command edge build iot edge solution select the deployment template json file for your solution from the command palette note be sure to check configuration section https github com azure iot edge modbus configuration to properly set each fields before deploying the module in azure iot hub devices explorer right click an iot edge device id then select create deployment for iot edge device open the config folder of your solution then select the deployment json file click select edge deployment manifest then you can see the deployment is successfully created with a deployment id in vs code integrated terminal you can check your container status in the vs code docker explorer or by run the docker ps command in the terminal configuration before running the module proper configuration is required here is a sample configuration for your reference json publishinterval 2000 version 2 slaveconfigs slave01 slaveconnection 192 168 0 1 hwid powermeter 0a 01 01 01 01 01 retrycount 10 retryinterval 50 operations op01 pollinginterval 1000 unitid 1 startaddress 400001 count 2 displayname voltage correlationid messagetype1 op02 pollinginterval 1000 unitid 1 startaddress 400002 count 2 displayname current correlationid messagetype1 slave02 slaveconnection ttys0 hwid powermeter 0a 01 01 01 01 02 baudrate 9600 databits 8 stopbits 1 parity odd flowcontrol none operations op01 pollinginterval 2000 unitid 1 startaddress 40001 count 1 displayname power op02 pollinginterval 2000 unitid 1 startaddress 40003 count 1 displayname status meaning of each field publishinterval interval between each push to iot hub in millisecond version switch between the pp public preview and the latest message payload format valid value for pp 1 all other values will switch to the latest format slaveconfigs contains one or more modbus slaves configuration in this sample we have slave01 and slave02 two devices slave01 slave02 user defined names for each modbus slave cannot have duplicates under slaveconfigs slaveconnection ipv4 address or the serial port name of the modbus slave retrycount max retry attempt for reading data default to 10 retryinterval retry interval between each retry attempt default to 50 milliseconds hwid unique id for each modbus slave user defined baudrate serial port communication parameter valid values 9600 14400 19200 databits serial port communication parameter valid values 7 8 stopbits serial port communication parameter valid values 1 1 5 2 parity serial port communication parameter valid values odd even none flowcontrol serial port communication parameter valid values only support none now operations contains one or more modbus read requests in this sample we have op01 and op02 two read requests in both slave01 and slave02 op01 op02 user defined names for each read request cannot have duplicates under the same operations section pollinginterval interval between each read request in millisecond unitid the unit id to be read startaddress the starting address of modbus read request currently supports both 5 digit and 6 digit format https en wikipedia org wiki modbus coil 2c discrete input 2c input register 2c holding register numbers and addresses count number of registers bits to be read displayname alternative name for the startaddress register s user defined correlationid the operations with same id with be grouped together in their output message for more about modbus please refer to the wiki https en wikipedia org wiki modbus link module endpoints and routing there are two endpoints defined in modbus tcp module modbusoutput this is a output endpoint for telemetries all read operations defined in configuration will be composed as telemetry messages output to this endpoint input1 this is an input endpoint for write commands input output message format and routing rules are introduced below read from modbus telemetry message message properties json content type application edge modbus json latest message payload json publishtimestamp 2018 04 17 12 28 53 content hwid powermeter 0a 01 01 01 01 02 data correlationid messagetype1 sourcetimestamp 2018 04 17 12 28 48 values displayname op02 address 40003 value 2785 displayname op02 address 40004 value 18529 displayname op01 address 40001 value 1840 displayname op01 address 40002 value 31497 correlationid messagetype1 sourcetimestamp 2018 04 17 12 28 50 values displayname op02 address 40003 value 21578 displayname op02 address 40004 value 26979 displayname op01 address 40001 value 13210 displayname op01 address 40002 value 13549 pp public preview message payload json displayname rotaryone hwid wise4012e address 40001 value 0 sourcetimestamp 2018 09 18 04 14 32 displayname switchone hwid wise4012e address 00001 value 1 sourcetimestamp 2018 09 18 04 14 33 displayname relayone hwid wise4012e address 00017 value 0 sourcetimestamp 2018 09 18 04 14 33 route to iot hub json routes modbustoiothub from messages modules modbus outputs modbusoutput into upstream route to other filter modules json routes modbustofilter from messages modules modbus outputs modbusoutput into brokeredendpoint modules filtermodule inputs input1 write to modbus modbus module use input endpoint input1 to receive commands currently it supports writing back to a single register cell in a modbus slave note currently iot edge only supports send messages into one module from another module direct c2d messages doesn t work command message the content of command must be the following message format message properties json command type modbuswrite message payload json hwid powermeter 0a 01 01 01 01 01 uid 1 address 40001 value 15 route from other filter modules the command should have a property command type with value modbuswrite also routing must be enabled by specifying rule like below json routes filtertomodbus from messages modules filtermodule outputs output1 into brokeredendpoint modules modbus inputs input1 howto run run as an iot edge module please follow the link https docs microsoft com en us azure iot edge tutorial csharp module to deploy the module as an iot edge module configure modbus rtu this is for modbus rtu only modbus tcp could skip this section in the container create option section enter the following for device mapping json hostconfig devices pathonhost device name on host machine pathincontainer device name in container cgrouppermissions rwm | server |
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db-capstone-project | db capstone project meta database engineering capstone in this course i completed a capstone project in which i created a database and client for little lemon restaurant the capstone project enabled me to demonstrate multiple skills from the certificate by solving an authentic real world problem in this case it was to design little lemon s database and access the data via the python mysql connector api i demonstrate my new skillset by designing and composing a database solution combining all the skills and technologies i ve learned throughout the program to solve the problem at hand by the end of this project i had proven your ability to set up a database project database modelling forward and backward engineering model synchronization add sales reports create a table booking system work with data analytics and visualization tableau data modelling snowflake and create a database client via python mysql connector api i also demonstrated my ability with the following tools and software git mysql workbench tableau and python | server |
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IE-CA-6 | ie ca 6 internet engineering course project 6 sql database | server |
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ComputerVision | computervision deep learning in computer vision | ai |
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fswb-torres | fswb programming examples for the full stack web development round one begin https www amazon com dp b08gfszgjt ref sr 1 11 dchild 1 keywords full stack web development round one begin qid 1598056453 sr 8 11 book purchase at amazon com https www amazon com dp b08gfszgjt ref sr 1 11 dchild 1 keywords full stack web development round one begin qid 1598056453 sr 8 11 notice mitre corporation prs case no 20 2148 | front_end |
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iotedge | iot edge build status https dev azure com msazure one apis build status custom azure iot edge core azure iot edge core 20ci branchname main https dev azure com msazure one build latest definitionid 45137 branchname main welcome to the home of iot edge iot edge moves cloud analytics and custom business logic to devices so that your organization can focus on business insights instead of data management enable your solution to truly scale by configuring your iot software deploying it to devices via standard containers and monitoring it all from the cloud this repository consists of three main projects the edge agent edge agent the edge hub edge hub and the iot edge security daemon edgelet documentation documentation for the azure iot edge product can be found at https docs microsoft com azure iot edge contributing if you would like to build or change the iot edge source code please follow the devguide doc devguide md this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | server |
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Stock-Ticker | stock ticker cs591 mobile development final project team members tiancheng zhu ruizhi jiang cedric wille alexander vardanyan tianqi tan powered by flutter firebase stock api https www alphavantage co app icon icons by icons8 to run paste api keys dart to flutter module lib if you encounter any trouble with the app accessing firebase or other services please paste the updated gradle files into the directory stockticker and allow app to replace the existing app folder and build gradle to replace the existing build gradle folder please contact one of the team members if you need a copy of api keys dart or the build files a href privacy html privacy policy a br a href term html terms conditions a | front_end |
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climin | climin climin is a python package for optimization heavily biased to machine learning scenarios distributed under the bsd 3 clause license it works on top of numpy and partially gnumpy the project was started in winter 2011 by christian osendorfer and justin bayer since then sarah diot girard thomas rueckstiess and sebastian urban have contributed if you use climin in your academic work please cite as tech report is in preparation j bayer and c osendorfer and s diot girard and t r ckstiess and sebastian urban climin a pythonic framework for gradient based function optimization tum tech report 2016 http github com brml climin important links official repository of source http github com brml climin documentation http climin readthedocs org mailing list climin librelist com archive http librelist com browser climin dependencies the software is tested under python 2 7 with numpy 1 10 4 scipy 0 17 the tests are run with nosetests installation use git to clone the official repository then run pip install user e in the clone to intall in your local user space testing from the download directory run nosetests tests | ai |
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awesome-it-merchandise | awesome it merchandise a curated list of merchandise offerings related to information technology all links either point to official organizations shops that donate part of the profit back to them or portfolio pages of artists who created the original designs contents nonprofit organizations nonprofit organizations officially endorsed shops officially endorsed shops open source firmware open source firmware open source software open source software programming languages programming languages nonprofit organizations apache software foundation merchandise https www apache org foundation buy stuff html a list of entities who sell apache branded merchandise and contribute profits back to the asf code org https www code org shop t shirts stickers buttons swag kits posters and classroom supplies to promote code org organizers of the annual hour of code electronic frontier foundation shop https supporters eff org shop stickers posters and clothing to support the work on defending civil liberties in the digital world free software foundation shop https shop fsf org books mugs stickers and clothing to support the work on promoting computer user freedom internet archive store https store archive org clothing accessories and more to support the digital library of internet sites and other cultural artifacts in digital form numfocus shop https shop spreadshirt com numfocus clothing and accessories featuring logos of projects like julia matplotlib pandas and pydata that are supported by numfocus referred from https numfocus org open source initiative store https opensource org store t shirt store to support open source advocacy and inter community work wikipedia store https store wikimedia org buy wikipedia apparel to thank a volunteer who helps make wikipedia possible officially endorsed shops freewear org https www freewear org clothing mugs and stickers with free and open source software designs with a share of the profit donated to the respective organizations hellotux https www hellotux com buy embroidered linux and free software t shirts in this shop that supports free software projects and developers open source firmware quantum mechanical keyboard https olkb com parts official store for qmk and olkb projects focused on making an open source keyboard firmware and hardware open source software blender store https store blender org buy clothes stickers books accessories and more to support blender projects open movies and software development the blender store is operated by blender institute in amsterdam the netherlands gentoo linux merchandise https www gentoo org inside gentoo stores stores offering gentoo products some of them have been licensed to use the gentoo logo by either the gentoo foundation inc or friends of gentoo e v gnome merchandise https www gnome org merchandise a list of vendors offering gnome merchandise some of them donate part of their profit to the gnome foundation inkscape merchandise https inkscape org shop a list of shops offering merchandise for the free vector graphics software linux mint store https www linuxmint com store php clothes featuring the linux mint logo and hardware running linux mint the joomla shop https community joomla org the joomla shop html clothing and accessories for the joomla content management system webpack shop https webpack threadless com custom shirts and prints featuring the logo of the javascript module bundler referred from https webpack js org programming languages go store https go store io official swag and merch store for the go programmming language with 100 of proceeds donated to the gobridge nonprofit organization haxe shirts and merch https haxe org foundation shop official store of the haxe foundation offering clothing drinkware and bags ldpl merchandise https www teepublic com user lartu official teepublic store of the ldpl programming language referred from https www ldpl lang org python merchandise https www python org community merchandise a list of manufacturers who pledged to donate a portion of the proceeds from their python branded sales to the python software foundation contribute contributions welcome read the contribution guidelines contributing md first license cc0 https mirrors creativecommons org presskit buttons 88x31 svg cc zero svg https creativecommons org publicdomain zero 1 0 to the extent possible under law ramiro g mez https ramiro org has waived all copyright and related or neighboring rights to this work | awesome awesome-list merchandise merch foss nonprofit-organizations open-source hacktoberfest | server |
spaCyTraining | natural language processing nlp with python and spacy by tertiary courses https www tertiarycourses com sg these are the exercise files used for natural language processing nlp with python and spacy https www tertiarycourses com sg deep learning nlp with spacy html course the course outline can be found in https www tertiarycourses com sg deep learning nlp with spacy html https www tertiarycourses com my deep learning nlp with spacy html p strong topic 1 overview of nlp and deep learning strong p ul li overview of natural language processing nlp li li applications of nlp li li deep learning approach to nlp li li nlp frameworks li ul p strong topic 2 language modeling strong p ul li introduction to spacy li li tokenization and stop words li li pre trained statistical language models li li part of speech pos amp syntactic dependency li li named entities recognition ner and lemmatization li li rule based matching li ul p strong topic 3 word embedding and similarity comparison strong p ul li overview of word embedding li li comparing similarity li ul p strong topic 4 nlp pipelines and text classification strong p ul li language processing pipeline li li create custom component li li model training and update li li text classification li ul p strong topic 5 attention and transformer based pipeline strong p ul li basics of recurrent neural network rnn li li introduction to attention and transformer li li transformer based pipelines li ul | spacy spacy-nlp | ai |
