names stringlengths 1 98 | readmes stringlengths 8 608k | topics stringlengths 0 442 | labels stringclasses 6
values |
|---|---|---|---|
Twitter-Sentiment-Analysis | twitter sentiment analysis it is a natural language processing problem where sentiment analysis is done by classifying the positive tweets from negative tweets by machine learning models for classification text mining text analysis data analysis and data visualization introduction natural language processing nlp is a hotbed of research in data science these days and one of the most common applications of nlp is sentiment analysis from opinion polls to creating entire marketing strategies this domain has completely reshaped the way businesses work which is why this is an area every data scientist must be familiar with thousands of text documents can be processed for sentiment and other features including named entities topics themes etc in seconds compared to the hours it would take a team of people to manually complete the same task we will do so by following a sequence of steps needed to solve a general sentiment analysis problem we will start with preprocessing and cleaning of the raw text of the tweets then we will explore the cleaned text and try to get some intuition about the context of the tweets after that we will extract numerical features from the data and finally use these feature sets to train models and identify the sentiments of the tweets this is one of the most interesting challenges in nlp so i m very excited to take this journey with you understand the problem statement let s go through the problem statement once as it is very crucial to understand the objective before working on the dataset the problem statement is as follows the objective of this task is to detect hate speech in tweets for the sake of simplicity we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it so the task is to classify racist or sexist tweets from other tweets formally given a training sample of tweets and labels where label 1 denotes the tweet is racist sexist and label 0 denotes the tweet is not racist sexist your objective is to predict the labels on the given test dataset note the evaluation metric from this practice problem is f1 score take a look at the pictures below depicting two scenarios of an office space one is untidy and the other is clean and organized tweets preprocessing and cleaning you are searching for a document in this office space in which scenario are you more likely to find the document easily of course in the less cluttered one because each item is kept in its proper place the data cleaning exercise is quite similar if the data is arranged in a structured format then it becomes easier to find the right information the preprocessing of the text data is an essential step as it makes the raw text ready for mining i e it becomes easier to extract information from the text and apply machine learning algorithms to it if we skip this step then there is a higher chance that you are working with noisy and inconsistent data the objective of this step is to clean noise those are less relevant to find the sentiment of tweets such as punctuation special characters numbers and terms which don t carry much weightage in context to the text in one of the later stages we will be extracting numeric features from our twitter text data this feature space is created using all the unique words present in the entire data so if we preprocess our data well then we would be able to get a better quality feature space let s first read our data and load the necessary libraries story generation and visualization from tweets in this section we will explore the cleaned tweets text exploring and visualizing data no matter whether its text or any other data is an essential step in gaining insights do not limit yourself to only these methods told in this tutorial feel free to explore the data as much as possible before we begin exploration we must think and ask questions related to the data in hand a few probable questions are as follows what are the most common words in the entire dataset what are the most common words in the dataset for negative and positive tweets respectively how many hashtags are there in a tweet which trends are associated with my dataset which trends are associated with either of the sentiments are they compatible with the sentiments end notes in this article we learned how to approach a sentiment analysis problem we started with preprocessing and exploration of data then we extracted features from the cleaned text using bag of words and tf idf finally we were able to build a couple of models using both the feature sets to classify the tweets did you find this article useful do you have any useful trick did you use any other method for feature extraction feel free to discuss your experiences in comments below or on the discussion portal and we ll be more than happy to discuss | nlp sentiment-analysis data-analysis bag-of-words data-visualization eda machine-learning classification cross-validation evaluation-metrics count-vectorizer wordcloud hashtags datacleaning | ai |
echo-android-framework | download https api bintray com packages echo mobile android framework stable images download svg version 4 2 2 https bintray com echo mobile android framework stable 4 2 2 link echo android framework echo android framework pure kotlin echo framework for android mobile development can be used to work with accounts transactions and contracts in kotlin and to easily obtain data from the blockchain via public apis install this framework can be obtained through gradle dependency implementation org echo mobile echoframework 5 1 1 or maven dependency dependency groupid org echo mobile groupid artifactid echoframework artifactid version 5 1 1 version type pom type dependency setup for setup framework use this simple code kotlin create and setup framework main class fun init val echo echoframework create here you can put your custom settings for our framework example custom api options which can be used settings configurator seturl echo url setapis api database api account history api network broadcast configure start framework connect to node and setup public apis echo start object callback any override fun onerror error localexception override fun onsuccess result any usage there are simple examples of usage framework accounts login kotlin fun login echo isownedby name wif object callback fullaccount override fun onsuccess result fullaccount override fun onerror error localexception info kotlin fun getinfo val name some name echo getaccount name object callback fullaccount override fun onsuccess result fullaccount override fun onerror error localexception echo getbalance name 1 3 0 object callback balance override fun onsuccess result balance override fun onerror error localexception echo isownedby name wif object callback fullaccount override fun onsuccess result fullaccount override fun onerror error localexception history kotlin fun gethistory echo getaccounthistroy nameorid some id startid 1 11 0 stopid 1 11 0 limit 100 object callback historyresponce override fun onsuccess result historyresponse override fun onerror error localexception subscribe to account kotlin fun subscribeonaccount echo subscribeonaccount account name or id object accountlistener override fun onchange updatedaccount fullaccount object callback boolean override fun onsuccess result boolean override fun onerror error localexception change password kotlin fun changewif echo changekeys account name or id old wif new wif object callback boolean override fun onsuccess result boolean override fun onerror error localexception resultcallback object callback transactionresult override fun onsuccess result transactionresult override fun onerror error localexception fee for transfer kotlin fun feefortransfer framework getfeefortransferoperation from name or id from account wif to name or id amount 10000 asset 1 3 0 feeasset 1 3 0 optional object callback string override fun onsuccess result string override fun onerror error localexception transfer kotlin fun send echo sendtransferoperation fromnameorid some name password some wif tonameorid some name amount 300 asset 1 3 0 feeasset 1 3 0 object callback boolean override fun onsuccess result boolean override fun onerror error localexception resultcallback object callback transactionresult override fun onsuccess result transactionresult override fun onerror error localexception assets create asset kotlin fun createasset val asset asset apply symbol asset symbol precision 4 issuer account issuer id setbtsoptions bitassetoptions feedlifetimesec 86400 minimumfeeds 7 val price price apply this quote assetamount unsignedlong valueof 1 asset 1 3 1 this base assetamount unsignedlong valueof 1 asset 1 3 0 val options assetoptions unsignedlong valueof 100000 assetoptions charge market fee assetoptions charge market fee price description asset assetoptions options echo createasset issuer name or id wif asset broadcastcallback object callback boolean override fun onsuccess result boolean override fun onerror error localexception resultcallback object callback transactionresult override fun onsuccess result transactionresult override fun onerror error localexception issue asset kotlin fun issueasset framework issueasset issuer account name or id wif 1 3 79 1 target account name or id object callback boolean override fun onsuccess result boolean override fun onerror error localexception resultcallback object callback transactionresult override fun onsuccess result transactionresult override fun onerror error localexception contracts create contract kotlin fun createcontract val bytecode some bytecode framework createcontract registrarnameorid wif assetid 1 3 0 feeasset 1 3 0 optional bytecode params listof optional broadcastcallback object callback boolean override fun onsuccess result boolean override fun onerror error localexception resultcallback object callback string override fun onsuccess result string override fun onerror error localexception optional call contract kotlin fun callcontract echo callcontract account name or id wif assetid 1 3 0 feeasset 1 3 0 optional contractid contractid methodname incrementcounter methodparams listof value 1 optional broadcastcallback object callback boolean override fun onsuccess result boolean override fun onerror error localexception resultcallback object callback string override fun onsuccess result string override fun onerror error localexception optional query contract kotlin fun querycontract echo querycontract account name or id assetid 1 3 0 contractid contractid getcount amount 0 methodparams listof object callback string override fun onsuccess result string override fun onerror error localexception | kotlin android echo echo-android echo-android-framework | front_end |
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code | 500 500 ai machine learning deep learning computer vision nlp projects with code https raw githubusercontent com ashishpatel26 500 ai machine learning deep learning computer vision nlp projects with code main images colorful 20futuristic 20technology 20poster gif follow me on linkedin https img shields io badge linkedin 0077b5 style for the badge logo linkedin logocolor white https www linkedin com in ashishpatel2604 this list is continuously updated you can take pull requests and contribute all links are tested and working fine please ping if any link doesn t work sr no name link 1 365 days computer vision learning https github com ashishpatel26 365 days computer vision learning linkedin post 2 125 nlp language models treasure of transformers https github com ashishpatel26 treasure of transformers 3 andrew ng ml notes https github com ashishpatel26 andrew ng notes 4 10 machine learning projects on time series forecasting https medium com coders camp 10 machine learning projects on time seri 20es forecasting ee0368420ccd 5 20 deep learning projects solved and explained with python https thecleverprogrammer com 2020 11 22 deep learning projects with python 6 20 machine learning project https amankharwal medium com 20 machine learning projects for portfolio 81e3dbd167b1 7 30 python project solved and explained https amankharwal medium com 30 python projects solved and explained 563fd7473003 8 machine learning course for free https thecleverprogrammer com 2020 09 24 machine learning course 9 5 web scraping projects with python https amankharwal medium com 5 web scraping projects with python 4bcc25ff039 10 20 machine learning projects on future prediction with python https amankharwal medium com 20 machine learning projects on future prediction with python 93932d9a7f7f 11 4 chatbot project with python https amankharwal medium com 4 chatbot projects with python 5b32fd84af37 12 7 python gui project https amankharwal medium com 7 python gui projects for beginners 87ae2c695d78 13 all unsupervised learning projects https amankharwal medium com all unsupervised machine learning algorithms explained aecf1ba95d8b 14 10 machine learning projects for regression analysis https amankharwal medium com 10 machine learning projects on regression with python e5494615a0d0 15 10 machine learning project for classification with python https medium datadriveninvestor com 10 machine learning projects on classification with python 9261add2e8a7 16 6 sentimental analysis projects with python https amankharwal medium com 6 sentiment analysis projects with python 1fdd3d43d90f 17 4 recommendations projects with python https medium com coders camp 4 recommendation system projects with python 5934de32ba7d 18 20 deep learning project with python https medium com coders camp 20 deep learning projects with python 3c56f7e6a721 19 5 covid19 projects with python https amankharwal medium com 5 covid 19 projects with python and machine learning 63d51cde96e2 20 9 computer vision project with python https becominghuman ai computer vision projects with python ecfac58ded18 21 8 neural network project with python https medium datadriveninvestor com 8 neural networks projects solved and explained a4f142bc10c 22 5 machine learning project for healthcare https medium datadriveninvestor com 5 machine learning projects for healthcare bbd0eac57b4a 23 5 nlp project with python https medium datadriveninvestor com 5 nlp projects for machine learning 72d3234381d4 24 47 machine learning projects for 2021 https data flair training blogs machine learning project ideas 25 19 artificial intelligence projects for 2021 https data flair training blogs artificial intelligence project ideas 26 28 machine learning projects for 2021 https data flair training blogs machine learning project ideas 27 16 data science projects with source code for 2021 https data flair training blogs data science project ideas 28 23 deep learning projects with source code for 2021 https data flair training blogs deep learning project ideas 29 25 computer vision projects with source code for 2021 https data flair training blogs computer vision project ideas 30 23 iot projects with source code for 2021 https data flair training blogs iot project ideas 31 27 django projects with source code for 2021 https data flair training blogs django project ideas 32 37 python fun projects with code for 2021 https data flair training blogs python project ideas 33 500 top deep learning codes https github com aymericdamien topdeeplearning 34 500 machine learning codes https github com josephmisiti awesome machine learning 35 20 machine learning datasets project ideas https www kdnuggets com 2020 03 20 machine learning datasets project ideas html 36 1000 computer vision codes https github com jbhuang0604 awesome computer vision 37 300 industry wise real world projects with code https github com ashishpatel26 real time ml project 38 1000 python project codes https github com vinta awesome python 39 363 nlp project with code https github com fighting41love funnlp 40 50 code ml models for ios 11 projects https github com likedan awesome coreml models 41 360 pretrained model projects for image text audio and video https github com paddlepaddle paddlehub 42 50 graph classification project list https github com benedekrozemberczki awesome graph classification 43 100 sentence embedding nlp resources https github com separius awesome sentence embedding 44 100 production machine learning projects https github com ethicalml awesome production machine learning 45 300 machine learning resources collection https github com ujjwalkarn machine learning tutorials 46 70 awesome ai https github com nirantk awesome project ideas 47 150 machine learning project ideas with code https github com src d awesome machine learning on source code 48 100 automl projects with code https github com hibayesian awesome automl papers 49 100 machine learning model interpretability code frameworks https github com jphall663 awesome machine learning interpretability 50 120 multi model machine learning code projects https github com pliang279 awesome multimodal ml 51 awesome chatbot projects https github com fendouai awesome chatbot 52 awesome ml demo project with ios https github com tucan9389 awesome ml demos with ios 53 100 python based machine learning application projects https github com prateekiiest code sleep python 54 100 reproducible research projects of ml and dl https github com leipzig awesome reproducible research 55 25 python projects https github com saadhaxxan awesome python projects 56 8 opencv projects https github com moadmmh awesome opencv 57 1000 awesome deep learning collection https github com christoschristofidis awesome deep learning 58 200 awesome nlp learning collection https github com keon awesome nlp 59 200 the super duper nlp repo https notebooks quantumstat com 60 100 nlp dataset for your projects https index quantumstat com dataset 61 364 machine learning projects definition https projectworlds in free projects machine learning projects with source code 62 300 google earth engine jupyter notebooks to analyze geospatial data https github com giswqs earthengine py notebooks 63 1000 machine learning projects information https 1000projects org projects machine learning projects 64 11 computer vision projects with code https github com akshaybhatia10 computervision projects 65 13 computer vision projects with code https github com anuragreddygv323 computer vision projects 66 13 cool computer vision github projects to inspire you https machinelearningknowledge ai cool computer vision github projects to inspire you 67 open source computer vision projects with tutorials https www theclickreader com open source computer vision projects with tutorials 68 opencv computer vision projects with python https github com packtpublishing opencv computer vision projects with python 69 100 computer vision algorithm implementation https github com gmalivenko awesome computer vision models 70 80 computer vision learning code https github com spmallick learnopencv 71 deep learning treasure https github com kmario23 deep learning drizzle 72 data analysis and machine learning projects https github com rhiever data analysis and machine learning projects 73 ai projects https github com stevenlei2017 ai projects 74 kaggle projects collection https github com alexattia data science projects 75 unique ai projects https github com robsalgado personal data science projects 76 data science project collection https github com tuangauss datascienceprojects 77 advance data science projects https github com thecodex me projects 78 deep and machine learning projects https github com nitinkaushik01 deep and machine learning projects 79 data science projects kaggle https github com alexattia data science projects 80 auto deep learning project https github com d x y autodl projects 81 180 machine learning project https medium com coders camp 180 data science and machine learning projects with python 6191bc7b9db9 82 amazing hackthon project collection https github com analyticsindiamagazine machinehack tree master hackathon solutions 83 awesome nlp project ideas https nirantk com awesome project ideas 84 12 nlp projects https github com gaoisbest nlp projects 85 advance nlp projects https github com packtpublishing advanced nlp projects with tensorflow 2 0 86 6 amazing nlp projects https github com anujvyas natural language processing projects 87 nlp beginner projects https github com positivepeng nlp beginner projects 88 paper with code by pwc collection https github com zziz pwc 89 sota models state of the art results https github com redditsota state of the art result for machine learning problems 90 best ai papers https github com louisfb01 best ai paper 2020 91 generative adversarial nets https github com zhangqianhui adversarialnetspapers 92 computer vision paper with code https github com dwctod cvpr2022 papers with code demo 93 nilms paper with code https github com klemenjak nilm papers with code 94 3d computer vision research projects https github com tom hardy 3d vision workshop awesome 3d vision 95 nlp and computer vision project collection https github com nelsonzhao zhihu 96 udacity collection of computer vision projects https github com bjarten computer vision nd 97 zero to hero tensorflow tutorial https github com mrdbourke tensorflow deep learning 98 deep learning in production https github com the ai summer deep learning in production 99 gans collection https github com the ai summer gans in computer vision 100 time series projects code https github com deshpandenu time series forecasting of amazon stock prices using neural networks lstm and gan 101 12 machine learning object detection https amankharwal medium com 12 machine learning projects on object detection 46b32adc3c37 102 20 nlp project with python https medium com coders camp 20 machine learning projects on nlp 582effe73b9c more projects list is coming | awesome machine-learning deep-learning machine-learning-projects deep-learning-project computer-vision-project nlp-projects artificial-intelligence-projects python artificial-intelligence data-science computer-vision nlp | ai |
csharp-web | csharp web this is the official github repository for the c web development courses | front_end | |
mlh-localhost-icon-blockchain-casino | icon blockchain casino this is the finished code for the icon blockchain casino localhost workshop to give participants test icx coins have them paste their wallet address at this link http mlhlocal host faucet | blockchain | |
design-system | turknet design system installation install the package in your project directory with shell npm install turk net design system or yarn add turk net design system usage js import alert from turk net design system const samplecomponent return alert severity warning this alert component the severity property changes alert color alert development quick start 1 install the dependencies navigate into your new site s directory and install the necessary dependencies shell navigate to the directory cd design system install the dependencies yarn 2 open the source code and start editing shell yarn start 3 build storybook shell yarn build 4 build library shell yarn build library learning storybook 1 read our introductory tutorial over at storybook tutorials https storybook js org tutorials intro to storybook react en get started 2 learn how to transform your component libraries into design systems in our design systems for developers https storybook js org tutorials design systems for developers tutorial 3 see our official documentation at storybook https storybook js org | os | |
pactus | codecov https codecov io gh pactus project pactus branch main graph badge svg token 8n6n60d5ui https codecov io gh pactus project pactus go report card https goreportcard com badge github com pactus project pactus https goreportcard com report github com pactus project pactus discord https badgen net badge icon discord icon discord label https discord gg zpqwqv85ch pactus blockchain a full node implementation of the pactus blockchain in go install please check the install docs install md document to build and run the pactus blockchain contribution contributions to the pactus blockchain are appreciated please read the contributing contributing md guidelines before submitting a pull request or opening an issue license the pactus blockchain is under mit license license | blockchain | |
CADOMonitoringWebsite-Backend | cadomonitoringwebsite backend this is the backend part that controls the database and sends all of the necessary information to the frontend | server | |
nlpbook | a href https ratsgo github io nlpbook img src https i imgur com tsjrqhv png width 400 title source imgur com a https ratsgo github io nlpbook ratsgo naver com | nlp | ai |
terraform-mackenzie | iac mackenzie arquivos e exemplos das aula de iac curso devops engineering and cloud solutions da mackenzie | cloud | |
design-systems-office-hours-playbook | design systems office hours playbook as our company and system scales it s important that design system team members have a common approach to attending and operating office hours office hours are our most direct channel of input from the product teams we serve making it an important part of our work below are some of the themes and behaviors you should bear in mind when you participate in or run office hours be attentive office hours are dedicated time for product teams to get face to face with the design system teams but that can only happen if our faces aren t buried in our laptops and phones when attending office hours refrain from using your phone or laptop to do work some exceptions are when you re taking notes or pulling up some work to share with the attendees in these cases just let the attendees know what you re doing so that they know they still have your full attention this applies to everyone in the room even if you re not giving direct feedback if you re too busy not to be on your phone or laptop for half an hour you should find someone who can take your place be supportive the teams that come to office hours are some of our most devoted colleagues they were able to recognize the importance of the work we do and want to give us the chance to work with them for this reason we should expect that all the work we see in office hours deserves our support and was produced with good intentions in cases where teams are in clear violation of guidelines we provide try to understand how they arrived at their solution and work with them to find a suitable compromise that puts them back on the right track be clear and direct just like any other feedback channel the best feedback we can give to teams coming to office hours should be succinct clear and actionable avoid simply telling teams that their work isn t design system compliant that s probably the reason they re present and avoid sugar coating your feedback make your feedback concise and clear provide actionable suggestions and remember that this is their time to talk to us not the other way around sometimes getting to good feedback is hard without the right context so make sure you have all the necessary facts you need to confidently make suggestions defer and observe it s wonderful when we have a strong design system presence at office hours that s a sign that our team is excited to see the work going on across the company but it can quickly become off putting for product teams when several people have feedback to give don t feel as though your being in office hours means you have to contribute feedback use the time to observe and take note of the work being presented so we can better anticipate similar work coming down the pike similarly let other people drive the feedback if it s clear they have a good handle on it and try to defer to other people if you re not confident you re the best person to support the attendees share your findings the final thing to talk about is about what happens after office hours take a moment to chat with the other design system representatives about the work you ve seen to make sure you re on the same page if there wasn t someone taking notes during the office hours make sure you agree on who will be responsible for writing notes for the rest of the team to see additionally just because people weren t in the room doesn t mean they aren t interested in the work being shown take the work back to your team to share with them in case they have their own relevant information or action items to share | template design-systems design-documentation | os |
Awesome-Visual-Transformer | awesome visual transformer awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome collect some transformer with computer vision cv papers if you find some overlooked papers please open issues or pull requests recommended papers transformer original paper attention is all you need https arxiv org abs 1706 03762 nips 2017 technical blog english blog transformers in vision link https davide coccomini medium com chinese blog 3w transformer link https zhuanlan zhihu com p 308301901 chinese blog vision transformer link https zhuanlan zhihu com p 348593638 survey multimodal learning with transformers a survey ieee tpami paper https arxiv org abs 2206 06488 2023 05 11 a survey of visual transformers paper https arxiv org abs 2111 06091 2021 11 30 transformers in vision a survey paper https arxiv org abs 2101 01169 2021 02 22 a survey on visual transformer paper https arxiv org abs 2012 12556 2021 1 30 a survey of transformers paper https arxiv org abs 2106 04554 2020 6 09 arxiv papers understanding gaussian attention bias of vision transformers using effective receptive paper https arxiv org abs 2305 04722 focuseddecoder focused decoding enables 3d anatomical detection by transformers paper https arxiv org abs 2207 10774v4 code https github com bwittmann transoar tag tag boosting text vqa via text aware visual question answer generation paper https arxiv org abs 2208 01813 code https github com henryjunw tag fastmetro cross attention of disentangled modalities for 3d human mesh recovery with transformers paper https arxiv org abs 2207 13820 code https github com postech ami fastmetro batchformer learning to explore sample relationships for robust representation learning paper https arxiv org abs 2203 01522 code https github com zhihou7 batchformer relvit relvit concept guided vision transformer for visual relational reasoning paper https arxiv org pdf 2204 11167 pdf code https github com nvlabs relvit mvitv2 improved multiscale vision transformers for classification and detection paper https arxiv org pdf 2112 01526 pdf code https github com facebookresearch mvit detr with improved denoising anchor boxes for end to end object detection paper https arxiv org pdf 2203 03605 pdf code https github com ideacvr dino three things everyone should know about vision transformers paper https arxiv org pdf 2203 09795 pdf deit iii deit iii revenge of the vit paper https arxiv org pdf 2204 07118 pdf davit davit dual attention vision transformers paper https arxiv org pdf 2204 03645 pdf code https github com dingmyu davit coformer collaborative transformers for grounded situation recognition paper https arxiv org abs 2203 16518 code https github com jhcho99 coformer gsrtr grounded situation recognition with transformers paper https arxiv org abs 2111 10135 code https github com jhcho99 gsrtr maxvit maxvit multi axis vision transformer paper https arxiv org abs 2204 01697 v2x vit v2x vit vehicle to everything cooperative perception with vision transformer paper https arxiv org abs 2203 10638 memmc mae unsupervised anomaly detection in medical images with a memory augmented multi level cross attentional masked autoencoder paper https arxiv org abs 2203 11725 code https github com tianyu0207 memmc mae contrastive transformer based multiple instance learning for weakly supervised polyp frame detection paper https arxiv org abs 2203 12121 code https github com tianyu0207 weakly polyp videomae videomae masked autoencoders are data efficient learners for self supervised video pre training paper https arxiv org abs 2203 12602 code https github com mcg nju videomae peco perceptual codebook for bert pre training of vision transformers paper https arxiv org pdf 2111 12710 pdf resvit residual vision transformers for multi modal medical image synthesis paper https arxiv org abs 2106 16031 crossefficientvit combining efficientnet and vision transformers for video deepfake detection paper https arxiv org abs 2107 02612 code https github com davide coccomini combining efficientnet and vision transformers for video deepfake detection discrete vit discrete representations strengthen vision transformer robustness paper https arxiv org abs 2111 10493 styleswin styleswin transformer based gan for high resolution image generation paper https arxiv org abs 2112 10762 code https github com microsoft styleswin sret sliced recursive transformer paper https arxiv org abs 2111 05297 code https github com szq0214 sret dynamic token normalization improves vision transformer paper https arxiv org abs 2112 02624 tokenlearner what can 8 learned tokens do for images and videos paper https arxiv org abs 2106 11297 code https github com google research scenic tree main scenic projects token learner improved robustness of vision transformer via prelayernorm in patch embedding paper https arxiv org abs 2111 08413 orvit object region video transformers paper https arxiv org abs 2110 06915 code https roeiherz github io orvit adaptively multi view and temporal fusing transformer for 3d human pose estimation paper https arxiv org abs 2110 05092 code https github com lelexx mtf transformer nvit nvit vision transformer compression and parameter redistribution paper https arxiv org abs 2110 04869 6d vit category level 6d object pose estimation via transformer based instance representation learning paper https arxiv org abs 2110 04792 adversarial token attacks on vision transformers paper https arxiv org abs 2110 04337 contextual transformer networks for visual recognition paper https arxiv org pdf 2107 12292 pdf code https github com jdai cv cotnet transalnet transalnet visual saliency prediction using transformers paper https arxiv org abs 2110 03593 mobilevit mobilevit light weight general purpose and mobile friendly vision transformer paper https arxiv org abs 2110 02178 a free lunch from vit adaptive attention multi scale fusion transformer for fine grained visual recognition paper https arxiv org abs 2110 01240 3d transformer 3d transformer molecular representation with transformer in 3d space paper https arxiv org abs 2110 01191 cctrans cctrans simplifying and improving crowd counting with transformer paper https arxiv org abs 2109 14483 ufo vit ufo vit high performance linear vision transformer without softmax paper https arxiv org abs 2109 14382 sparse spatial transformers for few shot learning paper https arxiv org abs 2109 12932 vision transformer hashing for image retrieval paper https arxiv org abs 2109 12564 oh former oh former omni relational high order transformer for person re identification paper https arxiv org abs 2109 11159 pix2seq pix2seq a language modeling framework for object detection paper https arxiv org abs 2109 10852 coatnet coatnet marrying convolution and attention for all data sizes paper https arxiv org pdf 2106 04803 pdf lotr lotr face landmark localization using localization transformer paper https arxiv org abs 2109 10057 transformer unet raw image processing with unet paper https arxiv org abs 2109 08417 graformer graformer graph convolution transformer for 3d pose estimation paper https arxiv org abs 2109 08364 cdtrans cdtrans cross domain transformer for unsupervised domain adaptation paper https arxiv org abs 2109 06165 pq transformer jointly parsing 3d objects and layouts from point clouds paper https arxiv org abs 2109 05566 code https github com open air sun pq transformer anchor detr query design for transformer based detector paper https arxiv org abs 2109 07107 code https github com megvii model anchordetr dab detr dab detr dynamic anchor boxes are better queries for detr paper https arxiv org abs 2201 12329 code https github com idea opensource dab detr esrt efficient transformer for single image super resolution paper https arxiv org abs 2108 11084 maskformer maskformer per pixel classification is not all you need for semantic segmentation paper http arxiv org abs 2107 06278 code https github com facebookresearch maskformer swinir swinir image restoration using swin transformer paper https arxiv org abs 2108 10257 code https github com jingyunliang swinir trans4trans trans4trans efficient transformer for transparent object and semantic scene segmentation in real world navigation assistance paper https arxiv org abs 2108 09174 do vision transformers see like convolutional neural networks paper https arxiv org abs 2108 08810 boosting salient object detection with transformer based asymmetric bilateral u net paper https arxiv org abs 2108 07851 light field image super resolution with transformers paper https arxiv org abs 2108 07597 code https github com zhengyuliang24 lft focal self attention for local global interactions in vision transformers paper https arxiv org abs 2107 00641 code https github com microsoft focal transformer polyp pvt polyp segmentation with pyramid vision transformers paper https arxiv org abs 2108 06932 code https github com dengpingfan polyp pvt mobile former bridging mobilenet and transformer paper https arxiv org abs 2108 05895 tritransnet tritransnet rgb d salient object detection with a triplet transformer embedding network paper https arxiv org abs 2108 03798 psvit psvit better vision transformer via token pooling and attention sharing paper https arxiv org abs 2108 03428 boosting few shot semantic segmentation with transformers paper https arxiv org abs 2108 02266 code https github com guoleisun trfs congested crowd instance localization with dilated convolutional swin transformer paper https arxiv org abs 2108 00584 evo vit slow fast token evolution for dynamic vision transformer paper https arxiv org abs 2108 01390 styleformer styleformer transformer based generative adversarial networks with style vector paper https arxiv org abs 2106 07023 code https github com jeeseung park styleformer cmt cmt convolutional neural networks meet vision transformers paper https arxiv org abs 2107 06263 transattunet transattunet multi level attention guided u net with transformer for medical image segmentation paper https arxiv org abs 2107 05274 transclaw u net claw u net with transformers for medical image segmentation paper https arxiv org abs 2107 05188 vitgan vitgan training gans with vision transformers paper https arxiv org abs 2107 04589 what makes for hierarchical vision transformer paper https arxiv org abs 2107 02174 trans4trans trans4trans efficient transformer for transparent object segmentation to help visually impaired people navigate in the real world paper https arxiv org abs 2107 03172 ffvt feature fusion vision transformer for fine grained visual categorization paper https arxiv org abs 2107 02341 transformerfusion transformerfusion monocular rgb scene reconstruction using transformers paper https arxiv org abs 2107 02191 escaping the big data paradigm with compact transformers paper https arxiv org pdf 2104 05704 pdf how to train your vit data augmentation and regularization in vision transformers paper https arxiv org pdf 2106 10270 pdf beyond self attention external attention using two linear layers for visual tasks paper https arxiv org pdf 2105 02358 pdf xcit xcit cross covariance image transformers paper https arxiv org pdf 2106 09681 pdf code https github com facebookresearch xcit shuffle transformer rethinking spatial shuffle for vision transformer paper https arxiv org abs 2106 03650 code https github com mulinmeng shuffle transformer video swin transformer paper https arxiv org abs 2106 13230 code https github com swintransformer video swin transformer volo volo vision outlooker for visual recognition paper https arxiv org abs 2106 13112 code https github com sail sg volo transformer meets convolution a bilateral awareness net work for semantic segmentation of very fine resolution ur ban scene images paper https arxiv org abs 2106 12413 end to end temporal action detection with transformer paper https arxiv org abs 2106 10271 code https github com xlliu7 tadtr how to train your vit data augmentation and regularization in vision transformers paper https arxiv org abs 2106 10270 efficient self supervised vision transformers for representation learning paper https arxiv org abs 2106 09785 space time mixing attention for video transformer paper https arxiv org abs 2106 05968 transformed cnns recasting pre trained convolutional layers with self attention paper https arxiv org abs 2106 05795 cat cat cross attention in vision transformer paper https arxiv org abs 2106 05786 scaling vision transformers paper https arxiv org abs 2106 04560 detreg detreg unsupervised pretraining with region priors for object detection paper https arxiv org abs 2106 04550 code https amirbar net detreg chasing sparsity in vision transformers an end to end exploration paper https arxiv org abs 2106 04533 mvit mvit mask vision transformer for facial expression recognition in the wild paper https arxiv org abs 2106 04520 demystifying local vision transformer sparse connectivity weight sharing and dynamic weight paper https arxiv org abs 2106 04263 on improving adversarial transferability of vision transformers paper https arxiv org abs 2106 04169 fully transformer networks for semantic imagesegmentation paper https arxiv org abs 2106 04108 visual transformer for task aware active learning paper https arxiv org abs 2106 03801 code https github com razvancaramalau visual transformer for task aware active learning efficient training of visual transformers with small size datasets paper https arxiv org abs 2106 03746 reveal of vision transformers robustness against adversarial attacks paper https arxiv org abs 2106 03734 person re identification with a locally aware transformer paper https arxiv org abs 2106 03720 refiner refiner refining self attention for vision transformers paper https arxiv org abs 2106 03714 vitae vitae vision transformer advanced by exploring intrinsic inductive bias paper https arxiv org abs 2106 03348 video instance segmentation using inter frame communication transformers paper https arxiv org abs 2106 03299 transformer in convolutional neural networks paper https arxiv org abs 2106 03180 code https github com yun liu transcnn uformer uformer a general u shaped transformer for image restoration paper https arxiv org abs 2106 03106 code https github com zhendongwang6 uformer patch slimming for efficient vision transformers paper https arxiv org abs 2106 02852 regionvit regionvit regional to local attention for vision transformers paper https arxiv org