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Model-based-design-of-Embedded-Systems | model based design of embedded systems model based design of embedded systems and control systems several models of embedded systems and control algorithms tested with simulink verfication and validation tools foc bldc hil is a model of a control algorithm to commute bldc motor model contains a control algorithm to commute a bldc motor with a three phase sub cycle averaged inverter with fault detection field oriented control vector control method with a three phase inverter and a bldc motor plant model inverter and bldc motor is fpga compatible model model is tested with mil then sil and compatible for hil testing and verification | os |
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bitcoinbook | mastering bitcoin mastering bitcoin is a book for developers although the first two chapters cover bitcoin at a level that is also approachable to non programmers anyone with a basic understanding of technology can read the first two chapters to get a great understanding of bitcoin this repository contains the complete first edition second print https github com bitcoinbook bitcoinbook releases tag edition1print2 published in december 2014 and the complete second edition third print https github com bitcoinbook bitcoinbook releases tag second edition print3 published in march 2018 as published by o reilly media in paperback and ebook formats issues errors comments contributions to contribute to this book see contributing md contributing md all contributions must be your original work and contributed under a public domain cc0 or attribution cc by license you must include your own attribution in the pull request as an edit to the github contrib asciidoc file if you know how to make a pull request to contribute a fix please write the correction and use a pull request to submit it for consideration against the develop branch https github com bitcoinbook bitcoinbook tree develop if you are making several changes please use a separate commit for each to make it easier to cherry pick or resolve conflicts otherwise please submit an issue explaining the error or comment if you would like to contribute extensive changes or new material please coordinate with the author first contact information can be found on his website https aantonop com reading this book to read this book see book asciidoc https github com bitcoinbook bitcoinbook blob develop book asciidoc click on each of the chapters to read in your browser other parties may choose to release pdfs of the book online for those editions where a pdf derivative is licensed chapters chapter 1 introduction https github com bitcoinbook bitcoinbook blob develop ch01 asciidoc chapter 2 how bitcoin works https github com bitcoinbook bitcoinbook blob develop ch02 asciidoc chapter 3 bitcoin core the reference implementation https github com bitcoinbook bitcoinbook blob develop ch03 asciidoc chapter 4 keys addresses https github com bitcoinbook bitcoinbook blob develop ch04 asciidoc chapter 5 wallets https github com bitcoinbook bitcoinbook blob develop ch05 asciidoc chapter 6 transactions https github com bitcoinbook bitcoinbook blob develop ch06 asciidoc chapter 7 advanced transactions and scripting https github com bitcoinbook bitcoinbook blob develop ch07 asciidoc chapter 8 the bitcoin network https github com bitcoinbook bitcoinbook blob develop ch08 asciidoc chapter 9 the blockchain https github com bitcoinbook bitcoinbook blob develop ch09 asciidoc chapter 10 mining and consensus https github com bitcoinbook bitcoinbook blob develop ch10 asciidoc chapter 11 bitcoin security https github com bitcoinbook bitcoinbook blob develop ch11 asciidoc chapter 12 blockchain applications https github com bitcoinbook bitcoinbook blob develop ch12 asciidoc published mastering bitcoin second edition third print programming the open blockchain is now available in paperback and ebook formats by many booksellers worldwide amazon https www amazon com mastering bitcoin programming open blockchain dp 1491954388 mastering bitcoin is also published in german polish japanese korean chinese and other languages by publishers in the respective countries mastering bitcoin open second edition based on the seond edition is also available in english and spanish at https aantonop com source the book s source code found in this repository is kept synchronized with the print and ebook editions mastering bitcoin first edition the tags edition1print1 https github com bitcoinbook bitcoinbook releases tag edition1print1 edition1print2 https github com bitcoinbook bitcoinbook releases tag edition1print2 correspond to the two existing prints of mastering bitcoin first edition as published by o reilly media a rel license href http creativecommons org licenses by sa 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by sa 4 0 88x31 png a br span xmlns dct http purl org dc terms href http purl org dc dcmitype text property dct title rel dct type mastering bitcoin first edition span by a xmlns cc http creativecommons org ns href https antonopoulos com property cc attributionname rel cc attributionurl andreas m antonopoulos llc a is licensed under a a rel license href http creativecommons org licenses by sa 4 0 creative commons attribution sharealike 4 0 international license a this free culture compliant license was approved by my publisher o reilly media http oreilly com who understands the value of open source o reilly media is not just the world s best publisher of technical books but is also a strong supporter of this open culture and the sharing of knowledge thank you o reilly media mastering bitcoin second edition the tags second edition print 1 https github com bitcoinbook bitcoinbook releases tag second edition print 1 second edition print2 https github com bitcoinbook bitcoinbook releases tag second edition print2 second edition print3 https github com bitcoinbook bitcoinbook releases tag second edition print3 correspond to the first june 8th 2017 second july 20th 2017 and third march 23rd 2018 print of mastering bitcoin second edition as published by o reilly media a rel license href http creativecommons org licenses by sa 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by sa 4 0 88x31 png a br span xmlns dct http purl org dc terms href http purl org dc dcmitype text property dct title rel dct type mastering bitcoin second edition span by a xmlns cc http creativecommons org ns href https antonopoulos com property cc attributionname rel cc attributionurl andreas m antonopoulos llc a is licensed under a a rel license href http creativecommons org licenses by sa 4 0 creative commons attribution sharealike 4 0 international license a translations if you are interested in translating this book please join our team of volunteers at https www transifex com aantonop mastering bitcoin | blockchain |
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RenderFarm | renderfarm project to cloud computing and virtualization course instituto superior t cnico structure of the project src folder contains all the code and resources needed i e input files configuration files and libraries needed bin folder is used as target to compiled code and to run our code script sh is used to launch our applications as shown below how run the project 1 run the script sh in the root of project directory renderfarm 1 1 pass argument 2 to the script to compile instrument and run the render farm instance with instrumented code 1 2 or pass argument 3 to the script to compile and run the render farm instance without instrumented code 1 2 or pass argument 4 to the script to compile and run the load balancer | amazon-web-services load-balancer auto-scaling cloud-computing java academic-project | cloud |
LLM-groundedDiffusion | llm grounded diffusion enhancing prompt understanding of text to image diffusion models with large language models long lian https tonylian com boyi li https sites google com site boyilics home adam yala https www adamyala org trevor darrell https people eecs berkeley edu trevor at uc berkeley ucsf paper https arxiv org pdf 2305 13655 pdf project page https llm grounded diffusion github io 5 minute blog post https bair berkeley edu blog 2023 05 23 lmd huggingface demo updated https huggingface co spaces longlian llm grounded diffusion citation citation tl dr text prompt llm as a request parser intermediate representation such as an image layout stable diffusion image main image https llm grounded diffusion github io main figure jpg visualizations enhanced prompt understanding https llm grounded diffusion github io visualizations jpg updates our repo has been largely improved now we have a repo with many methods implemented including our training free lmd and lmd lmd with gligen adapters our huggingface webui demo for stage 1 and 2 is updated now we support enabling each of the guidance components to get a taste of contributions check it out here https huggingface co spaces longlian llm grounded diffusion our webui is also available to run locally the instructions to run our webui locally to get faster generation without queues are here webui readme md our repo implements the following layout to image methods stage 2 these methods can be freely combined with our proposed llm based box to layout method stage 1 also implemented in this repo x training free lmd using original sd v1 v2 weights x lmd training free lmd that uses both cross attention control and gligen adapters x layout guidance backward guidance https arxiv org abs 2304 03373 x boxdiff iccv 23 https arxiv org abs 2307 10816 x multidiffusion region control icml 23 https arxiv org abs 2302 08113 x gligen cvpr 23 https arxiv org abs 2301 07093 feel free to contact me submit a pull request to add your methods our repo s features both web based chatgpt and openai api on gpt 3 5 4 supported allows generating bounding boxes by either asking chatgpt yourself free or in batch with openai api fully automated llm queries are cached to save on llm apis we cache each llm query for layout generation so it does not re generate the layouts from the same prompt open source llms supported host llms yourself for more freedom and lower costs we support vicuna llama 2 stablebeluga2 etc more in faq faq supports both lmd which uses sd weights without training and performs attention guidance and lmd which adds gligen adapters to sd in addition to attention guidance supports sd v1 and sd v2 in the same codebase if you implement a new feature or a new loss it s likely that it will work on both sd v1 and v2 several baseline stage 2 methods implemented in the same codebase handy if you want to benchmark and compare hackable we provide a minimal copy of diffusion unet architecture in our repo that exports the attention maps according to your need this allows you to change things without maintaining your own diffusers package parallel and resumable image generation supported you can generate in parallel to make use of multiple gpus servers if a generation fails on some images e g cuda oom you can simply rerun generation to regenerate those more in faq faq modular we implement different methods in different files copy from a file in generation and start creating your method without impacting existing methods web ui supported don t want to code or run anything try our public webui demo https huggingface co spaces longlian llm grounded diffusion or instructions to run webui locally webui readme md details summary and more exciting features expand to see summary flashattention and pytorch v2 supported unified benchmark same evaluation protocol on layouts stage 1 and generated images stage 1 2 for all methods implemented the benchmark is in beta and might change provides different presets to balance better control and fast generation in web ui details llm grounded diffusion lmd we provide instructions to run our code in this section installation pip install r requirements txt stage 1 text to layout generation note that we have uploaded the layout caches into this repo so that you can skip this step if you don t need layouts for new prompts since we have cached the layout generation which will be downloaded when you clone the repo you need to remove the cache in cache directory if you want to re generate the layout with the same prompts our layout generation format the llm takes in a text prompt describing the image and outputs three elements 1 captioned boxes 2 a background prompt 3 a negative prompt useful if the llm wants to express negation the template and examples are in prompt py https github com tonylianlong llm groundeddiffusion blob main prompt py you can edit the template and the parsing function to ask the llm to generate additional things or even perform chain of thought for better generation option 1 automated use an openai api key if you have an openai api key https openai com blog openai api you can put the api key in utils api key py or set openai api key environment variable then you can use openai s api for batch text to layout generation by querying an llm with gpt 4 as an example python prompt batch py prompt type demo model gpt 4 auto query always save template version v0 1 prompt type demo includes a few prompts for demonstrations the layout generation will be cached so it does not query the llm again with the same prompt lowers the cost you can visualize the bounding boxes in img generations imgs demo templatev0 1 option 2 free manually copy and paste to chatgpt python prompt batch py prompt type demo model gpt 4 always save template version v0 1 then copy and paste the template to chatgpt https chat openai com note that you want to use gpt 4 or change the model to gpt 3 5 in order to match the cache file name then copy the response back the generation will be cached if you want to visualize before deciding to save or not you don t need to pass in always save run our benchmark on text to layout generation evaluation we provide a benchmark that applies both to stage 1 and stage 2 this benchmarks includes a set of prompts with four tasks negation numeracy attribute binding and spatial relationships as well as unified benchmarking code for all implemented methods and both stages this will generate layouts from the prompts in the benchmark with prompt type lmd and evaluate the results python prompt batch py prompt type lmd model gpt 3 5 auto query always save template version v0 1 python scripts eval stage one py prompt type lmd model gpt 3 5 template version v0 1 details summary our reference benchmark results stage 1 evaluating the generated layouts only summary method negation numeracy attribution spatial overall gpt 3 5 100 97 100 99 99 0 gpt 4 100 100 100 100 100 0 gpt 3 5 eval type negation success 100 100 rate 1 00 eval type numeracy success 97 100 rate 0 97 eval type attribution success 100 100 rate 1 00 eval type spatial success 99 100 rate 0 99 overall success 396 400 rate 0 99 gpt 4 eval type negation success 100 100 rate 1 00 eval type numeracy success 100 100 rate 1 00 eval type attribution success 100 100 rate 1 00 eval type spatial success 100 100 rate 1 00 overall success 400 400 rate 1 00 details stage 2 layout to image generation note that since we provide caches for stage 1 you don t need to run stage 1 on your own for cached prompts that we provide i e you don t need an openai api key or to query an llm run layout to image generation using the gpt 4 cache and lmd python generate py prompt type demo model gpt 4 save suffix gpt 4 repeats 5 frozen step ratio 0 5 regenerate 1 force run ind 0 run model lmd plus no scale boxes default template version v0 1 save suffix is the suffix added to the name of the run you can change that if you change the args to mark the setting in the runs run model specifies the method to run you can set to lmd lmd or the implemented baselines with examples below use use sdv2 to enable sdv2 run our benchmark on layout to image generation evaluation we use a unified evaluation metric as stage 1 in stage 2 prompt type lmd since we have layout boxes for stage 1 but only images for stage 2 we use owl vit in order to detect the objects and ensure they are generated or not generated in negation in the right number with the right attributes and in the right place this benchmark is still in beta stage this runs generation with lmd and evaluate the generation shell use gpt 3 5 layouts python generate py prompt type lmd model gpt 3 5 save suffix gpt 3 5 repeats 1 frozen step ratio 0 5 regenerate 1 force run ind 0 run model lmd plus no scale boxes default template version v0 1 python scripts owl vit eval py model gpt 3 5 run base path img generations img generations templatev0 1 lmd plus lmd gpt 3 5 run0 skip first prompts 0 prompt start ind 0 verbose detection score threshold 0 15 nms threshold 0 15 class aware nms use gpt 4 layouts python generate py prompt type lmd model gpt 4 save suffix gpt 4 repeats 1 frozen step ratio 0 5 regenerate 1 force run ind 0 run model lmd plus no scale boxes default template version v0 1 python scripts owl vit eval py model gpt 4 run base path img generations img generations templatev0 1 lmd plus lmd gpt 4 run0 skip first prompts 0 prompt start ind 0 verbose detection score threshold 0 15 nms threshold 0 15 class aware nms our reference benchmark results method negation numeracy attribution spatial overall sd v1 5 28 39 52 28 36 8 lmd gpt 3 5 100 86 69 67 80 5 lmd gpt 4 100 84 79 82 86 3 lmd stablebeluga2 88 60 56 64 67 0 stablebeluga2 https huggingface co stabilityai stablebeluga2 is an open sourced model based on llama 2 we discover that the fact that llms spatial reasoning ability is also applicable to open sourced models however it can still be improved compared to proprietary models we leave llm fine tuning for better layout generation in stage 1 to future research to run generation with lmd with original sd weights and evaluate the generation details summary generate and evaluate samples with lmd summary shell use gpt 3 5 layouts python generate py prompt type lmd model gpt 3 5 save suffix gpt 3 5 repeats 1 frozen step ratio 0 5 regenerate 1 force run ind 0 run model lmd no scale boxes default template version v0 1 python scripts owl vit eval py model gpt 3 5 run base path img generations img generations templatev0 1 lmd lmd gpt 3 5 run0 skip first prompts 0 prompt start ind 0 verbose detection score threshold 0 15 nms threshold 0 15 class aware nms note you can enable autocast mixed precision to reduce the memory used in generation with use autocast 1 with potentially slightly lower generation quality details details summary generate samples with other stage 2 baseline methods summary shell sd v1 5 python generate py prompt type lmd model gpt 3 5 save suffix gpt 3 5 repeats 1 regenerate 1 force run ind 0 run model sd no scale boxes default template version v0 1 ignore negative prompt multidiffusion training free python generate py prompt type lmd model gpt 3 5 save suffix gpt 3 5 repeats 1 regenerate 1 force run ind 0 run model multidiffusion no scale boxes default template version v0 1 multidiffusion bootstrapping 10 backward guidance training free python generate py prompt type lmd model gpt 3 5 save suffix gpt 3 5 repeats 1 regenerate 1 force run ind 0 run model backward guidance no scale boxes default template version v0 1 boxdiff training free our reimplementation python generate py prompt type lmd model gpt 3 5 save suffix gpt 3 5 repeats 1 regenerate 1 force run ind 0 run model boxdiff no scale boxes default template version v0 1 gligen training based python generate py prompt type lmd model gpt 3 5 save suffix gpt 3 5 repeats 1 regenerate 1 force run ind 0 run model gligen no scale boxes default template version v0 1 note we set ignore negative prompt in sd v1 5 so that sd generation does not depend on the llm and follows a text to image generation baseline otherwise we take the llm generated negative prompts and put them into the negative prompt for other baselines you can feel free to generate evaluation is similar to lmd except you need to change the image path in the evaluation command details details summary our reference benchmark results stage 2 lmd without autocast summary method negation numeracy attribution spatial overall sd v1 5 28 39 52 28 36 8 lmd gpt 3 5 100 62 65 79 76 5 details ablation our reference benchmark results by combining lmd stage 1 with various layout to image baselines as stage 2 summary the stage 1 in this table is lmd gpt 3 5 unless stated otherwise we keep stage 1 in lmd the same and replace the stage 2 by other layout to image methods stage 1 stage 2 method negation numeracy attribution spatial overall none sd v1 5 28 39 52 28 36 8 training free br uses sd weights out of the box lmd multidiffusion 100 30 42 36 52 0 lmd backward guidance 100 42 36 61 59 8 lmd boxdiff 100 32 55 62 62 3 lmd lmd 100 62 65 79 76 5 training based lmd gligen 100 57 57 45 64 8 lmd lmd 100 86 69 67 80 5 lmd lmd gpt 4 100 84 79 82 86 3 all methods equipped with lmd stage 1 understand negation well because lmd stage 1 generates the negative prompts which is applicable to all methods that use classifier free guidance on sd note that lmd uses attention control that we proposed in addition to gligen which has much better generation compared to using only gligen showing that our proposed training free control is orthogonal to training based methods such as gligen faqs how do i use open source llms e g llama 2 stablebeluga2 vicuna you can install fastchat https github com lm sys fastchat and start a llm server note that the server does not have to be the same one as this repo using stablebeluga2 as an example which performs the best among all open source llms from our experience shell pip install fschat export fastchat worker api timeout 600 python3 m fastchat serve controller cuda visible devices 0 1 python3 m fastchat serve cli model path stabilityai stablebeluga2 num gpus 2 python3 m fastchat serve openai api server host localhost port 8000 stablebeluga2 is a 70b model so you need at least 2 gpus but you can run smaller models with only 1 gpu simply replace the model path to the huggingface model key e g meta llama llama 2 70b hf lmsys vicuna 33b v1 3 note that you probably want models without rlhf e g not llama 2 70b chat hf as we use text completion endpoints for layout generation then change the model argument to the intended model if your llm server is not on localhost 8000 you can change the api endpoint url in utils llm py https github com tonylianlong llm groundeddiffusion blob main utils llm py if you model name is not in the list in utils llm py https github com tonylianlong llm groundeddiffusion blob main utils llm py you can add it to the model names list we created this list to prevent typos in the command my llm queries finished very quickly why check whether you have a lot of cache hits in the output if so you might want to use the cache you are all set or remove the cache in cache directory to regenerate note that we allows different versions of templates so that you can manage several templates easily without cache overwrites contact us please contact long tony lian if you have any questions longlian berkeley edu acknowledgements this repo uses code from diffusers https huggingface co docs diffusers index gligen https github com gligen gligen and layout guidance https github com silent chen layout guidance this code also has an implementation of boxdiff https github com showlab boxdiff and multidiffusion region control https github com omerbt multidiffusion tree master using their code means adhering to their license citation if you use our work or our implementation in this repo or find them helpful please consider giving a citation article lian2023llmgrounded title llm grounded diffusion enhancing prompt understanding of text to image diffusion models with large language models author lian long and li boyi and yala adam and darrell trevor journal arxiv preprint arxiv 2305 13655 year 2023 | image-generation llm stable-diffusion stable-diffusion-webui text-to-image | ai |
All-OpenCV-Projects | br p align center a href https qxresearch github io qxresearch event 1 img width 640px src https github com xiaowuc2 xiaowuc2 blob master source hack2 png alt logo a h3 align center opencv project collection h3 p align center all opencv projects using python video explanation br br br p p twitter https img shields io twitter follow qxresearch svg style social label twitter https twitter com qxresearch github https img shields io static v1 svg label members message 116 color success logo github style social https github com orgs qxresearch people telegram https img shields io static v1 svg label telegram message 283 color success logo telegram style social https t me qxresearch linkedin https img shields io static v1 svg label linkedin message qxresearch color success logo linkedin style flat logocolor white colora blue https www linkedin com company 68716543 youtube https img shields io static v1 svg label youtube message qxresearch color grey logo youtube style flat logocolor white colora critical https www youtube com channel ucx7oe66v8zyfpajymfpl9va img alt aur last modified src https img shields io aur last modified google chrome this repository is the collection of all famous computer vision projects with opencv using python in collaboration with qxresearch https qxresearch github io qxresearch br br we re taking part in hacktoberfest 2020 so you can add your favorite opencv projects to this repository and get a chance to win cool swags from hacktoberfest team opencv projects 1 face mask detection https github com qxresearch all opencv projects tree main face 20mask 20detection 2 hand finger detection https github com qxresearch all opencv projects tree main hand 20finger 20detection 3 write in air https github com qxresearch all opencv projects tree main write 20in 20air 4 blue color detection from live video using hsv colorspace https github com varshakr1298 color detection using hsv colorspace contributing any kind of contributions to qxresearch opencv hacktoberfest are welcome make sure you follow our contribution guidelines to get your pull request merged contributions are what make the open source community such an amazing place to learn inspire and create 1 see video contributing guideline https youtu be jkgslym ltg 2 fork https github com qxresearch opencv project collection fork the project 3 commit your changes 4 push to the branch 5 open a pull request https github com qxresearch opencv project collection pulls requirements python 3 1 python libraries opencv video tutorial youtube playlist opencv project collection https www youtube com watch v b0 0gk cupm list plk zxbpeufmvpsxnl1wx1s6bd8ebujuom become official member join mozilla group qxresearch https www youtube com watch v cxi3r3a7ymq join telegram group qxresearch https t me qxresearch a href mailto rohitmandal814566 gmail com email a me your github id subject github id qxresearch p align center br br br a href https www youtube com watch v b0 0gk cupm list plk zxbpeufmvpsxnl1wx1s6bd8ebujuom watch on demand a a href https github com qxresearch opencv project collection issues report bug a a href https github com qxresearch opencv project collection issues request feature a p p | python opencv computer-vision hacktoberfest hacktoberfest2020 hacktoberfest-accepted | ai |
udacity-cloud-developer-microservice | udagram microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice following are the services involved in this project user allows users to register and log into a web client feed allows users to post photos and process photos using image filtering frontend acts as an interface between the user and the backend services reverseproxy for resolving multiple services running on same port in separate containers correspondingly the project is split into following parts 1 the restapi feed backend a node express feed microservice 2 the restapi user backend a node express user microservice 3 the simple frontend a basic ionic client web application which consumes the restapi backend 4 nginx as a reverse proxy server when different backend services are running on the same port then a reverse proxy server directs client requests to the appropriate backend server and retrieves resources on behalf of the client dependencies node express ionic aws credentials and setup to connect to the postgres database exercise instructions follow the below instructions instruction 1 clone the github repo 1 clone the following git repository https github com hariclerry udacity cloud developer microservice 2 to run the application in a docker container cd into course 03 exercises udacity c3 deployment docker directory and follow the below steps build docker images run docker compose f docker compose build yaml build parallel to pull docker images from docker hub run docker compose f docker compose build yaml pull to start the container run docker compose up navigate to the browser and run http localhost 8100 to see the udagram application up and running to remove the container run the following commands docker container ls docker container kill container name docker container prune 3 to run the application in a a kubernetes cluster cd into course 03 exercises udacity c3 deployment k8s directory and follow the below steps run all deployments in the k8s for example kubectl apply f reverseproxy deployment yaml run all services in the k8s for example kubectl apply f reverseproxy service yaml to start the backend services run kubectl port forward service reverseproxy 8080 8080 to start the frontend app run kubectl port forward service frontend 8100 8100 navigate to the browser and run http localhost 8100 to see the udagram application up and running to redeploy changes run kubectl apply f name deployment yaml with the name of the deployment to remove a deployment run for example kubectl delete reverseproxy deployment yaml to remove a service run for example kubectl delete svc reverseproxy | typescript microservices docker kubernetes nodejs expressjs aws | cloud |
nmp_qc | neural message passing for quantum chemistry implementation of different models of neural networks on graphs as explained in the article proposed by gilmer et al 1 installation pip install r requirements txt python main py installation of rdkit running any experiment using qm9 dataset needs installing the rdkit http www rdkit org package which can be done following the instructions available here http www rdkit org docs install html data the data used in this project can be downloaded here https github com priba nmp qc tree master data bibliography 1 gilmer et al neural message passing for quantum chemistry https arxiv org pdf 1704 01212 pdf arxiv 2017 2 duvenaud et al convolutional networks on graphs for learning molecular fingerprints https arxiv org abs 1606 09375 nips 2015 3 li et al gated graph sequence neural networks https arxiv org abs 1511 05493 iclr 2016 4 battaglia et al interaction networks for learning about objects https arxiv org abs 1612 00222 nips 2016 5 kipf et al semi supervised classification with graph convolutional networks https arxiv org abs 1609 02907 iclr 2017 6 defferrard et al convolutional neural networks on graphs with fast localized spectral filtering https arxiv org abs 1606 09375 nips 2016 7 kearnes et al molecular graph convolutions moving beyond fingerprints https arxiv org abs 1603 00856 jcamd 2016 8 bruna et al spectral networks and locally connected networks on graphs https arxiv org abs 1312 6203 iclr 2014 cite article gilmer2017 author justin gilmer and samuel s schoenholz and patrick f riley and oriol vinyals and george e dahl title neural message passing for quantum chemistry journal corr year 2017 authors pau riba priba webpage http www cvc uab es people priba anjan dutta anjandutta webpage https sites google com site 2adutta | ai |
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Online-Airline-Ticketing-System | online airline ticketing system database project for the database systems module taught at the department of computer science amp engineering university of moratuwa | server |
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bitthunder | bitthunder build status https git bitthunder org vitalelement bitthunder badges ci build svg https git bitthunder org vitalelement bitthunder commits ci a reliable real time operating system application framework c 2012 2014 james walmsley james fullfat fs co uk currently released under gnu gpl version 2 0 see license for more information directory tree dbuild dark builder build system bitthunder edition arch contains architecture specific code bootstrapping for the os arch contains common architecture components e g nvic gic drivers for arm mach subarch machine sub architectures e g zynq or cortex m3 stm32 etc etc doc documentation drivers architecture independent drivers e g i2c usb pcie devices etc kernel contains the rtos scheduler freertos lib contains all bitthunder library code i e the stuff not implementing the os useful structures etc os contains all of the bitthunder platform independent os code development process from now on the master branch is locked into a stable development cycle all work must be carried out on feature branches in the form feature username shortcode branchname feature branches may only enter the master branch through a rebase to keep the project history linear during which the commit history of the feature branch should be cleaned up or squashed if appropriate in order for a feature branch to be accepted into the master it must easily rebase onto the latest master with no conflicts cleanly compile no errors no warnings output should be pretty pass all unit tests when we have a test system in place in order to initiate the merging process you must make a merge request this is easily done on github using the pull request feature for those working with me on gitlab simply make a merge request merges are made with a no ff merge so that all commits pertaining to a feature can be easily identified it must be possible to build the kernel using a sensible configuration from any merge point on the master branch stable master branch as a consequence the master branch should remain relatively stable by stable it is meant that the build process is not broken and that a kernel image can be generated without much effort even though the master branch can be considered stable in this respect if you really need a stable kernel then you should use the last marked stable tag e g stable v1 0 0 getting started with bitthunder its easy to get a standalone kernel up and running make menuconfig make project you can create 3 types of bitthunder project a standalone bitthunder kernel usually calls an initscript on the filesystem an out of tree project a git managed out of tree project which pulls bitthunder as a submodule standalone kernel s these are simple make menuconfig make out of tree projects mkdir myproject create a directory for managing and building the new project cd myproject the next command creates an empty project with reference to the base kernel sources make c bitthunder project dir pwd project init make menuconfig make git managaed projects mkdir myproject git cd myproject git make c bitthunder project dir pwd project git init make menuconfig make project files all projects create the following files project dir makefile project dir kconfig project dir objects mk project dir main c after configuration the following files will be generated project dir include bt bsp config h project dir config makefile the project makefile configures a pair of variables and includes the kernel makefile project dir is the directory in which the project exists use shell pwd project config y default causes the config system to include project dir kconfig objects mk in a project the objects mk file lists the project objects that need to be additionally built and linked into the kernel to add a file to the build process you simply add a line to the objects mk file objs app main o objs app path to my object o include project dir path to module objects mk note you may also use configuration options as defined in your kconfig file to optionally compile objects objs bt config feature a support app feature a o note app is essentially the path to project dir but must be used to create the correct makefile targets it should also be noted that application objects mk files use a slightly different convention kconfig its possible for a project to hook into the kernel configuration system simply add any configuration options to this file and they will appear under project options on the menuconfig system note project config y must be set in the project makefile project dir makefile main c to begin with this file is blank booting the bitthunder kernel by default will cause it to only print welcome to bitthunder repeatedly on the boot logger device boot console to override this behaviour you simply need to create a function called main build system bitthunder uses a gnu make based build system called dbuild dark builder the core of the build system is found under base dbuild path conventions all path variables in dbuild must have a trailing slash appended for consistency and readability base root folder of the bitthunder kernel project dir root of the project based on bitthunder being developed build dir path where all objects and dependency files will be generated the convention was recently changed so that all variables are consistent you can use some sed rules to update old projects easily find name objects mk xargs sed i s base base g find name objects mk xargs sed i s project dir project dir g find name objects mk xargs sed i s build dir build dir g variables arch the architecture variant used e g arm blackfin mips subarch the machine variant used e g zynq these are used in paths of object files etc kernel development objects mk files the build system specifies all objects to be built in the objects mk files adding a new object is simple objects build dir arch arm mach zynq myobject o you can also use configuration variables like objects bt config feature a build dir feature a feature a o to include another folder in the build system include base drivers net objects mk you can include other folders based on configuration variables like ifeq bt config feature a y include base feature a objects mk endif | os |
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cnema | cnema database model with gui for a cinema made for data engineering course at tcs ju | server |
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rtos | what is packetngin o s packetngin operating system is a dpi deep packet inspection and network virtualization platform based on x86 64 architecture components packetngin o s is consists of some components packetngin bootloader packetngin kernel packetngin device drivers packetngin sdk license packetngin o s is licensed under both general public license gpl version 2 and a proprietary license that can be arranged with us each component of packetngin o s is licensed under different licesnes the reason of dual license and the role of gurumnetworks packetngin is very carefully designed to use dual licensing strategy most of the users are not necessary to use proprietary license gpl license is very attractive to most of users but some company need to make their own network device with their intellectual property need alternative license rather than gpl the role of gurumnetworks inc is make a virtuous cycle to develop the product intensively and continuously we promise most of income from commercial licensing will be reinvested to develop oss version of the product the components licensed under gpl2 and proprietary packetngin bootloader packetngin kernel packetngin sdk except 3rd parth libraries the components licensed under gpl2 licensed with special exceptions and proprietary packetngin sdk s core library the components licensed under 3rd party licenses packetngin sdk s libraries except core library the license of loadable kernel modules loadable kernel module is not affected by gpl and it s the tradition of linux kernel packetngin kernel follow the tradition too packetngin s loadable kernel module doesn t affected by the packetngin kernel s license please check the url http linuxmafia com faq kernel proprietary kernel modules html to arrange the proprietary license just send a inquiry to contact gurum cc to discuss this option | os |
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Sparkify-Data-Modelling-with-Apache-Cassandra | data modelling in apache cassandra this project is part of the udacity nanodegree on data engineering it aims to demonstrate a distributed database creation from a csv by creating appropriate database structures and populating correct data post query analysis in apache cassandra data that is loved tends to survive kurt bollacker core technologies shield https img shields io badge database apachecassandra lightgrey shield https img shields io badge language python cql lightgrey shield https img shields io badge rawdata csv lightgrey usage open the file bash executor ipynb in jupyter notebook execute all cells file dictionary event datafile new merged json data of datafile into a unified csv created by executor executor ipynb notebook to create insert and drop tables and database contributing pull requests are welcome for major changes please open an issue first to discuss what you would like to change please make sure to update tests as appropriate license mit https choosealicense com licenses mit | apache cassandra cql | server |
frontend-project | front end project this repository hosts the frontend components for the motion detection project in ict710 software design for embedded systems which is developed by narusorn srivaro crizyntch and menghorng bun menghorng96 system description overview this component receives query data from postgresql database api via get method and displays the movement status of each edge device in a simple html page the frontend html page is updated in real time via sse and javascript this repository is heroku deployable with procfile and requirements txt included heroku this project is currently hosted on heroku cloud server which can be accessed via this link url https taist2020 motion detection herokuapp com flask the frontend use flask as a lightweight python framework it handles the routing and sse pushing of the system web interface the interface is implemented using html and javascripts html is used for the general formatting of the page while javascripts handle real time sse and display the information prerequisite 1 python 3 7 2 flask library installation compile git clone https github com ict710 g4 frontent project git no further installation is required for hosting on the local device for heroku deployment please read here https devcenter heroku com categories deployment getting started cd frontent project pip install r requirements txt export flask app app py flask run running port can be configured in app py | os |