ParaKnowTransfer | paraknowtransfer code for seeking neural nuggets knowledge transfer in large language models from a parametric perspective | ai |
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MultiPL-E | multi programming language evaluation of large language models of code multipl e multipl e is a system for translating unit test driven neural code generation benchmarks to new languages we have used multipl e to translate two popular python benchmarks humaneval and mbpp to 18 other programming languages for more information multipl e is part of the bigcode code generation lm harness this is the easiest way to use multipl e the multilingual code models evaluation by bigcode evaluates code llms using several benchmarks including multipl e we have a tutorial on how to use multipl e directly read our paper multipl e a scalable and polyglot approach to benchmarking neural code generation the multipl e dataset of translated prompts is available on the hugging face hub versions version 0 4 0 work in progress new languages ocaml matlab using jsonl instead of json for prompts several bugfixes to prompts version 0 3 0 used to evaluate starcoder this version corrects several bugs in prompts and test cases that resulted in lower pass k rates for some of the statically typed languages the most significant difference is that the pass k for java increases by about 2 on humaneval version 0 2 0 used to evaluate santacoder tutorial https nuprl github io multipl e bigcode code generation lm harness https github com bigcode project bigcode evaluation harness multipl e a scalable and polyglot approach to benchmarking neural code generation https ieeexplore ieee org abstract document 10103177 santacoder https arxiv org abs 2301 03988 multipl e dataset https huggingface co datasets nuprl multipl e starcoder https arxiv org abs 2305 06161 multilingual code models evaluation https huggingface co spaces bigcode multilingual code evals | ai |
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vision_4_beginners | computer vision for beginners the series of computer vision for beginners computer vision is one of the hottest topics in artificial intelligence it is making tremendous advances in self driving cars robotics as well as in various photo correction apps steady progress in object detection is being made every day gans is also a thing researchers are putting their eyes on these days vision is showing us the future of technology and we can t even imagine what will be the end of its possibilities img https github com jjone36 vision 4 beginners blob master images main jpg so do you want to take your first step in computer vision and participate in this latest movement welcome you are at the right place this is the tutorial for computer vision and image processing for beginners which is also available on medium https towardsdatascience com computer vision for beginners part 1 7cca775f58ef br installing opencv the installation can be processed as follows pip install opencv python 3 4 2 pip install opencv contrib python 3 3 1 after you finish the installation try importing the package to see if it works well import cv2 cv2 version you can also find the detailed description here https pypi org project opencv python br the whole set of the series part 1 understanding color models and drawing figures on images https github com jjone36 vision 4 beginners blob master 01 image processing 01 introduction ipynb part 2 the basics of image processing with filtering and gradients https github com jjone36 vision 4 beginners blob master 01 image processing 02 image processing ipynb part 3 from feature detection to face detection https github com jjone36 vision 4 beginners blob master 01 image processing 03 object detection ipynb part 4 contour detection and having a little bit of fun https github com jjone36 vision 4 beginners blob master 01 image processing 04 contour mapping ipynb | artificial-intelligence computer-vision image-processing tutorial opencv-python object-detection medium-article beginner-friendly beginners-tutorial-series image-manipulation | ai |
FreeRTOS-ChibiOSAL | freertos with chibios hal this repository contains all code needed to build the chibios hal together with the freertos real time operating system an example project is included in the folder example first you should run make in the freertos directory this will create a static library containing freertos you can edit the settings in freertosconfig h to match your project use the makefile matching the type of cpu you use then run make in the example folder to create the binary | os |
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monorepo-react-component-library | h1 align center the design system h1 this is a proof of concept of a monorepo structure for serving react components and design tokens p align center a href https lerna js org img src https img shields io badge maintained 20with lerna cc00ff svg alt maintained with lerna a a href http commitizen github io cz cli img src https img shields io badge commitizen friendly brightgreen svg alt commitzen friendly a a href https conventionalcommits org img src https img shields io badge conventional 20commits 1 0 0 yellow svg alt conventional commits a p getting started this is a monorepo repository using lerna https lerna js org commitzen http commitizen github io cz cli and conventional commits https conventionalcommits org to maintain and manage component versions and for documentation we use storybook https storybook js org and compodoc https compodoc app you can access by clicking here https thedesignsystem gustavoribeiro dev list of packages containing in this repository name of package description thedesignsystem components components react components with each package json file setup local setup to run this project locally tools node version 10 20 1 https nodejs org download release v10 21 0 if you use nvm https github com nvm sh nvm just run the command nvm use in the root folder configuration install all the dependencies npm i you can see the components of this repo in storybook by running npm run start storybook installing components all components in this repository are installed separately that is each component has its own npm package for example if you want to install the button component npm i thedesignsystem button | react design-system monorepo lerna conventional-commits | os |
Skeleton-framework-jekyll | skeleton framework for jekyll an implementation of dave gamache s skeleton framework in jekyll the original skeleton framework and docs are available from http getskeleton com getting started as few changes to the base skeleton framework have been made if you have a feature request please make a formal request on the original skeleton framework start using skeleton with jekyll download a zip archive of the repository https github com bradfordlynch skeleton framework jekyll archive master zip clone the repository git clone https github com bradfordlynch skeleton framework jekyll git scaffolding included is skeleton s base css image files a favicon and index html the head content has been moved into an include and the default layout contains everything except for the content div s of skeleton s index html skeleton framework jekyll index html config yml includes head html layouts default html posts css normalize css skeleton css feed xml images favicon png license all parts of skeleton framework jekyll are free to use and abuse under the open source mit license https github com bradfordlynch skeleton framework jekyll blob master license md acknowledgement skeleton was created by dave gamache https twitter com dhg for a better web skeleton framework jekyll was created by bradford lynch https twitter com blynch41 for better skeleton and jekyll harmony | front_end |
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moko-widgets | moko widgets https user images githubusercontent com 5010169 70204294 93a45900 1752 11ea 9bb6 820d514ceef9 png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko widgets https repo1 maven org maven2 dev icerock moko widgets kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name widgets mobile kotlin widgets this is a kotlin multiplatform library that provides declarative ui and application screens management in common code you can implement full application for android and ios only from common code with it sample screen android ios sample android https user images githubusercontent com 5010169 70204616 d0bd1b00 1753 11ea 95d1 749341631ba7 png sample ios https user images githubusercontent com 5010169 70204576 aff4c580 1753 11ea 95b9 14e488edb689 png code of screen structure kotlin class loginscreen private val theme theme private val loginviewmodelfactory loginviewmodel widgetscreen args empty override fun createcontentwidget with theme val viewmodel getviewmodel loginviewmodelfactory constraint size widgetsize asparent val logoimage image size widgetsize const sizespec wrapcontent sizespec wrapcontent image const image resource mr images logo scaletype imagewidget scaletype fit val emailinput input size widgetsize widthasparentheightwrapcontent id id emailinputid label const email desc as stringdesc field viewmodel emailfield inputtype inputtype phone val passwordinput input size widgetsize widthasparentheightwrapcontent id id passwordinputid label const password desc as stringdesc field viewmodel passwordfield val loginbutton button size widgetsize const sizespec asparent sizespec exact 50f content buttonwidget content text value data login desc ontap viewmodel onloginpressed val registerbutton button id id registrationbuttonid size widgetsize const sizespec wrapcontent sizespec exact 40f content buttonwidget content text value data registration desc ontap viewmodel onregistrationpressed val copyrighttext text size widgetsize wrapcontent text const icerock development constraints passwordinput centerytocentery root passwordinput leftrighttoleftright root offset 16 emailinput bottomtotop passwordinput offset 8 emailinput leftrighttoleftright root offset 16 loginbutton toptobottom passwordinput loginbutton leftrighttoleftright root registerbutton toptobottom loginbutton registerbutton righttoright root logo image height must be automatic logoimage centerxtocenterx root logoimage verticalcenterbetween top root top bottom emailinput top copyrighttext centerxtocenterx root copyrighttext bottomtobottom root safearea offset 8 object id object emailinputid inputwidget id object passwordinputid inputwidget id object registrationbuttonid buttonwidget id code of theme kotlin val loginscreen theme basetheme factory constraintwidget defaultcategory constraintviewfactory padding paddingvalues 16f background background fill fill solid colors white factory inputwidget defaultcategory systeminputviewfactory margins marginvalues bottom 8f underlinecolor color 0x000000dd underlinefocusedcolor color 0x3949abff labeltextstyle textstyle size 12 color color 0x3949abff fontstyle fontstyle bold errortextstyle textstyle size 12 color color 0xb00020ff fontstyle fontstyle bold textstyle textstyle size 16 color color 0x000000ff fontstyle fontstyle medium val corners platformspecific android 8f ios 25f factory buttonwidget defaultcategory systembuttonviewfactory margins marginvalues top 32f background val bg color background background fill fill solid it shape shape rectangle cornerradius corners statebackground normal bg color 0x6770e0ff pressed bg color 0x6770e0ee disabled bg color 0x6770e0bb invoke textstyle textstyle color colors white factory loginscreen id registrationbuttonid systembuttonviewfactory background val bg color background background fill fill solid it shape shape rectangle cornerradius corners statebackground normal bg color 0xffffff00 pressed bg color 0xe7e7eeee disabled bg color 0x000000bb invoke margins marginvalues top 16f textstyle textstyle color color 0x777889ff androidelevationenabled false table of contents features features requirements requirements versions versions installation installation usage usage samples samples set up locally setup locally contributing contributing license license features compliance with platform rules declare structure not rendering compile time safety reactive data handling requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral project build gradle groovy dependencies commonmainapi dev icerock moko widgets 0 2 4 commonmainapi dev icerock moko widgets bottomsheet 0 2 4 show bottom sheets commonmainapi dev icerock moko widgets collection 0 2 4 collection widget commonmainapi dev icerock moko widgets datetime picker 0 2 4 show datepicker commonmainapi dev icerock moko widgets image network 0 2 4 images with load from url commonmainapi dev icerock moko widgets sms 0 2 4 input with sms autofill commonmainapi dev icerock moko widgets media 0 2 4 moko media integration commonmainapi dev icerock moko widgets permissions 0 2 4 moko permissions integration codegen for new widgets with widgetdef root build gradle groovy buildscript repositories gradlepluginportal dependencies classpath dev icerock moko widgets gradle plugin 0 2 4 project build gradle groovy apply plugin dev icerock mobile multiplatform widgets generator must apply before kotlin multiplatform plugin usage hello world multiplatform application definition at mpp library src commonmain kotlin app kt kotlin class app baseapplication override fun setup screendesc args empty val theme theme return registerscreen helloworldscreen class helloworldscreen theme screen definition mpp library src commonmain kotlin helloworldscreen kt kotlin class helloworldscreen private val theme theme widgetscreen args empty override fun createcontentwidget with theme container size widgetsize asparent center text size widgetsize wrapcontent text const hello world result android ios helloworld android https user images githubusercontent com 5010169 69857402 84408e00 12c2 11ea 945a 5f287a754e67 png helloworld ios https user images githubusercontent com 5010169 69857202 febcde00 12c1 11ea 8679 5b68b5b11c42 png configure styles setup theme config kotlin val theme theme factory textwidget defaultcategory systemtextviewfactory textstyle textstyle size 24 color colors black padding paddingvalues padding 16f result android ios custom style android https user images githubusercontent com 5010169 69857575 d1bcfb00 12c2 11ea 9cbb ee6b17357db2 png custom style ios https user images githubusercontent com 5010169 69857701 09c43e00 12c3 11ea 9e9d 181a298a7edf png bind data to ui kotlin class timerscreen private val theme theme widgetscreen args empty override fun createcontentwidget widget widgetsize const sizespec asparent sizespec asparent val viewmodel getviewmodel timerviewmodel return with theme container size widgetsize asparent center text size widgetsize wrapcontent text viewmodel text class timerviewmodel viewmodel private val iteration mutablelivedata int 0 val text livedata stringdesc iteration map it tostring desc init viewmodelscope launch while isactive delay 1000 iteration value iteration value 1 samples please see more examples in the sample directory sample set up locally the widgets directory widgets contains the widgets library the widgets bottomsheet directory widgets bottomsheet contains the widgets bottomsheet library the widgets sms directory widgets sms contains the widgets sms library the widgets datetime picker directory widgets datetime picker contains the datetime picker library the widgets collection directory widgets collection contains the collection library the gradle plugin directory gradle plugin contains the gradle plugin which apply compiler plugins for native and jvm the kotlin plugin directory kotlin plugin contains the jvm compiler plugin with code generation from widgetdef annotation the kotlin native plugin directory kotlin native plugin contains the native compiler plugin with code generation from widgetdef annotation the kotlin common plugin directory kotlin common plugin contains the common code of jvm and native compiler plugins the sample directory sample contains sample apps for android and ios plus the mpp library connected to the apps for local testing a library use publishtomavenlocal sh cd sample ios app pod install sample apps priority use the locally published version run android from android studio module android app run ios from xcode workspace sample ios app ios app xcworkspace contributing all development both new features and bug fixes is performed in the develop branch this way master always contains the sources of the most recently released version please send prs with bug fixes to the develop branch documentation fixes in the markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master on release for more details on contributing please see the contributing guide contributing md license copyright 2019 icerock mag inc 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 | android ios moko kotlin-native kotlin-multiplatform gradle-plugin kotlin-compiler-plugin kotlin ui ui-dsl | front_end |
