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react-native-multi-target-sample | react native multi target sample react native web development sample typescript react native for web image sound resources platform specific entry point shared directory demo web https rn multi target sample firebaseapp com storybook web https rn multi target sample firebaseapp com storybook expo expo qr code expo png https expo io nkzn react native multi target sample getting started bash yarn getting started on web bash yarn start web then webpack dev server is started getting started on native bash terminal 1 yarn start tsc bash terminal 2 yarn start then expo bundler is started thanks https soundeffect lab info | react-native expo react-native-web sample | front_end |
pi-videogames | hardcore gaming zone videogames app p align center img src videogame png p description development of a spa single page application using react for the front end and redux for managing states this spa consumes data from the rawg api through a back end developed in nodejs using express and sequelize it also allows you to create new video games in addition to editing and deleting them storing them in a postgres database this application is 100 responsive and is made with css modules without the use of external libraries used technologies react redux express sequelize postgres features main page landing page with designed background with css styles button to enter the home main route main route contains search input to find videogames by name area where the list of videogames will be seen image name genres buttons options to filter by genre and by existing video game or added by us buttons options to sort both ascending and descending the videogames by alphabetical order and by rating paginated to search and display the following videogames 15 games per page showing the first 15 on the first page videogame detail path contains the fields shown in the main route for each videogame image name and genres description release date rating platforms videogame creation route contains a form controllled with javascript with the following fields name description release date rating ability to select add multiple genres ability to select add multiple platforms button option to create a new video game videogame update route contains a form controlled with javascript that updates the following fields of the videogames created name description release date rating ability to select add multiple genres or delete ability to select add multiple platforms or delete button option to create a new video game videogame delete route contains a button in the created videogames detail that delete the game all forms are validate with javascript | server |
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flow-playground | flow playground the flow playground is the best way to learn and try cadence for newcomers to flow the flow developer documentation https docs onflow org includes a guide on how to use the playground philosophy how it s built we built the flow playground as a static website or typical jam stack website because of these properties portability it is easy to move a static website gui between platforms if desired we want to have the ability to deploy the playground on peer to peer networks like ipfs or dat fast build and deploy cycles we want to maximize the amount of potential contributions what is the playground we want the playground to have features that help you build on flow we also want to balance functionality with learning the playground is a learning tool first and an awesome development tool second although the two go hand in hand contributing read the contribution guidelines contributing md git workflow use merge squashing not commit merging eg here https blog dnsimple com 2019 01 two years of squash merge squash merge is your friend the staging branch is the base branch and contains the code deployed at https play staging onflow org developing pre requisites you ll need to have docker installed to develop installation clone the repo shell script git clone git github com onflow flow playground git install dependencies npm install rename env local to env start the api flow emulator and services docker run e flow debug true e flow sessioncookiessecure false p 8080 8080 gcr io dl flow playground api latest start the react app npm run start the playground is running on localhost 3000 if you are using vscode you can use this debugging config works with workspaces version 0 2 0 configurations name launch type chrome request launch port 9229 url http localhost 3000 webroot workspacefolder src sourcemaps true sourcemappathoverrides webpack src webroot webpack workspacefolder node modules webpack webroot webpack trace true deployment the runbook contains details on how to deploy the flow playground web app runbook md | react dapps javascript smart-contracts onflow | front_end |
M3Exam | m3exam a multilingual multimodal multilevel benchmark for llms this is the repository for m3exam a multilingual multimodal multilevel benchmark for examining large language models https arxiv org pdf 2306 05179 pdf tl dr we introduce m3exam a novel benchmark sourced from real and official human exam questions for evaluating llms in a multilingual multimodal and multilevel context image images m3exam examples jpg data access the data you can download the data from here https cutt ly m3exam data the downloaded folder will be encrypted to prevent some automatic crawling scripts please get the password from the bottom of this page after unzipping the file you will see the following file structure data multimodal questions questions requiring images xx questions image json file containing the questions xx is a language iamges xx folder containg all the images for xx text questions questions with pure text xx questions dev json held out data e g can be used as in context examples xx questions test json main test data for evaluation data format questions are stored in json format you can read each json file to check the data for example python with open f data text question lang questions dev json w as f data json load f data is a list of questions each question is stored in json format question text which civil war event occurred first background description answer text 2 options 1 battle of gettysburg 2 firing on fort sumter 3 assassination of president lincoln 4 emancipation proclamation need image no language english level mid subject social subject category social science year 2006 evaluation first you need to fill in your openai api key in the bash files python main py setting zero shot model chat use api selected langs english api key put your key here then you can quickly check by running quick run sh which will run on 10 english questions and produce english pred json in the corresponding output folder to evaluate you can also run eval sh to check the performance on this 10 examples to run on more data you can refer to run sh for more detailed settings python main py setting zero shot model chat use api selected langs english selected levels low mid high num samples all api key put your key here specify the languages you want to run through selected langs running on all questions set num samples all citation if you find this useful in your research please consider citing it article zhang2023m3exam title m3exam a multilingual multimodal multilevel benchmark for examining large language models author wenxuan zhang and sharifah mahani aljunied and chang gao and yew ken chia and lidong bing year 2023 eprint 2306 05179 archiveprefix arxiv primaryclass cs cl password 12317 | ai-education evaluation large-language-models llms multilingual multimodal chatgpt gpt-4 | ai |
NLP-in-the-real-world | nlp in the real world source code for crc press book natural language processing in the real world by jyotika singh https www linkedin com in jyotikasingh please create an issue if you encounter any missing code or bugs to help us improve the codebase for you this book is endorsed by several leading experts in the industry and academia this book does a phenomenal job capturing the real world techniques employed by industry experts to address complex problems with remarkable finesse and effectiveness from foundational techniques to cutting edge models this book seamlessly blends practical code examples and insightful applications to provide a comprehensive understanding whether you re a novice or an experienced practitioner this book will take you on a journey through the entire nlp landscape providing the knowledge and skills needed to tackle any linguistic challenge and enhance your grasp of nlp sumanik singh https www linkedin com in sumanik software engineer at amazon alexa smart home if you re stuck before you even start your nlp project this book is just what you need from key data storage tools for text to visualization techniques that make sense with language data to practical use cases in many verticals natural language processing in the real world will serve as your map trailguide and companion on your journey from fresh text dataset to prototype nlp app rebecca bilbro ph d https gufaculty360 georgetown edu s contact 00336000014tgfpaag rebecca bilbro founder and cto at rotational labs applied text analytics book author data science faculty at georgetown university natural language processing in the real world is a praiseworthy book that tackles a highly important subject it provides an accurate representation of real world applications and solutions effectively bridging the gap between theory and practice by exploring nlp across 15 different industry verticals this book offers readers a comprehensive understanding of how nlp is implemented in practical scenarios the inclusion of python code for implementing nlp applications further enhances its worth as it allows readers to apply their theoretical knowledge to real world projects as a researcher and academician i consider this book to be an invaluable resource and i believe it holds immense value for my students who are pursuing degrees in ml related subjects and aspire to build careers in data science vwani p roychowdhury http www vwaniroychowdhury com professor university of california los angeles ucla this book does an exceptional job of covering a wide range of nlp applications making it a must read for anyone interested in understanding the potential of this rapidly evolving field it introduces the fundamental concepts of nlp in a clear and concise manner ensuring that readers without a strong technical background can grasp the subject matter it then delves deeper into advanced techniques and algorithms providing readers with the necessary tools to implement nlp solutions effectively neha tiwari https www linkedin com in nehatiwari03 senior data scientist at nielsen the true value of natural language processing lies in its ability to quickly solve real world business problems while theory is important it is the practical application of nlp and its connection to a company s mission that drives meaningful innovation and impact this book provides a practical playbook offering insights and techniques that bridge the gap between theory and practice data science often carries the perception of being methodical and slow but this book focuses on leveraging prototyping stakeholder interaction and iteration to integrate data science into the core of value delivery for companies whether you re an experienced practitioner or new to the field this book empowers you to harness the power of nlp and transform the way we interact with language in the real world joey mccord https www linkedin com in joeymccord founder cto and adjunct professor often there is a gap between education and practice this book is an essential resource to cover the gap and must have for beginners as well as experienced professionals as a researcher in academia and professor of machine learning i find this book to be an eye opener for approaching nlp in a practical sense not only a great resource of people in academia it is all you need to build nlp solutions in the real world regardless of the industry vertical you work in dr v kalaichelvi https universe bits pilani ac in dubai kalaichelvi profile professor and head department of eee birla institute of technology science bits this exceptional guide offers profound insights into the various industries that leverage nlp its alignment with business objectives and practical guidance on developing python based applications whether you are a novice or an expert this book is an invaluable resource it comprehensively covers essential knowledge and has become an indispensable tool for enhancing my expertise and proficiency in nlp i consider it an immensely valuable asset and a frequent reference in my work dishant banga https globeeawards com dishant banga sr data analyst bridgetree | ai |
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NLP-Examples | h2 what is nlp natural language processing h2 the natural language processing is application of computational techniques to the analysis and synthesis of natural language and speech li summarize blocks of text using summarizer to extract the most important and central ideas while ignoring irrelevant li information li create a chat bot using parsey mcparseface a language parsing deep learning model made by google that uses point of speech tagging li li automatically generate keyword tags from content using autotag which leverages lda a technique that discovers topics contained within a body of text li li identify the type of entity extracted such as it being a person place or organization using named entity recognition li li use sentiment analysis to identify the sentiment of a string of text from very negative to neutral to very positive li li reduce words to their root or stem using porterstemmer or break up text into tokens using tokenizer li h1 common nlp tasks in software programs today include h1 ul li sentence segmentation part of speech tagging and parsing li li deep analytics li li named entity extraction li li co reference resolution li ul these are the some examples of some basics to advance natural language processing coming soon for all modules will update daily 1 day commit challange p require python doctype html html head head body div id mydiv class header h2 style margin 5px my to do list h2 div ul id myul li python libraries comparison li li class checked python 3 support li li deep learning li li web li li custom trainer li ul body html | python natural-language-processing nltk spacy parser machine-learning sklearn | ai |
MCTE4342-ESD | mcte 4342 embedded system design embedded system design | os |
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phoenix-rtos-ports | phoenix rtos ports ports of the open source tools | os |
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RS_steganalysis | hit information hiding technology lab2 lab2 lsb rs requests two weeks implement rs steganalysis for testing on lsb images and raw images any programming language other requirements remain unchanged extra implement anti rs steganalysis of lsb lsb rs steganalysis result div align center img width 500 height 300 src https github com airobotzhang rs steganalysis raw master result1 png alt rs div div anti rs steganalysis of lsb result div align center img width 500 height 300 src https github com airobotzhang rs steganalysis raw master result2 png alt anti rs div div | server |
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wfx-fullMAC-driver | wfx wi fi fmac driver the wfx wi fi full mac fmac driver enables a host device to communicate with silicon labs wi fi transceivers and take advantage of the capabilities listed below the fmac driver is built in a way that allows it to easily be ported on new platforms it fits bare metal architectures as well as rtos based implementations implementation examples are available on several platforms in the wfx fmac tools repository https github com siliconlabs wfx fullmac tools the latest driver documentation is available at docs silabs com https docs silabs com wifi wf200 rtos latest index wf200 wi fi transceiver the silicon labs wf200 is an ultra low power wi fi transceiver or network co processor ncp targeted for applications where optimal rf performance low power consumption and secure end to end solution together with fast time to market are key requirements the wf200 integrates the balun t r switch lna and pa for best possible rf performance wf200 has been optimized for resource and power constrained devices at the rf protocol and firmware levels power conscious devices can take advantage of these features in both active and idle sleep modes for security sensitive applications wf200 provides secure boot and a secure encrypted host interface robust security is made possible with a native integrated true random number generator and otp memory for confidential encryption key storage the wf200 fits well with linux based and rtos based host processors wf200 supports both the 802 11 split mac and the 802 11 full mac architectures it communicates with the external host controller over the spi or sdio interface the wf200 can act as a full mac wireless device and wpa2 personal supplicant the firmware exposes a full mac api at the ip packet level it also manages wpa2 personal authentication process as well as automatic roaming more information on silicon labs website https www silabs com products wireless wi fi | wi-fi rtos driver | os |
dbs | wahlinformations system study project for the databases lecture at tu munich as part of the software engineering masters program installation database scripts to create db contains shell and sql scripts to create and populate the database use postgres version 9 4 or above the following scripts are available runscripts basic initializes the database schema etc and populates the data of 2013 runscripts all archive2009 actual2013 as above but also sets 2013 as the current election and populates 2009s archives use runscripts all archive2009 actual2013 for the complete setup web server the node server directory contains the full node web server https nodejs org install dependencies using npm from within that directory npm install generator voting is only possible if wahlbezirke exist they can be generated using the stimmzettel generator which creates them alongside the stimmzettel citizenregistrations etc the generator uses the scala programming language http scala lang org using sbt http www scala sbt org you can run it from the sbt shell using runmain wahlinfo generator year wahlkreis id where year is the election year you want to create stimmzettel for and wahlkreis id can either be a single id of the wahlkreis or a range such as 15 42 omitting the wahlkreis id benchmarking the benchmarking client allows to specify urls a number of clients and a frequency at which the clients send requests to the urls and a probability with which the urls are selected in application conf e g benchmark n 10 t 100 millis deadline 30 seconds queries name q1 uri http localhost 3000 q1 propability 0 25 where n is the number of clients t is the average time between requests deadline is the total runtime of the benchmarking process and queries is a list of queries like the generator it uses scala run it using sbt with sbt run | server |
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glasses | glasses alt https github com francescosaveriozuppichini glasses blob develop docs static images background png raw true codecov https codecov io gh francescosaveriozuppichini glasses branch develop graph badge svg https codecov io gh francescosaveriozuppichini glasses compact concise and customizable deep learning computer vision library models have been stored into the hugging face hub https huggingface co glasses doc is here https francescosaveriozuppichini github io glasses index html tl dr this library has human readable code no research code common component are shared across models models same apis classification for all models you learn them once and they are always the same clear and easy to use model constomization see here block classification classification and segmentation segmentation emoji in the name stuff implemented so far training data efficient image transformers distillation through attention https arxiv org pdf 2012 12877 pdf vision transformer an image is worth 16x16 words transformers for image recognition at scale https arxiv org pdf 2010 11929 pdf resnest split attention networks https arxiv org abs 2004 08955 alexnet imagenet classification with deep convolutional neural networks https papers nips cc paper 2012 file c399862d3b9d6b76c8436e924a68c45b paper pdf densenet densely connected convolutional networks https arxiv org abs 1608 06993 efficientnet efficientnet rethinking model scaling for convolutional neural networks https arxiv org abs 1905 11946 efficientnetlite higher accuracy on vision models with efficientnet lite https blog tensorflow org 2020 03 higher accuracy on vision models with efficientnet lite html fishnet fishnet a versatile backbone for image region and pixel level prediction https arxiv org abs 1901 03495 mobilenet mobilenetv2 inverted residuals and linear bottlenecks https arxiv org pdf 1801 04381 pdf regnet designing network design spaces https arxiv org abs 2003 13678 resnet deep residual learning for image recognition https arxiv org abs 1512 03385 resnetd bag of tricks for image classification with convolutional neural networks https arxiv org pdf 1812 01187 pdf resnetxt aggregated residual transformations for deep neural networks https arxiv org pdf 1611 05431 pdf seresnet concurrent spatial and channel squeeze excitation in fully convolutional networks https arxiv org abs 1803 02579 vgg very deep convolutional networks for large scale image recognition https arxiv org pdf 1409 1556 pdf wideresnet wide residual networks https arxiv org pdf 1605 07146 pdf fpn feature pyramid networks for object detection https arxiv org abs 1612 03144 pfpn panoptic feature pyramid networks https arxiv org pdf 1901 02446 pdf unet u net convolutional networks for biomedical image segmentation https arxiv org abs 1505 04597 squeeze and excitation concurrent spatial and channel squeeze excitation in fully convolutional networks https arxiv org abs 1803 02579 eca eca net efficient channel attention for deep convolutional neural networks https arxiv org pdf 1910 03151 pdf dropblock a regularization method for convolutional networks https arxiv org abs 1810 12890 spatial pyramid pooling in deep convolutional networks for visual recognition https arxiv org pdf 1406 4729 pdf score cam score weighted visual explanations for convolutional neural networks https arxiv org abs 1910 01279 grad cam visual explanations from deep networks via gradient based localization https arxiv org abs 1610 02391 installation you can install glasses using pip by running pip install git https github com francescosaveriozuppichini glasses motivations almost all existing implementations of the most famous model are written with very bad coding practices what today is called research code i struggled to understand some of the implementations even if in the end were just a few lines of code most of them are missing a global structure they used tons of code repetition they are not easily customizable and not tested since i do computer vision for living i needed a way to make my life easier getting started the api are shared across all models python import torch from glasses models import automodel autotransform load one model model automodel from pretrained resnet18 eval and its correct input transformation tr autotransform from name resnet18 model summary device cpu thanks to torchinfo python at any time see all the models automodel models table python models name pretrained resnet18 true resnet26 true resnet26d true resnet34 true resnet34d true resnet50 true interpretability python import requests from pil import image from io import bytesio from glasses interpretability import gradcam saliencymap from torchvision transforms import normalize get a cute dog r requests get https i insider com 5df126b679d7570ad2044f3e width 700 format jpeg auto webp im image open bytesio r content un normalize when done mean std tr transforms 1 mean tr transforms 1 std postprocessing normalize mean std 1 0 std apply preprocessing x tr im unsqueeze 0 model interpret x using gradcam postprocessing postprocessing show alt https github com francescosaveriozuppichini glasses blob develop docs static images grad cam png raw true classification python from glasses models import resnet from torch import nn change activation model automodel from pretrained resnet18 activation nn selu eval or directly from the model class resnet resnet18 activation nn selu change number of classes resnet resnet18 n classes 100 freeze only the convolution weights model automodel from pretrained resnet18 model freeze who model encoder get the inner features python model encoder has special hooks ready to be activated call the features to trigger them model encoder features x torch randn 1 3 224 224 model x f shape for f in model encoder features change inner block python what about resnet with inverted residuals from glasses models classification efficientnet import invertedresidualblock resnet resnet18 block invertedresidualblock segmentation python from functools import partial from glasses models segmentation unet import unet unetdecoder vanilla unet unet unet let s change the encoder unet unet from encoder partial automodel from name efficientnet b1 mmm i want more layers in the decoder unet unet decoder partial unetdecoder widths 256 128 64 32 16 maybe resnet was better unet unet encoder lambda kwargs resnet resnet26 kwargs encoder same api unet summary input shape 1 224 224 unet more examples python change the decoder part model automodel from pretrained resnet18 my head nn sequential nn adaptiveavgpool2d 1 1 nn flatten nn linear model encoder widths 1 512 nn dropout 0 2 nn relu nn linear 512 1000 model head my head x torch rand 1 3 224 224 model x shape torch size 1 1000 pretrained models i am currently working on the pretrained models and the best way to make them available this is a list of all the pretrained models available so far they are all trained on imagenet i used a batch size 64 and a gtx 1080ti to evaluale the models top1 top5 time batch size vit base patch16 384 0 842 0 9722 1130 81 64 vit large patch16 224 0 82836 0 96406 893 486 64 eca resnet50t 0 82234 0 96172 241 754 64 eca resnet101d 0 82166 0 96052 213 632 64 efficientnet b3 0 82034 0 9603 199 599 64 regnety 032 0 81958 0 95964 136 518 64 vit base patch32 384 0 8166 0 9613 243 234 64 vit base patch16 224 0 815 0 96018 306 686 64 deit small patch16 224 0 81082 0 95316 132 868 64 eca resnet50d 0 80604 0 95322 135 567 64 resnet50d 0 80492 0 95128 97 5827 64 cse resnet50 0 80292 0 95048 108 765 64 efficientnet b2 0 80126 0 95124 127 177 64 eca resnet26t 0 79862 0 95084 155 396 64 regnety 064 0 79712 0 94774 183 065 64 regnety 040 0 79222 0 94656 124 881 64 resnext101 32x8d 0 7921 0 94556 290 38 64 regnetx 064 0 79066 0 94456 176 3 64 wide resnet101 2 0 7891 0 94344 277 755 64 regnetx 040 0 78486 0 94242 122 619 64 wide resnet50 2 0 78464 0 94064 201 634 64 efficientnet b1 0 7831 0 94096 98 7143 64 resnet152 0 7825 0 93982 186 191 64 regnetx 032 0 7792 0 93996 319 558 64 resnext50 32x4d 0 77628 0 9368 114 325 64 regnety 016 0 77604 0 93702 96 547 64 efficientnet b0 0 77332 0 93566 67 2147 64 resnet101 0 77314 0 93556 134 148 64 densenet161 0 77146 0 93602 239 388 64 resnet34d 0 77118 0 93418 59 9938 64 densenet201 0 76932 0 9339 158 514 64 regnetx 016 0 76684 0 9328 91 7536 64 resnet26d 0 766 0 93188 70 6453 64 regnety 008 0 76238 0 93026 54 1286 64 resnet50 0 76012 0 92934 89 7976 64 densenet169 0 75628 0 9281 127 077 64 resnet26 0 75394 0 92584 65 5801 64 resnet34 0 75096 0 92246 56 8985 64 regnety 006 0 75068 0 92474 55 5611 64 regnetx 008 0 74788 0 92194 57 9559 64 densenet121 0 74472 0 91974 104 13 64 deit tiny patch16 224 0 7437 0 91898 66 662 64 vgg19 bn 0 74216 0 91848 169 357 64 regnety 004 0 73766 0 91638 68 4893 64 regnetx 006 0 73682 0 91568 81 4703 64 vgg16 bn 0 73476 0 91536 150 317 64 vgg19 0 7236 0 9085 155 851 64 regnetx 004 0 72298 0 90644 58 0049 64 vgg16 0 71628 0 90368 135 398 64 vgg13 bn 0 71618 0 9036 129 077 64 efficientnet lite0 0 7041 0 89894 62 4211 64 vgg11 bn 0 70408 0 89724 86 9459 64 vgg13 0 69984 0 89306 116 052 64 regnety 002 0 6998 0 89422 46 804 64 resnet18 0 69644 0 88982 46 2029 64 vgg11 0 68872 0 88658 79 4136 64 regnetx 002 0 68658 0 88244 45 9211 64 assuming you want to load efficientnet b1 python