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blockchain-reading-list | blockchain reading list this is a list of readings and watchings on blockchain related topics preamble the word blockchain or block chain started off as a more or less narrowly defined technical term it blockchain later became a buzzword that represented anything related to bitcoin alt coins ex litecoin ethereum etc cryptocurrencies etc and now that the word blockchain is on the verge of becoming a household name it seems like it is starting to include what some others used to call p2p and are now calling distributed computing in some usages of the label blockchain neither blocks or hash lists seemed to be necessary for that thing to be called a blockchain this seems to somewhat mirror how previously the usage of the word p2p seemed to broaden and also include what others are calling distributed computing although this time around cryptography and various concepts of money seems to have prominence regardless this document focuses on a more traditional definition of blockchain but also includes some topics that the author of this document feels that while not strictly about blockchains will help provide readers with a deep er technical background that can be useful when working with or creating blockchain based technologies audience this document is targeted at technical people who are looking to gain a deep understand of the concepts behind blockchain based technologies topics abstract algebra abstract algebra behavioral science behavioral science bitcoin bitcoin bit gold bit gold b money b money colored coins colored coins consensus consensus content addressing content addressing cryptocurrency cryptocurrency cryptography cryptography distributed computing distributed computing distributed hash tables distributed hash tables ecash ecash error detection and correction error detection and correction ethereum ethereum handicap principle handicap principle hashcash hashcash hash lists hash lists history cypherpunk history cypherpunk history open source history open source identity identity mastercoin mastercoin money money onion routing onion routing paxos paxos peer to peer networks peer to peer networks privacy privacy proof of stake proof of stake proof of work proof of work raft raft rai rai randomness randomness selfish mining attack selfish mining attack smart contracts smart contracts solidity solidity sybil attack sybil attack tamper detection tamper detection trust networks trust networks worse is better worse is better zero knowledge proof protocol zero knowledge proof protocol abstract algebra abstract algebra socratica https www youtube com playlist list pli01xoe8jyoi3sgnngorr xow3ick tp6 by liliana de castro michael harrison hatch harrison behavioral science please not another bias an evolutionary take on behavioural economics https jasoncollins blog 2015 07 30 please not another bias an evolutionary take on behavioural economics by jason collins the hidden agenda of the political mind https www goodreads com book show 21981657 the hidden agenda of the political mind by jason weeden robert kurzban hierarchy in the forest https www goodreads com book show 2131522 hierarchy in the forest by christopher boehm the biology of moral systems https www goodreads com book show 1446851 the biology of moral systems by richard d alexander see also handicap principle handicap principle bitcoin bitcoin a peer to peer electronic cash system https bitcoin org bitcoin pdf by satoshi nakamoto annotated bitcoin a peer to peer electronic cash system https genius com 2698706 by satoshi nakamoto balaji srinivasan et al how the bitcoin protocol actually works http www michaelnielsen org ddi how the bitcoin protocol actually works by michael nielsen but how does bitcoin actually work https youtu be bbc nxj3ng4 by grant sanderson ever wonder how bitcoin and other cryptocurrencies actually work https youtu be bbc nxj3ng4 by grant sanderson mastering bitcoin https github com bitcoinbook bitcoinbook by andreas m antonopoulos why is it taking 20 minutes to mine this bitcoin block http r6 ca blog 20180225t160548z html by russell o connor op return and the future of bitcoin https bitzuma com posts op return and the future of bitcoin by rich apodaca explanation of what an op return transaction looks like https bitcoin stackexchange com questions 29554 explanation of what an op return transaction looks like hidden surprises in the bitcoin blockchain and how they are stored nelson mandela wikileaks photos and python software http www righto com 2014 02 ascii bernanke wikileaks photographs html by ken shirriff bit gold bit gold https unenumerated blogspot com 2005 12 bit gold html by nick szabo b money b money an anonymous distributed electronic cash system http weidai com bmoney txt by wei dai b money bitcoin wiki https en bitcoin it wiki b money colored coins colored coins bitcoin wiki https en bitcoin it wiki colored coins colored coins wikipedia https en wikipedia org wiki colored coin coloring scheme https github com colored coins colored coins protocol specification wiki coloring 20scheme consensus see also bitcoin bitcoin paxos paxos raft raft content addressing magnet uri project http magnet uri sourceforge net magnet v0 1 draft tech overview spec http magnet uri sourceforge net magnet draft overview txt by gordon mohr magnet uri scheme wikipedia https en wikipedia org wiki magnet uri scheme a urn namespace for identifiers based on cryptographic hashes https tools ietf org html draft thiemann hash urn 01 by peter thiemann ietf rfc 6920 naming things with hashes https tools ietf org search rfc6920 trusty uris verifiable immutable and permanent digital artifacts for linked data https arxiv org abs 1401 5775 by tobias kuhn michel dumontier bip 122 https github com bitcoin bips blob master bip 0122 mediawiki http extensions for a content addressable web http lists w3 org archives public www talk 2001novdec 0090 html ietf rfc 2397 the data url scheme https tools ietf org html rfc2397 hash uri specification https github com hash uri hash uri http want digest https developer mozilla org en us docs web http headers want digest http digest https developer mozilla org en us docs web http headers digest cache digests for http 2 https httpwg org http extensions cache digest html http 2 push is tougher than i thought https jakearchibald com 2017 h2 push tougher than i thought see also distributed hash tables distributed hash tables cryptocurrency see also bitcoin bitcoin bit gold bit gold b money b money ecash ecash ethereum ethereum cryptography everything you need to know about cryptography in 1 hour https youtu be jzy3m5kv7y8 by colin percival 5 wrench attack https xkcd com 538 by randall munroe communication theory of secrecy systems http netlab cs ucla edu wiki files shannon1949 pdf by claude elwood shannon new directions in cryptography http wwwknoll in tum de pub main teachingss2006einfuehrunginformatik2 diffie pdf by whitfield diffie martin e hellman a method for obtaining digital signatures and public key cryptosystems https people csail mit edu rivest rsapaper pdf by ronald linn rivest adi shamir leonard adleman how rsa works with examples http doctrina org how rsa works with examples html by barry steyn why rsa works three fundamental questions answered http doctrina org why rsa works three fundamental questions answered html by barry steyn cryptographic hash vs mac what you need to know http doctrina org cryptographic hash vs mac what you need to know html by barry steyn end to end encryption e2ee computerphile https youtu be jkv1kejgkra by mike pound secret key exchange diffie hellman computerphile https youtu be nmm9ha2mqgi by mike pound see also onion routing onion routing randomness randomness zero knowledge proof protocol zero knowledge proof protocol distributed computing introduction to distributed system design http www hpcs cs tsukuba ac jp tatebe lecture h23 dsys dsd tutorial html perspectives on the cap theorem http groups csail mit edu tds papers gilbert brewer2 pdf by seth gilbert nancy a lynch the log what every software engineer should know about real time data s unifying abstraction https engineering linkedin com distributed systems log what every software engineer should know about real time datas unifying by jay kreps notes on distributed systems for young bloods https www somethingsimilar com 2013 01 14 notes on distributed systems for young bloods by jeff hodges immutability changes everything video https vimeo com 52831373 by pat helland immutability changes everything paper http cidrdb org cidr2015 papers cidr15 paper16 pdf by pat helland kafka a distributed messaging system for log processing http notes stephenholiday com kafka pdf by jay kreps neha narkhede jun rao chord a scalable peer to peer lookup service for internet applications https pdos csail mit edu papers chord sigcomm01 chord sigcomm pdf by ion stoica robert morris david karger m frans kaashoek hari balakrishnan dynamo amazon s highly available key value store http www allthingsdistributed com files amazon dynamo sosp2007 pdf by giuseppe decandia deniz hastorun madan jampani gunavardhan kakulapati avinash lakshman alex pilchin swaminathan sivasubramanian peter vosshall werner vogels mapreduce simplified data processing on large clusters https research google com archive mapreduce html by jeffrey dean sanjay ghemawat the chubby lock service for loosely coupled distributed systems https research google com archive chubby html by mike burrows so you want to trace your distributed system key design insights from years of practical experience http www pdl cmu edu pdl ftp selfstar cmu pdl 14 102 pdf by raja r sambasivan rodrigo fonseca ilari shafer gregory r ganger dapper a large scale distributed systems tracing infrastructure https research google com pubs pub36356 html by benjamin h sigelman luiz andr barroso mike burrows pat stephenson manoj plakal donald beaver saul jaspan chandan shanbhag queues don t fix overload https ferd ca queues don t fix overload html by fred hebert see also consensus consensus peer to peer networks peer to peer networks distributed hash tables kademlia a peer to peer information system based on the xor metric https pdos csail mit edu petar papers maymounkov kademlia lncs pdf by petar maymounkov david mazi res ecash blind signatures for untraceable payments http sceweb sce uhcl edu yang teaching csci5234websecurityfall2011 chaum blind signatures pdf by david chaum ecash bitcoin wiki https en bitcoinwiki org wiki ecash an introduction to ecash https web archive org web 19971009044558 http digicash com publish ecash intro ecash intro html error detection and correction information theory coding computerphile https www youtube com playlist list plzh6n4zxuckpkaj1 88vs 8z6yn9zx p6 by david brailsford multiple dimension error correction computerphile https youtu be 1 x 7bghbe0 by david brailsford the perfect code computerphile https youtu be wpoqfkqlojg by david brailsford extra bits more on perfect codes computerphile https youtu be i4zc67yf5iw by david brailsford ethereum ethereum in 25 minutes https www youtube com watch v 66saedzlmp4 t 22s by vitalik buterin ethereum white paper https github com ethereum wiki wiki white paper ethereum yellow paper https github com ethereum yellowpaper by gav would et al ethereum beige paper rewrite of the yellowpaper in non yellowpaper syntax https github com chronaeon beigepaper by micah dameron mastering ethereum https github com ethereumbook ethereumbook by andreas m antonopoulos gavin wood see also solidity solidity handicap principle the handicap principle a missing piece of darwin s puzzle http www goodreads com book show 885547 the handicap principle by amotz zahavi avishag zahavi hashcash hashcash a denial of service counter measure http www hashcash org papers hashcash pdf by adam back hash lists how to time stamp a digital document https link springer com chapter 10 1007 3 540 38424 3 32 by stuart haber w scott stornetta history cypherpunk a cypherpunk s manifesto https www activism net cypherpunk manifesto html by eric hughes the crypto anarchist manifesto https www activism net cypherpunk crypto anarchy html by timothy c may cyphernomicon https www cypherpunks to faq cyphernomicron cyphernomicon html by timothy c may this machine kills secrets https www goodreads com book show 13586738 this machine kills secrets by andy greenberg crypto https www goodreads com book show 984428 crypto by steven levy history open source the cathedral and the bazaar http www catb org esr writings cathedral bazaar cathedral bazaar by eric steven raymond homesteading the noosphere http www catb org esr writings homesteading homesteading by eric steven raymond the wikipedia revolution https www goodreads com book show 4606921 the wikipedia revolution by andrew lih identity identity crisis how identification is overused and misunderstood https www goodreads com book show 171237 identity crisis by jim harper secrets and lies digital security in a networked world https www goodreads com book show 304482 secrets and lies by bruce schneier secure electronic commerce building the infrastructure for digital signatures and encryption https www goodreads com book show 1174424 secure electronic commerce by warwick ford michael s baum understanding windows cardspace an introduction to the concepts and challenges of digital identities https www goodreads com book show 22380161 understanding windows cardspace by vittorio bertocci garrett serack caleb baker 7 laws of identity https web archive org web 20090824224507 https www identityblog com p 1065 by kim camerons getting started with oauth 2 0 https www goodreads com book show 13228633 getting started with oauth 2 0 by ryan boyd digital identity https www goodreads com book show 155374 digital identity by phillip j windley identity management a primer https www goodreads com book show 7273193 identity management by graham williamson david yip ilan sharoni kent spaulding mastercoin mastercoin a second generation protocol on the bitcoin blockchain https bitcoinmagazine com articles mastercoin a second generation protocol on the bitcoin blockchain 1383603310 by vitalik buterin mastercoin complete specification version 1 0 https www scribd com document 157454619 mastercoin specification by j r willett money shelling out the origins of money http nakamotoinstitute org shelling out by nick szabo the ascent of money a financial history of the world documentary https youtu be fsrtb5lp60s by niall ferguson see also bitcoin bitcoin bit gold bit gold b money b money ecash ecash ethereum ethereum rai rai onion routing onion routing computerphile https youtu be qryzre4bf7i by mike pound extra bits onion routing computerphile https youtu be 6ewkdyrnfqy by mike pound tor the second generation onion router https www usenix org conference 13th usenix security symposium tor second generation onion router by roger dingledine nick mathewson paul syverson circuit fingerprinting attacks passive deanonymization of tor hidden services https people csail mit edu devadas pubs circuit finger pdf by albert kwon mashael alsabah david lazar marc dacier srinivas devadas garlic routing https geti2p net en docs how garlic routing paxos neat algorithms paxos http harry me blog 2014 12 27 neat algorithms paxos by harry brundage the paxos algorithm https youtu be d7nagi nzpk by luis quesada torres the part time parliament https www microsoft com en us research uploads prod 2016 12 the part time parliament pdf by leslie lamport peer to peer networks understanding churn in peer to peer networks http www barsoom org papers imc 2006 churn pdf by daniel stutzbach reza rejaie see also bitcoin bitcoin bit gold bit gold distributed hash tables distributed hash tables privacy an overview of blockchain privacy mechanisms https steemit com zcoin zcoinofficial an overview of blockchain privacy mechanisms and how zerocoin in zcoin usdxzc not zcash stacks up see also cryptography cryptography zero knowledge proof protocol zero knowledge proof protocol proof of stake on stake and consensus https download wpsoftware net bitcoin pos pdf by andrew poelstra cryptocurrencies without proof of work https arxiv org abs 1406 5694 by iddo bentov ariel gabizon alex mizrahi proof of work see also handicap principle handicap principle hashcash hashcash selfish mining attack selfish mining attack raft the raft consensus algorithm https raft github io by diego ongaro et al raft understandable distributed consensus http thesecretlivesofdata com raft by ben johnson in search of an understandable consensus algorithm extended version https raft github io raft pdf by diego ongaro john ousterhout tangaroa a byzantine fault tolerant raft http www scs stanford edu 14au cs244b labs projects copeland zhong pdf by christopher copeland hongxia zhong rai island money https www clevelandfed org en newsroom and events publications economic commentary economic commentary archives 2004 economic commentaries ec 20040201 island money aspx by michael f bryan the rai stones are huge stone wheels used as currency on the island of yap http www thevintagenews com 2017 10 25 the rai stones are huge stone wheels used as currency on the island of yap by boban docevski randomness uniform non random random numbers http changelog ca quote 2011 10 29 uniform non random random numbers by timothy masters tifu by using math random https medium com betable tifu by using math random f1c308c4fd9d by mike malone the lava lamps that help keep the internet secure https youtu be 1cuufmeoijg by tom scott selfish mining attack majority is not enough bitcoin mining is vulnerable https www cs cornell edu ie53 publications btcprocfc pdf by ittay eyal and emin g un sirer see also proof of work proof of work smart contracts the idea of smart contracts http www fon hum uva nl rob courses informationinspeech cdrom literature lotwinterschool2006 szabo best vwh net idea html by nick szabo see also b money b money solidity solidity solidity what s solidity https www youtube com watch v v hu0jptlto list pl16wqdaj66scodl6xifbke xqg2gw avg solidity bytecode and opcode basics https medium com blockchain101 solidity bytecode and opcode basics 672e9b1a88c2 by bernard peh cryptozombies https cryptozombies io sybil attack the sybil attack http research microsoft com pubs 74220 iptps2002 pdf by john r douceur tamper detection the playdough protocols http nakamotoinstitute org the playdough protocols by nick szabo see also hash lists hash lists trust networks advogato s trust metric https web archive org web 20121025175345 http www advogato org 80 trust metric html by raph levien attack resistant trust metrics http www levien com thesis compact pdf by raph levien advogato has failed https web archive org web 20170628190710 http www advogato org article 928 html by bryan taylor eigenmorality https www scottaaronson com blog p 1820 by scott aaronson see also sybil attack sybil attack worse is better the rise of worse is better https www jwz org doc worse is better html by richard p gabriel worse is better http dreamsongs com worseisbetter html by richard p gabriel rethinkdb why we failed worse is better rethinkdb vs mongodb http www defmacro org 2017 01 18 why rethinkdb failed html by slava akhmechet lisp good news bad news how to win big http dreamsongs com wib html by richard p gabriel worse is better wikiwikiweb http wiki c2 com worseisbetter zero knowledge proof protocol zero knowledge proofs computerphile https youtu be hus1bh85x9i by alberto sonnino zero knowledge proofs https youtu be 0sy6nb72gck by scott twombly introduction to zksnarks https youtu be jr95o k swi by christian reitwiessner bulletproofs short proofs for confidential transactions and more https eprint iacr org 2017 1066 pdf by benedikt b nz jonathan bootle dan boneh andrew poelstra pieter wuille greg maxwell blockchain reading list created and maintained by charles iliya krempeaux http changelog ca canonical url https github com reiver blockchain reading list | blockchain distributed-computing peer-to-peer cryptography identity abstract-algebra reading-list whitepapers bitcoin ethereum | blockchain |
Personal-Website | personal website template description a beginner friendly personal website template with helpful comments fork this repository and edit away | front_end |
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libmetal | libmetal overview libmetal provides common user apis to access devices handle device interrupts and request memory across the following operating environments linux user space based on uio and vfio support in the kernel rtos with and without virtual memory bare metal environments for more details on the framework please refer to the openamp docs https openamp readthedocs io en latest project configuration the configuration phase begins when the user invokes cmake cmake begins by processing the cmakelists txt file and the cmake directory some cmake options are available to help user to customize the libmetal to their own project with doc default on build with documentation add dwith doc off in cmake command line to disable with examples default on build with application examples add dwith doc off in cmake command line to disable the option with tests default on build with application tests add dwith doc off in cmake command line to disable the option with default logger default on build with default trace logger add dwith default logger off in cmake command line to disable the option with shared lib default on generate a shared library add dwith shared lib off in cmake command line to disable the option with static lib default on generate a static library add dwith static lib off in cmake command line to disable the option with zephyr default off build for zephyr environment add dwith zephyr on in cmake command line to enable the the option build steps building for linux host git clone https github com openamp libmetal git mkdir p libmetal build directory cd libmetal build directory cmake make verbose 1 destdir libmetal install location install cross compiling for linux target use meta openamp https github com openamp meta openamp to build libmetal library use package libmetal in your yocto config file building for baremetal to build on baremetal you will need to provide a toolchain file here is an example toolchain file set cmake system processor arm cache string set machine zynqmp r5 cache string set cross prefix armr5 none eabi cache string set cmake c flags mfloat abi soft mcpu cortex r5 wall werror wextra flto os i ws xsdk r5 0 bsp psu cortexr5 0 include cache string set cmake exe linker flags cmake exe linker flags flto set cmake ar gcc ar cache string set cmake c archive create cmake ar qcs target link flags objects set cmake c archive finish true include cross generic gcc note other toolchain files can be found in the cmake platforms directory compile with your toolchain file mkdir p build libmetal cd build libmetal cmake libmetal source dcmake toolchain file toolchain file make verbose 1 destdir libmetal install install note when building baremetal for xilinx 2018 3 or earlier environments add dxilinx pre v2019 to your cmake invocation this will include the xilmem and xilstandalone libraries in your build these libraries were removed in 2019 1 building for zephyr the zephyr libmetal https github com zephyrproject rtos libmetal implements the libmetal for the zephyr project it is mainly a fork of this repository with some add ons for integration in the zephyr project following instruction is only to be able to run test application on a qemu running a zephyr environment as zephyr uses cmake we build libmetal library and test application as targets of zephyr cmake project here is how to build libmetal for zephyr export zephyr gcc variant zephyr export zephyr sdk install dir where zephyr sdk is installed source git clone zephyr project source root zephyr env sh cmake libmetal source root dwith zephyr on dboard qemu cortex m3 dwith tests on make verbose 1 all if we have turned on tests with dwith tests on when we run cmake we launch libmetal test on zephyr qemu platform as follows make verbose 1 run interfaces the following subsections give an overview of interfaces provided by libmetal platform and os independent utilities these interfaces do not need to be ported across to new operating systems i o the libmetal i o region abstraction provides access to memory mapped i o and shared memory regions this includes primitives to read and write memory with ordering constraints and ability to translate between physical and virtual addressing on systems that support virtual memory log the libmetal logging interface is used to plug log messages generated by libmetal into application specific logging mechanisms e g syslog this also provides basic message prioritization and filtering mechanisms list this is a simple doubly linked list implementation used internally within libmetal and also available for application use other utilities the following utilities are provided in lib utilities h min max round up down etc bitmap operations helper to compute container structure pointers and more version the libmetal version interface allows user to get the version of the library the version increment follows the set of rule proposed in semantic versioning specification https semver org top level interfaces the users will need to call two top level interfaces to use libmetal apis metal init initialize the libmetal resource metal finish release libmetal resource each system needs to have their own implementation inside libmetal for these two apis to call metal sys init metal sys finish for the current release libmetal provides linux userspace and bare metal implementation for metal sys init and metal sys finish for linux userspace metal sys init sets up a table for available shared pages checks whether uio vfio drivers are avail and starts interrupt handling thread please note that on linux to access device s memory that is not page aligned an offset has to be added to the pointer returned by mmap this offset although it can be read from the device tree property exposed by the uio driver is not handled yet by the library for bare metal metal sys init and metal sys finish just returns atomics the libmetal atomic operations api is consistent with the c11 c 11 stdatomics interface the stdatomics interface is commonly provided by recent toolchains including gcc and llvm clang when porting to a different toolchain it may be necessary to provide an stdatomic compatible implementation if the toolchain does not already provide one alloc libmetal provides memory allocation and release apis locking libmetal provides the following locking apis mutex libmetal has a generic mutex implementation which is a busy wait it is recommended to have os specific implementation for mutex the linux userspace mutex implementation uses futex to wait for the lock and wakeup a waiter condition variable libmetal condition variable apis provide wait for user applications to wait on some condition to be met and signal to indicate a particular even occurs spinlock libmetal spinlock apis provides busy waiting mechanism to acquire a lock shmem libmetal has a generic static shared memory implementation if your os has a global shared memory allocation you will need to port it for the os the linux userspace shmem implementation uses libhugetlbfs to support huge page sizes bus and device abstraction libmetal has a static generic implementation if your os has a driver model implementation you will need to port it for the os the linux userspace abstraction binds the devices to uio or vfio driver the user applications specify which device to use e g bus platform bus device f8000000 slcr and then the abstraction will check if platform uio driver or platform vfio driver is there if platform vfio driver exists it will bind the device to the platform vfio driver otherwise if uio driver exists it will bind the device to the platform uio driver the vfio support is not yet implemented interrupt libmetal provides apis to register an interrupt disable interrupts and restore interrupts the linux userspace implementation will use a thread to call select function to listen to the file descriptors of the devices to see if there is an interrupt triggered if there is an interrupt triggered it will call the interrupt handler registered by the user application cache libmetal provides apis to flush and invalidate caches the cache apis for linux userspace are empty functions for now as cache operations system calls are not available for all architectures dma libmetal dma apis provide dma map and unmap implementation after calling dma map the dma device will own the memory after calling dma unmap the cpu will own the memory for linux userspace it only supports to use uio device memory as dma memory for this release time libmetal time apis provide getting timestamp implementation sleep libmetal sleep apis provide getting delay execution implementation compiler this api is for compiler dependent functions for this release there is only a gcc implementation and compiler specific code is limited to atomic operations how to contribute as an open source project we welcome and encourage the community to submit patches directly to the project as a contributor you should be familiar with common developer tools such as git and cmake and platforms such as github then following points should be rescpected to facilitate the review process licencing code is contributed to openamp under a number of licenses but all code must be compatible with version the bsd license https github com openamp libmetal blob master license md which is the license covering the openamp distribution as a whole in practice use the following tag instead of the full license text in the individual files spdx license identifier bsd 3 clause signed off by commit messages must contain signed off by line and your email must match the change authorship information make sure your gitconfig is set up correctly git config global user name first name last namer git config global user email yourmail company com gitlint before you submit a pull request to the project verify your commit messages meet the requirements the check can be performed locally using the the gitlint command run gitlint locally in your tree and branch where your patches have been committed gitlint note gitlint only checks head the most recent commit so you should run it after each commit or use the commits option to specify a commit range covering all the development patches to be submitted code style in general follow the linux kernel coding style with the following exceptions use for doxygen comments that need to appear in the documentation the linux kernel gpl licensed tool checkpatch is used to check coding style conformity checkpatch is available in the scripts directory to check your n commits in your git branch scripts checkpatch pl strict g head n send a pull request we use standard github mechanism for pull request please refer to github documentation for help communication and collaboration subscribe https lists openampproject org mailman3 lists openamp rp lists openampproject org to the openamp mailing list openamp rp lists openampproject org | os |
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xla | xla xla accelerated linear algebra is an open source machine learning ml compiler for gpus cpus and ml accelerators the xla compiler takes models from popular ml frameworks such as pytorch tensorflow and jax and optimizes them for high performance execution across different hardware platforms including gpus cpus and ml accelerators get started if you want to use xla to compile your ml project refer to the corresponding documentation for your ml framework pytorch https pytorch org xla tensorflow https www tensorflow org xla jax https jax readthedocs io en latest notebooks quickstart html if you re not contributing code to the xla compiler you don t need to clone and build this repo everything here is intended for xla contributors who want to develop the compiler and xla integrators who want to debug or add support for ml frontends and hardware backends contribute if you d like to contribute to xla review how to contribute contributing md and then see the developer guide docs developer guide md contacts for questions contact thea lamkin thealamkin at google com resources community resources https github com openxla community code of conduct while under tensorflow governance all community spaces for sig openxla are subject to the tensorflow code of conduct https github com tensorflow tensorflow blob master code of conduct md | ai |
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Factoid-based-Question-Answer-Chatbot | factoid question answering chatbot factoid question answering chatbot is a simpler version of question answering system it answers to the question asked by user in near real time using information retrieval and natural language processing technique chatbot is able to answer factoid and summarization based question from article of various domains with an accuracy of 70 16 measured on squad dataset of stanford synopsis to run bot sh python3 p2 py path to article for example sh python3 p2 py dataset marvel comics txt once bot is up and start running it will ask you to enter your question and respond with answer methodology architecture of this bot closely follow the architecture described in the book main modules of the qa system are question processing in this step bot identifies type of question and type of answer it expects passage retrieval it generates question vector and vectors of passage using tf idf as feature it computes cosine similarity between question vector and passage vector returning top 3 closely resembling passage furthur improvement to this step has been done by removing stop words and using porter stemmer sentence retrieval after retrieving passage it tokenize sentences and computes ngram similarity between question and sentence thus identifying most relevant sentences answer processing based on the expected answer type then it process the answer sentence to identify particular entity using name entity recognization technique and part of speech tagging technique text summarization if type of question is definition or bot is unable to identify named entity from question it summarize the text using ngram tilting technique running the test test script is included in this repository test script uses stanford question answer dataset squad to ask question and match bot s answer with human tagged answer sh python3 testqa py test script outputs the no of question and correct retrieval of the answer and match this answer with what was tagged by human it computes the accuracy of the qa answer prediction and stores final result in accuracy csv accuracy of prediction is defined by python accuracy no of correct prediction no of total prediction result result of passage retrieval accuracy of passage retrieval using tf idf was 69 69 when tested over 422 articles of squad dataset and approx 87599 questions cross domain after removing stopwords and using porter stemmer this accuracy parameter improved to 77 49 on further analysis 94 23 of passage retrieval contains the equivalant paragraph in top 3 returned paragraph result of question answer system overall average over entire dataset was 64 correct answer prediction dataset included in this repository has following accuracy dataset name no of question no of correct answer accuracy new york city 817 513 62 79 buddhism 610 428 70 16 queen victoria 680 378 55 59 modern history 448 325 72 54 windows 8 202 148 73 27 usb 235 208 88 51 marvel comics 123 77 62 6 mammal 88 59 67 05 alloy 96 65 67 71 rajasthan 119 82 68 91 anthropology 222 178 80 18 dataset bot is able to perform cross domain this submission includes following dataset and sample question asked from the article one can furthur ask question by reading article and asking factoid or complex question marvel comics dataset marvel comics txt sample questions what marvel character s stories are set in an area resembling the american southwest who originally founded marvel comics when was marvel founded who was marvel s first true full time editor who co created captain america besides simon who co created captain america what two real life persons were also part of the magazine feature alongside fictional marvel characters who took over as head of marvel in 1978 mammal dataset mammal txt sample questions about how small is the average bumble bee bat how many mammals were known to exist up to 2006 which early mammalian ancestor produced non mammalian dimetroden who wrote the principles of classification and a classification of mammals alloy dataset alloy txt sample questions who shouted eureka while checking the purity of a crown who created the first process for the mass production of tool steel who discovered precipitation hardening alloys what are alloys defined by what is solid solution metal elements classified as where is the only iron deposit on earth where is tin mostly found when were precipitation hardening alloys discovered rajasthan dataset rajasthan txt sample questions where were the marathas from what is the most populous city in the thar desert what is the largest city for polyster blouse pieces in rajasthan anthropology dataset anthropology txt sample questions what is anthropology a study of how many educational institutions had some curriculum in anthropology by 1898 where did early anthropology originate why type of anthropology is the study of social organization a central focus of when was anthropology used as a term for comparative anatomy buddhism dataset buddhism txt sample questions what type of religion is buddhism what country has the largest population of buddhists how many buddists are outside of asia what is the oldest surviving buddhist school the royal courts sponsored both buddhism and what queen victoria dataset queen victoria txt sample questions who did victoria marry when did queen victoria get married when did victoria give birth to her eigth child what was the name of the new anesthetic used during leopolds birth when was victoria s final child beatrice born what color was her dress modern history dataset modern history txt sample questions where did the united states support dictatorships in the 1970 how many people watched the apollo 11 landing when did the attack on pearl harbor occur who did imperial japan sign a tripartite pact with what institution was meant to bring stability windows 8 dataset windows 8 txt sample questions who is windows division president when was milestone 1 divulged when was milestone 2 divulged when did the developer preview expire how many downloads occured in the first 12 hours when was the consumer preview set to expire when was the release preview revealed to consumers when was the first game of the 2013 stanley cup finals when was the first windows 8 patch sent out who developed steam usb dataset usb txt sample questions usb has become what on other devices what has usb effectively replaced when did the seven companies begin developing usb s when was the first widely used version of usb released when was usb 3 1 released how many devices may be connected to a host controller a usb connection is based on what reference 1 jurafsky martin speech and language processing 2ed 2007 2 steven bird ewan klein and edward loper natural language processing with python 3 the stanford question answering dataset https rajpurkar github io squad explorer 4 amit singhal steve abney michiel bacchiani michael collins donald hindle fernando pereira at t at trec 8 5 fnu budianto reading comprehension on the squad dataset 6 xin li and dan roth learning question classifiers the role of semantic information | ai |
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nlptools | deprecated please see https github com dair iitd openie standalone which has combined multiple projects into a single project and maintains the latest version of open ie open ie 5 it is based on another repository https github com allenai openie standalone which has an older version of open ie uw nlptools this is a collection of natural language processing tools wrapped behind common interfaces the client can easily use the wrapped libraries through elegant scala interfaces it s also simple to switch implementations of a particular tool since all implementations of a particular tool extend a common interface this toolkit also aims to minimize the size of transitive dependencies each tool is broken into its own component so you can choose what you want to use through dependency management each component contains the requisite modules but no more saving you from needing to search for models while also protecting you from a dependencies that are hundreds of megabytes in order to contain every possible model if you want to avoid the default models that s ok too they are a transitive dependency of the tool so you can exclude them within your dependency manager licensing can be a nightmare each tool is split into its own component with the most permissive license allowable by the dependencies licenses are all clearly stated in the license file of the subcomponent the largest nlp components are opennlp toolkit apache 2 0 and stanford corenlp gpl 2 0 the interfaces are defined in the core component usage each component is usable through a java interface as well as on the command line here are some examples echo the quick brown fox jumps over the lazy dog sbt project nlptools parse stanford run main edu knowitall tool parse stanfordparsermain echo the quick brown fox jumps over the lazy dog sbt project nlptools chunk opennlp run main edu knowitall tool chunk opennlpchunkermain http you can also spin each tool up as an http server sbt project nlptools parse stanford run main edu knowitall tool parse stanfordparsermain server it s also possible to spin up all nlptools components as http servers scala scripts assignports scala servers config scala scripts runservers scala servers config inconveniently this spins up many programs on different ports that can receive post and get requests remembering which port corresponds to which tool is confusing at best there is a simple scala application in server that runs yet another server to unify the tools once you run the nlptools server you can post to the subpath corresponding to your tool sbt run servers config port 12000 now you can post to a convenient url curl localhost 12000 postag clear postag opennlp postag stanford tokenize clear tokenize breeze tokenize opennlp tokenize stanford curl localhost 12000 postag opennlp data binary let us postag this text let 0 vb us 4 prp postag 7 vb this 14 dt text 19 nn 23 or from stdin echo let us postag this text curl x post data binary localhost 12000 postag opennlp let 0 vb us 4 prp postag 7 vb this 14 dt text 19 nn 23 components stemmers morpha snowball porter porter2 lovins tokenizers opennlp penn treebank stanford part of speech pos taggers clear opennlp stanford chunkers opennlp constituency parsers opennlp stanford dependency parsers maltparser stanford clearparser coreference stanford sentence segmentation sentencer opennlp piao contributors michael schmitz http www schmitztech com | ai |
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ECE382N.23 | ece382n 23 project for ece382n 23 embedded systems design and modeling | os |
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IoTAR | iotar interenet of things augmented reality course by ritesh kanjee video course check out the full video course on udemy https www udemy com internet of things using augmented reality and unity iotar couponcode iotar github | server |
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Team07_MP3_Project | sj2 c development environment an sj2 board is used at san jose state university sjsu to teach embedded systems courses part of this git repository also includes development environment for not just an arm controller but also support to compile and test software on your host machine such as windows mac etc the sample project of the sj2 board contains code written in c that anyone can understand easily it was designed to learn the low level workings of a microcontroller platform with an rtos project highlights fully implemented in c minimalistic design with little to no abstractions follows good coding principles such as yagni and dry infrastructure highlights supports mac linux windows out of the box version controlled toolchains and other supplementary tools no vms no wsl dependency on windows next steps build and flash project readme getting started md read more about scons readme scons md to figure out how to build projects videos we have made some videos that you may find useful comprehensive development environment https www youtube com watch v ebicummaim0 compile arm project https www youtube com watch v al0vkzvu8 build system we use scons https scons org as our build platform the developers of sj2 c applied experience of diverse set of build systems acquired over many years and resorted to this one the candidates were scons used at tesla and many other companies bazel used at google zoox and many other companies make scons discusses the advantages and disadvantages of other existing build systems build system comparison https github com scons scons wiki sconsvsotherbuildtools from experience we can state that bazel is really cool but hard to extend and problematic in windows scons dependencies are tricky but it is based on python and easy to understand and extend scons takes advantage of a python interpreter making it portable across multiple platforms mac linux windows history we wanted to build a strong foundational sample project for sj 2 development board that is used at sjsu originally preet created sj1 development board which meant that there was one development board across multiple classes at sjsu i was an enthusiast and created a hybrid project composed of c and c sources i love c a little more than c because i can express artistic designs in the language but a language is really a tool and you need to select the right tool for the job presently i work on embedded firmware code for automotive industry which is in c and software code in c because c is the right tool for firmware and c is the right tool for software sj2 original https github com kammce sjsu dev2 software was also designed by an enthusiast khalil who is a very talented person but expressing a starter project in c increased the complexity so much that many developers had a difficult time adopting it this is where the sj2 c was born which was completely re designed to write simple code that everyone could understand many students are new to programming and it was not expected that c would come naturally the code may appear less fancy but it is simple to understand and traceable with minimal abstractions the goal is to avoid designing yet another arduino platform there is no such thing as magic in the field of firmware engineering | os |
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Advanced-Computer-Vision-with-TensorFlow | advanced computer vision with tensorflow | coursera-specialization tensorflow computer-vision | ai |
ctakes-rest-service | ctakes rest service the goal of this solution is to provide a json based rest service to process unstructured clinical text through a smart natural language processing system in a fast accurate and easy to setup way note that the install instructions are designed for a local environment and no guidance is provided for a secured cloud server deployment img demo png install based on ubuntu 18 04 prerequisites the following updates the list of available packages and installs necessary packages needed for this solution sudo apt get update y sudo apt get install y maven subversion git unzip wget curl java this solution has been tested on java jdk jre 8 newer java versions have been reported to have many bugs the following installs the exact java version and configures it so the default java 11 is not used sudo apt get install y openjdk 8 jdk openjdk 8 jre headless default jdk select java 8 with most likely selection 2 and press enter sudo update alternatives config java selection path priority status 0 usr lib jvm java 11 openjdk amd64 bin java 1111 auto mode 1 usr lib jvm java 11 openjdk amd64 bin java 1111 manual mode 2 usr lib jvm java 8 openjdk amd64 jre bin java 1081 manual mode select javac 8 with most likely selection 2 and press enter sudo update alternatives config javac selection path priority status 0 usr lib jvm java 11 openjdk amd64 bin javac 1111 auto mode 1 usr lib jvm java 11 openjdk amd64 bin javac 1111 manual mode 2 usr lib jvm java 8 openjdk amd64 bin javac 1081 manual mode mysql the mysql version to be installed must be 5 7 which is the default under ubuntu 18 04 the following also adds a default user for ctakes sudo apt get install y mysql server mysql client select low password validity deny validate password plugin enter pass as the password and confirm all other dialogs sudo mysql secure installation sudo service mysql restart sudo mysql u root ppass mysql alter user root localhost identified with mysql native password by pass mysql flush privileges mysql exit tomcat the following installs apache tomcat at version 8 5 42 sudo useradd m u d opt tomcat s bin false tomcat cd tmp for whatever reason when apache tomcat is updated older versions are removed from the server if the zip file is not found you ll need to locate the latest version and update the following command as well as a few others below the file server is found here http www us apache org dist tomcat tomcat 8 wget http www us apache org dist tomcat tomcat 8 v8 5 42 bin apache tomcat 8 5 42 zip unzip apache tomcat zip sudo mkdir p opt tomcat sudo mv apache tomcat 8 5 42 opt tomcat sudo ln s opt tomcat apache tomcat 8 5 42 opt tomcat latest sudo chown r tomcat opt tomcat sudo sh c chmod x opt tomcat latest bin sh tomcat service install using the nano editor create this file and note that it is opened for writing sudo nano etc systemd system tomcat service with nano opened paste the following with ctrl shift v save with with ctrl x y enter unit description tomcat 8 5 servlet container after network target service type forking user tomcat group tomcat environment java home usr lib jvm java 8 openjdk amd64 environment java opts djava security egd file dev urandom environment catalina base opt tomcat latest environment catalina home opt tomcat latest environment catalina pid opt tomcat latest temp tomcat pid environment catalina opts xms4000m xmx4000m server xx useparallelgc execstart opt tomcat latest bin startup sh execstop opt tomcat latest bin shutdown sh install wantedby multi user target start up tomcat and initialize the service at boot time sudo systemctl daemon reload sudo systemctl start tomcat sudo systemctl enable tomcat local repository the following pulls down and enters into the ctakes rest service repository directory cd desktop git clone https github com goteamepsilon ctakes rest service git cd ctakes rest service sql data scripts load in all sql data scripts in sno rx 16ab db this process may take several hours sudo mysql u root ppass sno rx 16ab db 01 setup sql sudo mysql u root ppass sno rx 16ab db 02 load sql sudo mysql u root ppass sno rx 16ab db 03 load sql sudo mysql u root ppass sno rx 16ab db 04 load sql sudo mysql u root ppass sno rx 16ab db 05 load sql sudo mysql u root ppass sno rx 16ab db 06 load sql sudo mysql u root ppass sno rx 16ab db 07 load sql sudo mysql u root ppass sno rx 16ab db 08 load sql building codebase the following instructs maven mvn to install and build ctakes proper as well as this codebase mkdir ctakes codebase area cd ctakes codebase area svn export https svn apache org repos asf ctakes trunk cd trunk ctakes distribution mvn install dmaven test skip true cd ctakes assertion zoner mvn install dmaven test skip true cd ctakes web rest mvn install launch the service the following moves the compiled web application resource war file to tomcat s webapps area to be served sudo mv target ctakes web rest war opt tomcat latest webapps real time tomcat logs while the deployed service is initializing run the following in another terminal tab to see the logs sudo tail f opt tomcat latest logs catalina out access the service there are two ways to access the service 1 access the url http localhost 8080 ctakes web rest index jsp to use a webpage with a textbox when the text is submitted a json structure is returned in a popup with all relevant clinical mentions 2 any http client such as curl or postman here is an example of a curl request curl x post http localhost 8080 ctakes web rest service analyze pipeline default h cache control no cache d patient has hypertension technical notes this solution assumes mysql uses the default port 3306 there is a minimum ram requirement of 5 gigabytes tomcat is set to use up to 4 gigabytes for an exact view of the custom dictionary used in this solution please see the following https github com goteamepsilon ctakes rest service blob master ctakes web rest src main resources org apache ctakes dictionary lookup fast customdictionary xml license apache license version 2 0 | ai |
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LLMFarm | llmfarm p align center img width 178px alt icon src dist llmfarm0 1 2 256 png a href https testflight apple com join 6spplivm img width 178px alt icon src dist testflight png a p p align center a href https testflight apple com join 6spplivm strong install with testflight strong a p p align center img alt icon height 400px src dist screen1 png img alt icon width 525px src dist screen2 png p llmfarm is an ios and macos app to work with large language models llm it allows you to load different llms with certain parameters br based on ggml https github com ggerganov ggml and llama cpp https github com ggerganov llama cpp by georgi gerganov https github com ggerganov also used sources from rwkv cpp https github com saharnooby rwkv cpp by saharnooby https github com saharnooby mia https github com byroneverson mia by byroneverson https github com byroneverson llamachat https github com alexrozanski llamachat by alexrozanski https github com alexrozanski features x macos 13 x ios 16 x various inferences x various sampling methods x metal dont work https github com ggerganov llama cpp issues 2407 issuecomment 1699544808 on intel mac x model setting templates x lora adapters support read more lora md lora train support lora export as model support other tokenizers support restore context state now only chat history inferences x llama https arxiv org abs 2302 13971 img src dist metal 96x96 2x png width 16px heigth 16px x gptneox https huggingface co docs transformers model doc gpt neox x replit https huggingface co replit replit code v1 3b x gpt2 https huggingface co docs transformers model doc gpt2 cerebras https arxiv org abs 2304 03208 img src dist metal 96x96 2x png width 16px heigth 16px x starcoder santacoder https huggingface co bigcode santacoder img src dist metal 96x96 2x png width 16px heigth 16px x rwkv https huggingface co docs transformers model doc rwkv 20b tokenizer x falcon https github com cmp nct ggllm cpp img src dist metal 96x96 2x png width 16px heigth 16px x mpt https huggingface co guinmoon mpt 7b storywriter gguf img src dist metal 96x96 2x png width 16px heigth 16px x bloom https huggingface co guinmoon bloomz 1b7 gguf img src dist metal 96x96 2x png width 16px heigth 16px note for falcon alpaca gpt4all chinese llama alpaca and chinese llama 2 alpaca 2 vigogne french vicuna koala openbuddy multilingual pygmalion metharme wizardlm baichuan 1 2 derivations aquila 1 2 mistral ai v0 1 refact persimmon 8b mpt bloom select llama inferece in model settings sampling methods x temperature temp tok k top p x tail free sampling tfs https www trentonbricken com tail free sampling x locally typical sampling https arxiv org abs 2202 00666 x mirostat https arxiv org abs 2007 14966 x greedy x grammar dont work for gptneox gpt 2 rwkv classifier free guidance getting started models must be quantized by ggml https github com ggerganov ggml after 154 https github com ggerganov ggml pull 154 llmfarm work in sandbox mode and put added models to the models directory you can find answers to some questions in the faq section https github com guinmoon llmfarm wiki faq inference options when creating a chat a json file is generated in which you can specify additional inference options the chat files are located in the chats directory you can see all inference options here inference options md models you can download some of the supported models here models md development llmfarm core has been moved to a separate repository https github com guinmoon llmfarm core swift to build llmfarm you need to clone this repository recursively bash git clone recurse submodules https github com guinmoon llmfarm p s the number of open models is continuously growing one of the advantages of using such models is the possibility of preserving their original content without censorship however the disadvantage may be the irrelevance of the information contained in them you can also get answers to questions from various industries for example there are models that specialize in medical terms or programming in addition with the help of these models you can create stories songs music and play quests etc | ai ggml gpt-2 gptneox ios llama macos swift starcoder rwkv | ai |