PsychCMS | psychcms psychcms is a content management system optimized for small bandwidth and self explained useability using asynchronus json interpreter optimized template user cacheing with dynamic content processing featured easy photographer friendly picture upload and thumbnail rendering processing for epical lightbox gallerys with comment and social bookmark and sharing feature for self driven marketing special uninterruptable multimedia player for unique and glamourous background or streaming use easy to setup search engine meta tag and sitemap creation simple and easy to manage web user session management with realtime tracking and live marketing feature with instant content push and browser script execution for epic content presentation using database stored javascript or jquery for animation dynamical script and css loader url api for simple and dsl based database and content integration for expandable and universal app development simple and dynamic group policies for publisher and user management simple backend with wysiwyg article editor based on jquery jquerymobile and jqueryui for easy content management made easy for musician photographer and people beeing unique and special like their portfolio please donate to admin musicfiler de this software is licensed under the mit license it online solutions ruhrig is member of the free software foundation this projekt is made public to support young entrepreneurs to help them with a quick start of their business if you use this software for commercial please donate to help me keeping this software free you are welcome to be a part of my team developing the future of web in case of using that software without ssl encryption online u need to know that everyone else can read the website data sended and recieved in cleartype to be sure to have a strong and secure data up and download you should use a self generated server certificate i am looking forward to add a client based zip and xtra datastream with binary file encryption for high secure content or microblogging for example business internals to heal paranoid users and help to keep secrets if you want you can add a plugin or a functionality to help the project to be more multifunctional staying easy and free for you and me and the future of web please send me your plugin if you want i can add it to the project i m interested in a cart and checkout system with dynamical product catalogue for example the ability to add attributes to a productid n m div articletypes with order reporting bill call helpdesk customer relationship phonebook warehouse and inkasso user friendly email reminder and automated bilance calculation and most important a backup email and export to pdf addon with a simple document based quality management | server |
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ICOM_4217_LABS | icom 4217 embedded systems design laboratory work br university of puerto rico mayaguez campus br using tiva c series tm4c123gh6pm br authors carlos rodriguez osvaldo ramirez | os |
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koyo | koyo ci cd https github com bogdanp koyo workflows ci cd badge svg koyo is a web application development toolkit that expands upon racket s built in web server with all the functionality that a web app typically needs in a complete package all of koyo s components are decoupled so you get to pick and choose what you use quickstart raco pkg install koyo raco koyo new example or raco koyo new b minimal example cd example cat readme md documentation you can find the documentation on the racket package server docs the package server only updates once every few hours so if you want the absolute most recent docs then you can visit koyoweb org docs master license koyo is licensed under the 3 clause bsd license docs https docs racket lang org koyo koyo doc index html docs master https koyoweb org | racket web framework | front_end |
Re-re | re re a collection of resources for linux and macos ios reverse engineering | os |
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e17-4yp-Large-Language-Models-in-Education | large language models in education table of content 1 introduction introduction 2 problem statement problemstatement 3 solution solution introduction in the last few years artificial intelligence has advanced significantly particularly in the fields of generative ai and large language models llms these cutting edge models have proven to be exceptionally capable of reading writing and producing content that is human like opening up new horizons in creativity and invention the rise of generative ai has sparked a lot of curiosity about the possible uses it could have in a variety of industries these generative ai models provide fascinating prospects in the field of education where technology integration is becoming more common intelligent tutoring systems have been sought after in education for a long time to improve and customize students learning experiences the effectiveness of generative ai paired with the current trends in educational technology use offer a potent synergy that has the potential to completely change the educational environment it offers great potential for involving students encouraging creativity and facilitating individualized learning journeys for them as generative ai models may produce tailored content adaptive learning materials and interactive simulations the dawn of a new era in education is on the horizon driven by the revolutionary potential of generative ai as we observe the convergence of cutting edge technology and educational methodology we explore how llm might be used in education as the basis for creating a smart tutor that is both affordable and successful we aim to develop a cutting edge teaching tool that can adapt to the demands of each individual student deliver interactive and interesting learning experiences and provide individualized assistance and support by harnessing the power of llms problem statement problems in llms in education high cost in llms inability to access our own materials llms can be expensive in terms of both time and computer resources especially when done at scale there are numerous ways to lower the cost and increase the effectiveness of the cost effective intelligent tutor then we are unable to get further readings of our own materials in the platforms we should be able to get urls for further reading solution as the solution a cost effective intelligent tutor is implemented as a web application our target domain is computer architecture course materials functionalities of the system 1 question categorizer 2 sentence similarity checker high level architectural diagram https github com cepdnaclk e17 4yp large language models in education blob main docs images diagram png team e 17 297 rupasinghe t t v n e17297 eng pdn ac lk mailto e17297 eng pdn ac lk e 17 206 manohora h t e17206 eng pdn ac lk mailto e17206 eng pdn ac lk e 17 148 kalpana m w v e17148 eng pdn ac lk mailto e17148 eng pdn ac lk supervisors dr damayanthi herath damayanthiherath eng pdn ac lk mailto damayanthiherath eng pdn ac lk prof roshan g ragel roshanr eng pdn ac lk mailto roshanr eng pdn ac lk dr isuru nawinne isurunawinne eng pdn ac lk mailto isurunawinne eng pdn ac lk dr shamane siriwardhana gshasiri gmail com mailto gshasiri gmail com related links project page https cepdnaclk github io e17 4yp large language models in education department website http www ce pdn ac lk | ai |
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introsde-2016-assignment-2 | introduction to service design and engineering assignment 2 general information in the assignment i worked alone server https mysterious waters 69770 herokuapp com the following are the supported rest endpoints get person get all persons get person id get a person with its id get person id measuretype get all the specified measures from a person specified with id get person id measuretype mid get a single measure specified with measure id measure type and person id get person measuretype measuretype min min max max return all persons that have measure that match min max values get person id measuretype before beforedate after afterdate return all measures of person specified by id that match the given before and after dates date format yyyy mm dd get measuretypes get all the measure types that are supported post person create a new person post person id measuretype create a new measure for person specified by id put person id update a person specified by id put person id measuretype mid update a measure specified by measure id measure type and person id delete person id delete a person specified by id about this assignment is about restful services in the assignment i have created a rest service that is running on heroku and a client that is making requests to the server structure the root folder contains mainly setup and configuration files and the database file the src package consist of all the needed java files for the program in addition with the persistence xml file webcontent folder contains files for server setup such as web xml file function the ivy xml and build xml files in root folder are for installing all the dependencies and running the program the lifecoach sqlite is the database file and app json and procfile in root folder contains setups for heroku log files contains logs from client server requests the src package contains all the java files and persistence xml file for persistence units the introsde rest ehealth package contains server configuration files introsde rest ehealt client contains the client introsde rest ehealth dao contains file for interacting with database introsde rest ehealt model contains the model files and introsde rest ehealth resources contains the resource handling files for the uri paths run the program uses ant build tool for running the program to execute the program please open the terminal and run the following command in the root directory of the program ant execute client the above command installs all dependencies and executes the client the client sends requests to the remote server running on heroku and prints the results to the terminal and also saves them to logs in the root folder | server |
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blockchainbean | build status https travis ci org ibm blockchainbean svg branch master https travis ci org ibm blockchainbean warning this repository is no longer maintained warning this pattern focuses on older technology e g hyperledger fabric apis prior to fabric 1 4 therefore there is no support for this pattern and it will be archived on may 1 2019 you are welcome to use up to that date but we recommend that you begin working with the updated release found at https developer ibm com patterns coffee supply chain network hyperledger fabric blockchain 2 see updated pattern here https github com horeaporutiu blockchainbean2 the steps outlined below will not work update is in progress here https github com horeaporutiu blockchainbean2 horea this code pattern is based on a recent proof of concept developed in collaboration with a coffee roasting company that was nice enough to let us use their supply chain documents the link to the application that the code pattern is based off of is here https www ibm com thought leadership blockchainbean all documents that were used in the supply chain are available online and can be found by clicking the view the blockchain button at https www ibm com thought leadership blockchainbean in this code pattern we will create a blockchain app that increases visibility and efficiency in the supply chain of a coffee retailer the private keys and credentials of the blockchain application will be stored on a cloudant database we will use different transactions to show different possible actions for the different participants in the supply chain this sample application will record all transactions on the ibm blockchain starter plan and enable a coffee retailer to ensure the customer that their coffee is organic and fair trade the code pattern can be useful to developers that are looking into learning more about creating applications that mimic a food trust supply chain with hyperledger composer when the reader has completed this code pattern they will understand how to interact with ibm blockchain starter plan build a blockchain back end using hyperledger composer create and use cloudant nosql database deploy a cloud foundry application that writes and queries to the ledger remember to dump an image in this path architecture docs app architecture png flow 1 user submits transaction proposal using a web app 2 the web app talks to a rest api which is running on cloud foundry 3 the rest api invokes chaincode on the peers of the blockchain network 4 the peers sign the transaction with their certificates which are held in cloudant the ledger is updated on the peers included components ibm blockchain starter plan https console bluemix net catalog services blockchain use the ibm blockchain platform to simplify the developmental governmental and operational aspects of creating a blockchain solution cloudant nosql db https console ng bluemix net catalog services cloudant nosql db a fully managed data layer designed for modern web and mobile applications that leverages a flexible json schema featured technologies ibm blockchain https www ibm com blockchain blockchain is a shared immutable ledger for recording the history of transactions databases https en wikipedia org wiki ibm information management system 22full function 22 databases repository for storing and managing collections of data cloud https www ibm com developerworks learn cloud accessing computer and information technology resources through the internet watch the video deploy a hyperledger composer blockchain network to cloud setup part 1 docs part1 png https www youtube com watch v fznf 5pdxdk t 5s watch the video deploy a hyperledger composer blockchain network to cloud setup part 2 docs part2 png https www youtube com watch v dt6yfemvizw t 4s prerequisites 1 if you do not have an ibm cloud account yet you will need to create one here https ibm biz bdjlxy 2 yeoman to generate app skeleton npm install g generator hyperledger composer npm install g yo steps 1 create the toolchain step 1 create the toolchain 2 clone the repo step 2 clone the repo 3 use yeoman to scaffold your project step 3 use yeoman to scaffold your project 4 push smart contract code step 4 push smart contract code 5 deploy smart contract to ibm blockchain starter plan step 5 deploy smart contract to ibm blockchain starter plan 6 post transactions and query the composer rest server swagger ui step 6 post transactions and query the composer rest server swagger ui 7 launch ibm blockchain starter plan step 7 launch ibm blockchain starter plan 8 inspect blocks on ibm blockchain starter plan step 8 inspect blocks on ibm blockchain starter plan 9 submit fair trade supply data step 9 submit fair trade supply data in this code pattern we will use the blockchain starter kit repository https github com sstone1 blockchain starter kit to deploy our smart contract to the cloud this repo will help us create a devops toolchain to automate deployment img src https i makeagif com media 7 24 2018 mattpg gif width 720 height 450 fd https i makeagif com media 7 24 2018 mattpg gif step 1 create the toolchain packagefile docs step12 gif go to https github com sstone1 blockchain starter kit go to step 2 and click on set up devops toolchain follow steps in the in the readme to create your devops toolchain and github repository at the end of this step you should have a toolchain with a github repo and a delivery pipeline as shown in the last part of step 2 of https github com sstone1 blockchain starter kit just refresh the toolchain page and you should see your toolchain have 3 parts think code delivery as shown in the gif below step 2 clone the repo packagefile docs gitclone gif now we need to clone the repo we have just created click on the github button in the middle