abs 2106 02689 associating objects with transformers for video object segmentation paper https arxiv org abs 2106 02638 code https github com z x yang aot few shot segmentation via cycle consistent transformer paper https arxiv org abs 2106 02320 glance and gaze vision transformer paper https arxiv org abs 2106 02277 code https github com yucornetto gg transformer unsupervised mri reconstruction via zero shot learned adversarial transformers paper https arxiv org pdf 2105 08059 pdf dynamicvit dynamicvit efficient vision transformers with dynamic token sparsification paper https arxiv org abs 2106 02034 code https dynamicvit ivg research xyz when vision transformers outperform resnets without pretraining or strong data augmentations paper https arxiv org abs 2106 01548 code unsupervised out of domain detection via pre trained transformers paper https arxiv org abs 2106 00948 transmil transmil transformer based correlated multiple instance learning for whole slide image classication paper https arxiv org abs 2106 00908 transvos transvos video object segmentation with transformers paper https arxiv org abs 2106 00588 kvt kvt k nn attention for boosting vision transformers paper https arxiv org abs 2106 00515 msg transformer msg transformer exchanging local spatial information by manipulating messenger tokens paper https arxiv org abs 2105 15168 code https github com hustvl msg transformer segformer segformer simple and efficient design for semantic segmentation with transformers paper https arxiv org abs 2105 15203 code https github com nvlabs segformer sdnet sdnet mutil branch for single image deraining using swin paper https arxiv org abs 2105 15077 code https github com h tfx sdnet dvt not all images are worth 16x16 words dynamic vision transformers with adaptive sequence length paper https arxiv org abs 2105 15075 gazetr gaze estimation using transformer paper https arxiv org abs 2105 14424 code https github com yihuacheng gazetr transformer based deep image matching for generalizable person re identification paper https arxiv org abs 2105 14432 less is more pay less attention in vision transformers paper https arxiv org abs 2105 14217 foveater foveater foveated transformer for image classification paper https arxiv org abs 2105 14173 transda transformer based source free domain adaptation paper https arxiv org abs 2105 14138 code https github com ygjwd12345 transda an attention free transformer paper https arxiv org abs 2105 14103 ptnet ptnet a high resolution infant mri synthesizer based on transformer paper https arxiv org abs 2105 13993 rest rest an efficient transformer for visual recognition paper https arxiv org abs 2105 13677 code https github com wofmanaf rest cogview cogview mastering text to image generation via transformers paper https arxiv org abs 2105 13290 nest aggregating nested transformers paper https arxiv org abs 2105 12723 tapg temporal action proposal generation with transformers paper https arxiv org abs 2105 12043 boosting crowd counting with transformers paper https arxiv org abs 2105 10926 cotr cotr convolution in transformer network for end to end polyp detection paper https arxiv org abs 2105 10925 transvod end to end video object detection with spatial temporal transformers paper https arxiv org abs 2105 10920 code https github com sjtu luhe transvod intriguing properties of vision transformers paper https arxiv org abs 2105 10497 code https git io js15x combining transformer generators with convolutional discriminators paper https arxiv org abs 2105 10189 rethinking the design principles of robust vision transformer paper https arxiv org abs 2105 07926 vision transformers are robust learners paper https arxiv org abs 2105 07581 code https git io j3vo0 manipulation detection in satellite images using vision transformer paper https arxiv org abs 2105 06373 swin unet swin unet unet like pure transformer for medical image segmentation paper https arxiv org abs 2105 05537 code https github com hucaofighting swin unet self supervised learning with swin transformers paper https arxiv org abs 2105 04553 code https github com swintransformer transformer ssl sctn sctn sparse convolution transformer network for scene flow estimation paper https arxiv org abs 2105 04447 relationtrack relationtrack relation aware multiple object tracking with decoupled representation paper https arxiv org abs 2105 04322 vgtr visual grounding with transformers paper https arxiv org abs 2105 04281 pst visual composite set detection using part and sum transformers paper https arxiv org abs 2105 02170 trtr trtr visual tracking with transformer paper https arxiv org abs 2105 03817 code https github com tongtybj trtr motr motr end to end multiple object tracking with transformer paper https arxiv org abs 2105 03247 code https github com megvii model motr attention for image registration air an unsupervised transformer approach paper https arxiv org abs 2105 02282 transhash transhash transformer based hamming hashing for efficient image retrieval paper https arxiv org abs 2105 01823 istr istr end to end instance segmentation with transformers paper https arxiv org abs 2105 00637 code https github com hujiecpp istr cat cat cross attention transformer for one shot object detection paper https arxiv org abs 2104 14984 cosformer cosformer detecting co salient object with transformers paper https arxiv org abs 2104 14729 end to end attention based image captioning paper https arxiv org abs 2104 14721 pmtrans pyramid medical transformer for medical image segmentation paper https arxiv org abs 2104 14702 handsformer handsformer keypoint transformer for monocular 3d pose estimation ofhands and object in interaction paper https arxiv org abs 2104 14639 gashis transformer gashis transformer a multi scale visual transformer approach for gastric histopathology image classification paper https arxiv org abs 2104 14528 emerging properties in self supervised vision transformers paper https arxiv org abs 2104 14294 intra inpainting transformer for anomaly detection paper https arxiv org abs 2104 13897 twins twins revisiting spatial attention design in vision transformers paper https arxiv org abs 2104 13840 code https github com meituan automl twins mlmspt point cloud learning with transformer paper https arxiv org abs 2104 13636 medical transformer universal brain encoder for 3d mri analysis paper https arxiv org abs 2104 13633 contnet contnet why not use convolution and transformer at the same time paper https arxiv org abs 2104 13497 code https github com yan hao tian contnet dtnet dual transformer for point cloud analysis paper https arxiv org abs 2104 13044 improve vision transformers training by suppressing over smoothing paper https arxiv org abs 2104 12753 code https github com chengyuegongr patchvisiontransformer transformer meets dcfam a novel semantic segmentation scheme for fine resolution remote sensing images paper https arxiv org abs 2104 12137 m3detr m3detr multi representation multi scale mutual relation 3d object detection with transformers paper https arxiv org abs 2104 11896 code https github com rayguan97 m3detr skeletor skeletor skeletal transformers for robust body pose estimation paper https arxiv org abs 2104 11712 facet learning to cluster faces via transformer paper https arxiv org abs 2104 11502 mvit multiscale vision transformers paper https arxiv org abs 2104 11227 code https github com facebookresearch slowfast vatt vatt transformers for multimodal self supervised learning from raw video audio and text paper https arxiv org abs 2104 11178 so vit so vit mind visual tokens for vision transformer paper https arxiv org abs 2104 10935 code https github com jiangtaoxie so vit token labeling training a 85 5 top 1 accuracy vision transformer with 56m parameters on imagenet paper https arxiv org abs 2104 10858 code https github com zihangjiang tokenlabeling transrppg transrppg remote photoplethysmography transformer for 3d mask face presentation attack detection paper https arxiv org abs 2104 07419 videogpt videogpt video generation using vq vae and transformers paper https arxiv org abs 2104 10157 m2tr m2tr multi modal multi scale transformers for deepfake detection paper https arxiv org abs 2104 09770 transformer transforms salient object detection and camouflaged object detection paper https arxiv org abs 2104 10127 transcrowd transcrowd weakly supervised crowd counting with transformer paper https arxiv org abs 2104 09116 code https github com dk liang transcrowd visual transformer pruning paper https arxiv org abs 2104 08500 self supervised video retrieval transformer network paper https arxiv org abs 2104 07993 vision transformer using low level chest x ray feature corpus for covid 19 diagnosis and severity quantification paper https arxiv org abs 2104 07235 transgan transgan two transformers can make one strong gan paper https arxiv org abs 2102 07074 code https github com vita group transgan geometry free view synthesis transformers and no 3d priors paper https arxiv org abs 2104 07652 code https git io jonwn coat co scale conv attentional image transformers paper https arxiv org abs 2104 06399 code https github com mlpc ucsd coat localvit localvit bringing locality to vision transformers paper https arxiv org abs 2104 05707 code https github com ofsoundof localvit cit cloth interactive transformer for virtual try on paper https arxiv org abs 2104 05519 code https arxiv org abs 2104 05519 handwriting transformers paper https arxiv org abs 2104 03964 sit sit self supervised vision transformer paper https arxiv org abs 2104 03602 code https github com sara ahmed sit on the robustness of vision transformers to adversarial examples paper https arxiv org abs 2104 02610 an empirical study of training self supervised visual transformers paper https arxiv org abs 2104 02057 a video is worth three views trigeminal transformers for video based person re identification paper https arxiv org abs 2104 01745 aot gan aggregated contextual transformations for high resolution image inpainting paper https arxiv org abs 2104 01431 code https github com researchmm aot gan for inpainting deepfake detection scheme based on vision transformer and distillation paper https arxiv org abs 2104 01353 atag augmented transformer with adaptive graph for temporal action proposal generation paper https arxiv org pdf 2103 16024 tuber tuber tube transformer for action detection paper https arxiv org abs 2104 00969 aaformer aaformer auto aligned transformer for person re identification paper https arxiv org abs 2104 00921 tfill tfill image completion via a transformer based architecture paper https arxiv org abs 2104 00845 group free 3d object detection via transformers paper https arxiv org abs 2104 00678 code https github com zeliu98 group free 3d stgt spatial temporal graph transformer for multiple object tracking paper https arxiv org abs 2104 00194 going deeper with image transformers paper https arxiv org abs 2103 17239 meta detr meta detr few shot object detection via unified image level meta learning paper https arxiv org abs 2103 11731 code https github com zhanggongjie meta detr da detr da detr domain adaptive detection transformer by hybrid attention paper https arxiv org abs 2103 17084 robust facial expression recognition with convolutional visual transformers paper https arxiv org abs 2103 16854 thinking fast and slow efficient text to visual retrieval with transformers paper https arxiv org abs 2103 16553 spatiotemporal transformer for video based person re identification paper https arxiv org abs 2103 16469 transunet transunet transformers make strong encoders for medical image segmentation paper https arxiv org abs 2102 04306 code https github com beckschen transunet cvt cvt introducing convolutions to vision transformers paper https arxiv org abs 2103 15808 code https github com leoxiaobin cvt tfpose tfpose direct human pose estimation with transformers paper https arxiv org abs 2103 15320 transcenter transcenter transformers with dense queries for multiple object tracking paper https arxiv org abs 2103 15145 face transformer for recognition paper https arxiv org abs 2103 14803 on the adversarial robustness of visual transformers paper https arxiv org abs 2103 15670 understanding robustness of transformers for image classification paper https arxiv org abs 2103 14586 lifting transformer for 3d human pose estimation in video paper https arxiv org abs 2103 14304 gsa net global self attention networks for image recognition paper https arxiv org abs 2010 03019 high fidelity pluralistic image completion with transformers paper https arxiv org abs 2103 14031 code http raywzy com ict dpt vision transformers for dense prediction paper https arxiv org abs 2103 13413 code https github com intel isl dpt transfg transfg a transformer architecture for fine grained recognition paper https arxiv org abs 2103 07976 timesformer is space time attention all you need for video understanding paper https arxiv org abs 2102 05095 multi view 3d reconstruction with transformer paper https arxiv org abs 2103 12957 can vision transformers learn without natural images paper https arxiv org abs 2103 13023 code https hirokatsukataoka16 github io vision transformers without natural images end to end trainable multi instance pose estimation with transformers paper https arxiv org abs 2103 12115 instance level image retrieval using reranking transformers paper https arxiv org abs 2103 12424 code https arxiv org abs 2103 12236 bossnas bossnas exploring hybrid cnn transformers with block wisely self supervised neural architecture search paper https arxiv org abs 2103 12424 code https github com changlin31 bossnas ceit incorporating convolution designs into visual transformers paper https arxiv org abs 2103 11816 deepvit deepvit towards deeper vision transformer paper https arxiv org abs 2103 11886 enhancing transformer for video understanding using gated multi level attention and temporal adversarial training paper https arxiv org abs 2103 10043 3d human pose estimation with spatial and temporal transformers paper https arxiv org abs 2103 10455 code https github com zczcwh poseformer sunetr sunetr transformers for 3d medical image segmentation paper https arxiv org abs 2103 10504 scalable visual transformers with hierarchical pooling paper https arxiv org abs 2103 10619 convit convit improving vision transformers with soft convolutional inductive biases paper https arxiv org abs 2103 10697 transmed transmed transformers advance multi modal medical image classification paper https arxiv org abs 2103 05940 u transformer u net transformer self and cross attention for medical image segmentation paper https arxiv org abs 2103 06104 spectr spectr spectral transformer for hyperspectral pathology image segmentation paper https arxiv org abs 2103 03604 code https github com hfut xc yun spectr transbts transbts multimodal brain tumor segmentation using transformer paper https arxiv org abs 2103 04430 code https github com wenxuan 1119 transbts sstn sstn self supervised domain adaptation thermal object detection for autonomous driving paper https arxiv org abs 2103 03150 transformer is all you need multimodal multitask learning with a unified transformer paper https arxiv org abs 2102 10772 code https mmf sh cpvt do we really need explicit position encodings for vision transformers paper https arxiv org abs 2102 10882 code https github com meituan automl cpvt deepfake video detection using convolutional vision transformer paper https arxiv org abs 2102 11126 training vision transformers for image retrieval paper https arxiv org abs 2102 05644 vtn video transformer network paper https arxiv org abs 2102 00719 botnet bottleneck transformers for visual recognition paper https arxiv org abs 2101 11605 cptr cptr full transformer network for image captioning paper https arxiv org abs 2101 10804 learn to dance with aist music conditioned 3d dance generation paper https arxiv org abs 2101 08779 code https google github io aichoreographer trans2seg segmenting transparent object in the wild with transformer paper https arxiv org abs 2101 08461 code https github com xieenze trans2seg investigating the vision transformer model for image retrieval tasks paper https arxiv org abs 2101 03771 trear trear transformer based rgb d egocentric action recognition paper https arxiv org abs 2101 03904 visualsparta visualsparta sparse transformer fragment level matching for large scale text to image search paper https arxiv org abs 2101 00265 trackformer trackformer multi object tracking with transformers paper https arxiv org abs 2101 02702 tape transformer guided geometry model for flow based unsupervised visual odometry paper https arxiv org abs 2101 02143 triq transformer for image quality assessment paper https arxiv org abs 2101 01097 code https github com junyongyou triq transtrack transtrack multiple object tracking with transformer paper https arxiv org abs 2012 15460 code https github com peizesun transtrack deit training data efficient image transformers distillation through attention paper https arxiv org abs 2012 12877 code https github com facebookresearch deit pointformer 3d object detection with pointformer paper https arxiv org abs 2012 11409 vit frcnn toward transformer based object detection paper https arxiv org abs 2012 09958 taming transformers taming transformers for high resolution image synthesis paper https arxiv org abs 2012 09841 code https compvis github io taming transformers sceneformer sceneformer indoor scene generation with transformers paper https arxiv org abs 2012 09793 pct pct point cloud transformer paper https arxiv org abs 2012 09688 ped detr for pedestrian detection paper https arxiv org abs 2012 06785 transformer guided geometry model for flow based unsupervised visual odometry paper https arxiv org abs 2101 02143 c tran general multi label image classification with transformers paper https arxiv org abs 2011 14027 2022 tpami p2t p2t pyramid pooling transformer for scene understanding paper https ieeexplore ieee org document 9870559 eccv x clip expanding language image pretrained models for general video recognition paper https arxiv org abs 2208 02816 code https aka ms x clip tinyvit tinyvit fast pretraining distillation for small vision transformers paper https arxiv org abs 2207 10666 code https github com microsoft cream tree main tinyvit fastmetro cross attention of disentangled modalities for 3d human mesh recovery with transformers paper https arxiv org abs 2207 13820 code https github com postech ami fastmetro aiatrack aiatrack attention in attention for transformer visual tracking paper https arxiv org abs 2207 09603 code https github com little podi aiatrack ostrack joint feature learning and relation modeling for tracking a one stream framework paper https arxiv org abs 2203 11991 code https github com botaoye ostrack unicorn towards grand unification of object tracking paper https arxiv org abs 2207 07078 code https github com masterbin iiau unicorn p3aformer tracking objects as pixel wise distributions paper https arxiv org abs 2207 05518 code https github com dvlab research eccv22 p3aformer tracking objects as pixel wise distributions cvpr mae masked autoencoders are scalable vision learners paper https arxiv org abs 2111 06377 code https github com facebookresearch mae cswin transformer a general vision transformer backbone with cross shaped windows paper https arxiv org abs 2107 00652 code https github com microsoft cswin transformer fast point transformer paper https arxiv org abs 2112 04702 edter edge detection with transformer paper https openaccess thecvf com content cvpr2022 html pu edter edge detection with transformer cvpr 2022 paper html code https github com mengyangpu edter bridged transformer for vision and point cloud 3d object detection paper https openaccess thecvf com content cvpr2022 html wang bridged transformer for vision and point cloud 3d object detection cvpr 2022 paper html mnsrnet multimodal transformer network for 3d surface super resolution paper https openaccess thecvf com content cvpr2022 html xie mnsrnet multimodal transformer network for 3d surface super resolution cvpr 2022 paper html hypertransformer a textural and spectral feature fusion transformer for pansharpening paper https openaccess thecvf com content cvpr2022 html bandara hypertransformer a textural and spectral feature fusion transformer for pansharpening cvpr 2022 paper html code https github com wgcban hypertransformer keypoint transformer solving joint identification in challenging hands and object interactions for accurate 3d pose estimation paper https openaccess thecvf com content cvpr2022 html hampali keypoint transformer solving joint identification in challenging hands and object cvpr 2022 paper html mpvit multi path vision transformer for dense prediction paper https openaccess thecvf com content cvpr2022 html lee mpvit multi path vision transformer for dense prediction cvpr 2022 paper html code https github com youngwanlee mpvit a vit adaptive tokens for efficient vision transformer paper https openaccess thecvf com content cvpr2022 html yin a vit adaptive tokens for efficient vision transformer cvpr 2022 paper html topformer token pyramid transformer for mobile semantic segmentation paper https openaccess thecvf com content cvpr2022 html zhang topformer token pyramid transformer for mobile semantic segmentation cvpr 2022 paper html code https github com hustvl topformer continual learning with lifelong vision transformer paper https openaccess thecvf com content cvpr2022 html wang continual learning with lifelong vision transformer cvpr 2022 paper html swin transformer v2 scaling up capacity and resolution paper https arxiv org abs 2111 09883 code https github com microsoft swin transformer voxel set transformer a set to set approach to 3d object detection from point clouds paper https arxiv org abs 2203 10314 code https github com skyhehe123 voxset multi class token transformer for weakly supervised semantic segmentation paper https openaccess thecvf com content cvpr2022 papers xu multi class token transformer for weakly supervised semantic segmentation cvpr 2022 paper pdf human object interaction detection via disentangled transformer paper https openaccess thecvf com content cvpr2022 html zhou human object interaction detection via disentangled transformer cvpr 2022 paper html lgt net indoor panoramic room layout estimation with geometry aware transformer network paper https openaccess thecvf com content cvpr2022 html jiang lgt net indoor panoramic room layout estimation with geometry aware transformer network cvpr 2022 paper html sparse local patch transformer for robust face alignment and landmarks inherent relation learning paper https openaccess thecvf com content cvpr2022 html xia sparse local patch transformer for robust face alignment and landmarks cvpr 2022 paper html vision transformer with deformable attention paper https openaccess thecvf com content cvpr2022 html xia vision transformer with deformable attention cvpr 2022 paper html dearkd data efficient early knowledge distillation for vision transformers paper https arxiv org abs 2204 12997 restormer restormer efficient transformer for high resolution image restoration paper https arxiv org abs 2111 09881 code https github com swz30 restormer sam detr accelerating detr convergence via semantic aligned matching paper https arxiv org abs 2203 06883 code https github com zhanggongjie sam detr bevt bevt bert pretraining of video transformers paper https arxiv org pdf 2112 01529 pdf code https github com xyzforever bevt mobileformer mobile former bridging mobilenet and transformer paper https arxiv org pdf 2108 05895 pdf strm spatio temporal relation modeling for few shot action recognition paper https arxiv org pdf 2112 05132 pdf code https github com anirudh257 strm minivit minivit compressing vision transformers with weight multiplexing paper https arxiv org abs 2204 07154 code https github com microsoft cream tree main minivit coformer collaborative transformers for grounded situation recognition paper https arxiv org abs 2203 16518 code https github com jhcho99 coformer dw vit beyond fixation dynamic window visual transformer paper https arxiv org pdf 2203 12856 pdf code https github com pzhren dw vit tokenfusion multimodal token fusion for vision transformers paper https arxiv org pdf 2204 08721 pdf cmt convolutional neural networks meet vision transformers paper https arxiv org pdf 2107 06263 pdf fine tuning image transformers using learnable memory paper https arxiv org pdf 2203 15243 pdf transmix attend to mix for vision transformers paper https arxiv org pdf 2111 09833 pdf code https github com beckschen transmix nommer nominate synergistic context in vision transformer for visual recognition paper https arxiv org pdf 2111 12994 pdf code https github com tencentyouturesearch visualrecognition nommer ssa shunted self attention via multi scale token aggregation paper https arxiv org pdf 2111 15193 pdf code https github com oliverrensu shunted transformer rvt towards robust vision transformer paper https arxiv org pdf 2105 07926 pdf code https github com vtddggg robust vision transformer lvt lite vision transformer with enhanced self attention paper https arxiv org pdf 2112 10809 pdf code https github com chenglin yang lvt stytr2 stytr2 image style transfer with transformers paper https arxiv org pdf 2105 14576 pdf code https github com diyiiyiii stytr 2 wacv image adaptive hint generation via vision transformer for outpainting paper https openaccess thecvf com content wacv2022 papers kong image adaptive hint generation via vision transformer for outpainting wacv 2022 paper pdf code https github com kdh4672 hgonet iclr relvit relvit concept guided vision transformer for visual relational reasoning paper https arxiv org pdf 2204 11167 pdf code https github com nvlabs relvit crossformer crossformer a versatile vision transformer based on cross scale attention paper https arxiv org abs 2108 00154 code https github com cheerss crossformer uniformer unified transformer for efficient spatiotemporal representation learning paper https arxiv org abs 2201 04676 code https github com sense x uniformer dab detr dab detr dynamic anchor boxes are better queries for detr paper https arxiv org abs 2201 12329 code https github com idea opensource dab detr 2021 neurips proto program guided transformer for program guided tasks paper https arxiv org abs 2110 00804 code https github com sjtuytc neurips21 proto program guided transformers for program guided tasks augvit augmented shortcuts for vision transformers paper https arxiv org abs 2106 15941 code https github com huawei noah cv backbones tree master augvit pytorch yolos you only look at one sequence rethinking transformer in vision through object detection paper https arxiv org abs 2106 00666 code https github com hustvl yolos cats semantic correspondence with transformers paper https arxiv org abs 2106 02520 code https github com sunghwanhong cats moment detr qvhighlights detecting moments and highlights in videos via natural language queries paper https arxiv org abs 2107 09609 code https github com jayleicn moment detr dual stream network for visual recognition paper https arxiv org abs 2105 14734 code https github com gaopengcuhk dsnet container container context aggregation network paper https arxiv org abs 2106 01401 code https github com gaopengcuhk container tnt transformer in transformer paper https arxiv org abs 2103 00112 code https github com huawei noah noah research tree master tnt t6d direct transformers for multi object 6d pose direct regression paper https arxiv org abs 2109 10948 long short term transformer for online action detection paper https papers nips cc paper 2021 hash 08b255a5d42b89b0585260b6f2360bdd abstract html transformerfusion monocular rgb scene reconstruction using transformers paper https papers nips cc paper 2021 hash 0a87257e5308197df43230edf4ad1dae abstract html transmatcher deep image matching through transformers for generalizable person re identification paper https papers nips cc paper 2021 hash 0f49c89d1e7298bb9930789c8ed59d48 abstract html transmil transformer based correlated multiple instance learning for whole slide image classification paper https papers nips cc paper 2021 hash 10c272d06794d3e5785d5e7c5356e9ff abstract html associating objects with transformers for video object segmentation paper https papers nips cc paper 2021 hash 147702db07145348245dc5a2f2fe5683 abstract html test time personalization with a transformer for human pose estimation paper https papers nips cc paper 2021 hash 1517c8664be296f0d87d9e5fc54fdd60 abstract html revitalizing cnn attention via transformers in self supervised visual representation learning paper https papers nips cc paper 2021 hash 21be992eb8016e541a15953eee90760e abstract html dynamic grained encoder for vision transformers paper https papers nips cc paper 2021 hash 2d969e2cee8cfa07ce7ca0bb13c7a36d abstract html hrformer high resolution vision transformer for dense predict paper https papers nips cc paper 2021 hash 3bbfdde8842a5c44a0323518eec97cbe abstract html searching the search space of vision transformer paper https papers nips cc paper 2021 hash 48e95c45c8217961bf6cd7696d80d238 abstract html not all images are worth 16x16 words dynamic transformers for efficient image recognition paper https papers nips cc paper 2021 hash 64517d8435994992e682b3e4aa0a0661 abstract html segformer simple and efficient design for semantic segmentation with transformers paper https papers nips cc paper 2021 hash 64f1f27bf1b4ec22924fd0acb550c235 abstract html do vision transformers see like convolutional neural networks paper https papers nips cc paper 2021 hash 652cf38361a209088302ba2b8b7f51e0 abstract html keeping your eye on the ball trajectory attention in video transformers paper https papers nips cc paper 2021 hash 67f7fb873eaf29526a11a9b7ac33bfac abstract html glance and gaze vision transformer paper https papers nips cc paper 2021 hash 6c524f9d5d7027454a783c841250ba71 abstract html mst masked self supervised transformer for visual representation paper https papers nips cc paper 2021 hash 6dbbe6abe5f14af882ff977fc3f35501 abstract html dynamicvit efficient vision transformers with dynamic token sparsification paper https papers nips cc paper 2021 hash 747d3443e319a22747fbb873e8b2f9f2 abstract html transgan two pure transformers can make one strong gan and that can scale up paper https papers nips cc paper 2021 hash 7c220a2091c26a7f5e9f1cfb099511e3 abstract html augmented shortcuts for vision transformers paper https papers nips cc paper 2021 hash 818f4654ed39a1c147d1e51a00ffb4cb abstract html improved transformer for high resolution gans paper https papers nips cc paper 2021 hash 98dce83da57b0395e163467c9dae521b abstract html all tokens matter token labeling for training better vision transformers paper https papers nips cc paper 2021 hash 9a49a25d845a483fae4be7e341368e36 abstract html xcit cross covariance image transformers paper https papers nips cc paper 2021 hash a655fbe4b8d7439994aa37ddad80de56 abstract html efficient training of visual transformers with small datasets paper https papers nips cc paper 2021 hash c81e155d85dae5430a8cee6f2242e82c abstract html iccv swin transformer hierarchical vision transformer using shifted windows marr prize paper https arxiv org abs 2103 14030 code https github com microsoft swin transformer ict high fidelity pluralistic image completion with transformers paper https arxiv org pdf 2103 14031 pdf code https github com raywzy ict pointr pointr diverse point cloud completion with geometry aware transformers oral paper https arxiv org abs 2108 08839 code https github com yuxumin pointr sttr revisiting stereo depth estimation from a sequence to sequence perspective with transformers paper https arxiv org abs 2011 02910v2 code https github com mli0603 stereo transformer tsp fcos rethinking transformer based set prediction for object detection paper https arxiv org abs 2011 10881 paint transformer feed forward neural painting with stroke prediction oral paper https arxiv org abs 2108 03798 code https github com huage001 painttransformer 3dvg transformer relation modeling for visual grounding on point clouds paper https openaccess thecvf com content iccv2021 papers zhao 3dvg transformer relation modeling for visual grounding on point clouds iccv 2021 paper pdf t2t vit training vision transformers from scratch on imagenet paper https arxiv org abs 2101 11986 code https github com yitu opensource t2t vit thundr thundr transformer based 3d human reconstruction with markers paper https openaccess thecvf com content iccv2021 html zanfir thundr transformer based 3d human reconstruction with markers iccv 2021 paper html multi scale vision longformer a new vision transformer for high resolution image encoding paper https arxiv org abs 2103 15358 pvt pyramid vision transformer a versatile backbone for dense prediction without convolutions paper https arxiv org abs 2102 12122 code https github com whai362 pvt spatial temporal transformer for dynamic scene graph generation paper https openaccess thecvf com content iccv2021 papers cong spatial temporal transformer for dynamic scene graph generation iccv 2021 paper pdf glit glit neural architecture search for global and local image transformer paper https openaccess thecvf com content iccv2021 papers chen glit neural architecture search for global and local image transformer iccv 2021 paper pdf trar trar routing the attention spans in transformer for visual question answering paper https openaccess thecvf com content iccv2021 papers zhou trar routing the attention spans in transformer for visual question iccv 2021 paper pdf unit unit multimodal multitask learning with a unified transformer paper https openaccess thecvf com content iccv2021 html hu unit multimodal multitask learning with a unified transformer iccv 2021 paper html code https mmf sh stochastic transformer networks with linear competing units application to end to end sl translation paper https openaccess thecvf com content iccv2021 papers voskou stochastic transformer networks with linear competing units application to end to end iccv 2021 paper pdf transformer based dual relation graph for multi label image recognition paper https openaccess thecvf com content iccv2021 papers zhao transformer based dual relation graph for multi label image recognition iccv 2021 paper pdf localtrans localtrans a multiscale local transformer network for cross resolution homography estimation paper https openaccess thecvf com content iccv2021 papers shao localtrans a multiscale local transformer network for cross resolution homography estimation iccv 2021 paper pdf improving 3d object detection with channel wise transformer paper https openaccess thecvf com content iccv2021 html sheng improving 3d object detection with channel wise transformer iccv 2021 paper html a latent transformer for disentangled face editing in images and videos paper https openaccess thecvf com content iccv2021 papers yao a latent transformer for disentangled face editing in images and iccv 2021 paper pdf code https github com interdigitalinc latent transformer groupformer groupformer group activity recognition with clustered spatial temporal transformer paper https openaccess thecvf com content iccv2021 html li groupformer group activity recognition with clustered spatial temporal transformer iccv 2021 paper html unified questioner transformer for descriptive question generation in goal oriented visual dialogue paper https openaccess thecvf com content iccv2021 papers matsumori unified questioner transformer for descriptive question generation in goal oriented visual iccv 2021 paper pdf wb detr wb detr transformer based detector without backbone paper https openaccess thecvf com content iccv2021 papers liu wb detr transformer based detector without backbone iccv 2021 paper pdf the animation transformer visual correspondence via segment matching paper https openaccess thecvf com content iccv2021 papers casey the animation transformer visual correspondence via segment matching iccv 2021 paper pdf the animation transformer visual correspondence via segment matching paper https openaccess thecvf com content iccv2021 papers casey the animation transformer visual correspondence via segment matching iccv 2021 paper pdf relaxed transformer decoders for direct action proposal generation paper https openaccess thecvf com content iccv2021 html tan relaxed transformer decoders for direct action proposal generation iccv 2021 paper html ppt net pyramid point cloud transformer for large scale place recognition paper https openaccess thecvf com content iccv2021 papers hui pyramid point cloud transformer for large scale place recognition iccv 2021 paper pdf code https github com fpthink ppt net multimodal co attention transformer for survival prediction in gigapixel whole slide images paper https openaccess thecvf com content iccv2021 papers chen multimodal co attention transformer for survival prediction in gigapixel whole slide iccv 2021 paper pdf uncertainty guided transformer reasoning for camouflaged object detection paper https openaccess thecvf com content iccv2021 papers yang uncertainty guided transformer reasoning for camouflaged object detection iccv 2021 paper pdf image harmonization with transformer paper https openaccess thecvf com content iccv2021 html guo image harmonization with transformer iccv 2021 paper html cpde https github com zhenglab harmonytransformer cotr cotr correspondence transformer for matching across images paper https openaccess thecvf com content iccv2021 papers jiang cotr correspondence transformer for matching across images iccv 2021 paper pdf musiq musiq multi scale image quality transformer paper https openaccess thecvf com content iccv2021 papers ke musiq multi scale image quality transformer iccv 2021 paper pdf episodic transformer for vision and language navigation paper https openaccess thecvf com content iccv2021 papers pashevich episodic transformer for vision and language navigation iccv 2021 paper pdf action conditioned 3d human motion synthesis with transformer vae paper https openaccess thecvf com content iccv2021 html petrovich action conditioned 3d human motion synthesis with transformer vae iccv 2021 paper html crackformer crackformer transformer network for fine grained crack detection paper https openaccess thecvf com content iccv2021 papers liu crackformer transformer network for fine grained crack detection iccv 2021 paper pdf hit hit hierarchical transformer with momentum contrast for video text retrieval paper https openaccess thecvf com content iccv2021 papers liu hit hierarchical transformer with momentum contrast for video text retrieval iccv 2021 paper pdf event based video reconstruction using transformer paper https openaccess thecvf com content iccv2021 papers weng event based video reconstruction using transformer iccv 2021 paper pdf stvgbert stvgbert a visual linguistic transformer based framework for spatio temporal video grounding paper https openaccess thecvf com content iccv2021 papers su stvgbert a visual linguistic transformer based framework for spatio temporal video grounding iccv 2021 paper pdf hift hift hierarchical feature transformer for aerial tracking paper https openaccess thecvf com content iccv2021 papers cao hift hierarchical feature transformer for aerial tracking iccv 2021 paper pdf code https github com vision4robotics hift docformer docformer end to end transformer for document understanding paper https arxiv org abs 2106 11539 levit levit a vision transformer in convnet s clothing for faster inference paper https arxiv org abs 2104 01136 code https github com facebookresearch levit signbert signbert pre training of hand model aware representation for sign language recognition paper https arxiv org abs 2110 05382 vidtr vidtr video transformer without convolutions paper https arxiv org abs 2104 11746 actor action conditioned 3d human motion synthesis with transformer vae paper https arxiv org abs 2104 05670 segmenter segmenter transformer for semantic segmentation paper https arxiv org abs 2105 05633 code https github com rstrudel segmenter visformer visformer the vision friendly transformer paper https arxiv org abs 2104 12533 code https github com danczs visformer pnp detr pnp detr towards efficient visual analysis with transformers iccv paper https arxiv org abs 2109 07036 code https github com twangnh pnp detr votr voxel transformer for 3d object detection paper https arxiv org abs 2109 02497 transvg transvg end to end visual grounding with transformers paper https arxiv org abs 2104 08541 3detr an end to end transformer model for 3d object detection paper https arxiv org abs 2109 08141 code https github com facebookresearch 3detr eformer eformer edge enhancement based transformer for medical image denoising paper https arxiv org abs 2109 08044 transfer transfer learning relation aware facial expression representations with transformers paper https arxiv org abs 2108 11116 oriented rcnn oriented object detection with transformer paper https arxiv org abs 2106 03146 vivit vivit a video vision transformer paper https arxiv org abs 2103 15691 stark learning spatio temporal transformer for visual tracking paper https arxiv org abs 2103 17154 code https github com researchmm stark ct3d improving 3d object detection with channel wise transformer paper https arxiv org abs 2108 10723 vst visual saliency transformer paper https arxiv org abs 2104 12099 pit rethinking spatial dimensions of vision transformers paper https arxiv org abs 2103 16302 code https github com naver ai pit crossvit crossvit cross attention multi scale vision transformer for image classification paper https arxiv org abs 2103 14899 code https github com ibm crossvit pointtransformer point transformer paper https arxiv org abs 2012 09164 ts cam ts cam token semantic coupled attention map for weakly supervised object localization paper https arxiv org abs 2103 14862 code https github com vasgaowei ts cam git vts visual transformers token based image representation and processing for computer vision paper https arxiv org abs 2006 03677 transdepth transformer based attention networks for continuous pixel wise prediction paper https arxiv org pdf 2103 12091 pdf code https github com ygjwd12345 transdepth conditional detr conditional detr for fast training convergence paper https arxiv org abs 2108 06152 code https github com atten4vis conditionaldetr pit pit position invariant transform for cross fov domain adaptation paper https arxiv org abs 2108 07142 code https github com sheepooo pit position invariant transform sotr sotr segmenting objects with transformers paper https arxiv org abs 2108 06747 code https github com easton cau sotr snowflakenet