from glasses models import efficientnet automodel autotransform load it using automodel model automodel from pretrained efficientnet b1 eval or from its own class model efficientnet efficientnet b1 pretrained true you may also need to get the correct transformation that must be applied on the input tr autotransform from name efficientnet b1 in this case tr is compose resize size 240 interpolation pil image bicubic centercrop size 240 240 totensor normalize mean 0 485 0 456 0 406 std 0 229 0 224 0 225 deep customization all models are composed by sharable parts block layer encoder head decoder block each model has its building block they are noted by block in each block all the weights are in the block field this makes it very easy to customize one specific model python from glasses models classification vgg import vggbasicblock from glasses models classification resnet import resnetbasicblock resnetbottleneckblock resnetbasicpreactblock resnetbottleneckpreactblock from glasses models classification senet import senetbasicblock senetbottleneckblock from glasses models classification resnetxt import resnetxtbottleneckblock from glasses models classification densenet import densebottleneckblock from glasses models classification wide resnet import wideresnetbottleneckblock from glasses models classification efficientnet import efficientnetbasicblock for example if we want to add squeeze and excitation to the resnet bottleneck block we can just python from glasses nn att import spatialse from glasses models classification resnet import resnetbottleneckblock class seresnetbottleneckblock resnetbottleneckblock def init self in features int out features int squeeze int 16 args kwargs super init in features out features args kwargs all the weights are in block we want to apply se after the weights self block add module se spatialse out features reduction squeeze seresnetbottleneckblock 32 64 then we can use the class methods to create the new models following the existing architecture blueprint for example to create se resnet50 python resnet resnet50 block resnetbottleneckblock the cool thing is each model has the same api if i want to create a vgg13 with the resnetbottleneckblock i can just python from glasses models import vgg model vgg vgg13 block seresnetbottleneckblock model summary some specific model can require additional parameter to the block for example mobilenetv2 also required a expansion parameter so our seresnetbottleneckblock won t work layer a layer is a collection of blocks it is used to stack multiple blocks together following some logic for example resnetlayer python from glasses models classification resnet import resnetlayer resnetlayer 64 128 depth 2 encoder the encoder is what encoders a vector so the convolution layers it has always two very important parameters widths depths widths is the wide at each layer so how much features there are depths is the depth at each layer so how many blocks there are for example resnetencoder will creates multiple resnetlayer based on the len of widths and depths let s see some example python from glasses models classification resnet import resnetencoder 3 layers with 32 64 128 features and 1 2 3 block each resnetencoder widths 32 64 128 depths 1 2 3 all encoders are subclass of encoder that allows us to hook on specific stages to get the featuers all you have to do is first call features to notify the model you want to receive the features and then pass an input python enc resnetencoder enc features enc torch randn 1 3 224 224 print f shape for f in enc features remember each model has always a encoder field python from glasses models import resnet model resnet resnet18 model encoder widths 1 the encoder knows the number of output features you can access them by features each encoder can return a list of features accessable by the features field you need to call it once before in order to notify the encoder we wish to also store the features python from glasses models classification resnet import resnetencoder x torch randn 1 3 224 224 enc resnetencoder enc features call it once enc x features enc features now we have all the features from each layer stage print f shape for f in features torch size 1 64 112 112 torch size 1 64 56 56 torch size 1 128 28 28 torch size 1 256 14 14 head head is the last part of the model it usually perform the classification python from glasses models classification resnet import resnethead resnethead 512 n classes 1000 decoder the decoder takes the last feature from the encoder and decode it this is usually done in segmentation models such as unet python from glasses models segmentation unet import unetdecoder x torch randn 1 3 224 224 enc resnetencoder enc features call it once x enc x features enc features we need to tell the decoder the first feature size and the size of the lateral features dec unetdecoder start features enc widths 1 lateral widths enc features widths 1 out dec x features 1 out shape this object oriented structure allows to reuse most of the code across the models name parameters size mb cse resnet101 49 326 872 188 17 cse resnet152 66 821 848 254 91 cse resnet18 11 778 592 44 93 cse resnet34 21 958 868 83 77 cse resnet50 28 088 024 107 15 deit base patch16 224 87 184 592 332 58 deit base patch16 384 87 186 128 357 63 deit small patch16 224 22 359 632 85 3 deit tiny patch16 224 5 872 400 22 4 densenet121 7 978 856 30 44 densenet161 28 681 000 109 41 densenet169 14 149 480 53 98 densenet201 20 013 928 76 35 eca resnet101d 44 568 563 212 62 eca resnet101t 44 566 027 228 65 eca resnet18d 16 014 452 98 41 eca resnet18t 1 415 684 37 91 eca resnet26d 16 014 452 98 41 eca resnet26t 16 011 916 114 44 eca resnet50d 25 576 350 136 65 eca resnet50t 25 573 814 152 68 efficientnet b0 5 288 548 20 17 efficientnet b1 7 794 184 29 73 efficientnet b2 9 109 994 34 75 efficientnet b3 12 233 232 46 67 efficientnet b4 19 341 616 73 78 efficientnet b5 30 389 784 115 93 efficientnet b6 43 040 704 164 19 efficientnet b7 66 347 960 253 1 efficientnet b8 87 413 142 505 01 efficientnet l2 480 309 308 2332 13 efficientnet lite0 4 652 008 17 75 efficientnet lite1 5 416 680 20 66 efficientnet lite2 6 092 072 23 24 efficientnet lite3 8 197 096 31 27 efficientnet lite4 13 006 568 49 62 fishnet150 24 960 808 95 22 fishnet99 16 630 312 63 44 mobilenet v2 3 504 872 24 51 mobilenetv2 3 504 872 13 37 regnetx 002 2 684 792 10 24 regnetx 004 5 157 512 19 67 regnetx 006 6 196 040 23 64 regnetx 008 7 259 656 27 69 regnetx 016 9 190 136 35 06 regnetx 032 15 296 552 58 35 regnetx 040 22 118 248 97 66 regnetx 064 26 209 256 114 02 regnetx 080 34 561 448 147 43 regnety 002 3 162 996 12 07 regnety 004 4 344 144 16 57 regnety 006 6 055 160 23 1 regnety 008 6 263 168 23 89 regnety 016 11 202 430 42 73 regnety 032 19 436 338 74 14 regnety 040 20 646 656 91 77 regnety 064 30 583 252 131 52 regnety 080 39 180 068 165 9 resnest101e 48 275 016 184 15 resnest14d 10 611 688 40 48 resnest200e 70 201 544 267 8 resnest269e 7 551 112 28 81 resnest26d 17 069 448 65 11 resnest50d 27 483 240 104 84 resnest50d 1s4x24d 25 677 000 97 95 resnest50d 4s2x40d 30 417 592 116 03 resnet101 44 549 160 169 94 resnet152 60 192 808 229 62 resnet18 11 689 512 44 59 resnet200 64 673 832 246 71 resnet26 15 995 176 61 02 resnet26d 16 014 408 61 09 resnet34 21 797 672 83 15 resnet34d 21 816 904 83 22 resnet50 25 557 032 97 49 resnet50d 25 576 264 97 57 resnext101 32x16d 194 026 792 740 15 resnext101 32x32d 468 530 472 1787 3 resnext101 32x48d 828 411 176 3160 14 resnext101 32x8d 88 791 336 338 71 resnext50 32x4d 25 028 904 95 48 se resnet101 49 292 328 188 04 se resnet152 66 770 984 254 71 se resnet18 11 776 552 44 92 se resnet34 21 954 856 83 75 se resnet50 28 071 976 107 09 unet 23 202 530 88 51 vgg11 132 863 336 506 83 vgg11 bn 132 868 840 506 85 vgg13 133 047 848 507 54 vgg13 bn 133 053 736 507 56 vgg16 138 357 544 527 79 vgg16 bn 138 365 992 527 82 vgg19 143 667 240 548 05 vgg19 bn 143 678 248 548 09 vit base patch16 224 86 415 592 329 65 vit base patch16 384 86 415 592 329 65 vit base patch32 384 88 185 064 336 4 vit huge patch16 224 631 823 080 2410 21 vit huge patch32 384 634 772 200 2421 46 vit large patch16 224 304 123 880 1160 14 vit large patch16 384 304 123 880 1160 14 vit large patch32 384 306 483 176 1169 14 vit small patch16 224 48 602 344 185 4 wide resnet101 2 126 886 696 484 03 wide resnet50 2 68 883 240 262 77 credits most of the weights were trained by other people and adapted to glasses it is worth cite pytorch image models https github com rwightman pytorch image models torchvision hhttps github com pytorch vision | deep-learning machine-learning computer-vision neural-network pretrained-models convolutional-networks deep-neural-networks classification pytorch | ai |
kaizen-design-system | kaizen design system build status https badge buildkite com 880e8b196b369c19ffcbef08a81d364059e0d6fb77e9d35563 svg branch main https buildkite com culture amp kaizen design system kaizen is culture amp s public design system it accelerates culture amp s designers and engineers as they collaborate on creating world class products visit cultureamp design https cultureamp design to learn more this repository is the source of truth for culture amp s design system documentation and hosts packages such as kaizen component library packages component library which are used across culture amp s products it is structured as a multi package repository with a number of independently versioned projects sharing common tools workflows and vision this document serves to orient you within the repository to get you set up and acquainted and then send you off in the right direction for more information on any specific package please refer to its readme quick links kaizen issues https github com cultureamp kaizen discourse issues kaizen design tokens readme packages design tokens readme md kaizen component library readme packages component library readme md contributing guidelines contributing md designers issues you can explore or create issues https github com cultureamp kaizen design system issues to track ideas enhancements or bugs use issues to track changes to design documentation missing documentation to learn more see the designer section of the contributing guidelines contributing md editing design documentation you can edit kaizen site documentation using github s interface to learn more see the designer section of the contributing guidelines contributing md getting started setup set up access to private culture amp packages on your laptop you will need to update npmrc with a github token linked to your account refer to the instructions here https github com cultureamp node packages blob master how to setup a project to use private cultureamp packages md installation to begin developing the design system locally run the following from the repository root yarn install we use storybook https github com storybooks storybook to provide a local development environment for react https reactjs org components all kaizen design system components have accompanying stories in storybook to run storybook locally run the following from the repository root yarn storybook having trouble running storybook try running yarn reset which includes yarn clean and yarn install force branch previews while the main site is located at https cultureamp design all branches in this repository are automatically deployed to a public url at https dev cultureamp design branch name this deployment is a storybook build for that branch for example pushing the branch louis more glitter would make its documentation and storybook build available at dev cultureamp design louis more glitter package scripts it s dangerous to go alone take these command summary yarn storybook develop components locally using storybook stories path to package yarn storybook develop just one package at a time using storybook builds faster yarn commit use commitizen to help you write your conventional commits yarn compile run all typechecks yarn lint run all linters yarn lint fix run all linters fixing violations yarn plop add a new component subcomponent yarn test storybook run tests on all storybook stories yarn test run all jest https jestjs io tests yarn reset reinstall all dependencies bugs and discussions please open a new github issue https github com cultureamp kaizen design system issues new to report bugs or suggest changes learn more culture amp employees can reach out to the design systems crew on slack | kaizen-design-system react typescript design-system kaizen storybook design-systems styleguide | os |
MrTipster | tipster codepath this is a tip calculator application for ios submitted as the pre assignment requirement for codepath time spent 17 hours completed required user can enter a bill amount choose a tip percentage and see the tip and total values required settings page to change the default tip percentage optional ui animations optional remembering the bill amount across app restarts if 10mins optional making sure the keyboard is always visible and the bill amount is always the first responder this way the user does not have to tap anywhere to use this app just launch the app and start typing optional locale and currency optional light dark theme video walkthrough tipsterrevision2 gif installation download the zip file and open the xcode project to run the app with the simulator | os |
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ATLAS-iOS | atlas ios these are my course materials for teaching the mobile app development course at the university of colorado s atlas institute https www colorado edu atlas website the html folder html in this repo is hosted as a website https zef github io atlas ios here on github pages you can see the syllabus slides and other materials there slides the slides folder contains markdown that produces all of the presentations using marp https marp app the rendered slides in the html folder can be downloaded and opened up in a browser window from your own computer a ruby rakefile is used to organize the shell commands needed for various tasks a symbolic link is created with ln s images slides images which enables me to see images when i run the marp server but still store the images in the html folder so that they can be served when opening an html file locally syllabus the syllabus folder contains files related to the syllabus which is here syllabus syllabus html pandoc https pandoc org is used to generate an html file of the syllabus as well as a pdf version which isn t very polished and is probably not necessary i might delete that later pdf syllabus generation i had to install pdflatex with brew install mactex and then manually add https github com zef dotfiles commit a5aa7a704ab1563d97aba5b4d381a094ab4ae0c8 the path to my environment to generate the pdf i m using this latex template https github com wandmalfarbe pandoc latex template for the design atlas html images atlas svg | front_end |
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Eureka | eureka human level reward design via coding large language models div align center website https eureka research github io arxiv https arxiv org abs 2310 12931 pdf https eureka research github io assets eureka paper pdf python version https img shields io badge python 3 8 blue svg https github com eureka research eureka img src https img shields io badge framework pytorch red svg https pytorch org github license https img shields io github license eureka research eureka https github com eureka research eureka blob main license https github com eureka research eureka assets 21993118 1abb960d 321a 4de9 b311 113b5fc53d4a images eureka png div large language models llms have excelled as high level semantic planners for sequential decision making tasks however harnessing them to learn complex low level manipulation tasks such as dexterous pen spinning remains an open problem we bridge this fundamental gap and present eureka a human level reward design algorithm powered by llms eureka exploits the remarkable zero shot generation code writing and in context improvement capabilities of state of the art llms such as gpt 4 to perform in context evolutionary optimization over reward code the resulting rewards can then be used to acquire complex skills via reinforcement learning eureka generates reward functions that outperform expert human engineered rewards without any task specific prompting or pre defined reward templates in a diverse suite of 29 open source rl environments that include 10 distinct robot morphologies eureka outperforms human expert on 83 of the tasks leading to an average normalized improvement of 52 the generality of eureka also enables a new gradient free approach to reinforcement learning from human feedback rlhf readily incorporating human oversight to improve the quality and the safety of the generated rewards in context finally using eureka rewards in a curriculum learning setting we demonstrate for the first time a simulated five finger shadow hand capable of performing pen spinning tricks adeptly manipulating a pen in circles at human speed installation eureka requires python 3 8 we have tested on ubuntu 20 04 and 22 04 1 create a new conda environment with conda create n eureka python 3 8 conda activate eureka 2 install isaacgym tested with preview release 4 4 follow the instruction https developer nvidia com isaac gym to download the package tar xvf isaacgym preview 4 package tar gz cd isaacgym python pip install e test installation python examples joint monkey py 3 install eureka git clone https github com eureka research eureka git cd eureka pip install e cd isaacgymenvs pip install e cd rl games pip install e 4 eureka currently uses openai api for language model queries you need to have an openai api key to use eureka here https platform openai com account api keys then set the environment variable in your terminal export openai api key your api key getting started navigate to the eureka directory and run python eureka py env environment iteration num iterations sample num samples environment is the task to perform options are listed in eureka cfg env num samples is the number of reward samples to generate per iteration default value is 16 num iterations is the number of eureka iterations to run default value is 5 below are some example commands to try out eureka python eureka py env shadow hand sample 4 iteration 2 model gpt 4 0314 python eureka py env humanoid sample 16 iteration 5 model gpt 3 5 turbo 16k 0613 each run will create a timestamp folder in eureka outputs that saves the eureka log as well as all intermediate reward functions and associated policies other command line parameters can be found in eureka cfg config yaml the list of supported environments can be found in eureka cfg env eureka pen spinning demo we have released eureka pen spinning policy in isaacgymenvs isaacgymenvs checkpoints try visualizing it with the following command cd isaacgymenvs isaacgymenvs python train py test true headless false force render true task shadowhandspin checkpoint checkpoints eurekapenspinning pth note that this script use the default isaac gym renderer and not the omniverse rendering in the paper videos running eureka on a new environment 1 create a new isaacgym environment instructions can be found in here isaacgymenvs docs framework md 2 verify that standard rl works for your new environment cd isaacgymenvs isaacgymenvs python train py task your new task 3 create a new yaml file your new task yaml in eureka cfg env env name your new task task your new task description 4 construct the raw environment code that will serve as context for eureka as well as the skeleton environment code on which the eureka reward will be appended to cd eureka utils python prune env py your new task 5 try out eureka python eureka py env your new task acknowledgement we thank the following open sourced projects our environments are from isaacgym https github com nvidia omniverse isaacgymenvs and dexteroushands https github com pku marl dexteroushands our rl training code is based on rl games https github com denys88 rl games license this codebase is released under mit license license citation if you find our work useful please consider citing us bibtex article ma2023eureka title eureka human level reward design via coding large language models author yecheng jason ma and william liang and guanzhi wang and de an huang and osbert bastani and dinesh jayaraman and yuke zhu and linxi fan and anima anandkumar year 2023 journal arxiv preprint arxiv arxiv 2310 12931 disclaimer this project is strictly for research purposes and not an official product from nvidia | ai |
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CECS347 | cecs347 embedded system design using arm cortex m4 microcontrollers | os |
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smart-mirror | h1 align center smart mirror h1 p align center a href https discord gg emb4ynw img src https discordapp com api guilds 258802311298547713 widget png alt discord chat a a href https travis ci org evancohen smart mirror img src https travis ci org evancohen smart mirror svg branch master alt build status a p p align center a voice controlled life automation hub most commonly powered by the raspberry pi p installation quick installation for the raspberry pi 2 curl sl https raw githubusercontent com evancohen smart mirror master scripts pi install sh bash documentation http docs smart mirror io check out the documentation for more information manual installation https docs smart mirror io installation configuration https docs smart mirror io configuration html troubleshooting https docs smart mirror io troubleshooting html discord a live chat to get help and discuss mirror related issues https discord gg emb4ynw usually there are a few folks hanging around in the lobby but if there arent you are probably better off filing an issue https github com evancohen smart mirror issues new development and contributing see the dev https github com evancohen smart mirror tree dev branch for features that are curently in development if you would like to contribue please follow the contribution guidelines https github com evancohen smart mirror blob master contributing md to launch the mirror with a debug window attached use the following command npm start dev you can also toggle the debug developers window on off with the keyboard ctrl ahift i sequence on the smart mirror screen for more information see the development and contributing http docs smart mirror io development and contributing html section of the documentation current feature requests suggestions please use feathub com to add feature requests and suggestions we also recommend looking through the already existing requests to help in ranking the most popular and useful features to smart mirror users p align center a href http feathub com evancohen smart mirror img src http feathub com evancohen smart mirror format svg alt feature requests a p license mit author evan cohen http evanbtcohen com more info favicon from in the wake of the king http walkingmind evilhat com 2014 03 17 in the wake of the king a head nod to the watcher a byblow of the king and a queen of the sea she has remained apart from the workings of her family more home beneath the waves watching all through water and mirror her ambitions lie outside the eternal kingdom but her secrets are valuable everywhere awesome | raspberry-pi diy home-automation smart-home voice-control | server |
llmon | llmon llmon pronounced lim n is a set of structured data tools optimized for large language models llmonconcentrate convert the structured input into a format that s more concise yet still understandable by llms llmonconcentrate can reduce your json s token usage by half or more while retaining all of the existing information in that structured data to use run replace berries json with your own file npx ts node concentrate ts f berries json yaml tokens for original json docs origtokens png tokens for concentrated yaml docs yamltokens png llmonslice llmonslices are subtrees of a larger data tree that can be fed into a large language model the basic idea is that each slice should be of small enough size to be consumed in a single llm prompt or bite to use run replace berries json with your own file npx ts node slice ts f berries json levelsdown 2 example output slice 0 berries name strawberry scientific name fragaria ananassa color red edible true origin continent north america region eastern united states discovery year 1750 discoverer unknown taste sweet and slightly tart uses jams pies smoothies slice 1 berries name blueberry scientific name vaccinium corymbosum color blue edible true origin continent north america region eastern united states discovery year 1750 discoverer unknown taste sweet and tangy uses pancakes muffins smoothies llmonaide llmonaide is a plain text description of the json data which is easier for large language models to consume to use run replace berries concentrated json with your own file since we are sending the data to the llm here we want to use the concentrated version openai api key your key here npx ts node aide ts f berries concentrated json example output berries are a popular fruit that come in many different varieties strawberries for example have the scientific name fragaria ananassa and are native to the eastern united states they are red in color and have a sweet and slightly tart taste strawberries are often used in jams pies and smoothies blueberries on the other hand have the scientific name vaccinium corymbosum and are native to the eastern united states they are blue in color and have a sweet and tangy taste blueberries are often used in pancakes muffins and smoothies blackberries with the scientific name rubus fruticosus are native to europe and western asia and are black in color they have a sweet and slightly tart taste and are often used in jams pies and cakes raspberries with the scientific name rubus idaeus are native to europe and eastern europe and are red in color they have a sweet and slightly tart taste and are often used in jams pies and cakes gooseberries with the scientific name ribes uva crispa are native to europe and western asia and are green in color they have a tart and slightly sweet taste and are often used in jams pies and sauces cranberries with the scientific name vaccinium oxycoccos are native to the eastern united states and are red in color they have a tart and slightly sweet taste and are often used in sauces juices and jellies boysenberries with the scientific name rubus ursinus idaeus are native to california and are red in color they have a sweet and tart taste and are often used in jams pies and cakes elderberries with the scientific name sambucus nigra are native to europe and western asia and are purple in color they have a tart and slightly sweet taste and are often used in jams syrups and wine mulberries with the scientific name morus are native to china and are black in color they have a sweet and slightly tart taste and are often used in jams pies and wine acai berries with the scientific name euterpe oleracea are native to the amazon rainforest and are purple in color they have a tangy and slightly sweet taste and are often used in smoothie bowls juices and energy bars bilberries with the scientific name vaccinium myrtillus are native to northern europe and are blue in color they have a sweet and tangy taste and are often used in jams pies and wine cloudberries with the scientific name rubus chamaemorus are native to northern europe and are orange in color they have a tangy and slightly sweet taste and are often used in jams pies and liqueurs finally currants with the scientific name ribes are native to northern europe and are red in color they have a tart and slightly sweet taste and are often used in jams jellies and syrups llmonbrothers llmonbrothers are pairs of data and its llmonaide the llmonaide is used for embedding similarity matching and the original llmon is used for question and answer to llms to use run replace berries concentrated json with your own file openai api key your key here npx ts node brothers ts f berries concentrated json example output brothers 0 berries name strawberry scientific name fragaria ananassa color red edible true origin continent north america region eastern united states discovery year 1750 discoverer unknown taste sweet and slightly tart uses jams pies smoothies the strawberry fragaria ananassa is a north american berry that is red in color and is edible it was discovered in the eastern united states in 1750 by an unknown discoverer the strawberry has a sweet and slightly tart taste and is used in jams pies and smoothies brothers 1 berries name blueberry scientific name vaccinium corymbosum color blue edible true origin continent north america region eastern united states discovery year 1750 discoverer unknown taste sweet and tangy uses pancakes muffins smoothies the blueberry scientifically known as vaccinium corymbosum is a north american berry that is native to the eastern united states it was discovered in 1750 by an unknown person and is a popular berry due to its sweet and tangy taste blueberries are edible and can be used in a variety of ways such as pancakes muffins and smoothies they are a blue color and are a popular ingredient in many recipes llmonpeel llmonpeel gets the structure of the json and then uses llms to generate jsonpaths which can than be used to search for the data you re looking for to use run replace berries concentrated json with your own file npx ts node peel ts f berries concentrated json | ai |