which will take you to your new github repo now click on the green clone or download button on the right side of the page this should give you a url save this you ll need it in the next step now in your terminal find a place where you would like to start your project in terminal execute the following git clone https github com yourusername projectname git go into your newly cloned repo i called my bsk horea 2 cd bsk horea 2 step 3 use yeoman to scaffold your project packagefile docs yeoman gif now to the fun part the smart contracts let s use yeoman npm install g generator hyperledger composer npm install g yo cd contracts yo hyperledger composer business network business network name coffeetracker4 description demo author name horea author email horea email com license apache 2 0 namespace org ibm coffee do you want to generate an empty template network yes step 4 push smart contract code packagefile docs packagejson gif first we need to modify some lines from your newly scaffoled application let s cut a few lines inside the package json file this is found in the bsk horea 2 contracts package json file let s remove the lines that start with pretest lint and test packagefile docs pastepermissions gif next let s first clone the blockchain bean directory git clone https github com ibm blockchainbean git next copy and paste the permissions acl file from blockchainbean contracts coffeetrackr permissions acl and overwrite your permissions acl file created from yeoman packagefile docs pushrest gif next we ll copy the queries qry file from blockchainbean contracts coffeetrackr queries qry and paste it in our directory we shouldn t have a queries qry yet after that let s rename our bsk horea 2 contracts models org ibm coffee cto file to bsk horea 2 contracts models model cto and copy and paste that same file from the blockchainbean directory as we have been doing the last file we need is blockchainbean contracts coffeetrackr lib logic js file and we can just grab that and paste the contents in bsk horea 2 contracts lib logic js packagefile docs gitpush gif now in terminal let s push our code up to the github repo with the following commands git add git commit m first commit git push origin master step 5 deploy smart contract to ibm blockchain starter plan packagefile docs delivery gif now we need to check our toolchain that we created in step 2 let s go back to our github repo that we just created click on the link that says created for toolchain in the title of the github repo you will be taken to your ibm cloud toolchains page click on the delivery stage the pipeline should be triggered now if it is not simply go to it and press the play button on the build stage as shown in the gif next wait for the pipeline to start if there are errors you may want to check the logs by pressing the view logs and history option link on the build stage packagefile docs cfapp gif once the app successfully builds you can check this with a simple page refresh the deploy stage should be triggered same as with the build stage you may want to check the logs if there are errors let s check the logs of the deploy stage by clicking the view logs and history button as shown in the gif we can find the url of our cloud foundry app by finding the rest server urls line close to the bottom of the logs as shown in the gif step 6 post transactions and query the composer rest server swagger ui once you click on your application url this is your cloud foundry node js application this will take you to your api documentation or swagger ui that was generated from the deployment scripts the deployment scripts essentially created a node js cloud foundry instance that is connected to a ibm blockchain starter plan instance we won t go into too much detail here but you can find more on simon s repo packagefile docs api gif next go to post pourcup and then paste the following json in the data field as shown in the picture above click try it out class org ibm coffee pourcup cupid cjb0119 next let s query our newly created cup with our unique cupid click on query and get queries getcupdata and enter in your cupid from above then click try it out you should see the relevant details registered from your recent post call on pourcup nice job you successfully queried the blockchain step 7 launch ibm blockchain starter plan packagefile docs launch1 gif next click on the ibm cloud in the top left corner and then use the search bar to find your blockchain service that you created from step 2 click on it and then on launch step 8 inspect blocks on ibm blockchain starter plan packagefile docs 5block gif after we launch our ibm blockchain starter plan let s click on channels on the left side of the page you will be greeted with your defaultchannel and a dashboard of your blockchain it will show you details such as number of blocks time since the last transaction and recent invokations we can click on the blue arrow to expand the details of our block in this gif we expland block number 4 we see the date and time of the transaction the type of transaction the uuid the chaincode id and some other actions we can take let s click on the 3 dot symbol under actions and then view details this will give you your block details you will see even more specific details of your transaction here such as the json object that is written to the ledger nice job you successfully registered your transaction on the ibm blockchain platform packagefile docs 2more gif i ll quickly show you two more transactions in the gif above mainly just to show you how fast your blocks are registered on the ibm blockchain starter plan each time we make a post request to pourcup as shown in the gif above we create a block on the blockchain you can imagine using those pourcup endpoints from the composer rest server instance with a mobile or web ui when certain button clicks or forms are submitted on that mobile or web ui each button click or form submission would trigger a post request to our composer rest server instance and then trigger a block to be added to your blockchain on the ibm blockchain starter plan service using these api endpoints you can create applications that leverage the industry standard for blockchain developers hyperledger fabric this pattern showed you how to build an app with hyperledger composer deploy it onto the ibm blockchain starter plan using a dev ops toolchain our deployed app was simply a swagger ui with endpoints that perform crud create read update delete on a blockchain step 9 submit fair trade supply data now that we have learned how to use our composer rest server with the ibm blockchain starter plan let s get into the smart contracts that we have written let s look at our model file first since that will show us the data schema for our blockchain you can find the model file at https github com ibm blockchainbean blob master contracts coffeetrackr models model cto if we just look at the first few lines in our file we will see that we have a few enums and concepts i won t go into detail here but you can learn more about these types here https hyperledger github io composer latest reference cto language html packagefile docs creategrower gif let s scroll down to the participants of our network and we will see we have 5 we have a grower a regulator a trader a retailer and a shipper by creating different participants in the network we can transfer ownership of the coffee throughout the full life cycle of the supply chain let s first create the grower the grower will have ownership of the coffee asset as the first part of the supply chain life cycle we will simply post to grower class org ibm coffee grower isfairtrade true growerid grower 0201 organization ethiopia gedeb 1 banko gotiti grainpro address class org ibm coffee address city gedeb country ethiopia next we can create the shipper just like we have created the grower above we will simply post to shipper class org ibm coffee shipper shipperid maersk organization a p moller maersk group address class org ibm coffee address city copenhagen country denmark next we can create the regulator just like we have created the grower above we will simply post to regulator class org ibm coffee regulator regulatorid ico organization international coffee organization address class org ibm coffee address city london country england street 22 berners street packagefile docs createtrader gif again we can create the trader just like we have created the grower above we will simply post to trader class org ibm coffee trader traderid trader 0791 organization royal coffee new york address class org ibm coffee address city south plainfield country usa street 661 hadley rd zip 07080 lastly we can create the retailer just like we have created the grower above we will simply post to retailer class org ibm coffee retailer retailerid brooklynroasting organization brooklyn roasting company address class org ibm coffee address city brooklyn country usa street 25 jay st zip 11201 packagefile docs addcoffee gif next we will create a batch of coffee on the network this is going to be a large batch and will make many cups of coffee we will simply post to addcoffee note that we are referencing the growerid the we have created in the step above the coffee will be owned by the grower at this in the supply chain class org ibm coffee addcoffee size large roast dark batchstate ready for distribution grower resource org ibm coffee grower grower 0201 what we now need to do is to do a get to coffee this will show us the batchid of the coffee we have just added from the step above grab that batchid and use it in the subsequent steps warning if you use mine you will get an error packagefile docs submitfairtradedocs gif next the fun part we need to upload the data received from our supply chain and post it to the blockchain to do this we will post to submitfairtradedata class org ibm coffee submitfairtradedata reportname fair trade coffee supply chain report organizationdescription ycfcu is an ethiopian coffee producing processing and exporting cooperative union founded in 2002 ycfcu represents 23 base level cooperatives all located in the gedeo zone within the southern nations nationalities and peoples region snnpr ethnically based region of ethiopia given that its members depend on coffee as their sole source of income ycfcu aims to maximize financial returns to its members through its linkages with international markets reportyear 2016 fairtradepremiuminvested 182273 investmenttitle1 school classroom addition investmentamount1 30 626 investmenttitle2 road infrastructure investmentamount2 43 251 investmenttitle3 food security investmentamount3 34 411 batchid 2vf5yiaey this data is based on the document that was given to us from the coffee roasting company you can see a screenshot of the document below we are simply taking some of the important fields from that document and uploading them https www ibm com thought leadership blockchainbean static fairtrade pdf packagefile docs fairtradedoc png packagefile docs submitpackinglistandcoffee gif next we will continue adding supply chain data we will submit the packing list invoice that shows the shipping details of our coffee batch to do this we will simply post to submitpackinglist note that all we did in our cto file is create the necessary fields based on the documents received from our supply chain in our logic file we just simply set the data to the data fields finally if we do a get on our coffee asset we will see that the asset will be updated with all of the data fields we have set from our previous transactions you will also see that the owner will be updated to be the trader after this submitpackinglist transaction class org ibm coffee submitpackinglist grower resource org ibm coffee grower grower 0201 trader resource org ibm coffee trader trader 0791 pl invoice no 0067 pl issuedate 2017 09 19 pl ico no 010 0150 0128 pl ico lot lot 7 pl fda no 15752850924 pl bill of lading no 961972237 pl loadedvessel northernmagnum pl vesselvoyage no 1707 pl container no redacted pl seal no ml dj0144535 20 dry 8 6 pl timestamp 2018 06 17 batchid 23wrt3jnh packagefile docs submitweighttally gif nice we re doing great so far keep it up we re almost done next we will submit the details of our coffee once we have received the shipment we will get some data from our supply chain about the status of our coffee does it have signs of insect activity does it have holes in the container how many bags are expected we will input all this data to the blockchain class org ibm coffee submitinboundweighttally coffeebatch resource org ibm coffee coffee u94sf62run datestripped 6 oct 2017 marks 010 0150 0128 lot 7 bagsexpected 150 condition class org ibm coffee condition condensation false holeincontainer false insectactivity false batchid 23wrt3jnh the last thing we will need to do is to submit our cupping details our retailer will rate how the coffee tastes and will give it an overall rating based on flavor aftertaste and other details to do this we will simply post to submitcupping we can also make a get to coffee and see that our batch is updated with all of our relevant supply chain data class org ibm coffee submitcupping coffeebatch resource org ibm coffee coffee 4210 date 12 april 2018 cupper brian aroma 9 flavor 8 aftertaste 8 acidity 8 body 9 finalscore 89 00 batchid 23wrt3jnh nice great job so at this point we have finished submitting all of the supply chain details give yourself a pat on the back you made it so now we have a working proof of concept for tracking the life cycle of coffee from the grower all the way to the retailer which sells it not only that but we even track the customer s purchase on the supply chain and give them a way to see where exactly their coffee is coming from how much the growers were paid who shipped the coffee what condition the coffee received in and how the roasters rated the coffee we have a lot here not only that but we track the customer s purchase on the blockchain using pourcup bonus step code pattern summary to summarize what we have done in the code pattern is model a supply chain network we have taken the documents given by the various participants in our supply chain network and we have submitted the data to the blockchain we have used the ibm blockchain starter plan to easily inspect and see our transactions details and we have created rest api endpoints in the cloud using the starter kit we have used a delivery toolchain to deploy our smart contracts to the cloud and have made it easy to submit subsequent changes to the chaincode by simply committing new code to our github repo the delivery pipeline will detect any changes and trigger our build and deliver stage to create a new cloud foundry app with our rest api endpoints those rest api endpoints are linked to our ibm blockchain starter plan so that each time we write data to the blockchain we will be able to view and inspect all the relevant details of our transactions on the platform all we need is a nice ui where we can submit our transactions and then in that ui we can simply use those rest api endpoints to submit data to the blockchain hope this was helpful and as always i am open to contributions thank you for reading i hope you enjoyed it go build something awesome deploy to ibm cloud deploy to ibm cloud https bluemix net deploy button png https bluemix net deploy repository https github com ibm watson second opinion links ibm blockchain marbles demo https github com ibm blockchain marbles hyperledger composer https hyperledger github io composer latest index html learn more blockchain code patterns enjoyed this code pattern check out our other blockchain code patterns https developer ibm com code technologies blockchain blockchain 101 learn why ibm believes that blockchain can transform businesses industries and even the world blockchain 101 https developer ibm com code technologies blockchain license this code pattern is licensed under the apache software license version 2 separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses contributions are subject to the developer certificate of origin version 1 1 dco https developercertificate org and the apache software license version 2 https www apache org licenses license 2 0 txt apache software license asl faq https www apache org foundation license faq html whatdoesitmean | blockchain |
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SLMTokBench | slmtokbench speech language model token benchmark existing speech tokens are not explicitly designed for speech language modeling and there has been no exploration into their suitability for building speech language models to address this gap we build speech language model token benchmark slmtokbench to assess the suitability of speech tokens for constructing speech language models in this benchmark we evaluate the alignment between speech tokens and text by estimating their mutual information we assess preservation of speech information within speech tokens by evaluating the quality of resynthesized speech | ai |