snowflakenet point cloud completion by snowflake point deconvolution with skip transformer paper https arxiv org abs 2108 04444 code https github com allenxiangx snowflakenet transpose transpose keypoint localization via transformer paper https arxiv org abs 2012 14214 code https github com yangsenius transpose transreid transreid transformer based object re identification paper https arxiv org abs 2102 04378 code https github com heshuting555 transreid cwt simpler is better few shot semantic segmentation with classifier weight transformer paper https arxiv org abs 2108 03032 code https github com zhihelu cwt for fss anticipative video transformer paper https arxiv org abs 2106 02036 code http facebookresearch github io avt rethinking and improving relative position encoding for vision transformer paper https arxiv org abs 2107 14222 code https github com microsoft cream tree main irpe vision transformer with progressive sampling paper https arxiv org abs 2108 01684 code https github com yuexy ps vit smca fast convergence of detr with spatially modulated co attention paper https arxiv org abs 2101 07448 code https github com abc403 smca replication autoformer autoformer searching transformers for visual recognition paper https arxiv org pdf 2107 00651 pdf code https github com microsoft automl cvpr diverse part discovery occluded person re identification with part aware transformer paper https arxiv org abs 2106 04095 hotr hotr end to end human object interaction detection with transformers oral paper https arxiv org abs 2104 13682 metro end to end human pose and mesh reconstruction with transformers paper https arxiv org abs 2012 09760 letr line segment detection using transformers without edges paper https arxiv org abs 2101 01909 transfuser multi modal fusion transformer for end to end autonomous driving paper https arxiv org abs 2104 09224 code https github com autonomousvision transfuser pose recognition with cascade transformers paper https arxiv org abs 2104 06976 seeing out of the box end to end pre training for vision language representation learning paper https arxiv org abs 2104 03135 loftr loftr detector free local feature matching with transformers paper https arxiv org abs 2104 00680 code https zju3dv github io loftr thinking fast and slow efficient text to visual retrieval with transformers paper https arxiv org abs 2103 16553 setr rethinking semantic segmentation from a sequence to sequence perspective with transformers paper https arxiv org abs 2012 15840 code https fudan zvg github io setr transt transformer tracking paper https arxiv org abs 2103 15436 code https github com chenxin dlut transt transformer meets tracker exploiting temporal context for robust visual tracking oral paper https arxiv org abs 2103 11681 vistr end to end video instance segmentation with transformers paper https arxiv org abs 2011 14503 transformer interpretability beyond attention visualization paper https arxiv org abs 2012 09838 code https github com hila chefer transformer explainability ipt pre trained image processing transformer paper https arxiv org abs 2012 00364 up detr up detr unsupervised pre training for object detection with transformers paper https arxiv org abs 2011 09094 iqt perceptual image quality assessment with transformers workshop paper https arxiv org abs 2104 14730 high resolution complex scene synthesis with transformers workshop paper https arxiv org abs 2105 06458 coformer collaborative transformers for grounded situation recognition paper https arxiv org abs 2203 16518 code https github com jhcho99 coformer icml generative video transformer can objects be the words paper https arxiv org abs 2107 09240 gansformer generative adversarial transformers paper https arxiv org abs 2103 01209 code https github com dorarad gansformer icra ndt transformer ndt transformer large scale 3d point cloud localisation using the normal distribution transform representation paper https arxiv org abs 2103 12292 iclr vtnet vtnet visual transformer network for object goal navigation paper https arxiv org abs 2105 09447 vision transformer an image is worth 16x16 words transformers for image recognition at scale paper https arxiv org abs 2010 11929 code https github com google research vision transformer deformable detr deformable detr deformable transformers for end to end object detection paper https arxiv org abs 2010 04159 code https github com fundamentalvision deformable detr lambdanetworks modeling long range interactions without attention paper https openreview net pdf id xtjen ggl1b code https github com lucidrains lambda networks acm mm video transformer for deepfake detection with incremental learning paper https arxiv org abs 2108 05307 hat hat hierarchical aggregation transformers for person re identification paper https arxiv org abs 2107 05946 token shift transformer for video classification paper https arxiv org abs 2108 02432 code https github com videonetworks tokshift transformer dpt dpt deformable patch based transformer for visual recognition paper https arxiv org abs 2107 14467 code https github com casia iva lab dpt miccai utnet utnet a hybrid transformer architecture for medical image segmentation paper https arxiv org abs 2107 00781 code https github com yhygao utnet medt medical transformer gated axial attention for medical image segmentation paper https arxiv org abs 2102 10662 code https github com jeya maria jose medical transformer mctrans multi compound transformer for accurate biomedical image segmentation paper https arxiv org abs 2106 14385 code https github com jiyuanfeng mctrans pns net progressively normalized self attention network for video polyp segmentation paper https arxiv org abs 2105 08468 code https github com gewelsji pns net mbt net a multi branch hybrid transformer networkfor corneal endothelial cell segmentation paper https arxiv org abs 2106 07557 bmvc act end to end object detection with adaptive clustering transformer paper https arxiv org abs 2011 09315 gsrtr grounded situation recognition with transformers paper https arxiv org abs 2111 10135 code https github com jhcho99 gsrtr transfusion transfusion cross view fusion with transformer for 3d human pose estimation paper https arxiv org abs 2110 09554 code https github com howiema transfusion pose isie vt adl a vision transformer network for image anomaly detection and localization isie paper https arxiv org abs 2104 10036 corl detr3d detr3d 3d object detection from multi view images via 3d to 2d queries paper https arxiv org abs 2110 06922 ijcai medical image segmentation using squeeze and expansion transformers paper https arxiv org abs 2105 09511 iros yogo you only group once efficient point cloud processing with token representation and relation inference module iros paper https arxiv org abs 2103 09975 code https github com chenfengxu714 yogo git ptt ptt point track transformer module for 3d single object tracking in point clouds paper https arxiv org abs 2108 06455 code https github com shanjiayao ptt wacv lstr end to end lane shape prediction with transformers paper https arxiv org abs 2011 04233 code https github com liuruijin17 lstr icdar vision transformer for fast and efficient scene text recognition paper https arxiv org abs 2105 08582 2020 detr end to end object detection with transformers eccv paper https arxiv org abs 2005 12872 code https github com facebookresearch detr fpt feature pyramid transformer cvpr paper https arxiv org abs 2007 09451 code https github com zhangdong njust fpt other resource awesome transformer attention https github com cmhungsteve awesome transformer attention acknowledgement thanks the template from awesome crowd counting https github com gjy3035 awesome crowd counting | transformer-cv transformer detr transformer-with-cv transformer-awesome visual-transformer | ai |
e6156_F23_p1 | e6156 f23 p1 starter microservice for e6156 topics in sw engineering cloud computing f23 | cloud | |
angular-python | python and angular experimentation project this project is under development so the structure and code may change in the near future this project provides a flexible environment for experimenting with python backend development and angular frontend development it includes a python server for backend functionality and an angular app for frontend user interfaces this project is ideal for developers who want to explore and test different features apis and interactions between the python backend and angular frontend python server angular frontend postgresql database socketio websocket openai integration different text to speech engines like gtts aws polly ll elevenlabs pyttsx3 webcam tools example usage this project incorporates various functions accompanied by code examples for both the backend and frontend components dynamic api provides get post put delete functions for dynamic api endpoints file manager allows file upload and management websocket chat enables real time chat functionality using websockets text to speech converts text into speech audio files speech recognition converts speech voice into text face recognition recognizes faces and gestures openai tool integrates with the openai api for various functionalities custom api manager test and implement third party apis prerequisites to run this project ensure that you have docker installed on your system docker provides a containerized environment for easy setup and deployment you can download and install docker from the official website https www docker com get started https www docker com get started project structure the project structure is as follows angular python postgres data database data will be generated on first start env env dotenv environment file docker compose yml docker compose yml docker composition package json package json node js npm scripts for angular app python server python server python server files dockerfile python server dockerfile docker configuration requirements txt python server requirements txt python requirements file server py python server server py main server routing server log python server server log log file modules python server modules file server py python server modules file server py file server file upload py python server modules file upload py file upload postgres api py python server modules postgres api py database api websocket py python server modules websocket py websocket text to speech py python server modules text to speech py text to speech speech recognition py python server modules speech recognition py speech voice recognition open ai py python server modules open ai py openai integration server logging py python server modules server logging py server logging www python server www tts files python server www tts files gets generated if needed text to speech files uploads python server www uploads gets generated if needed uploaded files ssl python server ssl certificate pem python server ssl certificate pem ssl certificate private key pem python server ssl private key pem ssl private key angular app angular app angular frontend app dockerfile angular app dockerfile build docker configuration for build mode localhost 80 package json angular app package json node js configuration src angular app src app angular app src app source files for frontend app app component ts angular app src app app component ts main app component app routing module ts angular app src app app routing module ts router configuration frontend urls file manager angular app src app file manager file manager for uploading and managing files api manager angular app src app api manager api manager for custom api integration websocket angular app src app websocket websocket integration text to speech angular app src app text to speech text to speech speech recognition angular app src app speech recognition speech voice recognition open ai angular app src app open ai openai integration welcome page angular app src app welcome page welcome page with angular starter content and router navigation example project angular app src app example project example project with documentation environment file env env dotenv mode dev user name admin user password password secret key secret key server host 0 0 0 0 server domain localhost server port 80 allowed origins ssl active 1 ssl private key path ssl private key pem ssl certificate path ssl certificate pem db host postgres database db port 5432 db user postgres db password postgres db database postgres postgres password postgres file upload root www file upload directory uploads text to speech directory tts files allowed extensions txt pdf png jpg jpeg gif mp3 wav wma mp4 ogg ogv webm csv max content length 10737418240 pythondontwritebytecode 1 pythonunbuffered 1 flask debug 1 flask env development openai api key your openai api key openai organisation id your openai organisation id eleven labs api key your ll elevenlabs api key aws access key id your aws access key aws secret access key your aws secret key aws default region us east 1 usage to run the project follow these steps 1 clone this repository bash git clone https github com jodermo angular python 2 change into the project directory bash cd angular python 3 run first build bash npm install npm run build 4 run docker images bash docker compose up this command will build and start the python server and angular app containers defined in the docker compose yml file 5 app is running at localhost 80 http localhost 80 6 optional start angular serve mode at localhost 4200 http localhost 4200 bash npm start this will watch for code changes and automatically recompile the angular app 7 optional use ssh and port 443 instead port 80 replace domain name com with your domain a docker compose file docker compose yml docker compose yml change 80 80 to 443 443 b server environment file env env change uncomment ssl active 1 to ssl active 1 change server domain localhost to server domain domain name com change allowed origins to allowed origins https domain name com change server port 80 to server port 443 c angular environment file angular app src environments environment prod ts angular app src environments environment prod ts change serverurl http localhost 80 to serverurl https domain name com d 1 if not exist the server will now create an ssl certificate to python server ssl python server ssl certificate pem python server ssl certificate pem and private key pem python server ssl private key pem on start up d 2 optional to use your own key replace files in python server ssl python server ssl certificate pem python server ssl certificate pem ssl certificate private key pem python server ssl private key pem ssl private key 8 attention before adding files to git exclude your local configuration files from git commits bash git update index assume unchanged docker compose yml git update index assume unchanged dockerfile git update index assume unchanged env git update index assume unchanged angular app src environments environment prod ts use openai integration to user openai functionality you need an openai api key signup to openai https platform openai com signup add your api key to the server environment file env env change uncomment openai api key your openai api key to openai api key your openai api key optional change uncomment openai organisation id your openai organisation id to openai organisation id your openai organisation id experiment and develop modify the python server code in the python server directory as per your experimentation requirements modify the angular app code in the angular app directory for frontend experimentation documentations angular deploayment documentation angular deployment md app service ts basic api functions documentation app service ts md example project documentation example project md this is an example project template that demonstrates data management functionality it consists of a form and a table the form allows you to enter a table name and a value you can load data add new values to the table and update or delete existing values the table displays the data retrieved from the server each row represents a record with an id and a corresponding value you can edit the values in the table and perform update and delete operations on each record the template provides a user friendly interface for managing data efficiently license this project is licensed under the mit license license please note that this project is still under development and the structure and code may change in the future author moritz petzka https petzka com https petzka com p s if you want to support me and the development take a look at my underwater unreal engine 5 project there is also a possibility to donate underwater ue game com https underwater ue game com | server | |
inkline | p align center a href https inkline io img src https raw githubusercontent com inkline inkline io main assets images logo logo black svg alt inkline width 72 height 72 a p h1 align center inkline h1 p align center inkline is the intuitive ui components library that gives you a developer friendly foundation for building high quality accessible and customizable vue js 3 design systems br br inkline is written and maintained by a href https github com alexgrozav alexgrozav a br br br a href https inkline io img src https raw githubusercontent com inkline inkline io main assets images github homepage png width 1009 alt vue js ui ux library inkline a br br br a href https inkline io homepage a a href https inkline io docs introduction documentation a a href https storybook inkline io storybook a a href https stackblitz com edit inkline file src app vue playground a a href https github com inkline inkline issues issue tracker a p br p align center a href https www npmjs com package inkline inkline img src https img shields io npm v inkline inkline svg alt npm version a a href https github com inkline inkline actions img src https github com inkline inkline workflows build badge svg alt build a a href https coveralls io github inkline inkline branch main img src https coveralls io repos github inkline inkline badge svg branch main alt coverage status a a href https www npmjs com package inkline inkline img src https img shields io npm dm inkline inkline svg alt downloads a a href https chat inkline io img src https img shields io discord 550436704482492429 svg alt discord a p br br table of contents installation installation bugs and feature requests bugs and feature requests contributing contributing community community releases releases versioning versioning creator creator copyright and license copyright and license installation read the getting started guide https inkline io docs introduction or choose one of the guides below inkline offers official integrations for vite js recommended vue js cli and nuxt js but can be easily installed for a custom vue js configuration as well a href https inkline io docs installation vite img src https raw githubusercontent com inkline inkline io main assets images github installation vitejs png width 526 alt nuxt js installation inkline a a href https inkline io docs installation nuxt img src https raw githubusercontent com inkline inkline io main assets images github installation nuxtjs png width 526 alt nuxt js installation inkline a a href https inkline io docs installation webpack img src https raw githubusercontent com inkline inkline io main assets images github installation vuejs png width 526 alt vue cli installation inkline a a href https inkline io docs installation manual img src https raw githubusercontent com inkline inkline io main assets images github installation generic png width 526 alt custom installation inkline a a href https inkline io docs installation cdn img src https raw githubusercontent com inkline inkline io main assets images github installation cdn png width 526 alt cdn installation inkline a bugs and feature requests have a bug or a feature request please first search for existing and closed issues if your problem or idea is not addressed yet please open a new issue https github com inkline inkline issues new community get updates on inkline s development and chat with the project maintainers and community members follow inkline on twitter https twitter com inkline join inkline on discord https chat inkline io developers should use the keyword inkline on packages which modify or add to the functionality of inkline when distributing through npm https www npmjs com browse keyword inkline or similar delivery mechanisms for maximum discoverability releases previous releases https github com inkline inkline releases and their documentation are also available for download versioning for increased transparency and backward compatibility inkline is maintained under the semantic versioning guidelines https semver org creator alex grozav https grozav com https twitter com alexgrozav https facebook com alexgrozav https github com alexgrozav if you use inkline in your daily work and feel that it has made your life easier please consider sponsoring me on github sponsors https github com sponsors alexgrozav contributing please read through our contributing guidelines github contributing md there you can find directions for opening issues feature requests coding standards and how to setup a local development environment thanks goes to these wonderful people all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tbody tr td align center valign top width 10 a href https github com alexgrozav img class avatar src https avatars3 githubusercontent com u 6179477 v 4 width 50px alt alex grozav a td td align center valign top width 10 a href https github com mtancoigne img class avatar src https avatars3 githubusercontent com u 1732268 v 4 width 50px alt manuel tancoigne a td td align center valign top width 10 a href https github com renieldev img class avatar src https avatars1 githubusercontent com u 14147493 v 4 width 50px alt reniel salvador a td td align center valign top width 10 a href https github com thejaredwilcurt img class avatar src https avatars1 githubusercontent com u 4629794 v 4 width 50px alt the jared wilcurt a td td align center valign top width 10 a href https github com eternal rise img class avatar src https avatars2 githubusercontent com u 34982358 v 4 width 50px alt bogdan saliuk a td td align center valign top width 10 a href https github com marbocub img class avatar src https avatars3 githubusercontent com u 1912821 v 4 width 50px alt marbocub a td td align center valign top width 10 a href https dimitrio dev img class avatar src https avatars3 githubusercontent com u 16154728 v 4 width 50px alt dimitrios mandamadiotis a td td align center valign top width 10 a href https github com roileo img class avatar src https avatars1 githubusercontent com u 9987732 v 4 width 50px alt roileo a td td align center valign top width 10 a href https github com milaan muc img class avatar src https avatars githubusercontent com u 3469112 v 4 width 50px alt milaan muc a td td align center valign top width 10 a href https github com masihtak img class avatar src https avatars githubusercontent com u 4639192 v 4 width 50px alt masih abjadi a td tr tr td align center valign top width 10 a href https github com idisposable img class avatar src https avatars githubusercontent com u 874059 v 4 width 50px alt marc brooks a td td align center valign top width 10 a href https github com onno timmerman img class avatar src https avatars githubusercontent com u 178425 v 4 width 50px alt code on a td td align center valign top width 10 a href https github com agarbutt img class avatar src https avatars githubusercontent com u 734757 v 4 width 50px alt andrew garbutt a td td align center valign top width 10 a href https github com skyroses img class avatar src https avatars githubusercontent com u 46625964 v 4 width 50px alt kamil a td td align center valign top width 10 a href https github com ferroeduardo img class avatar src https avatars githubusercontent com u 47820549 v 4 width 50px alt eduardo cabral a td td align center valign top width 10 a href https github com joewelds img class avatar src https avatars githubusercontent com u 28027932 v 4 width 50px alt joe a td td align center valign top width 10 a href https github com simondib img class avatar src https avatars githubusercontent com u 7483506 v 4 width 50px alt simon a td td align center valign top width 10 a href https github com thunfisch987 img class avatar src https avatars githubusercontent com u 43795814 v 4 width 50px alt thunfisch987 a td td align center valign top width 10 a href https github com theruziev img class avatar src https avatars githubusercontent com u 32102033 v 4 width 50px alt bakhtiyor ruziev a td td align center valign top width 10 a href https pkej github io img class avatar src https avatars githubusercontent com u 7883794 v 4 width 50px alt paul kenneth egell johnsen a td tr tr td align center valign top width 10 a href https github com intjelic img class avatar src https avatars githubusercontent com u 778121 v 4 width 50px alt jonathan de wachter a td td align center valign top width 10 a href https github com zozulinskyi img class avatar src https avatars githubusercontent com u 11585924 v 4 width 50px alt ihor a td td align center valign top width 10 a href https github com timbological img class avatar src https avatars githubusercontent com u 31364357 v 4 width 50px alt timbological a td tr tbody table markdownlint restore prettier ignore end all contributors list end copyright and license code copyright 2017 2022 inkline authors https github com inkline inkline graphs contributors code released under the mit license https github com inkline inkline blob main packages inkline license | inkline ui ux framework vue sass library vuejs vue3 css javascript dx | os |
ee-4341 | ee 4341 all of the projects are for the pic32mx470f512l lab0 asm delay implementation lab1 configuring uart lab2 configuring spi and making an spi library for lis3dh accelerometer lab3 rtos with spi library lab4 interfacing with sd card lab5 simple communication over can lab6 usb lab7 harmony graphics | os | |
listenbrainz-server | h1 align center br a href https listenbrainz org img src https github com metabrainz metabrainz logos blob master logos listenbrainz svg listenbrainz logo svg alt listenbrainz a h1 h4 align center server for the listenbrainz project h4 p align center a href https github com metabrainz listenbrainz server commits master img src https img shields io github last commit metabrainz listenbrainz server svg style flat square logo github logocolor white alt github last commit a a href https github com metabrainz listenbrainz server pulls img src https img shields io github issues pr raw metabrainz listenbrainz server style flat square logo github logocolor white alt github pull requests a p p align center a href https listenbrainz org website a a href https listenbrainz readthedocs io documentation a a href https tickets metabrainz org projects lb issues bug tracker a p about listenbrainz keeps track of music you listen to and provides you with insights into your listening habits we re completely open source and publish our data as open data you can use listenbrainz to track your music listening habits and share your taste with others using our visualizations we also have an api https listenbrainz readthedocs io en latest users api index html if you want to do more with our data listenbrainz is operated by the metabrainz foundation https metabrainz org which has a long standing history of curating protecting and making music data available to the public for more information about this project and its goals look at our website https listenbrainz org specifically the about page https listenbrainz org about changes and other important announcements about the listenbrainz services will be announced on our blog https blog metabrainz org if you start using our services in any production system we urge you to follow the blog commercial use all of our data is available for commercial use you can find out more about our commercial use support tiers https metabrainz org supporters account type on the metabrainz site contributing if you are interested in helping out consider donating https metabrainz org donate to the metabrainz foundation if you are interested in contributing code or documentation please have a look at the issue tracker https tickets metabrainz org browse lb or come visit us in the metabrainz irc channel on irc libera chat development environment these instructions help you get started with the development process installation in a production environment may be different read the development environment documentation https listenbrainz readthedocs io en latest developers devel env html setting up a development environment listenbrainz documentation in order to work with spark you ll have to setup the spark development environment read the documentation https listenbrainz readthedocs io en latest developers spark devel env html documentation full documentation for the listenbrainz api is available at listenbrainz readthedocs org https listenbrainz readthedocs org you can also build the documentation locally cd listenbrainz server docs pip install r requirements txt make clean html license notice copyright c 2017 metabrainz foundation inc this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 2 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license along with this program if not write to the free software foundation inc 51 franklin street fifth floor boston ma 02110 1301 usa | database listenbrainz-server web big-data music python spark typescript react | front_end |
grumbler | grumbler build status build badge build code coverage coverage badge coverage npm version version badge package build badge https img shields io github workflow status krakenjs grumbler build logo github style flat square build https github com krakenjs grumbler actions query workflow 3abuild coverage badge https img shields io codecov c github krakenjs grumbler svg style flat square coverage https codecov io github krakenjs grumbler version badge https img shields io npm v grumbler svg style flat square package https www npmjs com package grumbler https medium com bluepnume introducing grumbler an opinionated javascript module template 612245e06d00 a template for writing distributable javascript libraries javascript libraries are fun to write setting up all of the boilerplate to get your build up and running is not so fun this module provides a forkable module template which you can use to kick start a new javascript library once you ve done that feel free to come back and switch out the tooling for whatever you prefer features build minified and unminified versions of your code with source maps use es2015 out of the box write headless karma mocha tests which run in chrome headless and other browsers with code coverage reports integrate with travis ci out of the box write error free type safe code with eslint flow type and flow runtime technologies babel eslint flowtype flow runtime karma phantomjs chrome headless mocha istanbul webpack npm travis quick start getting started fork the module run setup npm run setup start editing code in src and writing tests in tests npm run build building bash npm run build tests edit tests in test tests run the tests bash npm run test testing with different multiple browsers bash npm run karma browser phantomjs npm run karma browser chrome npm run karma browser safari npm run karma browser firefox npm run karma browser phantomjs chrome safari firefox keeping the browser open after tests bash npm run karma browser chrome keep open publishing before you publish for the first time delete the example code in src test tests and demo edit the module name in package json edit readme md and contributing md then publish your code npm run release to add a patch or npm run release path npm run release minor npm run release major faq who is this for anyone who wants to get started quickly on a javascript library without setting up a lot of boilerplate anyone who wants a fairly healthy opinionated set of defaults to get started with anyone new to writing front end modules who doesn t want to immediately research which modules to use to build their code who this is not for anyone who needs wants tight control over their project and which specific build tools they want to use why use technology x y z probably because it s been a good fit for us in the past we wanted our focus to be around fairly standardized javascript as much as possible rather than compiled to javascript languages hence the use of babel flow etc so you just took a bunch of build tools and daisy chained them together yep pretty much this is not anything remotely technically impressive or new or innovative it s just a healthy set of defaults to get started with if you re building a front end distributable library can i improve this template by all means please feel to raise a pr but if it s a big change try to open an issue first so we can chat what about support for react ember framework x or y wanted to keep this module as framework agnostic as possible not to mention there are already pretty good boilerplates out there for whatever framework you re using i ll bet otherwise please feel free to be my guest and fork grumbler superawesomeframework if it s helpful | javascript-library javascript-modules javascript-tools boilerplate template | front_end |
Solutions-Stanford-cs131-Computer-Vision-Foundations-and-Application | cs131 computer vision foundations and applications this repository contains my solutions for assignments of the fall 2017 iteration of cs 131 http vision stanford edu teaching cs131 fall1718 a course at stanford taught by juan carlos niebles http www niebles net and ranjay krishna http ranjaykrishna com the assignments cover a wide range of topics in computer vision and should expose students to a broad range of concepts and applications | computer-vision stanford-course stanford solutions course image-processing | ai |
jswat | java debugger jswat is a graphical java debugger front end written to use the java platform debugger architecture and based on the netbeans platform its features include sophisticated breakpoints colorized source code display with code navigator movable display panels showing threads call stack visible variables and loaded classes command interface for more advanced features and java like expression evaluation including method invocation discontinued this project is effectively discontinued as of 2013 it is known not to work with java 8 and possibly even java 7 no one is working on this project any more please have a look at one of the much better alternatives netbeans https netbeans org eclipse http eclipse org intellij idea https www jetbrains com idea built on netbeans jswat is built on the netbeans platform https netbeans org features platform using some additional modules from the netbeans ide license the license for this application is the common development and distribution license cddl unless otherwise stated in the license text of the software in particular versions prior to 3 0 are licensed under the gnu general public license | front_end | |
source | source a component library for the guardian s source design system https theguardian design source packages have moved to https github com guardian csnx in 252 https github com guardian csnx pull 252 the last versions to be published from here were guardian source foundations 7 0 1 guardian source react components 9 1 0 guardian source react components development kitchen 7 0 0 guardian eslint plugin source foundations 7 0 0 guardian eslint plugin source react components 9 0 0 https github com guardian source releases | design-system production | os |
ComputerVision | computervision project measurement system this program needs python 2 7 pygame 1 9 and opencv 2 4 measurementsystem gui py this is the graphical user interface of the program measurementsystem measurementsystem py this contains all the functions to handle the image and measure stuff to run it execute python gui py | ai | |
ECUSTFD-resized- | ecustfd resized background obesity is a medical condition in which excess body fat has accumulated to the extent that it may have a negative effect on health obesity treatment requires the patients to eat healthy food and decrease the amount of daily calorie intake for those patients it is helpful that calories can be estimated from photos br br many methods based on computer vision have been created to estimate calories there are three steps in calorie estimation capturing food image s detecting food and calibration object estimating calorie of each food br br for those methods especially deep learning methods they need a corresponding image dataset to train and test however most of the image datasets concentrate on the food detection and just provide different categories of objects which is not helpful in visual measurement that s why we create our food image dataset named ecustfd ecust food dataset introduction ecustfd is a free public food image dataset our dataset has 19 types of food as shown in the figure the number of food images is 2978 the number of images and the number of objects for the same type are shown as follows div align center img src https github com liang yc images4readme blob master food sample jpg div br br for a single food portion we took several groups of images by using smart phones each group of images contains a top view and a side view of this food for each image there is only one coin as calibration object and no more than two foods in it if there are two food in the same image the type of one food is different from another we provide two datasets for researchers one includes original images and another includes resized images the size of each image in resized dataset is less than 1000 1000 br div align center img src https github com liang yc images4readme blob master coin 20sides jpg div br br as you see the diameter of the one yuan coin is 25 0mm in ecustfd only 2 kinds of plates are used when taking photos a white plate and a red plate if you want to use the circle plate as the calibration object you may need the diameter of each plate the white plate s diameter is about 20 7cm and its height is about 2 0cm the red plate s diameter is about 18 7cm and its height is about 2 0cm br br table thead tr th red plate th th white plate th tr thead tr td a href img width 100 style max width 30 max height 30 alt src https github com liang yc images4readme blob master red plate jpg a td td a href img width 100 style max width 30 max height 30 alt src https github com liang yc images4readme blob master white plate jpg a td tr table br assessment the dataset with original images and no annotations is publicly available at this baiduyun http pan baidu com s 1df866ut the small image dataset including annotations volume and quality information is available at this github https github com liang yc ecustfd resized or baiduyun http pan baidu com s 1o8qdnxc br br for ecustfd with original images we only provide images as referred but we provide the food s weight information in ecustfd weight http pan baidu com s 1df866ut list path 2fcalorie 20estimation 2fecustfd origin 2fecustfd weight parentpath 2fcalorie 20estimation folder if you are interested in character recognition you can use those images br br for the small size ecustfd the annotations of images are saved in the folder named annotations and images are saved in the jpegimages folder density xls saves food s volume and quality information citation if you used the code for your research please cite the paper article liang2017computer title computer vision based food calorie estimation dataset method and experiment author liang yanchao and li jianhua journal arxiv preprint arxiv 1705 07632 year 2017 notice mix002t 2 and mix005s 4 have not included calibration objects as they are used in model training rather than volume estimation experiment i have not found this problem till now please do not use these 2 images for estimation i m sorry for my carelessness 2017 10 23 | ai | |
OpenCV3-Computer-Vision-Application-Programming-Cookbook-Third-Edition | opencv 3 computer vision application programming cookbook third edition this is the code repository for opencv 3 computer vision application programming cookbook third edition https www packtpub com application development opencv 3 computer vision application programming cookbook third edition utm source github utm medium repository utm campaign 9781786469717 published by packt it contains all the supporting project files necessary to work through the book from start to finish about the book opencv 3 computer vision application programming cookbook third edition provides a complete introduction to the opencv library and explains how to build your first computer vision program you will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications instructions and navigations 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 compute laplacian using laplacianzc class laplacianzc laplacian laplacian setaperture 7 7x7 laplacian cv mat flap laplacian computelaplacian image laplace laplacian getlaplacianimage with the following software and hardware list you can run all code files present in the book chapter 1 14 software and hardware list chapter software required os required 1 14 opencv 3 windows mac os x and linux any 11 vtk windows mac os x and linux any 1 cmake windows mac os x and linux any related products opencv 3 blueprints https www packtpub com application development opencv 3 blueprints utm source github utm medium repository utm campaign 9781784399757 learning opencv 3 computer vision with python second edition https www packtpub com application development learning opencv 3 computer vision python second edition utm source github utm medium repository utm campaign 9781785283840 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 9781782161486 https packt link free ebook 9781782161486 a p | ai | |
image-filter-project | udagram image filtering 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 the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost author dims | cloud | |
Learning-DIY-RTOS | 0 1 github git sourcetree bug git 49 rtos http t elecfans com 1927 html github | os | |