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e18-3yp-non-invaisve-glucose-meter | layout home permalink index html please update this with your repository name and project title repository name e18 3yp non invasive glucometer title non invasive glucometer comment this is the standard layout for the project but you can clean this and use your own template non invasive glucometer team e 18 170 karunarathna w k e18170 eng pdn ac lk mailto e18170 eng pdn ac lk e 18 282 ranasinghe r a n s e18282 eng pdn ac lk mailto e18282 eng pdn ac lk e 18 382 weerarathne l d e18382 eng pdn ac lk mailto e18382 eng pdn ac lk sample image docs assets img solution png this is a sample image to show how to add images to your page to learn more options please refer this https projects ce pdn ac lk docs faq how to add an image sample image images sample png table of contents 1 introduction introduction 2 solution architecture solution architecture 3 hardware designs hardware designs 4 testing testing 5 detailed budget detailed budget 6 links links introduction with the rise of global diabetes among the older generation the international diabetes federation idf statistics show that one in every 12 adults in sri lanka are suffering from diabetes severe diabetes can even cause death or serious complications like stroke amputation kidney failure heart attack and heart failure therefore it should be monitored and the way to monitor diabetes is to measure blood glucose levels regularly to keep it under control for a diabetic patient having type 1 diabetes it is recommended to measure blood sugar 4 10 times a day people with type 2 diabetes should take blood sugar testing at least once a day the currently available and mostly used method is the invasive glucometer which is painful can cause damage to the blood vessels and can cause infections they are also costly as the strips need to be replaced over time the aim of this project is to eliminate these drawbacks and build a convenient method for frequent glucose monitoring for that we will be using the near infrared based method which is non invasive portable cost efficient reliable and accurate to a considerable extent solution architecture img src docs assets img sa png width 75 height 75 data and control flow img src docs assets img slide15 jpg width 75 height 75 high level diagram description hardware designs the hardware design comprises of following componets main processing unit atmega328p microcontroller nir emitter of 940 nm wavelength main two sensors nir detector and touch sensor lcd display bluetooth module batteries to supply constant power to the emitter img src docs assets img slide14 jpg width 75 height 75 software designs the software design has both a mobile and a web interface for two differnet users patients and the doctors features of the mobile application are user specific accounts get a graphical representation of real time glucose variation track previous glucose levels issue alerts on users diabetic conditions get health tips to control diabetes features of the web application are for the use of doctors to track the patients blood glucose levels and diabetic conditions track patients diabetic history if the user consents user interface design for the mobile app p float left img src docs assets img ui login page png width 17 height 17 emsp img src docs assets img ui register page1 png width 17 height 17 emsp img src docs assets img ui register page2 png width 17 height 17 emsp img src docs assets img ui glucose conc page png width 17 height 17 emsp img src docs assets img ui graph page png width 17 height 17 emsp p testing img src docs assets img testplan png width 75 height 75 detailed budget img src docs assets img budget png width 75 height 75 conclusion what was achieved future developments commercialization plans for more details visit our official project page from the below links links project repository https github com cepdnaclk e18 3yp non invaisve glucose meter project page https cepdnaclk github io e18 3yp non invaisve glucose meter department of computer engineering http www ce pdn ac lk university of peradeniya https eng pdn ac lk please refer this to learn more about markdown syntax https github com adam p markdown here wiki markdown cheatsheet | os |
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nn_nlp_tutorial | representation learning with neural networks and applications to natural language processing tutorial for more details visit http fagonzalezo github io nn nlp tutorial | ai |
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Web-a-Thon-20 | div align center web a thon 20 web development showdown div p align center a href https github com codemacrocosm web a thon 20 stargazers img src https img shields io github stars codemacrocosm web a thon 20 style flat square alt github stars a a href https github com codemacrocosm web a thon 20 network img src https img shields io github forks codemacrocosm web a thon 20 style flat square alt github forks a a href https github com codemacrocosm web a thon 20 graphs contributors img src https img shields io github contributors codemacrocosm web a thon 20 svg alt github contributors a img src https img shields io github license codemacrocosm web a thon 20 alt github p div align center hall of fame thanks goes to these amazing people for their contributions a href https github com codemacrocosm web a thon 20 graphs contributors img src https contrib rocks image repo codemacrocosm web a thon 20 a div table of contents 1 overview overview 2 how to contribute how to contribute 3 resources for beginners https github com codemacrocosm web a thon 20 edit master readme md resources for beginners 4 suggessions for beginners https github com codemacrocosm web a thon 20 edit master readme md suggestions for beginners 5 star it how to star 6 license license 7 thanks contributors contributors overview get ready to unleash your web development wizardry welcome to web a thon 20 contest hosted by shreya malogi where coding prowess meets creativity in a high voltage web development contest are you prepared to embark on an exhilarating journey showcasing your web development skills like never before hold on tight because this isn t just any contest it s the super duper mega exciting and professionally crafted challenge you ve been eagerly waiting for how to contribute let s make this contest journey as smooth as possible for you 1 fork this repository click the fork button at the top right corner of this repository to create your personal fork 2 clone your fork bash git clone https github com codemacrocosm web a thon 20 git 3 move inside the repository open your terminal and navigate to the repository folder 4 create a new branch it s time to make your mark create a new branch 5 bring your vision to life explore the contribute md file to discover various ways you can contribute to this web development extravaganza 6 commit your masterpiece once you ve poured your creativity into your work commit your changes 7 create separate files for resources and suggestions to share your resources and suggestions create separate markdown files for resources in the file named resources md and add your recommended resources there for suggestions in the file named suggestions md and add your suggestions there 8 create a pull request visit your github repository and click the compare pull request button submit your pull request and feel free to add a personal message resources for beginners in this contest our experienced contributors offer valuable resources to beginners each contributor has shared their resources to study coding and github profile so you can follow from their insights details summary click here summary github profile resources anuj khadka https github com anuj khadka telusko https www youtube com channel uc59k ug2a5ogwirhw4bmleg codewithharry https www youtube com channel ucevmnsshp iviwkknt83cww hemabhagnani https github com hemabhagnani flutter beginner tutorial https www youtube com watch v 1uksr1grtmu list pl4cuxegkcc9jlyyp2aoh6hcwuxfdx6pbj anjalisoni3655 https github com anjalisoni3655 sololearn https www sololearn com prakash sah lab https github com prakash sah lab mr sir g https youtu be wdejn42isey shruthi kotawar https github com shruthi kotawar nptel https nptel ac in sameera 7 https github com sameera 7 khan academy https www khanacademy org shruthi kotawar https github com shruthi kotawar nptel https nptel ac in niharikaponugoti https github com niharikaponugoti edwisely https www edwisely com chandrasangna noundla https github com chandrasangna noundla coursera https www coursera org dragon fly dev https github com dragon fly dev openclassroom https www openclassroom com srujana 55 https github com srujana 55 swoyam https www swoyam org anwarali https github com anwarali free code camp https www freecodecamp org brunopbb https github com brunopbb cbfcursos https www youtube com user canalfessorbruno jyoti88 https github com jyoti88 mr sir g https youtu be wdejn42isey khan academy https www khanacademy org bhargavirudravarapu https github com bhargavirudravarapu unschool https www unschool in sahil k1509 https github com sahil k1509 tech with tim https www youtube com channel uc4jx40jdee tinbkjycv4sg freecodecamp https www youtube com channel uc8butisfwt wl7ev0huk0bq parth93qa https github com parth93qa colt steele https www youtube com c coltsteelecode playlists the new boston https www youtube com user thenewboston gabbyj https github com gabbyj angela yu s https www udemy com course the complete web development bootcamp mike dane https www youtube com playlist list pllaz4kz9dfpmms5lskzbapyxn0bl7emsw shubhani https github com shubhani coursera https www coursera org ak0982 https github com ak0982 interviewbit https www interviewbit com zomsik https github com zomsik automatetheboringstuff https automatetheboringstuff com coderninjakashyap https github com coderninjakashyap coding omega https www youtube com c codingomega sadopsa https github com sadopsa w33schools https www w3schools com noob3426 https github com noob3426 codeforces https codeforces com anushree176 https github com anushree176 geeksforgeeks https www geeksforgeeks org codecademy https www codecademy com sanyammm https github com sanyammm apni kaksha https www youtube com channel ucf7bexjt2zh mmyqob139dg frontend mentor https www frontendmentor io challenges ocoderdude https github com ocoderdude traversy media https www youtube com user techguyweb aastha271100 https github com aastha271100 leetcode https www leetcode com hackerrank https www hackerrank com coursera https www coursera org dekydesigns https github com dekydesigns code pen https codepen io areen161 https github com areen161 sololearn https www sololearn com details suggestions for beginners in this competition seasoned contributors provide valuable guidance to beginners each contributor shares their advice and github profile allowing you to glean insights from their experiences details summary click here summary github profile with link suggestion hemabhagnani50 https github com hemabhagnani50 do not worry that others are better than you just keep in mind that you can and you will uzumakinaruto96 https github com uzumakinaruto96 always believe in yourself abhishek765 https github com abhishek765 learn complete web development from html css js to backend and deployment too follow the link snehakri022 https github com snehakri022 don t give up early it may take weeks to get started anjalisoni3655 https github com anjalisoni3655 explore different fields be it machine learning development cyber security etc at least once don t go with the current trend be yourself mousumidas625 https github com mousumidas625 first go through some videos relating to all the functionalities on github then start by editing some readme files and sending those as pr s you will feel more comfortable in solving the issues and contributing to the domain s of your interest prakash sah lab https github com prakash sah lab don t think coding is hard start learning from the zero level firstly watch some videos and know about the language shruthi kotawar https github com shruthi kotawar never sit for long between programming resolve real hard problems by taking a walk rd91 https github com rd91 for learning anything you don t need to pay for it you can get it on youtube the best place to learn sameera 7 https github com sameera 7 use version control software niharikaponugoti https github com niharikaponugoti learn by doing always play with the code while learning shruthi kotawar https github com shruthi kotawar starting your software development career can be scary but also very exciting github com srujana 55 https github com srujana 55 don t be afraid of coding you have to start from the basics parth93qa https github com parth93qa practice and learn do not worry about your mistakes you will eventually find your way masterbrian99 https github com masterbrian99 no one is perfect that s why ide has autocompletion never give up always be yourself shubhani25 https github com shubhani25 start everything from basics and do not worry at all practicing and doing everything on your own will fill you with confidence and then your hard work will pay off don t ever feel low everything will be by your side vivi3008 https github com vivi3008 persist even when you think you will not succeed imchigi https github com imchigi always play with the code while learning and pick a project you like try to enhance it every time you learn a new concept pahlevikun https github com pahlevikun always learn something new every day harpreet199 https github com harpreet199 understand your field of interest focus on one field and master it while moving towards other technology ocoderdude https github com ocoderdude do not memorize solutions to competitive problems instead understand the logic or the pattern used in the problem try to make your code optimized with good space and time complexity details how to star if you enjoyed this repo or find them inspiring don t forget to the repository your support is appreciated license this project is licensed under the mit license see the license license file for details mit license copyright c 2020 codemacrocosm contributors a big thanks to all the creative minds who have contributed now let the creation begin happy coding | codemacrocosm webathon-20 contribute contest-4 codemacrocosm-contests webdevelopment hacktoberfest hacktoberfest-accepted hacktoberfest2023 | front_end 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Deep-Learning-and-Computer-Vision | deep learning computer vision a z opencv ssd generative adversarial networks https www udemy com computer vision a z you ve definitely heard of ai and deep learning but when you ask yourself what is my position with respect to this new industrial revolution that might lead you to another fundamental question am i a consumer or a creator for most people nowadays the answer would be a consumer but what if you could also become a creator what if there was a way for you to easily break into the world of artificial intelligence and build amazing applications which leverage the latest technology to make the world a better place sounds too good to be true doesn t it but there actually is a way computer vision is by far the easiest way of becoming a creator and it s not only the easiest way it s also the branch of ai where there is the most to create why you ll ask that s because computer vision is applied everywhere from health to retail to entertainment the list goes on computer vision is already a 18 billion market and is growing exponentially just think of tumor detection in patient mri brain scans how many more lives are saved every day simply because a computer can analyze 10 000x more images than a human and what if you find an industry where computer vision is not yet applied then all the better that means there s a business opportunity which you can take advantage of so now that raises the question how do you break into the world of computer vision up until now computer vision has for the most part been a maze a growing maze as the number of codes libraries and tools in cv grows it becomes harder and harder to not get lost on top of that not only do you need to know how to use it you also need to know how it works to maximise the advantage of using computer vision to this problem we want to bring computer vision a z with this brand new course you will not only learn how the most popular computer vision methods work but you will also learn to apply them in practice can t wait to see you inside the class kirill hadelin what are the requirements only high school maths what am i going to get from this course have a toolbox of the most powerful computer vision models understand the theory behind computer vision master opencv master object detection master facial recognition create powerful computer vision applications what is the target audience anyone interested in computer vision or artificial intelligence | ai |
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altschool-sem3-final | altschool of cloud engineering 3rd semester exam submission circleci https dl circleci com status badge img gh onyekachukwu nweke altschool sem3 final tree main svg style svg https dl circleci com status badge redirect gh onyekachukwu nweke altschool sem3 final tree main i this project deploys two applications to the same kubernetes cluster using iac and ci cd approach i the following tools where used for this project which are code kubernetes code an open source container orchestration system for automating software deployment scaling and management code kubectl code a tool used to interact with kubernetes clusters code circleci code is a cloud based ci cd pipeline tool code terraform code is a cloud agnostic iac tool used to provision of the app code prometheus code is a tool used for monitoring and alerting incase things starts get weary code grafana code is an open source analytics and interactive visualization web application used to visualize performance metrics code aws code is the cloud provider used for this project code docker code and code dockerhub code code git code and code github code step1 setting up the infrastructure with terraform h3 terraform backend setup h3 p in the terraform folder we have another folder called backend this folder contains the configuration of where the terraform state file will be stored i created an s3 bucket to store the file and an aws dynamodb to ensure state locking and consistency of the state file p the bucket and dynamodb set up by the circleci pipeline s3 bucket img s3 png dynamodb img dynamodb png h3 setting up the kubernetes iam metrics and logging h3 p in the same terraform folder there is a folder called infrastructure the files in the folder has the configuration to provision an eks cluster create the vpc set up an iam role to secure access to who interacts with the cluster and other networking dependencies like lb and security group p eks cluster img eks png iam role img iam png vpc img vpc png route table img rtb png sg img sg png asg img asg png b also i setted up aws cloudwatch for logging and metrics of the eks cluster b cloudwatch img cw png h3 deployment of application h3 p this folder contains the deployment files which i used to setup domain name for the web application and the microservice application and also the kubernetes manifest file complete demo yaml used to install the apllication on the cluster and exposing of ports used in the various applications p r53 img r53 png p in the microservices application i setted up a service called front end and gave it a loadbalancer also the web application i dockerized it and pushed it to dockerhub built from nginx image and connected it to a mysql db also exposing the service 3306 p step2 dockerization of udagram p in the udagram app folder there is a dockerfile used to dockerize the application and push to docker through the pipeline p dockerhub img docker png step3 ci cd pipeline with circleci ci cd was implemented for this project using circleci the code for the ci cd pipeline can be found in the circleci folder which contains a config yml file containing the code for the ci cd pipeline the ci cd pipeline has six jobs 1 build docker image this job builds the docker image for the portfolio app and pushes the built image to dockerhub image repository 2 create backend state store this job applies the terraform script s in the terraform files backend directory this will create and s3 backend and dynamodb for storing and locking state this job will only execute if the backend branch of the repository is triggered 3 create infrastructure this job applies the terraform script s in the terraform files infrastructure directory this will create the network infrastructure as earlier stated 4 deploy applications this job applies the terraform script s in the terraform files deployment directory this will deploy the two applications to the eks cluster created in the previous job it also creates two subdomains and maps the loadbalancers from the two applications to the subdomains 5 configure monitoring this job applies the terraform script s in the terraform files monitoring directory this will deploy prometheus and grafana to the cluster 6 destroy everything this job will destroy the whole deployment and infrastructure by executing terraform destroy auto approve in the terraform files monitoring terraform files deployment and terraform files infrastructure directories this job will only execute if the destroy branch of the repository is triggered cicd image img cicd png cicd deploy image img deploycicd png the images shows the successful execution of the jobs in the ci cd pipeline after it was triggered step4 provisioning a monitoring system with prometheus and grafana p in the terraform another folder called monitoring has a series of yaml files kubernetes manifest files used to setup monitoring and alerting tools such as prometheus and grafana in the kubernetes using the cloud watch logs to analyze system performance p p in the grafana svc and prometheus svc files i changed code nodeport code to loadbalancer p prometheus access point a target blank href https prometheus onyekachukwuejiofornweke me 9090 targets prometheus url a prometheus img prometheus png grafana access point a target blank href https grafana onyekachukwuejiofornweke me 9090 targets grafana url a grafana img grafana png step5 encryption of sensitive data in the pipeline p i used circleci s envrionment variable to store sensitive data p env img env png view of deployed applications a href https blog onyekachukwuejiofornweke me blog url a blog img blog png a href https socks onyekachukwuejiofornweke me socks url a socks img socks png | cloud |
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traffic-light | traffic lights repository for the course of embedded system and formal verification deadlines embedded at the end of the term date tba formal verif written report and deliverables 20 05 2016 at 23 59 oral defense 27 05 2016 requirements embedded practical details the aim of the project is to design and implement an embedded system there is no fixed assignment each team has to devise a topic to work on and send a description of the project to the teacher by the end of march for aproval a special form is available on the main page for that purpose to be acknowledged the project should clearly demonstrate the ability of the team members to apply concepts tools and techniques studied in at least one of the course chapters for instance using scade to model and validate the system or using uppaal tiga or gvs to synthesise a part of the system or applying a pid controler or enforcing real time constraints thanks to rtai at the end of the term date tba students will hand out a report that presents the project and explains how the model centric approach has been applied the report should be clearly written in english french or dutch adopting a presentation style up to professional standards the report should clearly explain what has been done to meet the particular constraints that have been identified as pertaining to the project correctness real time a project oral defense will take place during the examination session during this defense students are expected to briefly present their project and to make a demo please note that during the defense each team member maybe interrogated about each part of the project in order to assess the individual participation of each member to the project grading will be in accordance to the following scheme 10 points to each group for the analysis 10 points to each group for the implementation 10 points to each student to evaluate his her capability of presenting the project this mark will take into account the common report of the team as well as the answers given by the individual during the oral defense these three marks will be averaged on 20 with the following weights 8 points for the analysis 6 points for the implementation and 6 points for the presentation formal verif goal the goal of the project is to implement a small reactive system provide a formal model of this system verify appropriate properties on this model using techniques presented in class and software verification tools and possibly discover bugs and correct the system using these techniques deliverables written report explaining the system its conception the verification process how it helped and so on and tool files models specifications evaluation quality of the written report and oral defense at the end of the semester please note that all students of the group are expected to be able to explain all parts of the project subject of the project our course is closely linked with infof410 embedded systems design taught by gilles geeraerts in which students have to undertake a larger project synthesizing correct controllers for reactive systems using appropriate tools therefore for all students following both courses we suggest but it is not mandatory that both projects are based on the same reactive system hence students could develop the full project for infof410 and for our course they could implement a smaller controller model it and verify it as discussed before and compare it with the other controller oral defenses could thus be merged the interest for the students is a reduced workload not having to develop two separate applications thus more time to spend on the interesting parts of both projects for all students undertaking a common project or not the choice of the application reactive system to model must be discussed with the teaching staff which must confirm that it is suitable before starting the project deadlines for group formation and project choice will be announced in class and via universit virtuelle | embedded formal-verification | os |
nlp-chatbot-poc | nlp chatbot poc simple chat bot using natural language processing technologies used django django restframework angular2 spacy setup create virtual environment install dependencies and run migrations to setup the app create virtualenv virtualenv env activate env source env bin activate if you re using linux env scripts activate if you re using windows install dependencies chmod x build sh build sh run the application run the server cd chatbox run the server python manage py runserver create superuser python manage py createsuperuser start the client app cd chatroom ng serve testing the parser using cli cd helper test nlp parser logic python parser py references elitedatascience com https elitedatascience com python nlp libraries spacy io https spacy io docs usage models djangoproject com https www djangoproject com django rest framework org http www django rest framework org angular io https cli angular io | chatbot natural-language-processing nlp nlp-parsing python django django-rest-framework angular2 typescript spacy-nlp | ai |