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aws-iot-device-sdk-arduino-yun | h1 align center aws iot arduino y n sdk h1 what is aws iot arduino y n sdk the aws iot arduino y n sdk allows developers to connect their arduino y n compatible board to aws iot by connecting the device to the aws iot users can securely work with the message broker rules and the thing shadow provided by aws iot and with other aws services like aws lambda amazon kinesis amazon s3 etc overview overview credentials credentials installation installation api documentation api key features keyfeatures using the sdk usingthesdk example example error code errorcode support support a name overview a overview this document provides step by step instructions to install the arduino y n sdk and connect your device to the aws iot the aws iot arduino y n sdk consists of two parts which take use of the resources of the two chips on arduino y n one for native arduino ide api access and the other for functionality and connections to the aws iot built on top of aws iot device sdk for python https github com aws aws iot device sdk python mqtt connection the aws iot arduino y n sdk provides apis to let users publish messages to aws iot and subscribe to mqtt topics to receive messages transmitted by other devices or coming from the broker this allows to interact with the standard mqtt pubsub functionality of aws iot more information on mqtt protocol is available here http docs aws amazon com iot latest developerguide protocols html thing shadow the aws iot arduino y n sdk also provides apis to provide access to thing shadows in aws iot using this sdk users will be able to sync the data status of their devices as json files to the cloud and respond to change of status requested by other applications more information on thing shadow is available here http docs aws amazon com iot latest developerguide iot thing shadows html a name credentials a credentials the sdk supports two types of credentials that correspond to the two connection types x 509 certificate for the certificate based mutual authentication connection type download the aws iot root ca https www symantec com content en us enterprise verisign roots verisign class 203 public primary certification authority g5 pem use the aws iot console to create and download the certificate and private key you must upload these credentials along with the python runtime code base to ar9331 on y n board and specify the location of these files in a configuration file aws iot config h iam credentials for the websocket with signature version 4 authentication type you will need iam credentials an access key id a secret access key you must also download and upload the aws iot root ca https www symantec com content en us enterprise verisign roots verisign class 203 public primary certification authority g5 pem a tooling script awsiotarduinoyunwebsocketcredentialconfig sh is provided for mac os linux users to update the iam credentials as environment variables on ar9331 y n board a name installation a installation download aws iot arduino y n sdk click here https s3 amazonaws com aws iot device sdk arduino yun aws iot arduino yun sdk latest zip to download aws iot arduino y n sdk zip package and extract it to aws iot arduino yun sdk on your computer set up your arduino y n board please follow the instructions from official website arduino y n guide https www arduino cc en guide arduinoyun installation on mac os linux before proceeding to the following steps please make sure that you have expect installed on your computer and correctly installed the arduino ide to install expect for ubuntu simply run sudo apt get install expect for mac expect is installed as default for arduino ide installation on linux please visit here http playground arduino cc linux all 1 setup the arduino y n board and connect it to wifi obtain its ip address and password 2 make sure your computer is connected to the same network local ip address range 3 download the aws iot ca file from here https www symantec com content en us enterprise verisign roots verisign class 203 public primary certification authority g5 pem 4 put your aws iot ca file private key and certificate into aws iot arduino yun sdk aws iot python runtime certs if you are using mqtt over websocket you can put only your aws iot ca file into the directory you should have a correctly configured aws identity and access management iam role with a proper policy and a pair of aws access key and aws secret access key id which will be used in step 6 for more information about iam please visit aws iam home page https aws amazon com iam 5 open a terminal cd to aws iot arduino yun sdk do chmod 755 awsiotarduinoyuninstallall sh and execute it as awsiotarduinoyuninstallall sh board ip username board password by default for arduino y n board your user name will be root and your password will be arduino this script will upload the python runtime code base and credentials to openwrt running on the more powerful micro controller on you arduino y n board this script will also download and install libraries for openwrt to implement the necessary scripting environment as well as communication protocols step 5 can take 10 15 minutes for the device to download and install the required packages distribute python openssl pip awsiotpythonsdkv1 0 0 note do not close the terminal before the script finishes otherwise you have to start over with step 5 make sure you are in your local terminal before repeating step 5 6 optional only for websocket open a terminal cd to aws iot arduino yun sdk do chmod 755 awsiotarduinoyunwebsocketcredentialconfig sh and execute it as awsiotarduinoyunwebsocketcredentialconfig sh board ip username board password aws access key id string aws secret access key string this script will add the given key id and secret key onto the openwrt as environment variables aws access key id and aws secret access key note1 you can always use this script to update your iam credentials used on the board the script will handle the update of the environment variables for you note2 please follow the instructions on the screen after the script completes to power cycle your board to enable all the environment changes on openwrt 7 copy and paste aws iot arduino yun sdk aws iot arduino yun library folder into arduino libraries that was installed with your arduino sdk installation for mac os default it should be under documents arduino libraries 8 restart the arduino ide if it was running during the installation you should be able to see the aws iot examples in the examples folder in your ide there are the other two scripts awsiotarduinoyunscp sh and awsiotarduinoyunsetupenvironment sh which are utilized in awsiotarduinoyuninstallall sh you can always use awsiotarduinoyunscp sh to upload your new credentials to your board when you are in the directory aws iot arduino yun sdk the command should be something like this awsiotarduinoyunscp sh board ip username board password file destination installation on windows before proceeding to the following steps please make sure that you have putty and winscp installed on your pc if you prefer to use other tools for ssh ing into your arduino y n board and transferring files you will have to adjust the steps below according to your tools putty can be downloaded from here http www chiark greenend org uk sgtatham putty download html winscp can be downloaded from here http winscp net eng download php 1 setup the arduino y n cloud board and connect it to wifi obtain its ip address and password 2 make sure your pc is connected to the same network local ip address range 3 download the aws iot ca file from here https www symantec com content en us enterprise verisign roots verisign class 203 public primary certification authority g5 pem 4 put your aws iot ca file private key and certificate into aws iot arduino yun sdk aws iot python runtime certs if you are using mqtt over websocket you can put only your aws iot ca file into the directory you should have a correctly configured aws identity and access management iam role with a proper policy and a pair of aws access key and aws secret access key id which will be used in step 7 for more information about iam please visit aws iam home page https aws amazon com iam 5 start winscp and upload aws iot python runtime folder to root on the board 6 use putty to ssh into openwrt on your board and execute the following commands to install the necessary libraries opkg update opkg install distribute opkg install python openssl easy install pip pip install awsiotpythonsdk 1 0 0 it can take 10 15 minutes for the device to download and install the required packages 7 optional only for websocket use putty to ssh into openwrt on your board and modify etc profile to include your iam credentials as environment variables export aws access key id aws acces key id string export aws secret access key aws secret access key string after that run source etc profile and power cycle the board to enable the changes 8 copy and paste aws iot arduino yun sdk aws iot arduino yun library folder into arduino libraries that was installed with your arduino sdk installation for windows default it should be under documents arduino libraries 9 restart the arduino ide if it was running during the installation you should be able to see the aws iot examples in the examples folder in your ide a name api a api documentation class name aws iot mqtt client api mqtt connection iot error t setup const char client id bool clean session mqttv t mqtt version bool usewebsocket setup iot error t config const char host unsigned int port const char cafile path const char keyfile path const char certfile path config iot error t configwss const char host unsigned int port const char cafile path configwss iot error t configbackofftiming unsigned int basereconnectquiettimesecond unsigned int maxreconnectquiettimesecond unsigned int stableconnectiontimesecond configbackofftiming iot error t configofflinepublishqueue unsigned int queuesize dropbehavior t behavior configofflinepublishqueue iot error t configdraininginterval float numberofseconds configdraininginterval iot error t connect unsigned int keepalive interval connect iot error t publish const char topic const char payload unsigned int payload len unsigned int qos bool retain publish iot error t subscribe const char topic unsigned int qos message callback cb subscribe iot error t unsubscribe const char topic unsubscribe iot error t yield yield iot error t disconnect disconnect thing shadow iot error t shadow init const char thingname shadow init iot error t shadow update const char thingname const char payload unsigned int payload len message callback cb unsigned int timeout shadow update iot error t shadow get const char thingname message callback cb unsigned int timeout shadow get iot error t shadow delete const char thingname message callback cb unsigned int timeout shadow delete iot error t shadow register delta func const char thingname message callback cb shadow register delta func iot error t shadow unregister delta func const char thingname shadow unregister delta func iot error t getdesiredvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getdesiredvaluebykey iot error t getreportedvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getreportedvaluebykey iot error t getdeltavaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getdeltavaluebykey iot error t getvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getvaluebykey message callback void message callback char unsigned int message status t message callback a name setup a iot error t setup const char client id bool clean session mqttv t mqtt version bool usewebsocket description start the python runtime and set up connection for aws iot mqtt client object must be called before any of aws iot mqtt client api is called syntax object setup myclientid setup a client with client id set to myclientid object setup myclientid true mqttv31 true setup a client with client id set to myclientid with clean session set to true using mqtt 3 1 over websocket parameters client id the client id for this connection clean session clear the previous connection with this id or not default value is true mqtt version version of mqtt protocol for this connection either mqttv31 mqtt version 3 1 or mqttv311 mqtt version 3 1 1 default value is mqttv311 usewebsocket enable the use of websocket or not default value is false iam credentials must be included in the environment variables on openwrt to make a successful mqtt connection over websocket returns none error if the setup on openwrt side and connection settings are correct null value error if input parameters have null value overflow error if input string exceeds the internal buffer size set up error if the setup failed serial1 communication error if there is an error in serial1 communication between the two chips generic error if an unknown error happens a name config a iot error t config const char host unsigned int port const char cafile path const char keyfile path const char certfile path description configure host port and certs location used to connect to aws iot if the input strings for host cafile path keyfile path and certfile path are set to null the default value will be used to connect must be called to load user settings right after aws iot mqtt client setup and before connect syntax object config example awsamazon com 1234 cafile keyfile certfile parameters host the endpoint to connect to must be a null terminated string port the port number to connect to cafile path the path of ca file on openwrt must be a null terminated string keyfile path the path of private key file on openwrt must be a null terminated string certfile path the path of certificate file on openwrt must be a null terminated string returns none error if the configuration is successful no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command config generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name configwss a iot error t configwss const char host unsigned int port const char cafile path description configure host port and rootca location used to connect to aws iot over websocket if the input strings for host and cafile path are set to null the default value will be used to connect must be called to load user settings right after aws iot mqtt client setup and before connect the client must be configured in setup to use websocket syntax object configwss example awsamazon com 1234 cafile parameters host the endpoint to connect to must be a null terminated string port the port number to connect to cafile path the path of ca file on openwrt must be a null terminated string returns none error if the configuration is successful no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command config generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name configbackofftiming a iot error t configbackofftiming unsigned int basereconnectquiettimesecond unsigned int maxreconnectquiettimesecond unsigned int stableconnectiontimesecond description configure the progressive back off timing on reconnect time interval for reconnect attempt will increase double from basereconnectquiettimesecond to maxreconnectquiettimesecond once a connection is live for longer than stableconnectiontimesecond the time interval will be reset to basereconnectquiettimesecond note that stableconnectiontimesecond must be greater than basereconnectquiettimesecond more details about progressive back off can be found here progressivebackoff note that if this api is not called the following default values will be used to configure the back off timing basereconnectquiettimesecond 1 maxreconnectquiettimesecond 128 stableconnectiontimesecond 20 syntax object configbackofftiming 1 32 20 configure basereconnectquiettimesecond to be 1 second configure maxreconnectquiettimesecond to be 32 seconds configure stableconnectiontimesecond to be 20 seconds parameters basereconnectquiettimesecond number of seconds to start with for progressive back off on reconnect maxreconnectquiettimesecond maximum number of seconds for progressive back off on reconnect stableconnectiontimesecond number of seconds for a valid connection to be considered stable a name configofflinepublishqueue a iot error t configofflinepublishqueue unsigned int queuesize dropbehavior t behavior description configure the internal queue size and its drop behavior once the queue is full in the python runtime on the openwrt side when the client is offline publish requests will be queued up and get resent once the network connection