leveraging-llms-for-mcqa | leveraging llms for mcqa overview this is the code for the iclr 2023 paper leveraging large language models for multiple choice question answering https arxiv org abs 2210 12353 it can be used to reproduce results in the paper and is designed to be extensible setup start by using your favorite package manager to install datasets numpy openai pandas scipy tqdm and transformers now register your api keys in api sectrets py to do this add a key and value for each api key you want to register to the dictionary in the get api key by name function you ll need an openai key for openai api experiments and a jurassic key for jurassic api experiments you can use the existing keys or choose your own names for the keys running experiments to run experiments and reproduce the results from the paper you will use main py the positional command line arguments are the name of the dataset to use must be a key from the dictionary inside get dataset info in dataset utils py e g mmlu the name of the model to use must be a key in one of the dictionaries in get model by name in models py e g codex the name of the prompting style to use either brown called cp in the paper or natural called mcp in the paper the number of shots to use 0 for zero shot 1 for one shot etc the name of the api key to use must be a key from the dictionary inside get api key by name in api secrets py the optional command line arguments are do strong shuffle for strong shuffling as used in appendix c do perm for passing all permutations of each question to the model as in the experiments in section 4 running main py will save a pickle file with experiment results analyzing results to analyze the results of an experiment from its saved pickle file you will use analyze py the positional and optional command line arguments are the same except for you don t need to supply the name of an api key to use these arguments will be used to look up the saved experiment pickle file other functionality you can visualize prompts that will be used by an experiment with viz prompts py the positional command line arguments are dataset name style name and number of shots as you d use with main py the optional argument longest will show the longest prompt instead of a random one you can add a custom model by adding a custom key and value to a dictionary in get model by name within models py you can add a custom dataset by adding a custom key and value to the dictionary in get dataset info within dataset utils py | ai | |
blade | br p align center picture source media prefers color scheme dark srcset branding blade original dark mode min svg source media prefers color scheme light srcset branding blade original min svg img width 450px alt blade design system logo src branding blade original min svg picture p br p align center a href https npmjs org package razorpay blade img alt blade latest version src https img shields io github package json v razorpay blade style for the badge labelcolor 322 logo npm label razorpay blade color darkred filename packages 2fblade 2fpackage json a nbsp a href https blade razorpay com img alt documentation blade razorpay com src https img shields io badge documentation blade razorpay com 0648ef style for the badge labelcolor 0012ad logo readthedocs logocolor eee a nbsp a href https github com razorpay blade tree master contributing md img alt discord join chat src https img shields io badge contributions open 333333 style for the badge logo github logocolor ffffff labelcolor 111111 a p h1 aria hidden true h1 br blade is the design system that powers razorpay https razorpay com links docs https blade razorpay com installation https blade razorpay com path docs guides installation page razorpay blade old https github com razorpay blade old deprecated private features cross platform works natively on react web https blade razorpay com path docs guides installation page ef b8 8f installation and react native https blade razorpay com path docs guides installation page react native projects white labelling https blade razorpay com path docs guides theming theme playground page css variables for non react projects https blade razorpay com path docs tokens css variables page accessible https github com razorpay blade blob master rfcs 2022 04 09 accessibility md manual testing documented rfcs https github com razorpay blade tree docs make docs pretty rfcs and api decisions https github com razorpay blade blob master packages blade src components alert decisions decisions md license licensed under the mit license https github com razorpay blade blob master license md h1 aria hidden true h1 p align center interested in working with us checkout our a href https razorpay com jobs jobs page a for open roles p | os | |
tesla-api | referrals are back need a vehicle to test with get a tesla with free supercharging http ts la timothy8449 do you work at tesla get in contact i d love to help with making this api official tesla json api view documentation https tesla api timdorr com this is unofficial documentation of the tesla json api used by the ios and android apps the api provides functionality to monitor and control the model s and future tesla vehicles remotely the project provides both a documentation of the api and a ruby library for accessing it if any folks at tesla are reading this i d love to help coordinate a developer program for your apis if there s any way i can be helpful please feel free to get in contact also i d love to be in the beta firmware program wink ruby gem gem version https img shields io gem v tesla api svg http rubygems org gems tesla api build status https img shields io travis timdorr tesla api master svg https travis ci org timdorr tesla api this gem provides a basic wrapper around the api to easily query and command the car remotely it also provides access to the streaming api and a means to process data coming from it installation add this line to your application s gemfile ruby gem tesla api or install it yourself sh gem install tesla api usage here s a quick example ruby require tesla api tesla api teslaapi client new email email client id client id client secret client secret tesla api login password or if you have an access token tesla api teslaapi client new access token access token model s tesla api vehicles first teslaapi vehicle model s wake up vehicle data model s vehicle data model s auto conditioning start unless vehicle data climate state is auto conditioning on model s set charge limit 90 model s charge start charge state vehicle data charge state puts your model s is charge state charging state with a soc of charge state battery level and an estimate range of charge state est battery range miles copyright ruby portions are copyright c 2014 present tim dorr released under the terms of the mit license see license for details | tesla ruby api car iot | server |
itseez-summer-school-2015-slides | itseez openvx https sites google com a itseez com openvx tutorial home https docs google com document d 1r8 py nklomhmxxbjx 6uitn0fjtcj1n oxpik7gs3m edit usp sharing https docs google com spreadsheets d 10py5z8crcrxcx1laivgzdpg2lvg nn1dwj1f1qom3to edit usp sharing | ai | |
application-ai-llm | application ai llm the llms ai project aims to integrate language and learning models llm powered by artificial intelligence into the xwiki platform for further information and documentation see this page https design xwiki org xwiki bin view proposal x ai | ai | |
road2blockchain | 1 pki 2 erc20 dapp 3 hyperledger fabric 1 0 4 ripple corda iota skycoin 5 fabric ripple corda kindle kindle kindle http book 8btc com master bitcoin http book 8btc com masterbitcoin2cn yeasy https www gitbook com book yeasy blockchain guide details hyperledger fabric https www gitbook com book yeasy hyperledger code fabric details http www 8btc com wiki bitcoin a peer to peer electronic cash system https www shiyanlou com courses 952 ibm https www ibm com developerworks community wikis home lang zh wiki w30b0c771924e 49d2 b3b7 88a2a2bc2e43 page hyperledger 20fabric e7 b3 bb e5 88 97 e5 be ae e8 ae b2 e5 a0 82 ibm fabric https www ibm com developerworks community wikis home lang zh wiki w30b0c771924e 49d2 b3b7 88a2a2bc2e43 page ibm e5 bc 80 e6 ba 90 e6 8a 80 e6 9c af e5 be ae e8 ae b2 e5 a0 82 https www zhihu com question 22076666 answer 69638270 https www zhihu com question 22076666 answer 232628039 https zhuanlan zhihu com p 22228902 http wangxiaoming com https mp weixin qq com s biz mziwoda3ndi5ma mid 2652525361 idx 1 sn e67635d03f120cc147f7ec7094fe1df9 https mp weixin qq com s biz mzu0nti0otuxoa mid 2247484687 idx 1 sn f49bab82482b4b0d4b189598df15d93d 200 https mp weixin qq com s biz mjm5odizndq3mw mid 2649967604 idx 1 sn 012645cc57f0d772fa262522c7c1be25 https mp weixin qq com s biz mzixnjc4nzkyma mid 2247484874 idx 2 sn 13eb05a1154e447ac322c0c976a3800c https www zhihu com question 51047975 answer 259779402 https github com yeasy cello platform to provide blockchain as a service python https github com hyperledger fabric golang 2017 12 21 fork pt at 1 pt at tbd tbd tbd tbd tbd tbd 1 2 3 4 5 9 1 2 3 4 5 1 2 3 4 5 9 10 11 it 1 2 2 pt at 2 tbd tbd tbd tbd tbd tbd 2 x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 x 10 x 11 x 12 6 1 3 2 3 pt at ibm 2017 2 14 tbd hyperledger 2017 2 28 tbd 4 pt at cello tbd tbd baas tbd tbd 5 pt at tbd tbd tbd tbd 6 pt at tbd tbd tbd tbd | blockchain cryptocurrency awesome-lists | blockchain |
Codehelp | mern stack development course dot batch this repository is used for maintaining my assignments class notes taken during web development bootcamp live course conducted by love babbar and lakshay kumar this repository contains br projects br cheatsheets br debug exercise solutions br homework solutions br handwritten notes br star this repo if you find it helpful | css css3 git html html5 javascript expressjs mongodb nodejs reactjs tailwind-css | front_end |
reforestar | welcome to reforestar repository final project of the computer engineering bachelor s degree application to simulate a reforestation plan using artificial reality for ios devices final score 19 20 reforestar is a native augmented reality mobile application for ios and android devices which allows the user to place virtual trees on horizontal surfaces and with that simulate a real experience of a reforestation plan this behavior is achieved through an algorithm that respects several minimum distances between trees the application also allows users to customize the experience with specific options such as modifying the scale type and quantity of trees to be placed it even has functionalities to save and load the progress of a reforestation session reforestar was implemented as a final project of the degree course later published as a scientific article and in the process of being submitted on the appstore the ios version was developed my myself while the android version by my colleague enrico along with my colleague we wrote reforestar an augmented reality mobile app for reforest purposes a paper presented at the b ranked conference visigrapp 17th international joint conference on computer vision imaging and computer graphics theory and applications on february 6 2022 published by insticc through its scitepress digital library available for free download at https www scitepress org papers 2022 108332 108332 pdf introduction reforestar is a project to raise awareness to environmental damages by immersing users into a real time reforestation experience using augmented reality ar by allowing users to virtually place 3d models of trees on a real surface using their own mobile phones helping in planning and visualize the replanting process to accomplish this purpose the creation of an augmented reality mobile app for both ios and android platforms the app named reforestar let users place virtual trees on detected surfaces and with this simulating a reforestation planning experience this behavior was achieved by the usage of an algorithm that acknowledges the minimum distance between trees the application also enables users to personalize the experience with specific options such as scale modification quantity of placed trees and the type of tree that is being placed saving loading and sharing reforestation projects is also possible installation instructions clone the repo to your local computer and open xcode select the option open existing project and select the file reforestar xcworkspace to test the app you need to run the app on any ios device which os version is at least 14 1 will not work on a simulator because of the arkit features that require connection to the camera s device usage examples video of the app in use https github com matiasortizluna reforestar assets 64530615 73f03413 e90c 4962 ac75 c2a68030fcd4 full list of features of the app the users can select different types of trees and the number to be placed one a single time select the size of the trees that will be placed save the progress of a session and load back the progress to continue working switch between reforestation plan mode allowing the user realistic experience of a reforestation plan with rules like spacing between trees and specific locations placement navigate through the application with the help of a bottom navigation bar create objects by touching the screen on a desired point through the camera s device activate a mode to only be able to plant trees within a specific area using geolocation store ar items placed for future use even when application is closed select different tree species to position in the ar chamber change the size interval of the trees 3d models they want to place view a tree s species catalog that includes information about the flora of the regions known by the application search specific species of trees within the catalog using a search engine view specific information about a selected tree s specie from the species catalog view a map with the selected specie s footprint highlighted view a map with the reforestation allowed areas highlighted view a map with the projects area of field highlighted view a projects catalog that include information about the projects associated to the user update a project from the projects catalog delete a project from the projects catalog register or log in the application with unique credentials view his personal information of his account update his personal information of his account delete his account log in or create an account to access premium features of the application or use it without one use the application with limited features without logged in account register or log in at any time after first usage by selecting the option in the user profile section navigate through the different sections of the application by clicking the one of the five available options of a bottom navigation bar select the 3d model of the tree s specie desired to create by selecting them in the menu delete the project if he has permission to do it update his personal information by editing specific textboxes and save the changes by clicking on the right button set the height interval of the species by selecting it with a slider present in the reforestation section menu load the progress of a project by selecting the option available in the specific project page accessed by selecting it on the catalog section user s 3d items created will have to be associated with a project to be saved for future use save the progress of the current session he should click on the screen save button view specific information about a selected tree s specie by clicking the desired option in the catalog not be allowed to update or delete any kind of information about tree s catalog delete his account by clicking on the specific button user s geolocation usage permissions will be asked at the beginning of the application for in use permissions policy application will read data from the firebase database each time user change section or menu option to show real time data to the user feature of save progress in different projects is limited to logged in users about development of the app the app took between 3 and 3 5 months to de developed all the details regarding their programming such as the algorithms used for its correct behavior the architecture of the app the scope their limitations the way it works will be detailed below hardware the computer used to develop this app was a macbook pro 13 from the year 2020 with 16 gb of ram and an intel i 7 2020 an iphone 7 2gb of ram from the year 2017 and an ipad mini 3g ram from the year 2019 and was developed using the apple ide xcode which is the only ide that allows development of ios native apps even tough xcode provides simulators of all the current devices that apple still gives support for running apps with ar features it is necessary to run or test the app with a physical device software the programming language that apple uses for their cross platform software macos ios ipados watchos and tvos is swift which is a robust language simple and intuitive language to learn before 2014 when swift was firstly introduced the main programming language was objective c which is more like to c 3d models the app has three 3d models available for the user to place in the ar reforestation scene but has information relative to sixteen species the 3d models available in the app were found online for free usage more detailed models were discovered but the license to use them were paid and or weren t consider as useful for the purpose of this project frameworks used the app has used the methods of different libraries and frameworks to implement their features here is a list of all of them with a brief description of its usage and capabilities foundation foundation is a framework of swift included in all initial classes when created a new project it provides basic functionalities like data storage persistence calculations filtering and networking uikit framework the uikit framework allows to display elements and content on screen interact with that content and let the system answer to those interactions another framework that does this is swiftui but uikit is an imperative framework that means the components of a screen can be designed like the drag and place method then associate actions to buttons or list to functions that are created in code and finally from the code of those functions deploy events that might be seen in device screen depends on what is changed a major limitation is that is not reactive and the state of variables needs to be tracked at every moment otherwise wrong information could be displayed and that is what swiftui appeared to solve swiftui although swift is the principal programming language one of the most important tools that developers use to create apps is swift ui a group of tools that allow to create ui elements it is easier intuitive and declarative and has reactive features swiftui is a framework or group of tools that simplify the way of developing apps in this case in a declarative way so no drag and drop items are available it s all just code but the most important ability it s that through layers the developer could create any element that want to be displayed call functions and classes when needed and deploy events on a more organized ways all at the same time the most important feature is their reactiveness swiftui has specific type of variables that allow bindings to be automatic and keep variable and user s state what simplifies the process for developers and avoid simple mistakes of showing the right data it is cross platform which means that is possible to use in different apple devices the only limitation is that because it was first introduced in 2019 it is just available for devices with ios 13 or higher so if any app is aimed to work on a device with an older version of ios it must use uikit components mapkit mapkit is a framework of the swift programming language that allows developers to display a map or satellite view on apps add polygons figures annotations and text so the user could have all the relevant information pointed out int the map it can also design polygons personalize their colors and add mark pointers to defined places corelocation corelocation framework determine device s geographical location in real time altitude orientation and let track user s location if is entering a specific area or zone ar kit apple s own ar sdk for ios apps that allows apps to display 2d or 3d objects into the camera s view from the device running the app ar kit uses device motion tracking camera scene capture to build an ar experience allowing to use the front and back camera od device with the usage of this ar sdk reforestar let the users place up until 20 models of tress at the same time functionality available just for specific 3d models with different scales save progress and load previous saved progress of 3d placed models run an algorithm that limit the number of 3d models placed at the same place so a perspective of forest is created reality kit reality kit framework works with ar kit to implement 3d rendering so ar objects can be displayed the way of working is by gather information provided by the ar kit framework to integrate all those virtual objects into the real world this framework allows to import assets add audio animate 3d objects respond to user input and changes of the environment and most recently to synchronize across devices the ar experiences scene kit scene kit is a 3d graphics framework that helps developers and designers create 3d animated scenes and effects inside the apps in resume it is a framework that works with the assets or 3d objects that will be placed inside the camera s view as an ar experience it s all about the geometry materials lights and cameras of the objects firebase ios sdk firebase is the mobile backend as a service that help developers reduce the effort of managing a database and a backend separately by having just one service that operates as both to use it it is necessary to integrate it with the ios app and that can be achieved by an installation through a command line agent cli cocoa pods is an agent than then will help install the firebase sdk into any app the process is easy every time a service of firebase needs to be added should be added to be pods file for this app the only needed were authentication and the normal firebase services which include the real time database support combine framework combine framework includes an api to handle asynchronous events inside classes in swift this is useful to communicate with different components classes and instances inside an app so when a change is done on one part of the code it can be communicated to other part of the app this allows events to be triggered now in different parts of the app architecture software design pattern because this app was my first it doesn t follow a specific pattern although and because apple s swiftui architecture and tutorials tend to follow an unofficial model called model view architecture where the view is automatically rendered and communicates with the model to perform the calls and the change of values database database 1 https github com matiasortizluna reforestar assets 64530615 2057e96d cb66 4ba8 9346 546727edfad5 google real time firebase is used to share the same information between the ios and the android app the database keeps the information of all users and the data of the app name of 3d models height descriptions of trees etc database 2 https github com matiasortizluna reforestar assets 64530615 c04790ce a395 48cf bac0 db3fc5d88665 data flow the app interacts with different entities and software to work correctly but for the internal components to work synchronously and all the features to be available some agents perform actions so other parts of the app could know about the updated data this process is thereafter explained because swiftui is a different ui framework a way of communicating the changes happening in swiftui components to ukit components is by using notification center and redundancy of variables this is why there is a singleton class called currentsession that holds all variables and data to correctly deploy changes like saving the progress to a specific project verify the location of the user or place 3 trees with a scale of 3 2 on the other hand because all of these values are part of a singleton class their changes are not visible in real time in the user s screen swiftui components framework in charge of ui are reactive a special type of variables that maintains their state thought the app cycle and is evident in the user s screen to solve this issue and make a good user experience when changing values and to allow the user to personalize the reforestation session even better a currentsessionswiftui class was created thus when the user changes a value in the ui interface the change will be experimented in the swiftui variables and will trigger in code the functions of the singleton class so the information is the same when deployed actions in the ar session data flow https github com matiasortizluna reforestar assets 64530615 afea614a d816 4493 9669 3420acd3adc3 seamless integration between swiftui and uikit there is an advantage when using both frameworks inside the same application uikit let developers drag and drop items to the screen and then program their actions and swiftui allows an easier program of components and their actions including the possibility of defining reactive variables that will update its value when it s changed everywhere in the app exactly because of this last feature is that using swiftui is a better option because the user interface will need to show different information according to the actions that the user makes noteless programming some of the components in swiftui could have taken much more time than doing it with uikit that s why it was used as well and considering that there is the possibility to integrate swiftui components into uikit or vice versa then it was considered a great option to use both reusing their best features at the same time in resume either swiftui and uikit are important tools when designing the ui interface of an ios app each one with their advantages and disadvantages but it s a useful thing to have the opportunity to use the best of both worlds at the same time views the app has a tab bar with 5 options that will help the user navigate through the different sections of the app each of them with a unique purpose and with features available inside them the 5 options are areas projects reforestar catalog and the user sections views https github com matiasortizluna reforestar assets 64530615 1e635140 72a3 411d b4b1 e1136d644665 areas section the areas section shows the map with the device s location and polygons overlaid representing the predefined areas that the system has defined https github com matiasortizluna reforestar assets 64530615 69cd8a16 6db7 4f90 90c6 70519a40b63e projects section the projects section shows a list of all the reforestation projects associated with the authenticated user when adding a virtual 3d tree s model with ar its placement is associated with a project each project has a specific view that is shown when selected from the list showing the project s name description status number of trees associated users and sharing deleting functions if the user that is accessing the page is the project owner https github com matiasortizluna reforestar assets 64530615 1a6a907f fb85 4d87 88d5 d8cdacd64e52 trees section the trees section shows a list of all the tree species considered in the project s scope a specific view for each tree is shown when selected and it shows the tree s latin name common name recommended space between each one when planted its minimum and maximum height and a description https github com matiasortizluna reforestar assets 64530615 d580a663 9df0 4dd2 9581 1a610670586c user section the user section shows the user s name email username statistics about the number of projects of the authenticated user and the number of planted trees the login logout and account registration options are also available here all registration and login forms have validation functions and an error message is presented when inserted values don t meet validation rules https github com matiasortizluna reforestar assets 64530615 9c8045f6 58c9 4e96 af71 11a258cd5e31 reforestar section the reforestar section holds the ar view where the main features of the application are available or in other words the ar camera along with some instructions reforestar section in ios was developed with swiftui and is compound by different buttons labels sliders switchers and pickers with each one of them with a specific function view reforestar 1 https github com matiasortizluna reforestar assets 64530615 519a426a 43eb 46a9 9294 01ebf58cc432 view reforestar 2 https github com matiasortizluna reforestar assets 64530615 42bbb73d e314 419a 83f3 2a654b5f2db5 some of the views have either swiftui uikit components or both the way of communicating the changes happening in swiftui components to uikit components is done with the api of the notification center 15 and redundancy of variables swiftui components are reactive so the variables state is kept through the app cycle and is evident in the user s screen notification center notifications are used to exchange information between the interface as for example the tap of a button to the ar class and vice versa making the user experience fluid and user friendly labels that show relevant information such as the current number of trees that have been placed in the current session and the real time geographic location of the user s device a toggle switch activates a functionality that compares the current user s device location to the one stored in the project once the load buttons to remove the last placed tree from the current session present a view with all available models that may be selected and a to display or hide a right bar menu this right bar menu contains a picker select for configuring the number of trees to be placed by a single tap from 1 to 10 a slider that modifies the scale of future placement models and another button to display or hide a small menu that includes features to delete all the placed trees existing in the current session select the associated project and save and load progress from the associated project view reforestar1 https github com matiasortizluna reforestar assets 64530615 31ac85b0 f9eb 4b22 a676 bce3f50c11d3 view reforestar3 https github com matiasortizluna reforestar assets 64530615 a5f6a51c fd1f 45f7 8bf4 614270694b26 view reforestar2 https github com matiasortizluna reforestar assets 64530615 7514635c 8863 4e6c 8d3b 079f5896c07c view reforestar4 https github com matiasortizluna reforestar assets 64530615 d2df9c9e 3f76 4796 9895 7ed953ab90f4 view reforestar5 https github com matiasortizluna reforestar assets 64530615 a0b1a86e 0bc8 49f1 a5f4 4d78ee9d033b reforestar placement algorithm when the user taps on an available horizontal surface on the screen an object is created in that position the algorithm was created so when the user taps to create objects it must respect some ground rules as if it was a real reforestation plan the user can t create an object in a position where there is another virtual object every object should have a distance value to prevent this from happening now when the user intends to create more than one object with a single click the first object will be placed in this position but it will also be used as the origin position to create new random positions for the new objects that are part of the same tap group the new positions will go through several comparisons that will verify that they are valid positions to simulate a reforestation environment if the positions created do not comply a new position is created and the verifications are made again having a limit of twenty attempts per position before failing and communicating to the user that the place where they intend to place the objects does not have enough space the three rules that the new random positions must follow are 1 to be between a min distance and max distance away from the initial position the first tap of the user 2 to be min distance and max distance away from any of the models from the same tap group 3 to be min distance and max distance away from any models that already existed in the scene before the tap group placement algorithm https github com matiasortizluna reforestar assets 64530615 8d1d571b 93bc 4e7b bfa4 41063a09e282 algorithm load functionality the process of loading is activated once the user taps in the button and it will retrieve the information about stored 3d models in the selected project in the google firebase database the user will select a point in the surface that will serve as the origin position to relate the stored 3d models previous positions limitations 3d models with a size of more than 200mb were found available but when they were used the devices would crash since the required ram was not available on the development equipment this thus being the main motive for why lighter models were chosen in future versions new 3d models are expected to be included apple s scene kit 3d object format universal scene description usdz keeps all the assets of the ar scenario including the 3d object as a folder structure of nested sub elements from a single zip file but when 3d objects were imported to apple s scene some of their features weren t correctly patched to the exported 3d object that s why the same models in ios look different from android on future versions the layout of 3d models is expected to have more details | os | |
DEIS_robot | deis robot autonomous robot snowplow fleet for the course design of embedded and intelligent systems | os | |
toolbox | toolbox devops container with various tools the available ansible version can be found here https pypi org project ansible core description usage if you can not use docker try lima https github com lima vm lima please note that the following examples contain both lima and docker you need to run the script command that matches your setup build bash docker docker build sh lima lima build sh run configure the variable code in the run sh script to mount a directory containing your playbooks repositories example code home code bash docker docker run sh lima lima run sh connect to the container with an interactive shell bash docker docker connect sh lima lima connect sh restart with new version bash docker docker run sh lima lima run sh shell alias examples there are some example scripts in the exec directory that allow you to use tools from the container on your native shell with path translation bash run alias toolboxs path to this repo toolbox lima run sh connect alias toolboxc path to this repo toolbox lima connect sh use lima as docker alias docker lima nerdctl make ansible vault from the container available on native shell alias ansible vault path to this repo exec docker ansible vault sh make ansible playbook from the container available on native shell alias ansible playbook path to this repo exec docker ansible playbook sh make ansible lint from the container available on native shell alias ansible playbook path to this repo exec docker ansible lint sh make azure cli from the container available on native shell alias az path to this repo exec docker az sh | hacktoberfest ansible docker devops cloud platform-engineering | cloud |
blockchain-lecture | blockchain lecture geth code copy paste pdf reader mkdir mynetwork cd mynetwork puppeth puppeth puppeth windows geth alltools path cmd puppeth macos vi bash profile jongkwang export path path users jongkwang bin geth alltools 1 8 15 terminal mynetwork json geth datadir init mynetwork json account geth datadir account new geth datadir account list code password txt 12341234 code startblockchain bat geth datadir networkid 9999 nodiscover rpc rpcport 8545 rpccorsdomain rpcapi eth web3 personal net nat any unlock 0 password password txt macos code startblockchain sh chmod 755 startblockchain sh startblockchain bat macos startblockchain sh geth console code startgethconsole bat geth attach ipc pipe geth ipc macos code startgethconsole sh chmod 755 startgethconsole sh geth console startgethconsole bat macos startgethconsole sh mist code startmist bat c program files mist mist exe ipc pipe geth ipc macos code startmist sh applications mist app contents macos mist ipc pipe geth ipc mist startmist bat macos startmist sh geth code eth accounts code 1 code eth accounts 0 code code personal newaccount code code personal unlockaccount code code miner start 1 code code miner stop code code eth pendingtransactions code id code eth gettransaction id code code eth blocknumber code web3 eth getblock latest debug verbosity 2 0 silent 1 error 2 warn 3 info 4 core 5 debug 6 debug detail solidity code solidity https github com jongkwang blockchain lecture tree master solidity | blockchain | |
iot-security-wiki | iot security wiki https iot security wiki gitbook github com yaseng iot security wiki https github com yaseng iot security wiki pdf epub iot security wiki 0 1 iot security wiki https iot security wiki future sec com https future sec com iot security iot security org https iot security org yaseng blog yaseng org https yaseng org zhuanlan zhihu com https zhuanlan zhihu com future sec info iot security wiki qq 306482276 https img 1253984064 cos ap guangzhou myqcloud com future sec qrcode jpg http img bg7ywl com wx jpg https github com yaseng iot security wiki 2018 6 20 0 1 | iot-security iot security wiki security-wiki | server |
pybroker | img src https github com edtechre pybroker blob master docs static pybroker logo png raw true alt pybroker python https img shields io badge python v3 brightgreen svg https www python org apache 2 0 with commons clause https img shields io badge license apache 202 0 20clause green https www pybroker com en latest license html documentation status https readthedocs org projects pybroker badge version latest https www pybroker com en latest badge latest package status https github com edtechre pybroker actions workflows main yml badge svg event push https github com edtechre pybroker actions downloads https static pepy tech badge lib pybroker https pepy tech project lib pybroker github stars https img shields io github stars edtechre pybroker style social https github com edtechre pybroker twitter https img shields io twitter follow libpybroker style social https twitter com intent follow screen name libpybroker algorithmic trading in python with machine learning are you looking to enhance your trading strategies with the power of python and machine learning then you need to check out pybroker this python framework is designed for developing algorithmic trading strategies with a focus on strategies that use machine learning with pybroker you can easily create and fine tune trading rules build powerful models and gain valuable insights into your strategy s performance key features a super fast backtesting engine built in numpy https numpy org and accelerated with numba https numba pydata org the ability to create and execute trading rules and models across multiple instruments with ease access to historical data from alpaca https alpaca markets yahoo finance https finance yahoo com akshare https github com akfamily akshare or from your own data provider https www pybroker com en latest notebooks 7 20creating 20a 20custom 20data 20source html the option to train and backtest models using walkforward analysis https www pybroker com en latest notebooks 6 20training 20a 20model html walkforward analysis which simulates how the strategy would perform during actual trading more reliable trading metrics that use randomized bootstrapping https en wikipedia org wiki bootstrapping statistics to provide more accurate results caching of downloaded data indicators and models to speed up your development process parallelized computations that enable faster performance with pybroker you ll have all the tools you need to create winning trading strategies backed by data and machine learning start using pybroker today and take your trading to the next level installation pybroker supports python 3 9 on windows mac and linux you can install pybroker using pip bash pip install u lib pybroker or you can clone the git repository with bash git clone https github com edtechre pybroker a quick example get a glimpse of what backtesting with pybroker looks like with these code snippets rule based strategy python from pybroker import strategy yfinance highest def exec fn ctx get the rolling 10 day high high 10d ctx indicator high 10d buy on a new 10 day high if not ctx long pos and high 10d 1 high 10d 2 ctx buy shares 100 hold the position for 5 days ctx hold bars 5 set a stop loss of 2 ctx stop loss pct 2 strategy strategy yfinance start date 1 1 2022 end date 7 1 2022 strategy add execution exec fn aapl msft indicators highest high 10d close period 10 run the backtest after 20 days have passed result strategy backtest warmup 20 model based strategy python import pybroker from pybroker import alpaca strategy def train fn train data test data ticker train the model using indicators stored in train data return trained model register the model and its training function with pybroker my model pybroker model my model train fn indicators def exec fn ctx preds ctx preds my model open a long position given my model s latest prediction if not ctx long pos and preds 1 buy threshold ctx buy shares 100 close the long position given my model s latest prediction elif ctx long pos and preds 1 sell threshold ctx sell all shares alpaca alpaca api key api secret strategy strategy alpaca start date 1 1 2022 end date 7 1 2022 strategy add execution exec fn aapl msft models my model run walkforward analysis on 1 minute data using 5 windows with 50 50 train test data result strategy walkforward timeframe 1m windows 5 train size 0 5 user guide getting started with data sources https www pybroker com en latest notebooks 1 20getting 20started 20with 20data 20sources html backtesting a strategy https www pybroker com en latest notebooks 2 20backtesting 20a 20strategy html evaluating with bootstrap metrics https www pybroker com en latest notebooks 3 20evaluating 20with 20bootstrap 20metrics html ranking and position sizing https www pybroker com en latest notebooks 4 20ranking 20and 20position 20sizing html writing indicators https www pybroker com en latest notebooks 5 20writing 20indicators html training a model https www pybroker com en latest notebooks 6 20training 20a 20model html creating a custom data source https www pybroker com en latest notebooks 7 20creating 20a 20custom 20data 20source html applying stops https www pybroker com en latest notebooks 8 20applying 20stops html rebalancing positions https www pybroker com en latest notebooks 9 20rebalancing 20positions html rotational trading https www pybroker com en latest notebooks 10 20rotational 20trading html faqs https www pybroker com en latest notebooks faqs html online documentation the full reference documentation is hosted at www pybroker com https www pybroker com for chinese users https www pybroker com zh cn latest courtesy of albert king https github com albertandking contact img src https github com edtechre pybroker blob master docs static email image png raw true | algotrading backtesting machine-learning python trading quantitative-finance stocks framework investment ai artificial-intelligence algorithmic-trading data-science finance trading-strategies crypto cryptocurrency | ai |