shards-react | p align center img src logo jpg width 230 p p align center shards react is a free beautiful and modern react ui kit br based on a href https github com designrevision shards ui shards a p br p align center a href img src https img shields io badge license mit brightgreen svg a a href https twitter com designrevision img src https img shields io twitter follow designrevision svg style social label follow a p br p align center a href https designrevision com docs shards react strong documentation demo strong a a href https designrevision com downloads shards react strong official page strong a p getting started getting started with shards react is fairly simple you can download shards react via the official website here on github as a release package or by using a package manager such as yarn or npm official website https designrevision com downloads shards react release package https github com designrevision shards react releases br quick start you can install shards react via yarn or npm bash yarn yarn add shards react npm npm i shards react br module bundlers if you are using a module bundler such as webpack https webpack js org or rollup https rollupjs org you can import components as follows javascript import alert from shards react import bootstrap dist css bootstrap min css import shards ui dist css shards min css br built using shards ui kit https designrevision com downloads shards react datepicker https reactdatepicker com react popper https github com fezvrasta react popper nouislider by l on gersen wtfpl license https refreshless com nouislider download br contributing please read contributing md contributing md for details on our code of conduct and the process for submitting pull requests to us br roadmap view the roadmap http designrevision com docs shards react roadmap br changelog view notable changes changelog md | react uikit ui design-system components bootstrap | os |
Tiva-C-FreeRTOS | tiva c freertos demos and tutorials that run freertos on the tiva c series arm development kits demos blinky this project is a port of the blinky basic demo from the freertos win32 msvc portable folder to run on the tiva c series ek tm4c123gxl launchpad tutorials tutorial freertos setup in this tutorial i will show how to set up a freertos project for the tiva c tm4c123x device in keil uvision in part 1 a freertos project will be created using the cmsis libraries and keil software packs and in part 2 i will show how to incorporate freertos directly into an empty uvision project and configure it manually the method used in part 1 requires fewer steps and is the quicker way to get a project up and running however if you prefer a minimalist approach and or a non cmsis implementation then you might want to use the second method part 2 instead i recommend going through the steps in part 1 before trying part 2 because with a few modifications you will be able to reuse the freertosconfig h file that is generated there step by step freertos for tiva c tm4c123x keil project setup tutorial freertos setup tutorial freertos setup pdf http img youtube com vi 2oqzpklucuy 2 jpg https youtu be 2oqzpklucuy br watch the video part 1 https youtu be 2oqzpklucuy http img youtube com vi lj56g2fwfrq default jpg https youtu be lj56g2fwfrq br watch the video part 2 https youtu be lj56g2fwfrq | os |
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launchpad-ui | launchpad a modern intuitive and accessible design system built and used by the launchdarkly team resources for a demo of all components available in launchpad visit our storybook https main 626696a2018c1f004a1cde86 chromatic com installation full installation instructions can be found here https www npmjs com package launchpad ui core running launchpad locally installation pnpm https pnpm io is the package manager used in this monorepo after installing the package manager https pnpm io installation run the following command to install the project s dependencies sh pnpm install storybook storybook https storybook js org is used for local development of components run this command to start a local instance in your browser sh pnpm storybook running tests unit tests vitest https vitest dev and react testing library https testing library com docs react testing library intro are used to unit test the code the following command will run unit tests in every package of the monorepo sh pnpm test a11y tests playwight https playwright dev with axe core playwright https github com dequelabs axe core npm blob develop packages playwright readme md is used to run accessibility checks on our stories in light and dark mode the following command will build storybook start the server and run a11y tests locally on them sh pnpm e2e a11y ssr tests ssr support is tested using a remix https remix run app found in the monorepo apps remix readme md the following command will build the app start the server and run playwright tests to verify the components have rendered sh pnpm e2e ssr launchpad architecture launchpad is set up as a monorepo where each component package is bundled and delivered as its own npm package while you can technically import each component package separately we do recommend using the bundled version of launchpad at launchpad ui core the team chose to treat each component as a separate package internally for improved testing version management and isolation guarantees at the time of writing this we are using the pnpm package manager https pnpm io for its workspace feature and the nx build system https nx dev for monorepo support each component is defined as a directory under the packages directory it s advised to understand generally how these works when contributing the packages directory when we talk about components in launchpad we re talking about a ui component library implemented as a package in our monorepo it is a standalone library that can be imported and used by other js applications while all launchpad ui components are packages not all packages are necessarily components a package can be defined to store utilities such as tokens used by other packages shared helper functions etc launchpad libraries may depend on one another as in the case of our modal library depending on the button library this dependency just like with shared utility libraries is simply represented as a package json import the apps directory the apps directory is essentially a standalone project that has easy access to our workspace packages currently we have a remix application that we use for ssr testing but you could build other apps such as a docs website or another testing playground versioning we are using major version zero 0 y z semantic versioning https semver org spec v0 1 0 html to indicate that the project is still in an initial development phase and anything may change at any time when a new package is introduced the initial version is set to 0 1 0 as mentioned above single components are deployed as versioned npm packages and launchpad ui core is versioned too when a component package version changes this will create a version bump in launchpad ui core as well according to this strategy core receives a version bump whenever one of its dependencies e g button modal is updated the core version bump is equivalent to the highest semver version bump of an underlying dependency in the release for example if core were at 0 1 0 and then the button package gets bumped from 0 2 1 to 0 3 0 minor and the alert package gets bumped from 0 5 1 to 0 5 2 patch core package will get bumped to 0 2 0 minor questions launchdarkly employees can reach out with questions or comments in our slack channel ask launchpad design system https launchdarkly slack com channels cdxefnmlp you can also open an issue https github com launchdarkly launchpad ui issues new in the launchpad repository to ask a question | accessibility component-library design-systems design-tokens launchdarkly react ssr launchdarkly-service-component managed-by-terraform | os |
URScript | urscript for rust cargo build test https github com spodeian urscript actions workflows rust yml badge svg https github com spodeian urscript actions workflows rust yml a library enabling efficient and effective control of ur5 cobots in std and no std environments this library currently provides support for no std environemnts preproccessing of variables related to urscript implementation of the pose type seen in urscript this library is aiming to provide formatting of variables and functions in a way compatible with urscript preproccessing of some functions and variables implementation of all types related to urscript async helper functions ability to deserialize data from ur5 robot into strings or native variables usage add this to your cargo toml toml dependencies replace with latest version ur script features this crate can be used with the standard library by enabling the std feature and optionally disabling the default libm feature use this in cargo toml toml dependencies ur script replace with latest version ur script features std the libm feature allows for the usage of floats in no std builds more features to come as this library is developed license a rel license href http creativecommons org licenses by nc 4 0 img alt creative commons licence style border width 0 src https i creativecommons org l by nc 4 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by nc 4 0 creative commons attribution noncommercial 4 0 international license a attributions this version of this library is built with nalgebra https nalgebra org as a core dependency which licensed under the apache 2 0 license http www apache org licenses license 2 0 | os |
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poseWrangler | posewrangler alt tag epic pose wrangler docs site html images v2 png overview posewrangler is a tool for interfacing with epic s mayauerbfplugin the plugin is distributed by epic games and installed via quixel bridge this is the same version distributed through quixel bridge with the maya plugin v6 9 2 or later supports scenes created with the uerbfsolvernode multiple driver support initial blendshape support wip supports maya 2018 2022 support for custom mirror mappings to allow for rigs with naming conventions that deviate from the default ue5 conventions fully automatable via python and mayapy serialization deserialization to dictionary or json file support for custom extensions and context menu actions contributors chris theodosius chris evans judd simantov david corral borna berc opening the tool to load the tool you can call it like so from epic pose wrangler import main pose wrangler main posewrangler | front_end |
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ALCE | enabling large language models to generate text with citations p align center img src https github com princeton nlp alce blob main assets moose png raw true alt alce width 15 br alce is pronounced as elk as alce is the latin word for elk europe or moose north america p this repository contains the code and data for paper enabling large language models to generate text with citations https arxiv org abs 2305 14627 in this paper we propose alce a benchmark for a utomatic l lms c itation evaluation alce contains three datasets asqa qampari and eli5 we provide automatic evaluation code of llm generations around three dimensions fluency correctness and citation quality this repository also includes code to reproduce the baselines in our paper img src https github com princeton nlp alce blob main assets alce png raw true alt alce width 100 quick links requirements requirements data data code structure code structure reproducing baselines reproducing baselines evaluation evaluation human evaluation human evaluation bug or questions bug or questions citation citation requirements please install the latest versions of pytorch torch huggingface transformers transformers huggingface accelerate accelerate and the openai api package openai this codebase is tested on torch 2 1 0 dev20230514 cu118 transformers 4 28 1 accelerate 0 17 1 and openai 0 27 4 with python 3 9 7 data you can download datasets along with retrieval results by running the following command bash bash download data sh all the data will be stored in data our data included top 100 dpr gtr retrieved results for asqa and qampari and top 100 bm25 retrieved results for qampari we also provide reranked oracle retrieval results where top 5 passages can achieve the same recall as the original top 100 recall retrieval you can reproduce the passage retrieval step with the following command bash python retrieval py data path to data retriever bm25 gtr output file path to output there are additional packages required for the retrieval steps specifically you need to install pyserini 0 21 0 their github repo https github com castorini pyserini tree master is helpful and sentence transformers 2 2 2 for the bm25 retrieval over common crawl using sphere you must first download the index from the sphere repo https github com facebookresearch sphere and set the environmental variable bm25 sphere path to the path of the downloaded index specifically you can use the following command bash wget p faiss index https dl fbaipublicfiles com sphere sphere sparse index tar gz tar xzvf faiss index sphere sparse index tar gz c faiss index export bm25 sphere path pwd faiss index it s important to note that given the large size of the corpus this step is extremely expensive and time consuming we found that larger cpu memory tends to help with the speed for gtr we first build an index using the dpr wikipedia snapshot which you can obtain using the download script from the dpr repo https github com facebookresearch dpr and then setting the environmental variable dpr wiki tsv to the path of the tsv file specifically you can use the following command bash wget https dl fbaipublicfiles com dpr wikipedia split psgs w100 tsv gz gzip xzvf psgs w100 tsv gz export dpr wiki tsv pwd psgs w100 tsv then you want to set gtr emb to the path of the gtr embeddings of the wikipedia corpus and running the retrieval script for the first time will automatically build and save the index building the dense index can be expensive for gpu memory we use 80gb gpus for this and time consuming the entire index will take about 31gb if you find this step to be too expensive you can also download it using bash wget https huggingface co datasets princeton nlp gtr t5 xxl wikipedia psgs w100 index resolve main gtr wikipedia index pkl export gtr pwd gtr wikipedia index pkl to reproduce the dpr retrieval we refer the dpr repo https github com facebookresearch dpr which we used the original dpr checkpoint trained on nq code structure run py run file to reproduce our baseline generations eval py eval file to evaluate generations prompts folder that contains all prompt files configs folder that contains all config files to reproduce baselines tools misc code generate summaries snippets reranking etc reproducing baselines you can reproduce baselines from our paper by bash python run py config configs config name you can also overwrite any arguments in the config file or add new arguments simply through command line python run py config configs config name seed 43 model vicuna 13b the naming of config files follow the rule of llm demos and passages retriever method yaml method names include default corresponds to the vanilla model in our paper summary corresponds to the summary model extraction corresponds to the snippet model interact doc id corresponds to the interact model interact search corresponds to the inlinesearch model closedbook corresponds to the closedbook model our code support both openai api and offline huggingface models for openai models for example chatgpt you need to set the environment variable openai api key and openai org id if you are using the azure openai api you need to set the environment variable of openai api key and openai api base you also need to add the flag azure note that in azure openai api chatgpt s name is different and you should set it by model gpt 35 turbo for the open source models you should set the model name equal to the input of huggingface models from pretrained method this could either be a local directory e g for the older llama models or a path to the huggingface hub for detailed argument usage please refer to run py model output along with gold answers and run configs will be stored in a json file in result post hoc citation for closed book models one can use post hoc cite py to add citations in a post hoc manner using gtr large to run post hoc citation execute bash python post hoc cite py f result result json file name external docs data corresponding data the output file with post hoc citations will be stored in result with a suffix post hoc cite gtr t5 large external evaluation acle evaluation is implemented in eval py for asqa use the following command bash python eval py f path to result file citations qa mauve for qampari use the following command bash python eval py f path to result file citations for eli5 use the following command bash python eval py f path to result file citations claims nli mauve the evaluation result will be saved in result with the same name as the input and a suffix score human evaluation the results from our human evaluation section 6 are located under the directory human eval human eval both the data and the analysis are available please refer to the directory for details bug or questions if you have any questions related to the code or the paper feel free to email tianyu tianyug cs princeton edu if you encounter any problems when using the code or want to report a bug you can open an issue please try to specify the problem with details so we can help you better and quicker citation please cite our paper if you use alce in your work bibtex article gao2023enabling title enabling large language models to generate text with citations author gao tianyu and yen howard and yu jiatong and chen danqi year 2023 | ai |
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Embedded-System-Design | embeddedsystemdesign this is the repository for the embedded system design class fall 2016 | os |
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Semaphore | semaphore a full body keyboard demo demo gif https youtu be h376w93gqq4 view a fuller demo and more background on the project at https youtu be h376w93gqq4 the next iteration of this project designed as a full body game controller is also available at https github com everythingishacked gamebody semaphore uses opencv https github com opencv opencv python and mediapipe s pose detection https google github io mediapipe solutions pose html python solution api to perform real time detection of body landmarks from video input from there relative differences are calculated to determine specific positions and translate those into keys and commands sent via keyboard https github com boppreh keyboard the primary input is to type letters digits and symbols via flag semaphore https en wikipedia org wiki flag semaphore by extending both arms at various angles rather than waiting a set time after every signal you can jump to repeat the last sent symbol see the semaphores dictionary in the code for a full set of angles which mostly conform to standard us semaphore with some custom additions most of the rest of the keyboard is included as other modifier gestures such as shift open both hands instead of fists backspace both hands over mouth digits and other extra symbols squat while signaling command lift left leg to horizontal thigh control lift right leg to horizontal thigh arrow left right up down cross arms and raise each straight leg leg arrow angle degrees repeat previous letter command jump running on latest macos from terminal toggle the following for keyboard access system settings privacy security accessibility terminal slide to allow for mac this uses a custom keyboard library https github com everythingishacked keyboard this is built for a mac keyboard but you can also swap e g windows key for command simply enough | mediapipe opencv python | ai |
CREATOR | creator we release here the code for most of the main experiments in paper creator tool creation for disentangling abstract and concrete reasoning of large language models including the code of our framework and various baselines we also release two new datasets creation challenge and tool transfer here please refer to our paper for more details preparation please first install the packages needed for the experiment through shell pip install r requirements txt we will use the api from openai to complete our experiment so please put your openai api key in file keys txt before the experiment if you have more than one api key please put one key in a line our code supports the polling of keys in the tool use setting we will also use the wolframalpha api please put the wolframalpha api key at the beginning of utils py replacing the wolfram key all the datasets including math tabmwp creation challenge and tool transfer dataset are provided in advance in corresponding folders main experiments all the codes are under the root folder in the main experiment we utilize three datasets including two existing benchmarks math and tabmwp and one new dataset creation challenge we construct a folder for the experiment on each dataset each folder contains the dataset prepared in advance and the prompt library that we will utilize to inspire the llm s ability creator setting please run generate creator py for the creator framework setting in the code please first choose one task from math tabmwp and creation challenge specially for creation challenge you can choose from normal utility hint and all hint to reproduce our experiment on the role of hints for creator framework the meaning of other hyper parameters are illustrated below python the index of the openai api key that we start to utilize start key 0 the temperature of the llm we apply in generation temperature 0 3 the path to the prompt file prompt creation f task prompt lib prompt creator creation md prompt decision f task prompt lib prompt creator decision md prompt rectification f task prompt lib prompt rectification md the file where we conduct execution code file code exec tmp0 the llm we use we also provide text davinci 003 api in utils gen func chat api the upper bound of the rounds of rectification rectify limit 0 you can also change the saving path after a successful execution the results will be recorded in the results folder for each dataset toolcreate whole setting we do not disentangle the tool creation and decision in this stage please run generate toolcreate whole py for this setting besides dataset different modes can also be chosen under this setting other hyper parameters keep the same meaning with the creator setting cot and pot setting please run generate cot py and generate pot py for these two settings respectively under the chain of thought cot and program of thought pot we do not have hint modes any more you can choose which dataset to conduct the experiments other hyper parameters keep the same meaning with the creator setting tool use setting please run generate tooluse py to conduct experiments under tool using setting we leverage wolframalpha as external api for the llm to utilize under this setting we test on math and tabmwp and do not apply hint modes other hyper parameters keep the same meaning with the creator setting tool transfer we also provide the codes and dataset about tool transfer in the discussion part of our paper same as the main experiment we create a folder specifically for this experiment and prepare the prompts and dataset in advanced please run test transfer py to reproduce our experiments there are two modes that could be chosen including normal and hint the meaning of all the other hyper parameters are same as the creator setting in our main experiment acknowledgement | ai |
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UI | ui this project contains a series of popular ios apps clones to level up my design skills in ios with autolayout project 1 apple s imessage uikit img src screenshots imessage png width 270 height 470 img technologies swift 4 2 autolayout uikit lesson learned lazy loading ui elements using the width and eight anchor with multipliers insteas of leading trailing top bottom for stack view subviews project 2 twitter project 3 apple music project 4 gmail | os |
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machine-learning-project-walkthrough | machine learning project walkthrough an implementation of a complete machine learning solution in python on a real world dataset this project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem | ai |
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iris | h1 img width 24 height 25 src https www iris go com images logo new lq 45 png news h1 iris version 12 2 0 has been released history md sa 11 march 2023 v1220 as always the latest version of iris comes with the promise of lifetime active maintenance try the official iris command line interface https github com kataras iris cli today a href https iris go com img src https iris go com images logo new lq 45 png a iris web framework a href readme gr md img width 20px src https iris go com images flag greece svg a href readme ja md img width 20px height 20px src https iris go com images flag japan svg a a a href readme fr md img width 20px src https iris go com images flag france svg a a href readme zh hant md img width 20px src https iris go com images flag taiwan svg a a href readme zh hans md img width 20px src https iris go com images flag china svg a a href readme es md img width 20px src https iris go com images flag spain png a a href readme fa md img width 20px src https iris 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githubusercontent com u 7625413 v 4 alt primadi title primadi width 75 height 75 style width 75px max width 75px height 75px a a href https github com th31nitiate img src https avatars1 githubusercontent com u 14749635 v 4 alt th31nitiate title th31nitiate width 75 height 75 style width 75px max width 75px height 75px a a href https github com stgrosshh img src https avatars1 githubusercontent com u 8356082 v 4 alt stgrosshh title stgrosshh width 75 height 75 style width 75px max width 75px height 75px a a href https github com rodrigoghm img src https avatars1 githubusercontent com u 66917643 v 4 alt rodrigoghm title rodrigoghm width 75 height 75 style width 75px max width 75px height 75px a a href https github com didainius img src https avatars1 githubusercontent com u 15804230 v 4 alt didainius title didainius width 75 height 75 style width 75px max width 75px height 75px a a href https github com dmarshaltu img src https avatars1 githubusercontent com u 59089266 v 4 alt dmarshaltu title dmarshaltu width 75 height 75 style width 75px max width 75px height 75px a a href https github com iwatekyle img src https avatars1 githubusercontent com u 658799 v 4 alt iwatekyle title iwatekyle width 75 height 75 style width 75px max width 75px height 75px a a href https github com little yangyang img src https avatars1 githubusercontent com u 10755202 v 4 alt little yangyang title little yangyang width 75 height 75 style width 75px max width 75px height 75px a a href https github com major2828 img src https avatars1 githubusercontent com u 19783402 v 4 alt major2828 title major2828 width 75 height 75 style width 75px max width 75px height 75px a a href https github com matejlach img src https avatars1 githubusercontent com u 531930 v 4 alt matejlach title matejlach width 75 height 75 style width 75px max width 75px height 75px a a href https github com agoncecelia img src https avatars1 githubusercontent com u 10442924 v 4 alt agoncecelia title agoncecelia width 75 height 75 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hieungm width 75 height 75 style width 75px max width 75px height 75px a a href https github com hieunmg img src https avatars1 githubusercontent com u 85067528 v 4 alt hieunmg title hieunmg width 75 height 75 style width 75px max width 75px height 75px a a href https github com homerious img src https avatars1 githubusercontent com u 22523525 v 4 alt homerious title homerious width 75 height 75 style width 75px max width 75px height 75px a a href https github com hzxd img src https avatars1 githubusercontent com u 3376231 v 4 alt hzxd title hzxd width 75 height 75 style width 75px max width 75px height 75px a a href https github com iysaleh img src https avatars1 githubusercontent com u 13583253 v 4 alt iysaleh title iysaleh width 75 height 75 style width 75px max width 75px height 75px a a href https github com jackptoke img src https avatars1 githubusercontent com u 54049012 v 4 alt jackptoke title jackptoke width 75 height 75 style width 75px max width 75px height 75px a a href 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com u 9495010 v 4 alt keymanye title keymanye width 75 height 75 style width 75px max width 75px height 75px a a href https github com khasanovrs img src https avatars1 githubusercontent com u 6076966 v 4 alt khasanovrs title khasanovrs width 75 height 75 style width 75px max width 75px height 75px a a href https github com kkdaypenny img src https avatars1 githubusercontent com u 47559431 v 4 alt kkdaypenny title kkdaypenny width 75 height 75 style width 75px max width 75px height 75px a a href https github com knavels img src https avatars1 githubusercontent com u 57287952 v 4 alt knavels title knavels width 75 height 75 style width 75px max width 75px height 75px a a href https github com kohakuhubo img src https avatars1 githubusercontent com u 32786755 v 4 alt kohakuhubo title kohakuhubo width 75 height 75 style width 75px max width 75px height 75px a a href https github com korowiov img src https avatars1 githubusercontent com u 5020824 v 4 alt korowiov title korowiov width 75 height 75 style width 75px max width 75px height 75px a a href https github com kostasvk img src https avatars1 githubusercontent com u 8888490 v 4 alt kostasvk title kostasvk width 75 height 75 style width 75px max width 75px height 75px a a href https github com lafayettedan img src https avatars1 githubusercontent com u 26064396 v 4 alt lafayettedan title lafayettedan width 75 height 75 style width 75px max width 75px height 75px a a href https github com leki75 img src https avatars1 githubusercontent com u 9675379 v 4 alt leki75 title leki75 width 75 height 75 style width 75px max width 75px height 75px a a href https github com liheyuan img src https avatars1 githubusercontent com u 776423 v 4 alt liheyuan title liheyuan width 75 height 75 style width 75px max width 75px height 75px a a href https github com lingyingtan img src https avatars1 githubusercontent com u 15610136 v 4 alt lingyingtan title lingyingtan width 75 height 75 style width 75px max width 75px height 75px a a 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height 75 style width 75px max width 75px height 75px a a href https github com mukunhao img src https avatars1 githubusercontent com u 45845255 v 4 alt mukunhao title mukunhao width 75 height 75 style width 75px max width 75px height 75px a a href https github com mulyawansentosa img src https avatars1 githubusercontent com u 29946673 v 4 alt mulyawansentosa title mulyawansentosa width 75 height 75 style width 75px max width 75px height 75px a a href https github com nasoma img src https avatars1 githubusercontent com u 19878418 v 4 alt nasoma title nasoma width 75 height 75 style width 75px max width 75px height 75px a a href https github com ngseiyu img src https avatars1 githubusercontent com u 44496936 v 4 alt ngseiyu title ngseiyu width 75 height 75 style width 75px max width 75px height 75px a a href https github com nikharsaxena img src https avatars1 githubusercontent com u 8684362 v 4 alt nikharsaxena title nikharsaxena width 75 height 75 style width 75px max width 75px height 75px a a href https github com nronzel img src https avatars1 githubusercontent com u 86695181 v 4 alt nronzel title nronzel width 75 height 75 style width 75px max width 75px height 75px a a href https github com onlysumitg img src https avatars1 githubusercontent com u 1676132 v 4 alt onlysumitg title onlysumitg width 75 height 75 style width 75px max width 75px height 75px a a href https github com ozfive img src https avatars1 githubusercontent com u 4494266 v 4 alt ozfive title ozfive width 75 height 75 style width 75px max width 75px height 75px a a href https github com paulxu21 img src https avatars1 githubusercontent com u 6261758 v 4 alt paulxu21 title paulxu21 width 75 height 75 style width 75px max width 75px height 75px a a href https github com pesquive img src https avatars1 githubusercontent com u 6610140 v 4 alt pesquive title pesquive width 75 height 75 style width 75px max width 75px height 75px a a href https github com phil535 img src https avatars1 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width 75 height 75 style width 75px max width 75px height 75px a a href https github com rbondi img src https avatars1 githubusercontent com u 81764 v 4 alt rbondi title rbondi width 75 height 75 style width 75px max width 75px height 75px a a href https github com relaera img src https avatars1 githubusercontent com u 26012106 v 4 alt relaera title relaera width 75 height 75 style width 75px max width 75px height 75px a a href https github com remopavithran img src https avatars1 githubusercontent com u 50388068 v 4 alt remopavithran title remopavithran width 75 height 75 style width 75px max width 75px height 75px a a href https github com rfunix img src https avatars1 githubusercontent com u 6026357 v 4 alt rfunix title rfunix width 75 height 75 style width 75px max width 75px height 75px a a href https github com rhernandez itemsoft img src https avatars1 githubusercontent com u 4327356 v 4 alt rhernandez itemsoft title rhernandez itemsoft width 75 height 75 style width 75px max width 75px height 75px a a href https github com opusmagna img src https avatars1 githubusercontent com u 33766678 v 4 alt opusmagna title opusmagna width 75 height 75 style width 75px max width 75px height 75px a a href https github com b4zz4r img src https avatars1 githubusercontent com u 7438782 v 4 alt b4zz4r title b4zz4r width 75 height 75 style width 75px max width 75px height 75px a a href https github com bobmcallan img src https avatars1 githubusercontent com u 8773580 v 4 alt bobmcallan title bobmcallan width 75 height 75 style width 75px max width 75px height 75px a a href https github com fangli img src https avatars1 githubusercontent com u 3032639 v 4 alt fangli title fangli width 75 height 75 style width 75px max width 75px height 75px a a href https github com galois tnp img src https avatars1 githubusercontent com u 41128011 v 4 alt galois tnp title galois tnp width 75 height 75 style width 75px max width 75px height 75px a a href https github com geoshan img src https 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rxrw title rxrw width 75 height 75 style width 75px max width 75px height 75px a a href https github com saleebm img src https avatars1 githubusercontent com u 34875122 v 4 alt saleebm title saleebm width 75 height 75 style width 75px max width 75px height 75px a a href https github com sbenimeli img src https avatars1 githubusercontent com u 46652122 v 4 alt sbenimeli title sbenimeli width 75 height 75 style width 75px max width 75px height 75px a a href https github com sebyno img src https avatars1 githubusercontent com u 15988169 v 4 alt sebyno title sebyno width 75 height 75 style width 75px max width 75px height 75px a a href https github com seun otosho img src https avatars1 githubusercontent com u 74518370 v 4 alt seun otosho title seun otosho width 75 height 75 style width 75px max width 75px height 75px a a href https github com solohiroshi img src https avatars1 githubusercontent com u 96872274 v 4 alt solohiroshi title solohiroshi width 75 height 75 style width 75px max width 75px height 75px a a href https github com su1gen img src https avatars1 githubusercontent com u 86298730 v 4 alt su1gen title su1gen width 75 height 75 style width 75px max width 75px height 75px a a href https github com sukiejosh img src https avatars1 githubusercontent com u 44656210 v 4 alt sukiejosh title sukiejosh width 75 height 75 style width 75px max width 75px height 75px a a href https github com svirmi img src https avatars1 githubusercontent com u 52601346 v 4 alt svirmi title svirmi width 75 height 75 style width 75px max width 75px height 75px a a href https github com thiennguyen93 img src https avatars1 githubusercontent com u 60094052 v 4 alt thiennguyen93 title thiennguyen93 width 75 height 75 style width 75px max width 75px height 75px a a href https github com unixedia img src https avatars1 githubusercontent com u 70646128 v 4 alt unixedia title unixedia width 75 height 75 style width 75px max width 75px height 75px a a href https github com vadgun img src 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vuhoanglam width 75 height 75 style width 75px max width 75px height 75px a a href https github com walter wang img src https avatars1 githubusercontent com u 7950295 v 4 alt walter wang title walter wang width 75 height 75 style width 75px max width 75px height 75px a a href https github com wixregiga img src https avatars1 githubusercontent com u 30182903 v 4 alt wixregiga title wixregiga width 75 height 75 style width 75px max width 75px height 75px a a href https github com yesudeep img src https avatars1 githubusercontent com u 3874 v 4 alt yesudeep title yesudeep width 75 height 75 style width 75px max width 75px height 75px a a href https github com ymonk img src https avatars1 githubusercontent com u 13493968 v 4 alt ymonk title ymonk width 75 height 75 style width 75px max width 75px height 75px a a href https github com yonson2 img src https avatars1 githubusercontent com u 1192599 v 4 alt yonson2 title yonson2 width 75 height 75 style width 75px max width 75px height 75px a a href https github com zhenggangpku img src https avatars1 githubusercontent com u 18161030 v 4 alt zhenggangpku title zhenggangpku width 75 height 75 style width 75px max width 75px height 75px a a href https github com sergeshin img src https avatars1 githubusercontent com u 402395 v 4 alt sergeshin title sergeshin width 75 height 75 style width 75px max width 75px height 75px a a href https github com img src https avatars1 githubusercontent com u 75544 v 4 alt title width 75 height 75 style width 75px max width 75px height 75px a a href https github com belmondus img src https avatars1 githubusercontent com u 159350 v 4 alt belmondus title belmondus width 75 height 75 style width 75px max width 75px height 75px a a href https github com martinjanda img src https avatars1 githubusercontent com u 122393 v 4 alt martinjanda title martinjanda width 75 height 75 style width 75px max width 75px height 75px a a href https github com martinlindhe img src https avatars1 githubusercontent com u 181531 v 4 alt martinlindhe title martinlindhe width 75 height 75 style width 75px max width 75px height 75px a a href https github com mdamschen img src https avatars1 githubusercontent com u 40914728 v 4 alt mdamschen title mdamschen width 75 height 75 style width 75px max width 75px height 75px a a href https github com netbaalzovf img src https avatars1 githubusercontent com u 98529711 v 4 alt netbaalzovf title netbaalzovf width 75 height 75 style width 75px max width 75px height 75px a a href https github com oliverjosefzimmer img src https avatars1 githubusercontent com u 24566297 v 4 alt oliverjosefzimmer title oliverjosefzimmer width 75 height 75 style width 75px max width 75px height 75px a a href https github com talebisinan img src https avatars1 githubusercontent com u 42139005 v 4 alt talebisinan title talebisinan width 75 height 75 style width 75px max width 75px height 75px a a href https github com valkuere img src https avatars1 githubusercontent com u 7230144 v 4 alt valkuere title valkuere width 75 height 75 style width 75px max width 75px height 75px a a href https github com lfaynman img src https avatars1 githubusercontent com u 16815068 v 4 alt lfaynman title lfaynman width 75 height 75 style width 75px max width 75px height 75px a a href https github com arturwierzbicki img src https avatars1 githubusercontent com u 23451458 v 4 alt arturwierzbicki title arturwierzbicki width 75 height 75 style width 75px max width 75px height 75px a a href https github com supersherm5 img src https avatars1 githubusercontent com u 7953550 v 4 alt supersherm5 title supersherm5 width 75 height 75 style width 75px max width 75px height 75px a a href https github com aaxx img src https avatars1 githubusercontent com u 476416 v 4 alt aaxx title aaxx width 75 height 75 style width 75px max width 75px height 75px a a href https github com crashcoder img src https avatars1 githubusercontent com u 1144298 v 4 alt crashcoder title crashcoder width 75 height 75 style width 75px max width 75px height 75px a a href https github com derekslenk img src https avatars1 githubusercontent com u 42957 v 4 alt derekslenk title derekslenk width 75 height 75 style width 75px max width 75px height 75px a a href https github com dochoaj img src https avatars1 githubusercontent com u 1789678 v 4 alt dochoaj title dochoaj width 75 height 75 style width 75px max width 75px height 75px a a href https github com evillgenius75 img src https avatars1 githubusercontent com u 22817701 v 4 alt evillgenius75 title evillgenius75 width 75 height 75 style width 75px max width 75px height 75px a a href https github com gog200921 img src https avatars1 githubusercontent com u 101519620 v 4 alt gog200921 title gog200921 width 75 height 75 style width 75px max width 75px height 75px a a href https github com mauricedcastro img src https avatars1 githubusercontent com u 6446532 v 4 alt mauricedcastro title mauricedcastro width 75 height 75 style width 75px max width 75px height 75px a a href https github com mwiater img src https avatars1 githubusercontent com u 5323591 v 4 alt mwiater title mwiater width 75 height 75 style width 75px max width 75px height 75px a a href https github com sj671 img src https avatars1 githubusercontent com u 7363652 v 4 alt sj671 title sj671 width 75 height 75 style width 75px max width 75px height 75px a a href https github com statik img src https avatars1 githubusercontent com u 983 v 4 alt statik title statik width 75 height 75 style width 75px max width 75px height 75px a a href https github com supersherm5 img src https avatars1 githubusercontent com u 7953550 v 4 alt supersherm5 title supersherm5 width 75 height 75 style width 75px max width 75px height 75px a a href https github com thejones img src https avatars1 githubusercontent com u 682850 v 4 alt thejones title thejones width 75 height 75 style width 75px max width 75px height 75px a a href https github com csraghunandan img src https avatars1 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com u 3864151 v 4 alt vcruzato title vcruzato width 75 height 75 style width 75px max width 75px height 75px a a href https github com evan img src https avatars1 githubusercontent com u 210 v 4 alt evan title evan width 75 height 75 style width 75px max width 75px height 75px a a href https github com grassshrimp img src https avatars1 githubusercontent com u 3070576 v 4 alt grassshrimp title grassshrimp width 75 height 75 style width 75px max width 75px height 75px a a href https github com hazmi e205 img src https avatars1 githubusercontent com u 12555465 v 4 alt hazmi e205 title hazmi e205 width 75 height 75 style width 75px max width 75px height 75px a a href https github com jtgoral img src https avatars1 githubusercontent com u 19780595 v 4 alt jtgoral title jtgoral width 75 height 75 style width 75px max width 75px height 75px a a href https github com ky2s img src https avatars1 githubusercontent com u 19502125 v 4 alt ky2s title ky2s width 75 height 75 style width 75px max width 75px height 75px a a href https github com lauweliam img src https avatars1 githubusercontent com u 4064517 v 4 alt lauweliam title lauweliam width 75 height 75 style width 75px max width 75px height 75px a a href https github com letmestudy img src https avatars1 githubusercontent com u 31943708 v 4 alt letmestudy title letmestudy width 75 height 75 style width 75px max width 75px height 75px a a href https github com mblandr img src https avatars1 githubusercontent com u 42862020 v 4 alt mblandr title mblandr width 75 height 75 style width 75px max width 75px height 75px a a href https github com midhubalan img src https avatars1 githubusercontent com u 13059634 v 4 alt midhubalan title midhubalan width 75 height 75 style width 75px max width 75px height 75px a a href https github com ndimorle img src https avatars1 githubusercontent com u 76732415 v 4 alt ndimorle title ndimorle width 75 height 75 style width 75px max width 75px height 75px a a href https github com rosales stephanie img src https avatars1 githubusercontent com u 43592017 v 4 alt rosales stephanie title rosales stephanie width 75 height 75 style width 75px max width 75px height 75px a a href https github com shyyawn img src https avatars1 githubusercontent com u 6064438 v 4 alt shyyawn title shyyawn width 75 height 75 style width 75px max width 75px height 75px a a href https github com wangbl11 img src https avatars1 githubusercontent com u 14358532 v 4 alt wangbl11 title wangbl11 width 75 height 75 style width 75px max width 75px height 75px a a href https github com wofka72 img src https avatars1 githubusercontent com u 10855340 v 4 alt wofka72 title wofka72 width 75 height 75 style width 75px max width 75px height 75px a a href https github com xsokev img src https avatars1 githubusercontent com u 28113 v 4 alt xsokev title xsokev width 75 height 75 style width 75px max width 75px height 75px a a href https github com oleang img src https avatars1 githubusercontent com u 142615 v 4 alt oleang title oleang width 75 height 75 style width 75px max width 75px height 75px a a href https github com michalsz img src https avatars1 githubusercontent com u 187477 v 4 alt michalsz title michalsz width 75 height 75 style width 75px max width 75px height 75px a a href https github com michaelsmanley img src https avatars1 githubusercontent com u 93241 v 4 alt michaelsmanley title michaelsmanley width 75 height 75 style width 75px max width 75px height 75px a a href https github com curtman img src https avatars1 githubusercontent com u 543481 v 4 alt curtman title curtman width 75 height 75 style width 75px max width 75px height 75px a a href https github com sridardhandapani img src https avatars1 githubusercontent com u 18103118 v 4 alt sridardhandapani title sridardhandapani width 75 height 75 style width 75px max width 75px height 75px a a href https github com tejzpr img src https avatars1 githubusercontent com u 2813811 v 4 alt tejzpr title tejzpr width 75 height 75 style width 75px max width 75px height 75px a a href https github com theantichris img src https avatars1 githubusercontent com u 1486502 v 4 alt theantichris title theantichris width 75 height 75 style width 75px max width 75px height 75px a a href https github com tuxaanand img src https avatars1 githubusercontent com u 9750371 v 4 alt tuxaanand title tuxaanand width 75 height 75 style width 75px max width 75px height 75px a a href https github com raphael brand img src https avatars1 githubusercontent com u 4279168 v 4 alt raphael brand title raphael brand width 75 height 75 style width 75px max width 75px height 75px a a href https github com willypuzzle img src https avatars1 githubusercontent com u 18305386 v 4 alt willypuzzle title willypuzzle width 75 height 75 style width 75px max width 75px height 75px a a href https github com hieulsw img src https avatars1 githubusercontent com u 1675478 v 4 alt hieulsw title hieulsw width 75 height 75 style width 75px max width 75px height 75px a a href https github com carlosmoran092 img src https avatars1 githubusercontent com u 10361754 v 4 alt carlosmoran092 title carlosmoran092 width 75 height 75 style width 75px max width 75px height 75px a a href https github com yangxianglong img src https avatars1 githubusercontent com u 55280276 v 4 alt yangxianglong title yangxianglong width 75 height 75 style width 75px max width 75px height 75px a p learning iris installation the only requirement is the go programming language https go dev dl create a new project sh mkdir myapp cd myapp go mod init myapp go get github com kataras iris v12 latest or v12 2 7 details summary install on existing project summary sh cd myapp go get github com kataras iris v12 latest run sh go mod tidy compat 1 20 compat 1 20 for windows go run details https www iris go com images gifs install create iris gif iris contains extensive and thorough documentation https www iris go com docs making it easy to get started with the framework iris contains extensive and thorough wiki https github com kataras iris wiki making it easy to get started with the framework https media giphy com media ur8iqy9fqfmpuyqpgy giphy gif for a more detailed technical documentation you can head over to our godocs https pkg go dev github com kataras iris v12 main and for executable code you can always visit the examples examples repository s subdirectory do you like to read while traveling a href https iris go com book img alt book cover src https iris go com images iris book cover sm jpg v 12 a follow author on twitter https img shields io twitter follow makismaropoulos color 3d8aa3 logocolor 3d8aa3 style for the badge logo twitter https twitter com intent follow screen name makismaropoulos follow iris web framework on twitter https img shields io twitter follow iris framework color ee7506 logocolor ee7506 style for the badge logo twitter https twitter com intent follow screen name iris framework follow iris web framework on facebook https img shields io badge follow 20 40iris framework 569 2d88ff svg style for the badge logo facebook https www facebook com iris framework you can request https www iris go com ebookdonateform a pdf and online access of the iris e book new edition future v12 2 0 today and be participated in the development of iris contributing we d love to see your contribution to the iris web framework for more information about contributing to the iris project please check the contributing md contributing md file list of all contributors https github com kataras iris graphs contributors security vulnerabilities if you discover a security vulnerability within iris please send an e mail to iris go outlook com mailto iris go outlook com all security vulnerabilities will be promptly addressed license this project is licensed under the bsd 3 clause license license just like the go project itself the project name iris was inspired by the greek mythology stargazers over time stargazers over time https starchart cc kataras iris svg https starchart cc kataras iris | go iris web-framework mvc golang dependency-injection http2 sessions websocket | front_end |