Small-Contact-List-App | small contact list app it is basic contact list app with frontend and backend part of web development | server |
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go-apla | throughput graph https graphs waffle io aplaproject go apla throughput svg https waffle io aplaproject go apla metrics throughput prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com go report card https goreportcard com badge github com aplaproject go apla https goreportcard com report github com aplaproject go apla build status https travis ci org aplaproject go apla svg branch master https travis ci org aplaproject go apla documentation https img shields io badge docs latest brightgreen svg style flat http apla readthedocs io en latest https tokei rs b1 github aplaproject go apla https github com aplaproject go apla https reposs herokuapp com path aplaproject go apla style flat api reference https camo githubusercontent com 915b7be44ada53c290eb157634330494ebe3e30a 68747470733a2f2f676f646f632e6f72672f6769746875622e636f6d2f676f6c616e672f6764646f3f7374617475732e737667 https godoc org github com aplaproject go apla gitter https badges gitter im join 20chat svg https gitter im go apla utm source badge utm medium badge utm campaign pr badge slack status https slack apla io badge svg https slack apla io about apla apla blockchain platform is a secure simple and compliant blockchain infrastructure for the fast growing global collaborative economy it was developed for building digital ecosystems the platform includes an integrated application development environment with a multi level system of access rights to data interfaces and smart contracts for more information about apla visit apla website https apla io we are open to new ideas and contributions and will be happy to see you among our active contributors to the source code documentation or whatever part you find inspiring in apla see our contribution guide https github com aplaproject go apla blob master contributing md for more information getting started you can get started with apla in several ways apla testnet apla testnet is the network for testing purposes you can explore apla features build apps from scratch and test your apps in the real network environment you can explore apla testnet from your browser you don t need to install anything to do so just visit https testapla0 apla io if you want to install apla frontend molis on your computer 1 download the latest apla front release for testnet https github com aplaproject apla front releases 2 follow the instructions in apla front https github com aplaproject apla front repository readme apla quickstart apla quickstart is a compact software package that you can use to deploy the apla blockchain network on a local computer quickstart installs 1 to 5 nodes alongside the platform s client software quickstart is aimed at providing end users with an idea of how apla blockchain works and includes usage examples of graphical interface elements as well as smart contracts quickstart is available for computers running macos and linux apla quickstart for linux and macos https github com aplaproject quick start deploying the apla blockchain platform ready to deploy your own network you can find out how to do that using our apla blockchain network deployment guide https apla readthedocs io en latest howtos deployment html about the backend components apla s backend has the following components go apla service tcp server api server postgresql database system centrifugo notification service postgresql database system each apla node use postgresql database system to store its current state database testing and production environment considerations testing environment you can deploy a single instance of postgresql database system for all nodes in this case you must create postgresql databases for each node all nodes will connect to their databases located on one postgresql instance production environment it is recommended to use a separate instance of postgresql database system for each node each node must connect only to its own postgresql database instance it is not requred to deploy this instance on the same host with other backend components centrifugo notification server centrifugo is a notification service that receives notifications from go apla tcp server and sends them to the frontend molis client so that users can see status of their transactions centrifugo is a unified notification service for all nodes in an apla blockchain platform when molis client connects to a go apla api service it receives the ip address of centrifugo host and connects to it via a websocket testing and production environment considerations testing environment you can deploy centrifugo service on the same host with other backend components it can be a single centrifugo service for all nodes or each node may connect to its own centrifugo instance production environment you must have at least several dedicated centrifugo hosts go apla go apla is the kernel of an apla node it consists of two services tcp server and api server tcp server supports the interaction between apla nodes api server supports the interaction between molis clients and apla nodes testing and production environment considerations testing environment you can deploy go apla service with other backend components on one host production environment you must deploy go apla services on dedicated hosts installation instructions for a detailed guide see apla blockchain network deployment guide https apla readthedocs io en latest howtos deployment html directories in this example backend components are locatesd in the following directories opt apla go apla go apla opt apla go apla node1 node data directory opt apla centrifugo centrifugo prerequisites and dependencies go versions 1 10 x and above centrifugo version 1 8 postgresql versions 10 and above postgres database 1 change user s password postgres to apla s default password bash sudo u postgres psql c alter user postgres with password apla 2 create a node current state database bash sudo u postgres psql c create database apladb centrifugo configuration 1 specify centrifugo secret in the centrifugo configuration file bash echo secret cent secret config json installing go apla 1 download and build the latest release bash go get v github com aplaproject go apla 2 copy the go apla binary from the go workspace to the destination directory opt apla go apla in this example bash cp home go bin go apla opt apla go apla configure the node 1 create the node configuration file bash opt apla go apla go apla config datadir opt apla go apla node1 dbname apladb centsecret cent secret centurl http 10 10 99 1 8000 httphost 10 10 99 1 httpport 7079 tcphost 10 10 99 1 tcpport 7078 2 generate node keys bash opt apla go apla go apla generatekeys config opt apla go apla node1 config toml 3 genereate the first block if you are creating your own blockchain network you must use the test true option otherwise you will not be able to create new accounts bash opt apla go apla go apla generatefirstblock config opt apla go apla node1 config toml test true 4 initialize the database bash opt apla go apla go apla initdatabase config opt apla go apla node1 config toml starting go apla to start the first node backend you must start two services centrifugo and go apla 1 run centrifugo bash opt apla centrifugo centrifugo a 10 10 99 1 p 8000 config opt apla centrifugo config json 2 run go apla bash opt apla go apla go apla start config opt apla go apla node1 config toml | blockchain smart-contracts | blockchain |
mobile-android | mobile android front end mobile development for android | front_end |
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EmbeddedSystemsProject | embeddedsystemsproject project repository for the embedded systems course spring 2022 design of magic curtain invisible cloak using system verilog and c to deploy in cyclone v fpga it s not easy for everyone to fly around on a broomstick but we want to show that it may be possible for one to hide oneself up and remain invisible atleast on video https www fpga4student com 2016 11 image processing on fpga verilog html https github com damdoy fpga image processing | os |
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chainquery | lbry chainquery build status https travis ci org lbryio chainquery svg branch master https travis ci org lbryio chainquery code climate https img shields io codeclimate tech debt lbryio chainquery svg go report card https goreportcard com badge github com lbryio chainquery https goreportcard com report github com lbryio chainquery maintainability https api codeclimate com v1 badges 3f60ac6b2d7e96f93992 maintainability https codeclimate com github lbryio chainquery maintainability github release https img shields io github release lbryio chainquery svg github commits since latest release https img shields io github commits since lbryio chainquery latest svg coverage status https coveralls io repos github lbryio chainquery badge svg branch master https coveralls io github lbryio chainquery branch master prerequisites os specifics osx in order to use wget you will need brew install wget used in build sh scripts build sh chainquery is built for linux by default in build sh scripts build sh so you will need to modify the cross compilation for an osx build be sure to give execute privileges to the scripts scripts you plan to use go make sure you have go 1 11 required for go releaser https goreleaser com ubuntu https launchpad net longsleep archive ubuntu golang backports or https github com golang go wiki ubuntu easier but maybe older recommended linux golang official install guide https golang org doc install tarball harder but up to date osx golang official install guide https golang org doc install macos windows golang official install guide https golang org doc install windows mysql install and run mysql 8 osx brew install mysql create chainquery database create user lbry with password lbry and grant it all permissions on chainquery db lbrycrd install lbrycrdd https github com lbryio lbrycrd releases ensure lbrycrd lbrycrd conf file exists with username and password if you don t have one run mkdir p lbrycrd echo e rpcuser lbryrpc nrpcpassword env lc ctype c lc all c tr dc a za z0 9 dev urandom head c 16 xargs lbrycrd lbrycrd conf run lbrycrdd server daemon txindex conf home lbrycrd lbrycrd conf if you get an error about indexing add the reindex flag for one run you will only need to reindex once configuration chainquery can be configured config default chainqueryconfig toml via toml file running from source go get u github com lbryio chainquery cd go env gopath src github com lbryio chainquery dev sh running from release this will likely eventually be the main supported method of running chainquery in your environment but this sections documentation is a wip so ymmv get a download link for your operating system specific release from the releases page https github com lbryio chainquery releases then use the following command with your download link wget o chainquery zip https example com path to your release zip example wget o chainquery zip https github com lbryio chainquery releases download v1 1 2 chainquery 1 1 2 linux x86 64 zip unzip the package you just downloaded with the following cd unzip chainquery zip your console should show you something similar to the following root 8fe4046b6d46 unzip chainquery zip archive chainquery zip inflating license inflating readme md inflating chainquery of course you don t have to extract all of this stuff to your machines home directory you must use whatever paths you prefer one that could be beneficial is adding these executables into your systems path this is out of the scope of this readme the main chainquery binary should be marked as executable by default but if not you can run the following chmod x chainquery finally running chainquery should be as simple as chainquery serve you can obtain information on the flags in chainqueries main binary by running the following chainquery help the model the model of chainquery at its foundation consists of the fundamental data types found in the block chain this information is then expounded on with additional columns and tables that make querying the data much easier latest schema db chainquery schema sql what does chainquery consist of chainquery consists of 4 main parts the api server the daemon the job scheduler and the upgrade manager api server the api server services either structured queries via defined apis or raw sql against the chainquery mysql database the apis are documented via chainquery apis https lbryio github io chainquery a work in progress daemon the daemon is responsible for updating the chainquery database to keep it in sync with lbrycrd data the daemon runs periodically to check if there are newly created blocks that need to be processed the daemon simply processes the block and its transactions it also handles blockchain reorganizations it will remove the orphaned block data and processing the new blocks from that height it diverged the entry points are daemon iterations daemon daemon go func daemoniteration block processing daemon processing block go func runblockprocessing height uint64 transaction processing daemon processing transaction go func processtx jsontx lbrycrd txrawresult blocktime uint64 job scheduler the job scheduler schedules different types of jobs to update the chainquery database example daemon jobs claimtriesync go these jobs synchronize different areas of the data either to make queries faster or ascertain information that is not directly part of the raw blockchain the example provided is leveraged to handle the status of a claim which is actually stored in the claimtrie of lbrycrd so it runs periodically to make sure chainquery has the most up to date status of claims in the trie the table job status stores the current state of a particular job like when it last synced upgrade manager the upgrade manager handles data upgrades between versions the table application status stores information about the state of the application as it relates to the data api and app versions this is all leveraged by the upgrade manager so it knows what scripts might need to be run to keep the data in sync across deployments the scripts daemon upgrademanager script go are foundation of the upgrade manager daemon upgrademanager upgrade go contributing contributions to this project are welcome encouraged and compensated for more details see https lbry tech contribute https lbry tech contribute the master branch is regularly built and tested but is not guaranteed to be completely stable releases https github com lbryio chainquery releases are created regularly to indicate new official stable release versions developers are strongly encouraged to write unit tests for new code and to submit new unit tests for old code unit tests can be compiled and run with go test from the source directory which should be gopath github com lbryio chainquery all contributions should run the e2e test via e2e ee2e sh this requires docker to be installed updating the generated models we use sqlboiler https github com lbryio sqlboiler to generate our data models based on the db schema if you make schema changes run gen models sh to regenerate the models a note of caution the models are generated by connecting to the mysql server and inspecting the current schema if you made any db schema changes by hand then the schema may be out of sync with the migrations here s the safe way to ensure that the models match the migrations put all the schema changes you want to make into a migration in mysql drop and recreate the db you re using so that it s empty run dev sh this will run all the migrations on the empty db run gen models sh to update the models this process ensures that the generated models will match the updated schema exactly so there are no surprises when the migrations are applied to the live db license this project is mit licensed for the full license see license license security we take security seriously please contact security lbry io regarding any security issues our pgp key is here https lbry com faq pgp key if you need it contact the primary contact for this project is tiger5226 https github com tiger5226 beamer at odysee com | blockchain sql lbry go | blockchain |
CoML | coml coml formerly mlcopilot is an interactive coding assistant for data scientists and machine learning developers empowered on large language models highlight features out of the box interactive natural language programming interface for data mining and machine learning tasks integration with jupyter lab and jupyter notebook built in large knowledge base of machine learning enhancing the ability of solving complex tasks installation bash pip install mlcopilot we can t have the name coml on pypi so we use mlcopilot instead coml in jupyter lab we recommend trying coml in a jupyter lab environment before using coml please make sure that 1 you have exported openai api key sk xxxx in your environment alternatively you can also use a env file 2 use load ext coml in your notebook to active coml extension then we have provided several commands to assist your journey with interactive coding in jupyter lab 1 coml task to prompt coml to write a cell for your task assets demo coml gif 2 comlfix to fix the cell just above the current cell you can also use comlfix reason to provide details for what s wrong assets demo comlfix gif 3 comlinspire to inspire you with a cell describing what to do next assets demo comlinspire gif limitations currently coml only supports jupyter lab and classical jupyter notebook nbclassic we are still working on supports of newer jupyter notebook jupyter vscode and google colab coml uses gpt 3 5 turbo 16k model in its implementation there is no way to change the model for now the cost of using this model is around 0 04 per request please be aware of this cost coml config agent coml config agent is the implementation of mlcopilot https arxiv org abs 2304 14979 which can suggest a ml configuration within a specific task for a specific task currently it is an independent component residing in coml configagent in the future we will integrate it into the coml system assets demo gif todo the demo needs an update extra preparation steps please follow the steps to use coml config agent 1 clone this repo git clone repo url cd coml 2 put assets coml db in your home directory cp assets coml db coml coml db 3 copy coml env template to coml env and put your api keys in the file command line utility currently it can only be invoked independently you can use the following command line coml configagent space space task task if you feel uncertain about what to put into space or task see the demo above or try the interactive usage below coml configagent interactive api usage python from coml configagent suggest import suggest space import space your space id task desc your task description for new task suggest configs knowledge suggest space task desc development development documentation stays here for now it shall be moved to a separate document later project structure important files and folders coml assets data examples demos coml python package examples example scripts install json jupyter lab extension installation file package json jupyter lab extension package file pyproject toml python package configuration src jupyter lab extension source code test python package tests tsconfig json jupyter lab extension typescript configuration installation and uninstallation you can use the following command for development installation pip install e dev if you are to develop jupyter lab extension you will also need to install nodejs and npm and run the following command link your development version of the extension with jupyterlab jupyter labextension develop overwrite rebuild extension typescript source after making changes jlpm run build to uninstall you can run the following commands bash server extension must be manually disabled in develop mode jupyter server extension disable coml uninstall the python package pip uninstall mlcopilot in development mode you will also need to remove the symlink created by jupyter labextension develop command to find its location you can run jupyter labextension list to figure out where the labextensions folder is located then you can remove the symlink named coml within that folder packaging bash hatch build citation if you find this work useful please cite the paper as below article zhang2023mlcopilot title mlcopilot unleashing the power of large language models in solving machine learning tasks author zhang lei and zhang yuge and ren kan and li dongsheng and yang yuqing journal arxiv preprint arxiv 2304 14979 year 2023 license the entire codebase is under mit license license | automated-machine automl hyperparameter-optimization large-language-models llm machine-learning copilot data-science jupyter jupyter-lab | ai |
DataEngineering | dataengineering contains cloud data engineering projects for azure and aws and gcp | cloud |
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ESD1_Lab1 | esd1 lab1 embedded systems design lab 1 intro into using the de1 soc board this lab was just used to navigate the toolchain the code was given by the professor and slightly modified as per lab guidlelines | os |
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deep-learning-cv | welcome this repo contains material for the application of deep leanrning in computer vision course held in national cheng kung university during 3 3 4 28 2018 thanks to google s generosity you can practice making deep neural net on google colaboratory using free gpu resource provided by google an instruction on how to do this can be found here week2 project syllabus for the course is as follows first week basics of deep neural network slide for the first week is prepared using gitpitch view slide here https gitpitch com chaoyuanyeh deep learning cv 1 introduction to programming tools 2 matrix math and numpy 3 calculus 4 image processing and opencv 5 perceptron 6 machine learning basics 7 forward propagation backward propagation 8 fully connected neural network second week convolutional neural network 1 convolutional neural network general concept 2 loss function 3 optimizer https docs google com presentation d 19bq4ty85kedzjwvnhjgso qqegt8wntglxuj6zvpnnm edit usp sharing 4 introduction to deep learning frameworks 5 convolutional neural network for image classification third week strategies and techniques for training convolutional neural networks 1 visualization of training progress 2 hyperparameter tuning 3 batch normalization https docs google com presentation d 1xlkmv zj1n0i8hp8p 9hyzt3gtvijnxewh6fyazlyqg edit usp sharing 4 regularization https docs google com presentation d 1ltljylp8n3pshnmysbrwpkqzcijrre q6si0nt knca edit usp sharing 5 dropout https docs google com presentation d 1zbluy5ccsvx43ro520su1d9hbjbp6r60jeqxbd o3ty edit usp sharing 6 data augmentation fourth week classical convolutional neural network architecture and other applications 1 introduction to keras 2 cnn case studies 3 cnn for object detection and image segmentation 4 introduction of rnn https docs google com presentation d 19cbdfmn dvd9ulimwvjniaw9qybsswlvltihefytisw edit usp sharing 5 sequence to sequence model 6 rnn for computer vision online resources stanford cs231n convolutional neural network for visual recognition by fei fei li http cs231n stanford edu caltech learning from data by yaser abu mustafa https work caltech edu telecourse html deep learning by ian goodfellow yoshua bengio and aaron courville http www deeplearningbook org introduction to machine learning by udacity https www udacity com course intro to machine learning ud120 deep learning by udacity https www udacity com course deep learning ud730 learn with google ai https ai google education modal active none | computer-vision machine-learning deep-learning convolutional-neural-networks | ai |
MasterBlockchain | https github com tianmingyun masterbitcoin2cn https github com zcc0721 masterblockchain blob master cn preface md https github com zcc0721 masterblockchain blob master second edition changes md https github com zcc0721 masterblockchain blob master preface md https github com zcc0721 masterblockchain blob master glossary md https github com zcc0721 masterblockchain blob master trans preface md https github com zcc0721 masterblockchain blob master ch01 md https github com zcc0721 masterblockchain blob master ch02 md https github com zcc0721 masterblockchain blob master ch03 md https github com zcc0721 masterblockchain blob master ch04 md https github com zcc0721 masterblockchain blob master ch05 md https github com zcc0721 masterblockchain blob master ch06 md https github com zcc0721 masterblockchain blob master ch07 md https github com zcc0721 masterblockchain blob master ch08 md https github com zcc0721 masterblockchain blob master ch09 md https github com zcc0721 masterblockchain blob master ch10 md https github com zcc0721 masterblockchain blob master ch11 md https github com zcc0721 masterblockchain blob master ch12 md 1 https github com zcc0721 masterblockchain blob master appdx bitcoinwhitepaper md 2 https github com zcc0721 masterblockchain blob master appdx scriptops md 3 bips https github com zcc0721 masterblockchain blob master appdx bips md 4 https github com zcc0721 masterblockchain blob master appdx segwit md 5 bitcore https github com zcc0721 masterblockchain blob master appdx bitcore md 6 pycoin https github com zcc0721 masterblockchain blob master appdx pycoin md 7 https github com zcc0721 masterblockchain blob master appdx bx md 8 https github com zcc0721 masterblockchain blob master appdx colored coins md | blockchain |
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blockchain | blockchain build status https travis ci org alfg blockchain svg branch master https travis ci org alfg blockchain go client for the blockchain data api https blockchain info api blockchain api documentation https godoc org github com alfg blockchain installation go get github com alfg blockchain example go package main import github com alfg blockchain fmt func main c e blockchain new resp e c getaddress 162fjqu7rydojnejcde6zrpdupalcv9hhq if e nil fmt print e fmt println resp hash160 fmt println resp address fmt println resp ntx fmt println resp totalreceived fmt println resp totalsent fmt println resp finalbalance for i range resp txs fmt println resp txs i result for j range resp txs i inputs fmt println resp txs i inputs j sequence fmt println resp txs i inputs j prevout spent license mit | bitcoin blockchain go | blockchain |