PinDB | pindb development of engineering based database | server | |
large_language_model_training_playbook | the large language model training playbook this playbook is a companion to the llm training handbook https github com huggingface llm training handbook which contains a lot more details and scripts an open collection of implementation tips tricks and resources for training large language models the following covers questions related to various topics which are interesting or challenging when training large language models deciding on a model architecture architecture deciding on a model parallelism strategy deciding on the model size scaling laws trade off of large language model sizes issues and questions related to tensor precision what to chose between fp32 fp16 bf16 mixed precisions for optimizers weights specifics modules how to finetune and integrate a model trained in a precision in another precision selecting training hyper parameters and model initializations hparams learning rate and learning rate schedules questions on batch size maximizing throughput throughput avoiding recovering from and understanding instabilities instabilities detecting instabilities early training tips to reduce instabilities issues with data and data processing debugging software and hardware failures debug tips on what metrics to follow during the training resources resources | cuda llm nccl nlp performance python pytorch scalability troubleshooting large-language-models | ai |
Azure-RTOS-on-Raspberry-Pi-Pico-RP2040 | azure rtos threadx on raspberry pi pr2040 this sample demonstrates azure rtos threadx https azure com rtos on raspberry pi rp2040 microcontroller contents file folder description demo threadx c sample source code threadx azure rtos threadx source code and cortex m0 port tx initialize low level s rp2040 specific low level initialization code main c rp2040 user application entry point prerequisites 1 raspberry pi pico https www raspberrypi org products raspberry pi pico board 2 a terminal emulator such as putty https www chiark greenend org uk sgtatham putty or tera term https ttssh2 osdn jp index html en to display the output 3 follow chapter 8 2 of getting started with raspberry pi pico https datasheets raspberrypi org pico getting started with pico pdf to setup the build environment on windows 10 build and run the sample 1 clone this repository and threadx as a submodule alongside the pico sdk 1 2 0 directory this project is an external project depends on pico sdk git clone recurse submodules https github com xiongyu0523 azure rtos on raspberry pi pico rp2040 2 open a developer command prompt for vs2019 and go to the cloned project to build cd azure rtos on azure sphere mediatek mt3620 mkdir build cd build assume pico sdk path is set to pico sdk cmake g nmake makefiles waiting for namke project generation nmake 3 hold the bootsel button on raspberry pi pico and connect micro usb cable you will see a new usb drive pri pr2 is mounted 4 drag and drop the azure rtos on raspberry pi pico rp2040 uf2 file in build folder to the usb drive observe the output open a terminal and connect to the usb serial device comx just enumerated you will see a statistical data of multiple tasks printed every second and led is blinking at 1hz threadx demo threadx demonstration on raspberry pi pico thread 0 events sent 108 thread 1 messages sent 3926897 thread 2 messages received 3926877 thread 3 obtained semaphore 268 thread 4 obtained semaphore 268 thread 5 events received 107 thread 6 mutex obtained 268 thread 7 mutex obtained 268 hardware license to use azure rtos on commerical project please see license txt license txt and licensed hardware txt licensed hardware txt | os | |
FreeRTOS-Examples | freertos examples applied on the avr atmega32 a makefile is included with each project freertos examples for tiva c tm4c123gh6pm arm cortex m4 are available in https github com mohammed ahmedaf arm tree master tiva c examples freertos | os | |
SPPU-SE-TE-Assignments | h1 align middle grey exclamation sppu se te assignments grey exclamation h1 p align middle a repository for assignments in second year and third year of information technology of sppu br b i do star the repository if you feel it helped drop a follow b i br br i please refer to the readme s or lab manuals to find the assignment you wish for i br black heart b happy learning b black heart br p index s no contents 1 semester 3 dsa lab https github com bhaumikmaan sppu second year assignments tree main semester 203 data 20structures 20and 20algorithms 20lab 2 semester 3 oop lab https github com bhaumikmaan sppu second year assignments tree main semester 203 object 20oriented 20programming 20lab 3 semester 4 computer graphics lab https github com bhaumikmaan sppu second year assignments tree main semester 204 computer 20graphics 20lab 4 semester 4 dbms lab https github com bhaumikmaan sppu second year assignments tree main semester 204 dbms 20lab 5 semester 4 psdl lab https github com bhaumikmaan sppu second year assignments tree main semester 204 psdl 20lab 6 semester 5 daa lab https github com bhaumikmaan sppu se te assignments tree main semester 205 daa 7 semester 5 machine learning lab https github com bhaumikmaan sppu se te assignments tree main semester 205 ml 8 semester 5 opearting system lab https github com bhaumikmaan sppu se te assignments tree main semester 205 os | data-structures object-oriented-programming computer-graphics microprocessor dsa daa machine-learning os | server |
vicuna-ts | vicuna ts i added vicuna 7b and 13b cpu callback support and custom model and executable path options sh npm install vicuna ts typescript import vicuna from vicuna ts const main async instantiate vicuna with default or custom settings interface constructoroptions const vicuna new vicuna model ggml vicuna 13b 4bit rev1 callback token console log token you can use this callback as stream processing but its optional you can use 13b and 7b vicuna initialize and download missing files await vicuna init open the connection with the model await vicuna open generate a response using a prompt const prompt hello i am shubham const response await vicuna prompt prompt console log prompt prompt console log response response const prompt2 do you still remember my name please tell me my name const response2 await vicuna prompt prompt2 console log prompt prompt2 console log response response2 close the connection when you re done vicuna close main catch console error options typescript interface constructoroptions model ggml vicuna 13b 4bit rev1 ggml vicuna 7b 4bit rev1 executablepath string modelpath string callback token void forcedownload boolean decoderconfig record string any modeloverride boolean br curruntly only win binary added if you have binary for your os u can set it in options and it will work credit s shubham badgujar model credits vicuna team https vicuna lmsys org | ai | |
azure-iot-predictive-maintenance | note as of december 10th 2020 predictive maintenance solution accelerator is no longer supported all supporting repositories have been archived check the faq https docs microsoft com en us azure iot accelerators iot accelerators faq to learn more microsoft azure iot suite you can deploy preconfigured solutions that implement common internet of things iot scenarios to microsoft azure using your azure subscrption you can use preconfigured solutions as a starting point for your own iot solution to learn about the most common patterns in iot solution design and development each preconfigured solution implements a common iot scenario and is a complete end to end implementation you can deploy the azure iot suite predictive maintenance preconfigured solution from https www azureiotsuite com https www azureiotsuite com following the guidance on provisioning pre configured solutions outlined in this document https azure microsoft com en us documentation articles iot suite getstarted preconfigured solutions in addition you can download the complete source code from this repository to customize and extend the solution to meet your specific requirements predictive maintenance pre configured solution the predictive maintenance pre configured solution illustrates how you can predict the point when failure is likely to occur the solution combines key azure iot suite services including an ml workspace complete with experiments for predicting the remaining useful life rul of an aircraft engine based on a public sample data set contents of this repository docs folder set up development environment windows docs dev setup md outlines the prerequisites for deploying the remote monitoring preconfigured solution cloud deployment docs cloud deployment md describes building and deploying the remote monitoring preconfigured solution fully on azure tutorial modify the remaining useful life predictive experiment docs tutorial rul md is a short walk through on modifying the azure ml model slightly and making appropriate modifications to accommodate this other useful iot suite documentation https azure microsoft com documentation suites iot suite frequently asked questions for iot suite https azure microsoft com documentation articles iot suite faq permissions on the azureiotsuite com site https azure microsoft com documentation articles iot suite permissions configuring azure iot suite preconfigured solutions for demo purposes https github com azure azure iot remote monitoring blob master docs configure preconfigured demo md walks you through changing the footprint of the underlying azure services for your solution eventprocessor folder azure worker role that hosts an event hub eventprocessorhost instance to handle the event data from the devices forwarding event data to other back end services or to the remote monitoring site visual studio solution predictivemaintenance contains the source code for the whole preconfigured solution including the solution portal web app the eventprocessor web job and the simulator web job feedback have ideas for how we can improve azure iot give us feedback https feedback azure com forums 916438 azure iot solution accelerators code of conduct this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | server | |
Dive-Into-Blockchain | dive into blokchain 2008 satoshi bitcoin web2 0 ethereum gavin wood web3 2017 web3 foundation web 3 0 web3 web 3 0 web3 programmer push code is law https github com dessertheart dive into blockchain tree main learnblockchain learnethereum dapp e5 bc 80 e5 8f 91 e5 85 a5 e9 97 a8 pos go ethereum https github com dessertheart dive into blockchain tree main learnblockchain dapp learnblockchain learnethereum dapp e5 bc 80 e5 8f 91 e5 85 a5 e9 97 a8 ethereum learnblockchain learnethereum evm e5 ba 95 e5 b1 82 e7 bb 93 e6 9e 84 go ethereum learnblockchain learnethereum zk https github com dessertheart dive into blockchain tree master learnblockchain learnzero knowledge learnblockchain learnzero knowledge e5 9f ba e6 9c ac e6 a6 82 e5 bf b5 commitment learnblockchain learnzero knowledge e6 89 bf e8 af ba e6 96 b9 e6 a1 88 learnblockchain learnzero knowledge e7 ae 97 e6 9c af e5 8c 96 zk snarks learnblockchain learnzero knowledge zk snark circom learnblockchain learnzero knowledge e7 ae 97 e6 9c af e7 94 b5 e8 b7 af e5 ba 94 e7 94 a8 e4 b8 8ecircom damn vulnerable defi https github com dessertheart diveintoblockchain tree main safeblockchain damnvulnerabledefi evm puzzles https github com dessertheart dive into blockchain tree main safeblockchain evm puzzles chainflag https github com dessertheart dive into blockchain tree main safeblockchain chainflag 0 1 bitcoin https github com dessertheart dive into blockchain tree main devblockchain mini bitcoin zap aave uniswapv2 uniswapv3 https github com dessertheart dive into blockchain tree main playblockchain flashloan demo | blockchain ethereum solidity bitcoin zero-knowledge damn-vulnerable-defi evm flashloan geth opcodes plonk | blockchain |
mros2-mbed | build app https github com mros base mros2 mbed actions workflows build app yaml badge svg https github com mros base mros2 mbed actions workflows build app yaml build board https github com mros base mros2 mbed actions workflows build board yaml badge svg https github com mros base mros2 mbed actions workflows build board yaml mros2 mbed mros 2 mros2 as casually codename realizes a agent less and lightweight runtime environment compatible with ros 2 for embedded devices mros 2 mainly offers pub sub apis compatible with rclcpp https docs ros org en rolling p rclcpp index html for embedded devices mros 2 consists of communication library for pub sub apis rtps protocol udp ip stack and real time kernel this repository provides the reference implementation of mros 2 that can be operated on the mbed enabled board please also check mros2 repository https github com mros base mros2 for more details and another implementations supported environment mbed device board mbed enabled boards having an ethernet port for now these boards below are confirmed to run the example on them stm32 nucleo f767zi https www st com en evaluation tools nucleo f767zi html stm32 nucleo h743zi2 https www st com en evaluation tools nucleo h743zi html these boards below are also confirmed but not always supported in the latest version due to our development resources stm32 nucleo f429zi https www st com en evaluation tools nucleo f429zi html stm32 f746ng discovery https www st com ja evaluation tools 32f746gdiscovery html stm32 f769ni discovery https www st com ja evaluation tools 32f769idiscovery html seeed arch max v1 1 https wiki seeedstudio com arch max v1 1 renesas gr mango https www renesas com products gadget renesas boards gr mango kernel mbed os 6 https github com armmbed mbed os check the mbed website for the boards list https os mbed com platforms q mbed os 6 bare metal mbed os 6 rtos communication ethernet where mros2 may work please let us know if you find a new board that can work as mros2 enabled device host environment ros 2 humble hawksbill https docs ros org en humble index html on ubuntu 22 04 lts ros 2 foxy fitzroy https docs ros org en foxy index html on ubuntu 20 04 lts network setting make sure both the device and the host are connected to the wired network with the following setting since they are statically configured to the board ip address 192 168 11 x 2 will be assigned to the board netmask 255 255 255 0 gateway 192 168 11 1 you can configure them by editing platform mros2 platform h note that we have not confirmed the operation using dhcp setting yet so you cannot comment out the define mros2 ip address static line to assign static ip address the firewall on the host ubuntu needs to be disabled for ros 2 dds communication e g sudo ufw disable if the host is connected to the internet with other network adapters communication with mros2 may not work properly in that case please turn off them getting started 1 prepare these items below host pc having an ethernet port whose network is set to the above and a docker environment mbed board having an ethernet port listed above 2 build mbed executable binary using the docker environment for mbed cli2 https github com armmbed mbed os pkgs container mbed os env you can also use the native environment where mbed cli2 could work well please add native to 4th arg see the example instruction to prepare native env https github com mros base mros2 mbed commit 90225c77e07e5cedc8473285b457827cb047e481 git clone https github com mros base mros2 mbed cd mros2 mbed please replace the target with the ones as below your target board target nucleo f767zi nucleo f767zi nucleo h743zi2 nucleo h743zi2 nucleo f429zi nucleo f429zi f746ng discovery disco f746ng f769ni discovery disco f769ni arch max v1 1 arch max gr mango gr mango build bash all target echoback string after that you will find an executable binary is created in the path below cmake build target develop gcc arm mros2 mbed bin 3 connect the pc and mbed board with usb and lan cables 4 open serial console of the mbed board 115200bps 5 copy the executable binary above to the mbed board you may find it in the nautilus file manager as node f429zi f767zi or daplink mros2 mbed start app name echoback string successfully connected to network ip address 192 168 11 2 mros2lib set ip address for rtps communication mros2lib mros2 init task start mros 2 initialization is completed mros2lib create node mros2lib start creating participant mros2lib successfully created participant mros2lib create publisher complete mros2lib create subscription complete mros2lib initilizing domain complete ready to pub sub message publishing msg hello from mros2 mbed onto nucleo f767zi 0 publishing msg hello from mros2 mbed onto nucleo f767zi 1 publishing msg hello from mros2 mbed onto nucleo f767zi 2 publishing msg hello from mros2 mbed onto nucleo f767zi 3 snipped 6 one of the easiest way to operate the host is using docker on the host terminal type the command below docker run rm it net host ros humble bin bash c source opt ros humble setup bash cd git clone https github com mros base mros2 host examples cd mros2 host examples colcon build packages select mros2 echoreply string source install setup bash ros2 run mros2 echoreply string echoreply node then we can confirm the communication between the host and mbed board via ros 2 topic cloning into mros2 host examples remote enumerating objects 831 done remote counting objects 100 85 85 done remote compressing objects 100 68 68 done remote total 831 delta 46 reused 26 delta 15 pack reused 746 receiving objects 100 831 831 96 01 kib 7 38 mib s done resolving deltas 100 448 448 done starting mros2 echoreply string finished mros2 echoreply string 9 02s summary 1 package finished 9 17s info 1666012200 122092282 mros2 echoreply node subscribed msg hello from mros2 mbed onto nucleo f767zi 7 info 1666012200 122210443 mros2 echoreply node publishing msg hello from mros2 mbed onto nucleo f767zi 7 info 1666012201 127168943 mros2 echoreply node subscribed msg hello from mros2 mbed onto nucleo f767zi 8 info 1666012201 127216518 mros2 echoreply node publishing msg hello from mros2 mbed onto nucleo f767zi 8 info 1666012202 132162620 mros2 echoreply node subscribed msg hello from mros2 mbed onto nucleo f767zi 9 info 1666012202 132208473 mros2 echoreply node publishing msg hello from mros2 mbed onto nucleo f767zi 9 info 1666012203 137265544 mros2 echoreply node snipped serial console on the board snipped publishing msg hello from mros2 mbed onto nucleo f767zi 5 publishing msg hello from mros2 mbed onto nucleo f767zi 6 mros2lib subscriber matched with remote publisher mros2lib publisher matched with remote subscriber publishing msg hello from mros2 mbed onto nucleo f767zi 7 subscribed msg hello from mros2 mbed onto nucleo f767zi 7 publishing msg hello from mros2 mbed onto nucleo f767zi 8 subscribed msg hello from mros2 mbed onto nucleo f767zi 8 publishing msg hello from mros2 mbed onto nucleo f767zi 9 subscribed msg hello from mros2 mbed onto nucleo f767zi 9 snipped examples this repository contains some example applications in workspace workspace to communicate with ros 2 nodes on the host you can switch the example by specifying the third argument of build bash of course you can also create a new program file and specify it as your own application please also check mros base mros2 host examples https github com mros base mros2 host examples repository for more detail about the host examples echoback string default description the mros 2 node on the embedded board publishes string std msgs msg string message to to linux topic the node on the host will echoreply this message as it is the mros 2 node subscribes the replied message from to stm topic host operation ros2 run mros2 echoreply string echoreply node echoreply string description the mros 2 node on the embedded board subscribes string std msgs msg string message from to stm topic and then publishes this string message as it is to to linux as the reply host operation at first terminal ros2 run mros2 echoback string sub node and then at second terminal ros2 run mros2 echoback string pub node or at one terminal ros2 launch mros2 echoback string pubsub launch py pub float32 description the mros 2 node on the embedded board publishes float32 std msgs msg float32 message to to linux topic note that this application just print whether the value of message is less than 0 0 between 0 0 and 1 0 or greater than 1 0 if you want to print float value in serial console you need to add target printf lib std into mbed app json see detail https forums mbed com t float printf doesnt work in desktop version 9164 note that linking std lib will increase the size of flash memory host operation ros2 run mros2 sub float32 sub node or ros2 launch mros2 sub float32 sub launch py sub uint16 description the mros 2 node on the embedded board subscribes uint16 std msgs msg uint16 message from to stm topic host operation ros2 run mros2 pub uint16 pub node or ros2 launch mros2 pub uint16 pub launch py pub twist description the mros 2 node on the embedded board publishes twist geometry msgs msg twist message to cmd vel topic this application requires to generated header files for twist and vector3 see detail in repo root readme md generating header files for custom msgtypes readme md generating header files for custom msgtypes host operation ros2 run mros2 sub twist sub node or ros2 launch mros2 sub twist sub launch py sub pose description the mros 2 node on the embedded board subscibes pose geometry msgs msg pose message to cmd vel topic this application requires to generated header files for pose point and quartenion see detail in repo root readme md generating header files for custom msgtypes readme md generating header files for custom msgtypes host operation ros2 run mros2 pub pose pub node or ros2 launch mros2 pub pose pub launch py mturtle teleop description this is a sample application along with mturtlesim https github com mros base ros tutorials tree mros2 humble devel turtlesim mros2 dedicated version of turtlesim the mros 2 node on the embedded board publishes twist geometry msgs msg twist message to turtle1 cmd vel topic according to the input from keyboard via serial console please see mturtle teleop readme md workspace mturtle teleop readme md for more detail including host operation mturtle teleop joy description this is a sample application along with mturtlesim https github com mros base ros tutorials tree mros2 humble devel turtlesim mros2 dedicated version of turtlesim the mros 2 node on the embedded board publishes twist geometry msgs msg twist message to turtle1 cmd vel topic according to the input from joystick module please see mturtle teleop joy readme md workspace mturtle teleop joy readme md for more detail including host operation pub long string sub crc description this sample application is an example to demonstrate the fragmented message feature added by the pull request below https github com mros base mros2 mbed issues 32 the mros 2 node on the embedded board publishes too long string std msgs msg string message prepared in long text txt to the to linux topic the node on the host will calculate the crc value of subscribed long string and publish the value as the reply the mros 2 node subscribes uint32 std msgs msg uint32 message as the calculated crc value from to stm topic host operation ros2 run mros2 sub long string pub crc sub long string pub crc node pub camera image description this sample application is an example to demonstrate the fragmented message feature added by the pull request below https github com mros base mros2 mbed issues 32 the mros 2 node on the embedded board publishes an image sensor msgs msg image message to the to linux topic this sample uses the dcmi i f of stm32 mcu as a camera i f the camera module to be used is the prevalence one like below https papers ssrn com sol3 papers cfm abstract id 4166233 if you don t have the camera module but want to try publishing image you can use pub image sample instead that publises a dummy image defined in mros image h for some nucleo boards e g nucelo f429zi you might have to solder two pin headers on both pa8 and pb7 to connect them to xclk and vsync respectively the whole of the pin connections between the mcu and the camera module should be below mcu camera module 3 3v 3v3 gnd gnd pb9 sda pb8 scl pb7 vsync pa4 hsync or href pa6 pixclk or pclk pa8 xclk pe6 d7 pe5 d6 pb6 d5 pe4 d4 pc9 d3 pc8 d2 pc7 d1 pc6 d0 pa0 reset gnd pwndn refs https www stmcu jp design document users manual 52234 host operation rqt and then subscribe to the topic to linux and visualize it by image view on the gui like below movie sample https github production user asset 6210df s3 amazonaws com 90823686 243369057 839cf812 eb1d 45bf 820e e0166253899c webm note file s for the application the main source of the application is app cpp you can change and or add filename by editing app srcs in cmakelists txt if you have several directories that contain application code files you also need to edit cmakelists txt see details in comment generating header files for custom msgtypes if you want to use your own customized message type followed by the ros 2 manner please refer to mros2 generating header files for custom msgtypes https github com mros base mros2 generating header files for custom msgtypes section this section was moved because it is common feature for mros 2 tips 1 configure the network platform rtps config h is the configuration file for embeddedrtps we may be able to realize the rtps communication to the appropriate configuration by editting this file and also you can configure for lwip udp ip by mbed app json we should seek the appropreate configurations or how to fit them to the demand of applications please let us know about them if you have any opinions or awesome knowledges tips 2 development with the latest mros2 repo a k a memo for me when using the docker environment for build it will try to clone mros2 dir repo automatically during the cmake process to prevent this we can simply clone code from github as the development mode and or add to the beginning of mros2 lib tips 3 getting started in 5 minutes with the online compiler caution although you can easily try out the basic features of mros2 online this environment has not been fully maintained note that new features such as uniquely defined message types are not available we also provide an online development environment for mros2 mbed by using the following codes on keil studio cloud a k a mbed online complier you can try out the power of mros2 just in 5 minute we wish d example application mbed os example mros2 echoreply string https os mbed com users smoritaemb code mbed os example mros2 mbed os example mros2 pub twist pub twist https os mbed com users smoritaemb code example mbed mros2 pub twist mbed os example mros2 sub pose sub pose https os mbed com users smoritaemb code example mbed mros2 sub pose mbed mros2 core library for mros2 mbed https os mbed com users smoritaemb code mbed mros2 please feel free to let us know in issues on this repository https github com mros base mros2 mbed issues if you have any troubles or causes submodules and licenses the source code of this repository itself is published under apache license 2 0 https github com mros base mros2 blob main license please note that this repository requires the following stacks as the submodules and also check their licenses mros2 https github com mros base mros2 the pub sub apis compatible with ros 2 rclcpp embeddedrtps https github com mros base embeddedrtps rtps communication layer including lwip and micro cdr mbed os 6 https github com armmbed mbed os an open source embedded operating system designed specifically for the things in the internet of things | cortex-m embedded iot pubsub ros2 stm32 dds mros2 rtos rtps | os |
Modernistik | modernistik cocoa framework https raw githubusercontent com modernistik modernistik master modernistik png this framework represents extensions utilities design patterns and practices adopted for modernistik software development in swift installation you can install modernistik in two forms cocoapods or using swift package manager swift package manager modernistik is available as a swift package on xcode 12 and later add the repository url to the list of swift packages in xcode cocoapods modernistik is available through cocoapods http cocoapods org to install it simply add the following line to your podfile ruby pod modernistik 0 5 swift 5 author anthony persaud https www modernistik com | swift sdk coresdk hyperdrive parse-server ios tvos | front_end |
GPU-Viewer | gpu viewer donate https user images githubusercontent com 30646692 209199473 a51dbd6c d7f2 4bfe a392 0abf20cbc4ec png https www paypal com donate hosted button id 7m3pmm78fbr4q a front end to glxinfo vulkaninfo clinfo and es2 info packaging status https repology org badge vertical allrepos gpu viewer svg https repology org project gpu viewer versions this project aims to capture all the important details of glxinfo vulkaninfo and clinfo in a gui the project is being developed using python 3 pygobject with gtk4 all the important details are extracted using glxinfo vulkaninfo clinfo with the combination of grep cat awk commands and displayed in the front end there is no hard opengl programming involved until glxinfo vulkaninfo and clinfo works the gpu viewer will also work screenshot from 2023 07 18 21 29 32 https github com arunsivaramanneo gpu viewer assets 30646692 ebd8e397 7d21 4119 a6a0 196cf81f86c0 screenshot from 2023 07 18 21 29 42 https github com arunsivaramanneo gpu viewer assets 30646692 cf9525ac efa1 4372 a011 fd45793921bc screenshot from 2023 07 18 21 29 54 https github com arunsivaramanneo gpu viewer assets 30646692 3cdd34a6 8e0d 451f 8b47 1643428917d8 screenshot from 2023 07 18 21 30 00 https github com arunsivaramanneo gpu viewer assets 30646692 19066f11 8e9b 45f6 b31b 9800b685098a screenshot from 2023 07 18 21 30 05 https github com arunsivaramanneo gpu viewer assets 30646692 927e5553 9a5f 4c28 8335 beb955711abd installation steps those who are cloning and installing the application through install file please note the application will work with vulkan tools 1 2 141 or higher for anything below please use the ppa to install the latest stable version working on integrating the latest vulkan tools to the installation 1 before downloading the files please see the known issues mentioned below 2 ensure python is installed 3 ubuntu 23 04 lunar ubuntu 23 10 mantic ubuntu 22 04 jammy linux mint 20 x linux mint 19 x users should be able to install this application using the below ppa sudo add apt repository ppa arunsivaraman gpuviewer sudo apt get update sudo apt get install gpu viewer please note all the dependencies python vulkan tools clinfo es2 info will be installed if not installed before 4 debian based distro users should be able to install the application by just running the deb file attached in the release notes 5 arch based distro users should be able to grab the application at https aur archlinux org packages gpu viewer or by running command yay s gpu viewer from the terminal this should automatically take care of the dependencies thanks to dan johnson strit for maintaining the aur package 6 fedora based distro run the command sudo dnf y install clinfo egl utils mesa demos mesa vulkan drivers python3 vdpauinfo vulkan tools from the terminal then complete steps 8 to 11 7 opensuse based distro run the command sudo zypper install clinfo mesa demo mesa vulkan device select libvulkan intel libvulkan lvp libvulkan radeon python3 libvdpau1 vulkan tools xdpyinfo xev xlsatoms xlsclients xlsfonts xprop xvinfo xwininfo from the terminal then complete steps 8 to 11 8 for others please follow steps 8 to 11 9 download the file and extract to a folder 10 navigate to extracted folder open terminal and enter below commands meson build cd build ninja install 11 once completed application can be accessed at menu system administration system tools gpu viewer 12 for vulkan tab to work install vulkan tools sudo apt get install vulkan tools in ubuntu vulkan tools in arch vulkan in solus also vulkan enabled drivers should be installed the installer should be able to take care of this dependency in debian based distro and solus 12 for opencl tab to work install clinfo sudo apt install clinfo in ubuntu clinfo in solus sudo eopkg install clinfo clinfo in arch also ensure you have opencl installed for your respective platforms ex nvidia cuda for nvidia hardware beignet for intel graphics or pocpl for cpu or amd opencl for amd hardware 13 for egl information to be displayed in opengl tab users should install mesa utils extra package in debian based systems on arch please install latest version of mesa demos 14 for vdpau information to be displayed please install vdpauinfo 15 incase of issues launching the application please see the faq in wiki section uninstall steps 1 debian users should be able to uninstall in the default way i e sudo apt remove gpu viewer 2 for others remove gpu viewer directory in usr share or run sudo rm usr share gpu viewer r to remove also you need to remove the symlink by running sudo rm usr bin gpu viewer what s developed and available 1 opengl tab opengl information opengl es information opengl hardware limits and extensions displayed as per different vendors view glx frame buffer configuration and egl information egl information 2 vulkan tab device features device limits device extensions formats memory types heaps partial queue families implemented instance and layers surface tab 3 opencl tab platform details device details device memory image details device queue and execution capabilities device vector details total no of platforms no of devices for the platform 4 about tab about gpu viewer application ability to report a bug view license view change log donate via paypal gpu viewer github main page under development 1 general bug fixes code optimizations high priority important 1 requires python3 to run this application works only on linux operating system 2 for vulkan tab to work nvidia mesa and amd vulkan enabled drivers should be installed along with vulkan utils 3 for opencl tab to work install clinfo along with opencl drivers for your respective gpu s known issues 1 minor ui issues 2 the extensions drop down menu in opengl tab will not render well if there are too many items users may see a big empty space at the start this is a gtk issue https github com arunsivaramanneo gpu viewer issues 9 3 the application does not render well under dark themes development test environment 1 operating system ubuntu 23 04 2 desktop gnome 42 3 kernel 5 15 x xx 4 ide vscode system setup 1 msi pe62 laptop huawei matebook 13 amd 2 quad core intel core i7 4710hq amd ryzen 3500 u 3 nvidia gefore gtx 1050ti discrete gpu drivers nvidia proprietary mesa drivers 4 intel hd r graphics 630 drivers mesa open source 5 8 gb ram if you find the project interesting enough please consider making a donation even a small one would mean the world to me more than a mere financial act donate means that you simply believe in this project and want it to be better donate https user images githubusercontent com 30646692 209199473 a51dbd6c d7f2 4bfe a392 0abf20cbc4ec png https www paypal com donate hosted button id 7m3pmm78fbr4q | gpu-viewer glxinfo vulkan-utils gtk3 hardware-information front-end linux python pygobject gpu-info vulkaninfo opengl-viewer opengl vulkan clinfo opencl | front_end |
partclone | partclone is a project similar to the well known backup utility partition image a k a partimage partclone provides utilities to back up and restore used blocks of a partition and it is designed for higher compatibility of the file system by using existing library e g e2fslibs is used to read and write the ext2 partition partclone now supports ext2 ext3 ext4 hfs reiserfs reiser4 btrfs vmfs3 vmfs5 xfs jfs ufs ntfs fat 12 16 32 exfat we made some utilities partclone ext2 partclone ext3 partclone ext4 partclone extfs partclone reiserfs partclone reiser4 partclone xfs partclone exfat partclone fat fat 12 fat 16 fat 32 partclone ntfs partclone hfsp partclone vmfs v3 and v5 partclone ufs partclone jfs partclone btrfs partclone minix partclone f2fs partclone nilfs partclone info partclone restore partclone chkimg partclone dd basic usage clone partition to image partclone ext4 d c s dev sda1 o sda1 img restore image to partition partclone ext4 d r s sda1 img o dev sda1 partiiton to partition clone partclone ext4 d b s dev sda1 o dev sdb1 display image information partclone info s sda1 img check image partclone chkimg s sda1 img limitations filesystem being backedup must be unmounted and inaccessible to other programs for more info about partclone check our website http partclone org or github wiki | os | |
SQL-Database | sql database this is a research project about people whom a particular company employed during the 1980s and 1990s in this project tables are designed to hold the data from the csv files import the csv files into a sql database data modelling data engineering and data analysis are performed instructions step 1 to clone the repo use the following command https github com bharatguturi sql database git step 2 find the erd document erd employee relations in the folder step 3 create the tables according to the erd document using the scripts inside the following folders in order 1 titles sql 2 employees sql 3 department sql 4 salaries sql 5 dept manager sql 6 dept emp sql alternately you can execute the code in the schema erd employee relations to create all the tables together step 4 import csvs for each table from data folder into respective tables step 5 run the script in the schema employees data analysis step 6 create env file with the following details db username your database username db password your database password step 6 for bonus run the script in the file bonus employee salary plotting | database postgresql sql | server |
Web-Application-Development-with-R-Using-Shiny-third-edition | web application development with r using shiny a href https www packtpub com web development web application development r using shiny third edition utm source github utm medium repository utm campaign 9781788993128 img src https d1ldz4te4covpm cloudfront net sites default files imagecache ppv4 main book cover b10166 png alt web application development with r using shiny height 256px align right a this is the code repository for web application development with r using shiny https www packtpub com web development web application development r using shiny third edition utm source github utm medium repository utm campaign 9781788993128 published by packt build stunning graphics and interactive data visualizations to deliver cutting edge analytics what is this book about web application development with r using shiny helps you become familiar with the complete r shiny package the book starts with a quick overview of r and its fundamentals followed by an exploration of the fundamentals of shiny and some of the things that it can help you do you ll learn about the wide range of widgets and functions within shiny and how they fit together to make an attractive and easy to use application this book covers the following exciting features harness the power of javascript to customize your applications build dashboards with predefined ui and layouts engage your users and build better analytics using interactive plots learn advanced code patterns to make your applications easy to write and maintain develop a full understanding of shiny s ui functions to give you the power to build a wide variety of attractive applications if you feel this book is for you get your copy https www amazon com dp 1788993128 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following ul li first bullet li li second bullet li li third bullet li ul following is what you need for this book web application development with r using shiny is for you if you are interested in creating compelling web applications and interactive data visualization over the web using shiny programming experience with r is required with the following software and hardware list you can run all code files present in the book chapter 1 9 software and hardware list chapter software required os required 1 8 r 3 0 1 rstuio 1 1 456 mac os x 10 6 or higher ubuntu 12 or higher windows 7 8 0 8 1 or 10 32 bit and 64 bit 9 rstuio 1 1 456 amazon aws ec2 mac os x 10 6 or higher ubuntu 12 or higher windows 7 8 0 8 1 or 10 32 bit and 64 bit we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https www packtpub com sites default files downloads 9781788993128 colorimages pdf related products other books you may enjoy data analysis with r second edition packt https www packtpub com big data and business intelligence data analysis r second edition utm source github utm medium repository utm campaign 9781788393720 amazon https www amazon com dp 1788393724 r data visualization recipes packt https www packtpub com big data and business intelligence r data visualization recipes utm source github utm medium repository utm campaign 9781788398312 amazon https www amazon com dp 1788398319 get to know the authors chris beeley has been using r and other open source software for ten years to better capture analyze and visualize data in the healthcare sector in the uk he is the author of web application development with r using shiny he works full time developing software to store collate and present questionnaire data using open technologies mysql php r and shiny with a particular emphasis on using the web and shiny to produce simple and attractive data summaries chris is working hard to increase the use of r and shiny both within his own organization and throughout the rest of the healthcare sector as well to enable his organization to better use a variety of other data science tools chris has also delivered talks about shiny all over the country shitalkumar r sukhdeve is a senior data scientist at pt smartfren telecom tbk jakarta indonesia on his career journey he has worked with reliance jio as a data scientist entrepreneur and corporate trainer he has trained over 1 000 professionals and students and has delivered over 200 lectures on r and machine learning research and development in ai driven self optimizing networks predictive maintenance optimal network quality anomaly detection and customer experience management for 4g lte networks are all areas of interest to shitalkumar he is very experienced with r spark r shiny h2o python knime the hadoop ecosystem mapreduce hive and configuring the open source r shiny server for machine learning models and dashboard deployment other books by the authors hands on dashboard development with shiny https www packtpub com big data and business intelligence hands dashboard development shiny utm source github utm medium repository utm campaign 9781789611557 web application development with r using shiny https www packtpub com web development web application development r using shiny utm source github utm medium repository utm campaign 9781783284474 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781782174349 https packt link free ebook 9781782174349 a p | front_end | |