alienMarkovNetworks | alien markov networks experiments in mrfs for image segmentation please report errors in these instructions this library contains a set of python tools for performing image segmentation using markov random fields mrfs and or conditional random fields crfs the default matrix orientation is numberexamples x numberdimensions which is in line with scikit learn and matlab the first aim is to get some standard results on the msrc data set the target platform is ubuntu 13 04 by far the easiest way to use the software is on a vm using vagrant if you re running ubuntu 13 04 you could try running parts of the provisioning script in the vagrant directory bootstrap sh to install the necessary dependencies msrc data is available here http research microsoft com en us downloads b94de342 60dc 45d0 830b 9f6eff91b301 default aspx to install on machine clone the repo install some needed dependencies apt get update apt get install y emacs git core python pip python dev python numpy python matplotlib python scipy python pandas libboost dev python opencv pip install cython scikit learn scikit image upgrade get a local installation of the slic python library cd mylibdir git clone https github com amueller slic python git cd slic python make build the cython components cd alienmarkovnetworks maxflow python setup py build ext inplace add these libraries to your python path possibly in your bashrc or the like echo export pythonpath pythonpath mylibdir slic python mycodedir alienmarkovnetworks maxflow bashrc to install on vm note vagrant uses virtualbox by default for me this required switching virtualisation on in the bios run these commands sudo apt get install vagrant vagrant box add raring64 http cloud images ubuntu com vagrant raring current raring server cloudimg amd64 vagrant disk1 box git clone https github com rockstarcoders alienmarkovnetworks git cd alienmarkovnetworks vagrant vagrant up vagrant ssh cd vagrant alienmarkovnetworks go get some coffee and after that you should be up and running inside the vm on the host machine you can provide the msrc data to the vm as follows cd alienmarkovnetworks createmsrcpartition sh c path to msrc objcategimagedatabase v2 msrc datasplit shotton train txt msrc datasplit shotton validation txt msrc datasplit shotton test txt vagrant msrcdata run these instructions will allow you to reproduce a result on the msrc data set 1 download the msrc data http research microsoft com en us downloads b94de342 60dc 45d0 830b 9f6eff91b301 default aspx let s call that path datapath 2 copy it to partitioned sub directories mkdir datapath msrcpartitioned cd alienmarkovnetworks createmsrcpartition sh c datapath msrc objcategimagedatabase v2 msrc datasplit shotton train txt msrc datasplit shotton validation txt msrc datasplit shotton test txt datapath msrcpartitioned 3 run the following script mkdir outputdir reproducemsrc sh datapath msrcpartitioned outputdir 8 the number 8 above is the number of cores to use limiting factor is ram i have 32 gb ram and 8 is good for me in outputdir this produces labelled images as well as accuracy metrics to standard out | ai |
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react-C--TS-app | this readme will be compiled based on my own learnings as i stumble progress through the process of learning getting the site running to make the site run dotnet build in the main project folder this will build the site then serve forth with dotnet watch editors note yet to confirm if hot ideally it would be pages as of latest commit there are no other pages than the standard ones provided by the template from visual studio this will be amended in the future | server |
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vehicle-detection | vehicle detection video demo https www youtube com watch v i71chxfswxw feature youtu be png output top png overview vehicle detection using these machine learning and computer vision techniques linear svm hog histogram of oriented gradients feature extraction color space conversion space binning histogram of color extraction sliding window note first you need to get training data cars and not cars you can get car images from gti vehicle image database http www gti ssr upm es data vehicle database html kitti vision benchmark http www cvlibs net datasets kitti and over 1500 images per each is good for this project dependencies python 3 4 set up environment pip install r requirements txt run jupyter notebook jupyter notebook defining utility functions python import glob import time import cv2 import numpy as np import matplotlib image as mpimg import matplotlib pyplot as plt from skimage feature import hog from sklearn model selection import train test split from sklearn svm import linearsvc from sklearn preprocessing import standardscaler from skimage feature import hog matplotlib inline python a function to extract features from a list of images def extract features imgs color space rgb spatial size 32 32 hist bins 32 orient 9 pix per cell 8 cell per block 2 hog channel 0 spatial feat true hist feat true hog feat true create a list to append feature vectors to features iterate through the list of images for file in imgs file features read in each one by one image mpimg imread file apply color conversion if other than rgb if color space rgb if color space hsv feature image cv2 cvtcolor image cv2 color rgb2hsv elif color space luv feature image cv2 cvtcolor image cv2 color rgb2luv elif color space hls feature image cv2 cvtcolor image cv2 color rgb2hls elif color space yuv feature image cv2 cvtcolor image cv2 color rgb2yuv elif color space ycrcb feature image cv2 cvtcolor image cv2 color rgb2ycrcb else feature image np copy image if spatial feat true spatial features bin spatial feature image size spatial size file features append spatial features if hist feat true apply color hist hist features color hist feature image nbins hist bins file features append hist features if hog feat true call get hog features with vis false feature vec true if hog channel all hog features for channel in range feature image shape 2 hog features append get hog features feature image channel orient pix per cell cell per block vis false feature vec true hog features np ravel hog features else hog features get hog features feature image hog channel orient pix per cell cell per block vis false feature vec true append the new feature vector to the features list file features append hog features features append np concatenate file features return list of feature vectors return features def get hog features img orient pix per cell cell per block vis false feature vec true call with two outputs if vis true if vis true features hog image hog img orientations orient pixels per cell pix per cell pix per cell cells per block cell per block cell per block transform sqrt false visualise vis feature vector feature vec return features hog image otherwise call with one output else features hog img orientations orient pixels per cell pix per cell pix per cell cells per block cell per block cell per block transform sqrt false visualise vis feature vector feature vec return features def bin spatial img size 32 32 color1 cv2 resize img 0 size ravel color2 cv2 resize img 1 size ravel color3 cv2 resize img 2 size ravel return np hstack color1 color2 color3 def color hist img nbins 32 bins range 0 256 compute the histogram of the color channels separately channel1 hist np histogram img 0 bins nbins channel2 hist np histogram img 1 bins nbins channel3 hist np histogram img 2 bins nbins concatenate the histograms into a single feature vector hist features np concatenate channel1 hist 0 channel2 hist 0 channel3 hist 0 return the individual histograms bin centers and feature vector return hist features collecting data python get image file names images glob glob training data png cars notcars all cars all notcars for image in images if nonvehicle in image all notcars append image else all cars append image get only 1 5 of the training data to avoid overfitting for ix notcar in enumerate all notcars if ix 5 0 notcars append notcar for ix car in enumerate all cars if ix 5 0 cars append car car image mpimg imread cars 5 notcar image mpimg imread notcars 0 def compare images image1 image2 image1 exp image 1 image2 exp image 2 f ax1 ax2 plt subplots 1 2 figsize 6 3 f tight layout ax1 imshow image1 ax1 set title image1 exp fontsize 20 ax2 imshow image2 ax2 set title image2 exp fontsize 20 plt subplots adjust left 0 right 1 top 0 9 bottom 0 compare images car image notcar image car not car png output output 7 0 png extracting features python color space yuv can be rgb hsv luv hls yuv ycrcb orient 15 hog orientations pix per cell 8 hog pixels per cell cell per block 2 hog cells per block hog channel all can be 0 1 2 or all spatial size 32 32 spatial binning dimensions hist bins 32 number of histogram bins spatial feat true spatial features on or off hist feat true histogram features on or off hog feat true hog features on or off converted car image cv2 cvtcolor car image cv2 color rgb2yuv car ch1 converted car image 0 car ch2 converted car image 1 car ch3 converted car image 2 converted notcar image cv2 cvtcolor notcar image cv2 color rgb2yuv notcar ch1 converted notcar image 0 notcar ch2 converted notcar image 1 notcar ch3 converted notcar image 2 car hog feature car hog image get hog features car ch1 orient pix per cell cell per block vis true feature vec true notcar hog feature notcar hog image get hog features notcar ch1 orient pix per cell cell per block vis true feature vec true car ch1 features cv2 resize car ch1 spatial size car ch2 features cv2 resize car ch2 spatial size car ch3 features cv2 resize car ch3 spatial size notcar ch1 features cv2 resize notcar ch1 spatial size notcar ch2 features cv2 resize notcar ch2 spatial size notcar ch3 features cv2 resize notcar ch3 spatial size def show images image1 image2 image3 image4 image1 exp image 1 image2 exp image 2 image3 exp image 3 image4 exp image 4 f ax1 ax2 ax3 ax4 plt subplots 1 4 figsize 24 9 f tight layout ax1 imshow image1 ax1 set title image1 exp fontsize 20 ax2 imshow image2 ax2 set title image2 exp fontsize 20 ax3 imshow image3 ax3 set title image3 exp fontsize 20 ax4 imshow image4 ax4 set title image4 exp fontsize 20 plt subplots adjust left 0 right 1 top 0 9 bottom 0 show images car ch1 car hog image notcar ch1 notcar hog image car ch 1 car ch 1 hog not car ch 1 not car ch 1 hog show images car ch1 car ch1 features notcar ch1 notcar ch1 features car ch 1 car ch 1 features not car ch 1 not car ch 1 features show images car ch2 car ch2 features notcar ch2 notcar ch2 features car ch 2 car ch 2 features not car ch 2 not car ch 2 features show images car ch3 car ch3 features notcar ch3 notcar ch3 features car ch 3 car ch 3 features not car ch 3 not car ch 3 features users hatanaka anaconda3 envs carnd term1 lib python3 5 site packages skimage feature hog py 119 skimage deprecation default value of block norm l1 is deprecated and will be changed to l2 hys in v0 15 be changed to l2 hys in v0 15 skimage deprecation png output output 9 1 png png output output 9 2 png png output output 9 3 png png output output 9 4 png training classifier python car features extract features cars color space color space spatial size spatial size hist bins hist bins orient orient pix per cell pix per cell cell per block cell per block hog channel hog channel spatial feat spatial feat hist feat hist feat hog feat hog feat notcar features extract features notcars color space color space spatial size spatial size hist bins hist bins orient orient pix per cell pix per cell cell per block cell per block hog channel hog channel spatial feat spatial feat hist feat hist feat hog feat hog feat x np vstack car features notcar features astype np float64 fit a per column scaler x scaler standardscaler fit x apply the scaler to x scaled x x scaler transform x define the labels vector y np hstack np ones len car features np zeros len notcar features split up data into randomized training and test sets rand state np random randint 0 100 x train x test y train y test train test split scaled x y test size 0 2 random state rand state print using orient orientations pix per cell pixels per cell and cell per block cells per block print feature vector length len x train 0 use a linear svc svc linearsvc check the training time for the svc t time time svc fit x train y train t2 time time print round t2 t 2 seconds to train svc check the score of the svc print test accuracy of svc round svc score x test y test 4 check the prediction time for a single sample t time time users hatanaka anaconda3 envs carnd term1 lib python3 5 site packages skimage feature hog py 119 skimage deprecation default value of block norm l1 is deprecated and will be changed to l2 hys in v0 15 be changed to l2 hys in v0 15 skimage deprecation using 15 orientations 8 pixels per cell and 2 cells per block feature vector length 11988 2 56 seconds to train svc test accuracy of svc 0 9789 sliding window python def convert color img conv rgb2ycrcb if conv rgb2ycrcb return cv2 cvtcolor img cv2 color rgb2ycrcb if conv bgr2ycrcb return cv2 cvtcolor img cv2 color bgr2ycrcb if conv rgb2luv return cv2 cvtcolor img cv2 color rgb2luv if conv rgb2yuv return cv2 cvtcolor img cv2 color rgb2yuv def find cars img ystart ystop scale svc x scaler orient pix per cell cell per block spatial size hist bins draw img np copy img img img astype np float32 255 img tosearch img ystart ystop sub sampling ctrans tosearch convert color img tosearch conv rgb2yuv if scale 1 imshape ctrans tosearch shape ctrans tosearch cv2 resize ctrans tosearch np int imshape 1 scale np int imshape 0 scale ch1 ctrans tosearch 0 ch2 ctrans tosearch 1 ch3 ctrans tosearch 2 define blocks and steps as above nxblocks ch1 shape 1 pix per cell cell per block 1 nyblocks ch1 shape 0 pix per cell cell per block 1 nfeat per block orient cell per block 2 64 was the orginal sampling rate with 8 cells and 8 pix per cell window 64 nblocks per window window pix per cell cell per block 1 nblocks per window window pix per cell 1 cells per step 2 instead of overlap define how many cells to step nxsteps nxblocks nblocks per window cells per step nysteps nyblocks nblocks per window cells per step compute individual channel hog features for the entire image hog1 get hog features ch1 orient pix per cell cell per block vis false feature vec false hog2 get hog features ch2 orient pix per cell cell per block vis false feature vec false hog3 get hog features ch3 orient pix per cell cell per block vis false feature vec false bboxes for xb in range nxsteps for yb in range nysteps ypos yb cells per step xpos xb cells per step extract hog for this patch hog feat1 hog1 ypos ypos nblocks per window xpos xpos nblocks per window ravel hog feat2 hog2 ypos ypos nblocks per window xpos xpos nblocks per window ravel hog feat3 hog3 ypos ypos nblocks per window xpos xpos nblocks per window ravel hog features np hstack hog feat1 hog feat2 hog feat3 xleft xpos pix per cell ytop ypos pix per cell extract the image patch subimg cv2 resize ctrans tosearch ytop ytop window xleft xleft window 64 64 get color features spatial features bin spatial subimg size spatial size hist features color hist subimg nbins hist bins scale features and make a prediction test stacked np hstack spatial features hist features hog features reshape 1 1 test features x scaler transform test stacked test features scaler transform np array features reshape 1 1 test features x scaler transform np hstack shape feat hist feat reshape 1 1 test prediction svc predict test features if test prediction 1 xbox left np int xleft scale ytop draw np int ytop scale win draw np int window scale cv2 rectangle draw img xbox left ytop draw ystart xbox left win draw ytop draw win draw ystart 0 0 255 6 bboxes append int xbox left int ytop draw ystart int xbox left win draw int ytop draw win draw ystart return draw img bboxes def apply sliding window image svc x scaler pix per cell cell per block spatial size hist bins bboxes ystart 400 ystop 500 out img bboxes1 find cars image ystart ystop 1 0 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 400 ystop 500 out img bboxes2 find cars out img ystart ystop 1 3 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 410 ystop 500 out img bboxes3 find cars out img ystart ystop 1 4 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 420 ystop 556 out img bboxes4 find cars out img ystart ystop 1 6 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 430 ystop 556 out img bboxes5 find cars out img ystart ystop 1 8 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 430 ystop 556 out img bboxes6 find cars out img ystart ystop 2 0 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 440 ystop 556 out img bboxes7 find cars out img ystart ystop 1 9 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 400 ystop 556 out img bboxes8 find cars out img ystart ystop 1 3 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 400 ystop 556 out img bboxes9 find cars out img ystart ystop 2 2 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 500 ystop 656 out img bboxes10 find cars out img ystart ystop 3 0 svc x scaler orient pix per cell cell per block spatial size hist bins bboxes extend bboxes1 bboxes extend bboxes2 bboxes extend bboxes3 bboxes extend bboxes4 bboxes extend bboxes5 bboxes extend bboxes6 bboxes extend bboxes7 bboxes extend bboxes8 bboxes extend bboxes9 bboxes extend bboxes10 return out img bboxes image1 mpimg imread test series series1 jpg image2 mpimg imread test series series2 jpg image3 mpimg imread test series series3 jpg image4 mpimg imread test series series4 jpg image5 mpimg imread test series series5 jpg image6 mpimg imread test series series6 jpg output image1 bboxes1 apply sliding window image1 svc x scaler pix per cell cell per block spatial size hist bins output image2 bboxes2 apply sliding window image2 svc x scaler pix per cell cell per block spatial size hist bins output image3 bboxes3 apply sliding window image3 svc x scaler pix per cell cell per block spatial size hist bins output image4 bboxes4 apply sliding window image4 svc x scaler pix per cell cell per block spatial size hist bins output image5 bboxes5 apply sliding window image5 svc x scaler pix per cell cell per block spatial size hist bins output image6 bboxes6 apply sliding window image6 svc x scaler pix per cell cell per block spatial size hist bins image mpimg imread test images test4 jpg draw image np copy image output image bboxes apply sliding window image svc x scaler pix per cell cell per block spatial size hist bins def show images image1 image2 image3 image1 exp image 1 image2 exp image 2 image3 exp image 3 f ax1 ax2 ax3 plt subplots 1 3 figsize 24 9 f tight layout ax1 imshow image1 ax1 set title image1 exp fontsize 20 ax2 imshow image2 ax2 set title image2 exp fontsize 20 ax3 imshow image3 ax3 set title image3 exp fontsize 20 plt subplots adjust left 0 right 1 top 0 9 bottom 0 show images output image1 output image2 output image3 show images output image4 output image5 output image6 users hatanaka anaconda3 envs carnd term1 lib python3 5 site packages skimage feature hog py 119 skimage deprecation default value of block norm l1 is deprecated and will be changed to l2 hys in v0 15 be changed to l2 hys in v0 15 skimage deprecation png output output 13 1 png png output output 13 2 png creating heatmap python from scipy ndimage measurements import label def add heat heatmap bbox list iterate through list of bboxes for box in bbox list add 1 for all pixels inside each bbox assuming each box takes the form x1 y1 x2 y2 heatmap box 0 1 box 1 1 box 0 0 box 1 0 1 return updated heatmap return heatmap iterate through list of bboxes def apply threshold heatmap threshold zero out pixels below the threshold heatmap heatmap threshold 0 return thresholded map return heatmap def draw labeled bboxes img labels iterate through all detected cars for car number in range 1 labels 1 1 find pixels with each car number label value nonzero labels 0 car number nonzero identify x and y values of those pixels nonzeroy np array nonzero 0 nonzerox np array nonzero 1 define a bounding box based on min max x and y bbox np min nonzerox np min nonzeroy np max nonzerox np max nonzeroy draw the box on the image cv2 rectangle img bbox 0 bbox 1 0 0 255 6 return the image return img heat np zeros like output image 0 astype np float add heat to each box in box list heat add heat heat bboxes apply threshold to help remove false positives threshold 1 heat apply threshold heat threshold visualize the heatmap when displaying heatmap np clip heat 0 255 find final boxes from heatmap using label function labels label heatmap draw img draw labeled bboxes np copy image labels def show images image1 image2 image1 exp image 1 image2 exp image 2 f ax1 ax2 plt subplots 1 2 figsize 24 9 f tight layout ax1 imshow image1 ax1 set title image1 exp fontsize 20 ax2 imshow image2 cmap hot ax2 set title image2 exp fontsize 20 plt subplots adjust left 0 right 1 top 0 9 bottom 0 show images output image heatmap car positions result png output output 15 0 png more heatmaps python def get heatmap bboxes threshold 1 heat np zeros like output image 0 astype np float heat add heat heat bboxes heat apply threshold heat threshold heatmap np clip heat 0 255 return heatmap def show images image1 image2 image1 exp image 1 image2 exp image 2 f ax1 ax2 plt subplots 1 2 figsize 24 9 f tight layout ax1 imshow image1 ax1 set title image1 exp fontsize 20 ax2 imshow image2 cmap hot ax2 set title image2 exp fontsize 20 plt subplots adjust left 0 right 1 top 0 9 bottom 0 heatmap1 get heatmap bboxes1 heatmap2 get heatmap bboxes2 heatmap3 get heatmap bboxes3 heatmap4 get heatmap bboxes4 heatmap5 get heatmap bboxes5 heatmap6 get heatmap bboxes6 show images output image1 heatmap1 show images output image2 heatmap2 show images output image3 heatmap3 show images output image4 heatmap4 show images output image5 heatmap5 show images output image6 heatmap6 png output output 17 0 png png output output 17 1 png png output output 17 2 png png output output 17 3 png png output output 17 4 png png output output 17 5 png labeled image python plt imshow labels 0 cmap gray matplotlib image axesimage at 0x11c9d32e8 png output output 19 1 png resulting bonding boxes python plt imshow draw img matplotlib image axesimage at 0x11ca0eb38 png output output 21 1 png applying to video python from collections import deque history deque maxlen 8 def detect cars image bboxes ystart 400 ystop 500 out img bboxes1 find cars image ystart ystop 1 0 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 400 ystop 500 out img bboxes2 find cars image ystart ystop 1 3 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 410 ystop 500 out img bboxes3 find cars out img ystart ystop 1 4 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 420 ystop 556 out img bboxes4 find cars out img ystart ystop 1 6 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 430 ystop 556 out img bboxes5 find cars out img ystart ystop 1 8 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 430 ystop 556 out img bboxes6 find cars out img ystart ystop 2 0 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 440 ystop 556 out img bboxes7 find cars out img ystart ystop 1 9 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 400 ystop 556 out img bboxes8 find cars out img ystart ystop 1 3 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 400 ystop 556 out img bboxes9 find cars out img ystart ystop 2 2 svc x scaler orient pix per cell cell per block spatial size hist bins ystart 500 ystop 656 out img bboxes10 find cars out img ystart ystop 3 0 svc x scaler orient pix per cell cell per block spatial size hist bins bboxes extend bboxes1 bboxes extend bboxes2 bboxes extend bboxes3 bboxes extend bboxes4 bboxes extend bboxes5 bboxes extend bboxes6 bboxes extend bboxes7 bboxes extend bboxes8 bboxes extend bboxes9 bboxes extend bboxes10 heat np zeros like out img 0 astype np float add heat to each box in box list heat add heat heat bboxes apply threshold to help remove false positives threshold 1 heat apply threshold heat threshold visualize the heatmap when displaying current heatmap np clip heat 0 255 history append current heatmap heatmap np zeros like current heatmap astype np float for heat in history heatmap heatmap heat find final boxes from heatmap using label function labels label heatmap draw img draw labeled bboxes np copy image labels return draw img img detect cars image plt imshow img python import imageio imageio plugins ffmpeg download from moviepy editor import videofileclip from ipython display import html python history deque maxlen 8 output test result mp4 clip videofileclip test video mp4 video clip clip fl image detect cars time video clip write videofile output audio false moviepy building video test result mp4 moviepy writing video test result mp4 97 38 39 00 42 00 01 1 14s it moviepy done moviepy video ready test result mp4 cpu times user 38 7 s sys 3 22 s total 41 9 s wall time 44 6 s python history deque maxlen 8 output result mp4 clip videofileclip project video mp4 video clip clip fl image detect cars time video clip write videofile output audio false | machine-learning svm svm-classifier computer-vision hog-features sliding-windows self-driving-car udacity | ai |
booterator | booterator front end boilerplate using harp js bootstrap amp less a bootstrap theme generator speeds up production and maintenance of bootstrap themes includes bootstrap 3 3 2 minified framework node js harp js integration to generate theme less compiler built in with harp less variable mixin library starter less theme flat color palette dependancies bootstrap 3 3 2 node js harp js less changelog feb 13 2015 upgraded to bootstrap 3 3 2 sep 18 2015 updated readme file sep 18 2015 sass version is now available at https github com cardeo booterator sass license the mit license mit copyright c 2014 matt lambert 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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LLM_Evaulation | llm evaulation all files stored in this folder are test results for large language models especially chatgpt and gpt 4 developed by openai showing the performance of large language models in completing these test tasks these tests are mainly designed from the perspective of linguistics the language involved in the questions is mainly mandarin chinese since openai released chatgpt at the end of november 2022 i have designed a variety of questions based on the chinese linguistics to investigate language performance of chatgpt some of these test results reflect the amazing ability of large language models to understand chinese and some reflect that large language models still have insufficient understanding of natural language by testing the language capabilities of large language models it may help us gain a deeper understanding of the nature of natural language weidong zhan br dept of chinese language literature br peking university br http ccl pku edu cn doubtfire br br various evaluations of large language models br br b chatgpt llm errors tracker b br https researchrabbit typeform com llmerrors typeform source garymarcus substack com br https docs google com spreadsheets d 1kdsernrov5fghbvn8z bxh9gak2ixrtoqz0nwhrvicw edit usp sharing b sparks of artificial general intelligence early experiments with gpt 4 b https arxiv org abs 2303 12712 br https www youtube com watch v qbik7 jpb2c b theory of mind may have spontaneously emerged in large language models b br https arxiv org abs 2302 02083 br b agieval a human centric benchmark for evaluating foundation models b br https arxiv org abs 2304 06364 | ai |
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microros_zephyr_docker | microros zephyr docker this package supplies a docker image with everything needed to start using micro ros https micro ros github io with the zephyr rtos https www zephyrproject org use this together with micro ros docker images https github com micro ros docker to build and run your first micro ros app example of usage building this docker image building an example application in it and running it doc usage sample gif this docker image contains a full toolchain to build and flash a micro ros application to a microcontroller or to run it in host emulation combine this with the micro ros agent image from micro ros docker https github com micro ros docker to get a fully functional micro ros workbench disclaimer this package is still under construction and not yet ready for any serious use table of contents building the image building the image how to build this docker image for your specific setup running the image running the image how to run this image correctly example applications example applications examples of real world application serial communication with microcontroller board serial communication with microcontroller board using this image to build an example for a microcontroller board using serial communication udp based communication with host emulation udp based communication with host emulation using this image to run a micro ros application in a host emulation using udp communcation building the image before using this image you need to install docker on your system if you don t have this already take a look at the docker installation instructions https docs docker com engine install you can build the image using console git clone git github com robertwilbrandt microros zephyr docker git docker build microros zephyr docker tag wilbrandt microros zephyr foxy build arg platform board your board the platform board argument specifies which board you are using the format it uses depends on if your board is already supported by micro ros use the name shown in the micro ros setup https github com micro ros micro ros setup supported platforms section if your board is not yet supported by micro ros but supported by zephyr and you just want to try out this configuration use the name used by zephyr as found in its board side in the documentation https docs zephyrproject org latest boards index html this will give you warnings during your build running the image you can use this image just like any other docker image recommended usage is console docker run it rm privileged volume absolute path to your apps folder uros apps wilbrandt microros zephyr foxy the effects of these flags are it keeps stdin open and creates a pseudo tty for you to interact with with these two options you can use this like a normal shell rm removes the container after it exits you might not want to use this if you want to keep an internal state in the image and attach to it later on privileged this is needed to give the container all permissions needed to flash your microcontroller volume maps your custom apps into the image this has to be an absoulte path and it should contain the directories of your custom apps the toolchain inside the image expects your apps to be mapped to uros apps and will error out if it doesn t find any there example applications serial communication with microcontroller board suppose you want to build one of the example applications distributed in zephyr apps https github com micro ros zephyr apps as part of micro ros for example ping pong you use the stm32f429i disc1 discovery kit https docs zephyrproject org latest boards arm stm32f429i disc1 doc index html the first step to achieve this consists of getting the application and building this docker image console git clone git github com micro ros zephyr apps git git clone git github com robertwilbrandt microros zephyr docker git docker build microros zephyr docker tag wilbrandt microros zephyr stm32f429i disc1 foxy build arg platform board stm32f429i disc1 okay i am cheating a bit here as the micro ros applications in zephyr apps will check if the board is supported which is not the case for stm32f429i disc1 you can just comment out the check in cmakelists txt though and i will try to get a patch in that removes this for basic applications this will take a while as a full ros foxy image with a complete micro ros and zephyr installation needs to be installed when this is done you can run the image like this console docker run it rm privileged volume pwd zephyr apps apps uros apps wilbrandt microros zephyr stm32f429i disc1 foxy inside this image you can now configure build and flash the application the example board is connected using a serial transport piped through the on board debugger connected via usb and the zephyr board page tells us we want to use uart1 console ros2 run micro ros setup configure firmware sh ping pong transport serial dev 1 ros2 run micro ros setup build firmware sh f ros2 run micro ros setup flash firmware sh you have now flashed your micro ros image to your board when developing you might want to keep this docker image open to prevent having to fully rebuild every time an easy way to connect to your mcu is to use the micro ros micro ros agent docker image console docker run it rm net host privileged microros micro ros agent foxy serial dev dev ttyacm0 make sure to replace the dev ttyacm0 with your actual device you might need to reset your microcontroller for most development boards out there this should be possible by just pressing a button the agent should show some lines notifying you about a new connection now you should be ready to try out your demo open two shells and enter console shell 1 source opt ros foxy setup bash ros2 topic echo microros pong console shell 2 source opt ros foxy setup bash ros2 topic pub 1 microros ping std msgs msg header frame id hello world you should now see your message in the first shell udp based communication with host emulation zephyr also allows you to build an application right on your computer emulating some of the driver interfaces seamlessly even complex peripherals like displays network stacks and ble if you don t have access to a microcontroller board right now but still want to take a look at micro ros i encourage getting started using this it can also be incredibly useful for prototyping and debugging embedded applications more information on this can be found in the zephyr documentation https docs zephyrproject org latest boards posix native posix doc index html using this host emulation is nearly identical to the above example you start by cloning your application in this case int32 publisher and building this docker image console git clone git github com micro ros zephyr apps git git clone git github com robertwilbrandt microros zephyr docker git docker build microros zephyr docker tag wilbrandt microros zephyr host foxy build arg platform board host after this start the container inside it you can now configure and build an image note that we need to replace the privileged flag with net host as we are now communicationg via network console docker run it rm net host volume pwd zephyr apps apps uros apps wilbrandt microros zephyr stm32f429i disc1 foxy ros2 run micro ros setup configure firmware sh int32 publisher transport udp ip 127 0 0 1 port 8888 ros2 run micro ros setup build firmware sh f note that we did not yet start the application using flash firmware sh as this will just error out when it doesn t find an agent running we can again start an agent in a seperate container and as this doesn t require any device access we don t need any privileged flag for this to work console docker run it rm net host microros micro ros agent foxy udp4 port 8888 now go back to your first container and start the application console ros2 run micro ros setup flash firmware sh you should see some messages in the agent container in a normal ros 2 foxy machine your can now check the functionality console source opt ros foxy setup bash ros2 topic echo zephyr int32 publisher | os |
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DataScienceTools | datasciencetools aim a collection of useful data science and machine learning tools libraries and packages data science tools list based on general work flow work flow datascienceworkflow1 png work flow datascienceworkflow2 png fetching data web scrabing wget curl beautifulsoup mechanicalsoup request urllib selenium headless browser framework for extracting javascript data scrapy oop framework newspaper3k https newspaper readthedocs io easily extract text from articles requests html requests html https github com kennethreitz requests html sql msql sqlite data cleaning pandas dedupe remove duplicates fuzzywuzzy string matching scrubadab anonymize privacy address parsing usaddress dora working with missing nulls pdftables extracting tables in pdf tabulate arrow dates timezone pendulum dates inflect num2words convert numbers to text imbalance learn flashtext data analysis numpy scipy pandas tabel data visualization matplotlib seaborn plotly bokeh altair dash dashboard library from plotly dataspyre dashboard framework with flask backend folium geoplot d tale plotnine clone of r s ggplot2 joypy https github com sbebo joypy blob master joyplot ipynb bqplot jmpy pyqtgraph toyplot ipyleaflet https github com jupyter widgets ipyleaflet probscale easily make probability scaled axis https github com matplotlib mpl probscale adjusttext easily add non overlapping annotated text https github com phlya adjusttext blob master docs source examples ipynb make matplotlib animations with animatplot https animatplot readthedocs io en stable machine learning deep learning model building scikitlearn keras tensorflow theano pytorch sklearn pandas imbalanced learn hyperopt sklearn https github com hyperopt hyperopt sklearn not pip installable yet tpot xgboost lightgbm fasttext natural language processing nltk spacy textblob stanford nlp allennlp polyglot computer vision opencv scikit images scikit video pillow workspace and environment virtual env anaconda pipenv venv ide jupyter lab jupyter notebook nteract vs code sublime text juno atom etc pycharm etc cloud colab serializers joblib pickle ray csvkit json speed and large dataset pandas modin dask pyarrow fastparquet vaex https github com maartenbreddels vaex pandas on ray https github com modin project modin dampr https github com refefer dampr cloud computing services gcp bigquery aws azure forecasting pyramid arima https github com tgsmith61591 pyramid fbprophet time series forecasting additive model which performs best with high frequency data pyflux time series library https github com rjt1990 pyflux pydata stack numpy scipy pandas jupyter statsmodels profiling pandas profiling https github com pandas profiling pandas profiling dataprofiler https github com capitalone dataprofiler memory profiler https github com pythonprofilers memory profiler py spy https github com benfred py spy blob master readme md pyflame https github com uber pyflame does not support windows pyinstrument scalene cprofile forecasting pyramid arima https github com tgsmith61591 pyramid fbprophet time series forecasting additive model which performs best with high frequency data pyflux time series library https github com rjt1990 pyflux niche stats libraries lifelines survival analysis https github com camdavidsonpilon lifelines convoys https better engineering convoys jupyter notebook related ipysheet https github com quantstack ipysheet ipypivot https github com pierremarion23 ipypivot ipytree https github com quantstack ipytree papermill scrapbook https github com nteract papermill parameterized notebooks nteract scapbook https github com nteract scrapbook how to pass a dataframe between notebooks https github com nteract papermill issues 215 but instead you should really save the dataframe somewhere and then just pass the path of the saved dataframe jupytext edit notebooks as text files https github com mwouts jupytext database related pyodbc turbodbc ipython sql db py dead project sqlalchemy sqlalchemy turbodbc postgresql sqlmodel etl or data engineering related sorted from lightest to heaviest framework papermill scrapbook https github com nteract papermill parameterized notebooks nteract scapbook https github com nteract scrapbook for passing data between notebooks dequindre https github com vogt4nick dequindre petl https github com petl developers petl bonobo pypeln https github com cgarciae pypeln botflow https github com kkyon botflow https github com mara data integration dbt https www getdbt com spotify luigi works with windows apache airflow windows not supported it is a pita to try to install on windows prefect https docs prefect io data validation and cleaning frameworks https github com pyeve cerberus https github com great expectations great expectations https github com cosmicbboy pandera https pyjanitor readthedocs io https github com keleshev schema https github com tmiguelt pandasschema https github com tomaugspurger engarde r related rpy2 plydata dplyr clone plotnine ggplot2 clone machine learning related mostly for tabular data or non nn scikit learn sklearn pandas imbalanced learn hyperopt sklearn https github com hyperopt hyperopt sklearn not pip installable yet tpot xgboost lightgbm fasttext https github com kvh recurrent extract datetimes from english sentence webscraping beautifulsoup4 mechanicalsoup selenium headless browser framework for extracting javascript data scrapy oop framework newspaper3k https newspaper readthedocs io easily extract text from articles requests html requests html https github com kennethreitz requests html data web apps and ml web apps streamlit https github com streamlit gradio https github com gradio app gradio mercury mljar https github com mljar mercury panel https github com holoviz panel dash databutton flask django fastapi utilities https github com tldr pages tldr python client replacement for man pages bropages http bropages org sudo apt get install ruby dev sudo gem install bropages https github com gleitz howdoi inspect https docs python org 3 library inspect html prettypandas https github com seatgeek fuzzywuzzy https github com robinl fuzzymatcher pytest requests requests html https github com kennethreitz requests html psutil pdir2 https github com laike9m pdir2 helping https github com conquerprogramming1 helping amazon web services pyathena use plain ol sql https github com laughingman7743 pyathena cli click for making cli fire for making cli https github com tmbo questionary progress bars tqdm https github com tqdm tqdm fastprogress https github com fastai fastprogress misc sending windows 10 notifications https github com jithurjacob windows 10 toast notifications another windows notification library https github com malja zroya glances cpu memory monitoring pendulum a better datetime library better than arrow visidata https jsvine github io intro to visidata index html schedule job scheduling for humans https github com dbader schedule pyautogui ptpython better repl xlwings excel vba but with python instead openpyxl https openpyxl readthedocs io en stable https github com simonbiggs scriptedforms black source code formatter debugging ipdb pudb https github com inducer pudb windows not supported need cygwin logging https github com delgan loguru languages tools etc languages r python julia scala matlab octave c c c perl ruby javascript tensorflow js web apps java clojure fortran tools sql hadoop sas java hive pig spss perl tableau e nosql aws c hbase bash spark elasticsearch php scala shark awk cascading cassandra javascript jmp mahout objective c qlickview redis redshift sed by jesse e agbe jcharis jesus saves jcharistech j secur1ty inspired by others | ai |