iotmap | iotmap iotmap is a tool that models iot networks using one or multiple protocols simultaneously this is work in progress as a part of a phd thesis on internet of things security this repository is regularly updated as new results are obtained this project supports 3 protocol as this time ble zigbee and os4i more are coming requirements python 3 requirements python 3 5 scapy can be installed through the requirements file but building from the latest source https github com secdev scapy is recommended neo4j 1 7 6 docopt 0 6 2 prompt toolkit 3 0 5 terminaltables 3 1 0 pycryptodomex 3 9 7 you can use the requirements txt file to install the packages pip3 install r requirements txt os libraries libgcrypt20 dev needed for sniffing capabilities through killerbee depending on the system you used debian based os archlinux based os you can use sudo apt get install libgcrypt20 dev debian based distribs sudo pacman s libgcrypt archlinux based distribs installation clone this repo and install all requirements described above for any distrib git clone https github com algosecure iotmap git cd iotmap sudo pip install r requirements txt if debian based sudo apt get install libgcrypt20 dev if archlinux based sudo pacman s libgcrypt now we gonna install neo4j most distributions ships neo4j through the built in package manager however it may be simpler to install and use it from the tarball cd path to iotmap cd database replace the version number with the latest in the url if necessary wget o neo4j community tar https neo4j com artifact php name neo4j community 3 5 9 unix tar gz mkdir neo4j community tar xvf neo4j community tar c neo4j community strip components 1 first run for the first run of the project you need to define a username and a password for the database start the database with the following cd database neo4j community bin neo4j console neo4j will start and is accessible at http localhost 7474 default username and password are neo4j and neo4j respectively iotmap uses the default username and iotmap for password if you want to set different credentials you must update the values in core databasecontroller py at line 46 as follows model model bolt http localhost 7474 username password how to use iotmap a more detailed documentation on how to use iotmap with an example is available here doc started md start the framework python3 iotmap py iotmap will start the neo4j database before running however the database is not immediately available sometimes the sleep of 10 seconds is enough for the database to be available sometimes not and you need to rerun iotmap iotmap provides 3 modules database modelling and sniffing the sniffing module is a work in progress and not fully operational to switch between modules simply type the name of the module python3 iotmap py starting the database database is available at http localhost 7474 iiiiiiiiii tttttttttttttttttttttttmmmmmmmm mmmmmmmm i i t tm m m m i i t tm m m m ii ii t tt tt tm m m m i i oooooooooootttttt t t ttttttm m m m aaaaaaaaaaaaa ppppp ppppppppp i i oo oo t t m m m m a a p ppp p i i o o t t m m m m m m aaaaaaaaa ap p i i o ooooo o t t m m m m m m m m a app ppppp p i i o o o o t t m m m m m m m aaaaaaa a p p p p i i o o o o t t m m m m m m aa a p p p p i i o o o o t t m m m m m m a aaaa a p p p p i i o o o o t t m m mmmmm m ma a a a p p p p ii iio ooooo o tt tt m m m ma a a a p ppppp p i io o t t m m m ma aaaa a p p i i oo oo t t m m m m a aa ap pp iiiiiiiiii ooooooooooo ttttttttttt mmmmmmmm mmmmmmmm aaaaaaaaaa aaaap pppppppp p p p p p p p p p p ppppppppp version 0 1 iotmap help core commands commands description database use database mode sniffing use sniffing mode exploit use exploit mode iotmap each module and functions provide a help menu to list the functions available and how to use them database module this module manages and interacts with the neo4j database iotmap database iotmap database help core commands commands description database use database mode sniffing use sniffing mode exploit use exploit mode database commands interact with the neo4j database list of available commands addnodes cleardatabase exportdb getnodes help importdb importpcaps mergenodes removenode for more information about any commands hit command name h iotmap database to populate the database you can import an existing database or pcaps files importpcaps converts pcaps to our unified format used to generate the modelling this module uses different extractors according to the protocol given in argument that you can find in the extractors folder the main program chooses the appropriate extractor then runs the packets generator gen packets py in a multithreading way to generate the pcap with the unified format modelling module iotmap modelling help core commands commands description database use database mode sniffing use sniffing mode exploit use exploit mode modelling commands map the network of iot devices detected by sniffing list of available commands appgraph compareto dlgraph help nwkgraph option run set transgraph for more information about any commands hit command name h iotmap modelling this program starts the neo4j database before creating the modelling once the database is up the modelling begins it starts with the analysis of the pcap given in input to extract and create nodes then edges that link nodes after the 4 graphs created the result can be viewed on the web application provided by neo4j available at http localhost 7474 you can also request the database directly from the web application by using cypher request in the input box neo4j imports doc images application graph png | server |
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CS-T680-CNSE | cs t680 cnse cloud native software engineering welcome to the cs t680 cnse repository this repository is dedicated to storing all my assignments for the cloud native software engineering course it focuses on using golang for developing cloud native applications throughout the course i will be submitting my assignments in this repository please follow assignment specific instructions in each directory s readme md let s dive into the world of cloud native software engineering together and sharpen our skills | cloud |
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KosmosG | multi modality agorabanner png https discord gg qutxnk2nmf kosmosg my implementation of the model kosmosg from kosmos g generating images in context with multimodal large language models installation pip install kosmosg usage python import torch from kosmosg main import kosmosg usage img torch randn 1 3 256 256 text torch randint 0 20000 1 1024 model kosmosg output model img text print output architecture text image kosmosg text tokens with multi modality understanding license mit todo create aligner in pytorch create diffusion module integrate these pieces create a training script | attention-is-all-you-need attention-mechanism attention-mechanisms computer-vision multimodal multimodal-learning | ai |
alpine-sqs | alpine sqs alpine sqs banner https raw githubusercontent com roribio alpine sqs master banner png https images microbadger com badges image roribio16 alpine sqs svg https microbadger com images roribio16 alpine sqs get your own image badge on microbadger com https images microbadger com badges version roribio16 alpine sqs svg https microbadger com images roribio16 alpine sqs get your own version badge on microbadger com docker pulls https img shields io docker stars roribio16 alpine sqs svg https hub docker com r roribio16 alpine sqs docker pulls https img shields io docker pulls roribio16 alpine sqs svg https hub docker com r roribio16 alpine sqs standard readme compliant https img shields io badge readme 20style standard brightgreen svg style flat square https github com richardlitt standard readme dockerized elasticmq server web ui over alpine linux for local development alpine sqs provides a containerized java implementation of the amazon simple queue service aws sqs it is based on elasticmq running alpine linux and the oracle java 8 server jre it is compatible with aws s api cli as well as the amazon java sdk this allows for quicker local development without having to incurr in infrastructure costs the goal of this repository is to maintain an updated docker environment for elasticmq with an integrated web ui for visualizing queues and messages table of contents background background install install usage usage maintainer maintainer contribute contribute license license background when searching for existing local implementations of sqs i came across a docker image by vsouza https github com vsouza called docker sqs local https github com vsouza docker sqs local with over 11k pulls at the time this introduced me to elasticmq which this project is based on and is described by it s creators as a message queue system offering an actor based scala and an sqs compatible rest query interface using his work as inspiration i decided to improve upon it by implementing the following reduce the docker image foot print as much as possible automatically update to the latest elasticmq server integrated ui for message queue visualization automatic tests builds work in progress thorough documentation see also for more information on the different projects this work is based on please visit elasticmq https github com adamw elasticmq by adamw https github com adamw sqs insight https github com kobim sqs insight fork by kobim https github com kobim fork of finanzcheck s now archived sqs insight https github com finanzcheck sqs insight docker alpine java https github com anapsix docker alpine java by anapsix https github com anapsix install pre requisites to be able to use this environment please make sure you have installed the latest version of docker https docs docker com engine installation if you intend to build the environment yourself it is recommended that you also install the latest version of docker compose https docs docker com compose install installation methods you can obtain the environment in two ways the easiest is to pull the image directly from docker hub also you may clone this repository and build run it using docker compose 1 pulling from docker hub docker pull roribio16 alpine sqs 2 building from scratch git clone https github com roribio alpine sqs git usage running the environment depending on how you chose to install the environment you can initialize it in three ways 1 docker run method use this method if you re pulling directly from docker hub and do not have a docker compose yml file docker run name alpine sqs p 9324 9324 p 9325 9325 d roribio16 alpine sqs latest custom configuration files may be used to override default behaviors you can mount a volume mapping the container s opt custom directory to a folder in your host machine where the custom configuration files are stored providing for sake of example that in your host machine directory opt alpine sqs you have both elasticmq conf and sqs insight conf files you can run the container with docker run name alpine sqs p 9324 9324 p 9325 9325 v opt alpine sqs opt custom d roribio16 alpine sqs latest for any configuration file not explicitly included in the container s opt custom directory alpine sqs will fall back to using the default configuration files listed here https github com roribio alpine sqs tree master opt 2 docker compose up method if you ve cloned the repository you can still take advantage of the image present in docker hub by running the container from the default docker compose yml file this will pull the pre built image from the public registry and run it with the same values stated in the previous method docker compose up d 3 docker compose up build method to build the image from scratch and then run the corresponding container use this method docker compose f docker compose build up d build note to use any of the docker compose methods you need to clone this repository as well as have docker compose installed note 2 depending on your platform you may need to adjust how you declare mounted volumes you can find instructions for your specific platform here https github com roribio alpine sqs wiki sharing files with host machine working with queues elasticmq provides an amazon sqs compatible interface this means you may use the aws command line tool api calls and the java sdk to interact with local queues the same as if interacting with the actual sqs default queue the default configuration provisions elasticmq with a initial queue of the same name at run time this allows you to start pushing messages to the queue without further configuration to make use of this queue point your client to http localhost 9324 queue default dead letter queue the default dead letter queue is called dlq configured in opt elasticmq conf https github com softwaremill elasticmq automatically creating queues on startup sending a message to send messages to a queue you need to specify the new endpoint url and queue url along with the message payload the following example uses the aws cli to send a message to the default queue aws endpoint url http localhost 9324 sqs send message queue url http localhost 9324 queue default message body hello queue viewing messages to view messages navigate to the web ui sqs insight https github com finanzcheck sqs insight by pointing your web browser to http localhost 9325 you can also poll for messages from the command line like so aws endpoint url http localhost 9324 sqs receive message queue url http localhost 9324 queue default wait time seconds 10 creating new queues you can create new queues by using the command line or configuring elasticmq directly aws cli aws endpoint url http localhost 9324 sqs create queue queue name newqueue edit elasticmq configuration file navigate to the directory where the configuration files reside and edit the elasticmq conf file to add a new entry for each queue to the queue block queues default defaultvisibilitytimeout 10 seconds delay 5 seconds receivemessagewait 0 seconds newqueue defaultvisibilitytimeout 10 seconds delay 5 seconds receivemessagewait 0 seconds note the configuration directory location inside the container is located at opt config if you mounted that volume onto your host you can also find the configuration files there after editing the elasticmq conf file you need to restart the elasticmq server by running the supervisorctl restart elasticmq command inside the container if you re editing the configuration file outside of the container use this command docker exec it alpine sqs sh c supervisorctl restart elasticmq registering new queues with the ui to be able to visualize newly created queues you need to edit the sqs insight conf file to register the new queue with the ui server edits to this file are automatically detected by the server and does not require a restart configure a new endpoint like this endpoints key notvalidkey secretkey notvalidsecret region eu central 1 url http localhost 9324 queue default key notvalidkey secretkey notvalidsecret region eu central 1 url http localhost 9324 queue newqueue all the fields except the url field are required by sqs insight to function but are not used when pointing it to a local queue server this means that the values in those fields are not relevant for the ui to work correctly consult the aws cli command reference http docs aws amazon com cli latest reference sqs index html cli aws sqs or the aws sdk for java http docs aws amazon com sdk for java v1 developer guide examples sqs message queues html guide for more examples and information maintainer ronald e oribio r roribio https github com roribio contribute prs are accepted and encouraged please direct any questions requests or comments to the issues https github com roribio alpine sqs issues section of this project note if editing this readme please conform to the standard readme https github com richardlitt standard readme specification license copyright 2017 ronald e oribio r this project is licensed under the gnu general public license version 3 0 see the license license file for details | docker aws-sqs elasticmq webui sqs elasticmq-server sqs-insight alpine | front_end |
azure-sphere-samples | azure sphere samples this repository contains samples for the azure sphere platform https www microsoft com azure sphere that are created and maintained by microsoft you may also be interested in https github com azure azure sphere hardware designs maintained hardware designs for azure sphere https github com azure azure sphere gallery gallery of further unmaintained content from microsoft https github com azure azure sphere tools useful tools for device builders and application developers using the azure sphere platform including the manufacturing tools which used to live in this repository please also see the codethink mediatek and azure rtos repositories for more sample applications for the mt3620 real time cores https github com codethinklabs mt3620 m4 samples https github com mediatek labs mt3620 m4 software https github com azure samples azure rtos on azure sphere mediatek mt3620 using the samples see the azure sphere getting started https www microsoft com azure sphere get started page for details on getting an azure sphere development kit https aka ms azurespherehardware and setting up your pc for development you should complete the azure sphere installation quickstarts https learn microsoft com azure sphere install overview and tutorials https learn microsoft com azure sphere install qs overview to validate that your environment is configured properly before using the samples here clone this entire repository locally the repository includes the hardware definition files hardwaredefinitions that are required to run the samples on a range of azure sphere hardware each folder within the samples subdirectory contains a readme md file that describes the samples therein follow the instructions for each individual sample to build and deploy it to your azure sphere hardware to learn about the features that the sample demonstrates contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately for example label comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments licenses for information about the licenses that apply to a particular sample see the license and readme md files in each subdirectory samples by category categories samples application lifecycle deferred update https github com azure azure sphere samples tree main samples deferredupdate deferredupdate highlevelapp br device to cloud https github com azure azure sphere samples tree main samples devicetocloud externalmculowpower br power down https github com azure azure sphere samples tree main samples powerdown powerdown highlevelapp external mcus device to cloud https github com azure azure sphere samples tree main samples devicetocloud externalmculowpower br external mcu update https github com azure azure sphere samples tree main samples externalmcuupdate br wi fi setup via ble https github com azure azure sphere samples tree main samples wifisetupanddevicecontrolviable microsoft azure azure iot https github com azure azure sphere samples tree main samples azureiot multi core samples hello world https github com azure azure sphere samples tree main samples helloworld br inter core communication https github com azure azure sphere samples tree main samples intercorecomms networking time certificates https github com azure azure sphere samples tree main samples certificates cert highlevelapp br custom ntp https github com azure azure sphere samples tree main samples customntp customntp highlevelapp br dhcp https github com azure azure sphere samples tree main samples dhcp br dns service discovery https github com azure azure sphere samples tree main samples dnsservicediscovery br https curl easy https github com azure azure sphere samples tree main samples https https curl easy br https curl multi https github com azure azure sphere samples tree main samples https https curl multi br private network services https github com azure azure sphere samples tree main samples privatenetworkservices br system time https github com azure azure sphere samples tree main samples systemtime br wi fi https github com azure azure sphere samples tree main samples wifi wifi highlevelapp br wi fi setup via ble https github com azure azure sphere samples tree main samples wifisetupanddevicecontrolviable br wolfssl api https github com azure azure sphere samples tree main samples wolfssl wolfssl highlevelapp peripherals sensors devices hello world https github com azure azure sphere samples tree main samples helloworld br adc https github com azure azure sphere samples tree main samples adc adc highlevelapp br gpio https github com azure azure sphere samples tree main samples gpio gpio highlevelapp br i2c https github com azure azure sphere samples tree main samples i2c i2c lsm6ds3 highlevelapp br pwm https github com azure azure sphere samples tree main samples pwm pwm highlevelapp br spi https github com azure azure sphere samples tree main samples spi spi lsm6ds3 highlevelapp br uart https github com azure azure sphere samples tree main samples uart uart highlevelapp power memory external mcu low power https github com azure azure sphere samples tree main samples devicetocloud externalmculowpower br power down https github com azure azure sphere samples tree main samples powerdown powerdown highlevelapp storage mutable storage https github com azure azure sphere samples tree main samples mutablestorage | iot samples | server |
chameleon | chameleon opensuse design system for web based on bootstrap 4 https getbootstrap com extend remix icon https remixicon com comply opensuse brand guidelines https opensuse github io branding guidelines use graphics from opensuse artwork https github com opensuse artwork combine eos design system https opensuse eosdesignsystem com use cdn chameleon asset files are hosted at https static opensuse org to improve webpage loading speed this is the most recommended way to use chameleon html chameleon style link rel stylesheet href https static opensuse org chameleon 3 0 dist css chameleon css jquery slim script src https static opensuse org chameleon 3 0 dist js jquery slim js defer script bootstrap script script src https static opensuse org chameleon 3 0 dist js bootstrap bundle js defer script chameleon script script src https static opensuse org chameleon 3 0 dist js chameleon js defer script if the site is based on bootstrap 3 here is a minimal build to use html chameleon style link rel stylesheet href https static opensuse org chameleon 3 0 dist css chameleon bs3 css chameleon script script src https static opensuse org chameleon 3 0 dist js chameleon bs3 js defer script script src https static opensuse org chameleon 3 0 dist js chameleon js defer script use npm if the website requires a lot of sass variables and mixins from chameleon and bootstrap then you need to install the package locally bash npm install save opensuse chameleon here are different build system you can use webpack gulp grunt rollup but ways to import scss and js files are similiar scss scss import opensuse chameleon js cjs require opensuse chameleon es6 import opensuse chameleon examples here are already several websites integrated with chameleon https news opensuse org https en opensuse org https software opensuse org | os |
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homework-front-end | eaze front end homework our front end code challenge for engineering applicants project using the github api and your framework of choice display a list of your public github repos include the description and sort them by number of watchers most popular first folder structure we ve included all of the boilerplate to get started with this project using react feel free to use this structure if you d like if you d prefer to use something other than react or you d prefer a different project structure go ahead and make any changes you d like my app readme md index html favicon ico node modules package json src app css app js index css index js logo svg for the project to build these files must exist with exact filenames index html is the page template favicon ico is the icon you see in the browser tab src index js is the javascript entry point you can delete or rename the other files you may create subdirectories inside src for faster rebuilds only files inside src are processed by webpack you need to put any js and css files inside src or webpack won t see them you can however create more top level directories they will not be included in the production build so you can use them for things like documentation known issue you may encounter an issue where changing a file inside src doesn t trigger a recompilation most likely this happens because the path in your filesystem differs in its casing from the path you imported for example if a file is called app js but you are importing app js the watcher might not recognize changes to it we are considering https github com facebookincubator create react app issues 240 enforcing some checks to prevent this if this doesn t help check out the page on troubleshooting watching https webpack github io docs troubleshooting html watching available scripts in the project directory you can run npm start runs the app in the development mode br open http localhost 3000 http localhost 3000 to view it in the browser the page will reload if you make edits br you will also see any lint errors in the console npm run build builds the app for production to the build folder br it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes br your app is ready to be deployed npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it how to install a dependency the project includes react and reactdom as dependencies it also includes a set of scripts used by create react app as a development dependency you may install other dependencies for example react router with npm npm install save library name import a component this project setup supports es6 modules thanks to babel while you can still use require and module exports we encourage you to use import and export http exploringjs com es6 ch modules html instead for example button js js import react component from react class button extends component render export default button don t forget to use export default dangerbutton js js import react component from react import button from button import a component from another file class dangerbutton extends component render return button color red export default dangerbutton be aware of the difference between default and named exports http stackoverflow com questions 36795819 react native es 6 when should i use curly braces for import 36796281 36796281 it is a common source of mistakes we suggest that you stick to using default imports and exports when a module only exports a single thing for example a component that s what you get when you use export default button and import button from button named exports are useful for utility modules that export several functions a module may have at most one default export and as many named exports as you like learn more about es6 modules when to use the curly braces http stackoverflow com questions 36795819 react native es 6 when should i use curly braces for import 36796281 36796281 exploring es6 modules http exploringjs com es6 ch modules html understanding es6 modules https leanpub com understandinges6 read leanpub auto encapsulating code with modules add a stylesheet this project setup uses webpack https webpack github io for handling all assets webpack offers a custom way of extending the concept of import beyond javascript to express that a javascript file depends on a css file you need to import the css from the javascript file button css css button padding 20px button js js import react component from react import button css tell webpack that button js uses these styles class button extends component render you can use them as regular css styles return div classname button this is not required for react but many people find this feature convenient you can read about the benefits of this approach here https medium com seek ui engineering block element modifying your javascript components d7f99fcab52b however you should be aware that this makes your code less portable to other build tools and environments than webpack in development expressing dependencies this way allows your styles to be reloaded on the fly as you edit them in production all css files will be concatenated into a single minified css file in the build output if you are concerned about using webpack specific semantics you can put all your css right into src index css it would still be imported from src index js but you could always remove that import if you later migrate to a different build tool post process css this project setup minifies your css and adds vendor prefixes to it automatically through autoprefixer https github com postcss autoprefixer so you don t need to worry about it for example this css app display flex flex direction row align items center becomes this css app display webkit box display ms flexbox display flex webkit box orient horizontal webkit box direction normal ms flex direction row flex direction row webkit box align center ms flex align center align items center there is currently no support for preprocessors such as less or for sharing variables across css files add images and fonts with webpack using static assets like images and fonts works similarly to css you can import an image right in a javascript module this tells webpack to include that image in the bundle unlike css imports importing an image or a font gives you a string value this value is the final image path you can reference in your code here is an example js import react from react import logo from logo png tell webpack this js file uses this image console log logo logo 84287d09 png function header import result is the url of your image return img src logo alt logo export default function header this works in css too css logo background image url logo png webpack finds all relative module references in css they start with and replaces them with the final paths from the compiled bundle if you make a typo or accidentally delete an important file you will see a compilation error just like when you import a non existent javascript module the final filenames in the compiled bundle are generated by webpack from content hashes if the file content changes in the future webpack will give it a different name in production so you don t need to worry about long term caching of assets please be advised that this is also a custom feature of webpack it is not required for react but many people enjoy it and react native uses a similar mechanism for images however it may not be portable to some other environments such as node js and browserify if you prefer to reference static assets in a more traditional way outside the module system please let us know in this issue https github com facebookincubator create react app issues 28 and we will consider support for this deploy github pages note this feature is available with react scripts 0 2 0 and higher first open your package json and add a homepage field it could look like this js name my app homepage http myusername github io my app now whenever you run npm run build you will see a cheat sheet with a sequence of commands to deploy to github pages sh git checkout b gh pages git add f build git commit am rebuild website git push origin gh pages git subtree push prefix build origin gh pages git checkout you may copy and paste them or put them into a custom shell script you may also customize them for another hosting provider note that github pages doesn t support routers that use the html5 pushstate history api under the hood for example react router using browserhistory this is becasue when there is a fresh page load for a url like http user github io todomvc todos 42 where todos 42 is a frontend route the github pages server returns 404 because it knows nothing of todos 42 if you want to add a router to a project hosted on github pages here are a couple of solutions you could switch from using html5 history api to routing with hashes if you use react router you can switch to hashhistory for this effect but the url will be longer and more verbose for example http user github io todomvc todos 42 k yknaj read more https github com reactjs react router blob master docs guides histories md histories about different history implementations in react router alternatively you can use a trick to teach github pages to handle 404 by redirecting to your index html page with a special redirect parameter you would need to add a 404 html file with the redirection code to the build folder before deploying your project and you ll need to add code handling the redirect parameter to index html you can find a detailed explanation of this technique in this guide https github com rafrex spa github pages testing use whatever testing library you prefer the tests should be run using npm test | front_end |