RTOS-ChibiOS | rtos chibios this is the repo for rtos chibios firmware development using stm32f407 discovery board and chibios 20 3 using rt kernel | rtos chibios chibios-rtos ardupilot | os |
blockchain | blockchain node js module to access the blockchain websocket api build status https travis ci org abrkn blockchain png https travis ci org abrkn blockchain installation npm install blockchain donations 1kk26tmvgxfavxultndkmh7ihzs2a7524y methods constructor creates a new socket and calls open subscribe address callback unsubscribe address callback open close events connect function connectfailed function errordescription disconnect function error function error notes subscriptions persist see tests for example license isc | blockchain | |
hsds-react | https ddwva799xzrph cloudfront net items 110k3c0l3v183j3j0g2i hsds logo readme png hsds react build status https travis ci org helpscout hsds react svg branch main https travis ci org helpscout hsds react coverage status https coveralls io repos github helpscout hsds react badge svg branch main https coveralls io github helpscout hsds react branch main npm version https badge fury io js 40helpscout 2fhsds react svg https badge fury io js 40helpscout 2fhsds react node https img shields io badge node 16 14 0 blue svg npm https img shields io badge npm 8 3 1 blue svg 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 react components for help scout s design system live demo check out hsds s storybook https hsds helpscout com https hsds helpscout com install npm install helpscout hsds react save getting started after cloning this repo down run npm install once everything is installed run npm start check out hsds s storybook at http localhost 8900 in your browser svg adapter as of v2 18 0 the svg set is opt in this was done to reduce the compiled bundle size to load the svg icons add the appropriate adapter to your app it is recommended that the adapter be loaded somewhere within your main entry point e g src index js js src index js for a lighter weight svg set for embeddables import helpscout hsds react adapters embed for the complete svg set import helpscout hsds react adapters app note this loads all the svg images including icon src components icon and illo src components illo testing to run jest in watch mode run npm run dev to execute all the tests with coverage reporting run npm run test publishing merging a pr to merge a pr when the release is completed please do a squash and merge from the pr page it will keep the history clean in the main branch we tend to do a release with only one pr at the time if possible releasing on npm you can learn more about releasing main https helpscout gitbook io hsds react contributing release or releasing a beta build https helpscout gitbook io hsds react testing local integration testing to publish and release a new version of hsds run the following command npm run release you ll be presented with a cli prompt with options pick the one you want and that s it the script will take care of the rest from testing to publishing deploying storybook to deploy the storybook run the following command npm run deploy learning hsds documentation https hsds helpscout com https hsds helpscout com developer in depth documentation dev docs docs readme md also check out our videos videos md for more learning resources blue on nov 8 2018 we rebranded blue to hsds react tada the original blue https www npmjs com package helpscout blue library will still exist on npm and is still installable via npm install helpscout blue save however continued development of this component library will continue under hsds react which is installable via npm install helpscout hsds react save a big thanks to all the folks involved in blue blue will always be your boy blue heart | open-source react components library javascript design-systems | os |
nebular | nebular img src https i imgur com omcxwz0 png alt eva design system height 20px https eva design utm campaign eva design 20 20home 20 20nebular 20github 20readme utm source nebular utm medium referral utm content github readme hero pic npm https img shields io npm l nebular theme svg npm https img shields io npm dt nebular theme svg https www npmjs com package nebular theme codecov https img shields io codecov c github akveo nebular master svg style flat square https codecov io gh akveo nebular branch master documentation https akveo github io nebular docs getting started what is nebular utm campaign nebular 20 20home 20 20nebular 20github 20readme utm source nebular utm medium referral utm content documentation stackblitz template https stackblitz com github akveo nebular seed angular templates https www akveo com templates utm campaign services 20 20github 20 20templates utm source nebular utm medium referral utm content github 20readme 20top 20angular 20templates 20link nebular is a customizable angular ui library with a focus on beautiful design and ability to adapt it to your brand easily it comes with 4 stunning visual themes a powerful theming engine with runtime theme switching and support of custom css properties mode nebular is based on eva design system specifications a href https akveo github io nebular utm campaign nebular 20 20home 20 20nebular 20github 20readme utm source nebular utm medium referral utm content nebular readme pic img src https i imgur com vu5ro3a jpg a what s included 4 visual themes including new dark easily customizable to your brand 35 angular ui components with a bunch of handy settings and configurations configurable options colors sizes appearances shapes and other useful settings 3 auth strategies and security authentication and security layer easily configurable for your api powerful theming engine with custom css properties mode svg eva icons support 480 general purpose icons repository state and engagement with the community repository is currently in a state of minimal maintenance our primary focus is on ensuring that the angular version used in this project is kept up to date our capacity to engage in other aspects of repository management is currently limited we are not actively reviewing or merging pull requests responding to or resolving issues at this time we appreciate the effort and contributions from the community and we understand that issues are crucial for the community but now our current focus is solely on maintaining angular quick start you can install nebular with angular cli bash ng add nebular theme configuration will be done automatically if you want to have more control over setup process you can use manual setup guide https akveo github io nebular docs guides install nebular utm campaign nebular 20 20home 20 20nebular 20github 20readme utm source nebular utm medium referral utm content install manually manually browser support nebular supports most recent browsers browser support list can be found a href https angular io guide browser support target blank here a starters ngx admin http github com akveo ngx admin 20k stars application based on nebular modules with beautiful e commerce iot components for boosting your developing process live demo https www akveo com ngx admin utm campaign ngx admin 20 20demo 20 20nebular 20github 20readme 20 20traffic utm source nebular utm medium referral utm content github readme ngx admin starter https github com akveo ngx admin tree starter kit clean application based on nebular modules with a limited number of additional dependencies license mit license txt license more from akveo eva icons https github com akveo eva icons 480 beautiful open source icons akveo templates https www akveo com templates utm campaign services 20 20github 20 20templates utm source nebular utm medium referral utm content nebular 20github 20readme 20more 20from 20akveo 20link 10 ready to use apps templates to speed up your apps developments how can i support the developers star our github repo star create pull requests submit bugs suggest new features or documentation updates wrench read us on medium https medium com akveo engineering follow us on twitter https twitter com akveo inc feet like our page on facebook https www facebook com akveo thumbsup from developers made with heart by akveo team https www akveo com utm campaign service 20 20akveo 20website 20 20nebular 20github 20readme 20 20traffic utm source nebular utm medium referral utm content github readme follow us on twitter https twitter com akveo inc to get the latest news first we re always happy to receive your feedback | angular ui ui-kit authentication theme multitheme modular typescript sass webpack angular-cli angular5 angular4 angular2 angular-components ngx-admin angular6 nebular-modules akveo angular7 | os |
blockchain-tutorial | blockchain dapp for my blog post note this is a sample project for my blog on medium which can be found here https betterprogramming pub blockchain introduction using real world dapp react solidity web3 js 546471419955 get started 1 clone this repository 2 cd blockchain tutorial contacts 3 run yarn install 3 run yarn start 4 go to http localhost 3000 in your browser to see it in action please click here to buy me a coffee a href https www buymeacoffee com zafarsaleem target blank img src https cdn buymeacoffee com buttons v2 default yellow png alt buy me a coffee style height 60px important width 217px important a | react blockchain truffle truffle-framework ganache metamask solidity smart-contracts dapps blog medium-article web3js | blockchain |
pando-cloud | pando cloud build status https travis ci org ruizeng pando cloud svg https travis ci org ruizeng pando cloud coverage status https coveralls io repos pandocloud pando cloud badge svg branch master service github https coveralls io github pandocloud pando cloud branch master pandocloud english document docs en readme md pando demo architecture docs img architecture jpeg http xmpp mqtt coap http mqtt api registry devicemanger apiprovider restful api controller mongodb mongodb mysql mysql redis redis nsq etcd elk elk logstash elasticsearch kibana4 micro services http martinfowler com articles microservices html 12 factor apps http 12factor net golang http golang org mqtt http mqtt org docker http www docker com mysql http www mysql com mongodb https www mongodb org redis http redis io etcd https github com coreos etcd nsq http nsq io elk https www elastic co products docs zh cn quick start readme md pc pando pando pando https github com pandocloud pando protocol pando https github com pandocloud pando embeded framework docs zh cn api doc device md pc rest api pando http url docs zh cn api doc application md pando issue https github com pandocloud pando cloud issues new issue https github com pandocloud pando cloud issues new qq 488074716 pando docs zh cn contribution work flow md | iot iot-platform iot-framework iot-cloud | server |
iTsai-Webtools | itsai webtools ul li strong author strong chiroc chiroc cai qq com li li strong version strong 0 2 8 li ul p itsai webtools is a web development kit that integrates some of the commonly used javascript api p | front_end | |
terraform-and-opennebula-by-example | terraform and opennebula by example this repository contains examples used in practical labs related to cloud technologies at the department of computer science and engineering university of west bohemia pilsen cz | cloud | |
AI_For_Social_Good | div align center ai for social good suicidal ideation detection in online social content img src assets web gif div getting started the rise of social media and online communities creates safe and anonymous spaces for individuals to share their thoughts about their mental health and express their feelings and sufferings in online communities to prevent suicide it is necessary to detect suicide related posts and user s suicide ideation in cyberspace by natural language processing methods i focused on the online community called reddit and the social networking website twitter and classify user s posts with potential suicide and without suicidal risk through text features processing machine learning and deep learning based methods datasets collected two sets of data from reddit and twitter the reddit data set includes 2958 suicidal ideation samples and a number of non suicide texts 5381 the twitter dataset has a total 3000 tweets with suicidal ideation reddit data was scraped from subreddits like suicide watch depression anxiety etc twitter data was collected by querying keywords like end my life die etc the twitter word cloud left and reddit word cloud right are shown as follow div align center img alt demo src wordclouds twitter png height 300px width 400px nbsp nbsp img alt demo src wordclouds reddit png height 300px width 400px div feature processing and training performed text cleaning and removed some corpus specific stopwords and plotted word cloud to visualize the frequently occurring words in a corpus performed vectorization using both bag of words and tfidf vectorizer used grid search cv to find the best parameters to train the model using random forest classifier and archived an accuracy of 96 trained the model using multilayer bidirectional lstm with globe embedding to attain an accuracy of 97 results results of different methods applied model acc pre rec f1 rf tfidf 0 96 0 96 0 96 0 96 lstm globe 0 97 0 97 0 97 0 97 usage dataset all the collected and cleaned dataset data collection code for scraping data from reddit and twitter src all the source code for text preprocessing and building ml models pretrained models all the pretrained models and tokenizers flask code for server and model deployment to run the server cd flask python app py license distributed under the mit license see license for more information br | natural-language-processing lstm tfidf-vectorizer web-scraping random-forest | ai |
Web-Developers-Figma-Resources | this repository helps those people who started learning web development and struggle for finding design templates google drive download templates https drive google com drive folders 1lpp2nq7290h9hd oyfjdkc6kmxbnp8cd usp sharing download files if you want how to use it watch this video guide video https youtu be y zpzsbhz6e download files if you want figma banner images figma 20design 20template s png how to use this repository this repository is simple to use just fork this repository or download it and then go to the figma templates folder and use it if you can not understand yet please follow steps by step guide step 01 fork this repository or download and star this repository fork or download images fork png step 02 open this figma templates folder and use those templates download folder images folder png if you have some figma templates and you want to contribute to this repository please follow those steps and guidelines coming soon | beginner-project figma-templates figmadesign frontend-design templates business open-source static-site ui-components ui-design cwm | front_end |
llm-python | llm python a set of instructional materials code samples and python scripts featuring llms gpt etc through interfaces like llamaindex langchain chroma chromadb pinecone etc mainly used to store reference code for my langchain tutorials on youtube img src assets youtube png width 50 alt langchain youtube tutorials langchain youtube tutorials assets llmseries png learn langchain from my youtube channel 8 hours of llm hands on building tutorials each lesson is accompanied by the corresponding code in this repo and is designed to be self contained while still focused on some key concepts in llm large language model development and tooling feel free to pick and choose your starting point based on your learning goals part llm tutorial link video duration 1 openai tutorial and video walkthrough tutorial video https youtu be skw togjy7q 26 56 2 langchain openai tutorial building a q a system w own text data tutorial video https youtu be dyou z0hawo 20 00 3 langchain openai to chat w query own database csv tutorial video https youtu be fz0wjwzfnpi 19 30 4 langchain huggingface s inference api no openai credits required tutorial video https youtu be dd xnmepdd0 24 36 5 understanding embeddings in llms tutorial video https youtu be 6uybc0jm1xq 29 22 6 query any website with llamaindex gpt3 ft chromadb trafilatura tutorial video https youtu be 6k1lyyzpxtk 11 11 7 locally hosted offline llm w llamaindex opt open source instruction tuning llm tutorial video https youtu be qavhs6unb2k 32 27 8 building an ai language tutor pinecone llamaindex gpt 3 beautifulsoup tutorial video https youtu be k8g1edzgf1e 51 08 9 building a queryable journal w openai markdown llamaindex tutorial video https youtu be ozdhjor5ifq 40 29 10 making a sci fi game w cohere llm stability ai generative ai tutorial tutorial video https youtu be ur93ytngtp4 1 02 20 11 gpt builds entire party invitation app from prompt ft smol developer tutorial video https www youtube com watch v ll visijufg 41 33 12 a language for llm prompt design guidance tutorial video https youtu be k4ejc3blqiu 43 15 13 you should use langchain s caching tutorial video https youtu be uk sjsnqru8 25 37 14 build chat ai apps with steamlit langchain tutorial video https youtu be 7qr6hxx nms 32 11 the full lesson playlist can be found here https www youtube com playlist list plxsftk46hzxuqerrbomugoqbmd kwlkos quick start 1 clone this repo 2 install requirements pip install r requirements txt 3 some sample data are provided to you in the news foldeer but you can use your own data by replacing the content or adding to it with your own text files 4 create a env file which contains your openai api key you can get one from here https beta openai com huggingfacehub api token and pinecone api key are optional but they are used in some of the lessons lesson 10 11 worldbuilding py uses cohere and stability ai both of which offers a free tier no credit card required you can add the respective keys as cohere api key and stability api key in the env file the env file should look like this openai api key your api key here optionals not required for most of the series huggingfacehub api token your api token here pinecone api key your api key here huggingface and pinecone are optional but is recommended if you want to use the inference api and explore those models outside of the openai ecosystem this is demonstrated in part 3 of the tutorial series 5 run the examples in any order you want for example python 6 team py will run the website q a example which uses gpt 3 to answer questions about a company and the team of people working at supertype ai watch the corresponding video to follow along each of the examples dependencies thanks to the work of vanillamacchiato this project is updated as of 2023 06 30 to use the latest version of llamaindex 0 6 31 and langchain 0 0 209 installing the dependencies should be as simple as pip install r requirements txt if you encounter any issues please let me know if you re watching the llm video tutorials they may have very minor differences typically 1 2 lines of code that needs to be changed from the code in this repo since these videos have been released with the respective versions at the time of recording llamaindex 0 5 7 and langchain 0 0 157 please refer to the code in this repo for the latest version of the code i will try to keep this repo up to date with the latest version of the libraries but if you encounter any issues please 1 raise a discussion through issues or 2 volunteer a pr to update the code mentorship and support i run a mentorship program under supertype fellowship https fellowship supertype ai the program is self paced and free with a community of other learners and practitioners around the world english speaking you can optionally book a 1 on 1 session with my team of mentors to help you through video tutoring and code reviews license mit supertype https supertype ai 2023 in a big data era increasingly defined by the velocity and volume of information businesses are turning to streaming analytics to make sense of their data in real time streaming data pipelines are designed for ingesting processing and analyzing data as it arrives from different sources affording businesses the opportunity to act on the most up to date information available in this article we ll explore the benefits of streaming analytics and how to build an end to end streaming data pipeline using apache kafka apache spark cassandra mysql streamlit and docker | langchain chromadb gpt-3 langchain-python llamaindex openai-api llm llmops pinecone tutorial | ai |
key-value-database | key value database with hash index how to run development environment 1 install python 3 10 2 install poetry shell windows invoke webrequest uri https install python poetry org usebasicparsing content py nix curl ssl https install python poetry org python3 more info at https python poetry org docs if you got error with python version try to add to pythonpath python root dir for example c program files python3 10 3 check installation poetry version 4 run in project root dir poetry shell 5 in opened shell install dependencies with poetry install 6 install pre commit hooks pre commit install run tests shell pytest use db shell python main py d test db db lab commands usage select h table table limit limit use index all counter filter filter options h help show this help message and exit table table t table table name limit limit l limit rows limit use index i use indexes in select all do not pause select counter only count items filter filter f filter key val or key val usage create table h table positional arguments table name keys key type options h help show this help message and exit usage create index h table table key key options h help show this help message and exit table table t table table name key key k key table key usage list tables h options h help show this help message and exit usage insert h table table data data options h help show this help message and exit table table t table table name data data d data key val usage insert auto h table table amount amount options h help show this help message and exit table table t table table name amount amount a amount rows amount commands examples create table name test keys id int content str create table name cats keys name str age int owner str list tables create index t cats k age create index t cats k name create index t cats k owner insert t cats d name kitty age 2 owner lilly insert t cats d name murmur age 3 owner lilly insert t cats d name pretty cat age 1 owner barry insert auto t cats amount 30 insert auto t cats amount 300000 select t cats counter select t cats counter use index select t cats f name kitty select t cats f name pretty cat select t cats f age 0 all use index select t cats f age 0 all use index counter select t cats f age 0 all select t cats f age 0 all counter select t cats f name kitty all counter select t cats f name kitty all use index select t cats f name kitty all counter use index select t cats f age 1 all select t cats f age 1 all use index select t cats f age 1 age 2 all use index select t cats f age 1 age 2 all select t cats f age 1 2 owner lilly age 3 all use index counter select t cats f age 1 age 2 counter all use index select t cats f age 1 age 2 counter all select t cats all counter | dbms python | server |
Proximity-Marketing-App | proximity marketing app pma final year project bsc hons software systems development waterford institute of technology br img src https user images githubusercontent com 20372577 56387130 6d5d9780 621b 11e9 856c 3783af311a7b png height 539 width 954 br using bluetooth low energy ble beacon technology this web application provides shoppers with relevant and timely information about in store products straight to their mobile devices users can easily access product reviews and price comparisons before making a purchase and also take advantage of special offers through digital discount vouchers combining the in store experience where a shopper can pick up and get the feel of a product with the online experience where reviews and product insights are at your fingertips the pma brings the benefits of both worlds together br take this typical scenario a customer walks into an electronics store and heads directly to a tv she wants to buy standing in front of the tv she receives a browser notification on her smartphone she clicks the link upon which the pma provides her with product info product reviews special offers and a price comparison with similar products br table tr td notifications screen td td product info screen td tr tr td img src https user images githubusercontent com 20372577 46041881 d6f54780 c10b 11e8 9fa1 a9d1a0f2b435 jpg height 700 width 375 td td img src https user images githubusercontent com 20372577 46042532 841c8f80 c10d 11e8 8e76 9b22af68b668 png height 700 width 375 td tr table br prerequisites install npm https www npmjs com get npm install firebase npm install firebase save installation install node dependencies start application npm install npm start check browser at localhost 3000 samsung tv localhost 3000 asics runners features product description product reviews price comparison digital vouchers technologies used bluetooth beacons eddystone react js javascript firebase firestore hosting authentication security webhose api voucherify api systems overview img src https user images githubusercontent com 20372577 46046923 c8faf300 c11a 11e8 9aef 1c038383097e png br documentation see analysis and design documentation a href https github com shane walsh proximity marketing app wiki analysis design here a on github wikis please see the development journey documentation a href https shanewalsh gitbook io proximity marketing app here a on gitbooks | server | |
Lab-Project-FreeRTOS-Cellular-Demo | freertos labs freertos cellular interface demo introduction freertos offers a suite of networking stacks designed for iot applications applications can access communication protocols at different levels mqtt http secure sockets etc common connectivity technologies such as ethernet wi fi and ble have been integrated with the networking stacks of freertos with a wide selection of microcontrollers and modules https devices amazonaws com search page 1 sv freertos pre integrated freertos supported demos for freertos cellular interface can be found in the freertos repository https github com freertos freertos tree main freertos plus demo freertos cellular interface windows simulator this repository contains community supported demos the demos in this project demonstrate how to establish mutually authenticated mqtt connections to mqtt brokers such as aws iot core by using cellular connectivity the demos use the freertos cellular interface https github com freertos freertos cellular interface sub moduled from an external project the freertos cellular interface exposes the capability of a few popular cellular modems through a uniform api 1 1nce zero touch provisioning https 1nce com en help center tutorials documentations 1nce connectivity suite 1 simcom sim7080 https cn simcom com product sim7080g html the mqtt and http libraries of freertos use an abstract transport interface https github com freertos coremqtt blob main source interface transport interface h to send receive data in a generic way the demos in this project offer a implementation https github com freertos lab project freertos cellular demo blob master source coremqtt using mbedtls c of the transport interface on top of the uniform api exposed by the freertos cellular interface hardware setup the demos in this project can be run in the freertos windows simulator https freertos org freertos windows simulator emulator for visual studio and eclipse mingw html you will need a windows pc and one of the supported cellular modems to run a demo a version of visual studio such as the free community version of visual studios https visualstudio microsoft com vs community will be needed for building the demos freertos windows simulator make use of com port to communicate with cellular module setup your cellular module communication with the following steps 1 connect the cellular module to pc most cellular dev kits have usb in that case just connect it to pc s usb port and look for the com port in window s device manager for example you will see a new com69 showing up when you connect the modem like below if your cellular dev kit does not have usb use a usb adaptor like these https www amazon com serial usb adapter s k serial to usb adapter p align center img src doc windows device manager png width 70 br screenshot 1 cellular module com port in windows device manager p 2 use putty https www putty org or any terminal tool to verify connection with the cellular module refer to you cellular module s manual for settings like baud rate parity and flow control input ate1 the modem should return ok depending on your modem setting you may see an echo of ate1 as well input at the modem should return ok p align center img src doc at command terminal png width 70 br screenshot 2 testing the com port with at commands in putty p components and interfaces this project makes use of five 5 sub modules from other github projects shown as yellow boxes in the diagram below p align center img src doc cellular component and interface png width 70 br figure 1 components and interfaces p the other components shown as blue boxes and dotted lines are implemented by this project the demo application https github com freertos lab project freertos cellular demo blob main source it is largely the same as the coremqtt demo https github com freertos freertos tree master freertos plus demo coremqtt windows simulator mqtt mutual auth with added logic to set up cellular as the transport the original coremqtt demo was designed for wi fi on freertos windows simulator there is also a demo application that integrates 1nce zero touch provisioning https 1nce com en help center tutorials documentations 1nce connectivity suite with the freertos cellular interface and coremqtt for connecting to aws iot core the transport interface https github com freertos lab project freertos cellular demo blob main source coremqtt using mbedtls c is needed by the mqtt library sub moduled from the coremqtt https github com freertos coremqtt project to send and receive packets the tls porting interface https github com freertos lab project freertos cellular demo blob main source mbedtls mbedtls freertos port c is needed by the mbedtls library to run on freertos the comm interface https github com freertos lab project freertos cellular demo blob main source cellular comm if windows c is used by the freertos cellular interface to communicate with the cellular modems over uart connections developer references and api documents please refer to freertos cellular interface api document https www freertos org documentation api ref cellular index html download the source code the source code can be downloaded from the freertos labs or by itself through github to clone using https git clone https github com freertos lab project freertos cellular demo git recurse submodules using ssh git clone git github com freertos lab project freertos cellular demo git recurse submodules if you have downloaded the repo without using the recurse submodules argument you need to run git submodule update init recursive source code organization the demo project files for visual studio are named xyz mqtt mutual auth demo sln where xyz is the name of the cellular modem they can be found on github https github com freertos lab project freertos cellular demo tree main projects in the following directory projects sim70x0 mqtt mutual auth demo https github com freertos lab project freertos cellular demo tree master projects sim70x0 mqtt mutual auth demo there is also a demo for 1nce zero touch provisioning with quectel bg96 gsm modules tested with m95 m66 projects 1nce bg96 zero touch provisioning demo https github com freertos lab project freertos cellular demo tree master projects 1nce bg96 zero touch provisioning demo projects 1nce qgsm zero touch provisioning demo https github com freertos lab project freertos cellular demo tree master projects 1nce qgsm zero touch provisioning demo lab project freertos cellular demo lib backoff algorithm submodule backoffalgorithm cellular submodule freertos cellular interface coremqtt submodule coremqtt freertos submodule freertos kernel thirdparty mbedtls submodule mbedtls projects sim70x0 mqtt mutual auth demo demo project for simcom sim7080 sim7090 project dependent demo tasks and configuration files 1nce bg96 zero touch provisioning demo demo project for 1nce zero touch provisioning with bg96 project dependent demo tasks and configuration files 1nce qgsm zero touch provisioning demo demo project for 1nce zero touch provisioning with quectel gsm modules project dependent demo tasks and configuration files source common source files to adapt libraries cellular code for adapting freertos cellular interface with this demo coremqtt code for adapting coremqtt with this demo mbedtls code for adapting mbedtls with this demo logging code for freertos logging cellular setup c configure application settings configure cellular network the following parameters in the cellular configuration cellular config h https github com freertos lab project freertos cellular demo tree main source cellular located in b projects project name cellular config h b must be modified for your network environment configuration description value cellular comm interface port cellular communication interface make use of com port on computer to communicate with cellular module on windows simulator your com port connected to cellular module cellular apn default apn for network registration specify the value according to your network operator cellular pdn context id pdn context id for cellular network default value is cellular pdn context id min cellular pdn connect timeout pdn connect timeout for network registration default value is 100000 milliseconds configure mqtt broker the configuration for connecting to a mqtt broker can be found in b projects project name demo config h b for more information about the settings configure com port settings reference the cellular module documentation for com port settings update the comm if windows c https github com freertos lab project freertos cellular demo blob main source cellular comm if windows c if necessary configure other sub modules b projects project name freertosconfig h b b projects project name mbedtls config h b and b projects project name core mqtt config h b are configurations for the corresponding sub modules demo execution step flow the demo app performs three types of operations by searching the names of functions in the diagram below you can find the exact places these operations are made in the source code 1 register to a cellular network see cellular setup c https github com freertos lab project freertos cellular demo blob main source cellular setup c 2 establish a secure connection with the mqtt broker of aws iot see using mbedtls c https github com freertos lab project freertos cellular demo blob main source coremqtt using mbedtls c 3 perform mqtt operations see mutualauthmqttexample c https github com freertos lab project freertos cellular demo blob main source mutualauthmqttexample c the following diagram illustrates the interactions between the demo app and other components p align center img src doc cellular demo sequence png br figure 2 demo application sequence diagram p build and run the mqtt mutual authentication demos 1 in visual studio open one of the mqtt mutual auth demo sln projects that matches your cellular modem 2 compile and run the following is the console output of a successful execution of the bg96 mqtt mutual auth demo sln project info cellular commtaskthread 287 cellular commtaskthread started cellular sim okay cellular getservicestatus failed 0 ps registration status 0 cellular module registered cellular module registered ip address 10 160 13 238 info mqttdemo prvconnecttoserverwithbackoffretries 583 creating a tls connection to a2zzppv7s4siea ats iot us west 2 amazonaws com 8883 info mqttdemo mqttdemotask 465 creating an mqtt connection to a2zzppv7s4siea ats iot us west 2 amazonaws com info mqttdemo prvcreatemqttconnectionwithbroker 683 an mqtt connection is established with a2zzppv7s4siea ats iot us west 2 amazonaws com info mqttdemo prvmqttsubscribewithbackoffretries 741 attempt to subscribe to the mqtt topic testclient13 24 47 example topic info mqttdemo prvmqttsubscribewithbackoffretries 748 subscribe sent for topic testclient13 24 47 example topic to broker info mqttdemo prvmqttprocessresponse 872 subscribed to the topic testclient13 24 47 example topic with maximum qos 1 info mqttdemo mqttdemotask 479 publish to the mqtt topic testclient13 24 47 example topic info mqttdemo mqttdemotask 485 attempt to receive publish message from broker info mqttdemo prvmqttprocessresponse 853 puback received for packet id 2 info mqttdemo mqttdemotask 490 keeping connection idle info mqttdemo mqttdemotask 479 publish to the mqtt topic testclient13 24 47 example topic info mqttdemo mqttdemotask 485 attempt to receive publish message from broker info mqttdemo prvmqttprocessincomingpublish 908 incoming qos 1 info mqttdemo prvmqttprocessincomingpublish 919 incoming publish topic name testclient13 24 47 example topic matches subscribed topic incoming publish message hello world build and run the 1nce zero touch provisioning demo 1nce is a global iot carrier specialized in providing managed connectivity services for low bandwidth iot applications in this demo 1nce service a 1nce sim card aws iot device onboarding server and supported cellular modules are used to demonstrate how to provision device with zero touch and connect to aws iot core refer to the 1nce blueprint for freertos https github com 1nce gmbh blueprint freertos in particular this flow chart https 1nce com wp content uploads 2020 07 identity2 png to learn how the zero touch provisioning works 1 in visual studio open the 1nce bg96 zero touch provisioning demo sln project in this visual studio solution file the macro of use 1nce zero touch provisioning is defined please look for ifdef use 1nce zero touch provisioning in the source files to see how it does differently to provision the device by using the 1nce service otherwise this demo performs the same mutually authenticated mqtt operations as the other demos 2 generate a self signed certificate and its private key locally https docs aws amazon com iot latest developerguide create device cert html update source demo config h https github com freertos lab project freertos cellular demo blob main source demo config h with the certificate and private key these are for the purpose of establishing tls connection to 1nce server note that adding keys into a header file is done for convenience of demonstration only production devices should use secure storage to store the keys 3 get apn for your sim card from 1nce update cellular apn in file cellular config h https github com freertos lab project freertos cellular demo blob main source cellular bg96 cellular config h for bg96 and follow configure application settings steps above to finish the rest configuration 4 compile and run | os | |
NLIB_NSBM_Library_Assistant | nlib library assistant a new flutter project getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https docs flutter dev get started codelab cookbook useful flutter samples https docs flutter dev cookbook for help getting started with flutter development view the online documentation https docs flutter dev which offers tutorials samples guidance on mobile development and a full api reference | front_end | |
BiasExploration4J | java ci with maven https github com lmartinezexex biasexploration4j actions workflows maven yml badge svg https github com lmartinezexex biasexploration4j actions workflows maven yml tests https github com lmartinezexex biasexploration4j actions workflows testing yml badge svg https github com lmartinezexex biasexploration4j actions workflows testing yml biasexploration4j description the language models and word embeddings used today made use of huge amounts of unlabeled bias data for their respective training phase which was increased by the attention mechanism of the new transformers as a new unsupervised learning technique with all these training samples there are bound to be biased samples that affect the results of the resulting systems sometimes we have to make biased decisions with our systems but it is also nice to know the bias within our model biasexploration4j allows word embedding exploration and bert based language model tendency measurement the latter based on nangia et al 2020 https aclanthology org 2020 emnlp main 154 work to explore different biases within the given system download and use this tool is composed by different classes that aims to evaluate some aspect of bias so you can instatiate it anywhere you like bash clone the repository git clone https github com lmartinezexex biasexploration4j git change cwd inside the cloned repository cd biasexploration4j for use as a dependency from other projects mvn clean install how to use and documentation check out our wiki pages https github com lmartinezexex biasexploration4j wiki where you will find information notes and tips on how to use biasexploration4j definitions of core concepts and certain limitations to keep in mind when exploring bias in your data as well as examples to help you better internalize it all license information this project is under a mit license license | bias java language-model nlp-machine-learning | ai |
siwesfreecodecamp | siwesfreecodecamp siwes full stack web development 2016 | front_end | |
MoreFreeTime-SE575 | morefreetime cs575 morefreetime is a project created for a software engineering course at drexel university it is a basic functioning scheduling ios application that functions with the use of a local sqlite database | os | |
FuzzyLogicForEmbeddedSystems | fuzzylogicforembeddedsystems this code blocks designed using c language this code is designed using the c language if you want to use fuzzy logic in embedded systems you can add them to your system | os | |