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UCR-investigation-center-webapp | el sistema grupos investigaci n ucr tabla de contenidos 1 definiciones markdown header definiciones acronimos y abreviaciones 2 introducci n markdown header introduccion 3 equipos markdown header listado de equipos y miembros de los equipos 4 descripci n del sistema markdown header descripcion general 4 1 contexto y situaci n actual markdown header contexto actual 4 2 problema que resuelve markdown header problema que resuelve 4 3 interesados del proyecto y tipos de usuarios markdown header interesados del proyecto y tipos de usuarios 4 4 soluci n propuesta markdown header solucion propuesta 4 5 an lisis del entorno markdown header analisis del entorno 4 6 visi n del producto markdown header vision del producto 4 7 relaci n con otros sistemas externos markdown header relacion con otros sistemas externos 4 8 descripci n de los sistemas markdown header descripcion de los temas modulos asignados a cada equipo 4 9 requerimientos funcionales markdown header requerimientos funcionales 4 10 mapa de ruta del producto markdown header mapa de ruta del producto 4 11 requerimientos no funcionales markdown header requerimientos no funcionales que debe cumplir toda la aplicacion web 5 decisiones t cnicas markdown header decisiones tecnicas 5 1 metodolog as utilizadas y procesos definidos markdown header metodologias utilizadas en el desarrollo del proyecto 5 2 artefactos utilizados en el desarrollo del proyecto markdown header artefactos utilizados en el desarrollo del proyecto 5 3 tecnolog as utilizadas con sus respectivas versiones markdown header tecnologias utilizadas con sus respectivas versiones 5 4 repositorio de c digo y estrategia git para el proyecto markdown header repositorio de codigo y estrategia git para el proyecto 5 5 definici n de listo markdown header definicion de listo 6 referencias bibliogr ficas markdown header referencias bibliograficas definiciones acr nimos y abreviaciones citic centro de investigaciones en tecnolog as de informaci n y comunicaci n ucr universidad de costa rica odi oficina de divulgaci n e informaci n ecci escuela de ciencias de computaci n e inform tica ddd domain driven design vpn virtual private network introducci n este documento especifica la organizaci n entre los equipos de trabajo del proyecto cada uno de los roles de cada miembro al igual que las decisiones t cnicas para el desarrollo del proyecto se estructura en 3 secciones principales listado de equipos descripci n general del proyecto y decisiones t cnicas listado de equipos y miembros de los equipos equipo panas nombre rol sebasti n montero castro scrummaster andrea alvarado acon database carlos mora look and feel dylan arias git greivin s nchez garita ambassador and documentation equipo monkey madness nombre rol nels n lvarez scrum master and database tyron fonseca look and feel rodrigo contreras git roberto m ndez documentaci n dean vargas ambassador equipo cheetos nombre rol angie sofia castillo campos scrummaster sebastian gonzalez varela documentaci n oscar navarro c spedes github steven n ez murillo look and feel esteban quesada quesada bases de datos gabriel revillat zeled n ambassador equipo pollos hermanos nombre rol frank alvarado alfaro bases de datos diana luna pacheco scrum master pablo ot rola rodr guez documentaci n christian rojas r os look and feel david s nchez l pez git elvis badilla mena ambassador descripci n general contexto actual el centro de investigaciones en tecnolog as de la informaci n y comunicaci n de la ucr tiene desea promover la investigaci n en las reas relacionadas con las tics es por esto que desea desarrollar una aplicaci n web para administrar la informaci n de los grupos de investigaci n cient fica que trabajan en esta rea y ayudar a facilitar la divulgaci n cient fica que realizan estos grupos actualmente la universidad de costa rica no cuenta con una nica aplicaci n web para los distintos centros de investigaci n que existen es por esto que este proyecto plantea ser una primera soluci n escalable que comience a utilizarse en el citic y luego pueda ser usada por los otros centros de investigaci n problema que resuelve dado la ausencia de una nica aplicaci n web centralizada que permita acceder de manera sencilla y ordena a la informaci n sobre proyectos de investigaci n publicaciones tesis noticias de los centros de investigaci n de la ucr esta aplicaci n web viene a solucionar el problema de una informaci n de dif cil acceso y desactualizada que existe actualmente en el contexto de las diferentes p ginas de los centros de investigaci n interesados del proyecto y tipos de usuarios en primera instancia la aplicaci n ser desarrollada con un enfoque en el centro de investigaciones en tecnolog as de la informaci n y comunicaci n citic de la ucr de modo que las personas interasadas son todas aquellas que se relacionan directa o indirectamente con el citic tales como visitantes personal administrativo e investigadores estudiantes publicadores entre otros dado que la aplicaci n busca expandirse a los dem s centros de investigaci n dentro de la universidad las personas interesadas son tambi n todas aquellas que se ver n beneficiadas de una aplicaci n centralizada de f cil acceso respuesta r pida e intuitiva soluci n propuesta desarrollar una aplicaci n web que permita administrar la informaci n de los grupos de investigaci n cient fica y facilitar la divulgaci n cient fica que se realiza an lisis del entorno requerimientos del usuario el sistema permitir acceder a la informaci n del centro de investigaci n el sistema muestra la informaci n de manera ordenada el sistema permite el filtrado de informaci n la informaci n presentada est actualizada al d a regulaci n este sistema se regula bajo las normas de la universidad de costa rica ser un sistema que cumpla con los lineamientos t cnicos del centro de inform tica as como los lineamientos formales establecidos por la oficina de divulgaci n e informaci n odi y la vicerector a de investigaci n competidores competidores internos distintas p ginas web existentes de los centros de investigaci n otros grupos de desarrollo dentro de la universidad competidores externos empresas desarrolladoras de software desarrolladores independientes visi n del producto para personas interesadas en proyectos de los centros de investigaci n de la ucr quienes requieren acceder a investigaciones de los diferentes centros y sus grupos de investigaci n de la ucr el sistema grupos investigaci n ucr es una aplicaci n web que permite la b squeda de proyectos de investigaci n publicaciones tesis y personas investigadoras de forma distinta a las p ginas actuales de centros de investigaci n de la ucr nuestro producto proveer un sitio web simple de usar que facilitar la administraci n y publicaci n de la investigaci n que realizan los grupos de investigaci n de la ucr relaci n con otros sistemas externos los diversos centros de investigaci n de la ucr administran de manera distinta su informaci n principalmente en el acceso a los recursos que proporcionan dado a que algunos casos tienen recursos de manera privativa como proyectos que se est n realizando tesis o publicaciones donde solo proporcionan un resumen adem s algunos de los sitios de los centros de investigaci n son lentos o tienen un look and feel distinto con dise os que en ocasiones pueden llegar a dificultar el acceso a la informaci n de inter s nuestra aplicaci n viene a permitir centralizar los proyectos de investigaci n publicaciones y tesis en un mismo sitio para que pueda ser accedido por las personas interesadas por medio de una interfaz amigable y al igual que facilitar la administraci n proporcionando informaci n estad stica y a su vez permitiendo una mejor divulgaci n de la informaci n cient fica descripci n de los temas m dulos asignados a cada equipo pollos hermanos publicaciones y estad sticas publicaciones este m dulo se encarga de gestionar las publicaciones del centro de investigaci n y de la visualizaci n de cada una de est s publicaciones estad sticas este m dulo se encarga de desplegar las estad sticas de la cantidad de publicaciones en cuatro apartados por grupo de invstigaci n por a o por rea de investigaci n y por tipo de publicaci n monkeymadness centro de investigaci n grupos de investigaci n reas de investigaci n centro de investigaci n este m dulo contiene a la persona directora personal administrativo y grupos de investigaci n asociados grupos de investigaci n este m dulo es el encargado de desplegar la informaci n de personal administrativo y personas relacionadas investigadores estudiantes colaboradores adem s de las publicaciones proyectos tesis entre otros reas de investigaci n este m dulo contiene descripciones y palabras clave que permiten la clasificaci n de la informaci n proyectos tesis entre otros que se encuentra dentro de los centros y grupos de investigaci n flaming hot cheetos proyectos de investigaci n y tesis de grado y posgrado proyectos de investigaci n este m dulo se encarga de la administraci n y visualizaci n de los proyectos de investigaci n con sus caracter sticas y diferentes relaciones tesis grado y posgrado este m dulo se encarga de la administraci n y visualizaci n de las tesis de grado y posgrado con sus caracter sticas y diferentes relaciones panas personas noticias contacto trabajar con nosotros personas este m dulo se encarga de mostrar la informaci n de contacto publicaciones asociadas y rol dentro del centro de investigaci n para cada persona as como de la administraci n de cuentas es el m dulo m s grande del equipo informaci n genereal noticias despliegue de la informaci n relacionada a las noticias actualizadas del centro de investigaci n contacto m dulo encargado de brindar informaci n pertinente al contacto con el centro de investigaci n trabaja con nosotros brinda la informaci n necesaria para conocer las labores que realiza el centro de investigaci n y c mo participar requerimientos funcionales los requerimientos funcionales se administran por medio de la herramienta de jira el cual puede ser accedido por este enlace http 10 1 4 22 8080 projects piib22021 summary para poder acceder a este proyecto de jira es necesario utilizar el vpn proporcionado por la ecci las instrucciones se pueden en este enlace https www ecci ucr ac cr colaboradores procedimientos acceso remoto por red privada virtual vpn la ecci mapa de ruta del producto si desea conocer el mapa de ruta del producto puede hacerlo accediendo a este enlace https docs google com spreadsheets d 1ga4vefm1qp1bqun9uzckp5slvnvys1ng37ylmm5xcnm edit usp sharing requerimientos no funcionales que debe cumplir toda la aplicaci n web el sistema ser desarrollado para navegadores web basados en chromium y gecko firefox eficiencia toda la funcionalidad del sistema debe ser capaz de responder en menos de 10 segundos el sistema debe ser capaz de operar de manera adecuada con hasta 1000 usuarios de manera concurrente los datos que se modifican deben ser actualizados en la base de datos para todos los usuarios en menos de 2 segundos usabilidad el sistema debe proporcionar mensajes de error que sean informativos y orientados a usuario final el sistema debe poseer interfaces gr ficas bien formadas el tiempo de aprendizaje del sistema por un usuario deber ser menor a 4 horas seguridad el sistema se debe desarrollar aplicando recomendaciones de programaci n que mantengan la seguridad de los datos los datos solo podr n ser modificados por el administrador del sistema o miembro con los permisos correspondientes a su rol organizaci n la metodolog a de desarrollo de software ser domain driven development ddd y arquitectura limpia cada semana se debera producir reportes de las reuniones diarias en los cuales cada equipo muestre los avances del proyecto decisiones t cnicas clean code la especificaci n definida por los equipos desarrolladores se encuentra en c digo limpio https docs google com document d 1hw9ravlhxjj9bz xtidhbhkgfuqr4v2q edit usp sharing ouid 105993574609311310782 rtpof true sd true arquitectura limpia para la realizaci n de este proyecto se trabaj en la arquitectura limpia de tipo ddd la decisi n se toma en conjunto con el asesor t cnico que recomienda la arquitectura pues suele ir de la mano con blazor es necesario recalcar que las capas exteriores pueden depender de las capas interiores pero no al rev s es decir la capa de dominio no puede depender de la capa de presentaci n esta arquitectura cuenta con 4 capas bien definidas dominio contiene las entidades servicios del dominio interfaces para repositorio aplicaci n casos de uso y servicios de aplicaci n services infraestructura implementaciones de las interfaces de dominio y fuentes de datos contextos presentaci n interfaz de usuario en general es quien interact a con los servicios de la aplicaci n razor metodolog as utilizadas y procesos definidos metodolog a gil scrum scrum es un marco de gesti n para el desarrollo incremental de productos vali ndose de uno o m s equipos multifuncionales autoorganizados de aproximadamente siete personas cada uno estos equipos son responsables de la creaci n y adaptaci n de los procesos mediante una estructura de roles reuniones reglas y artefactos para el trabajo en equipo que scrum posee en scrum se realizan entregas parciales y regulares del producto final priorizadas por el beneficio que aportan al receptor del proyecto el mayor beneficio de la metodolog a scrum se experimenta en el trabajo complejo que implica la creaci n de conocimiento y colaboraci n roles se definieron distintos roles para trabajar en equipos transversales ambassador encargado git encargado documentaci n encargado look and feel scrum master reuniones se decide realizar por lo menos una reuni n semanal con el objetivo de conocer los avances problemas y sidebar topics que enfrenta cada equipo en el desarrollo de sus m dulos reglas cada equipo es responsable de las reglas bajo las que se trabaja pero en conjunto todos deben cumplir con los requisitos de clean code arquitectura limpia y definici n de acordados artefactos en el siguiente punto del documento se encuentran los artefactos utilizados markdown header artefactos utilizados en el desarrollo del proyecto cada equipo cuenta con un backlog de prioridades donde se almacenan las historias de usuario de acuerdo a su importancia se trabaja mediante ciclos o iteraciones llamados sprints estos consisten de per odos de trabajo continuo de entre 2 semanas y 2 meses al final de cada per odo se procede a una revisi n del trabajo revisado con los inversores y el due o del producto artefactos utilizados en el desarrollo del proyecto se realizaron los siguientes artefactos para el dise o modelo conceptual de la aplicaci n web https miro com app board o9j ly1kqic modelo conceptual y relacional base de datos https lucid app lucidchart invitations accept inv 16199716 9634 40d5 9347 320976039096 tecnolog as utilizadas con sus respectivas versiones microsoft visual studio enterprise 2019 v16 11 2 microsoft sql management studio 2018 v15 0 18386 0 microsoft sql server blazor asp net core 5 0 repositorio de c digo y estrategia git para el proyecto el c digo del proyecto se encuentra en un repositorio de bitbucket https cristian quesadalopez bitbucket org cristian quesadalopez ecci ci0128 ii2021 g01 pi git dado que se utiliz la metolod a gil scrum se defini que se utilizar a una rama main de la cual se crea una rama por cada equipo de acuerdo a sus mod los en cada subrama de equipo se crea una subrama por cada historia de usuario definida por los equipos definici n de listo para subir al master no romper la arquitectura tiene que completar una tarea t cnica comentario en el c digo especificando la ltima tarea t cnica que modific esa parte espec fica del c digo comentario explicando algo en el c digo que se considere complicado si se hace un cambio a los modelos se debe de hacer un pull request a los otros equipos seguir principios de clean code b sico verificar que corra todas las tareas de integraci n deben tener pull requests entre los equipos involucrados para presentar en el sprint review estar en el master y actualizado en el backlog tiene que estar cumpliendo los criterios de aceptaci n tiene que haber sido revisada y aprovada por el p o referencias bibliogr ficas elmasri r and navathe s 2010 fundamentals of database systems 6th ed boston united states addison wesley cohn m 2005 agile estimating and planning 1st ed pearson india uml y patrones craig larman ingenier a de software roger pressman | server |
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VisioZoezi | p align center img src resources app icon png alt visiozoezi width 150px p p align center img src https img shields io badge kotlin 013200 logo kotlin logocolor green style for the badge img src https img shields io badge android 013200 logo android logocolor green style for the badge img src https img shields io badge windows 013200 logo windows logocolor green style for the badge img src https img shields io badge ubuntu 013200 logo linux logocolor green style for the badge p visiozoezi a compose multiplatform fitness application the application utilizes kotlin multiplatform https kotlinlang org docs multiplatform html to share code and compose multiplatform https www jetbrains com lp compose mpp to share user interfaces between different platforms table of contents libraries libraries used project structure project structure modules modules layers layers screenshots screenshots computer vision computer vision prerequisites ide you can either use intellij idea https www jetbrains com idea or android studio https developer android com studio to develop the project exercise db api the application utilizes exercise db api https rapidapi com justin wfnsxh t6 api exercisedb to display list of workouts you can obtain an api key from rapidapi https rapidapi com justin wfnsxh t6 api exercisedb libraries used multiplatform libraries sqldelight https cashapp github io sqldelight multiplatform database used for persistence ktor https ktor io docs create client html network client voyager https voyager adriel cafe multiplatform navigation library android libraries jetpack compose https developer android com jetpack compose accompanist permission https google github io accompanist permissions camera x https developer android com training camerax kotlin dl https github com kotlin kotlindl tensorflow lite https www tensorflow org lite glide compose https bumptech github io glide int compose html desktop libraries compose jb https github com jetbrains compose jb webcam capture https github com sarxos webcam capture deep learning java https djl ai s designed using material 3 guidelines https m3 material io project structure modules the project contains 3 main modules common https github com bkmbigo visiozoezi tree main common manages code sharing between platform the module contains the following sub modules commonmain contains shared code commontest androidmain androidtest desktopmain desktoptest android https github com bkmbigo visiozoezi tree main android contains android code desktop https github com bkmbigo visiozoezi tree main desktop contains desktop specific code layers the application is divided into four modules data layer domain layer ml layer presentation layer screenshots p align center img src resources home screen android dark png alt visiozoezi width 200px p computer vision camera capture camera capture is achieved using camera x https developer android com training camerax on android and webcam capture https github com sarxos webcam capture on desktop jvm s the use of webcam capture requires the use of swing panel which overlays over compose components thus disabling preview of pose detection results pose estimation the project was aimed at utilizing computer vision techniques particularly pose estimation and classification to assist in fitness tracking pose estimation utilizes movenet singlepose lightning https tfhub dev google movenet singlepose lightning 4 to infer human pose from images captures from the camera the model was implemented using kotlin dl https github com kotlin kotlindl and deeplearningjava https djl ai s on jvm the use of different frameworks was due to issues encountered while using one framework to instantiating models on different platforms | android kotlin compose compose-multiplatform kotlin-multiplatform ktor-client pose-estimation sqldelight jvm | ai |
RTO-Hackathon | rto hackathon here is the link to a short demo for this project https drive google com file d 18cpxfcs2plwjw mkenrmqapz k 8dzp view usp share link | os |
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LWC-Mobile-Samples | samples for previewing lightning web components on mobile welcome to the mobile preview samples repo here you can find sample projects and apps that let you preview lightning web components several ways in your desktop browser in the mobile browser of an ios or android virtual device in a native app running on an ios or android virtual device important before running this sample lightning web component project you must configure it to use a scratch org if you re not familiar with this concept we recommend completing the following trails these trails demonstrate how to set up your development environment and configure a lightning web component project with a salesforce scratch org quick start lightning web components https trailhead salesforce com content learn projects quick start lightning web components trail id build lightning web components set up your lightning web components developer tools https trailhead salesforce com content learn projects set up your lightning web components developer tools trail id build lightning web components what s included samples in this repo include lightning web component projects and native mobile apps the native apps are configured to recognize and display a component running on a local server lightning web component projects these samples define lightning web components and demonstrate configurations for previewing them details summary b helloworld b summary this sample lightning web component project demonstrates how to preview locally it contains a basic lightning web component along with code mobile apps json code a configuration file that defines how to preview this component in native mobile apps this file points to code configure android test app ts code and code configure ios test app ts code files together these files demonstrate how you can configure your apps to show up in the vscode preview dialog boxes define the optional code get app bundle code parameter you can implement it to compile the app or perform any other setup steps for more information on code mobile apps json code see configuring a native mobile app to host previews in preview your components from the command line https developer salesforce com docs component library documentation en lwc lwc mobile extensions cli commands preview in custom mobile apps https developer salesforce com docs component library documentation en lwc lwc mobile extensions integrate custom app details native mobile apps these samples let you preview lightning web components in standalone native apps details summary b apps ios lwctestapp b summary a sample app that you can use to preview a lightning web component in a native ios app details details summary strong apps android lwctestapp strong summary a sample app that you can use to preview a lightning web component in a native android app details previewing the hello world sample after you ve set up your environment connect the helloworld sample project to your scratch org and preview your component you can use the following instructions as a model for previewing other lightning web components 1 in vs code open the helloworld folder 2 configure the helloworld project to use a scratch org if you already have a scratch org skip to step 3 if you don t have a scratch org 1 in vs code press cmd shift p macos or ctrl shift p windows 2 type in scratch and select sfdx create a default scratch org 3 follow the onscreen steps to create a scratch org the recommended trails provide detailed information on these steps 4 ensure that a success message appears in the vs code output window 3 authorize the helloworld project to use your scratch org 1 in vs code press cmd shift p macos or ctrl shift p windows 2 type in authorize and select sfdx authorize an org 3 follow the onscreen steps to log into your salesforce org with your credentials the recommended trails provide detailed information on these steps 4 ensure that a success message appears in the vs code output window 4 now that your helloworld project is connected to your scratch org preview it locally 1 in vs code navigate to force app main default lwc 2 right click helloworld and select sfdx preview component locally 3 select whether you d like to preview it in your desktop browser or on an ios or android device 4 if you chose ios android either select an available virtual device from the presented list if one appears or choose to create a virtual device 5 indicate whether you d like to preview the component on your mobile browser or in the provided native lwc test app your virtual device launches and your component preview appears for full documentation see preview lightning web components on mobile https developer salesforce com docs component library documentation en lwc lwc mobile extensions | front_end |
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nlp-data-readiness | data readiness for natural language processing this document concerns data readiness in the context of machine learning and natural language processing it describes how an organization may proceed to identify make available validate and prepare data to facilitate automated analysis methods the contents of the document is based on the practical challenges and frequently asked questions we nlu group at rise https ri se have encountered in our work as an applied research institute with helping organizations and companies both in the public and private sectors to use data in their business processes the purpose of this document is to describe how an organization may proceed to identify make available validate and prepare data to facilitate automated analysis methods machine learning natural language processing read the documentation here click here https nlp data readiness readthedocs io en latest for the latest working version at read the docs click here docs readme md for the latest working version at github pre print papers data readiness for natural language processing https arxiv org abs 2009 02043 olsson sahlgren 2020 we need to talk about data the importance of data readiness in natural language processing https arxiv org abs 2110 05464 olsson sahlgren 2021 how to contribute the nlp data readiness document is work in progress if you have any questions suggestions for edits or other input please email the author fredrik olsson at sign gavagai io or submit a pull request in the document s github repository available at https github com fredriko nlp data readiness https github com fredriko nlp data readiness general step by step instructions for how to contribute to create a pull request is descibed by e g step by step guide to contributing on github https www dataschool io how to contribute on github | data-readiness data-readiness-levels machine-learning natural-language-processing nlp nlp-machine-learning data data-quality | ai |
IKnowAGuy | iknowaguy about i know a guy is a learning team project created to explore the development of a web app from scratch the idea behind is to create a platform where people needing to get a project done and handymen specialized on certain services can advertise their job requirements or offers and get in touch to make the project come to life technologies the development was done with c and the asp net core framework for the backend functionalities using net core entity for the database and net core identity for the users management for access security a jwt token verification was implemented the frontend design is made using the reactjs library screenshots home page ikag preview 1 update 1 https user images githubusercontent com 94113923 208301283 d1d56403 cd12 4993 b076 2ee940703da4 png ikag preview 2 update 1 https user images githubusercontent com 94113923 208301290 168f7718 5a84 4507 9d2e a18e6533ab97 png filtered home page ikag preview 3 update 1 https user images githubusercontent com 94113923 208301298 275205d0 98ff 4b5d 8efb a6e87eae1704 png ad creation page ikag preview 4 update 1 https user images githubusercontent com 94113923 208301305 72a6ec58 f452 4d67 af21 b00d9faca751 png ad details ikag preview 5 update 1 https user images githubusercontent com 94113923 208301314 df515cb1 d914 474c ade7 98d4dcbbb9e0 png | server |
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personal-budget-final | getting started with create react app this project was bootstrapped with create react app https github com facebook create react app available scripts in the project directory you can run npm start runs the app in the development mode open http localhost 3000 http localhost 3000 to view it in the browser the page will reload if you make edits you will also see any lint errors in the console npm test launches the test runner in the interactive watch mode see the section about running tests https facebook github io create react app docs running tests for more information npm run build builds the app for production to the build folder it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes your app is ready to be deployed see the section about deployment https facebook github io create react app docs deployment for more information npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it learn more you can learn more in the create react app documentation https facebook github io create react app docs getting started to learn react check out the react documentation https reactjs org code splitting this section has moved here https facebook github io create react app docs code splitting https facebook github io create react app docs code splitting analyzing the bundle size this section has moved here https facebook github io create react app docs analyzing the bundle size https facebook github io create react app docs analyzing the bundle size making a progressive web app this section has moved here https facebook github io create react app docs making a progressive web app https facebook github io create react app docs making a progressive web app advanced configuration this section has moved here https facebook github io create react app docs advanced configuration https facebook github io create react app docs advanced configuration deployment this section has moved here https facebook github io create react app docs deployment https facebook github io create react app docs deployment npm run build fails to minify this section has moved here https facebook github io create react app docs troubleshooting npm run build fails to minify https facebook github io create react app docs troubleshooting npm run build fails to minify | server |
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mirage | mirage reverse proxy frontend for docker mirage is reverse proxy for docker container and container manager mirage can launch and terminate docker container and serve http request with specified subdomain additionaly mirage passes variable to dockerfile using environment variables usage 1 setup mirage and edit configuration see setup section for detail 2 run mirage following instructions use below settings host webapi docker dev example net reverse proxy suffix dev example net listen http 80 prerequisite you should resolve dev example net to your docker host using cli 3 launch docker container using curl curl http docker dev example net api launch d subdomain cool feature d branch feature cool d image myapp latest 4 now you can access to container using http cool feature dev exmaple net 5 terminate container using curl curl http docker dev example net api terminate d subdomain cool feature using web interface 3 access to mirage web interface via http docker dev example net 4 press launch new container 5 fill launch options subdomain cool feature branch feature cool image myapp latest 6 now you can access to container using http cool feature dev exmaple net 7 press terminate button customization mirage now supports custom parameter write your parameter on config yml mirage contains default parameter branch as below parameters name branch env git branch rule required true you can add custom parameter rule option is regexp string setup 1 download binary https github com acidlemon mirage releases or go get 2 rename config sample yml to config yml 3 edit config yml to your environment 4 change your domain setting to fit your config yml 5 run mirage license the mit license mit c 2014 acidlemon c 2014 kayac inc | front_end |
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blinky-mbed-rtos | blinky mbed rtos simple blinky example using mbed s rtos | os |
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FrontendVibes | frontendvibes front end for conuhacks vibes | front_end |
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CodeIgniter-Bootstrap | codeigniter bootstrap join the chat at https gitter im sjlu codeigniter bootstrap https badges gitter im join 20chat svg https gitter im sjlu codeigniter bootstrap utm source badge utm medium badge utm campaign pr badge utm content badge bundles the following packages together codeigniter https github com bcit ci codeigniter codeigniter rest server https github com chriskacerguis codeigniter restserver bootstrap https github com twbs bootstrap font awesome https github com fortawesome font awesome lodash https github com lodash lodash use if you re planning on just using developing with codeigniter bootstrap don t clone the repository instead use these steps to get a pre compiled version of it download the latest package https github com sjlu codeigniter bootstrap releases download 1 0 4 codeigniter bootstrap zip or alternatively download another release https github com sjlu codeigniter bootstrap releases use like any other codeigniter install http codeigniter com user guide installation index html build if you plan on extending the build process you should follow these steps this will only work on a nix machine and not on a windows machine first clone this reposistory git clone https github com sjlu codeigniter bootstrap git cd codeigniter bootstrap run the makefile make unzip the build unzip codeigniter bootstrap zip docs check out the wiki https github com sjlu codeigniter bootstrap wiki for information and codeigniter guides license mit with codeigniter amendments http codeigniter com user guide license html | front_end |
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MDEV | mdev a href https play google com store apps details id com ibektillo mobiledevelop img src https cdn afterdawn fi v3 news original get it on google play png width 250px a hr mobile development xamarin forms android applicaton br hr p img src https play lh googleusercontent com e5cdxdi2vcmqxrf2o9y1 5wg5p0aa5pakqbwcssmzwx stzaj7qy4o3bscyezpi0zoc w1464 h685 rw widht 350px img src https play lh googleusercontent com y5em54b2r3bcx1u94ldvvr6ndusa5zfeqcjpmyhyhx1jrmjhqnehinbh5xqvzmqgshgo w1464 h685 rw widht 350px p | front_end |
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OpenOPS | openops openops for information technology bellow projects is prototype for dell emc storage monitor with python you can not use them in product enviroment directly openops powerstore https github com qd888 openops powerstore openops xtremio https github com qd888 openops xtremio openops unity https github com qd888 openops unity openops vmax https github com qd888 openops vmax openops isilon https github com qd888 openops isilon openops ecs https github com qd888 openops ecs | server |
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webdev | webdev s a bokhari web development 2016 week end projects front end web development is fun let s build wow projects s a bokhari | front_end |
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smockin | p align center img src public image smockin logo png width 400 p p align center version 2 20 1 p br new in version 2 20 0 ngrok is now built into smockin your api mocks can now be made publicly accessible in seconds change of jdk version java 11 or later is now required to build the main branch java 8 users can continue to build smockin from this branch https github com matthewgallina smockin tree jdk8 br br dynamic api s3 bucket mail server mocking for application development qa testing visit us https www smockin com contact us or follow us on twitter b smockin com b user guide https www smockin com help br br overview smockin is a development tool used to dynamically mock api endpoints s3 buckets email accounts featuring a rich ui an built in mock servers creating and managing mocks can be done quickly both with or without code whether you are a developer who needs to simulate restful api endpoints or an engineer working with complex microservice infrastructure smockin can help by mimicking any services that are either unavailable or otherwise too difficult or time consuming to set up smockin runs as a small web app which can be either installed locally onto a personal machine or hosted centrally and used by multiple users br dashboard https raw githubusercontent com mgtechsoftware smockin master public image dashboard png client call https raw githubusercontent com mgtechsoftware smockin master public image client call png br key features create dynamic api mocks to mimic real world application behaviour create manage s3 bucket mocks where an aws account may not be available new create manage mock mail inboxes run smockin centrally and create user accounts for your team import export mocks to share between your team version control monitor and log traffic going to the http or s3 mock servers choose whether to build your api mocks using javascript or without code a rich and complete ui solution br requirements java 8 maven 3 please note all bash scripts were written and tested on gnu bash version 3 2 57 1 release all bat files were were written and tested on windows 7 br quick start clone this repo git clone https github com mgtechsoftware smockin git change the current directory to smockin and build the project cd smockin mvn clean install run the install and start scripts for windows use the equivalent bat files install sh start sh finally from your browser open the dashboard http localhost 8000 index html if running smockin for the first time then please allow 20 30 secs for the app to fully start up br for further details please consult the installation https github com mgtechsoftware smockin wiki installation and getting started https github com mgtechsoftware smockin wiki api mock tutorial getting started guides in the wiki you can also check out the latest help guide here https www smockin com help br troubleshooting please consult the configuration troubleshooting https github com mgtechsoftware smockin wiki configuration troubleshooting guide br licence smockin is licensed in accordance with the terms of the apache license version 2 0 the full text of this license can be found at https www apache org licenses license 2 0 br acknowledgements third parties smockin is built upon the following frameworks spring boot https projects spring io spring boot hibernate http hibernate org spark http sparkjava com maven https maven apache org angularjs https angularjs org ui bootstrap https angular ui github io bootstrap vkbeautify https github com vkiryukhin vkbeautify h2 http www h2database com hikaricp https brettwooldridge github io hikaricp apache commons https commons apache org apache http components https hc apache org apache commons io https commons apache org proper commons io junit http junit org mockito http site mockito org apache activemq http activemq apache org apache ftpserver https mina apache org ftpserver project raml parser 2 https github com raml org raml java parser jasypt http www jasypt org java jwt https github com auth0 java jwt jwt decode https github com auth0 jwt decode jquery https jquery com code mirror https codemirror net s3proxy https github com gaul s3proxy greenmail https greenmail mail test github io greenmail java ngrok https github com alexdlaird java ngrok ngrok https ngrok com br br recent release features br important new in version 2 19 0 from 2 19 0 onwards smockin s internal h2 database has been upgraded to v2 1 moving away from v1 4 unfortunately v2 of h2 is not backwards compatible with v1 4 which means you will need to export your existing data and migrate this to the new db br note the latest version of smockin to continue using v1 4 of h2 will be kept here https github com matthewgallina smockin tree 2 18 4 br to upgrade to 2 19 0 please follow the steps below 1 in your current version of smockin 2 18 x or earlier use the export feature to backup any mock data your wish to keep 2 next go to the user home directory smockin is running from and rename the config directory smockin to smockin 2 18 4 3 download version 2 19 0 or later of smockin we recommend keeping the previous version of smockin on your system also for the short term so you can switch between versions until all of your data has been moved across 4 using the new version of smockin run the install script this will create a new smockin config directory containing the newer h2 database 5 launch the new version of smockin using one of the start run scripts 6 from the smockin dashboard use the import feature to save your mock data to the new database br unfortunately not all data can be automatically exported and so will need to be handled manually this includes mail mocks and messages server configuration key value data br should you wish to switch between versions of smockin as suggested above then this can be done by simply switching the smockin config directory in your user home and ensuring this aligns with version of smockin you wish to run a good practice we advise is to rename suffix any unused smockin config directories with the version of the smockin application they align too so as to make them easily identifiable for example smockin 2 18 4 unused config dir br smockin 2 19 0 unused config dir br smockin this is the active config dir br br new in version 2 18 0 introducing the email mock server 1 swap in the smockin mock mail server running on port 8003 as your application s delivery mail server 2 create email accounts in the smockin ui 3 view all received email messages from the ui including mail attachments 4 support for mail inbox auto generation so as to support random addresses during testing mail messages can be stored in memory or saved to database br br new in version 2 17 0 introducing s3 bucket mocking simply create a mock bucket build your s3 content and point your application to the s3 mock server featuring different synchronisation modes developing and testing around s3 has never been easier once you ve created your first bucket this can be accessed from port 8002 using any s3 client like so s3 client https raw githubusercontent com mgtechsoftware smockin master public image s3 client png br br new in version 2 11 0 introducing block mock and swap with a number of users discovering smockin as a great way to monitor live dev traffic you can now intercept and manipulate on the fly any requests via the live logging tool combined with the proxy server this now gives developers and qa engineers complete visibility and control over all http traffic running between your applications br br new in version 2 10 0 the smockin proxy server now supports multiple url destinations which can be mapped to specific paths for example say you are running 3 independent microservices for each of the following services v1 product localhost 9001 v1 customer localhost 9002 v1 address localhost 9003 you can now add these path to url mappings to smockin and then decide whether swap out any traffic between these services and the end user with mocked responses using various strategies this can be highly useful for either temporarily modifying an existing api or adding mocking an entirely new endpoint br br about smockin is designed and actively maintained by matthew gallina why the name smockin whilst it may sound like an old english middle ages embroidery technique the name actually came about more in relation to a classic jim carey movie quote smokin and yes far too much thought and time went into it | api raml api-mocking api-rest mock-server mocking java-8 simulation api-simulation api-mock mock-api s3-bucket java s3 | front_end |
bitcoin_book_2nd | https bitcoinbook info mastering bitcoin 2nd edition programming the open blockchain https bitcoinbook info in chinese simplified mastering bitcoin 2nd edition images cover png asciidoc asciidoc asciidoc asciidoc bitcoin core asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc amazon https www amazon com mastering bitcoin programming open blockchain dp 1491954388 asciidoc images thanks jpeg | bitcoin blockchain bitcoinbook | blockchain |
Computer_Vision_with_OpenCV | computer vision with opencv computer vision projects with opencv and python | ai |
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amsterdam-styled-components | amsterdam styled components asc style styled components https img shields io badge style f0 9f 92 85 20styled components orange svg colorb daa357 colora db748e https github com styled components styled components storybook https github com storybooks brand blob master badge badge storybook svg https github com storybooks storybook lerna https img shields io badge maintained 20with lerna cc00ff svg https lernajs io typescript https badges frapsoft com typescript version typescript next svg v 101 https github com ellerbrock typescript badges build status https travis ci org amsterdam amsterdam styled components svg branch main https travis ci org amsterdam amsterdam styled components netlify status https api netlify com api v1 badges 8662c9dc e673 434b 9a95 a642e4b45825 deploy status https app netlify com sites amsterdam styled components deploys code style prettier https img shields io badge code style prettier ff69b4 svg style flat square https github com prettier prettier demo site with the storybook of the components https amsterdam github io amsterdam styled components path docs introduction welcome page use components in your project please read the getting started https github com amsterdam amsterdam styled components tree main docs getting started md documentation contributing creating components please read the contribution md https github com amsterdam amsterdam styled components tree main docs contributing md design system we aim the components to be aligned with the amsterdam design system https designsystem amsterdam nl 7awj1hc9f p 39359e design system in order to align these components we contact people on the dst design system team slack group https dstamsterdam slack com to let them know which components we want to align usually some modifications need to be made in both storybook and amsterdam design system the state of a component indicates whether it is approved as officially aligned there are two possible states 1 stable stable components are aligned and approved by the design system these components are usually embedded in the corresponding the design system 2 experimental these components are simply not yet aligned and reviewed by the design system but they can be used in your project if you want keep in mind they can change in the near future when aligning them some components don t have a state in the stories consider these experimental more info can be found here https github com amsterdam amsterdam styled components tree main docs contributing md aligning if you have any questions please contact one of the maintainers https github com amsterdam amsterdam styled components tree main docs maintainers md to get access to the dst slack group https dstamsterdam slack com structure this project is a monorepo with 2 packages asc assets packages asc assets contains fonts and icons in directory static and react icons asc ui packages asc ui the react implementation of the components vision consistency is always an issue in software engineering especially when it comes to web styling and ux that is why we think a component library who captures styling but also certain ux aspects e g button loading state is highly beneficial for organisations with large or multiple applications such as the municipality of amsterdam we acknowledge that such a library entails some risks and pitfalls and we aim to cover these as much as possible on the other hand we think that the benefits outweigh the downsides quality assurance and durable maintainability one of the biggest risks is the way a library needs be maintained in order to guarentee quality and keep developers motivated to continue using it this is at risk when maintainers stop maintaining e g they leave the organisation or company maintainers do not have the time to properly review pr s e g there is no budget time to spend on the project tests are neglected dev guidelines are violated our goal is to set up strict guidelines for development and limit the amount of reviewers in the repo creating these guidelines is an iterative process and we invite all who are interested to contribute the guidelines can be found here tbd tradeoff pros able to reuse components this will not only save development time in the long term but it also introduces consistency in design and code no more copy paste code easier to test strong separation of concerns every component keeps its own logic and style a monorepo one source for styled components and everything that is related to that also updates won t immediately affect other repo s because of versioning great to be used in a living styleguide like storybook attractive for the internal open source community risks quality assurance we need to set up some well founded contribution guidelines and make sure the repo doesn t get polluted versioning updating the component package might contain breaking changes this could be prevented for most of the time if we have a proper updating strategy in our guidelines more time to set up develop and maintain however this is an investment for future products that will result in decreasing development time drastically extra information more detailed information can be found in the readme md of each package asc assets packages asc assets readme md asc ui packages asc ui readme md | team-dataportaal | os |