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volcano | volcano logo https raw githubusercontent com volcano volcano master public assets img logo large png introduction volcano is an api first billing solution that is capable of interfacing with a variety of payment gateways volcano offers both a robust restful api https github com volcano volcano wiki api as well as a fully featured front end control panel volcano control panel https raw githubusercontent com volcano volcano master public assets img screenshot customer png mission volcano is meant to be a flexible gateway agnostic billing system the system can be setup with one or more sellers each of which has its own set of configurable products and customer base volcano s core elements include sellers customers products product options product fees orders and transactions current goals for the project include additional features for these core elements and new light weight crm tools such as customer support ticket management design volcano is built on top of the fuelphp http fuelphp com framework the app is designed with multitenancy http en wikipedia org wiki multitenancy in mind and heavily leverages the adapter pattern http en wikipedia org wiki adapter pattern for multi gateway support and the service pattern http en wikipedia org wiki service layers pattern for the core structure that powers both the api and front end control panel additionally both the api and control panel use the same core validation classes volcano supports event based callbacks https github com volcano volcano wiki callbacks for example volcano can post data to an external callback url when a new customer is created this allows 3rd party apps to handle their own email product messaging and feature acl event callbacks can be setup via the api https github com volcano volcano wiki api callbacks or the control panel settings page installation first ensure that your system meets fuelphp s minimum requirements http fuelphp com docs requirements html next clone the repo git clone b master https github com volcano volcano git volcano initialize all submodules cd volcano git submodule update init recursive install composer https getcomposer org doc 00 intro md install composer dependencies php composer phar install configuration app create a new database add these new database credentials to the appropriate database environment config s fuel app config environment db php run the setup task http fuelphp com docs packages oil refine html sets file permissions and runs migrations php oil r setup setup the statistics and recurring tasks as crons http en wikipedia org wiki cron examples that run every night something like this 00 00 fuel env production usr bin php oil r recurring 00 01 fuel env production usr bin php oil r statistics optional run the simulate task to auto generate faux seller product customer and order data this will allow you to more easily test out various control panel features run the statistics task to compute stats for the simulated data php oil r simulate php oil r statistics api for api development and testing https github com volcano volcano wiki api you ll want to first create a seller see usage below and then create an api key via the control panel settings page next copy the new seller api key and add it to fuel app config development api php so that you don t have to specify the api key param when testing locally usage app you ll be redirected to your domain setup the first time you access the volcano control panel this will allow you to create your first seller you may access your domain setup at any time to easily create additional sellers api the api documentation https github com volcano volcano wiki api contains a full list of available apis here are a few examples create a product http f post api products 71687 options name product abc create a customer http f post api customers contact first name scatman contact last name john contact email imthescatman scatman com create a multi product order for a customer http f post api customers 120488428 orders products 4 myappdomain com products 7 my app instance | front_end |
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frontend-fridays | welcome to your new front end fridays project this project has been created using webpack cli here you will be able to pull all answers npm run build or yarn build to bundle your application participate fork this repository so you can participate on all things front end on fridays at slack channel | front_end |
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TrustSecSiCode | trustsecsicode trustzone assisted securesilicon on a codesign framework team number xohw18 133 project name trustsecsicode trustzone assisted secure silicon on a codesign framework date 30 of june 2018 version of uploaded archive 1 0 university name universidade do minho supervisor name dr sandro pinto supervisor e mail sandro pinto dei uminho pt participant s rgio pereira email a70669 alunos uminho pt participant jos martins email a60141 alunos uminho pt board used zedboard vivado version 2018 1 brief description of project embedded systems were for a long time single purpose and closed systems characterized by hardware resource constraints and real time requirements nowadays their functionality is ever growing coupled with an increasing complexity and heterogeneity virtualization which enables multiple operating systems oses to run on top of the same hardware platform is gaining momentum in the embedded systems arena driven by the growing interest in consolidating and isolating multiple and heterogeneous environments programable system on chip soc are becoming leading players in the embedded systems space because the combination of a plethora of hard resources with configurable logic enables the efficient implementation of systems that perfectly fit the heterogeneous nature of embedded applications this work presents a hardware software co design framework for easing the economy of meeting the new generation of embedded systems requirements arm trustzone technology is exploited to implement the root of trust of a virtualization based architecture that allows the execution of a general purpose operating system gpos side by side with a real time operating system rtos rtos services were offloaded to hardware so that we can present simultaneous improvements on performance and determinism instead of focusing in a concrete application we provide a complete framework specifically tailored for zynq base devices that developers can use to accelerate a bunch of distinct applications across different embedded industries link to youtube video s https youtu be t6ukp4thmoi | os |
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blockchainer-roadmap | blockchainer roadmap 2023 2023 by zhejiang university blockchain association zjubca 2018 2019 2020 2021 2022 heart eyes bowtie 6 https movie douban com subject 27941811 b https www bilibili com bangumi play ep290332 from search seid 4820733908948631673 mastering bitcoin 1 0 https book douban com subject 30180770 https www 8btc com books 261 master bitcoin book https book douban com subject 22993903 http book 8btc com blockchain credit https book douban com subject 26860970 https book douban com subject 26860970 2014 01 22 http www csrc gov cn pub newsite flb flfg bmgf zh gfxwjfxq 201401 t20140122 242972 html 2017 09 04 http www csrc gov cn pub newsite zjhxwfb xwdd 201709 t20170904 323047 html https www yeebee com cn knowledge detail id 32 2021 09 03 https www ndrc gov cn xxgk zcfb tz 202109 t20210924 1297474 html 2021 09 15 http www gov cn zhengce zhengceku 2021 10 08 content 5641404 htm coingecko https www coingecko com coinmarkcap https coinmarketcap com mytoken http pre mytoken io app imtoken https token im tokenpocket https www tokenpocket pro https www binance com zh cn okx https www okx com https www huobi com zh cn metaverse https www yuque com books share a8a2de86 76de 4531 8486 ac3d40944557 pyu4w0 http www 8btc com app odaily https www odaily news https theblockbeats info mataki https www matataki io panews https www panewslab com https www jinse com roadmap consortium blockchain consortium blockchian roadmap md hyperledger fabric r3 corda 80 hyperledger fabric https hyperledger fabric readthedocs io en latest https book douban com subject 30177480 hyperledger fabric hyperchain https book douban com subject 27127839 hyperledger fabric pow https github com indutny proof of work pos https github com ethereum consensus specs pbft https pmg csail mit edu papers osdi99 pdf dpos pbft https github com sqfasd dpos pbft https ethereum org en developers docs solidity https docs soliditylang org en latest https book douban com subject 33424766 cto polygon dfinity hyperledger fabric dapp dev dapp md evm evm awesome list https github com ethereum wiki wiki ethereum virtual machine evm awesome list evm https github com coinculture evm tools blob master analysis guide md wasm webassembly eth ewasm https github com ewasm https book douban com subject 1088180 https book douban com subject 24733262 dag iota https docs iota org introduction byteball https byteball org ipfs filecoin https github com blockchain zju blockchainer roadmap tree master ipfs ico ico erc 20 ico ico token staking staking economy defi decentralized finance defi how to defi defi how to defi chn pdf nft non fungible token nft erc 721 roadmap nft nft nfter roadmap md bitcoin bitcoin a peer to peer electronic cash system https bitcoin org bitcoin pdf ethereum a next generation smart contract and decentralized application platform https github com ethereum wiki wiki white paper ethereum a secure decentralised generalised transaction ledger https ethereum github io yellowpaper paper pdf wiki https github com ethereum wiki wiki go ethereum code analysis https github com ztesoftcs go ethereum code analysis teaspring https blog csdn net teaspring overview and finalit https ethfans org posts ethereum sharding and finality eos https github com eosio documentation blob master technicalwhitepaper md polkadot polkadot vision for a heterogeneous multi chain framework 33 5 1 https www chainnews com articles 363251574672 htm google scholar blockchain distribute ledger bitcoin https github com bitcoin bitcoin ethereum go ethereum https github com ethereum go ethereum eos https github com eosio eos hyperledger fabric https github com hyperledger fabric v buterin v chain interoperabilit https www r3 com wp content uploads 2017 06 chain interoperability r3 pdf https mp weixin qq com s fgrpwzvpb3si5ahscacvag https mp weixin qq com s x7ztdbg6kqgrbagwrrsfqw polkadot overview of polkadot and its design considerations polkadot rust https github com paritytech polkadot bitxhub go https github com meshplus bitxhub subtrate https substrate dev eth2 0 eth1 0 eth2 0 roadmap layer2 layer2 rollup optimistic rollup zk rollup roadmap consortium blockchain consortium blockchian roadmap md defi nft defi defi defi defi defi defi report pdf nft nft roadmap nft nft nfter roadmap md roadmap consortium blockchain consortium blockchian roadmap md 2018 2020 https book douban com subject 35030217 awesome blockchain https github com chaozh awesome blockchain cn awesome metaverse https github com m3 org awesome metaverse awesome zjubca https github com blockchain zju awesome zjubca journey to become a blockchain engineer https github com spo0ds journey to become a blockchain engineer google web3 web3 web3 wiki https github com w3f web3 wiki wiki metaverse metaverse wiki https en wikipedia org wiki metaverse | blockchain learn wiki roadmap bitcoin ethereum eos learn-blockchains | blockchain |
fwk_rtos | rtos framework repository this repository is a collection of c c rtos libraries used to develop for xcore base libraries include freertos smp kernel board support package configurations operating system abstraction layer osal intertile driver support supported peripheral rtos drivers include gpio uart i2c i2s spi qspi flash pdm microphones usb additional rtos drivers include clock control l2 cache swmem wifi supported rtos stacks and software services include tinyusb generic processing pipeline inferencing device control fatfs http tls dhcp json mqtt wifi build status build type status docs ci https github com xmos fwk rtos actions workflows docs yml badge svg branch develop event push cloning some dependent components are included as git submodules these can be obtained by cloning this repository with the following command git clone recurse submodules https github com xmos fwk rtos git testing several tests for the rtos framework modules exist in the test folder https github com xmos fwk rtos tree develop test documentation information on building the documentation can be found in the docs readme https github com xmos fwk rtos blob develop doc readme rst license this software is subject to the terms of the xmos public licence version 1 https github com xmos fwk rtos blob develop license rst third party copyrighted code is specified in the fwk rtos copyrights and licenses https github com xmos fwk rtos blob develop doc copyright rst | os |
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DACSDC-DeepZ | solution of team deepz for 2018 dacsdc this repository contains the proposed solution of team deepz gpu platform for 2018 system design contest https dac com content 2018 system design contest upd official dataset is available in this repo https github com xyzxinyizhang 2018 dac system design contest and we just learn that the dataset has been updated and reduced from 98 classes to 95 classes unfortunately we did not notice the update during the contest which means all of our experiments were based on the former 98 classes dataset this should not have a big impact on our model but the division of train valid set will be different with the new dataset breaking some of the scripts for now we do not have time to review and update those scripts so feel free to ask here if you encounter any problems introduction due to the speed limitation of 20 fps we started with yolov2 tiny detector https pjreddie com darknet yolov2 which consists of a backbone network for feature extraction and a detection network for candidate bounding box generation considering that there is no need to classify in our task we reduced the detection network to a location network in which a candidate bounding box is only represented by a confidence socre and a position however with such a simple model we were soon faced with the challenges of tiny objects occlusions and distractions from the provided data set in order to tackle to the aforementioned challenges we investigated various network architectures for both training and inference p align center img src https raw githubusercontent com jndeng dacsdc deepz master train cfg architecture png alt network architecture width 380px height 400px p we later combined feature pyramid network https arxiv org abs 1612 03144v2 to fuse fine grained features with strong semantic features to enhance the ability in detecting small objects meanwhile we utilized focal loss https arxiv org abs 1708 02002 function to mitigate the imbalance between the single ground truth box and the candidate boxes at training phase thereby partially resolving occlusions and distractions with the combined techniques we achieved the inference network as shown in the figure with an accuracy improvement of 0 042 moreover we used multithreading to accelerate the process of prediction by loading images and infering in parallel which improved about 7 fps on nvidia jetson tx2 the performance of our model is as follow self test accuracy mean iou organizer test accuracy mean iou speed fps on jetson tx2 0 866 0 691 25 note we develop two projects for different purposes in this repository project train is mainly used for model training and accuracy evaluation on powerful gpu nvidia titan x pascal in our experiments while project inference is dedicated to inference on embedded gpu nvidia jetson tx2 with better optimization in speed and energy consumption usage installation prerequisites opencv cuda cudnn python2 python2 numpy project download and installation 1 download the source code on the appropriate devices respectively project train is recommended using on device with powerful gpu while project inference should be used on nvidia jetson tx2 in order to make a fair evaluation of speed shell you may use this command twice to download the source code on different devices git clone https github com jndeng dacsdc deepz git 2 build the source code of two projects separately on the corresponding device we will use train root and inference root to call the directory of project train and project inference respectively shell for project train cd train root make j8 shell for project inference cd inference root make j8 note 1 our implementation is based on darknet framework https pjreddie com darknet you can also refer to the installation guide https pjreddie com darknet install of the original darknet framework 2 for convenience we only implement the code for single gpu mode which means cpu mode and multiple gpus mode are not supported in both of our projects data preparation 1 download the raw dataset dac origin tar 6 3gb about 100 000 images and the corresponding labels and unzip it to train root data raw dataset 1 download the official dataset unzip it rename and move the folder contains all subclass folders to train root data raw dataset 2 use the raw dataset train root data raw dataset to generate the proper dataset train root data train dataset for training the entire process of dataset generation takes about 14gb of hard disk space and the raw dataset will no longer be needed once we obtain train root data train dataset shell cd train root data script python generate dataset py 3 randomly divide the entire dataset into two disjoint parts training set and validation set according to 8 2 ratio the result of division will be stored in train root data dataset as the meta files you can make a new division by yourself or just apply the pre divided dataset used in our experiments shell make a new division cd train root data script python divide dataset randomly py shell use a pre divided dataset cd train root data script python divide dataset py train validation train 1 download the convolutional weights https drive google com open id 1wljtqkobdztsxauvh33zi pzr07n5zox which are pre trained on coco dataset into train root model to initialize our model 2 configurate project path in train root script train model sh 3 start training shell cd train root script bash train model sh by default training log will be written to file train root log yolo tiny dacsdc out and validation will be performed on validation set every 20000 batch automatically the accuracy of each validation will be stored in file train root log yolo tiny dacsdc log besides weights of the best model among all validated models will be saved as train root model yolo tiny dacsdc best weights validation you can also validate a model trained by yourself manually or just download our trained model here 43mb https drive google com open id 1wljtqkobdztsxauvh33zi pzr07n5zox and put it into train root model 1 configurate project path in train root script valid model sh 2 start validating shell cd train root script bash valid model sh inference on jetson tx2 we provide a python interface for inference on jetson tx2 assume that all the images to be detected are stored in inference root data images 1 copy the trained weights of the model from train root model yolo tiny dacsdc best weights to inference root model yolo tiny dacsdc best weights 2 start inference shell cd inference root script python main py 3 wait until the process is finished and then you can obtain the inference result of each image in inference root data result xml where each xml file contains the predicted bounding box of the corresponding image besides the speed of the model will be recorded in inference root data result time time txt | os |
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awesome-webdev-learning | awesome web development learning resources build status https travis ci org melevir awesome webdev learning svg branch master https travis ci org melevir awesome webdev learning a curated list of resources to learn different topics fo web development mostly for python web developers all resources are free contents beginners git https github com melevir awesome webdev learning blob master topics git md sql https github com melevir awesome webdev learning blob master topics sql md python https github com melevir awesome webdev learning blob master topics python md command line https github com melevir awesome webdev learning blob master topics cmd md advanced open source https github com melevir awesome webdev learning blob master topics opensource md style guides code style https github com melevir awesome webdev learning blob master topics style md unit testing https github com melevir awesome webdev learning blob master topics unit testing md web api design https github com melevir awesome webdev learning blob master topics api md more python team work | front_end |
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devcontainers.github.io | development containers website this repo holds the website for the development containers specification https github com devcontainers spec you may view the site at containers dev https containers dev building if you d like to build and preview the site yourself we make it as smooth as possible through a dev container in this repo dev container you may build github pages sites with jekyll https jekyllrb com which is a ruby gem you could manually install these tools on your machine or you can easily get started with the setup you already need through a dev container you may review this repo s dev container in the devcontainer https github com devcontainers containers dev tree gh pages devcontainer folder it is from this jekyll dev container template https github com devcontainers templates tree main src jekyll steps to build and run clone or open this repo in the dev container supporting editor of your choosing you may review supporting tools and services here https containers dev supporting reopen this repo in the dev container so that the container builds and you may develop inside it using the included tools once the dev container finishes building execute the following command in your dev container to start the site bundle exec jekyll serve check out the site http localhost 4000 containers dev feedback and contributing if you d like to provide feedback on or contribute to the dev containers website please feel free to open an issue or pr in this repo for issues on and contributions to the dev container specification itself please visit the dev containers spec repo https github com devcontainers spec license license for this repository https github com devcontainers containers dev blob gh pages license | containers devcontainers | front_end |
Computer-Vision | computer vision computer vision projects | ai |
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TheCodas | thecodas a music database website for software engineering project | server |