MT2503 | readme mtk gsm gprs gps mt2503 https item taobao com item htm spm a230r 1 14 13 3b5a1defjoiovk id 564119400071 ns 1 abbucket 19 detail 203cs qq 657996991 mtk 0028 gps md docs 0028 gps md 0027 bt md docs 0027 bt md 0026 task debug printf md docs 0026 task debug printf md 0025 task sleep md docs 0025 task sleep md 0024 task message communication md docs 0024 task message communication md 0023 task create md docs 0023 task create md 0022 catcher database path md docs 0022 catcher database path md 0021 catcher usb uart md docs 0021 catcher usb uart md 0020 flashtool download md docs 0020 flashtool download md 0019 gpio driver md docs 0019 gpio driver md 0018 task understand message md docs 0018 task understand message md 0017 power on md docs 0017 power on md 0016 task init sequence hacking md docs 0016 task init sequence hacking md 0015 hacking launcher md docs 0015 hacking launcher md 0014 home page names modify md docs 0014 home page names modify md 0013 simulation vs2008 env md docs 0013 simulation vs2008 env md 0012 init analysis md docs 0012 init analysis md 0011 architecture of maui md docs 0011 architecture of maui md 0010 refer docs md docs 0010 refer docs md 0009 mt2503 make hacking md docs 0009 mt2503 make hacking md 0008 perl md docs 0008 perl md 0007 mt2503 env build md docs 0007 mt2503 env build md 0006 wz203cs v3 0 mqtt app md docs 0006 wz203cs v3 0 mqtt app md 0005 example gpio hacking md docs 0005 example gpio hacking md 0004 opencpu quick start application note md docs 0004 opencpu quick start application note md 0003 make hacking md docs 0003 make hacking md 0002 download prebuild fw md docs 0002 download prebuild fw md 0001 just run test md docs 0001 just run test md | server | |
data-dockerfiles | data engineering open source tools databases a repository for storing various data engineering docker compose files in one place how to use it go into the target folder set the required settings in env file it may be hidden comment uncomment necessary services in each docker compose file check out the exposed ports and if you have detected some conflicts with the ports on your system change them the left one run docker compose up in command line | server | |
SQL-employee-database | employee database analysis ins dataset ins six csv files were provided to conduct a research on employees of the corporation from the 1980s and 1990s ins objective ins perform data modeling data engineering and data analysis ins data modelling ins csv files were inspected to sketch out an entity relationship diagram erd of the tables quick database diagrams http www quickdatabasediagrams com was used to design the erd ins data engineering ins table schema was designed for the six csv files and the files the imported into the corresponsing sql table in a postgresql database ins data analysis ins sql queries were designed to query the database for the following 1 list the following details of each employee employee number last name first name gender and salary 2 list employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name and start and end employment dates 4 list the department of each employee with the following information employee number last name first name and department name 5 list all employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name ins further analysis ins the sql database was imported into a pandas dataframe sql alchemy was used to make the connection with postgresql database a histogram was created to visualize the most common salary ranges for employees a bar chart of average salary by title was also created | sql postgresql pandas | server |
FreeRTOS-FAT-CLI-for-RPi-Pico | freertos fat cli for rpi pico sd cards on the pico this project is essentially a freertos fat https www freertos org freertos plus freertos plus fat index html media driver https www freertos org freertos plus freertos plus fat creating a file system media driver html for the raspberry pi pico https www raspberrypi org products raspberry pi pico using serial peripheral interface spi based on sdblockdevice from mbed os 5 https os mbed com docs mbed os v5 15 apis sdblockdevice html it is wrapped up in a complete runnable project with a command line interface provided by freertos cli https www freertos org freertos plus freertos plus cli freertos plus command line interface html image https github com carlk3 freertos fat cli for rpi pico blob master images img 1473 jpg prototype features supports multiple sd cards supports multiple spis supports multiple sd cards per spi supports real time clock for maintaining file and directory time stamps supports cyclic redundancy check crc resources used at least one depending on configuration of the two serial peripheral interface spi controllers is used for each spi controller used two dma channels are claimed with dma claim unused channel dma irq 0 is hooked with irq add shared handler and enabled for each spi controller used one gpio is needed for each of rx tx and sck note each spi controller can only use a limited set of gpios for these functions for each sd card attached to an spi controller a gpio is needed for cs and optionally another for cd card detect size simple example elf text data bss dec hex filename 68428 44 4388 72860 11c9c simple example elf performance writing and reading a file of 0x10000000 268 435 456 bytes 1 4 gib on a sandisk 32gb card with spi baud rate 12 500 000 writing elapsed seconds 376 transfer rate 697 kib s reading elapsed seconds 315 transfer rate 832 kib s surprisingly to me i have been able to push the spi baud rate as far as 20 833 333 with a sandisk class 4 16 gb card writing elapsed seconds 226 transfer rate 1159 kib s reading elapsed seconds 232 transfer rate 1129 kib s but sdio would be faster prerequisites raspberry pi pico something like the sparkfun microsd transflash breakout https www sparkfun com products 544 breadboard and wires raspberry pi pico c c sdk optional a couple of 5 10k resistors for pull ups optional a couple of 100 pf capacitors for decoupling image https github com carlk3 freertos fat cli for rpi pico blob master images img 1478 jpg prototype dependencies freertos kernel https github com freertos freertos kernel lab project freertos fat https github com freertos lab project freertos fat image https www raspberrypi org documentation microcontrollers images pico r3 sdk11 pinout svg pinout spi0 gpio pin spi microsd 0 description miso rx 16 21 do do master in slave out cs0 csn 17 22 ss or cs cs slave or chip select sck sck 18 24 sclk clk spi clock mosi tx 19 25 di di master out slave in cd 22 29 cd card detect gnd 18 23 gnd ground 3v3 36 3v3 3 3 volt power construction the wiring is so simple that i didn t bother with a schematic i just referred to the table above wiring point to point from the pin column on the pico to the microsd 0 column on the transflash card detect is optional some sd card sockets have no provision for it even if it is provided by the hardware if you have no requirement for it you can skip it and save a pico i o pin you can choose to use either or both of the pico s spis wires should be kept short and direct spi operates at hf radio frequencies pull up resistors the spi miso do on sd card spi x rx on pico is open collector or tristate it should be pulled up the pico internal gpio pull up is weak around 56ua or 60k it s best to add an external pull up resistor of around 5k to 3 3v you might get away without one if you only run one sd card and don t push the spi baud rate too high the spi slave select ss or chip select cs line enables one spi slave of possibly multiple slaves on the bus this is what enables the tristate buffer for data out do among other things it s best to pull cs up so that it doesn t float before the pico gpio is initialized it is imperative to pull it up for any devices on the bus that aren t initialized for example if you have two sd cards on one bus but the firmware is aware of only one card see hw config you can t let the cs float on the unused one notes about prewired boards with sd card sockets i don t think the pimoroni pico vga demo base https shop pimoroni com products pimoroni pico vga demo base can work with a built in rp2040 spi controller it looks like rp20040 spi0 sck needs to be on gpio 2 6 or 18 pin 4 9 or 24 respectively but pimoroni wired it to gpio 5 pin 7 the sparkfun rp2040 thing plus https learn sparkfun com tutorials rp2040 thing plus hookup guide hardware overview looks like it should work on spi1 for sparkfun rp2040 thing plus spi0 gpio description miso rx 12 master in slave out cs0 csn 09 slave or chip select sck sck 14 spi clock mosi tx 15 master out slave in cd card detect maker pi pico https www cytron io p maker pi pico looks like it could work on spi1 it has cs on gpio 15 which is not a pin that the rp2040 built in spi1 controller would drive as cs but this driver controls cs explicitly with gpio put so it doesn t matter firmware follow instructions in getting started with raspberry pi pico https datasheets raspberrypi org pico getting started with pico pdf to set up the development environment install source code git clone recurse submodules https github com carlk3 freertos fat cli for rpi pico git freertos fat cli customize tailor freertos fat cli freertos fat cli portable rp2040 hw config c to match hardware customize pico enable stdio uart and pico enable stdio usb in cmakelists txt as you prefer see 4 1 serial input and output on raspberry pi pico in getting started with raspberry pi pico https datasheets raspberrypi org pico getting started with pico pdf and 2 7 1 standard input output stdio support in raspberry pi pico c c sdk https datasheets raspberrypi org pico raspberry pi pico c sdk pdf build cd freertos fat cli example mkdir build cd build cmake make program the device operation connect a terminal putty https www putty org or tio work ok for example tio m odelbs dev ttyacm0 press enter to start the cli you should see a prompt like sunday 24 february 9 04 23 2021 freertos cli the help command describes the available commands task stats displays a table showing the state of each freertos task run time stats displays a table showing how much processing time each freertos task has used cls clear screen diskinfo device name print information about mounted partitions e g diskinfo sdcard setrtc dd mm yy hh mm ss set real time clock parameters new date dd mm yy new time in 24 hour format hh mm ss date print current date and time die kill background tasks undie allow background tasks to live again reset soft system reset format device name format device name e g format sd0 mount device name mount device name at device name e g mount sd0 eject device name eject device name e g eject sd0 lliot device name destructive low level i o driver test e g lliot sd0 simple device name run simple fs tests expects card to already be formatted but not mounted e g simple sd0 cdef device name create disk and example files expects card to be already formatted but not mounted e g cdef sd0 swcwdt device name stdio with cwd test expects card to be already formatted but not mounted note run cdef first e g swcwdt sd0 mtswcwdt device name multitask stdio with cwd test expects card to be already formatted but not mounted e g mtswcwdt sd0 big file test pathname size in bytes seed writes random data to file pathname size in bytes must be multiple of 512 e g big file test sd0 bf 1048576 1 dir lists the files in the current directory cd dir name changes the working directory type filename prints file contents to the terminal del filename deletes a file use rmdir to delete a directory rmdir directory name deletes a directory copy source file dest file copies source file to dest file ren source file dest file moves source file to dest file pwd print working directory data log demo launch data logging task image https github com carlk3 freertos fat cli for rpi pico blob master images img 1481 jpg prototype troubleshooting the first thing to try is lowering the spi baud rate see hw config c this will also make it easier to use things like logic analyzers make sure the sd card s are getting enough power try an external supply try adding a decoupling capacitor between vcc and gnd hint check voltage while formatting card it must be 2 7 to 3 6 volts hint if you are powering a pico with a picoprobe try adding a usb cable to a wall charger to the pico under test try another brand of sd card some handle the spi interface better than others most consumer devices like cameras or pcs use the sdio interface i have had good luck with sandisk tracing most of the source files have a couple of lines near the top of the file like define trace printf fmt args disable tracing define trace printf printf trace with printf you can swap the commenting to enable tracing of what s happening in that file logic analyzer for less than ten bucks something like this comidox 1set usb logic analyzer device set usb cable 24mhz 8ch 24mhz 8 channel uart iic spi debug for arduino arm fpga m100 hot https smile amazon com gp product b07kw445dj and pulseview sigrok https sigrok org make a nice combination for looking at spi as long as you don t run the baud rate too high get yourself a protoboard and solder everything so much more reliable than solderless breadboard image https github com carlk3 freertos fat cli for rpi pico blob master images pxl 20211214 165648888 mp jpg next steps there is a simple example of using the api in the simple example subdirectory there is a demonstration data logging application in freertos fat cli for rpi pico src data log demo c it runs as a separate task and can be launched from the cli with the data log demo command stop it with the die command it records the temperature as reported by the rp2040 internal temperature sensor once per second in files named something like sd0 data 2021 02 27 21 csv use this as a starting point for your own data logging application if you want to use freertos fat cli as a library embedded in another project use something like git submodule add git github com carlk3 freertos fat cli for rpi pico git or git submodule add https github com carlk3 freertos fat cli for rpi pico git you will need to pick up the library in cmakelists txt add subdirectory freertos fat cli for rpi pico freertos fat cli build target link libraries my app freertos fat cli happy hacking image https github com carlk3 freertos fat cli for rpi pico blob master images img 20210322 201928116 jpg prototype appendix adding additional cards when you re dealing with information storage it s always nice to have redundancy there are many possible combinations of spis and sd cards one of these is putting multiple sd cards on the same spi bus at a cost of one or two additional pico i o pins depending on whether or you care about card detect i will illustrate that example here to add a second sd card on the same spi connect it in parallel except that it will need a unique gpio for the card select slave select csn and another for card detect cd optional name spi0 gpio pin spi sdio microsd 0 microsd 1 cd1 14 19 cd cs1 15 20 ss or cs dat3 cs miso rx 16 21 do dat0 do do cs0 17 22 ss or cs dat3 cs sck sck 18 24 sclk clk sck sck mosi tx 19 25 di cmd di di cd0 22 29 cd gnd 18 23 gnd gnd 3v3 36 3v3 3v3 wiring as you can see from the table above the only new signals are cd1 and cs1 otherwise the new card is wired in parallel with the first card firmware sd driver hw config c must be edited to add a new instance to static sd card t sd cards | os | |
table-tennis-computer-vision | description the goal of the project is to apply computer vision with opencv http opencv org and maybe depth sensors like kinect to keep track of the score at table tennis furthermore data analysis can be made to infer statistics about the players installation opencv 2 4 mac os x brew tap homebrew science brew install opencv the opencv is usually installed in usr local cellar opencv to use the python bindings you must create symlinks in the directory where python is installed usually in library python 2 7 site packages pointing to the opencv directory sudo ln s usr local cellar opencv 2 4 11 lib python2 7 site packages cv py library python 2 7 site packages cv py sudo ln s usr local cellar opencv 2 4 11 lib python2 7 site packages cv2 so library python 2 7 site packages cv2 so advancement 1 first step camera calibration camera calibration 2 second step distortion correction distortion correction 3 third step table lines detection table lines detection 4 fourth step ball tracking ball tracking 5 fifth step 3d position calculation with stereovision stereovision 1 calibration stereovision calibration distortion correction distortion correction before after before images distortion correction before jpg after images distortion correction after jpg table lines detection table lines detection hough transform rectangle detection hough after k mean hough transform images table lines detection hough transform jpg rectangle detection images table lines detection detect rectangles jpg hough transform after k mean images table lines detection hough3 jpg ball tracking ball tracking ball tracking images ball tracking ball tracking jpg | computer-vision opencv table-tennis ball-tracking ping | ai |
deep-learning-models | aws deep learning models this is a repository with implementations optimized to run well on aws infrastructure structure folder description models models a collection of implementations for models that use tf 2 x apis the models are reasonably optimized to run on aws models nlp models nlp natural language processing understanding models models vision models vision computer vision models legacy legacy older scripts for resnet training contribution guidelines if you want to contribute to models please review the contribution guidelines contributing md license apache license 2 0 license | deep-learning aws | ai |
Embedded-Systems-Design | embedded systems design embedded systems design | os | |
python-bitshares | python library for bitshares https img shields io pypi v bitshares svg style for the badge https img shields io github release bitshares python bitshares svg style for the badge https img shields io github downloads bitshares python bitshares total svg style for the badge https img shields io pypi pyversions bitshares svg style for the badge https img shields io pypi l bitshares svg style for the badge https cla assistant io readme badge bitshares python bitshares stable docs master https readthedocs org projects python bitshares badge version latest http python bitshares readthedocs io en latest travis master https travis ci org bitshares python bitshares png branch master https travis ci org bitshares python bitshares codecov https codecov io gh bitshares python bitshares branch master graph badge svg https codecov io gh bitshares python bitshares develop docs develop https readthedocs org projects python bitshares badge version develop http python bitshares readthedocs io en develop travis develop https travis ci org bitshares python bitshares png branch develop https travis ci org bitshares python bitshares codecov develop https codecov io gh bitshares python bitshares branch develop graph badge svg https codecov io gh bitshares python bitshares documentation visit the pybitshares website http docs pybitshares com en latest for in depth documentation on this python library installation install with pip3 sudo apt get install libffi dev libssl dev python dev python3 dev python3 pip pip3 install bitshares manual installation git clone https github com bitshares python bitshares cd python bitshares python3 setup py install user upgrade pip3 install user upgrade bitshares contributing python bitshares welcomes contributions from anyone and everyone please see our guidelines for contributing contributing md and the code of conduct code of conduct md discussion and developers discussions around development and use of this library can be found in a dedicated telegram channel https t me pybitshares license a copy of the license is available in the repository s license license txt file | bitshares python-library blockchain python | blockchain |
SNU-esd | snu esd 2015 embedded system design course term project for 2015 seoul national univ embedded system desginclass the project is to build a system that simulates black scholes option pricing model 1 000 times faster than general purpose computer the system consists of an jni android application that interfaces with a human a linux kernel driver to interface fpga embedded system and a digital system wrote in verilog running on fpga board that computes heavy main part of entire option pricing computation | os | |
llm-composability-demo | llm composability app demo the demo uses the slack events api and openai s gpt 3 5 chatcompletion api to simulate a community platform ai guide the bot s primary mode is onboarding where it will interact with members providing inights and recommendations and links to other channels this code accompanies post i did here https medium com ncoronges composability in llm apps to run locally grab source and activate environment git clone branch master https github com ncoronges llm composability demo cd llm composability demo source env bin activate pip install r requirements txt to do local development you will need to set up the app to listen to events from the slack api event dispatcher that means your app has to be accessible on the internet more on that below and you will need an instance of slack configured to point to your local instance to set up the app in slack you ll need to create a new app and provide the necessary scopes you may be able to setup with the slack app manifest yaml file included in repo but this is still a beta feature in slack channels history chat write im write pins read reactions read users profile read users read chat write channels read groups read mpim read grab the client signing secret and oauth access token create keys for openai and slack instance export slack secret your slack app secret signing secret export slack token your slack token oauth permissions add to workspace bot user oauth access token export openai key open ai secret key you ll need to enable events in the slack app and set up a local server address using something like npx https github com localtunnel localtunnel or ngrok to create a reverse proxy for slack callbacks python3 app py npx localtunnel port 3000 a tip when running localtunnel is to fix the subdomain once you get one lt does crash when the underlying service changes unlike ngrok npx localtunnel subdomain your subdomain port 3000 once you grab your public url from npx go back to your slack app https api slack com apps under event subscriptions click on enable events set https npx url slack events subscribe to the following events message channels message groups app mention | ai | |
nlpo3 | nlpo3 thai natural language processing library in rust with python and node bindings formerly oxidized thainlp features thai word tokenizer use maximal matching dictionary based tokenization algorithm and honor thai character cluster boundaries 2 5x faster https github com pythainlp nlpo3 blob main nlpo3 python notebooks nlpo3 segment benchmarks ipynb than similar pure python implementation pythainlp s newmm load a dictionary from a plain text file one word per line or from vec string dictionary file for the interest of library size nlpo3 does not assume what dictionary the developer would like to use it does not come with a dictionary a dictionary is needed for the dictionary based word tokenizer for tokenization dictionary try words th tx https github com pythainlp pythainlp blob dev pythainlp corpus words th txt from pythainlp https github com pythainlp pythainlp around 62 000 words cc0 word break dictionary https github com tlwg libthai tree master data from libthai https github com tlwg libthai consists of dictionaries in different categories with make script lgpl 2 1 usage command line interface nlpo3 cli nlpo3 cli a href https crates io crates nlpo3 cli img alt crates io src https img shields io crates v nlpo3 cli svg a bash echo nlpo3 segment bindings node js nlpo3 nodejs python nlpo3 python a href https pypi python org pypi nlpo3 img alt pypi src https img shields io pypi v nlpo3 svg a python from nlpo3 import load dict segment load dict path to dict file dict name segment dict name as rust library a href https crates io crates nlpo3 img alt crates io src https img shields io crates v nlpo3 svg a in cargo toml toml dependencies nlpo3 1 3 2 create a tokenizer using a dictionary from file then use it to tokenize a string safe mode true and parallel mode false rust use nlpo3 tokenizer newmm newmmtokenizer use nlpo3 tokenizer tokenizer trait tokenizer let tokenizer newmmtokenizer new path to dict file let tokens tokenizer segment true false unwrap create a tokenizer using a dictionary from a vector of strings rust let words vec to string to string let tokenizer newmmtokenizer from word list words add words to an existing tokenizer rust tokenizer add word remove words from an existing tokenizer rust tokenizer remove word build requirements rust 2018 edition https www rust lang org tools install steps generic test bash cargo test build api document and open it to check bash cargo doc open build remove release to keep debug information bash cargo build release check target for build artifacts development documents notes on custom string src note on string md issues please report issues at https github com pythainlp nlpo3 issues | thai-language natural-language-processing text-processing tokenizer python rust nodejs hacktoberfest | ai |
CS224n-Natural-Language-Processing-with-Deep-Learning | assignment 1 http web stanford edu class cs224n assignment1 index html in this assignment we will familiarize you with basic concepts of neural networks word vectors and their application to sentiment analysis setup note please be sure you have python 2 7 x installed on your system the following instructions should work on mac or linux get the code download the starter code here and the complementary written problems here optional virtual environment once you have unzipped the starter code you might want to create a virtual environment for the project if you choose not to use a virtual environment it is up to you to make sure that all dependencies for the code are installed on your machine to set up a virtual environment run the following cd assignment1 sudo pip install virtualenv this may already be installed virtualenv env create a virtual environment source env bin activate activate the virtual environment pip install r requirements txt install dependencies work on the assignment for a while deactivate exit the virtual environment install requirements without a virtual environment to install the required packages locally without setting up a virtual environment run the following cd assignment1 pip install r requirements txt install dependencies download data once you have the starter code you will need to download the stanford sentiment treebank dataset and pretrained glove vectors run the following from the assignment1 directory sh get datasets sh tasks there will be four parts to this assignment q1 softmax q2 neural network basics q3 word2vec q4 sentiment analysis assignment 2 tensorflow neural dependency parsing and rnn language modeling in this assignment you will learn the fundamentals of tensorflow use tensorflow to implemented a feed forward neural network for transition based dependency parsing and delve into backpropagation through time by computing the gradients for a recurrent neural network language model setup note please be sure you have python 2 7 x installed on your system the following instructions should work on mac or linux get the code download the starter code here and the assignment handout here python package requirements the core requirements for this assignment are tensorflow numpy if you have a recent linux ubuntu 14 04 and later install or mac os x the default tensorflow installation directions will work well for you if not we recommed that you work on the corn clusters tensorflow is already installed for the system default python on corn assignment overview tasks there will be three parts to this assignment q1 tensorflow softmax q2 neural transition based dependency parsing q3 recurrent neural networks language modeling assignment 3 named entity recognition and recurrent neural networks in this assignment you will learn about named entity recognition and implement a baseline window based model as well as a recurrent neural network model the assignment also covers gated recurrent units applying them to simple 1d sequences and the named entity recognition setup note please be sure you have python 2 7 x installed on your system the following instructions should work on mac or linux get the code download the starter code here and the assignment handout here python package requirements the core requirements for this assignment are tensorflow numpy matplotlib if you have a recent linux ubuntu 14 04 and later install or mac os x the default tensorflow installation directions will work well for you if not we recommed that you work on the corn clusters tensorflow is already installed for the system default python on corn we will also be providing access to gpu computing facilities on microsoft azure details on how to access these facilities will be uploaded soon assignment overview tasks there will be three parts to this assignment q1 a window into ner q2 recurrent neural nets for ner q3 grooving with grus assignment 4 reading comprehension check out the assignment 4 squad leaderboard http cs224n leaderboard southcentralus cloudapp azure com you are becoming a researcher in nlp with deep learning with this programming assignment you will implement a neural network architecture for reading comprehension using the recently published stanford question answering dataset squad setup note please be sure you have python 2 7 x installed on your system the following instructions should work on mac or linux get the code download the starter code here and the assignment handout here python package requirements the core requirements for this assignment are tensorflow nltk tqdm joblib if you have a recent linux ubuntu 14 04 and later install or mac os x the default tensorflow installation directions will work well for you if not we recommed that you work on the corn clusters tensorflow is already installed for the system default python on corn we will also be providing access to gpu computing facilities on microsoft azure details on how to access these facilities will be uploaded soon assignment overview everything you need to start doing the assignment is detailed in the assignment handout we are not setting explicit tasks for this assignment we only provide starter code for general apis data preprocessing and evaluation | ai | |
machine-learning-yearning-cn | machine learning yearning https deeplearning ai github io machine learning yearning cn release https github com deeplearning ai machine learning yearning cn releases 1 issues 2 pull request 3 mailto xiaowei deeplearning ai mly img img src pic name jpg prepend site imgurl 4 0 cc by nc sa 4 0 https creativecommons org licenses by nc sa 4 0 legalcode zh hans https creativecommons org licenses by nc sa 4 0 deed zh https creativecommons org licenses by nc sa 4 0 deed zh https creativecommons org licenses by nc sa 4 0 deed zh https creativecommons org licenses by nc sa 4 0 deed zh https creativecommons org licenses by nc sa 4 0 deed zh https creativecommons org licenses by nc sa 4 0 deed zh | machine-learning deep-learning book | ai |
graphene-ui | graphene ui this is a light wallet that connects to a graphene based api server such as the bitshares witness node executable it stores all keys locally in the browser never exposing your keys to anyone as it signs transactions locally before transmitting them to the api server which then broadcasts them to the blockchain network the wallet is encrypted with a password of your choosing and encrypted in a browser database getting started graphene ui depends node node js and version 6 is required it has not yet been tested with v7 on ubuntu and osx the easiest way to install node is to use the node version manager https github com creationix nvm to install nvm for linux osx simply copy paste the following in a terminal curl o https raw githubusercontent com creationix nvm v0 30 2 install sh bash nvm install v6 nvm use v6 once you have node installed you can clone the repo git clone https github com cryptonomex graphene ui git cd graphene ui before launching the gui you will need to install the npm packages for each subdirectory cd web npm install running the dev server the dev server uses express in combination with wepback 2 once all the packages have been installed you can start the development server by going to the web folder and running npm start once the compilation is done the gui will be available in your browser at localhost 8080 or 127 0 0 1 8080 hot reloading is enabled so the browser will live update as you edit the source files testnet by default graphene ui connects to the live bitshares network but it s very easy to switch it to the testnet run by xeroc to do so open the ui in a browser go to settings then under access select the public testnet server in the dropdown menu you should also change the faucet if you need to create an account the testnet faucet address is https testnet bitshares eu the ui will reload and connect to the testnet where you can use the faucet to create an account and receive an initial sum of test bts image https cloud githubusercontent com assets 6890015 22055747 f8e15e68 dd5c 11e6 84cd 692749b578d8 png production if you d like to host your own wallet somewhere you should create a production build and host it using nginx or apache in order to create a prod bundle simply run the following command npm run build this will create a bundle in the dist folder that can be hosted with the web server of your choice installable wallets we use electron to provide installable wallets available for windows osx and linux debian platforms such as ubuntu first install the required packages in the electron folder then go to the web folder and run npm run electron this will compile the ui with some special modifications for use with electron and copy the result to the root electron build folder now go back to the electron folder and run npm run release in order to build a wallet for your platform contributing graphene ui is open source and anyone is free to contribute pr s are welcomed and will be reviewed in a timely manner and long term contributors will be given access to the repo if you would like to get involved we have a slack channel where you can ask questions and get help for more info please contact one of the following people fabian bitshares org cass bitshares org bitsharesblocks gmail com valentine cryptonomex com there s also a very active telegram chatroom https web telegram org im p g33416306 development process bugs are always worked before enhancements developers should work each issue according to a numbered branch corresponding to the issue git checkout b 123 coding style guideline our style guideline is based on airbnb javascript style guide https github com airbnb javascript with few exceptions strings are double quoted additional trailing comma in arrays and objects declaration is optional 4 spaces tabs spaces inside curly braces are optional we strongly encourage to use eslint to make sure the code adhere to our style guidelines | front_end | |
tuya-convert | tuya convert a chinese company named tuya offers a free to brand turnkey smart home solution to anyone using their offer is dead simple since everything can be done by clicking through the tuya web page https en tuya com from choosing your pre designed products or pre programmed wifi modules mostly esp8266 to building your own app in the end this has resulted in as they claim over 11 000 devices made by over 10 000 vendors using tuyas firmware and cloud services aside from that they claim their cloud solution has military grade security michael steigerwald founder of the german it security startup vtrust was able to disprove this claim and presented his results in the smart home smart hack talk at 35c3 in leipzig https media ccc de v 35c3 9723 smart home smart hack in the following days vtrust and the german tech magazine c t decided to work together since reflashing devices using the esp8266 85 is widespread among diy smart home enthusiasts we wanted to provide an easy way for everyone to free their devices from the cloud without the need for a soldering iron please make sure to visit vtrust https www vtrust de since the hack is their work warning please be sure that you understand what you re doing before using this software flashing an alternative firmware can lead to unexpected behavior and or render the device unusable so that it might be permanently damaged highly unlikely or require soldering a serial connection to the processor in order to reflash it likely be aware that you use this software at your own risk so neither vtrust nor c t heise can be held accountable for any damage done or loss of functionality tuya convert only provides with the means to backup the original and flash an alternative firmware please do not ask for hardware support for your favorite alternative firmware in this repository rather open an issue in the corresponding repository documentation since tuya devices are spread around the world with likely a vast amount of different brand names please tell the community if you find one there is a device list in the wiki that you can help extend please at least add the device model number brand name geographical area where you have bought the device and its flash mode as seen in the device information add the gpio assignments as well if you have found them to save the developers of alternative firmwares some time please note that we do not develop or maintain alternative firmwares and so post installation issues should be directed to the respective project asciicast https asciinema org a 2adzwevgfliwc9tjb1ncwmkvm png https asciinema org a 2adzwevgfliwc9tjb1ncwmkvm requirements linux computer with a wifi adapter secondary wifi device e g smartphone dependencies will be installed by install prereq sh these scripts were tested in kali linux 2018 4 in vmware a raspberry pi zero w with rasbian a raspberry pi 3b 3b 4b with raspbian stretch and its internal wifi chip a raspberry pi 3b 3b raspberry pi os buster previously called raspbian and its internal wifi chip a raspberry pi 3b usb wifi with an image from here https www offensive security com kali linux arm images ubuntu 18 04 3 64bit in virtualbox on win10 with a cheap rtl8188cu wifi adapter connected to the vm any linux with a wifi adapter which can act as an access point should also work please note that we have tested the raspberry pi with clean installations only if you use your raspberry pi for anything else we recommend using another sd card with a clean installation procedure on january 28th 2019 tuya started distributing a patch https www heise de newsticker meldung smart home hack tuya veroeffentlicht sicherheitsupdate 4292028 html that prevented older versions of tuya convert from completing successfully we have since developed a work around to enable ota flashing on some newer devices but tuya has since released yet another patch https github com ct open source tuya convert issues 483 to ensure the best chance of success do not connect your device with the official app as it may automatically update the device preventing you from flashing with tuya convert it is up to the individual brands to update their firmware so some devices may be affected sooner than others unfortunately many devices have already been shipping with the new patched firmware evident by a psk id beginning with 02 in smarthack psk log there is no workaround at this time additionally manufacturers have been silently switching from the esp82xx based modules to other chipsets making it impossible to install alternative esp firmware on these devices installation git clone https github com ct open source tuya convert cd tuya convert install prereq sh flash third party firmware be sure the firmware fits your device 1 place your binary file in the files directory or use one of the included firmware images currently a tasmota https github com arendst tasmota tasmota lite bin build is included in the tuya convert package you can easily update to the current maintenance release http thehackbox org tasmota via ota after the flashing process completes successfully the included binary does not have any specific hardware configured once flashed using tuya convert you will need to configure your device s properly please note that while we include this firmware for your convenience we are not affiliated with the tasmota project and cannot provide support for post installation issues please refer to the tasmota project https github com arendst tasmota and its documentation http tasmota com for configuration and support important if you still want to update the tasmota binary before using tuya convert always pickup tasmota lite bin never ever use tasmota minimal bin as you will brick your device an espurna 1 13 5 https github com xoseperez espurna releases tag 1 13 5 binary is also included espurna base bin like before the binary included does not have any specific hardware defined once flashed using tuya convert you can update to the device specific version via any of the means that espurna provides ota web interface update update via telnet or mqtt please refer to the espurna project page http espurna io for more info and support binary requirements full binary including first stage bootloader tested with arduino eboot and open rtos rboot maximum filesize 512kb for first flash 2 start flashing process execute start flash sh and follow the instructions it will install our flash loader onto the esp and connect to the access point created by your wifi adapter a backup of the original firmware will be automatically downloaded and stored locally you can then proceed to flash your desired firmware or revert to the stock firmware 3 initial configuration if you flashed the included tasmota firmware file it will broadcast a tasmota xxxx access point ap when the device boots connect to this ap and open the browser to 192 168 4 1 to configure the device s wi fi credentials when entering the wi fi password click the checkbox to view the password you enter to ensure that it is correct and that your mobile device has not inadvertently capitalized the first letter if it is supposed to be lower case nor autocorrected what you entered double triple check the wi fi credentials before clicking save to apply the settings if you flashed the included espurna firmware file the procedure will be very similar the device will broadcast a espurna xxxxxx access point you will have to connect to it using the default password fibonacci once connected open the browser to 192 168 4 1 and follow the initial configuration instructions then go to the wifi tab and configure your home wifi connection remember to save or go to the admin tab to upgrade the firmware to the device specific image using docker you may want to use a docker image instead advantage of this solution you don t have to install anything on your host except docker everything goes into the docker image requirements linux computer with a wifi adapter secondary wifi device e g smartphone docker is installed docker compose is installed create docker image git clone https github com ct open source tuya convert cd tuya convert docker build t tuya latest setup docker compose copy docker docker compose sample yml to a new folder you created the file should be named docker compose yml you may adjust this docker compose yml if necessary environment variables may be different for example network adapter may be different from wlan0 adjust the volume folder where you want your backups stored run the image go into the folder you copied docker compose yml docker compose up d docker compose exec tuya start tuya convert now starts within docker stop the image docker compose exec tuya stop docker compose down contributing this project is currently maintained by colin kuebler kueblc significant time and resources are devoted to supporting and maintaining this project research development and testing requires obtaining and often breaking iot devices and related hardware to help offset costs and support the developers who make this project possible please consider making a one time or recurring donation this allows us to spend less time worrying about putting food on the table and more time making great software accessible to everyone become a patron https www patreon com kueblc buy me a coffee https www buymeacoffee com kueblc paypal https paypal me kueblc you can also give back by providing or improving documentation tutorials issue support bug reports feature requests and pull requests when planning to contribute major code changes please post your intention beforehand so we can coordinate avoid redundant contributions and ensure the changes match project philosophy any major pr should be made against the development branch related works tuyapi https github com codetheweb tuyapi npm library for lan control of tuya devices with stock firmware tuyota https github com synackfin tuyota perl based tuya flashing script using a similar strategy mocktuyacloud https github com kueblc mocktuyacloud framework replicating much of the tuya cloud functionality | tuya tuya-smart smarthome iot 35c3 esp8266 mqtt | server |