Machine-Learning-Algorithms | machine learning algorithms this is the code repository for machine learning algorithms https www packtpub com big data and business intelligence machine learning algorithms utm source github utm medium repository utm campaign 9781785889622 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish about the book in this book you will learn all the important machine learning algorithms that are commonly used in the field of data science these algorithms can be used for supervised as well as unsupervised learning reinforcement learning and semi supervised learning a few famous algorithms that are covered in this book are linear regression logistic regression svm na ve bayes k means random forest xgbooster and feature engineering in this book you will also learn how these algorithms work and their practical implementation to resolve your problems this book will also introduce you to the natural processing language and recommendation systems which help you run multiple algorithms simultaneously on completion of the book you will have mastered selecting machine learning algorithms for clustering classification or regression based on for your problem instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 the code will look like the following nn nearestneighbors n neighbors 10 radius 5 0 metric hamming nn fit items there are no particular mathematical prerequisites however to fully understand all the algorithms it s important to have a basic knowledge of linear algebra probability theory and calculus chapters 1 and 2 do not contain any code as they cover introductory theoretical concepts all practical examples are written in python and use the scikit learn machine learning framework natural language toolkit nltk crab langdetect spark gensim and tensorflow deep learning framework these are available for linux mac os x and windows with python 2 7 and 3 3 when a particular framework is employed for a specific task detailed instructions and references will be provided scikit learn nltk and tensorflow can be installed by following the instructions provided on these websites http scikit learn org http www nltk org and https www tensorflow org related products learning functional data structures and algorithms https www packtpub com application development learning functional data structures and algorithms utm source github utm medium repository utm campaign 9781785888731 machine learning using advanced algorithms and visualization in r video https www packtpub com big data and business intelligence machine learning using advanced algorithms and visualization r vi utm source github utm medium repository utm campaign 9781788294980 learning javascript data structures and algorithms second edition https www packtpub com web development learning javascript data structures and algorithms second edition utm source github utm medium repository utm campaign 9781785285493 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 9781785889622 https packt link free ebook 9781785889622 a p | ai |
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data-engineering-on-google-cloud | data engineering on google cloud | cloud |
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MWbalanced-firmware-none | mwbalanced 8051 git miaowlabs gpl fork windows keil c51 ide http www miaowlabs com http www miaowlabs com http bbs miaowlabs com http bbs miaowlabs com mwbalanced 8051 stm32 q 153337261 1 2 20161010 by 1 common src servo c common inc servo h 20160521 by alex g 1 2 3 isp 4 5 app 1 4 20150513 by 20160415 by 20160402 by mwbalancedstc15 20160329 by 33m 20m 20151205 by bug 20160117 by | os |
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zss | zss sass for design systems zss is a sass framework that helps you build design systems with configurable design scales documentation documentation background background what it does what it does what it doesn t do what it doesnt do principles principles setup setup structure structure class names class names source order source order responsive responsive how to use how to use configuration and variables configuration and variables design scales design scales other variables other variables development checks development checks helpers helpers generic generic base base abstract objects abstract objects utility classes utility classes add ons and tools add ons and tools third party third party documentation this overview provides an in depth view of all of the available properties classes in zss property index docs property index md background what it does provides a simple base for your typography provides abstractions of common ui patterns so you can focus on the specific design details functional css generates immutable configurable and responsive utility classes provides a flexible and configurable flexbox grid provide compile time checking of your configuration for design smells what it doesn t do no pre defined colors unlike bootstrap zss does not contain pre made components or javascript principles zss is built on a few principles composition over repetition the class attribute is perfect for composing your design with multiple classes it leads to better re usability of your design and smaller css stylesheets configurable and optional in zss generated utility classes have configurable values need an extra value or don t like the default values just override a variable in your project don t need the button abstraction class just don t import it responsiveness all utility classes have responsive equivalents so you can use different values on different screens design scales zss uses scales to limit the number of available values for certain properties like color and margin setup install cli npm install zss save after that import the files you need into your sass stylesheet sass import node modules zss src utilities background color please note that some utility classes make use of the responsive helper mixins to make sure zss compiles correctly import these before you import the utility classes sass import node modules zss src helpers responsive import node modules zss src utilities margin you can use the accompanying file zss scss src zss scss as a starting point for your project s stylesheet it imports all files that are available in zss structure class names zss uses namespaces similar to the system css wizardry https csswizardry com 2015 03 more transparent ui code with namespaces wrote about o prefix for abstract objects u prefix for utility classes source order zss advocates importing sources files in the way itcss http itcss io prescribes with an inverted specificity 1 settings and variables 2 mixins and other sass helpers 3 generic common resets 4 base unclassed html and page setup 5 abstract objects 6 components 7 scopes 8 theming 9 visual classes 10 utility classes responsive zss uses an breakpoint suffix to indicate if a class only works on a certain breakpoint e g u black md how to use configuration and variables all zss variables are defined in variables zss defaults scss src variables zss defaults scss override them at will in your project sass import node modules zss src variables zss defaults import variables zss overrides design scales variable explanation zss opacity scale limited set of values for opacity utility classes zss border radius scale limited set of values for border radius utility classes zss border width scale limited set of values for border width classes zss spacing scale limited set of values for margin and padding utility classes zss type scale limited set of values for font size utility classes zss line height scale limited set of values for line height utility classes zss width scale limited set of values for width utility classes zss width percentage scale limited set of values for width utility classes in percentages zss max width scale limited set of values for max width utility classes zss height scale limited set of values for height utility classes zss height percentage scale limited set of values for height utility classes in percentages other variables variable explanation zss colors sass map with color definitions for text backgrounds and borders utility classes zss grid max widths sass map containing the max width of the grid on each breakpoint zss grid columns the number of columns a grid row can have zss grid gutter the amount of whitespace between grid column zss breakpoints sass map containing breakpoint names and screen sizes zss border colors sass map containing color definitions for border utility classes zss base font size font size used a base for rem values zss base line height base line height applied to the body element zss media margin the margin between the image and text of media objects development checks zss provides compile time checking of the configuration import dev strict if you want to check if the design scales are properly ordered check if there are any so called design smells like too many font sizes in your scales see the documentation docs tools strict md for more details helpers helpers are mixins functions and similar programmatic constructs in sass that help with code re use zss provides these helpers color docs tools color md function for accessing pre defined colors grid docs tools grid md mixin for generating grid classes hover docs tools hover md mixin for generating flexible hover effects responsive docs tools responsive md mixin for dealing with responsive declarations units docs tools units md functions for dealing with units and values generic the generic src generic folder contains css applied at a global level border box docs global border box md hard reset docs global hard reset md normalize css docs global normalize css md base the base src base folder contains css applied to specific elements typography docs global typography md abstract objects objects are classes that define the structure of a common ui pattern like the popular media object zss provides these objects button docs layout button md a gathering of properties most used in buttons grid docs layout grid md a grid structure to create a layout with rows and columns list reset docs layout list reset md resets ordered and unordered lists media docs layout media md image next to text utility classes utility classes have a single responsibility in other words they affect the value of just one single css property this construct is also known as functional css the most common css properties are available in zss and are listed in the property index docs property index md add ons and tools these tools and add ons make working with zss even easier third party color name https github com joppe color name cli that parses the output of http www color blindness com color name hue for easy converting of a hexadecimal color value to a name that can be used in zss to generate class names e g u yellow tangerine maintainer annemieke van der hoek avanderhoek https github com avanderhoek zicht https github com zicht jurg roessen hangloozz https github com hangloozz zicht https github com zicht | sass css design-systems functional-css responsive-design | os |
TurkcellFrontEnd | turkcellfrontend turkcell front end e itimi | front_end |
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system_design | system design preparation how to prepare for and answer system design questions objective learning about and implementing large scale distributed system is not easy i do not want to give the impression that it s something that can be learnt in a month what this repository aims to achieve is for software engineers and students to get a rough idea of how the thought process of designing a large scale works and how big companies have managed to solve really hard problems along with that there is a recent trend for companies to have an open ended interview with system design questions which is at times hard for engineers of all levels if they haven t gotten the opportunity to work on such systems themselves this is a collection of links documents for the following use cases a prepare for a system design or open ended rounds b learn more about how large scale systems work and thought process of designing a new system index starting point start basics basics how to answer in interviews howtoans steps how i approach the system design questions in interviews myapproach common design questions designques architecture architecture company engineering blog links blog low on time tldr a name start starting point a for a very broad overview please go through these lectures really useful gaurav sen s system design series https www youtube com playlist list plmcxhnjxntnvo6alsjvkgxv vh6epyvox starts from simple stuff like load balancing and message queues then moves to building full systems like whatsapp and tinder david malans cs75 scalability talk https www youtube com watch v w9f d3oy4 list plmhrnzyyvpdmlpavqm3mk5py5kb 4hlje index 10 feel free to go through other lectures if needed david huffman s talk scaling up talk https www udacity com course web development cs253 youtube link https www youtube com watch v pjntgulvvf4 list plvi1lmrukq0ninqfjklven7j2lzfl35wp index 1 scalability for dummies http www lecloud net tagged scalability designing data intensive appliations https dataintensive net this is by far one of the best books about large scale systems and the practical challenges encountered during building them it s focussed more on data oriented applications though system design interview preperation series by codekarle https www youtube com watch v 3loacsxowru list plhgw50vuymycjpn6zbgtpvkaj0cl4oeh3 this is a collection of some of the most commonly asked system design interview question explain in a very detailed and straight forward manner these talks should give you a starting point on how to think about such problems a name basics basics a but before you begin here are some topics in no particular order which in my opinion you should have a decent idea of before proceeding 1 operating system basics how a file system virtual memory paging instruction execution cycle etc work for starters silbershatz should be enough if you already have decent knowledge try stallings book on os 2 networking basics should know the tcp ip stack basics of how internet http tcp ip work at the minimum cs75 on youtube 1st lecture should give a broad overview i personally love networking a top down approach http www amazon com computer networking top down approach edition dp 0132856204 3 concurrency basics threads processes threading in the language you know locks mutex etc 4 db basics types of db s sql vs nosql etc hashing and indexing eav based databases sharding caching for databases master slave etc 5 a basic idea of how a basic web architecture is say load balancers proxy servers database servers caching servers precompute logging big data etc just know broadly what is each layer for 6 very basic summary of what the cap theorem http robertgreiner com 2014 08 cap theorem revisited is have never been asked about the theorem itself but knowing it will help you in designing large scale systems a name howtoans how to answer in interviews a i found hiredintech http www hiredintech com system design videos an excellent place to start with the way how to approach a design question as given in the link is really useful it goes into how we start with clearing the use cases of the system then thinking in the abstract manner of the various component and the interactions think about the bottlenecks of the system and what is more critical for your system eg latency vs reliability vs uptime etc address those giving the tradeoff of your approach system design in crack the coding interview http www flipkart com cracking coding interview 150 programming questions solutions english 5th p itmdz4pvzbhcv6uv good approach on how to begin attacking a problem by first solving for a small usecase then expanding the system the best way to prepare for such questions is do mock interviews pick any topic given below try to come up with a design and then go and see how and why it is designed in that manner there is absolutely no alternative to practice whiteboarding a system design question is similar to actually writing code and testing it just reading will only take you so far a name myapproach steps how i approach the system design questions in interviews a these are the steps i go through mentally in the interviews followed by actual interview experiences a be absolutely sure you understand the problem being asked clarify on the onset rather than assuming anything b use cases this is critical you must know what is the system going to be used for what is the scale it is going to be used for also constraints like requests per second requests types data written per second data read per second c solve the problem for a very small set say 100 users this will broadly help you figure out the data structures components abstract design of the overall model d write down the various components figured out so far and how will they interact with each other e as a rule of thumb remember at least these 1 processing and servers 2 storage 3 caching 4 concurrency and communication 5 security 6 load balancing and proxy 7 cdn 8 monetization if relevant how will you monetize eg what kind of db is postgres enough if not why do you need caching and how much is security a prime concern f special cases for the question asked say designing a system for storing thumbnails will a file system be enough what if you have to scale for facebook or google will a nosql based database work g after i have my components in place what i generally try to do is look for minor optimization in various places according to the use cases various tradeoffs that will help in better scaling in 99 cases h scaling out or up http highscalability com blog 2014 5 12 4 architecture issues when scaling web applications bottlene html i check with the interviewer is there any other special case he is looking to solve also it really helps if you know about the company you are interviewing with what its architecture is what will the interviewer have more interest in based on the company and what he works on a name designques common design questions a it generally depends what you are and you will be working on also what your level is but these are some of the more frequent interview questions design amazon s frequently viewed product page eg which shows the last 5 items you saw design an online poker game for multiplayer solve for persistence concurrency scale draw the er diagram for this design a url compression system http www hiredintech com system design the system design process search engine http infolab stanford edu backrub google html generally asked with people who have some domain knowledge basic crawling collection hashing etc depends on your expertise on this topic design dropbox s architecture good talk on this https www youtube com watch v pe4gwstwhmc design a picture sharing website http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html how will you store thumbnails photos usage of cdns caching at various layers etc design a news feed eg facebook twitter news feed http www quora com software engineering best practices what are best practices for building something like a news feed design a product based on maps eg hotel atm finder given a location design malloc free and garbage collection system http courses cs washington edu courses csep521 07wi prj rick pdf what data structures to use decorator pattern over malloc etc design a site like junglee com http www junglee com i e price comparision availability on e commerce websites when and will you cache how much to query how to crawl efficiently over e commerce sites sharding of databases basic database design a web application for instant messaging eg whatsapp http highscalability com blog 2014 2 26 the whatsapp architecture facebook bought for 19 billion html facebook chat issues of each scaling problems status and availability notification etc design a system for collaborating over a document simultaneously eg google docs https neil fraser name writing sync very common top n or most frequent items of a running stream of data design election commission architecture let s say we work with the election commission on counting day we want to collate the votes received at the lakhs of voting booths all over the country each booth has a voting machine which when connected to the network returns an array of the form party id num votes party id 2 num votes 2 we want to collect these and get the current scores in real time the report we need continuously is how many seats is each party leading in please design a system for this design a logging system for web applications it is common to have a large number of servers running the same application with a load balancer in front to distribute the incoming requests in this scenario we want to check and alarm in case an exception is thrown in any of the servers we want a system that checks for the appearance of specific words exception disk full etc in the logs of any of the servers how would you design this system design google maps https www codekarle com system design google maps system design html design a video conferencing system like zoom webex https www codekarle com system design zoom system design html a name architecture architectures a personally i looked into the following architectures basics of google search http infolab stanford edu backrub google html basics of messaging frameworks like kafka queuing architectures like rabbitmq broad overview and advantages of redis mongodb cassandra google file system http static googleusercontent com media research google com en archive gfs sosp2003 pdf google architecture http highscalability com google architecture instagram http instagram engineering tumblr com post 13649370142 what powers instagram hundreds of instances and other image based social networks memcache scaling by facebook https cs uwaterloo ca brecht courses 854 emerging 2014 readings key value fb memcached nsdi 2013 pdf twitter scaling https www youtube com watch v z8lu0cj6bou and facebook feeds facebook graph api https cs uwaterloo ca brecht courses 854 emerging 2014 readings data store tao facebook distributed datastore atc 2013 pdf facebook haystack needle architecture https www usenix org legacy event osdi10 tech full papers beaver pdf youtube architecture and optimizations for video https www youtube com watch v zw5 eekec28 a name blog company engineering blog links a courtesy checkcheckzz https github com checkcheckzz system design interview toc depending on where you are interviewing go through the company blog very useful in interviews it really helps if you have an idea of the architecture as the questions asked will generally be of that domain and your prior knowledge will help out here airbnb engineering http nerds airbnb com amazon https developer amazon com blogs amazon aws https aws amazon com blogs bandcamp tech http bandcamptech wordpress com banksimple simple blog https www simple com engineering bitly engineering blog http word bitly com cloudera developer blog http blog cloudera com blog dropbox tech blog https tech dropbox com engineering at quora http engineering quora com etsy code as craft http codeascraft com facebook engineering https www facebook com engineering flickr code http code flickr net foursquare engineering blog http engineering foursquare com google research blog http googleresearch blogspot com groupn engineering blog https engineering groupon com high scalability http highscalability com instagram engineering http instagram engineering tumblr com linkedin engineering http engineering linkedin com blog oyster tech blog http tech oyster com pinterest engineering blog http engineering pinterest com songkick technology blog http devblog songkick com soundcloud backstage blog https developers soundcloud com blog square the corner http corner squareup com the reddit blog http www redditblog com the github blog https github com blog category engineering the netflix tech blog http techblog netflix com twilio engineering blog http www twilio com engineering twitter engineering https engineering twitter com uber engineering https eng uber com walmart labs tech blog https medium com walmartlabs webengage engineering blog http engineering webengage com yammer engineering http eng yammer com blog yelp engineering blog http engineeringblog yelp com smarkets blog https smarketshq com a name tldr low on time a i would highly recommend you do not take a shortcut unless you have a week or so for an interview system design is best learnt by practising shortcuts might help you in the short term but would recommend coming back to this link for an in depth understanding after the interview a go through cs76 and udacity s links given above for scaling systems b go through the engineering blog of the company you are interviewing in or if its a startup go through the link of the company closest to yours c see this talk http www hiredintech com system design the system design process and develop a process for how to answer such questions d remember these terms just roll over them in your interview in your mind and if relevant mention it in the interview 1 processing and servers 2 storage 3 caching 4 concurrency and communication 5 security 6 load balancing and proxy 7 cdn 8 monetization best of luck 1 feel free to send pull requests to add more content to this git | interview engineering architecture scale-systems facebook concurrency scalability preparation system-design interview-preparation interview-preparation-resources interview-questions | os |
bedrock | bedrock https bedrockapp org wp content themes bedrock images logo black svg welcome to bedrock bedrock release https img shields io github release usebedrock bedrock svg bedrock is a powerful tool that lets you prototype complex web applications and document design systems for the full documentation please check out the bedrock website https bedrockapp org demo see https bedrock monocompany vercel app try to hit ctrl m to see the page tree and navigate to https bedrock monocompany vercel app styleguide docs introduction html basic requirements first make sure you have node 14 or later installed you can find the latest version of node at nodejs org https nodejs org en go for the lts version create your first prototype we recommend using a href https github com rich harris degit degit a to be able to grab a bedrock install without actually cloning the project repository see contributing to bedrock here is the series of commands to use npx degit usebedrock bedrock my bedrock project cd my bedrock project npm install npm start contributing to the bedrock project read the active issues on a href https github com usebedrock bedrock issues github a clone this repository git clone git github com usebedrock bedrock git the latest development usually happen in feature branches or in the development branch make sure to make a branch for yourself and if you have something meaningful to contribute send us a pr contact the authors johan s dm inbox is open for questions https twitter com wolfr 2 major commands npm start runs the prototype npm run build create a build which ends up in the dist folder that can be deployed to a web server upgrading bedrock upgrading your bedrock prototype is done through a cli tool which you install globally npm i g bedrock cli bedrock upgrade to upgrade to the development branch bedrock upgrade dev see the readme at https github com mono company bedrock cli using icon fonts icon font generation is optional set icons generateiconfont to true in bedrock config js to activate icon fonts windows windows usage is not supported at the moment we have used bedrock on windows successfully though if you encounter any windows related bugs please log them under issues license bedrock is mit licensed credits bedrock was made by the team at mono https mono company bedrock was initially written by thomas tuts http thomastuts com | static-site-generator html-prototypes | os |
aws-iot-device-sdk-cpp-v2 | aws iot device sdk for c v2 this document provides information about the aws iot device sdk for c v2 this sdk is built on the aws common runtime https docs aws amazon com sdkref latest guide common runtime html jump to installation installation samples samples getting help getting help faq documents faq md api docs https aws github io aws iot device sdk cpp v2 mqtt5 user guide documents mqtt5 userguide md installation minimum requirements c 11 or higher clang 3 9 or gcc 4 8 or msvc 2015 cmake 3 1 step by step instructions documents prerequisites md build from source sh create a workspace directory to hold all the sdk files mkdir sdk workspace cd sdk workspace clone the repository git clone recursive https github com aws aws iot device sdk cpp v2 git make a build directory for the sdk can use any name mkdir aws iot device sdk cpp v2 build cd aws iot device sdk cpp v2 build continue with the build steps below based on os macos and linux sh generate the sdk build files dcmake install prefix needs to be the absolute full path to the directory example users example sdk workspace dcmake build type can be release relwithdebinfo or debug cmake dcmake install prefix absolute path to sdk workspace dcmake build type debug aws iot device sdk cpp v2 build and install the library once installed you can develop with the sdk and run the samples cmake build target install windows sh generate the sdk build files dcmake install prefix needs to be the absolute full path to the directory example c users example sdk workspace cmake dcmake install prefix absolute path sdk workspace dir aws iot device sdk cpp v2 build and install the library once installed you can develop with the sdk and run the samples config can be release relwithdebinfo or debug cmake build target install config debug windows specific notes due to maximum path length limitations in the windows api we recommend cloning to a short path like c dev iotsdk config is only required for multi configuration build tools visualstudio msbuild being the most common linux specific notes if your application uses openssl configure with duse openssl on the iot sdk does not use openssl for tls on apple and windows the os s default tls library is used on linux s2n tls https github com aws s2n tls is used but s2n tls uses libcrypto the cryptography math library bundled with openssl to simplify the build process the source code for s2n tls and libcrypto are included as git submodules and built along with the iot sdk but if your application is also loading the system installation of openssl i e your application uses libcurl which uses libssl which uses libcrypto there may be crashes as the application tries to use two different versions of libcrypto at once setting duse openssl on will cause the iot sdk to link against your system s existing libcrypto instead of building its own copy samples samples readme samples getting help the best way to interact with our team is through github you can open a discussion https github com aws aws iot device sdk cpp v2 discussions for guidance questions or an issue https github com aws aws iot device sdk cpp v2 issues new choose for bug reports or feature requests you may also find help on community resources such as stackoverflow https stackoverflow com questions tagged aws iot with the tag aws iot https stackoverflow com questions tagged aws iot or if you have a support plan with aws support https aws amazon com premiumsupport you can also create a new support case please make sure to check out our resources too before opening an issue faq documents faq md api docs https aws github io aws iot device sdk cpp v2 iot guide https docs aws amazon com iot latest developerguide what is aws iot html source https github com awsdocs aws iot docs check for similar issues https github com aws aws iot device sdk cpp v2 issues aws iot core documentation https docs aws amazon com iot dev blog https aws amazon com blogs awsf blog master iot category internet of things 23amazon freertos 7ccategory internet of things 23aws greengrass 7ccategory internet of things 23aws iot analytics 7ccategory internet of things 23aws iot button 7ccategory internet of things 23aws iot device defender 7ccategory internet of things 23aws iot device management 7ccategory internet of things 23aws iot platform integration with aws iot services such as device shadow https docs aws amazon com iot latest developerguide iot device shadows html and jobs https docs aws amazon com iot latest developerguide iot jobs html is provided by code that been generated from a model of the service secure tunnel user guide documents secure tunnel userguide md contributions guidelines documents contributing md license this library is licensed under the apache 2 0 license documents license latest released version v1 30 0 | hacktoberfest | server |
esp-aws-iot | esp aws iot important please choose the branch of this repo based on the freertos lts release https github com freertos freertos lts releases that you would like to base your application on if you are using the older 3 x x aws iot device sdk embedded c release please checkout the release v3 1 x https github com espressif esp aws iot tree release v3 1 x branch this sdk enables aws iot cloud connectivity with esp32 based platforms using the libraries provided as a part of the aws iot device embedded c sdk https github com aws aws iot device sdk embedded c getting started please clone this branch of the repository using git clone b name of the release branch recursive https github com espressif esp aws iot for example to clone just release 202210 01 lts you may run git clone b release 202210 01 lts recursive https github com espressif esp aws iot please refer to https docs espressif com projects esp idf en latest get started index html for setting up esp idf esp idf can be downloaded from https github com espressif esp idf this sdk supports release v5 1 release v5 0 release v4 4 and release v4 3 of esp idf please set your branch to the idf release that you would like to use and pull in the latest changes note please read support policy support policy below for more details please refer to the example readme examples readme md for more information on setting up examples for a production ready starting point for making applications that connect to aws iot core using esp aws iot refer to the reference example https github com freertos iot reference esp32c3 please refer to the security guide examples ota securityguide md for steps to enable security features on your espressif chip this sdk supports multiple ways to securely store the pki credentials the default method is to use pki credentials which are embedded in the binary using the certs from the certs in every example for using secure element atecc608a you will need to use esp cryptoauthlib https github com espressif esp cryptoauthlib how to use esp cryptoauthlib with esp idf the security guide includes detailed steps and pointers to configure and use the digital signature peripheral on supported chips using esp secure cert mgr https components espressif com components espressif esp secure cert mgr supported socs the following table shows esp aws iot support with the esp idf support of espressif socs alt text supported and alt text unsupported denote supported and unsupported status respectively table thead tr align center th rowspan 2 soc th th rowspan 2 supported idf version th th colspan 3 esp aws iot release th tr tr align center th a href https github com espressif esp aws iot tree release 202012 04 lts 202012 04 lts a th th a href https github com espressif esp aws iot tree release 202210 01 lts 202210 01 lts a th th a href https github com espressif esp aws iot tree master target blank rel noopener noreferrer master branch a th tr thead tbody tr align center td rowspan 4 esp32 td td idf v4 3 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v4 4 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 0 td td td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 1 td td td td td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td rowspan 4 esp32 s2 td td idf v4 3 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v4 4 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 0 td td td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 1 td td td td td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td rowspan 4 esp32 c3 td td idf v4 3 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v4 4 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 0 td td td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 1 td td td td td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td rowspan 3 esp32 s3 td td idf v4 4 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 0 td td td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 1 td td td td td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td rowspan 2 esp32 c2 td td idf v5 0 td td td td img src https img shields io badge supported green alt image width 65 height 20 td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td idf v5 1 td td td td td td img src https img shields io badge supported green alt image width 65 height 20 td tr tr align center td esp32 c6 td td idf v5 1 td td td td td td img src https img shields io badge supported green alt image width 65 height 20 td tr tbody table supported https img shields io badge supported green supported unsupported https img shields io badge unsupported red unsupported support policy idf version support for esp aws iot releases is based on idf release support schedule https github com espressif esp idf esp idf release support schedule for example support for idf v4 3 for release 202210 01 lts will expire on 15th dec 2023 | server |
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EHRKit-2022 | p align center img src https github com yale lily ehrkit blob master wrapper functions ehrlogo png alt ehrkit p ehrkit a python natural language processing toolkit for electronic health record texts python 3 6 13 https img shields io badge python 3 6 13 green svg https www python org downloads release python 360 this library aims at processing medical texts in electronic health records we provide specific functions to access the mimic iii https physionet org content mimiciii demo record efficiently the method includes searching by record id searching similar records searching with an input query we also support functions for some nlp tasks including abbreviation disambiguation extractive and abstractive summarization for more specific evaluaiton please check this pre print url https arxiv org abs 2204 06604 moreover if users want to deal with general medical texts we integrate third party libraries including hugginface https huggingface co scispacy https allenai github io scispacy allennlp https github com allenai allennlp stanza https stanfordnlp github io stanza and so on please checkout the special verison of this library ehrkit wf https github com yale lily ehrkit tree master wrapper functions p align center img src https github com yale lily ehrkit 2022 blob main ehrkit jpg alt ehrkit p table of contents 1 updates updates 2 data data 3 setup setup 4 toolkit toolkit 5 get involved get involved 6 off shelf functions get involved 6 citation get involved updates 24 05 2023 new release pretrained models for machine translation br 15 03 2022 merged a wrapper function folder to support off shelf medical text processing br 10 03 2022 made all tests avaiable in a ipynb file and updated the most recent version br 12 17 2021 new folder collated tasks containing fall 2021 functionalities added br 05 11 2021 cleaned up the notebooks fixed up the readme using depth 1 br 05 04 2021 tests run through added in tests br 04 22 2021 freezing development br 04 22 2021 completed the tutorials and readme br 04 20 2021 spring functionality finished mimic classification summarization and query extraction br data ehrkit is built for use with medical information mart for intensive care iii mimic iii it requires this dataset to be downloaded this dataset is freely available to the public but it requires completion of an online training course information on accessing mimic iii can be found at https mimic physionet org gettingstarted access once this process is complete it is recommended to download the mimic files to the folder data the other dataset that is required for some of the modules is the pubmed dataset https www ncbi nlm nih gov cbbresearch wilbur iret dataset this dataset contains a large number of medical articles the required downloading and parsing is all performed in the pubmed folder first run bash download pubmed sh and then python parse articles py this process is also detailed in the tutorial notebook for summarization tutorials naivebayes ipynb setup download repository you can download ehrkit as a git repository simply clone to your choice of directories keep depth small to keep the old versions out and reduce size git clone https github com yale lily ehrkit git depth 1 environment option 1 using conda see the environment yml file for specific requirements aside from basic conda packages pytorch and transformers are required for more advanced models to create the required environment go to the root directory of ehrkit and run conda env create f environment yml name env name for local lily lab users on tangra setup will work a little differently pip install nltk pip install pymysql pip install requests pip install gensim pip install torch pip install scikit learn pip install spacy python m spacy download en core web sm pip install u pip setuptools wheel pip install u spacy cuda102 pip install transformers environment option 2 using virtualenv cd ehrkit python3 m venv ehrvir source ehrvir bin activate pip install r requirements txt then you are good to go testing you can test your installation assuming you re in the ehrkit folder and get familiar with the library through tests note that this will only work with the sql mimic database setup python m spacy download en core web sm some spacy extras must be downloaded python m tests tests py if you want to run all the tests including the longer tests python m test all tests py most of the modules access the data through a sql database the construction of the database is described in database readmes mimic ehrkit requires medical information mart for intensive care iii mimic iii database to be installed this database is freely available to the public but it requires completion of an online training course information on accessing mimic iii can be found at https mimic physionet org gettingstarted access once you have gotten access you can put the mimic data in the folder data pubmed the other dataset that is required for some of the modules is the pubmed dataset https www ncbi nlm nih gov cbbresearch wilbur iret dataset this dataset contains a large number of medical articles the required downloading and parsing is all performed in the pubmed folder first run bash download pubmed sh and then python parse articles py this process is also detailed in the tutorial notebook for summarization tutorials naivebayes ipynb getting started once the required data has been downloaded a new user is recommended to explore the tutorials folder these tutorials cover summarization code classification and query extraction to get a new user started there are a number of jupyter notebooks in tutorials these tutorials cover summarization icd9 code classification query extraction toolkit jupyter notebook walkthroughs for some of these packages are available in tutorials these tutorials are the best way for a novice to familiarize themselves with these works and in the interest of consolidation that information will not be repeated here in addition to these most recent models there are a number of other packages which do not have full tutorials as they are built for different interactions readmes are written out for these packages before we switched to the tutorial model a full list of the modules is below modules summarization has a naive bayes model developed by jeremy this model is built for extractive summarization of medical text trained on the pubmed corpus mimic icd9 coding contains a general pipeline for classifying clinical notes from the mimic dataset into icd9 billing codes these codes contain diagnoses among other information queryextraction demonstrates simple tools for performing automated query based extraction from text which can easily be run on mimic data extractivesummarization contains an implementation of lexrank for mimic iii and pubmed in the future we will make a test in tests py that runs it on an ehr it was developed by sanya nijhawan b s cs 20 allennlp has scripts utilized by tests 6 1 and 6 2 in tests py for efficiently calling functions in the allennlp library ehrkit contains scripts that enable interaction with mimic data pubmed contains scripts for downloading the pubmed corpus of biomedical research papers once downloaded the papers are stored inside this directory tests has tests on the mimic dataset in tests py and the pubmed corpus in pubmed tests py collated tasks has a collection of tasks on mimic data including extracting named entities abbreviations hyponyms linked entities machine translation sentence segmentation document clustering and retrieving similar documents it also contains auxiliary functions including retrieving notes from noteevents csv and creating vector representations using bag of words or pre trained transformer models tutorials for tasks on non mimic data are also available for de identification inference and medical question answering developed by keen during fall 2021 get involved please create a github issue if you have any questions suggestions requests or bug reports we welcome prs off shelf functions for medical text processing ehrkit wf if you want to process some general medical text please check the ehrkit wf in wrapper functions https github com yale lily ehrkit tree master wrapper functions note you do not need mimic iii access for running this we support the following key functions abbreviation detection expansion hyponym detection entity linking named entity recognition translation sentencizer document clustering similar document retrieval word tokenization negation detection section detection umls concept extraction | ai |
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Nawodya_HelloWorldLabs | nawodya helloworldlabs repository for eee307 embedded system design | embedded-system-design | os |
Face-Recognition | face recognition capstone design project face recognition system using opencv 3 1 c and dlib description this program is a fully end to end video face recognition system done as the final capstone design project for my undergraduate degree the program also had an accompanying mobile app given a set of user face images the system is trained to recognize the faces of the individuals in the given photos the system detects the faces in the videos crops them using facial landmark detection preprocesses them and then applies a face recognition algorithm to identify the person in the frame the system is able to run at a real time speed this program was writting in c 11 using opencv 3 1 c api and dlib files fld cpp the main file that runs the full pipeline videofacedetector cpp runs the face detection from the webcam feed preprocess cpp pre processing of every webcam video frame tantriggs cpp tan triggs method of preprocessing before face recognition trainmodel cpp train the face recognizer on the collected images information the step by step pipeline is as follows 1 collect a set of photos for each user using the mobile app the user can take photos of themself from several angles for better results in training the face recognizer alternatively a video can be taken and frames that have a high similarity to each other will be discarded 2 to run the program build and run the fld cpp program this will access the current available webcam 3 firstly the local binary patterns histograms sup 1 sup lbph face recognizer will be trained using the set of collected photos along with their classification labels id video capture will then begin via webcam 4 faces are initally detected using the viola jones sup 4 sup vj face detector by searching through the entire image frame as this is a bit slow we use a smart speed up technique if a face was detected in the previous frame then we only search for a face using the vj detector in a small region of interest roi around where that previous face was detected as depending on how far away from the camera the face was it may not have moved or changed in orientation very much if we can t find a face within that small roi then either the face has completely moved has changed orientation or is slightly occluded we will then run a template matching a template of the face detected in the previous frame is saved and is used as a reference for matching in the current framce we found this to be a bit more robust to occlusions and changes in orientation as the vj frontal face detector is largely trained on straight unoccluded faces we find that this pipeline runs very fast acheiving a speed of 26 fps on an i5 2500k processor for 640x480 video frames it also provides some robustness to occlusions and rotations 5 next we apply a facial landmark detection using the dlib library which uses the method of kazemi et al sup 2 sup since the lbph sup 1 sup face recognizer is only trained on a small number of images we would prefer the images being fed in for recognition to be the same orientation e g perfectly straight and upright thus we use the detected landmarks to align the face by rotating the face such that the angle between two corresponding points are the same for example the angle between the points on the corners of the left and right eye should be zero if the head is perfectly upright 6 before passing the aligned faces to the face recognizer we must first pre process them we reject false positives used a simple skin detection based on the hue saturation and value in the hsv colour space secondly we apply the pre procesisng of tan et al sup 3 sup to normalize lighting and contrast and to combat noise and aliasing 7 the final step is to pass the processed faces to the face recognition object for recognition alt text https github com georgeseif face recognition blob master motivation png alt text https github com georgeseif face recognition blob master design png alt text https github com georgeseif face recognition blob master results png references 1 ahonen t hadid a and pietik inen m 2006 face description with local binary patterns application to face recognition ieee trans pattern analysis and machine intelligence 28 12 2037 2041 2 v kazemi and j sullivan one millisecond face alignment with an ensemble of regression trees 1st ed stockholm sweden computer vision and active perception lab 2014 3 x tan and b triggs enhanced local texture feature sets for face recognition under difficult lighting conditions 1st ed montbonnot france inria laboratoire jean kuntzmann 2007 4 p viola and m jones robust real time face detection 1st ed netherlands kluwer academic publishers 2004 | os |