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homer-app | img src https user images githubusercontent com 1423657 55069501 8348c400 5084 11e9 9931 fefe0f9874a7 png width 200 homer webapp this repository hosts homer app the the go webapplication for the hep homer 7 7 stack instructions if you want to install homer https github com sipcapture homer please refer to the project readme https github com sipcapture homer if you re just interested in using homer app download configure and run the latest release or package requirements golang 1 13 postgres 11 git 2 7 optional prometheus influxdb loki installation local to get dependencies and compile the latest homer app on your system use the following commands make modules make all docker to get dependencies and compile the latest homer app using a docker builder use the following command make binary make frontend configuration before using the application configure all database parameters using the example configuration file usr local homer etc webapp config json note the default location for settings and provisioning files is usr local homer manual dist if you are installing the homer app manualy you can download the latest compiled version of the frontend https github com sipcapture homer app releases latest once you have download a tar gz of homer ui copy the entire files and directories from the archive to the local dist directory usualy it s usr local homer dist but check your webapp config json for a correct path if you want to install the latest master please go to https github com sipcapture homer ui and follow the instruction how to build it using npmn and angular cli 1 1 32 an example here please use the latest version wget https github com sipcapture homer app releases download 1 1 32 homer ui 7 7 028 tgz tar xzf homer ui 7 7 028 tgz cp rp dist usr local homer dist note the default location for settings and provisioning files is usr local homer usage command help homer app h custom config in etc homer app webapp config path etc initialization the application is able to initialize its database and tables it requires with the following commands create user homer app create homer user database root user postgres database host localhost database root password postgres show user homer app show db users database root user postgres database host localhost database root password postgres create homer dbs homer app create config db database root user postgres database host localhost database root password postgres database homer user homer user homer app create data db database root user postgres database host localhost database root password postgres database homer user homer user create user permissions homer app create homer role database root user postgres database host localhost database root password postgres database homer data homer data database homer config homer config save it or edit the webapp config json manualy homer app save homer db config settings database host localhost database homer config homer config database homer user homer user database homer password homer password homer app save homer db data settings database host localhost database homer data homer data database homer user homer user database homer password homer password please setup the correct credentials for homer config and homer data db in your webapp config json if your webapp config json isn t in the default directory usr local homer etc use the flag webapp config path to correct it same have to be applied to all steps there you read settings from webapp config json create table migration connection data will be read from webapp config json homer app create table db config or homer app create table db config webapp config path etc webapp config json populate db homer app populate table db config upgrade migration connection data will be read from webapp config json homer app upgrade table db config re populate config db homer app populate table db config force populate re populate config db for specific table tables homer app populate table db config force populate populate table mapping schema populate table user settings reset ui password for any user homer app update ui user admin update ui password mypassword usage env webappenv config file extension local webapppath path for config webapplogpath path to the log dir webapplogname prefix name of the log swagger apis requirements go swagger 2 0 https github com go swagger go swagger swagger apis can be generated from inside the homer app to generate swagger json file run below command insider homer app swagger generate spec m o swagger json to serve swagger json file run below command swagger serve f swagger swagger json deb rpm packages to build a full package including the latest frontend code make package the application will deploy to usr local bin with config in etc docker image this application is available on dockerhub as sipcapture webapp to build a full bundle locally including the latest frontend code make docker for working examples and ready to run recipes see homer7 docker https github com sipcapture homer7 docker tree 7 7 heplify server this project is part of homer img src https camo githubusercontent com c287bf83f8d5969635b5bed047a3e70854bc1840 687474703a2f2f736970636170747572652e6f72672f646174612f696d616765732f736970636170747572655f6865616465722e706e67 width 300 license copyright this project is released under the gnu affero general public license as published by the free software foundation either version 3 of the license or at your option any later version a href https github com sipcapture homer app graphs contributors img src https contributors img web app image repo sipcapture homer app a made by humans this open source project is made possible by actual humans without corporate sponsors angels or patreons br if you use this software in production please consider supporting its development with contributions or donations https www paypal com cgi bin webscr cmd donations business donation 40sipcapture 2eorg lc us item name sipcapture no note 0 currency code eur bn pp 2ddonationsbf 3abtn donatecc lg 2egif 3anonhostedguest donate https www paypalobjects com en us i btn btn donatecc lg gif https www paypal com cgi bin webscr cmd donations business donation 40sipcapture 2eorg lc us item name sipcapture no note 0 currency code eur bn pp 2ddonationsbf 3abtn donatecc lg 2egif 3anonhostedguest c 2008 2020 qxip bv | sipcapture homer metrics sip rtp rtcp logs rtcpxr heplify influxdb prometheus elasticsearch voip rtc freeswitch kamailio opensips asterisk monitoring loki | front_end |
gamboa-project | what is it it is a project structure for rapid web application development based on scala apache wicket and your flavor of base framework java ee with jpa or spring with mongodb it also comes with velocity for email templates processing it has a clean tuned directory structure that fits the common scenario where designers and developers work together to build a web application but want clear separation of design layout and programming code how to create a new project gamboa is based on apache maven and has archetypes previous knowledge on maven is required to start your project follow these steps 1 install apache maven 3 0 go to http maven apache org 2 clone the gamboa project locally using git git clone git github com brunoborges gamboa project git 3 go to the gamboa project folder and install the maven artifacts mvn install 4 go to your workspace choose your archetype gamboa jee archetype or gamboa mongodb archetype and create your project cd workspace mvn archetype generate darchetypegroupid org gamboa project darchetypeartifactid gamboa jee archetype darchetypeversion 1 0 snapshot dgroupid com mycompany dartifactid myproject dinteractivemode false 5 run it it will download and start glassfish embedded cd myproject mvn install 6 now fire your browser at http localhost 8080 7 to have access just fill the login form with some valid email and a password remember it create an account and then login project structure here is the project structure gamboa suggests for rapid development myproject tree src scala files code data your jpa pojo entities for jee or mongodb services your ejb3 or spring services email your email template processing classes webapp your base classes for the web application pages your wicket pages using the scala dsl config configuration files xml and some properties templates velocity templates for email delivery layout html files and presentation css js images css js your custom js scripts vendor js libs like jquery modernizr and respond maven eclipse xml pom xml project structure dismistified gamboa does not follow maven s convention because it believes the convention is not suited for rapid web application development having to define a java package and all those folders under src is not straightforward for developers who don t know maven even for those who know it still is not something simple gamboa suggests a different approach taking advantage of maven s build process and scala capabilities all html files must go into the layout folder for each html page there must be a scala class under code pages this is the wicket convention other web resources css javascript and images will go to the context root of the web application this way the prototype will be functional and during runtime everything will work as expected thanks to wicket there s no need to programmatically reference resources logical services are ejb3 or spring based pojos and should go to code services as this is the convention for annotations scan defined in the applicationcontext xml if you are using the spring based archetype if you want to change this modify that xml as you wish the code services email package has some useful classes for sending emails templates should go to config templates and can be velocity templates in html all configuration like smtp properties are located in the emailcontext xml for spring based the jee archetype requires a jndi name for the mailsession resource want a clean structure if you want a clean project structure without any code just create a project based on gamboa jee archetype and delete everything from code webapp pages and layout then drop your functional prototype to layout and start coding from scratch remember to follow wicket s convention one webpage panel border class for each html file ide support projects created with gamboa archetype already have eclipse files configured they were generated for eclipse helios 3 6 also it is better to have the scala ide installed to have better support at editing and hot deployment during development another great tool is the aptana studio for html css and javascript edition scala ide update site for scala 2 9 z http download scala ide org releases 29 stable site debugging it is possible to debug by simply running the debug sh script that will start the jvm with a debug server at port 9009 then on eclipse launch a remote application debug agent and attach to that port frameworks and technologies here s the list of all gamboa integrated frameworks and technologies 1 server side libraries scala 2 9 2 apache wicket 6 0 velocity 1 7 junit 4 10 slf4j 1 7 1 logback 2 client side libraries jquery 1 6 1 modernizr 1 7 html5 initializr http initializr com roadmap the gamboa project aims to be not only a maven archetype but also a solid quickstart for java and scala developers who want to quickly build web applications that already have a functional prototype following the top down design strategy contributing as any other github project fork it and contribute if you want to share your changes request a pull | front_end |
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DBMS | dbms contains all personalized queries done by me in mysql oracle database and mongodb in my third year of bachelors engineering in pict | server |
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AssignmentOne-COSC2196 | assignment one bach it cosc2196 introuduction to information technology | server |
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awtk-stm32h743iitx-freertos | awtk stm32h743 awtk https github com zlgopen awtk toolkit anywhere zlg http www zlg cn gui web pc gui gui awtk stm32h743iitx freertos https github com zlgopen awtk stm32h743iitx freertos awtk stm32h743 stm32h743 stm32h7 https item taobao com item htm id 571115692251 ali refid a3 430582 1006 1103191143 n higgptz 2bahqpeevxe 2fk1rsppjjrvcvoc 5b41a06d9f58d787690cd30a8865e887 ali trackid 1 5b41a06d9f58d787690cd30a8865e887 spm a230r 1 14 8 detail issue rtos freertos fatfs sd sqlite google 1 git clone https github com zlgopen awtk stm32h743iitx freertos git cd awtk stm32h743iitx freertos git clone https github com zlgopen awtk git git clone https github com zlgopen awtk sqlite3 git git clone https github com zlgopen awtk fs adapter git 2 keil user awtk uvprojx | os |
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opencv | opencv open source computer vision library resources homepage https opencv org courses https opencv org courses docs https docs opencv org 4 x q a forum https forum opencv org previous forum read only http answers opencv org issue tracking https github com opencv opencv issues additional opencv functionality https github com opencv opencv contrib contributing please read the contribution guidelines https github com opencv opencv wiki how to contribute before starting work on a pull request summary of the guidelines one pull request per issue choose the right base branch include tests and documentation clean up oops commits before submitting follow the coding style guide https github com opencv opencv wiki coding style guide | opencv c-plus-plus computer-vision deep-learning image-processing | ai |
fileuploader | file uploader example project this project was bootstrapped with create react app it serves as an example of component decomposition for building react applications | front_end |
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amazon-efs-tutorial | https s3 amazonaws com aws us east 1 tutorial aws logo pms 300x180 png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit available png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit ingergration png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit ecryption lock png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit fully managed png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit lowcost affordable png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit performance png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit scalable png https s3 amazonaws com aws us east 1 tutorial 100x100 benefit storage png amazon elastic file system amazon efs tutorials version 1 2 2 efs t 1 2 1 2020 amazon web services inc and its affiliates all rights reserved this sample code is made available under the mit 0 license see the license file errors or corrections email us at darrylo amazon com mailto darrylo amazon com table of contents tutorials tutorials 1 create a file system 1 create a file system 2 performance 2 performance 3 scale out 3 scale out 4 monitoring 4 monitoring 5 in cloud transfer 5 in cloud transfer 6 data science 6 data science tutorials these six 6 tutorials are designed to help you better understand the performance characteristics of amazon elastic file system amazon efs and how parallelism i o size and amazon ec2 instance types affects file system iops and throughput 1 create a file system this tutorial is a set of aws cloudformation templates that will create an amazon efs file system and pre load data to grow the file system to obtain higher levels of iops and throughput throughput and iops on amazon efs scales as a file system grows so larger file systems are able to achieve higher levels of throughput and iops because file based workloads are typically spiky driving high levels of throughput for short periods of time and low levels of throughput the rest of the time amazon efs is designed to burst to high throughput levels for periods of time amazon efs uses a credit system to determine when file systems can burst file systems can be monitored using aws cloudwatch metrics these cloudformation templates will also create an aws cloudwatch dashboard custom metrics alarms scheduled events aws lambda function sns notification and an auto scaling group to monitor and dynamically adjust alarm thresholds as the file system grows and shrinks click on the images efs tutorial png link below to go to the create a file system tutorial once you ve finished that tutorial move on to performance tutorial link create a file system images efs tutorial png create file system 2 performance this tutorial is a set of scripts that will demonstrate different instance types provide different levels of network performance when accessing a file system different i o sizes block sizes and sync freqencies the rate data is persisted to disk effects file system throughput increasing the number of threads accessing a file system will increase iops and throughput click on the images efs tutorial png link below to go to the performance tutorial once you ve finished that tutorial move on to scale out tutorial link performance images efs tutorial png performance 3 scale out this tutorial is a cloudformation template that will create an amazon ec2 spot fleet and download objects in parallel from an amazon s3 bucket click on the images efs tutorial png link below to go to the scale out tutorial once you ve finished that tutorial move on to the scenarios tutorial link scale out images efs tutorial png scale out 4 monitoring this tutorial is designed to help you better understand how amazon efs is performing by using amazon cloudwatch and metric math to monitor file system performance click on the images efs tutorial png link below to go to the monitoring tutorial tutorial link monitoring images efs tutorial png monitoring 5 in cloud transfer the aws datasync in cloud transfer quick start and scheduler creates a one time or recurring schedule to transfer files between source and destination amazon efs file systems these file systems could be in the same or different aws regions click on the images efs tutorial png link below to go to the in cloud transfer quick start quick start link in cloud transfer images efs tutorial png in cloud transfer 6 data sciense workshop this tutorial covers how to use amazon efs a highly available highly durable and elastic cloud native file storage for data science workloads click on the images efs tutorial png link below to go to the data science tutorial tutorial link data science images efs tutorial png data science troubleshooting for feedback suggestions or corrections please email me at darrylo amazon com mailto darrylo amazon com license this sample code is made available under the mit 0 license see the license file | os |
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My-Wallet-V3-Frontend | deprecated use https github com blockchain blockchain wallet v4 frontend instead blockchain info wallet build status https travis ci org blockchain my wallet v3 frontend svg branch master https travis ci org blockchain my wallet v3 frontend coverage status https coveralls io repos blockchain my wallet v3 frontend badge svg branch master service github https coveralls io github blockchain my wallet v3 frontend branch master license agpl v3 https img shields io badge license agpl 20v3 blue svg https www gnu org licenses agpl 3 0 be your own bank at blockchain info wallet https blockchain info wallet please contact support https support blockchain com if you have any issues using the wallet run the wallet on your own computer the normal and easiest way to use our wallet is to go to blockchain info wallet https blockchain info wallet however if you like more control over the exact code that runs in your browser you can download the source code and run the wallet from a simple server on your own machine here s how 1 install node js http nodejs org 2 git clone git github com blockchain my wallet v3 frontend git b v1 11 11 single branch depth 1 3 make server login to your existing wallet or create a new one at http localhost 8080 you can replace v1 11 11 with any tagged version you like but we recommend always using the latest release https github com blockchain my wallet v3 frontend releases the versions marked as pre release have not gone through extensive internal testing yet note that the wallet itself is still stored on blockchain info servers it is encrypted with your password the wallet also uses the blockchain info servers to show you your balance notify you of new payments submit transactions etc about the frontend code in this repository uses angularjs the bitcoin specific tasks are handled by my wallet v3 https github com blockchain my wallet v3 which is included via bower develop make sure you have node js http nodejs org installed you also need sass use sudo if you re not using a ruby version manager https rvm io sh gem install sass install dependencies sh npm install g bower grunt cli yarn bower install create a file called env in the root of the project put the following in it root url https blockchain info optionally you can add auto reload 1 web socket url wss ws blockchain info inv api domain https api blockchain info wallet helper url http localhost 8081 to inspect individual directives run sh yarn run start parts build grunt watches and compiles the pug view templates and css keep it running sh grunt lint to run the file linter sh yarn lint test to run test and monitor for changes sh yarn test vet to ensure builds will succeed linting and unit tests passing sh yarn vet a coverage report is generated after you run the test for the first time just open coverage index html in your browser run run local http server sh yarn start visit localhost 8080 http localhost 8080 developing my wallet v3 if you are making changes to my wallet v3 https github com blockchain my wallet v3 that you want to try out in the frontend create a symlink sh rm my wallet v3 frontend bower components blockchain wallet dist my wallet js ln s my wallet v3 dist my wallet js my wallet v3 frontend bower components blockchain wallet dist my wallet js to automatically login and go back to where you last were in the app after every page refresh create a file env and add auto reload 1 to it if you enable handle bitcoin links in your wallet settings you can open bitcoin uri s like this one bitcoin address 1feerpcgswvgrlvkme759c96dubtf7sva2 amount 0 01 contribute bug fixes and feedback on our code is always appreciated security security issues can be reported to us in the following venues email security blockchain info bug bounty https hackerone com blockchain | bitcoin wallet unit-tested ethereum javascript angular frontend html grunt yarn | blockchain |
Building-Blockchain-Projects | building blockchain projects this is the code repository for building blockchain projects https www packtpub com big data and business intelligence building blockchain projects utm source github utm medium repository utm campaign 9781787122147 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish about the book this book will teach you what blockchain is how it maintains data integrity and how to create real world blockchain projects using ethereum with interesting real world projects you will know learn how to write smart contracts which run exactly as programmed without any chance of fraud censorship or third party interference and build end to end applications for blockchain you will learn concepts such as cryptography in cryptocurrencies ether security mining smart contracts and solidity instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 chapters 1 2 3 do not have any code files chapter 1 is introduction chapter 2 is setup and chapter 3 have minimal one class codes the code will look like the following var solc require solc var input contract x function g var output solc compile input 1 1 activates the optimizer for var contractname in output contracts logging code and abi console log contractname output contracts contractname bytecode console log contractname json parse output contracts contractname interface you require windows 7 sp1 8 10 or mac os x 10 8 related products learn nodejs by building 12 projects video https www packtpub com web development learn nodejs building 12 projects video utm source github utm medium repository utm campaign 9781787122215 building machine learning projects with tensorflow https www packtpub com big data and business intelligence building machine learning projects tensorflow utm source github utm medium repository utm campaign 9781786466587 learn meteorjs by building 10 real world projects video https www packtpub com application development learn meteorjs building 10 real world projects video utm source github utm medium repository utm campaign 9781787129726 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781787122147 https packt link free ebook 9781787122147 a p | blockchain |
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OpenMoE | openmoe blog https xuefuzhao notion site aug 2023 openmoe v0 2 release 43808efc0f5845caa788f2db52021879 twitter https twitter com xuefz discord https discord gg bjgngfjegu openmoe is a project aimed at igniting the open source moe community we are releasing a family of open sourced mixture of experts moe large language models since we are a small team working on a huge project we cannot handle everything instead we release some intermediate checkpoints in this repo to invite more contributors to work on open sourced moe project together news 2023 08 we released an intermediate openmoe 8b checkpoint openmoe v0 2 along with two other models check out the blog post https xuefuzhao notion site aug 2023 openmoe v0 2 release 43808efc0f5845caa788f2db52021879 todo list pytorch implementation with colossal ai more evaluation continue training to 1t tokens paper contents model weights model weights get started get started approach approach license license authors authors citation citation model weights currently three models are released in total model name description param gcs huggingface gin file openmoe base 16e a small moe model for debugging 637m gs openmoe openmoe base checkpoint 500000 link https huggingface co fuzhao openmoe base link https github com xuefuzhao t5x blob main t5x examples t5 t5 1 1 examples openmoe base gin openllama base a dense counter part of openmoe base 310m gs openmoe openllama base checkpoint 500000 link https huggingface co fuzhao openllama base link https github com xuefuzhao t5x blob main t5x examples t5 t5 1 1 examples openllama base gin openmoe 8b 32e 8b moe with comparable flops of a 2b llama 8b gs openmoe openmoe 8b checkpoint 100000 link https huggingface co fuzhao openmoe 8b link https github com xuefuzhao t5x blob main t5x examples t5 t5 1 1 examples openmoe large gin we release all these checkpoints on huggingface and google cloud storage for instance you can download openmoe 8b with gsutil cp r gs openmoe openmoe 8b checkpoint 100000 your dir the base models are trained with 128b tokens the openmoe 8b checkpoint with 4 moe layers and 32 experts has been trained by 200b tokens we are still training openmoe 8b so if you are interested in the latest checkpoint please feel free to drop fuzhao an email f xue u nus edu in addition we are highly interested in training this model until saturate by performing multi epoch training which means we may train our model for over 2t and even more tokens this depends on the resource we can get in the coming months note downloading data from google cloud storage is not free but you can sign up to google cloud and get some credits get started training get a tpu vm and run the following code on all tpus researcher can apply tpu research cloud https sites research google trc about to get the tpu resource we are working on the pytorch gpu implementation with colossal ai https github com hpcaitech colossalai git clone https github com xuefuzhao openmoe git bash openmoe script run pretrain sh eval get a tpu vm and run the following code on all tpus git clone https github com xuefuzhao openmoe git bash openmoe script run eval sh approach data 50 the redpajama 50 the stack dedup we use a high ratio of coding data to improve reasoning ability below are scripts to generate tfds for pre training datasets the redpajama https github com orion zheng redpajama tfds the stack dedup https github com orion zheng the stack tfds tokenizer we use the umt5 tokenizer https arxiv org abs 2304 09151 to support multi lingual continue learning in the future which can be downloaded on huggingface https huggingface co google umt5 small tree main or google cloud https github com google research t5x blob main docs models md umt5 checkpoints model architecture openmoe is based on st moe https arxiv org abs 2202 08906 but uses decoder only architecture the detailed implementation can be found in fuzhao s t5x https github com xuefuzhao t5x and flaxformer https github com xuefuzhao flaxformer repo training objective we use a modified ul2 training objective but casual attention mask we use more prefix lm and high mask ratio because it saves computation 50 prefix lm 10 span len 3 mask ratio 0 15 10 span len 8 mask ratio 0 15 10 span len 3 mask ratio 0 5 10 span len 8 mask ratio 0 5 10 span len 64 mask ratio 0 5 other designs rope swiglu activation 2k context length we will release a more detailed report soon evaluation we evaluate our model on bigbench lite as our first step we plot the cost effectiveness curve in the figure below relative cost is approximated by multiplying activated parameters and training tokens the size of dots denotes the number of activated parameters for each token the lightgray dot denotes the total parameters of moe models plot figure bblite 3 shot png for more detailed results please see our blog https www notion so aug 2023 openmoe v0 2 release 43808efc0f5845caa788f2db52021879 license our code is under apache 2 0 license since the models are trained on the redpajama and the stack dataset please check the license of these two datasets for your model usage authors this project is currently contributed by the following authors fuzhao xue https xuefuzhao github io zian zheng https www linkedin com in zian zheng 21a715239 yao fu https franxyao github io jinjie ni http jinjie one zangwei zheng https zhengzangw github io wangchunshu zhou https michaelzhouwang github io yang you https www comp nus edu sg youy citation please cite the repo if you use the model and code in this repo bibtex misc openmoe2023 author fuzhao xue zian zheng yao fu jinjie ni zangwei zheng wangchunshu zhou and yang you title openmoe open mixture of experts language models year 2023 publisher github journal github repository howpublished url https github com xuefuzhao openmoe | ai |