MOOC-JS-Full-Stack-Web-Development-Specialization | full stack web development specialization learn front end and mobile hybrid development build back end support and implement a fully functional application created by the hong kong university of science and technology ibm academic initiative the first two courses in this specialization include an orientation to client side development covering html css javascript jquery and frameworks such as angularjs and bootstrap on the server side you ll learn to implement nosql databases using mongodb work within a node js environment and communicate to the client side through a restful api you ll also learn to create hybrid mobile applications using the ionic framework and cordova in the final capstone project you ll apply your skills to build a fully functional web and hybrid mobile application with back end support course 1 html css and javascript course 2 front end web ui frameworks and tools course 3 front end javascript frameworks angularjs course 4 multiplatform mobile app development with web technologies course 5 server side development with nodejs updates 06 11 2015 enrolled in the program finished week 1 html css in course 1 html css and javascript 08 11 2015 finished week 1 front end web ui frameworks overview bootstrap in course 2 front end web ui frameworks and tools finished week 2 bootstrap css components in course 2 front end web ui frameworks and tools confusion project updated week 2 course 2 version 24 11 2015 finished week 2 introduction to javascript in course 1 html css and javascript 25 11 2015 finished week 3 advanced javascript in course 1 html css and javascript finished week 3 bootstrap javascript components in course 2 front end web ui frameworks and tools confusion project updated 26 11 2015 finished week 4 web tools in course 2 front end web ui frameworks and tools 28 11 2015 confusion project updated 02 12 2015 finished week 1 front end javascript frameworks angularjs overview in course 3 front end javascript frameworks angularjs 23 12 2015 confusion project updated with dishdetail html 05 02 2016 review week 1 html css in course 1 html css and javascript review week 2 introduction to javascript in course 1 html css and javascript 06 02 2016 review week 3 advanced javascript in course 1 html css and javascript 08 02 2016 review week 1 front end web ui frameworks overview bootstrap in course 2 front end web ui frameworks and tools 11 02 2016 review week 2 bootstrap css components in course 2 front end web ui frameworks and tools 14 02 2016 review week 3 bootstrap javascript components in course 2 front end web ui frameworks and tools review week 4 web tools in course 2 front end web ui frameworks and tools 15 02 2016 review week 1 front end javascript frameworks angularjs overview in course 3 front end javascript frameworks angularjs 17 02 2016 finished week 2 task runners angular scope forms and form validation in course 3 front end javascript frameworks angularjs 11 03 2016 finished week 3 single page applications in course 3 front end javascript frameworks angularjs 13 03 2016 finished week 4 client server communication and angular testing in course 3 front end javascript frameworks angularjs 15 04 2016 finished week 1 hybrid mobile app development frameworks an introduction in course 4 multiplatform mobile app development with web technologies | front_end | |
venom-blockchain.github.io | p align center a href https github com venom blockchain developer program img src https raw githubusercontent com venom blockchain developer program main vf dev program png alt logo width 366 8 height 146 4 a p the venom knowledge base table of contents about about getting started getting started usage usage contributing contributing license license about this website serves as the official documentation https docs venom foundation for the venom blockchain here you can find guides tutorials and documentation to help you get started with building on the venom blockchain getting started to get started with the venom blockchain you can visit the official website https venom foundation for more information you can also join our community chat https discord venom foundation dev to connect with other developers and ask questions usage local development this website is built using docusaurus 2 https docusaurus io a modern static website generator installation yarn run yarn start this command starts a local development server and opens up a browser window most changes are reflected live without having to restart the server build yarn build this command generates static content into the build directory and can be served using any static contents hosting service contributing we welcome contributions to the the venom knowledge base if you notice any issues or errors feel free to open an issue or submit a pull request license this project is licensed under the cc by sa 4 0 license see the license license file for details | blockchain venom-blockchain venom-developer-program | blockchain |
System-Design-Resources | system design resources this repository contains free videos articles support links and other relevant materials for preparation of system design technical interview i will be adding more resources soon feel free to contribute relevant topics articles which may be missing about cracking interviews of technical companies like amazon uber etc require a strong understanding of topics like data structures algorithms dbms os and networking it has been noticed that more and more companies are inclining towards taking a separate system design interview especially for sde roles since the resources for system design preparation are either paid or scattered here is a compilation of topics that one may go through for their preparation prerequisites one must revise basics of dbms os and networking before moving to designing systems some resources are shared below dbms dbms tutorial in hindi by sanchit jain https www youtube com playlist list plmxkhu9fnesr1rses7oldjanfgmuj0syv dbms and sql complete course https www youtube com playlist list plyrxbafkyiwbkas4b39rrbaj37s4ycoga os os complete course https www youtube com playlist list plyrxbafkyiwdhoiosjz7peqdeywhix az os tutorial in hindi by sanchit jain https www youtube com playlist list plmxkhu9fnessfvj6gasuwmqd23ul5omtd computer networks computer networks top down approach https www youtube com playlist list plyrxbafkyiwdswpbyomyfty3j8rfycu l networking tutorial in hindi by sanchit jain https www youtube com playlist list plmxkhu9fnessjfbxszgf8jf 4lvwwofcd resources pdf of full course the pdf guide to all the topics of system design along with sample system design examples is given here system design pdf system design introduction how to approach a design question https www hiredintech com system design key characteristics of a distributed system https github com lei hsia grokking system design blob master basics key characteristics md cap theoram https www youtube com watch v k yaq8ahlfa importance of scalibility issues while scaling web applications http highscalability com blog 2014 5 12 4 architecture issues when scaling web applications bottlene html making a service scalable https www lecloud net tagged scalability scaling databases how does indexing work https chartio com learn databases how does indexing work intro to nosql databases https www youtube com watch v xqnin9bw0og list plmcxhnjxntnvo6alsjvkgxv vh6epyvox index 15 sql vs nosql https github com lei hsia grokking system design blob master basics sql vs nosql md sharding database partitioning https www youtube com watch v ynrvexoenfm basics of cache https github com lei hsia grokking system design blob master basics caching md site cache vs browser cache vs server cache what s the difference https wp rocket me blog different types of caching cache server https whatis techtarget com definition cache server what is distributed caching https www youtube com watch v u3rkdlts7uy what is a content delivery network https www youtube com watch v 0qkjncu6tam what are proxy servers https www varonis com blog what is a proxy server load balancing and consistent hashing basics of hash tables tutorials notes https www hackerearth com practice data structures hash tables basics of hash tables tutorial what are load balancers load balacing algorithms https kemptechnologies com load balancer load balancing algorithms techniques sticky and non sticky sessions https stackoverflow com questions 10494431 sticky and non sticky sessions intro to consistent hashing https www youtube com watch v zarkonvygr8 list plmcxhnjxntnvo6alsjvkgxv vh6epyvox index 4 consistent hashing with simple implementation http michaelnielsen org blog consistent hashing message queues what is message queuing https www cloudamqp com blog 2014 12 03 what is message queuing html asynchronous processing in web applications part 1 a database is not a queue http blog codepath com 2012 11 15 asynchronous processing in web applications part 1 a database is not a queue asynchronous processing in web applications part 2 developers need to understand message queues http blog codepath com 2013 01 06 asynchronous processing in web applications part 2 developers need to understand message queues designing apis intro to designing apis https www youtube com watch v ylyunmtcc8 list plmcxhnjxntnvo6alsjvkgxv vh6epyvox index 12 what are restful apis https www youtube com watch v bpnfu0izhoe difference between soap and rest https www youtube com watch v bpnfu0izhoe representational state transfer rest architecture style https www ics uci edu fielding pubs dissertation rest arch style htm http interactions must be stateless explained https ruben verborgh org blog 2012 08 24 rest wheres my state microservices architecture introduction to microservices https www youtube com watch v qyhrvh9tjkw how to design microservices architecture uber architecture a case study https www youtube com watch v zed6udtpgro do you have too many microservices http highscalability com blog 2018 4 5 do you have too many microservices five design attributes th html distributed systems distributed file system https www youtube com watch v lcut0ynb ks map reduce distributed data processing https www youtube com watch v maj0aw5g17c distributed systems architectural patterns https youtu be tpspo9k28pm medium articles approach a system design interview https medium com system designing interviews approach a system design interview f3594e243730 sharding https medium com system designing interviews system design chapter 2 sharding 484960c18f6 load balancing https medium com system designing interviews system design chapter 3 load balancing e1c89148e37 caching https medium com system designing interviews system design chapter 4 caching b59a4cf83f10 indexes in databases https medium com nishantnitb system design chapter 5 indexes in databases beb90295dbcf proxies https medium com system designing interviews system design chapter 6 proxies f77be8858023 queues https medium com system designing interviews system design chapter 7 queues 5f3f9bed81 example system design questions what are the best practices for building something like a news feed https www quora com what are the best practices for building something like a news feed tinder as a microservice architecture https www youtube com watch v tndzlznxq40 mock system desugn interview design a code deployment system https www youtube com watch v q0kgywnbf 0 tips to system design interview system design cheatsheet https gist github com vasanthk 485d1c25737e8e72759f scalable system design patterns http horicky blogspot com 2010 10 scalable system design patterns html contributors kartik mahendru https github com kartikmahendru | os | |
iot | iot iot with mqtt apache kafka arduino raspberry pi you can read more on this project in this blog a href http shazsterblog blogspot com 2014 12 iot with mqtt apache kafka arduino html post a | server | |
non-profit-blockchain | amazon managed blockchain workshop amazon managed blockchain images amazonmanagedblockchain png amazon managed blockchain updated to support fabric v1 4x building and deploying an application for hyperledger fabric on amazon managed blockchain this workshop builds a hyperledger fabric blockchain network using amazon managed blockchain once the fabric network has been created you will deploy a 3 tier application that uses the fabric network to track donations to a non profit organisation and track how those donations are spent by the non profit donations and spending are tracked on a hyperledger fabric blockchain network with both donors and non profits ngo s being members of the network the 3 tier application consists of the following components node js angular user interface application accessing services provided by a restful api restful api running as a node js express application using the hyperledger fabric client sdk to query and invoke chaincode fabric chaincode written in node js deployed to a hyperledger fabric network this workshop will build a hyperledger fabric blockchain network using amazon managed blockchain deploy the chaincode start the restful api server and finally run a ui application that uses the restful api to interact with the fabric network the workshop is divided into four parts 1 building a hyperledger fabric blockchain network using amazon managed blockchain instructions can be found in the folder ngo fabric ngo fabric 2 deploying the chaincode or smart contract that provides the donation and spend tracking functionality instructions can be found in the folder ngo chaincode ngo chaincode 3 starting the restful api server that exposes the chaincode functions to client applications instructions can be found in the folder ngo rest api ngo rest api 4 running the user interface application instructions can be found in the folder ngo ui ngo ui getting started to build the network deploy the chaincode start the restful api server and run the application follow the readme instructions in parts 1 4 in this order part 1 ngo fabric readme md start the workshop by building the hyperledger fabric blockchain network using amazon managed blockchain part 2 ngo chaincode readme md deploy the non profit chaincode part 3 ngo rest api readme md run the restful api server part 4 ngo ui readme md run the application part 5 new member readme md add a new member to the network part 6 ngo lambda readme md read and write to the blockchain with amazon api gateway and aws lambda part 7 ngo events readme md use blockchain events to notify users of ngo donations part 8 blockchain explorer readme md deploy hyperledger explorer part 9 ngo identity readme md integrating blockchain users with amazon cognito cleanup to clean up your resources delete the hyperledger fabric network managed by amazon managed blockchain and the aws cloudformation template as follows in the aws cloudformation console delete the stack with the stack name your network fabric client node in the amazon managed blockchain console delete the member for your network this will delete the peer node the member and finally the fabric network assuming you created only one member in the aws cloud9 console delete your aws cloud9 instance license this library is licensed under the apache 2 0 license | blockchain | |
bitcore | bitcore monorepo p align center img alt npm src https img shields io npm v bitcore lib img alt github commit activity src https img shields io github commit activity m bitpay bitcore a href https opensource org licenses mit target blank img alt mit license src https img shields io badge license mit blue svg style display inherit a img alt github contributors src https img shields io github contributors bitpay bitcore br img src https circleci com gh bitpay bitcore svg style shield alt master build p infrastructure to build bitcoin and blockchain based applications for the next generation of financial technology applications bitcore node packages bitcore node a standardized api to interact with multiple blockchain networks bitcore wallet packages bitcore wallet a command line based wallet client bitcore wallet client packages bitcore wallet client a client for the wallet service bitcore wallet service packages bitcore wallet service a multisig hd service for wallets bitpay wallet https github com bitpay wallet an easy to use multiplatform multisignature secure bitcoin wallet insight packages insight a blockchain explorer web user interface libraries bitcore lib packages bitcore lib a powerful javascript library for bitcoin bitcore lib cash packages bitcore lib cash a powerful javascript library for bitcoin cash bitcore lib doge packages bitcore lib doge a powerful javascript library for dogecoin bitcore lib litecoin packages bitcore lib ltc a powerful javascript library for litecoin bitcore mnemonic packages bitcore mnemonic implements mnemonic code for generating deterministic keys bitcore p2p packages bitcore p2p the peer to peer networking protocol for bitcoin bitcore p2p cash packages bitcore p2p cash the peer to peer networking protocol for bitcoin cash bitcore p2p doge packages bitcore p2p doge deprecated 1 the peer to peer networking protocol for dogecoin crypto wallet core packages crypto wallet core a coin agnostic wallet library for creating transactions signing and address derivation extras bitcore build packages bitcore build a helper to add tasks to gulp bitcore client packages bitcore client a helper to create a wallet using the bitcore v8 infrastructure contributing see contributing md https github com bitpay bitcore blob master contributing md on the main bitcore repo for information about how to contribute license code released under the mit license https github com bitpay bitcore blob master license copyright 2013 2023 bitpay inc bitcore is a trademark maintained by bitpay inc 1 the bitcore p2p doge library is no longer maintained as all the core functionality is contained in bitcore p2p | blockchain | |
rtos_disp | rtos disp rtos display stm32f401 discovery freertos tft display with ili9341 connected via spi some piece of code used by me to work with tft display nowtime it is possible to initialize display draw rectangles draw simple functions one y pixel per one x pixel | os | |
Local-Feature-Matching | local feature matching local feature matching computer vision university project this project aims at learning to generate image features around a local point in the image the project consists of three parts in student py 1 generating interest points with harris get interest points 2 generating sift like features around each interest point get features 3 matching features between two images match features results notre dame images notre dame matches jpg br matches after whole pipeline on notre dame matches 1113 accuracy on 50most confident 100 accuracy on 100 most confident 99 accuracy on all matches 75 mt rushmore images mt rushmore matches jpg br matches after whole pipeline on mount rushmore matches 55 accuracy on50 most confident 94 accuracy on all matches 92 e gaudi images e gaudi matches jpg br matches after whole pipeline on epicopal gaudi matches 13 accuracy onall matches 23 | ai | |
Mobility | mobility mobile development | front_end | |
S3Eval | div align center img src figs logo png border 0 width 512 br br div s3eval a synthetic scalable and systematic evaluation suite for large language models the official repository which contains the code and data for our paper s3eval a s ynthetic s calable and s ystematic evaluation suite for large language models updates 2023 10 24 we released our code https github com lfy79001 sqleval and paper https arxiv org abs 2310 15147 task p align center img src figs pipeline png p features sqleval is a platform for large language model held out evaluation and analysis it has the following features reasoning sql contains a rich grammar in which each keyword implies a different reasoning capability and sqleval can effectively test model reasoning capabilities long context understanding the difficulty of long text evaluation is how to collect meaningful texts and tasks our work can theoretically evaluate any long context capability of any llm context windows length controllable analysis this platform allows fine grained control of data generation such as detailed attributes of the table fine grained difficulty control of the generated sql and so on users have the flexibility to use it to explore more features of llm dynamic without data leakage randomly construct synthetic data that has never been seen by llms which greatly alleviates data leakage problem in llm evaluation customizable users can use their own xlsx csv tables to generate sql of any complexity some insight benchmark alignment div style display flex img src figs bbh general png style width 48 alt img 1 img src figs code general human png style width 48 alt img 2 div we use exact match em metric as our evaluation function and we consider the pearson correlation coefficient r and the kendall rank correlation coefficient as our correlation functions the results show strong alignment between s3eval and bbh for codellm it shows the alignment between s3eval and humaneval long context analysis div align center img src figs long context png width 50 div we can clearly figure out that the performance of almost all llms on s3eval decreases significantly as context length increasing div align center img src figs neighbor png width 50 div it shows that existing long context extension methods while improving performance on sliding windows perform poorly on modelling of a truly global nature answer position analysis div style display flex img src figs gpt dot png style width 48 alt image 1 img src figs llama dot png style width 48 alt image 2 div quickstart install required packages bash bash requirements sh generate data bash python quick start py python s3eval s3eval general general easy long2k long4k long8k long16k long32k long64k long128k output path data general1 json custom output file name data s3eval generate data 500 output path total number output path using this code you can easily generate the same setting data as in the paper detailed data generation method you just run this scripts to generate tables and sqls bash bash run sh bash parameter introduction python synthetic py db path db db1 location of the generated tables new db true true create new tables in this db path then generate data false use existing tables to generate data total number 1000 how many training data do you want to generate each table number 50 how much training data do you want to generate on one table database config config database config json fine grained config for table properties sql config config sql config json the most important file sql config file synthetic mode general general standard diverse sql generation methods recommended template custom sql template generation template template general json sql grammar template location data mode eval data format styles eval is more commonly used changes are not recommended if you want to change the database config bash database config json col min 5 the min number of cols col max 8 the max number of cols row min 15 the min number of rows row max 40 the max number of rows text int date 0 55 0 35 0 1 text int date header ratio if you want to change the sql config bash nest 1 number of sql nestings options 1 2 1 2 1 2 3 keywords setting if a keyword is false then no sql containing this keyword is generated select true where true group by true having true order by true length setting control the length of sql is available false to enable this setting you need to adjust is available to true first value value can be set to specific values such as 13 14 15 if value is null then the range is used min max min 6 max 16 column ratio controlling the ratio of columns involved in sql is available false to enable this setting you need to adjust is available to true first value value can be set to specific values such as 1 2 control the number of columns involved in sql min 0 1 if value is null then the range is used min max it s the used ratio used columns all columns max 0 3 select row ratio controlling the ratio of rows involved in select keyword is available false to enable this setting you need to adjust is available to true first value value can be set to specific values such as 1 2 3 4 control the number of rows involved in sql min 0 1 if value is null then the range is used min max it s the used ratio select rows all rows max 0 2 calculate times controlling the calculate times of the sql sum count min max avg is available false to enable this setting you need to adjust is available to true first value 1 2 3 4 value can be set to specific values means the calculate times filter times controlling the filter times of the sql in like is available false to enable this setting you need to adjust is available to true first value 1 2 3 4 5 value can be set to specific values means the calculate times answer location controlling the location of answer in the table usually used in long context understanding is available false to enable this setting you need to adjust is available to true first value null min 0 1 if value is null then the range is used min max means that 0 1 row where answer is located row number 0 9 max 0 9 answer cells number 1 usually remains 1 in this repo we often just test the sql whose answer is from one cell include exclude template or general general mode is recommended to use cause it has more grammar you only need to control the config or simply control the file template mode can also be used but this template is slightly more difficult to write can be used when testing long context see template template long txt your own tables if you want to general sqls with your own xlsx csv tables use the following steps bash convert xlsx csv tables to sqlite3 python convert csv py db path new db files path csv path your tables folder path generate data python systhetic py new db 0 db path new db files path other setting is the same as normal examples for example if you want to generate where sql with easy level you have two options method1 change sql config to this bash nest 1 keywords setting select true where true group by false having false order by false length setting is available false value min 6 max 16 column ratio is available false value min 0 1 max 0 3 select row ratio is available false value min 0 1 max 0 2 calculate times is available true value 0 filter times is available false value 1 2 3 4 5 answer location is available false value null min 0 1 max 0 9 answer cells number 1 include exclude method2 direct use template file template easy txt this approach has low sql diversity you have to change the synthetic mode for general to template leardboard p align center img src figs leardboard png p bibtex bash misc lei2023s3eval title s3eval a synthetic scalable systematic evaluation suite for large language models author fangyu lei and qian liu and yiming huang and shizhu he and jun zhao and kang liu year 2023 eprint 2310 15147 archiveprefix arxiv primaryclass cs cl | ai | |
CNN_captcha_demo | python3 cnn requirement version description anaconda3 optional 5 3 1 python conda br python python 3 6 7 pip optional 18 1 conda numpy 1 15 4 tensorflow gpu 1 12 0 tensorboard br gpu cuda 9 2 148 cudnn 7 4 captcha 0 3 python pillow 5 3 0 python pil python2 python3 captchacnn import python captcha gen py capcthacnn captcha gen py imgsave python cnn train py parameter default description acc from 0 95 minimun of accepted accuracy rate acc to 0 98 maximun of accepted accuracy rate acc inc 0 05 increasement after each acception acc step 200 step number of sample batches between two checkpoints acc batch 110 the number of every sample batch checkpoint train python captcha cnn py captsize 10 modelname 5 963636 parameter default description captsize 10 the number of captchas which need testing modelname 5 963636 the name of tensorflow model in folder trainok python webservice py py cgi localhost 8888 index html demo index html index html assets js trainok json demo package version description jquery 3 3 1 javascript bootstrap 3 3 7 twitter html css js echarts 4 2 0 gl 1 1 1 baidu javascript br chancejs 1 0 16 random generator helper for javascript fontawesome optional 4 7 0 javascript pdf word | demo python tensorflow | server |
pessimism | pessimism because you can t always be optimistic pessimism is a public good monitoring service that allows for op stack https stack optimism io and evm compatible blockchains to be continuously assessed for real time threats using custom defined user heuristic rule sets to learn about pessimism s architecture please advise the documentation badge row 1 status github contributors https img shields io github contributors base org pessimism https github com base org pessimism graphs contributors github commit activity https img shields io github commit activity w base org pessimism https github com base org pessimism graphs contributors github stars https img shields io github stars base org pessimism svg https github com base org pessimism stargazers github repo size https img shields io github repo size base org pessimism github https img shields io github license base org pessimism color blue https github com base org pessimism blob main license badge row 2 detailed status github pull requests by label https img shields io github issues pr raw base org pessimism https github com base org pessimism pulls github issues https img shields io github issues raw base org pessimism svg https github com base org pessimism issues warning pessimism is currently experimental and very much in development it means pessimism is currently unstable so code will change and builds can break over the coming months if you come across problems it would help greatly to open issues so that we can fix them as quickly as possible setup to use the template run the following command s 1 create local config file config env to store all necessary environmental variables there s already an example config env template in the repo that stores default env vars 2 download https go dev doc install or upgrade to golang 1 19 3 install all project golang dependencies by running go mod download to run 1 compile pessimism to machine binary by running the following project level command s using make make build app 2 to run the compiled binary you can use the following project level command s using make make run app direct call bin pessimism docker 1 ensure docker https docs docker com engine install is installed on your machine 2 pull the latest image from github container registry ghcr via docker pull ghcr io base org pessimism latest 3 make sure you have followed the above instructions to create a local config file config env using the config env template 4 run the following without genesis json bash docker run p 8080 8080 p 7300 7300 env file config env it ghcr io base org pessimism latest with genesis json bash docker run p 8080 8080 p 7300 7300 env file config env it v pwd genesis json app genesis json ghcr io base org pessimism latest note if you want to bootstrap the application and run specific heuristics pipelines upon start update config env bootstrap path value to the location of your genesis json file then run building and running new images run make docker build at the root of the repository to build a new docker image run make docker run at the root of the repository to run the new docker image linting golangci lint https golangci lint run is used to perform code linting configurations are defined in golangci yml golangci yml it can be ran using the following project level command s using make make lint direct call golangci lint run linting markdown files to ensure consistent formatting and avoid common mistakes in our markdown documents we use markdownlint before submitting a pull request you can check your markdown files for compliance installation 1 install node js if you haven t already install node js https nodejs org en 2 install markdownlint cli globally bash npm install g markdownlint cli linting with markdownlint to lint your markdown files navigate to the root directory of the project and run bash markdownlint md if markdownlint reports any issues please fix them before submitting your pull request testing unit tests unit tests are written using the native go test https pkg go dev testing library with test mocks generated using the golang native mock https github com golang mock library these tests live throughout the project s internal directory and are named with the suffix test go unit tests can run using the following project level command s using make make test direct call go test integration tests integration tests are written that leverage the existing op e2e https github com ethereum optimism optimism tree develop op e2e testing framework for spinning up pieces of the bedrock system additionally the httptest https pkg go dev net http httptest library is used to mock downstream alerting services e g slack s webhook api these tests live in the project s e2e directory running integration tests requires generating devnet allocation files for compatibility with the optimism monorepo the following scripts devnet allocs sh can be run to do this generation if successful a new devnet directory will be created in the project s root directory integration tests can run using the following project level command s using make make e2e test direct call go test e2e bootstrap config a bootstrap config file is used to define the initial state of the pessimism service the file must be json formatted with its directive defined in the bootstrap path env var e g bootstrap path genesis json example file json network layer1 pipeline type live type contract event start height null alerting params message destination slack heuristic params address 0xfc0157aa4f5db7177830acddb3d5a9bb5be9cc5e args transfer address address uint256 network layer1 pipeline type live type balance enforcement start height null alerting params message destination slack heuristic params address 0xfc0157aa4f5db7177830acddb3d5a9bb5be9cc5e lower 1 upper 2 spawning a heuristic session to learn about the currently supported heuristics and how to spawn them please advise the heuristics documentation docs heuristics markdown | blockchain | |
ITP_Project | please go under edit and edit this file as needed for your project project name optical and eye channeling management system batch wd b02 itp g05 group leader it20657482 wijesingha w a a d ashenwijesingha member 2 it20638290 dayaratne k m a k h dayaratnekmakh member 3 it20654412 wijesinghe d l dilinilakshikawijesinghe member 4 it20666460 ramanayaka r r t raveesharamanayaka member 5 it20651756 karunathilake d n m dnmadhawa member 6 it20651060 sirikumara k d t m it20651060 member 7 it20647582 bandara t w m i p s ireshaprabodhani this is mine member 8 it20647278 udeshika p k i ishaniudeshika brief description of project this project primarily focused on the management of customers patients surgeons designs payments and the staff members with accurate and concurrently and this system helps to manage their day today activities easily without irritating also this system helps with newly designed sub systems and innovative parts with so many easy to use interfaces and user guidelines in this project our objective is to uplift their day today paperwork and shift those methods and procedures to a computer based web application who can use this any ware at any time with authentication and authorization processes so this system is more secure and more accurate than humanoid workflows technologies used we used uml apparatuses and strategies in our information technology project as well as a database modeling tool such as mongodb and a project management software such as ms project we have divided our work into sections and are working on it independently we used draw io staruml and mockflow to create the illustrations and models for our projects as a result one and each colleague fulfilled their respective commitments model one part used microsoft powerpoint to create the task proposition presentation and it is also used to create the scrum presentation in the second model in order to complete our task proposition record we used microsoft word consequently the microsoft office package has proven to be extremely beneficial in helping us complete our project proposal planning and finish our sanction archive each colleague independently depicted their capability in microsoft word and then they all combined their work into a unified word archive the adobe xd design software is used to ensure that the design is clear and effective when it comes to designing interfaces for websites or apps adobe xd is a powerful vector program that was created specifically for those who want to be productive and creative at the same time draw io is a valuable free outlining administration that offers strong collaboration features to its users it has several different shape libraries each of which contains hundreds of visual components in this section we can create and share graphs directly from within an internet browser as a result we were able to use it to create our framework outline chart aside from that we ve made use of staruml as well it is a collection of language display apparatuses that have been brought together it is also employed in the creation of uml diagrams a hackolade will also be used as the database displaying apparatus for our database in addition to the rest of the equipment mongodb these are the tools and technological advancements that we have made use of as part of our executive project management process we used the microsoft project ms project software we will make use of a variety of resources in order to complete our project on time and budget as a programming language we will use javascript and node js to accomplish our goals it is a javascript runtime environment that allows you to run javascript code that is not contained within a web browser this environment is completely based on the javascript engine in version 8 nodejs offers a genuine opportunity to build high performance web applications from the ground up visual studio code and eclipse will serve as our primary integrated development environments with sublime text serving as our secondary editor in addition we will use react as the front end framework and express as the back end framework for our application express is a nodejs web application framework that adds features to web and multi platform applications while maintaining simplicity it provides a rationale for worker side applications on the web and in portable devices combined with the mongodb data store express serves as the backend component of the mern stack while react serves as the frontend framework react is a javascript library for creating user interfaces that is both effective and adaptable it creates complex user interfaces out of small and discrete pieces of code known as components it is used to put together single page applications on the web bootstrap html and xml will be the web technologies that we will be utilizing for our itp project bootstrap is a well known css structure that in addition allows for the incorporation of javascript augmentations it incorporates plan layouts that are based on html and css in addition we use xml to display information on our website it has the potential to be used for both frontend and backend purposes in a single application we will make use of apache as the web service and apache tomcat as the webserver in this implementation apache is a web server that is free and open source software that distributes web content over the internet it is the apache s responsibility to establish a connection between the apache tomcat worker and the web browsers of web application guests in this situation apache is a multi stage programming language apache tomcat worker is a servlet compartment that can serve java servlet demands note the student s github account should be given in brackets e g asirirepos this ideally should be your student id | server |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.