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BELLE | img src assets belle logo png style vertical align middle width 35px belle be everyone s large language model engine read this in english readme en md div align center a href https github com lianjiatech belle stargazers github repo stars https img shields io github stars lianjiatech belle style social a code license https img shields io badge code 20license apache 2 0 green svg https github com lianjiatech belle blob main license generic badge https img shields io badge discord belle 20group green svg logo discord https discord gg pmpy53uugq generic badge https img shields io badge wechat belle green svg logo wechat https github com lianjiatech belle blob main assets belle wechat jpg generic badge https img shields io badge huggingface 20repo green svg https huggingface co bellegroup generic badge https img shields io badge huggingface 20repo2 green svg https huggingface co belle 2 div llm engine belle belle belle chatgpt br 2023 09 26 rlhf ppo dpo https arxiv org abs 2305 18290 readme rlhf md train readme rlhf md 2023 08 16 train 3 5m cn https huggingface co datasets bellegroup train 3 5m cn 13 train 3 5m cn with category https huggingface co datasets belle 2 train 3 5m cn with category 2023 08 10 zero inference train readme zero inference md train readme zero inference md 2023 08 07 flash attention 2 train readme md train readme md train docker readme md train docker readme md 2023 07 31 https arxiv org abs 2307 15290 2023 07 27 belle llama2 13b chat 0 4m https huggingface co belle 2 belle llama2 13b chat 0 4m llama 2 13b 40 https github com lianjiatech belle blob main eval eval set json belle llama ext 13b 2023 05 14 belle llama ext 13b https huggingface co bellegroup belle llama ext 13b llama 13b 400 2023 05 11 belle data 10m data 10m 350 train 3 5m cn https huggingface co datasets bellegroup train 3 5m cn 2023 04 19 llama7b bellegroup belle llama ext 7b https huggingface co bellegroup belle llama ext 7b llama 7b bellegroup belle on open datasets https huggingface co bellegroup belle on open datasets 2023 04 18 train belle train https github com lianjiatech belle tree main train deepspeed chat docker 2023 04 18 lora finetune 2023 04 12 chatbelle app chat readme md llama cpp https github com ggerganov llama cpp flutter https flutter dev belle 7b 2023 04 11 eval 2023 04 08 belle data 10m data 10m 40 generated chat https huggingface co datasets bellegroup generated chat 0 4m 200 train 2m cn https huggingface co datasets bellegroup train 2m cn br app 4bit belle 7b m1 max cpu app app chat readme md app https github com lianjiatech belle releases download v0 95 chatbelle dmg mac os img src chat chatbelle demo gif img br belle train train deepspeed chat finetune lora docker belle data 1 5m data 1 5m stanford alpaca https github com tatsu lab stanford alpaca 1m https huggingface co datasets bellegroup train 1m cn 0 5m https huggingface co datasets bellegroup train 0 5m cn belle data 10m data 10m belle eval https github com lianjiatech belle tree main eval 1k prompt gpt 4 chatgpt case pr belle models models meta llama2 https github com facebookresearch llama belle llama2 13b chat 0 4m https huggingface co belle 2 belle llama2 13b chat 0 4m meta llama https github com facebookresearch llama belle llama 7b 0 6m enc https huggingface co bellegroup belle llama 7b 0 6m enc belle llama 7b 2m enc https huggingface co bellegroup belle llama 7b 2m enc belle llama 7b 2m gptq enc https huggingface co bellegroup belle llama 7b 2m gptq enc belle llama 13b 2m enc https huggingface co bellegroup belle llama 13b 2m enc belle on open datasets https huggingface co bellegroup belle on open datasets llama belle llama ext 7b https huggingface co bellegroup belle llama ext 7b meta llama license https github com facebookresearch llama blob main license llama llama diff xor llama belle models https github com lianjiatech belle tree main models bloomz 7b1 mt belle 7b 0 2m https huggingface co bellegroup belle 7b 0 2m belle 7b 0 6m https huggingface co bellegroup belle 7b 0 6m belle 7b 1m https huggingface co bellegroup belle 7b 1m belle 7b 2m https huggingface co bellegroup belle 7b 2m gptq belle gptq https github com lianjiatech belle tree main models gptq gptq colab open in colab https colab research google com assets colab badge svg https colab research google com github lianjiatech belle blob main models notebook belle infer colab ipynb colab colab https colab research google com github lianjiatech belle blob main models notebook belle infer colab ipynb chatbelle app belle chat chat readme md belle https github com lianjiatech belle app flutter macos windows android ios belle docs docs issue prompts br towards better instruction following language models for chinese investigating the impact of training data and evaluation https github com lianjiatech belle blob main docs towards 20better 20instruction 20following 20language 20models 20for 20chinese pdf chatgpt llama 34 chatgpt 1 alpaca gpt3 5 self instruct 2 alpaca gpt4 self instruct 3 chatgpt sharegpt table tr td factor td td base model td td training data td td score w o others td tr td rowspan 2 td td llama 7b ext td td zh alpaca 3 5 4 sharegpt td td 0 670 td tr tr td llama 7b td td zh alpaca 3 5 4 sharegpt td td 0 652 td tr tr td rowspan 2 td td llama 7b ext td td zh alpaca 3 5 td td 0 642 td tr tr td llama 7b ext td td zh alpaca 4 td td 0 693 td tr tr td rowspan 4 td td llama 7b ext td td zh alpaca 3 5 4 td td 0 679 td tr tr td llama 7b ext td td en alpaca 3 5 4 td td 0 659 td tr tr td llama 7b ext td td zh alpaca 3 5 4 sharegpt td td 0 670 td tr tr td llama 7b ext td td en alpaca 3 5 4 sharegpt td td 0 668 td tr tr td rowspan 2 td td llama 7b ext td td zh alpaca 3 5 4 sharegpt td td 0 670 td tr tr td llama 7b ext td td zh alpaca 3 5 4 sharegpt br belle 0 5m clean td td 0 762 td tr tr td td td chatgpt td td td td 0 824 td table belle 0 5m clean 230 0 5m 0 5m p align center img src assets eval set distribution jpg alt llm eval width 400 p chatgpt a comparative study between full parameter and lora based fine tuning on chinese instruction data for instruction following large language model https github com lianjiatech belle blob main docs a 20comparative 20study 20between 20full parameter 20and 20lora based pdf lora lora llama lora model average score additional param training time hour epoch llama 13b lora 2m 0 648 28m 8 llama 7b lora 4m 0 624 17 9m 11 llama 7b lora 2m 0 609 17 9m 7 llama 7b lora 0 6m 0 589 17 9m 5 llama 7b ft 2m 0 710 31 llama 7b lora 4m 0 686 17 llama 7b ft 2m br lora math 0 25m 0 729 17 9m 3 llama 7b ft 2m br ft math 0 25m 0 738 6 score 1000 llama 13b lora 2m llama 13b lora 2m llama 7b ft 2m llama 7b ft 2m lora math 0 25m 0 25m llama 7b ft 2m lora llama 7b ft 2m ft math 0 25m 8 nvidia a100 40gb gpu math 0 25m 0 25m 0 5 lora 0 25m math 0 25m llama 7b ft 2m 5e 7 2 llama 7b ft 2m lora math 0 25m llama 7b ft 2m ft math 0 25m lora lora 0 586 0 559 1 lora 2 lora 3 lora lora lora sft 1 2 3 https github com lianjiatech belle blob main disclaimer br misc belle author bellegroup title belle be everyone s large language model engine year 2023 publisher github journal github repository howpublished url https github com lianjiatech belle article belle2023exploring title exploring the impact of instruction data scaling on large language models an empirical study on real world use cases author yunjie ji yong deng yan gong yiping peng qiang niu lei zhang baochang ma xiangang li journal arxiv preprint arxiv 2303 14742 year 2023 article wen2023chathome title chathome development and evaluation of a domain specific language model for home renovation author wen cheng and sun xianghui and zhao shuaijiang and fang xiaoquan and chen liangyu and zou wei journal arxiv preprint arxiv 2307 15290 year 2023 bloom llama stanford alpaca self instruct br details human assitant i am the spring rain br dancing on your face and body br my dew drenched petals dance with you br bringing life to every plant br br shakespeare i am here to meet you br your works have filled my heart with joy br the beauty of nature in its fullness br makes me feel like a partaker br br as the leaves sway and rustle by br my dew filled hand reaches out for them br the sweet scent of flowers fill the air br and bring me peace from all around br br butterflies flit about as well br glistening in the sun s rays br their wings are made of dew br a beautiful sight to behold br br you see shakespeare i too am one with nature br my dew drenched hair is a symbol of hope br for when we can connect back to our roots br we will be able to make it through this season i am the spring rain br dancing on your face and body br my dew drenched petals dance with you br bringing life to every plant br br shakespeare i am here to meet you br your works have filled my heart with joy br the beauty of nature in its fullness br makes me feel like a partaker br br as the leaves sway and rustle by br my dew filled hand reaches out for them br the sweet scent of flowers fill the air br and bring me peace from all around br br butterflies flit about as well br glistening in the sun s rays br their wings are made of dew br a beautiful sight to behold br br you see shakespeare i too am one with nature br my dew drenched hair is a symbol of hope br for when we can connect back to our roots br we will be able to make it through this season br br br br br br br br br br br br br br br br br br br classify the sentiment of the following sentence into positive neutral or negative br super excited about teaching stanford s first course on large language models check the syllabus out here positive python def binary search arr target br nbsp nbsp low 0 br nbsp nbsp high len arr 1 br nbsp nbsp while low high br nbsp nbsp nbsp nbsp mid low high 2 br nbsp nbsp nbsp nbsp if arr mid target br nbsp nbsp nbsp nbsp nbsp nbsp return mid br nbsp nbsp nbsp nbsp elif target arr mid br nbsp nbsp nbsp nbsp nbsp nbsp low mid 1 br nbsp nbsp nbsp nbsp else br nbsp nbsp nbsp nbsp nbsp nbsp high mid 1 br nbsp nbsp return 1 what s the weather like today 5 br 1 br 2 br 3 br 4 br 5 br 1 br 2 br 3 br br 1 br 2 br 3 details br issue https github com lianjiatech belle blob main how to contribute md discord https discord gg pmpy53uugq https github com lianjiatech belle blob main assets belle wechat jpg star history star history chart https api star history com svg repos lianjiatech belle type date https star history com lianjiatech belle date | bloom instruction-set llama open-models gpt-q instruct-gpt gpt-evaluation chinese-nlp lora instruct-finetune | ai |
elm-bulma | elm bulma is a simple and beautiful front end library documentation learn about the components at bulma io http bulma io for api information head over to the elm package http package elm lang org packages surprisetalk elm bulma latest getting started 1 make a new project mkdir bulma example cd bulma example 2 install the package elm package install yes surprisetalk elm bulma 3 create a new file example elm and copy this example file https github com surprisetalk elm bulma blob master src example elm or the code below 4 start elm reactor and navigate to http localhost 8000 example elm http localhost 8000 elm module example exposing import browser import bulma cdn exposing import bulma modifiers exposing import bulma elements exposing import bulma columns exposing import bulma layout exposing import html exposing html main text type alias model main program model msg main browser sandbox init view view update msg model model view model html msg view model main stylesheet examplehero examplecolumns examplehero html msg examplehero hero heromodifiers size medium color primary herobody container title h1 text hero title title h2 text hero subtitle examplecolumns html msg examplecolumns section notspaced container columns columnsmodifiers column columnmodifiers text first column column columnmodifiers text second column column columnmodifiers text third column contributions feel free to report bugs on github https github com surprisetalk elm bulma issues if you have any suggestions on how to make the api more friendly please reach out to me at surprisetalk gmail com surprisetalk gmail com friends elm bootstrap http package elm lang org packages rundis elm bootstrap latest | front_end |
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covid19_projections | covid 19 projections using machine learning the main repository for covid19 projections com https covid19 projections com created by youyang gu https youyanggu com this contains a comprehensive library of not only the most recent updates but also all historical updates if you are only interested in the last infections estimates made in march 2021 click here https github com youyanggu covid19 infection estimates latest see a list of all of our covid 19 repositories below other repositories warning if you plan to clone this repository please be aware that this repository takes approximately 4gb of space this is because it contains all of our historical estimates and projections not just the latest ones 2021 03 08 update we made our last daily update on march 7 this repository will no longer be maintained read youyang gu s one year later https youyanggu com blog one year later blog post for a detailed explanation and for a list of alternate resources 2020 11 19 update we have uploaded our raw us infection estimates in the infection estimates infection estimates directory for a slimmed down version that only has the latest infection estimates visit this repository https github com youyanggu covid19 infection estimates latest 2020 10 06 update projections dated 2020 10 04 was our last model update for more information read youyang gu s blog post https youyanggu com blog six months later follow youyanggu https twitter com youyanggu on twitter for continued covid 19 insights we take a data driven approach rooted in epidemiology to forecast infections and deaths for the covid 19 coronavirus epidemic in the us and around the world description of folders you can click on each folder to view the readme for that folder c19pro score c19pro score raw data for our c19pro score https covid19 projections com maps c19pro score implied ifr implied ifr implied infection fatality rate iifr estimates infection estimates infection estimates infection estimates for the us projections projections raw daily projections generated by our model r values r values reproduction number r estimates reich forecasts reich forecasts reich forecasts follow the format specified by the reich lab https github com reichlab covid19 forecast hub tests target tests target testing targets for every region based on our forecasts the underlying seir simulator used to generate these projections is open source https github com youyanggu yyg seir simulator the simulator repository also includes the best learned values of the various parameters https github com youyanggu yyg seir simulator parameters for every region our projections are featured on the cdc website https www cdc gov coronavirus 2019 ncov covid data forecasting us html we have also open sourced our evaluation of our model and other cdc models https github com youyanggu covid19 forecast hub evaluation other repositories in addition to our main repository here we have several other covid 19 related repositories infections estimates https github com youyanggu covid19 infection estimates latest historical cdc vaccination data https github com youyanggu covid19 cdc vaccination data covid 19 datasets https github com youyanggu covid19 datasets seir simulator https github com youyanggu yyg seir simulator model evaluations https github com youyanggu covid19 forecast hub evaluation questions contact me on twitter at youyanggu https twitter com youyanggu covid19 projections com is powered by github pages https pages github com | ai |
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GRIP | logo https cloud githubusercontent com assets 3964980 11156885 6fa1967a 8a1c 11e5 8c78 e552ffba31c0 png join the chat at https gitter im wpiroboticsprojects grip https badges gitter im join 20chat svg https gitter im wpiroboticsprojects grip utm source badge utm medium badge utm campaign pr badge utm content badge build status https dev azure com wpiroboticsprojects grip apis build status wpiroboticsprojects grip branchname master https dev azure com wpiroboticsprojects grip build latest definitionid 1 branchname master codecov io http codecov io github wpiroboticsprojects grip coverage svg branch master http codecov io github wpiroboticsprojects grip branch master github releases https img shields io github downloads wpiroboticsprojects grip total svg https github com wpiroboticsprojects grip releases latest grip computer vision engine grip the graphically represented image processing engine is an application for rapidly prototyping and deploying computer vision algorithms primarily for robotics applications developing a vision program can be difficult because it is hard to visualize the intermediate results grip simplifies and accelerates the creation of vision systems for experienced users and reduces the barrier to entry for inexperienced users as a result many teams with minimal computer vision knowledge have successfully used grip since the 2016 first robotics competition game features intuitive drag and drop ui active development community generates java c and python code directly from the pipeline example usage here https github com wpiroboticsprojects grip code generation extensible deploys and runs headless supports various network protocols network tables https github com wpilibsuite allwpilib tree master ntcore robot operating system ros http www ros org http cuda acceleration os support windows osx linux minimum ubuntu 18 04 or libc version 2 27 embedded linux arm ni roborio getting started 1 download the newest release https github com wpiroboticsprojects grip releases for your operating system 2 run the included installer 3 open grip check out the release notes and the wiki https github com wpiroboticsprojects grip wiki for more information note for linux users grip requires gtk2 to be installed most ubuntu based distributions include it but some other distros such as arch may require it to be manually installed grip also requires libc version 2 27 or higher for ubuntu based distributions this requires ubuntu 18 04 or newer building grip from source to build and run use the included gradle wrapper script on a unix system gradlew ui run on windows gradlew ui run if you don t have an internet connection you can build using the offline flag if you have built grip once before running tests to do a complete check of functionality of grip use the included gradle wrapper script on a unix system gradlew check on windows gradlew check cuda support to enable cuda acceleration cuda 10 0 needs to be installed on your computer cuda 10 1 may work on linux systems but windows must use 10 0 when running or building from source add the gradle flag pcuda to enable cuda acceleration eg gradlew ui run pcuda note that cuda acceleration is not available for all operations code generation does not support cuda it is only used for operations running in grip contributing see the guide on setting up build tools https github com wpiroboticsprojects grip wiki setting up build tools in the wiki | computer-vision first-frc firstrobotics first-robotics-competition opencv vision robotics camera wpi | ai |
stock-prediction | implementaion of leveraging financial news for stock trend prediction with attention based recurrent neural network leveraging financial news for stock trend prediction with attention based recurrent neural network huicheng liu preprint arxiv https arxiv org abs 1811 06173 i won t write everthing in detail here so it is likely that you will face some error when runnning the scripits below usually something wrong with path and the commented codes please contace me if you want to do the entire process from scratch 1 download the meta data from https drive google com drive folders 0b3c8gefwm08qy3aysme2z1daaue 2 data preprocessing cd stuff don t forget to change the paths in the scripts listed below download the historical data for the s p 500 index from yahoo finance and use python3 split data py to get the labels use python3 extrac whole py to extract the titles abstracts and articles from the raw data use python3 read j news py to preprocess the news use python3 filter news py to further preprocess the news and split them so they are reading for the models input 3 build the model the core codes are in the scripts directory the lstm folder stores the script that helps me achieve the result wrote in the paper the clstm adds cnn before the prediction layer the cha lstm adds character level embedding the slstm uses the contents from the article which is much more complicate the wlstm uses bag of words instead of lstm elstm adds elmo to the code but are almost impossible to train because it needs powerful gpu besides it doesn t give us any performance improvment to run the scripts you will have to change the path in train py and also need glove which can be download here http nlp stanford edu data glove 840b 300d zip the script can be run with train sh the result will be shown in log result txt and log txt you can see an example run from 65 53 lstm i really don t have much time to arrange all the codes and make them more clear for the readers please contact me if you need a ready to run version | ai |
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tuya-os-api-examples | tuyaos api examples english readme md readme zh md what is it this repository provides samples of how to use the rtos for more information click here how to use them 1 set up the development environment for more information see this documentation https developer tuya com en docs iot soc redevelopment windows id kauqrxxka0wv6 2 see the following tutorial and build your project tuya os examples gif https images tuyacn com smart shiliu zone tuya os examples tuya os examples gif gif technical support you can get support from tuya with the following methods tuya developer platform https developer tuya com en help center https support tuya com en help service support https service console tuya com | os |
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dopebase | dopebase all in one web mobile development platform powered by ai dopebase the next generation open source app builder redefining the frontier of digital creations in a world driven by dynamic digital landscapes dopebase emerges as a revolutionary open source app builder platform positioned as a formidable alternative to wordpress engineered with the contemporary developer in mind it offers a canvas that is both expansive and intuitive ready to facilitate your most ambitious projects here is why dopebase stands a cut above the rest scalable websites and blogs create adaptable websites and blogs that grow with your ambitions from personal blogs to large scale corporate websites dopebase supports it all ensuring that your platform can handle the influx of traffic as it scales innovative saas products build saas products of the future today dopebase offers a conducive environment to foster innovation allowing you to craft saas products that meet the evolving needs of the market always staying a step ahead of the competition ai agents at your fingertips leverage the power of artificial intelligence to create ai agents capable of transforming user experiences dopebase nurtures a space where technology meets creativity enabling the development of ai agents that are not just responsive but intuitively understand user needs cross platform mobile apps develop mobile apps that resonate with your audience across all platforms whether it is android or ios dopebase ensures a seamless transition providing tools that empower developers to create apps that offer a unified user experience across all platforms dive deeper with dopebase join a community of forward thinking developers who are leveraging dopebase to bring their visions to life with a rich feature set and a robust framework at your disposal the potential is limitless build with dopebase and craft digital experiences that are not just modern but monumental reshaping the digital landscape one creation at a time get started with dopebase https dopebase com explore features https dopebase com in this expanded content i introduced dopebase as a next generation open source app builder i detailed individual features and benefits to offer a more rounded view of what users can accomplish with dopebase i included a call to action at the end to encourage users to get started or learn more about the features offered | angular appwrite aws firebase flutter ghost kotlin nextjs nodejs react react-native reactjs supabase swift wordpress | front_end |
Bike-Store-ETL | extracting and preparing bike store data for analysis credit sqlservertutorial net http sqlservertutorial net data source https www kaggle com datasets dillonmyrick bike store sample database select brands csv introduction project summary the project demonstrates how to effectively gather transform and store data in a structured manner leveraging azure s suite of tools making it a valuable blueprint for businesses aiming to streamline their data analytics processes and providing scalability for improved cost blank diagram https github com chrisxj33 bike store data analytics assets 53899548 5e165ff6 d06a 4a76 935b 82eb50f2b642 project importance centralized data storage by setting up a relational database businesses can ensure that their data is stored in a centralized and structured manner this makes it easier to access manage and update data as needed efficient data ingestion using tools like azure data factory simplifies the data ingestion process allowing businesses to quickly pull in new data from various sources and keep their databases up to date scalability cloud platforms like azure offer scalable database solutions as the company grows and its data needs increase the database can be scaled up without significant changes to the existing infrastructure data transformation with azure databricks businesses can transform and preprocess their data as needed this ensures that the data stored in the database is in the right format and quality for analytics automation the entire end to end process can be easily scheduled to efficiently update the database with the latest data enhanced decision making with a properly set up database and the right data in place companies can leverage analytics and business intelligence tools to derive insights from their data this can guide strategic decision making and optimize operations methodology step 1 database schema and creation first we visualized our desired database schema using dbdiagram this involved defining tables specifying columns setting data types and delineating the relationships between tables schema final https github com chrisxj33 bike store data analytics assets 53899548 8bb6029d fb63 4881 8558 db0f0ca59f00 subsequently we initialized our database within azure s sql database service using azure data studio we remotely accessed the database from a local machine and executed an sql notebook to apply the visualized schema this process involved creating tables and setting up relationships with this step completed the database was primed to receive data step 2 data ingestion with azure data factory our primary step was to establish a data storage container to archive both raw and processed data sets we leveraged azure data factory to retrieve data directly from github using a provided raw file link subsequently storing the csv file within our data storage container image https github com chrisxj33 bike store data analytics assets 53899548 703b19a4 78a9 448a 9916 4ea4dfbecb8d step 3 data transformation for data transformation we utilized azure databricks utilizing a python based notebook we imported data from our storage container executed necessary transformations to ensure compatibility with our database schema like data type conversions and saved the refined data back to the storage container in a separate directory step 4 inserting data into the relational database azure data factory came into play once again for this step here our task was to map our data source the storage container to our database and initiate the data transfer it s imperative to ensure that the sequence of data transfer respects the database s constraints notably primary and foreign key associations the recommended order for data insertion is illustrated below image https github com chrisxj33 bike store data analytics assets 53899548 28fc04ca f764 4a90 845a d51c981bb13f | cloud |
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FreeRTOS_STM32 | freertos stm32 exemples de code cours de p foubet | os |
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GPT-InvestAR | gpt investar enhancing stock investment strategies through annual report analysis with large language models this repository contains a set of tools and scripts designed to enhance stock investment strategies through the analysis of annual reports using large language models the components in this repository are organized as follows 1 download 10k py this python script downloads 10 k filings of companies from the sec website which contain crucial financial information 2 convert html to pdf py converts html files to pdf files pdfs are preferred due to their token efficiency for further analysis 3 make targets py generates a dataframe of stock tickers with target values of different time resolutions which can be used as investment targets for a machine learning model 4 embeddings save py generates embeddings of pdf files and saves them using cromadb these embeddings are numerical representations of the textual content in annual reports 5 gpt scores as features py utilizes saved embeddings to query all questions for each annual report using a large language model llm such as gpt 3 5 and uses the scores or answers as features 6 modeling and return estimation ipynb this jupyter notebook contains the core modeling process it uses machine learning techniques specifically linear regression to model the dataset and estimate returns the goal is to create a portfolio of top k predicted stocks and compare their returns with the s p 500 index by following the sequence of these components you can analyze annual reports generate embeddings and build predictive models to potentially enhance stock investment strategies feel free to explore each component for more details and usage instructions dependencies 1 llama index https github com jerryjliu llama index and related dependencies 2 openbb https github com openbb finance openbbterminal and related dependencies 3 scikit learn https github com scikit learn scikit learn 4 pdfkit https github com jazzcore python pdfkit and related dependencies it is recommended to install libraries 1 and 2 in separate virtual conda environments the python scripts mentioned above do not require both these libraries to be installed in the same environment citation if you use the code or find this repository helpful please consider citing the paper gpt investar enhancing stock investment strategies through annual report analysis with large language models udit gupta publication links 1 arxiv link https arxiv org abs 2309 03079 2 ssrn link https papers ssrn com sol3 papers cfm abstract id 4568964 bibtex article gpt investar author udit gupta title gpt investar enhancing stock investment strategies through annual report analysis with large language models journal arxiv e prints year 2023 eprint arxiv 2309 03079 url https arxiv org abs 2309 03079 | ai |
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sql-challenge | sql challenge this repository demonstrates skills with data modelling data engineering and database operations table schema image below shows the schema for this exercise employees schema image images employees schema image png navigation directory csv data contains all the csvs that will be loaded into the postgre database directory employee sql contains the sql file to build the schema the sql queries required for the exercise and the jupyter notebook python script for the bonus directory images contains the image for the sql schema requirements 1 anaconda with jupyter notebook installed 2 postgre sql server v11 or higher 3 psycopg2 installed instructions 1 run pgadmin4 and create a new database named employees 2 open employeesql employee schema sql in pgadmin4 and change line 26 to the csv data path where the repository for this challenge is copied do not add a slash at the end of the path string 3 run all the codes in the employee schema sql file 4 open the employeesql employee sql sql file and run each sql queries individually to display the responses for aech challenge 5 open employeesql db credentials py and update user and password with your postgres username and password default password is postgres 6 employeesql open employes analysis ipynb and run each code block from top to bottom the last two blocks will show the required plots for the bonus epiloge apparently this was a very informative april fool s prank aprilfools images epilogue png | server |
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db-fmi-2019 | db fmi 2019 learning repository for database course at fmi 2019 software engineering | server |
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frontloops-challenges | frontloops frontend challenges logo jpg frontloops provide frontend development challenges to improve your skills in html css and javascript for more informations check frontloops website https frontloops io markup challenges theses are web interface challenges it is more based on html and css you can find below my solutions loop 1 step 1 pricing table https github com zathio frontloops challenges tree master markup challenges loop1 step1 step 2 payement form https github com zathio frontloops challenges tree master markup challenges loop1 step2 step 3 payement confirmation screen https github com zathio frontloops challenges tree master markup challenges loop1 step3 step 4 message box https github com zathio frontloops challenges tree master markup challenges loop1 step4 step 5 boarding pass https github com zathio frontloops challenges tree master markup challenges loop1 step5 step 6 sign up form https github com zathio frontloops challenges tree master markup challenges loop1 step6 step 7 blog post https github com zathio frontloops challenges tree master markup challenges loop1 step7 step 8 travel article https github com zathio frontloops challenges tree master markup challenges loop1 step8 step 9 online wallet https github com zathio frontloops challenges tree master markup challenges loop1 step9 step 10 apple watch landing page https github com zathio frontloops challenges tree master markup challenges loop1 step10 loop 2 step 1 washing machine program https github com zathio frontloops challenges tree master markup challenges loop2 step1 step 2 account settings form https github com zathio frontloops challenges tree master markup challenges loop2 step2 step 3 files manager https github com zathio frontloops challenges tree master markup challenges loop2 step3 step 4 travel card https github com zathio frontloops challenges tree master markup challenges loop2 step4 step 5 article title page https github com zathio frontloops challenges tree master markup challenges loop2 step5 step 6 calendar https github com zathio frontloops challenges tree master markup challenges loop2 step6 step 7 press page https github com zathio frontloops challenges tree master markup challenges loop2 step7 step 8 mobile app mockup https github com zathio frontloops challenges tree master markup challenges loop2 step8 step 9 navigation app https github com zathio frontloops challenges tree master markup challenges loop2 step9 step 10 linkedin website https github com zathio frontloops challenges tree master markup challenges loop2 step10 loop 3 step 1 product page https github com zathio frontloops challenges tree master markup challenges loop3 step1 step 2 gallery overview https github com zathio frontloops challenges tree master markup challenges loop3 step2 step 3 movie cards https github com zathio frontloops challenges tree master markup challenges loop3 step3 step 4 music card https github com zathio frontloops challenges tree master markup challenges loop3 step4 step 5 charts https github com zathio frontloops challenges tree master markup challenges loop3 step5 step 6 payment page https github com zathio frontloops challenges tree master markup challenges loop3 step6 step 7 cinema ticket https github com zathio frontloops challenges tree master markup challenges loop3 step7 step 8 news app https github com zathio frontloops challenges tree master markup challenges loop3 step8 step 9 media homepage https github com zathio frontloops challenges tree master markup challenges loop3 step9 elements challenges theses are interactive widgets challenges it is more based on javascript you can find below the lives versions of my solutions loop 1 step 1 segmented control select https github com zathio frontloops challenges tree master elements challenges loop1 step1 step 2 tab switcher https github com zathio frontloops challenges tree master elements challenges loop1 step2 step 3 random toggle switches https github com zathio frontloops challenges tree master elements challenges loop1 step3 step 4 multi step form https github com zathio frontloops challenges tree master elements challenges loop1 step4 step 5 typewriter effect https github com zathio frontloops challenges tree master elements challenges loop1 step5 step 6 3d tilt hover effect https github com zathio frontloops challenges tree master elements challenges loop1 step6 step 7 show on scroll https github com zathio frontloops challenges tree master elements challenges loop1 step7 step 8 side menu https github com zathio frontloops challenges tree master elements challenges loop1 step8 step 9 sticky element with javascript https github com zathio frontloops challenges tree master elements challenges loop1 step9 step 10 draggable card https github com zathio frontloops challenges tree master elements challenges loop1 step10 loop 2 step 1 code verification https github com zathio frontloops challenges tree master elements challenges loop2 step1 step 2 multiselect https github com zathio frontloops challenges tree master elements challenges loop2 step2 step 3 card slider https github com zathio frontloops challenges tree master elements challenges loop2 step3 step 4 form validation https github com zathio frontloops challenges tree master elements challenges loop2 step4 step 5 color picker https github com zathio frontloops challenges tree master elements challenges loop2 step5 step 6 boiling dots https github com zathio frontloops challenges tree master elements challenges loop2 step6 step 7 custom scrollbar https github com zathio frontloops challenges tree master elements challenges loop2 step7 step 8 custom video player https github com zathio frontloops challenges tree master elements challenges loop2 step8 step 9 input slider https github com zathio frontloops challenges tree master elements challenges loop2 step9 step 10 accordion https github com zathio frontloops challenges tree master elements challenges loop2 step10 loop 3 step 1 to do list with local storage https github com zathio frontloops challenges tree master elements challenges loop3 step1 step 2 moving dot https github com zathio frontloops challenges tree master elements challenges loop3 step2 step 3 calendar week changer https github com zathio frontloops challenges tree master elements challenges loop3 step3 step 4 toast event https github com zathio frontloops challenges tree master elements challenges loop3 step4 step 5 paging component https github com zathio frontloops challenges tree master elements challenges loop3 step5 step 6 onboarding https github com zathio frontloops challenges tree master elements challenges loop3 step6 step 7 ripple cursor https github com zathio frontloops challenges tree master elements challenges loop3 step7 step 8 suggest https github com zathio frontloops challenges tree master elements challenges loop3 step8 step 9 textarea highlight https github com zathio frontloops challenges tree master elements challenges loop3 step9 step 10 image magnifier https github com zathio frontloops challenges tree master elements challenges loop3 step10 | frontloops frontend challenge frontend-challenge frontloops-challenges | front_end |
Job-Portal-Full-Stack-Web-Application | professional job portal full stack web application description designed and developed a job portal using java j2ee spring mvc and hibernate orm the application enabled job seekers to register for viewing the jobs posted by employers as well as apply to them the employers can also register post jobs and receive applications for the job they posted the application had the functionality to create retrieve update and delete information implemented cross site scripting interceptors and session management techniques to secure data enhanced database interaction using hibernate annotations based mapping with mysql built a user friendly and responsive website to enhance user experience programming language java javascript sql technologies j2ee spring mvc hibernate maven html css bootstrap jquery ajax tools ide spring tool suite eclipse dreamweaver mysql | web-application spring mvc-framework hibernate ajax jquery javascript bootstrap maven tomcat-server json html-css servlets jsp job-portal job-seekers employer javascript-library | front_end |