is back if the number of publish requests exceeds the maximum size of the queue configured dropping will happen according the drop behavior configured by this api more details about offline publish requests queuing can be found here offlinepublishqueuedraining syntax object configofflinepublishqueue 20 drop oldest configure size of the offline publish requests queue to be 20 configure the queue to drop the oldest requests once the queue is full object configofflinepublishqueue 20 drop newest configure size of the offline publish requests queue to be 20 configure the queue to drop the newest requests once the queue is full object configofflinepublishqueue 0 drop oldest configure size of the offline publish requests queue to be infinite the queue full drop behavior is ignored when the size if infinite object configofflinepublishqueue 1 drop oldest disable the offline publish requests queue the queue full drop behavior is ignored when the queue is disabled parameters queuesize the size of the offline publish requests queue determining how many publish requests can be queued up while the client it offline behavior the drop behavior when the offline publish requests queue is full can be configured to drop the oldest requests or the newest requests in the queue returns none error if the configuration is successful no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command config generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name configdraininginterval a iot error t configdraininginterval float numberofseconds description configure the draining interval for requests to be sent out when client is back online and gets connected this will affect the outbound rate for republish and resubscribe requests more details about draining can be found here offlinepublishqueuedraining syntax object configdraininginterval 0 5 configure the draining interval to be 0 5 seconds in this way resubscribe requests if any will be sent per 0 5 seconds offline publish requests if any in the queue will be sent per 0 5 seconds parameters numberofseconds number of seconds to wait between every resubscribe republish request when the client is back online and connected returns none error if the configuration is successful no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command config generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name connect a iot error t connect unsigned int keepalive interval description connect to aws iot using user specific keepalive setting syntax object connect connect to aws iot with default keepalive set to 60 seconds object connect 55 connect to aws iot with keepalive set to 55 seconds parameters keepalive interval amount of time for mqtt ping request interval in seconds default is set to 60 seconds returns none error if the connect is successful no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command connect ssl error if the tls handshake failed connect error if the connection failed connect timeout if the connection gets timeout connect credential not found if the specified credentials are not found on openwrt websocket credential not found if the iam credentials do not exist as environment variables on openwrt connect generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name publish a iot error t publish const char topic const char payload unsigned int payload len unsigned int qos bool retain description publish a new message to the desired topic with qos and retain flag settings using mqtt protocol syntax object publish mytopic mymessage strlen mymessage 0 false publish mymessage to topic mytopic in qos 0 with retain flag set to false parameters topic topic name to publish to must be a null terminated string payload payload to publish payload len length of payload qos qualiy of service could be 0 or 1 retain retain flag returns none error if the publish is successful null value error if input parameters have null value overflow error if topic payload exceeds the internal buffer size no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command publish error if the publish failed publish timeout if the publish gets timeout publish queue full if the internal offline publish requests queue is full publish queue disabled if the internal offline publish requests queue is disabled publish generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name subscribe a iot error t subscribe const char topic unsigned int qos message callback cb description subscribe to the desired topic and register a callback for new messages from this topic syntax object subscribe mytopic 0 mycallbackfunc subscribe to topic mytopic in qos 0 and register its callback function as mycallbackfunc parameters topic the topic to subscribe to must be a null terminated string qos quality of service could be 0 or 1 cb function pointer to user specific callback function to call when a new message comes in for the subscribed topic the callback function should have a parameter list of char unsigned int message status t to store the incoming message content and the length of the message returns none error if the subscribe is successful null value error if input parameters have null value overflow error if topic payload exceeds the internal buffer size out of sketch subscribe memory if the number of current subscribe exceeds the configured number in aws iot config sdk h no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command subscribe error if the subscribe failed subscribe timeout if the subscribe gets timeout subscribe generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name unsubscribe a iot error t unsubscribe const char topic description unsubscribe to the desired topic syntax object unsubscribe mytopic parameters topic the topic to unsubscribe to must be a null terminated string returns none error if the unsubscribe is successful null value error if input parameters have null value overflow error if topic exceeds the internal buffer size no set up error if no setup is called before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command unsubscribe error if the unsubscribe failed unsubscribe timeout if the unsubscribe gets timeout unsubscribe generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name yield a iot error t yield description called in the loop to check if there is a new message from all subscribed topics as well as thing shadow topics registered callback functions will be called according to the sequence of messages if there is any specifically unnecessary shadow thing topics accepted rejected will be unsubscribed according to the incoming new messages to free subscribe slots users should call this function frequently to receive new messages and free subscribe slots for new subscribes especially for shadow thing requests syntax object yield parameters none returns none error if the yield is successful whether there is a new message or not overflow error if the new message exceeds the internal buffer size yield error if the yield failed a name disconnect a iot error t disconnect description disconnect from aws iot syntax object disconnect parameters none returns none error if disconnect is successful no set up error if no setup is called before this call disconnect error if the disconnect failed disconnect timeout if the disconnect gets timeout disconnect generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name shadow init a iot error t shadow init const char thingname description initialize thing shadow configuration and a shadow instance with thingname should be called before any of the thing shadow api call for thingname shadow operations syntax object shadow init newthingname init thing shadow configuration and set thing name to newthingname parameters thingname thing name for the shadow instance to be created must be a null terminated string returns none error if thing shadow is successfully initialized null value error if input parameters have null value overflow error if thing name exceeds the internal buffer size shadow init error if thing shadow initialization failed generic error if an unknown error happens a name shadow update a iot error t shadow update const char thingname const char payload unsigned int payload len message callback cb unsigned int timeout description update the thing shadow data in the cloud by publishing a new json file onto the corresponding thing shadow topic and subscribing accepted rejected thing shadow topics to get feedback of whether it is a successful failed request timeout can be set in seconds as the maximum waiting time for feedback once the request gets timeout a timeout message will be received the registered callback function will be called whenever there is an accepted rejected timeout feedback subscription to accepted rejected topics will be processed in a persistent manner and will not be unsubscribed once this api is called for this shadow syntax object shadow update userthingname json file strlen json file usercallbackfunction 5 update the data of userthingname thing shadow in the cloud to json file with a timeout of 5 seconds and usercallbackfunction as the callback function parameters thingname the name of the thing shadow in the cloud must be a null terminated string payload the data that needs to be updated into the cloud in json file format payload len length of payload cb function pointer to user specific callback function to call when a new message comes in for the subscribed topic the callback function should have a parameter list of char unsigned int message status t to store the incoming message content and the length of the message timeout the maximum time to wait for feedback returns none error if the shadow update request succeeds null value error if input parameters have null value overflow error if thing name payload exceeds the internal buffer size out of sketch subscribe memory if the number of current subscribe exceeds the configured number in aws iot config sdk h no shadow init error if the shadow with thingname is initialized before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command subscribe error if the subscribe accepted rejected failed subscribe timeout if the subscribe gets timeout publish error if the publish payload failed publish timeout if the publish payload gets timeout publish queue full if the publish action failed because of a full internal offline publish requests queue publish queue disabled if the publish action failed because of a disabled internal offline publish requests queue shadow update generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name shadow get a iot error t shadow get const char thingname message callback cb unsigned int timeout description obtain the thing shadow data in the cloud by publishing an empty json file onto the corresponding thing shadow topic and subscribing accepted rejected thing shadow topics to get feedback of whether it is a successful failed request timeout can be set in seconds as the maximum waiting time for feedback once the request gets timeout a timeout message will be received the registered callback function will be called whenever there is an accepted rejected timeout feedback subscription to accepted rejected topics will be processed in a persistent manner and will not be unsubscribed once this api is called for this shadow thing shadow data will be available as a json file in the callback syntax object shadow get userthingname usercallbackfunction 5 get the data of the thing shadow userthingname with a timeout of 5 seconds and usercallbackfunction as the callback function parameters thingname the name of the thing shadow in the cloud must be a null terminated string cb function pointer to user specific callback function to call when a new message comes in for the subscribed topic the callback function should have a parameter list of char unsigned int message status t to store the incoming message content and the length of the message timeout the maximum time to wait for feedback returns none error if the shadow get request succeeds null value error if input parameters have null value overflow error if thing name exceeds the internal buffer size out of sketch subscribe memory if the number of current subscribe exceeds the configured number in aws iot config sdk h no shadow init error if the shadow with thingname is initialized before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command subscribe error if the subscribe accepted rejected failed subscribe timeout if the subscribe gets timeout publish error if the publish payload failed publish timeout if the publish payload gets timeout publish queue full if the publish action failed because of a full internal offline publish requests queue publish queue disabled if the publish action failed because of a disabled internal offline publish requests queue shadow get generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name shadow delete a iot error t shadow delete const char thingname message callback cb unsigned int timeout description delete the thing shadow data in the cloud by publishing an empty json file onto the corresponding thing shadow topic and subscribing accepted rejected thing shadow topics to get feedback of whether it is a successful failed request timeout can be set in seconds as the maximum waiting time for feedback once the request gets timeout a timeout message will be received the registered callback function will be called whenever there is an accepted rejected timeout feedback after the feedback comes in it will automatically unsubscribe accepted rejected shadow topics syntax object shadow delete userthingname usercallbackfunction 5 delete the data of the thing shadow userthingname with a timeout of 5 seconds and usercallbackfunction as the callback function parameters thingname the name of the thing shadow in the cloud must be a null terminated string cb function pointer to user specific callback function to call when a new message comes in for the subscribed topic the callback function should have a parameter list of char unsigned int message status t to store the incoming message content and the length of the message timeout the maximum time to wait for feedback returns none error if the shadow delete request succeeds null value error if input parameters have null value overflow error if thing name exceeds the internal buffer size out of sketch subscribe memory if the number of current subscribe exceeds the configured number in aws iot config sdk h no shadow init error if the shadow with thingname is initialized before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command subscribe error if the subscribe accepted rejected failed subscribe timeout if the subscribe gets timeout publish error if the publish payload failed publish timeout if the publish payload gets timeout publish queue full if the publish action failed because of a full internal offline publish requests queue publish queue disabled if the publish action failed because of a disabled internal offline publish requests queue shadow delete generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name shadow register delta func a iot error t shadow register delta func const char thingname message callback cb description subscribe to the delta topic of the corresponding thing shadow with the given name and register a callback whenever there is a difference between the desired and reported state data the registered callback will be called and the feedback message will be available in the callback syntax object shadow register delta func userthingname usercallbackfunction register usercallbackfunction as the delta callback function for the thing shadow userthingname parameters thingname the name of the thing shadow in the cloud must be a null terminated string cb function pointer to user specific callback function to call when a new message comes in for the subscribed topic the callback function should have a parameter list of char unsigned int message status t to store the incoming message content and the length of the message return none error if the shadow delta topic is successfully subscribed and the callback function is successfully registered null value error if input parameters have null value overflow error if thing name exceeds the internal buffer size out of sketch subscribe memory if the number of current subscribe exceeds the configured number in aws iot config sdk h no shadow init error if the shadow with thingname is initialized before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command subscribe error if the subscribe accepted rejected failed subscribe timeout if the subscribe gets timeout shadow register delta callback generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name shadow unregister delta func a iot error t shadow unregister delta func const char thingname description unsubscribe to the delta