seldon-core | seldon core blazing fast industry ready ml an open source platform to deploy your machine learning models on kubernetes at massive scale seldon core v2 now available scv2 image https raw githubusercontent com seldonio seldon core master doc source static scv2 banner png https docs seldon io projects seldon core en v2 index html seldon core v2 https docs seldon io projects seldon core en v2 index html is now available if you re new to seldon core we recommend you start here https docs seldon io projects seldon core en v2 contents getting started index html check out the docs here https docs seldon io projects seldon core en v2 index html and make sure to leave feedback on our slack community https join slack com t seldondev shared invite zt vejg6ttd ksziqs3o hotpqsen labg and submit bugs or feature requests on the repo https github com seldonio seldon core issues new choose the codebase can be found in this branch https github com seldonio seldon core tree v2 continue reading for info on seldon core v1 video play icon https raw githubusercontent com seldonio seldon core master doc source images core play logo png https www youtube com watch v 5q 03we8ade overview seldon core converts your ml models tensorflow pytorch h2o etc or language wrappers python java etc into production rest grpc microservices seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including advanced metrics request logging explainers outlier detectors a b tests canaries and more read the seldon core documentation https docs seldon io projects seldon core en latest join our community slack https join slack com t seldondev shared invite zt vejg6ttd ksziqs3o hotpqsen labg to ask any questions get started with seldon core notebook examples https docs seldon io projects seldon core en latest examples notebooks html join our fortnightly online working group calls https docs seldon io projects seldon core en latest developer community html google calendar https calendar google com event action template tmeid mxbtnzi1cjk0dg9kczhsztrkcwlmcm1kdjvfmjaymda3mdlumtuwmdawwibzzwxkb24uaw9fbtrumnztcmzubdi3m3fsczvnyjlwnjvpmhnazw tmsrc seldon io m4n2vmrfnl273qls5gb9p65i0s 40group calendar google com scp all learn how you can start contributing https docs seldon io projects seldon core en latest developer contributing html check out blogs https docs seldon io projects seldon core en latest tutorials blogs html that dive into seldon core components watch some of the videos and talks https docs seldon io projects seldon core en latest tutorials videos html using seldon core https raw githubusercontent com seldonio seldon core master doc source images seldon core high level jpg high level features with over 2m installs seldon core is used across organisations to manage large scale deployment of machine learning models and key benefits include easy way to containerise ml models using our pre packaged inference servers https docs seldon io projects seldon core en latest servers overview html custom servers https docs seldon io projects seldon core en latest servers custom html or language wrappers https docs seldon io projects seldon core en latest wrappers language wrappers html out of the box endpoints which can be tested through swagger ui https docs seldon io projects seldon core en latest reference apis openapi html highlight swagger seldon python client or curl grpcurl https docs seldon io projects seldon core en latest python python module html seldon core python api client cloud agnostic and tested on aws eks azure aks google gke alicloud digital ocean and openshift https docs seldon io projects seldon core en latest examples notebooks html cloud specific examples powerful and rich inference graphs made out of predictors transformers routers combiners and more https docs seldon io projects seldon core en latest examples graph metadata html metadata provenance to ensure each model can be traced back to its respective training system data and metrics https docs seldon io projects seldon core en latest reference apis metadata html advanced and customisable metrics with integration to prometheus and grafana https docs seldon io projects seldon core en latest analytics analytics html full auditability through model input output request logging integration with elasticsearch https docs seldon io projects seldon core en latest analytics log level html microservice distributed tracing through integration to jaeger https docs seldon io projects seldon core en latest graph distributed tracing html for insights on latency across microservice hops secure reliable and robust system maintained through a consistent security updates policy https github com seldonio seldon core blob master security md getting started deploying your models using seldon core is simplified through our pre packaged inference servers and language wrappers below you can see how you can deploy our hello world iris example you can see more details on these workflows in our documentation quickstart https docs seldon io projects seldon core en latest workflow quickstart html install seldon core quick install using helm 3 you can also use kustomize bash kubectl create namespace seldon system helm install seldon core seldon core operator repo https storage googleapis com seldon charts set usagemetrics enabled true namespace seldon system set istio enabled true you can set ambassador instead with set ambassador enabled true deploy your model using pre packaged model servers we provide optimized model servers for some of the most popular deep learning and machine learning frameworks that allow you to deploy your trained model binaries weights without having to containerize or modify them you only have to upload your model binaries into your preferred object store in this case we have a trained scikit learn iris model in a google bucket console gs seldon models v1 18 0 dev sklearn iris model joblib create a namespace to run your model in bash kubectl create namespace seldon we then can deploy this model with seldon core to our kubernetes cluster using the pre packaged model server for scikit learn sklearn server by running the kubectl apply command below yaml kubectl apply f end apiversion machinelearning seldon io v1 kind seldondeployment metadata name iris model namespace seldon spec name iris predictors graph implementation sklearn server modeluri gs seldon models v1 18 0 dev sklearn iris name classifier name default replicas 1 end send api requests to your deployed model every model deployed exposes a standardised user interface to send requests using our openapi schema this can be accessed through the endpoint http ingress url seldon namespace model name api v1 0 doc which will allow you to send requests directly through your browser https raw githubusercontent com seldonio seldon core master doc source images rest openapi jpg or alternatively you can send requests programmatically using our seldon python client https docs seldon io projects seldon core en latest python seldon client html or another linux cli console curl x post http ingress seldon seldon iris model api v1 0 predictions h content type application json d data ndarray 1 2 3 4 meta data names t 0 t 1 t 2 ndarray 0 000698519453116284 0 00366803903943576 0 995633441507448 deploy your custom model using language wrappers for more custom deep learning and machine learning use cases which have custom dependencies such as 3rd party libraries operating system binaries or even external systems we can use any of the seldon core language wrappers you only have to write a class wrapper that exposes the logic of your model for example in python we can create a file model py python import pickle class model def init self self model pickle loads open model pickle rb def predict self x output self model x return output we can now containerize our class file using the seldon core s2i utils https docs seldon io projects seldon core en latest wrappers s2i html to produce the sklearn iris image console s2i build seldonio seldon core s2i python3 0 18 sklearn iris 0 1 and we now deploy it to our seldon core kubernetes cluster yaml kubectl apply f end apiversion machinelearning seldon io v1 kind seldondeployment metadata name iris model namespace model namespace spec name iris predictors componentspecs spec containers name classifier image sklearn iris 0 1 graph name classifier name default replicas 1 end send api requests to your deployed model every model deployed exposes a standardised user interface to send requests using our openapi schema this can be accessed through the endpoint http ingress url seldon namespace model name api v1 0 doc which will allow you to send requests directly through your browser https raw githubusercontent com seldonio seldon core master doc source images rest openapi jpg or alternatively you can send requests programmatically using our seldon python client https docs seldon io projects seldon core en latest python seldon client html or another linux cli console curl x post http ingress seldon model namespace iris model api v1 0 predictions h content type application json d data ndarray 1 2 3 4 json pp meta data names t 0 t 1 t 2 ndarray 0 000698519453116284 0 00366803903943576 0 995633441507448 dive into the advanced production ml integrations any model that is deployed and orchestrated with seldon core provides out of the box machine learning insights for monitoring managing scaling and debugging below are some of the core components together with link to the logs that provide further insights on how to set them up table tr valign top td width 50 a href https docs seldon io projects seldon core en latest analytics analytics html br b standard and custom metrics with prometheus b br br img src https raw githubusercontent com seldonio seldon core master doc source analytics dashboard png a td td width 50 a href https docs seldon io projects seldon core en latest analytics logging html br b full audit trails with elk request logging b br br img src https raw githubusercontent com seldonio seldon core master doc source images kibana custom search png a td tr tr valign top td width 50 a href https docs seldon io projects seldon core en latest analytics explainers html br b explainers for machine learning interpretability b br br img src https raw githubusercontent com seldonio seldon core master doc source images anchors jpg a td td width 50 a href https docs seldon io projects seldon core en latest analytics outlier detection html br b outlier and adversarial detectors for monitoring b br br img src https raw githubusercontent com seldonio seldon core master doc source images adversarial attack png a td tr tr valign top td width 50 a href https docs seldon io projects seldon core en latest analytics cicd mlops html br b ci cd for mlops at massive scale b br br img src https raw githubusercontent com seldonio seldon core master doc source images cicd seldon jpg a td td width 50 a href https docs seldon io projects seldon core en latest graph distributed tracing html br b distributed tracing for performance monitoring b br br img src https raw githubusercontent com seldonio seldon core master doc source graph jaeger ui rest example png a td tr table where to go from here getting started quickstart guide https docs seldon io projects seldon core en latest workflow github readme html overview of components https docs seldon io projects seldon core en latest workflow overview html install seldon core on kubernetes https docs seldon io projects seldon core en latest workflow install html join the community https docs seldon io projects seldon core en latest developer community html seldon core deep dive detailed installation parameters https docs seldon io projects seldon core en latest reference helm html pre packaged inference servers https docs seldon io projects seldon core en latest servers overview html language wrappers for custom models https docs seldon io projects seldon core en latest wrappers language wrappers html create your inference graph https docs seldon io projects seldon core en latest graph inference graph html deploy your model https docs seldon io projects seldon core en latest workflow deploying html testing your model endpoints https docs seldon io projects seldon core en latest workflow serving html troubleshooting guide https docs seldon io projects seldon core en latest workflow troubleshooting html usage reporting https docs seldon io projects seldon core en latest workflow usage reporting html upgrading https docs seldon io projects seldon core en latest reference upgrading html changelog https docs seldon io projects seldon core en latest reference changelog html pre packaged inference servers mlflow server https docs seldon io projects seldon core en latest servers mlflow html sklearn server https docs seldon io projects seldon core en latest servers sklearn html tensorflow serving https docs seldon io projects seldon core en latest servers tensorflow html xgboost server https docs seldon io projects seldon core en latest servers xgboost html language wrappers production python language wrapper production https docs seldon io projects seldon core en latest python index html language wrappers incubating java language wrapper incubating https docs seldon io projects seldon core en latest java readme html r language wrapper alpha https docs seldon io projects seldon core en latest r readme html nodejs language wrapper alpha https docs seldon io projects seldon core en latest nodejs readme html go language wrapper alpha https docs seldon io projects seldon core en latest go go wrapper link html ingress ambassador ingress https docs seldon io projects seldon core en latest ingress ambassador html istio ingress https docs seldon io projects seldon core en latest ingress istio html production supported api protocols https docs seldon io projects seldon core en latest graph protocols html ci cd mlops at scale https docs seldon io projects seldon core en latest analytics cicd mlops html metrics with prometheus https docs seldon io projects seldon core en latest analytics analytics html payload logging with elk https docs seldon io projects seldon core en latest analytics logging html distributed tracing with jaeger https docs seldon io projects seldon core en latest graph distributed tracing html replica scaling https docs seldon io projects seldon core en latest graph scaling html budgeting disruptions https docs seldon io projects seldon core en latest graph disruption budgets html custom inference servers https docs seldon io projects seldon core en latest servers custom html advanced inference model explanations https docs seldon io projects seldon core en latest analytics explainers html outlier detection https docs seldon io projects seldon core en latest analytics outlier detection html routers incl multi armed bandits https docs seldon io projects seldon core en latest analytics routers html examples notebooks https docs seldon io projects seldon core en latest examples notebooks html articles blogs https docs seldon io projects seldon core en latest tutorials blogs html videos https docs seldon io projects seldon core en latest tutorials videos html reference annotation based configuration https docs seldon io projects seldon core en latest graph annotations html benchmarking https docs seldon io projects seldon core en latest reference benchmarking html general availability https docs seldon io projects seldon core en latest reference ga html helm charts https docs seldon io projects seldon core en latest graph helm charts html images https docs seldon io projects seldon core en latest reference images html logging log level https docs seldon io projects seldon core en latest analytics log level html private docker registry https docs seldon io projects seldon core en latest graph private registries html prediction apis https docs seldon io projects seldon core en latest reference apis index html python api reference https docs seldon io projects seldon core en latest python api modules html release highlights https docs seldon io projects seldon core en latest reference release highlights html seldon deployment crd https docs seldon io projects seldon core en latest reference seldon deployment html service orchestrator https docs seldon io projects seldon core en latest graph svcorch html kubeflow https docs seldon io projects seldon core en latest analytics kubeflow html developer overview https docs seldon io projects seldon core en latest developer readme html contributing to seldon core https docs seldon io projects seldon core en latest developer contributing html end to end tests https docs seldon io projects seldon core en latest developer e2e html roadmap https docs seldon io projects seldon core en latest developer roadmap html build using private repo https docs seldon io projects seldon core en latest developer build using private repo html about the name seldon core the name seldon s ld n core was inspired from the foundation series scifi novel https en wikipedia org wiki foundation series where it s premise consists of a mathematician called hari seldon who spends his life developing a theory of psychohistory a new and effective mathematical sociology which allows for the future to be predicted extremely accurate through long periods of time across hundreds of thousands of years commercial support https raw githubusercontent com seldonio seldon core master doc source images deploy logo png we offer commercial support via our enterprise product seldon deploy please visit https www seldon io https www seldon io for details and a trial | kubernetes machine-learning deployment serving mlops aiops machine-learning-operations production-machine-learning | ai |
iot-journey | iot journey an exploration into building a solution in the cloud memo this project is not under active development please see the azure iot hub https azure microsoft com en us documentation services iot hub and azure iot suite https www azureiotsuite com for microsoft s latest offerings in this space why there is no one size fits all answer when it comes to building an iot solution intro to iot our approach to guidance is to embark on a collaborative journey into understanding the mechanics and challenges surrounding an end to end system our purpose is not to tell you all the answers that you ll need but rather to help you ask the right questions what we will be constructing an iot solution hosted in azure our focus will be on problems related to the back end architecture such as high scale event ingestion event stream processing facilitation of analysis we have identified a number of high level scenarios described and explored in the docs docs we will be using the same tools and services that are available to you we are creating a set of reference implementations our fancy name for an end to end sample some of the reference implementation are meant to work in concert while others are alternative implementations in addition to the source code we ll also produce a set of written articles covering the general concepts and patterns the rationale behind design choices and a few other things to help you navigate the guidance we intend this to be an interactive act of discovery how we are basing our scenarios on requirements we ve gathered from customers and advisors the scenario is not meant to be realistic but rather representative that is it should represent the most common needs and challenges encountered in this space we are very interested in your feedback we are publishing updates about the work on our video blog https channel9 msdn com blogs mspnp we encourage you to ask questions in the chat room or to open issue on this repo any and all feedback is welcome who our intended audience for this guidance is any senior developer or architect interested in developing an iot solution we want all developers regardless of their preferred development stack to benefit from the written guidance if you feel that there is anything more that we can do to make this guidance accessible to a broader audience you are encouraged to share next step go to the docs docs folder review the readme and begin reviewing the content problems concerns and feedback if something doesn t make sense start with the faq faq md if that doesn t help open an issue if you want to contribute directly please review our contribution guidelines contributing md current backlog status ready https badge waffle io mspnp iot journey svg label ready title ready https waffle io mspnp iot journey in progress https badge waffle io mspnp iot journey svg label in progress title in progress https waffle io mspnp iot journey awaiting review https badge waffle io mspnp iot journey svg label awaiting review title awaiting review https waffle io mspnp iot journey intro to iot docs 01 what is an iot solution md | server |
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forgeconfigapiport | forge config api port a minecraft mod downloads can be found on curseforge https www curseforge com members fuzs projects and modrinth https modrinth com user fuzs https raw githubusercontent com fuzss modresources main pages data forgeconfigapiport banner png about the project forge config api port is in no way affiliated with the minecraft forge project the sole purpose of this library is to enable usage of the minecraft forge config api on both fabric and quilt mod loaders this is done in the hopes of removing one more obstacle for developers wishing to maintain their mods on various mod loaders this is a direct port from minecraft forge all package names are the same so you don t even have to readjust imports when porting from forge as fabric and quilt are very different mod loaders from forge there obviously have to be some differences even though they re quite small the main advantage of forge config api port over other config libraries lies in the fact that no additional library is required on forge since this very exact config api is built in only fabric quilt needs this one library for more information regarding the licensing of this project and different parts of it check the licensing md licensing md file developer information adding forge config api port to your gradle workspace details via fuzs mod resources fuzs mod resources is the recommended way of adding forge config api port to your project in your build gradle file groovy repositories maven name fuzs mod resources url https raw githubusercontent com fuzss modresources main maven dependencies modapi fuzs forgeconfigapiport forgeconfigapiport fabric modversion modversion e g 7 0 0 for minecraft 1 20 when developing for both multiple mod loaders simultaneously using a multi loader setup forge config api port can also be included in the common project to provide all classes common to both loaders instead of the mod loader specific version simply include the common publication in your build gradle file the common publication is not obfuscated it therefore is sufficient to use api over modapi groovy dependencies api fuzs forgeconfigapiport forgeconfigapiport common modversion modversion e g 7 0 0 for minecraft 1 20 via curse maven modrinth maven alternatively you can use the curse maven modrinth maven to include forge config api port in your workspace javadoc and sources will be absent in that case groovy repositories exclusivecontent forrepository maven name curseforge url https cursemaven com filter includegroup curse maven exclusivecontent forrepository maven name modrinth url https api modrinth com maven filter includegroup maven modrinth dependencies modapi curse maven forgeconfigapiport 547434 fileid fileid e g 3671141 for mod version 3 2 0 for minecraft 1 18 2 modapi maven modrinth forge config api port fileid fileid e g v6 0 2 1 19 4 fabric for mod version 6 0 2 for minecraft 1 19 4 before minecraft 1 20 details versions of forge config api port for minecraft before 1 19 3 are distributed using the net minecraftforge maven group instead of fuzs forgeconfigapiport it is important to note that there is a minor difference from the production jars released on curseforge and modrinth jars from this maven do not have a dependency set on night config in fabric mod json this is necessary as there is no proper way of getting night config to be recognized as a fabric quilt mod with this in mind when you plan to include forge config api port as a jar in jar absolutely make sure to set a proper dependency on night config within your own mod s fabric mod json since forge config api port won t have any set json depends com electronwill night config core com electronwill night config toml there s also one more thing that will have to be done when including forge config api port from the curse maven the mod will not be able to recognize the required night config libraries you ll know that is the case when upon running the game you are greeted with the following message net fabricmc loader impl formattedexception net fabricmc loader impl discovery modresolutionexception mod resolution encountered an incompatible mod set a potential solution has been determined install com electronwill night config core any version install com electronwill night config toml any version to resolve this issue manually add dependency overrides check the fabric wiki https fabricmc net wiki tutorial dependency overrides for more information on this topic to your run configuration do that by creating a new file at run config fabric loader dependencies json in which you put the following contents json version 1 overrides forgeconfigapiport depends com electronwill night config core com electronwill night config toml also don t forget to manually add this file to your vcs since the whole run directory is usually ignored by default a dedicated common publication does not exist for these versions details details working with forge config api port details these instructions exist only to highlight differences between the original forge implementation and forge config api port it is assumed you are generally familiar with forge s configs registering configs the recommended point for registering your configs is directly in your modinitializer oninitialize method registering your configs works via fuzs forgeconfigapiport api config v2 forgeconfigregistry obtain an instance of the implementation from forgeconfigregistry instance you ll have to provide the mod id of your mod as there is no context which would be aware of the current mod as there is on forge java void register string modid modconfig type type iconfigspec spec and as on forge there is also a version which supports a custom file name java void register string modid modconfig type type iconfigspec spec string filename config loading as forge s mod loading process is split into multiple stages configs aren t loaded immediately upon being registered on fabric quilt though no such mod loading stages exist therefore forge config api port loads all registered configs immediately listening for config loading reloading and unloading forge s net minecraftforge fml event config modconfigevent loading and net minecraftforge fml event config modconfigevent reloading events are both adapted for fabric s quilt s callback event style they can be accessed from the fuzs forgeconfigapiport api config v2 modconfigevents class additionally there is an event that fires when server configs are unloading as on forge all these events provide is the config being handled all mod config related events run on the modlifecycle event bus instead of the primary event bus on forge this essentially means events do not run globally but are instead only handed to the mod that initially registered a listener as there is no mod specific event bus on fabric quilt your mod id must be provided when registering a listener for a config callback to achieve the same behavior as on forge where the listener will only run for your mod as an example a complete implementation of the reloading callback looks something like this java modconfigevents reloading modid register modconfig config details other differences from forge in forge config api port details apart from the obviously necessary differences in implementation details from forge mentioned above forge config api port additionally includes minor tweaks to certain aspects of the config system these tweaks are optional via a separate config file found at minecraft config forgeconfigapiport toml and only concern the implementation of certain features their implementations do not result in changes to public facing code these options are also available for forge as a separate mod project night config fixes https www curseforge com minecraft mc mods night config fixes a fix for parsingexception not enough data available recreateconfigswhenparsingfails true if your game has ever crashed with the following exception this workaround is just for you and the main reason why night config fixes was made in the first place caused by com electronwill nightconfig core io parsingexception not enough data available sometimes and very randomly also only reported on windows systems existing config files just loose all of their data and go completely blank this is when the exception mentioned above is thrown as night config is unable to read the file with this workaround enabled instead of the game crashing the invalid blank file is simply deleted and a new file with default values is created in its place no settings from the previous file can be restored unfortunately note when enabling this workaround in a mod pack which ships some already configured configs make sure to place those configs in the defaultconfigs directory not just in config so that when restoring a faulty config the desired default values from defaultconfigs are used instead of the built in values apply default config values from defaultconfigs correctconfigvaluesfromdefaultconfig true when only individual options in a config are invalid like an option is missing or contains a set value that cannot be parsed forge corrects those individual options by restoring them to their default values in the config file you can observe forge doing this in the console when the following message is printed net minecraftforge fml config configfiletypehandler config configuration file config path is not correct correcting the problem with that is forge uses the built in default value defined by the mod providing the config but ignores any value from a possibly present default config in defaultconfigs which a mod pack might ship this workaround changes this behavior and checks if an entry in a config in defaultconfigs exists first before falling back to correcting to the built in default value example a config contains an option which requires an integer value the default value for this option defined by the mod the config is from is 3 the default value for this option defined by the current mod pack via the config placed in defaultconfigs is 5 though when the user now accidentially enters a value such as 10 5 forge corrects the input back to the default 3 since 10 5 is a double not an integer and therefore invalid with this workaround enabled the value will instead be corrected to 5 global server configs forceglobalserverconfigs true changes forge s server config type to generate in the global config directory instead of on a local basis per world in saves world name serverconfig this design decision by forge simply causes too much confusion and frustration among users so this mod felt like a good enough opportunity to include a fix for that details forge config api port in a multi loader workspace details as the sole purpose of forge config api port is to allow for config parity on forge and fabric quilt it works especially great when developing your mod using a multi loader workspace gradle setup such as this one https github com jaredlll08 multiloader template arranged by jaredlll08 https github com jaredlll08 configs can be created and used within the common project without having to use any abstractions at all simply add forge config api port to the common project use the dedicated common publication so no mod loader specific code makes its way into your common project groovy api fuzs forgeconfigapiport forgeconfigapiport common modversion as all class and package names are the same as forge your code will compile on both forge and fabric quilt without any issues the only thing where you ll actually have to use mod loader specific code is when registering configs that s all an example implementation of this can be found here https github com thexaero open parties and claims details in game configuration using forge config api port details just as with forge itself in game configuration is not available in forge config api port by default instead users will have to rely on third party mods to offer that capability forge config api port includes default support for and recommends the configured fabric https www curseforge com minecraft mc mods configured fabric mod which already is the most popular way of handling in game configs on forge to use the configs provided by configured in game mod menu https github com terraformersmc modmenu needs to be installed too adding configured and mod menu to your workspace is not a requirement but highly recommended groovy repositories maven name curse maven url https cursemaven com maven name terraformers url https maven terraformersmc com dependencies configured modlocalruntime curse maven configured fabric 667378 4166864 configured version 2 0 2 for minecraft 1 19 3 mod menu modlocalruntime com terraformersmc modmenu 5 0 2 details config library alternatives to forge s configs details forge config api port is really useful when porting a forge mod to fabric quilt or maintaining a mod on multiple loaders as the existing config implementation does not need any major adjustments and also no new library dependency needs to be added to the forge project as with every library though the forge config system does have a number of shortcomings such as existence of three different config types with different functionalities that can be annoying to work with as a developer and easily become confusing for users handling of server config files per world without easy access to the file and a major effort when changing server config values globally this is addressed by forge config api port by moving server configs to the common config directory in minecraft config lack of in game configuration possible via the third party configured mod though lack of annotation support when defining new config files so when starting on a brand new mod project it might be advisable to consider a completely different config library with more features than the forge system has here is an overview with some recommendations project forge fabric quilt minecraft versions comments forge config api 1 16 1 17 1 18 1 19 fabric and quilt support provided by forge config api port in game config screens provided by the configured mod spectre lib 1 19 not primarily a config library implementation is very much based on forge s configs no in game config screens pollen 1 16 1 18 not primarily a config library implementation is very much based on forge s configs no in game config screens cloth config api 1 14 1 15 1 16 1 17 1 18 1 19 very extensive config library with annotation support in game config screens and great api documentation omega config 1 18 1 19 config library with annotation support and in game config screens owo lib 1 17 1 18 1 19 not primarily a config library with annotation support and the most beautiful in game config screens customizable via xml also the best api documentation midnight lib 1 17 1 18 1 19 great config library with in game screens also contains non config related features though yetanotherconfiglib 1 19 great library with lots of features in game screens are heavily inspired by sodium great api documentation details configured https www curseforge com minecraft mc mods configured spectre lib https github com illusivesoulworks spectrelib pollen https www curseforge com minecraft mc mods pollen cloth config api https www curseforge com minecraft mc mods cloth config omega config https www curseforge com minecraft mc mods omega config owo lib https www curseforge com minecraft mc mods owo lib midnight lib https modrinth com mod midnightlib yetanotherconfiglib https github com isxander yetanotherconfiglib | os |
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Core2-for-AWS-IoT-Kit | m5stack core2 for aws iot kit code repository this is the accompanying code repository for microcontroller tutorials presented in the aws iot kit https aws iot kit docs m5stack com program using the m5stack core2 for aws iot kit https m5stack com products m5stack core2 esp32 iot development kit for aws iot kit reference hardware each of the folders in this repository contains a separate project as described below all projects are compatible with esp idf v4 2 https www espressif com en products sdks esp idf or platformio https platformio org and runs on espressif s port of the freertos kernel https www freertos org please ensure that your installation of platformio is updated to the latest version of platformio core 5 1 using the command pio upgrade follow the aws iot kit getting started https aws iot kit docs m5stack com en getting started tutorial for instructions on how to setup your environment for arduino uiflow or micropython content and code please view the official m5stack docs https docs m5stack com included projects core2 for aws iot kit hardware features demo this project is a demo of the hardware features available on the m5stack core2 for aws iot kit reference hardware it uses at least one available api of each hardware feature in the board support package bsp the bsp drivers are located in the components core2foraws directory there is also a ported version of espressif s esp cryptoauthlib to be used with the bsp for the microchip atecc608 trust go secure element to function core2 for aws iot kit factory firmware this project is the factory firmware that comes loaded with the device it contains basic functionality and can be used to restore the device to factory state getting started this project is used in the aws iot kit getting started https aws iot kit docs m5stack com en getting started tutorial it contains a port of esp rainmaker https rainmaker espressif com it is a quick end to end demonstration of a cloud connected smart home application please follow the tutorial for usage cloud connected blinky hello world this project is used in the aws iot kit cloud connected blink https aws iot kit docs m5stack com en blinky hello world tutorial it is a blinky led demo that uses the on board secure element for provisioning the device to aws iot and a freertos task to flash the on board sk6812 led bars this example uses the espressif ported aws iot device sdk for embedded c https github com espressif esp aws iot please follow the tutorial for usage smart thermostat this project is used in the aws iot kit smart thermostat https aws iot kit docs m5stack com en smart thermostat and aws iot kit smart spaces https aws iot kit docs m5stack com en smart spaces tutorials it is a demonstration that uses aws iot device shadow feature to provide an example of a smart thermostat and how to update device states through the device shadow feature this example uses the espressif ported aws iot device sdk for embedded c https github com espressif esp aws iot please follow the tutorial for usage alexa for iot intro beta this project is used in the aws iot kit intro to alexa for iot https aws iot kit docs m5stack com en intro to alexa for iot tutorial it contains several alexa for aws iot aia features including english alexa wake word detection smart home device audio player and others please follow the tutorial for usage this is preview software based on the esp va sdk https github com espressif esp va sdk and is not a stable port support to get support with aws iot kit post your question in the content repo s discussions https github com m5stack aws iot kit tutorials discussions for issues with the aws iot kit this repo please submit an issue https github com m5stack core2 for aws iot kit issues to this repository | aws-iot-edukit m5stack-core2 esp-idf platformio edukit aws iot | server |
50.001_1D_planner | 50 001 1d planner andriod mobile app development overview currently there are 9 activities layouts each of them corresponds to one java class which encodes themselves for a layout class pair both of them have the same name the log in page is named as main and activity main after the log in page the first activity appeared is our time table if you click the top right checklist button you ll go to the checklist these are the only two activities without titles in their headings they are replaced by date there are also other classes which do not have their corresponding layouts user class for user task class for task dbhelper sqlite database helper userdao class contains basic operations for the user table taskdao class contains basic operations for the task table these five classes relates to our database our database is called calendar db it contains two tables user table and task table to visualize the database in android studio while the app is running on your phone or emulator go to view tool windows device file explorer data data com example calendar databases calendar db save it or export it to your laptop and open it with corresponding app then you ll see what s inside if you want to see it again after you modify the database you need to redo the above process and download it again todo ui part 1 for headings in all activities we should always set the activity title a textview at the center of the screen width currently it stays at the center of its own textview box so if there s only one back button at its left the title will be pushed towards right 2 optional for now all activities are using linear layout which does not look flexible since the screen is not fully used an alternative layout can be used if it can arrange the content better 3 in the two main activities time table checklist instead of an activity title like time table and check list the date should be displayed at the center of the heading it can be displayed in the form of textview for now i am leaving it as button 4 optional when inputing time slots we might want to have an actual calendar that can be used to select days instead of key in everything 5 in set working hours the user should be able to select multiple time slots this can be done in the form of spinner a list you can drop down and select one of the options for now the input only takes one entire time slot 6 optional we should think more options that can be included in the settings activity for now it only has an account option possible choices can be background color text size day night mode and so on if no time to do these then just compress settings and account into one thing 7 optional inside account the user can choose to upload his her picture and display it in the account page or select one from our default images 8 optional inside account we can also include the user s personal information like age and birthday if we managed to send notifications to the user don t forget to tell him her happy birthday 9 hopefully our app can looks nicer algo part 1 all tasks should be sorted and displayed in both time table and checklist maybe in checklist the tasks don t need to be sorted and scrollable after one task is finished the user can mark it as finished either by modify it under edit tasks mode or using the checklist directly 2 optional to edit tasks we can go menu edit tasks or when we are in time table activity we can press and hold the screen and directly enter edit tasks mode in the edit task mode the user should be able to first select which tasks need to be edited then select the operation such as remove mark as finished or edit task itself 2 1 a third options for me is to remove the checklist mode and only use the time table mode since the only usage of the checklist is to mark one task as finished if we can press and hold then be able to edit the entire timetable then an additional checklist might not be meaningful 3 optional we might want to save the finished tasks for the user so it means mark a task as finished and delete a task are two different things 4 optional our app can send notifications to the user 5 optional when switching between accounts the user can switch directly from settings for now the only way to switch is to log out first and log in with another account 6 optional maybe we want to limit the form of user s password such as it cannot be pure numbers | front_end |
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doordash-database-lifecycle | the database lifecycle final database design and development project re engineering the early stage of doordash database what s included in phase1 pdf research on the business origin current functioning competitors value proposition draft user roles and user stories draft conceptual model relational model what s included in phase2 pdf refined user roles and user stories conceptual model relational model functional dependencies normalized schema queries each query is written to fetch data for a corresponding user story using python and psycopg2 | server |
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flax-blockchain | flax blockchain flax is a modern community centric green cryptocurrency based on a proof of space and time consensus algorithm it is a community supported fork of the chia network https github com chia network chia blockchain codebase | blockchain |
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engcloud | engcloud this repository contains scripts and utilities for engineering openstack cloud there are several directories each with their own readme files dev project contains a script to create a basic project and user and to create the control node and a devstack environment for the suer scripts this directory contains script for adding nios images and flavors as well as older versions of various scripts to manage grids and other vms dns vnf scripts and templates for demonstrating dns as an auto scaling vnf grid templates scripts and templates for creating various grid master and member configurations | cloud |
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worklink | ini adalah platform freelancer dengan pembagian 3 role user admin dan company digunakan untuk memenuhi tugas kuliah dibuat sepenuhnya hardcode dengan template dari internet features 1 upload bukti bayar 2 upload update kerjaan 3 timeline kerjaan 4 available job 5 ongoing job tidak akan di update karena hanya sebatas tugas | server |
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digitraffic | digitraffic sis lt sivujen p ivitys sis lt sivut l ytyv t pages kansiosta muokattavissa olevat sivut ovat md p tteisi sivujen sis lt voi muokata prose io https prose io palvelulla navigoi palvelussa pages kansioon ja haluttuun sis lt sivuun avaa sis lt sivu prosessa voit muokata sivun sis lt avautuvassa tekstieditorissa voit tyylitell teksti otsikoita listoja ja lainauksia maalaamalla tyylitelt v n tekstin ja valitsemalla tyylin tekstieditorin yl laidassa olevan tyylin painikkeesta editori kirjoittaa valitun tekstin markdown merkint tavalla suoraan tekstiin voit k ytt markdownia https github com adam p markdown here wiki markdown cheatsheet my s kirjoittamalla merkinn t suoraan tekstin sekaan kuvan lis minen voit lis t sivulle kuvia valitsemalla kuvapainikkeen prosen editorista nelj s painike oikealta valitse ladattava kuva ja anna sille alt teksti editori lataa kuvan pages kansioon ja sijoittaa kuvan merkint tavan mukaisesti valitsemaasi kohtaan tekstiss voit kirjoittaa 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n ytt itse osoite tekstin lis t n vain yksi rivi jossa on edess kaksi v liviivaa https tie digitraffic fi api weathercam v1 stations data huom l muokkaa muita meta datan tietoja tiedotteiden artikkelien ja tapahtumien julkaisu uuden tiedotteen tai artikkelin julkaisu tiedotteet artikkelit ja tapahtumat sijaitsevat posts kansiossa ja ovat md p tteisi tiedostoja tiedotteita voit julkaista ja muokata prose io https prose io palvelulla navigoi palvelussa posts kansioon ja valitse new file ruudun yl laidasta hakukent n oikealta puolelta julkaisun tiedoston nime minen ja p iv m r n asettaminen kirjoita julkaisulle otsikko klikkaamalla sivun yl laidassa olevaa kentt jossa on placeholder tekstin luontip iv m r vastaava posts 2017 09 22 your filename md tyylinen teksti voit kirjoittaa t m n tekstin tilalle halutun tiedostonimen jonka alussa on julkaisun p iv m r esimerkiksi jos julkaisun p iv m r on 26 9 2017 ja otiskoksi tulee vanhat rajapinnat suljetaan voi tiedostonimeksi antaa posts 2017 09 26 vanhat rajapinnat suljetaan md huom varmista ett tiedostonimen alkuun tulee posts v litt m sti per n p iv m r muodossa vvvv kk pp t m n j lkeen haluttu nimi v liviivan per ss haluttu nimi ja lopuksi tiedostop te md posts vvvv kk pp haluttu nimi md julkaisun sis ll n kirjoittaminen kirjoita julkaisulle varsinainen sis lt otsikon alapuolella olevaan tekstieditoriin sivusto muotoilee julkaisun ensimm isen kappaleen aina ingressiksi ingressi k ytet n my s nostotekstin koontisivulla voit vaihtaa kappaletta lis m ll kappaleiden v liin kaksi rivinvaihtoa t m on ensimm inen kappale t m on toinen kappale t m ei ole viel kolmatta kappaletta mutta t m on voit tyylitell teksti lis t v liotsikoita listoja ja lainauksia ks sis lt sivujen p ivitys sis lt sivujen p ivitys voit lis t kuvia tekstin sekaan ks kuvan lis minen kuvan lis minen julkaisun otsikointi voit lis t julkaisulle hero kuvan valitsemalla prosesta meta data painikkeen sivun oikeasta laidasta kolmas ylh lt uuden julkaisun meta data on tyhj joten meta data editorin sis lt alue on viel tyhj voit halutessasi kopioida metadatalle pohjan aikaisemmasta julkaisusta valmiin julkaisun meta data n ytt seuraavalta published true title vanhojen soap rajapintojen elinkaari p ttyi image 2017 08 31 old api eol data eol jpg categories tiedotteet traffictypes tieliikenne tags rajapinnat yll pito endoflife kirjoita julkaisun otsikko meta datan title tietoon esimerkiksi seuraavalla tavalla title vanhojen soap rajapintojen elinkaari p ttyi julkaisun hero kuvan lis minen jotta voit lis t kuvan julkaisun meta datan image tietoon sinun tulee ensin ladata kuva repositorion juuresta l ytyv n img kansioon esim julkaisun mukaan nimettyyn alikansioon 2017 08 31 old api eol kun kuva l ytyy repositoriosta voit lis t kuvatiedoston img kansion alaisen polun image avaimen per n esimerkiksi kun kuva on sijoitettu kansioon img 2017 08 31 old api eol data eol jpg tulee meta dataan kirjoittaa seuraava rivi image 2017 08 31 old api eol data eol jpg huom voit my s j tt hero kuvan lis m tt jolloin julkaisussa ei n y hero kuvaa vaan tekstisis lt n ytet n suoraan otsikon alla t ll in voit j tt image tiedon kirjoittamatta kokonaan meta dataan julkaisun kategorian lis minen julkaisut voivat olla tiedotteita artikkeleita tai tapahtumia n it vastaa kolme katgoriaa tidotteet artikkelit ja tapahtumat kategoria m ritell n meta datan categories tietoon esimerkiksi jos julkaisu on tiedote kirjoita meta dataan seuraava rivi categories tiedotteet huom muista kirjoittaa varsinainen kategoria isolla alkukirjaimella artikkelit on oikein artikkelit on v rin julkaisun liikennetyyppien lis minen julkaisulla voi olla yksi tai useampi kolmesta liikennetyypist tieliikenne rautatieliikenne meriliikenne liikennetyypit m ritell n meta datan traffictypes tietoon listaksi jokainen liikennetyyppi kirjoitetaan omalle rivilleen meta dataan ja kunkin eteen kirjoitetaan v liviiva ja v lily nti esimerkiksi kun haluat lis t julkaisulle liikennetyypit rautatieliikenne ja meriliikenne kirjoita metadataan seuraavat rivit traffictypes rautatieliikenne meriliikenne huom m rittele julkaisulle v hint n yksi liikennetyyppi julkaisun avainsanojen lis minen julkaisulle voidaan antaa yksi tai useampi avainsana julkaisuja voidaan suodattaa avainsanojen perusteella avainsanat ovat vapaamuotoisia tarkista aikaisemmista julkaisuista mit avainsanoja on jo k ytetty ja pyri k ytt m n yhdenmukaisia avainsanoja avainsanat m ritell n meta dataan tags tietoon jokainen avainsana kirjoitetaan omalle rivilleen ja kunkin eteen kirjoitetaan v liviiva ja v lily nti esimerkiksi kun haluat lis t julkaisulle avainsanat kelikamerat rajapinnat ja yll pito kirjoita meta dataan seuraavat rivit tags kelikamerat rajapinnat yll pito huom voit olla antamatta julkaisulle yht k n avainsanaa mutta on suositeltavaa ett jokaiselle julkaisulle annetaan ainakin yksi avainsana julkaiseminen julkaise uusi tiedote artikkeli tai tapahtuma lis m ll meta dataan seuraava rivi published true t m n j lkeen valitse prosen oikeasta laidasta changes to save painike alin oikean laidan painikkeista kirjoita aukeavaan tekstialueeseen kommentti github committia varten esim tiedote vanhojen rajapintojen p ttymisest luotu julkaise tiedote valitsemalla commit komenttikent n alta julkaistu tiedote n kyy sivustolla muutaman minuutin kuluttua aikaisemmin julkaistun julkaisun muokkaaminen voit muokata aikasemmin julkaistuja julkaisuja samaan tapaan kuin sis lt sivuja ks sis lt sivujen p ivitys sis lt sivujen p ivitys avaa julkaisu klikkaamalla sen otsikkoa posts kansiossa t m n j lkeen voit muokata sis lt ja meta dataa kuten uutta lis tt ess otskon muokkaaminen jo julkaistun julkaisun otsikko n kyy nyt sis lt editorin yl puolella muokkaa julkaisun otsikkoa klikkaamalla otsikkoa ja kirjoittamalla uusi otiskko vanhan tilalle p iv m r n muokkaaminen jo julkaistun julkaisun p iv m r voit muokata muokkaamalla tiedoston tiedostonime muokkaa tiedostonime valitsemalla settings prosen oikeasta laidasta nelj s painike ylh lt auenneeseen file path kentt n voit muokata p iv m r n tiedostonimeen muodossa vvvv kk pp posts vvvv kk pp julkaisun nimi md ohje kuvatiedostojen vaatimuksista k yt vain kuvatiedostoja joihin voi antaa loppuk ytt jille vapaat oikeudet kuten liikenneviraston kuvapankki https unsplash com tms itse ottamasi valokuvat 1 p kuva vaakakuva korkeintaan 1500 px leve optimi 1200 x 675 px 2 sis lt kuva vaakakuva korkeintaan 1000 px leve optimi 960 x 540 px 3 oikean palstan kuva vaakakuva korkeintaan 500 px leve optimi 480 x 270 px 4 hero taustakuva vaakakuva mustavalkoinen korkeintaan 1500 px leve optimi 1200 x 675 px edited install with docker docker build t digitraffic pages docker run p 3000 3000 p 3001 3001 v pwd app digitraffic pages latest or one liner docker build t digitraffic pages docker run p 3000 3000 p 3001 3001 v pwd app digitraffic pages latest or the script build and run in docker sh that uses build docker image sh and run in docker sh scripts in addition additionally this repository contains a wiki https github com tmfg digitraffic wiki for fintraffic https fintraffic fi s road digitraffic service the wiki source can be cloned with bash git clone https github com tmfg digitraffic wiki git keeping up to date command bundle update is excecuted every time docker container is started so it will do updates https help github com articles setting up your github pages site locally with jekyll step 2 install jekyll using bundler https help github com articles setting up your github pages site locally with jekyll https pages github com versions run bundle update to catch up | opendata traffic-data | front_end |
weibo | weibo natural language processing about micro blogs | ai |
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