topic of the corresponding thing shadow with the given name and unregister the callback there will be no message coming after this api call if another difference occurs between the desired and reported state data for this thing shadow syntax object shadow unregister delta func userthingname unregister the delta topic of the thing shadow userthingname parameters thingname the name of the thing shadow in the cloud must be a null terminated string returns none error if the shadow delta topic is successfully unsubscribed and the callback function is successfully unregistered null value error if input parameters have null value overflow error if thing name exceeds the internal buffer size no shadow init error if the shadow with thingname is initialized before this call wrong parameter error if there is an error for the python runtime to get enough input parameters for this command unsubscribe error if the subscribe accepted rejected failed unsubscribe timeout if the subscribe gets timeout shadow unregister delta callback generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name getdesiredvaluebykey a iot error t getdesiredvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize description get the value by key in the desired section in the shadow json document denoted by the provided identifier the corresponding value will be stored as a string into a user specified externalbuffer nested key value access is available more information can be found here individualkvaccess syntax object getdesiredvaluebykey json 0 property1 somebuffer somebuffersize access jsondocument state desired property1 denoted by jsonidentifier json 0 and store the value in somebuffer object getdesiredvaluebykey json 0 property2 subproperty somebuffer somebuffersize access jsondocument state desired property2 subproperty denoted by jsonidentifier json 0 and store the value in somebuffer parameters jsonidentifier the json identifier string to access a certain json document stored in python runtime on the openwrt side this is obtained from the registered shadow callback as shadow responses key the key for dereferencing out the value in the desired section of the json document nested key can be specified using as the delimiter externaljsonbuf buffer specified by the user to store the incoming value as string bufsize size of the buffer to store the incoming value as string returns none error if the value is retrieved successfully no set up error if no setup is called before this call overflow error if the length of the incoming value exceeds the size of the provided externaljsonbuf json file not found if the json document with the provided json identifier does not exist json key not found if the specified key does not exist in the json document denoted by the provided json identifier json generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name getreportedvaluebykey a iot error t getreportedvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize description get the value by key in the reported section in the shadow json document denoted by the provided identifier the corresponding value will be stored as a string into a user specified externalbuffer nested key value access is available more information can be found here individualkvaccess syntax object getreportedvaluebykey json 0 property1 somebuffer somebuffersize access jsondocument state reported property1 denoted by jsonidentifier json 0 and store the value in somebuffer object getreportedvaluebykey json 0 property2 subproperty somebuffer somebuffersize access jsondocument state reported property2 subproperty denoted by jsonidentifier json 0 and store the value in somebuffer parameters jsonidentifier the json identifier string to access a certain json document stored in python runtime on the openwrt side this is obtained from the registered shadow callback as shadow responses key the key for dereferencing out the value in the reported section of the json document nested key can be specified using as the delimiter externaljsonbuf buffer specified by the user to store the incoming value as string bufsize size of the buffer to store the incoming value as string returns none error if the value is retrieved successfully no set up error if no setup is called before this call overflow error if the length of the incoming value exceeds the size of the provided externaljsonbuf json file not found if the json document with the provided json identifier does not exist json key not found if the spedified key does not exist in the json document denoted by the provided json identifier json generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name getdeltavaluebykey a iot error t getdeltavaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize description get the value by key in the state section in the shadow json document denoted by the provided identifier the corresponding value will be stored as a string into a user specified externalbuffer nested key value access is available more information can be found here individualkvaccess syntax object getdeltavaluebykey json 0 property1 somebuffer somebuffersize access jsondocument state property1 denoted by jsonidentifier json 0 and store the value in somebuffer object getdeltavaluebykey json 0 property2 subproperty somebuffer somebuffersize access jsondocument state property2 subproperty denoted by jsonidentifier json 0 and store the value in somebuffer parameters jsonidentifier the json identifier string to access a certain json document stored in python runtime on the openwrt side this is obtained from the registered shadow callback as shadow responses key the key for dereferencing out the value in the state section of the json document nested key can be specified using as the delimiter externaljsonbuf buffer specified by the user to store the incoming value as string bufsize size of the buffer to store the incoming value as string returns none error if the value is retrieved successfully no set up error if no setup is called before this call overflow error if the length of the incoming value exceeds the size of the provided externaljsonbuf json file not found if the json document with the provided json identifier does not exist json key not found if the specified key does not exist in the json document denoted by the provided json identifier json generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name getvaluebykey a iot error t getvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize description get the value by key in the shadow json document denoted by the provided identifier the corresponding value will be stored as a string into a user specified externalbuffer nested key value access is available more information can be found here individualkvaccess syntax object getvaluebykey json 0 property1 somebuffer somebuffersize access jsondocument property1 denoted by jsonidentifier json 0 and store the value in somebuffer object getvaluebykey json 0 property2 subproperty somebuffer somebuffersize access jsondocument property2 subproperty denoted by jsonidentifier json 0 and store the value in somebuffer parameters jsonidentifier the json identifier string to access a certain json document stored in python runtime on the openwrt side this is obtained from the registered shadow callback as shadow responses key the key for dereferencing out the value in the json document nested key can be specified using as the delimiter externaljsonbuf buffer specified by the user to store the incoming value as string bufsize size of the buffer to store the incoming value as string returns none error if the value is retrieved successfully no set up error if no setup is called before this call overflow error if the length of the incoming value exceeds the size of the provided externaljsonbuf json file not found if the json document with the provided json identifier does not exist json key not found if the specified key does not exist in the json document denoted by the provided json identifier json generic error if there is an error in executing the command in python runtime generic error if an unknown error happens a name message callback a void message callback char unsigned int message status t description callback function for received mqtt messages used for plain mqtt communications as well as shadow communications for plain mqtt messages message payload and size will be passed into the callback message status t will be status normal see parameters below for more details for shadow messages json identifier and its size will be passed into the callback message status t varies for shadow messages from different topics for accept shadow responses message status t will be status shadow accepted for reject shadow responses message status t will be status shadow rejected for delta shadow responses message status t will be status normal see parameters below for more details syntax void custom message callback char msg unsigned int length message status t status access the incoming message from msg length message payload for plain mqtt messages json identifer for shadow messages access the message status from status status normal for plain mqtt shadow delta messages status shadow accepted for shadow accept responses status shadow rejected for shadow reject responses parameters char incoming message message payload for plain mqtt messages and json identifier for shadow message responses unsigned int length of bytes of the incoming message message status t status of the incoming responses messages it has the following values typedef enum status debug 1 status normal 0 status shadow timeout 1 status shadow accepted 2 status shadow rejected 3 status message overflow 4 message status t status normal indicates that a new plain mqtt message shadow delta message has arrived status shadow timeout indicates that the incoming message is a shadow response for an operation timeout there was no response received for the corresponding shadow operation within the preconfigured timeout status shadow accepted indicates that the incoming message is a shadow response for accept the corresponding shadow operation was accepted by the aws iot service and has succeeded status shadow rejected indicates that the incoming message is a shadow response for reject the corresponding shadow operation was rejected by the aws iot service and has failed status message overflow indicates that the size of the incoming message has exceeded the size of the internal message buffer internal message buffer size configured in aws iot config sdk h needs to be increased to receive the complete incoming message status debug is for sdk internal use returns no returns a name keyfeatures a key features a name individualkvaccess a individual key value access for shadow as for shadow operations get update delete and shadow delta messages instead of detailed the message content for shadow json document a json identifier will be passed through into the registered callback so that it can be used for key value pair access on demand through the individual key value pair access apis a json identifier of a shadow json response received looks like this json i where i is an integer number note that there is a limitation on the total number of history json documents that can be kept on the openwrt side for key value pair retrieval the limits are 512 entries for shadow json accepted responses 512 entries for shadow json rejected responses 512 entries for shadow json delta messages once the limits are exceeded new incoming shadow json documents will overwrite history entries starting from the beginning json 0 json 1 and json 2 the following apis are provided for uses to access shadow json key value pair from arduino sketch in an easier manner iot error t getdesiredvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getdesiredvaluebykey iot error t getreportedvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getreportedvaluebykey iot error t getdeltavaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getdeltavaluebykey iot error t getvaluebykey const char jsonidentifier const char key char externaljsonbuf unsigned int bufsize getvaluebykey for a typical shadow json document responses for get update delete it should look like this state desired reported getdesiredvaluebykey and getreportedvaluebykey can be used to access the key value pair in the desired reported section respectively for a typical shadow json document messages for delta it should look like this state getdeltavaluebykey can be used to access the key value pair in the delta shadow json messages more generally getvaluebykey can be used to access key value pair in a more generic way where users can specify their own key patterns nested json key is denoted using as the delimiter for example we have the following shadow json document with a json identifier json 0 state desired property1 value1 property2 subproperty value2 reported property3 value3 to access a series of key value pair we can use the following function calls object getdesiredvaluebykey json 0 property1 buffer buffersize access jsondocument state desired property1 object getreportedvaluebykey json 0 property3 buffer buffersize access jsondocument state reported property3 object getdesiredvaluebykey json 0 property2 subproperty buffer buffersize access jsondocument state desired property2 subproperty note that the following two function calls are equivalent they both access the nested json key value pair jsondocument state desired property2 subproperty object getdesiredvaluebykey json 0 property2 subproperty buffer buffersize object getvaluebykey json 0 state desired property2 subproperty buffer buffersize see that getvaluebykey is a more generic way for shadow json key value access for detailed use cases please check out examples example a name progressivebackoff a progressive reconnect back off api is provided to configure the progressive back off timing parameters iot error t configbackofftiming unsigned int basereconnectquiettimesecond unsigned int maxreconnectquiettimesecond unsigned int stableconnectiontimesecond configbackofftiming the auto reconnect happens with a progressive back off which follows the following mechanism for reconnect quiet time h5 align center t sup current sup min 2 sup n sup t sup base sup t sup max sup h5 where t sup current sup is the current reconnect quiet time t sup base sup is the base reconnect quiet time t sup max sup is the maximum reconnect quiet time the reconnect quiet time will be doubled on disconnect and reconnect attempt until it reaches the preconfigured maximum reconnect quiet time once the connection is stable for over the stableconnectiontime the reconnect quiet time will be reset to the basereconnectquiettime if no configbackofftiming gets called the following default configuration for back off timing will be done on setup call basereconnectquiettimesecond 1 maxreconnectquiettimesecond 128 stableconnectiontimesecond 20 a name offlinepublishqueuedraining a offline publish requests queuing with draining apis are provided to configure the offline publish requests queuing size and drop behavior as well as draining intervals iot error t configofflinepublishqueue unsigned int queuesize dropbehavior t behavior configofflinepublishqueue iot error t configdraininginterval float numberofseconds configdraininginterval when the client is temporarily offline and gets disconnected due to some network failure publish requests will be queued up into an internal queue in the python runtime on the openwrt side until the number of queued up requests reaches to the size limit of the queue once the queue is full offline publish requests will be discarded or replaced according to different configuration of the drop behavior typedef enum drop oldest 0 drop newest 1 dropbehavior t lets say we configure the size of offlinepublishqueue to be 5 and we have 7 offline publish requests coming in in a drop oldest configuration myclient configofflinepublishqueue 5 drop oldest the internal queue should be like this when the queue is just full head pub req0 pub req1 pub req2 pub req3 pub req4 when the 6th and the 7th publish requests are made offline the internal queue will be like this head pub req2 pub req3 pub req4 pub req5 pub req6 since the queue is already full the oldest requests pub req0 and pub req1 are discarded in a drop newest configuration myclient configofflinepublishqueue 5 drop newest the internal queue should be like this when the queue is just full head pub req0 pub req1 pub req2 pub req3 pub req4 when the 6th and the 7th publish requests are made offline the internal queue will be like this head pub req0 pub req1 pub req2 pub req3 pub req4 since the queue is already full the newest requests pub req5 and pub req6 are discarded when the client is back online connected and resubscribed to all topics that it has previously subscribed to the draining starts all requests in the offline publish queue will be resent at the configured draining rate if no configofflinepublishqueue or configdraininginterval is called the following default configuration for offline publish queuing and draining will be done on setup call offlinepublishqueuesize 20 dropbehavior drop newest draininginterval 0 5 sec note that before the draining process finishes any new publish request within this time will be added to the queue therefore draining rate should be higher than the normal publish rate to avoid an endless draining process after reconnect also note that disconnect event is detected based on pingresp mqtt packet loss offline publish queuing will not be triggered until the disconnect event gets detected configuring a shorter keep alive interval allows the client to detect disconnects more quickly any qos0 publish requests issued after the network failure and before the detection of the pingresp loss will be lost a name usingthesdk a using the sdk make sure you have properly installed the aws iot arduino y n sdk and setup the board a name slowstartup a make sure to start the sketch after openwrt is ready and gets connected to wifi it takes about 80 90 seconds for arduino y n board to start the openwrt and get connected to wifi for each power cycle make sure you have properly configured sdk settings in aws iot config h inside each sketch directory define aws iot mqtt host random string iot region amazonaws com your endpoint define aws iot mqtt port 8883 your port use 443 for mqtt over websocket define aws iot client id my clientid your client id define aws iot my thing name my board your thing name define aws iot root ca filename aws iot rootca crt your root ca filename define aws iot certificate filename cert pem your certificate filename define aws iot private key filename privkey pem your private key filename these settings can be downloaded from the aws iot console after you created a device and clicked on connect a device make sure you have included the aws iot arduino y n sdk library include aws iot mqtt h make sure you have included your configuration header file include aws iot config h make sure you have enough memory for subscribe messages and sketch runtime internal buffer size is defined in sdk library source directory libraries aws iot arduino yun library aws iot config sdk h the following are default settings define max buf size 256 maximum number of bytes to publish receive define max sub 15 maximum number of subscribe define cmd time out 200 maximum time to wait for feedback from ar9331 200 10 sec make sure you setup the client configure it using your configuration and connect it to aws iot first remember to use certs path macros for configuration aws iot mqtt client myclient myclient setup aws iot client id myclient setup aws iot client id true mqttv31 true use websocket myclient config aws iot mqtt host aws iot mqtt port aws iot root ca path aws iot private key path aws iot certificate path myclient configwss aws iot mqtt host aws iot mqtt port aws iot root ca path use websocket myclient connnect remember to check incoming messages in a loop void loop myclient yield when you are using thing shadow api for a specific device shadow name make sure you initialize the shadow with your device shadow name first myclient shadow init aws iot my thing name for shadow of this device myclient shadow init anotherdevice for another shadow when you are using thing shadow api always make sure max sub is big enough for a thing shadow request in the loop myclient shadow get mythingname mycallback 5 need 2 in max sub void loop myclient shadow get mythingname mycallback 5 need 4 in max sub myclient yield unsubscribe thing shadow topics when necessary when you are using thing shadow api make sure you set the timeout to a proper value and frequently call yield to free subscribe resources long timeout with low rate of yielding and high rate of shadow request will result in exhaustion of subscribe resources void loop myclient shadow get mythingname mycallback 5 5 sec timeout is fine for a request per 5 sec myclient shadow get mythingname mycallback 50 50 sec timeout is too long when missing feedback happens frequently with a rate of 1 request per 5 sec subscribed topics will soon accumulate and exceed max sub before any of the previously subscribed topic gets timeout and unsubscribed myclient yield delay 5000 5 sec delay enjoy the internet of things a name example a example basicpubsub this example https github com aws aws iot device sdk arduino yun tree master aws iot arduino yun library examples basicpubsub demonstrates a simple mqtt publish subscribe using aws iot from arduino y n board it first subscribes to a topic once and registers a callback to print out new messages to serial monitor and then publishes to the topic in a loop whenever it receives a new message it will be printed out to serial monitor indicating the callback function has been called hardware required arduino y n computer connected with arduino y n using usb serial software required none circuit required none attention please make sure to start the example sketch after the board is fully set up and openwrt is up and connected to wifi see here slowstartup code create an instance of aws iot mqtt client aws iot mqtt client myclient in setup open the serial set the instance up and connect it to the aws iot serial begin 115200 if rc myclient setup aws iot client id 0 load user configuration if rc myclient config aws iot mqtt host aws iot mqtt port aws iot root ca path aws iot private key path aws iot certificate path 0 use default connect 60 sec for keepalive if rc myclient connect 0 success connect true else else else in setup subscribe to the desired topic and wait for some delay time if rc myclient subscribe topic1 1 msg callback 0 serial println f subscribe failed serial println rc delay 2000 in loop publish to this topic and call yield function to receive the message every 5 seconds sprintf msg new message d cnt if rc myclient publish topic1 msg strlen msg 1 false 0 serial println f publish failed serial println rc if rc myclient yield 0 serial println f yield failed serial println rc delay 5000 the full sketch can be found in aws iot arduino yun library examples basicpubsub thingshadowecho sample app this example https github com aws aws iot device sdk arduino yun tree master aws iot arduino yun library examples thingshadowecho demonstrates arduino y n board as a device communicating with aws iot syncing data into the thing shadow in the cloud and receiving commands from an app whenever there is a new command from the app side to change the desired state of the device the board will receive this request and apply the change by publishing it as the reported state by registering a delta callback function users will be able to see this incoming message and notice the syncing of the state hardware required arduino y n computer connected with arduino y n using usb serial software required app side code that updates the state of the corresponding thing shadow in the cloud note you can also use aws iot console https aws amazon com iot to update the shadow data circuit required none attention please make sure to start the example sketch after the board is fully set up and openwrt is up and connected to wifi see here slowstartup code create an instance of aws iot mqtt client aws iot mqtt client myclient create logging function for execution tracking bool print log const char src int code in setup open the serial set the instance up and connect it to the aws iot init the shadow and register a delta callback function all steps are tracked using logging function if print log setup myclient setup aws iot client id if print log config myclient config aws iot mqtt host aws iot mqtt port aws iot root ca path aws iot private key path aws iot certificate path if print log connect myclient connect success connect true print log shadow init myclient shadow init aws iot my thing name print log register thing shadow delta function myclient shadow register delta func aws iot my thing name msg callback delta in loop yield to check and receive new incoming messages every 1 second if myclient yield serial println yield failed delay 1000 for delta callback function obtain the desired delta state and put it as the reported state in the json file that needs to be updated void msg callback delta const char src unsigned int len message status t flag if flag status normal get the whole delta section print log getdeltakeyvalue myclient getdeltavaluebykey src json buf 100 string payload state reported payload delta payload payload tochararray json buf 100 print log update thing shadow myclient shadow update aws iot my thing name json buf strlen json buf null 5 once an update of the desired state for this device is received a delta message will be received and displayed in the serial monitor the device will update this data into the cloud the full sketch can be found in aws iot arduino yun library examples thingshadowecho simple thermostat simulator this example https github com aws aws iot device sdk arduino yun tree master aws iot arduino yun library examples thermostatsimulatordevice demonstrates arduino y n as a device accepting instructions and syncing reported state in shadow in aws iot which simulates a thermostat to control the temperature of a room with the provided example app script users will be able to get real time temperature data coming from the board and be able to remotely set the desired temperature this example also demonstrates how to retrieve shadow json data received on arduino y n board aws iot device sdk for python https github com aws aws iot device sdk python is used in app scripts for mqtt connections users can modify it to use other mqtt library according to their needs hardware required arduino y n computer connected with arduino y n using usb serial and running example app scripts software required tkinter for app gui aws iot device sdk for python https github com aws aws iot device sdk python for mqtt connectivity example app script which is included in the exampleappscripts thermostatsimulatorapp circuit required none attention please make sure to start the example sketch after the board is fully set up and openwrt is up and connected to wifi see here slowstartup getting started before proceeding to the following steps please make sure you have your board set up with all code base and credentials properly installed please make sure you attach the correct policy to your certificate 1 modify your configuration file to match your own credentials and host address 2 start the sketch when the board boots up it should pass the initialization steps and then start to update shadow data it should have a similar display in the serial monitor as follows p img align center src https s3 amazonaws com aws iot device sdk arduino yun suppliemental images thermostatsimulatordevice sketch png 3 on the app side please make sure you have tkinter http tkinter unpythonic net wiki how to install tkinter and aws iot device sdk for python https github com aws aws iot device sdk python pre installed on your computer 4 copy and paste your credentials certificate private key and rootca into thermostatsimulatorapp certs please make sure to keep the file names as they are downloaded and make sure the ca file name ends with ca crt 5 in the directory thermostatsimulatorapp start the app script by executing python thermostatsimulatorapp py e your aws iot endpoint for x 509 certificate based mutual authentication python thermostatsimulatorapp py e your aws iot endpoint w for mqtt over websocket using iam credentials for more details about command line options you can use the following command python thermostatsimulatorapp py h you should be able to see a gui prompt up with default reported temperature p img align center src https s3 amazonaws com aws iot device sdk arduino yun suppliemental images thermostatsimulatorapp start png 6 try to input a desired temperature and click kbd set kbd if it succeeds you should be able to see the desired temperature on the panel and a log printed out in the console space the board will start continuously syncing temperature settings p img align center src https s3 amazonaws com aws iot device sdk arduino yun suppliemental images thermostatsimulatorapp settemp png note the temperature is configured to be lower than 100 f and higher than 100 f error message will be printed out if there is a malformed setting p img align center src https s3 amazonaws com aws iot device sdk arduino yun suppliemental images thermostatsimulatorapp setabovelimits png p img align center src https s3 amazonaws com aws iot device sdk arduino yun suppliemental images thermostatsimulatorapp setbelowlimits png code create an instance of aws iot mqtt client aws iot mqtt client myclient create logging function for execution tracking bool print log const char src int code in setup open the serial set the instance up and connect it to the aws iot init the shadow and register a delta callback function all steps are tracked using logging function if print log setup myclient setup aws iot client id if print log config myclient config aws iot mqtt host aws iot mqtt port aws iot root ca path aws iot private key path aws iot certificate path if print log connect myclient connect success connect true print log shadow init myclient shadow init aws iot my thing name print log register thing shadow delta function myclient shadow register delta func aws iot my thing name msg callback delta in loop simulate the behavior of a thermostat check to see the difference between the desired and the reported temperature if the desired temperature is higher increase the reported temperature by 0 1 degree per 1 second per loop if the desired one is lower decrease the reported temperature by 0 1 degree per 1 second per loop increase decrease action will happen until the reported reaches the desired update the reported temperature and then yield to check if there is any new delta message void loop if success connect if desiredtemp reportedtemp 0 001 reportedtemp 0 1 else if reportedtemp desiredtemp 0 001 reportedtemp 0 1 dtostrf reportedtemp 4 1 float buf float buf 4 0 sprintf p json buf pstr state reported temp s float buf print log shadow update myclient shadow update aws iot my thing name json buf strlen json buf null 5 if myclient yield serial println yield failed delay 1000 check for incoming delta per 1000 ms for delta callback function get the desired state and the desired temperature data in it update the desired temperature record on board so that the board knows what to do heating or cooling void msg callback delta const char src unsigned int len message status t flag if flag status normal get temp section in delta messages print log getdeltakeyvalue myclient getdeltavaluebykey src temp json buf 50 string delta data substring st ed desiredtemp string json buf tofloat each time the board receives a new desired temperature different from its reported temperature changes will happen synced into shadow and captured by the example app users will be able to see the whole process of temperature updating from the app side the full sketch can be found in aws iot arduino yun library examples thermostatsimulatordevice a name errorcode a error code the following error codes are defined in aws iot arduino yun library aws iot error h typedef enum none error 0 generic error 1 null value error 2 overflow error 3 out of sketch subscribe memory 4 serial1 communication error 5 set up error 6 no set up error 7 wrong parameter error 8 config generic error 9 connect ssl error 10 connect error 11 connect timeout 12 connect credential not found 13 connect generic error 14 publish error 15 publish timeout 16 publish generic error 17 subscribe error 18 subscribe timeout 19 subscribe generic error 20 unsubscribe error 21 unsubscribe timeout 22 unsubscribe generic error 23 disconnect error 24 disconnect timeout 25 disconnect generic error 26 shadow init error 27 no shadow init error 28 shadow get generic error 29 shadow update generic error 30 shadow update invalid json error 31 shadow delete generic error 32 shadow register delta callback generic error 33 shadow unregister delta callback generic error 34 yield error 35 websocket credential not found 36 json file not found 37 json key not found 38 json generic error 39 publish queue full 40 publish queue disabled 41 iot error t a name support a support if you have technical questions about aws iot device sdk please use aws iot forum https forums aws amazon com forum jspa forumid 210 for any other questions on aws iot please contact aws support https aws amazon com contact us | server |
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