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azure-flask-react | this is academic project based on python flask and js react backend azure samples https github com azure azure sdk for python tree master sdk storage azure storage blob samples deploy locally backend dependency packages install python 3 but it has been developed on 3 8 and works for sure go to f r flask modules directory create venv flask modules python3 m venv venv activate venv flask modules source venv bin activate run command venv pip3 install r requirements txt add global variable to pycharm https stackoverflow com questions 42708389 how to set environment variables in pycharm flask app init py initiate db from the shell app flask db init upload developed models to db with migrate package move into app directory and run app flask db migrate m initial migration if the last command failed with importerror cannot import name post from app models models users alex downloads web f r app models models py then comment all the models but user then uncomment that and run flask db upgrade its needed to update db after models updated run the instruction above written to run project from pycharm run from the shell follow the official instruction https flask palletsprojects com en 1 1 x cli frontend install frontend dependency packages run to install f a npm install run run the reactapp from shell f a npm start open http localhost 3000 http localhost 3000 to view it in the browser the page will be reloaded if source code changed br you will also see any lint errors in the console | server |
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ResourceHub | resourcehub banner images resourcehub banner png div align center open source love https firstcontributions github io open source badges badges open source v1 open source svg https github com firstcontributions open source badges awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome contributors welcome https img shields io badge contributors welcome 0b7cbd https github com nikohoffren fork commit merge pulls first contributors https img shields io badge first contributors 0b7cbd https github com nikohoffren fork commit merge pulls pr s welcome https img shields io badge pr s welcome 0b7cbd https github com nikohoffren fork commit merge pulls issues https img shields io github issues jfmartinz resourcehub svg style flat https github com jfmartinz resourcehub issues stars https img shields io github stars jfmartinz resourcehub svg style flat https github com jfmartinz resourcehub stars forks https img shields io github forks jfmartinz resourcehub svg style flat https github com jfmartinz resourcehub forks mit licence https badges frapsoft com os mit mit svg v 103 https opensource org licenses mit license php contributor covenant https img shields io badge contributor 20covenant 2 1 4baaaa svg code of conduct md div resourcehub is an open source project that serves as a collection of valuable resources for web development carefully curated by the amazing contributors in the community br you can contribute helpful resources you ve discovered on the internet and share them with us category here are the currently available categories in the project br web development https github com jfmartinz resourcehub tree main web 20development ui ux design https github com jfmartinz resourcehub tree main ui ux 20design contributing read contributing md https github com jfmartinz resourcehub blob main contributing md before contributing 1 fork the repository click the fork button at the top right of this repository to create your own copy 2 clone your fork now clone the forked repository to your local machine go to your github account open the forked repository click on the code button and then click the copy to clipboard icon open a terminal and run the following git command bash git clone url you just copied for example bash git clone https github com jfmartinz resourcehub git 3 create a branch change to the repository directory on your computer if you are not already there bash cd resourcehub make a meaningful name for your new branch and then create and switch to it using this command bash git checkout b new branch name note replace your new branch name with the actual branch name 4 make your changes navigate to the relevant category where you want to add your resource once you ve identified the appropriate category and have your resource in mind proceed to make the necessary changes within that specific category stage your changes after making your changes you need to stage the changes for commit using the git add command to stage all changes use bash git add commit your changes commit your staged changes with a descriptive commit message that explains the purpose of your changes use the git commit command bash git commit m your descriptive commit message here note replace your descriptive commit message here with a concise and informative message push your changes bash git push origin your branch name note replace your branch name with the name of your branch 5 submit your changes for review if you go to your repository on github you ll see a compare pull request button click on that button now submit the pull request congratulations on successfully submitting your pull request pr to our project we want to express our gratitude for your valuable contribution your effort not only benefits our project but also help the entire community please consider sharing this repository with other developers to help increase its visibility and reach our ontributors a href https github com jfmartinz resourcehub graphs contributors img src https contrib rocks image repo jfmartinz resourcehub a license resourcehub is licensed under the mit license see the license https github com jfmartinz resourcehub blob main license file for details br if you find this repository useful please consider giving it a | beginner-friendly contributions open-source resources web-development webdesign webdevelopment collections contributors-welcome hacktoberfest hacktoberfest2023 css html webdev webresource awesome | front_end |
machine_learning | machine learning this repo contains a collection of ipython notebooks detailing various machine learning algorithims in general the mathematics follows that presented by dr andrew ng s machine learning course taught at stanford university materials available from itunes u http www apple com education itunes u stanford machine learning dr tom mitchell s course at carnegie mellon materials avialable here http www cs cmu edu tom 10701 sp11 and christopher m bishop s pattern recognition and machine learning unless otherwise noted the python code is orginal and any errors or ommissions should be attribued to me and not the aforemention authors each ipynb provides a list of the pertinent reading material it is suggested that the material be read in the order provided if you do not have ipython installed or notebook configured why not the src directory has py versions of the notebook files and some of the pdf output files are in this repository s downloads section however they are not always as updated as the ipynb files python version 2 7 2 ipython version 0 13 | ai |
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Steward | steward a smart stock portfolio manager that provides short term predictions and social media sentimental analysis for stocks selections team members kevin jin kjin2 will povell wpovell matt robins mrobins philip xu jx24 mentor sean segal sean segal brown edu project specs and mockup specs interfaces 1 graphical user interface the user interface of this project will be a web application the layout is demonstrated in the mockups features 1 signup login and data retention users should be able to sign up on our service using an username password and email users should be able to log in to the service and create edit their portfolio that information should be retained and everytime the user logs in they should be able to have access to the same portfolio they created users should also be able to log out change their passwords or delete their accounts 2 portfolio editting users should be able to create and edit their portfolios using a search box through symbol or stock name query there should be an extensive stock database containing all currently traded nyse or more stocks 3 stock information display when a user clicks on a stock for example aapl the stock s candlestick chart statistics fundamentals predictions should be displayed users should be able to zoom in on the graph mouse over for more daily information and draw trendlines with percentage displays the current traded price volumes traded volatility and fundamentals of the company will be displayed below the graph and a user can click to see more details news articles or twitter feeds regarding the stock will be displayed on the right along with a sentamental analysis of social media feeds 2 backend interface the backend interface will do the following 1 a neural network api a stock data api a twitter feed api and other relevant finance news apis such as yahoo finance will be used 2 a neural network will be trained using the quantitative variables of a stock s fundamentals and a sentiment value as obtained from using our sentiment api on relevant news data 1 at the very least the sentiment value will be obtained by applying the sentiment api to tweets that use the stock s ticker symbol however it would be ideal to extend this model by using multiple keywords to assess sentiment such as company name ceo name sector name etc through different news mediums 2 the neural network will be used to make both a one day price prediction and a five day price prediction 3 when a stock s home page is presented on the front end all of the fundamentals graph data and prediction data will come from a gson variable sent from the backend this gson will include the following key value pairs 1 daily data dd mm yyyy contains an array of the following 1 open close daily high daily low volume 2 minute data dd mm yyyy hh mm ss is used for displaying a stock chart for more recent intervals including the past week and the current day and will contain an array of 1 current price total daily volume 3 fundamental fundamental i e p e ratio dividend yield shares outstanding and other fundamentals will be included as key value pairs where the key is the name of the fundamental string and the value is a number representing the value of the fundamental 4 5 day prediction fiveday is generated by the neural network in the backend and contains a number representing a five day prediction for the given stock price 5 1 day prediction oneday is generated by the neural network in the backend and contains a number representing a one day prediction for the given stock price 4 predictions for stocks in the portfolio will be cached and predictions for portfolio stocks should be generated in less than 2 second 5 predictions for stocks not in the portfolio will be generated in under twenty seconds a loading bar with approximate time remaining will be displayed functionalities 1 user login signup users should be able to sign up on our service using an username password and email users should be able to log in to the service and create edit their portfolio that information should be retained and everytime the user logs in they should be able to have access to the same portfolio they created users should also be able to log out change their passwords or delete their accounts 2 portfolio management users will have access to a complete set of all stocks currently trading in the u s users should be able to edit their portfolio and have access to it when they re log in 3 stock users should be able to have access to all relevant stock information current price fundamentals daily open daily close daily low daily low daily volume volatility news articles projections and sentimental analysis informations users should be able to draw trendlines zoom onto graphs have access to info on the graphs performance minimal computations will be performed on the frontend most of the data manipulation and preperations will be done on the backend 1 user data management user data will be stored in a sql database insertions upon creation and query upon login all performance will be limited by sql operations which will likely o log n 2 prediction the backend will performance prediction using apis for recurrent neural networks and sentimental analysis the performance of this will depend on the apis used after the initial run the results will be cached and any redundant queries will be retrieved from the cache instead to speed up querying 3 stock info querying the backend will provide information of charts and fundamentals this information will be queried from a sql database and cached for future uses safety our app will not have any financial transactions or irreversible actions we will store the usernames and passwords for logins on the site in a database on the server we will find an online package to use as a password hasher compatibility our portfolio manager will be a web app that supports at least chrome that can be accessed from personal computers our stretch goals include support for firefox safari ie edge and smartphone browsing we will not require any files to be provided from the client mockups main page main page readme src index mock png register page register page readme src reg mock png graph page graph page readme src graph mock png | stock stock-market stock-price-prediction sentiment-analysis porfolio webapp java | ai |
free_rtos_v7.0.1_c2000 | freertos v7 0 1 port for c2000 mcus this repository contains an abandoned and obsolete freertos https www freertos org port for the ti c2000 mcu family the files are solely made available for reference purposes interested developer may also want to review this post http www freertos org freertos support forum archive august 2012 freertos freertos on ti 28335 5525049 html for a current and real solution check out this project https github com ivanzuy freertos c28x on github | os |
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neo | p align center a href https neo org img src https neo3 azureedge net images logo 20files dark svg width 250px alt neo logo a p h3 align center neo blockchain h3 p align center a modern distributed network for the smart economy br a href https docs neo org docs en us index html strong documentation strong a br br a href https github com neo project neo strong neo strong a a href https github com neo project neo vm neo vm a a href https github com neo project neo modules neo modules a a href https github com neo project neo devpack dotnet neo devpack a a href https github com neo project neo node neo node a p p align center a href https twitter com neo blockchain img src github images twitter logo png width 25px a nbsp a href https medium com neo smart economy img src github images medium logo png width 23px a nbsp a href https neonewstoday com img src github images nnt logo jpg width 23px a nbsp a href https t me neo en img src github images telegram logo png width 24px a nbsp a href https www reddit com r neo img src github images reddit logo png width 24px a nbsp a href https discord io neo img src github images discord logo png width 25px a nbsp a href https www youtube com neosmarteconomy img src github images youtube logo png width 32px a nbsp how to get a link a href https neo org img src github images we chat logo png width 25px a nbsp a href https weibo com neosmarteconomy img src github images weibo logo png width 28px a p p align center a href https github com neo project neo releases img src https badge fury io gh neo project 2fneo svg alt current neo version a a href https coveralls io github neo project neo img src https coveralls io repos github neo project neo badge svg alt coverage status a a href https github com neo project neo blob master license img src https img shields io badge license mit blue svg alt license a p table of contents 1 overview overview 2 project structure project structure 3 related projects related projects 4 opening a new issue opening a new issue 5 bounty program bounty program 6 license license overview this repository contain main classes of the neo https www neo org blockchain visit the documentation https docs neo org docs en us index html to get started project structure an overview of the project folders can be seen below folder content consensus classes used in the dbft consensus algorithm including the consensusservice actor cryptography general cryptography classes including ecc implementation io data structures used for caching and collection interaction ledger classes responsible for the state control including the memorypool and blockchain classes network peer to peer protocol implementation classes persistence classes used to allow other classes to access application state plugins interfaces used to extend neo including the storage interface smartcontract native contracts applicationengine interopservice and other smart contract related classes vm helper methods used to interact with the vm wallet wallet and account implementation related projects code references are provided for all platform building blocks that includes the base library the vm a command line application and the compiler neo https github com neo project neo neo core library contains base classes including ledger p2p and io modules neo vm https github com neo project neo vm neo virtual machine is a decoupled vm that neo uses to execute its scripts it also uses the interopservice layer to extend its functionalities neo node https github com neo project neo node executable version of the neo library exposing features using a command line application or gui neo modules https github com neo project neo modules neo modules include additional tools and plugins to be used with neo neo devpack dotnet https github com neo project neo devpack dotnet these are the official tools used to convert a c smart contract into a neo executable file opening a new issue please feel free to create new issues to suggest features or ask questions feature request https github com neo project neo issues new assignees labels discussion template feature or enhancement request md title bug report https github com neo project neo issues new assignees labels template bug report md title questions https github com neo project neo issues new assignees labels question template questions md title if you found a security issue please refer to our security policy https github com neo project neo security policy bounty program you can be rewarded by finding security issues please refer to our bounty program page https neo org bounty for more information license the neo project is licensed under the mit license license | neo blockchain | blockchain |
PHP-and-MySQL-Web-Development-5th-Edition | php and mysql web development 5th edition php and mysql web development 5th edition | php7 mysqli pdo-mysql example-code | front_end |
llmpromptboosting | boosted prompt ensembles for large language models silviu pitis michael r zhang andrew wang jimmy ba arxiv https arxiv org abs 2304 05970 img src figs illustration png width 500 this repo contains code for our paper on prompt boosting please see the paper for detailed experiments and ablations this code is built off the official implementation of large language models are zero shot reasoners https github com kojima takeshi188 zero shot cot abstract methods such as chain of thought prompting and self consistency have pushed the frontier of language model reasoning performance with no additional training to further improve performance we propose a prompt ensembling method for large language models which uses a small dataset to construct a set of few shot prompts that together comprise a boosted prompt ensemble the few shot examples for each prompt are chosen in a stepwise fashion to be hard examples on which the previous step s ensemble is uncertain we show that this outperforms single prompt output space ensembles and bagged prompt space ensembles on the gsm8k and aqua datasets among others we propose both train time and test time versions of boosted prompting that use different levels of available annotation and conduct a detailed empirical study of our algorithm example command python dynamicprompting py dataset aqua size limit 12 min prompt size 3 installation make sure you have python 3 8 installed on your machine pip install upgrade pip pip install python dotenv openai transformers pip install datasets cohere git clone https github com kojima takeshi188 zero shot cot git cd zero shot cot pip install torch 1 8 2 cu111 torchtext 0 9 2 f https download pytorch org whl lts 1 8 torch lts html pip install r requirements txt set your openai api key https beta openai com account api keys export openai api key your openai api key set arguments model gpt3 xl gpt3 gpt3 medium gpt3 large gpt3 xl gpt3 is the smallest model dataset multiarith we can use other datasets see help for the details limit dataset size 10 this is important to save your budget if you want to use all the samples in a dataset set 0 api time interval 1 0 caution the api allows users request api up to 60 times in a minutes otherwise errors happen quick start prompt boosting on gsm8k 200 train to boost entire test set python dynamicprompting py dataset gsm8k split train min agreement 0 9 size limit 200 seed 0 prompt mode boosted tag train base200 python dynamicprompting py dataset gsm8k min agreement 0 9 size limit 3000 seed 0 boosted prompts logs gsm8k boosted code davinci 002 200 0 9 0 append train base200 pickle tag base200 self consistency 100 on gsm8k python dynamicprompting py dataset gsm8k min agreement 1 0 seed 0 prompt mode self consistency tag sc test time prompt boosting on mmlu570 python dynamicprompting py dataset mmlu570 split train min agreement 0 9 size limit 300 seed 0 prompt mode boosted tag train base python dynamicprompting py dataset mmlu570 min agreement 0 9 seed 0 boosted prompts logs mmlu570 boosted code davinci 002 300 0 9 0 append train base pickle tag base | ai |
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ZHAW-Data-Science-Fundamentals | zhaw data science fundamentals this is github repo for the course data science fundamentals in msc business information technology at zhaw zurich university of applied sciences the repository is organised in different folders each correspomding to a different week in the course instruction and solutions are provided within each week folder | server |
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ui | how this ui repo is used language implementations should clone this repo as a submodule http git scm com book en git tools submodules miniprofiler data structures this document specifies the miniprofiler data structures new languages should use this as the implementation specification profile the root element id string a unique identifier like a guid name string request name generally controller route started number request start time in milliseconds since unix epoch durationmilliseconds number duration of request in milliseconds machinename string name of the server customlinks object optional object of links keys are names values are urls root timing optional root timing object clienttimings clienttimings optional client timings object timing id string a unique identifier like a guid name string step name startmilliseconds number step start time in milliseconds since start of request durationmilliseconds number duration of step in milliseconds including children children array of timing optional array of child steps customtimings object optional object with keys as call type redis sql etc and values as arrays of customtiming of recorded calls does not include child step s custom timings customtiming id string a unique identifier like a guid executetype string optional execute type of call read write query etc commandstring string html escaped command rendered in a pre so supports newlines stacktracesnippet string shortened one line stack trace generally all function names on the stack separated by a space startmilliseconds number call start time in milliseconds since start of request durationmilliseconds number duration of call in milliseconds firstfetchdurationmilliseconds number optional duration in milliseconds of the time to first result clienttimings this data should be recorded by the browser and reported back to miniprofiler redirectcount number redirect count timings array of clienttiming clienttiming name string start number duration number example json id 7659f15a 6c47 4731 2854 67c6f2b92a3f name goapp listfeeds started 1368211081000 machinename mjibson mbp local root id 0ee41f07 c4a7 44f9 2b81 b300cba7e856 name get http localhost 8080 user list feeds durationmilliseconds 17 595 startmilliseconds 0 children id 9f6660ee 286b 4d2a 2d1d 90d2dcbfb4f3 name unmarshal user data durationmilliseconds 0 03399999999999981 startmilliseconds 5 828 children null customtimings null id 8f35fc4b 57dd 4455 22d4 e25fb27a13c8 name fetch feeds durationmilliseconds 2 6899999999999995 startmilliseconds 5 865 children null customtimings memcache id 7345c3d3 6fd0 4417 21e9 1b3b2014b18e executetype get commandstring get n nkey 34 agtkzxz z28tcmvhzhilcxibriieahr0cdovl21hdhrqawjzb24uy29tl2f0b20ueg1sda 34 for cas true stacktracesnippet call call getmulti step listfeeds startmilliseconds 6 221 durationmilliseconds 1 442 firstfetchdurationmilliseconds 0 id 62078d64 7f70 4194 2a3f d778d1844a35 name feed fetch wait durationmilliseconds 8 904000000000002 startmilliseconds 8 571 children null customtimings datastore v3 id decc860e be66 4274 2c9e bf63380c59c0 executetype runquery commandstring runquery n napp 34 dev go read 34 kind 34 s 34 ancestor lt app 34 dev go read 34 path lt element type 34 f 34 name 34 http mattjibson com atom xml 34 gt gt filter op greater than prope stacktracesnippet call call getall startmilliseconds 8 963 durationmilliseconds 5 435 firstfetchdurationmilliseconds 0 memcache id d8ccb046 f3e8 41cc 22a6 fee03497f4fb executetype get commandstring get n nfor cas true stacktracesnippet call call getmulti startmilliseconds 14 921 durationmilliseconds 2 486 firstfetchdurationmilliseconds 0 id 92011933 a3d9 40e8 23ff 799492f7530a name json marshal durationmilliseconds 0 06099999999999994 startmilliseconds 17 529 children null customtimings null customtimings memcache id e5a454d0 d434 4c97 29b7 1c383a8711d5 executetype get commandstring get n nkey 34 agtkzxz z28tcmvhzhiccxibvsivmtg1oda0nzy0mjiwmtm5mti0mte4da 34 key 34 agtkzxz z28tcmvhzhiocxibvsivmtg1oda0nzy0mjiwmtm5mti0mte4dassalveigrkyxrhda 34 for stacktracesnippet call call getmulti listfeeds startmilliseconds 0 535 durationmilliseconds 4 032 firstfetchdurationmilliseconds 0 clienttimings null durationmilliseconds 17 595 customlinks appstats http localhost 8080 ah stats details time 542064000 client timings example json id 779f22e9 2c1d 45ae 2677 ae5f34c3f39f name goapp main started 1368211203000 machinename mjibson mbp local root id 4362ec49 b625 4184 2bb4 95d904cb466d name get http localhost 8080 durationmilliseconds 5 964 startmilliseconds 0 children null customtimings memcache id 83a9d0a9 ed37 4e9d 27cd 4f8813ac6408 executetype get commandstring get n nkey 34 agtkzxz z28tcmvhzhiccxibvsivmtg1oda0nzy0mjiwmtm5mti0mte4da 34 for cas true stacktracesnippet call call getmulti get includes main startmilliseconds 0 978 durationmilliseconds 2 323 firstfetchdurationmilliseconds 0 clienttimings redirectcount 0 timings name connect start 2 duration 0 name request start 2 duration 1 name response start 17 duration 1 name unload event start 18 duration 0 name dom content loaded event start 183 duration 67 name load event start 320 duration 1 durationmilliseconds 5 964 customlinks appstats http localhost 8080 ah stats details time 511483000 | front_end |
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jiny | p align center img width 150 src https i imgur com opg00ct png p github workflow status branch https img shields io github workflow status keva dev jiny java 20ci 20runner master label build style flat square lines of code https img shields io tokei lines github huynhminhtufu jiny style flat square github code size in bytes https img shields io github languages code size huynhminhtufu jiny style flat square github https img shields io github license huynhminhtufu jiny style flat square maven central https img shields io maven central v com jinyframework core style flat square what is jiny jiny features a lightweight servlet free and no dependency expressive and unopinionated web framework if you need a quick start simple way to write a jvm server you would love this framework unlike others web frameworks you can structure your application as you want quick start get started in 5 minutes https jinyframework com changelogs learn about the latest improvements https jinyframework com guide changelogs html development want to file a bug contribute some code or improve documentation excellent first see developer guide https jinyframework com guide developer guide html pull requests are encouraged and always welcome pick an issue https github com keva dev jiny issues and help us out to install and work on jiny locally git clone git github com keva dev jiny git cd jiny gradlew dependencies to build and run tests for the modules included in the package gradlew parallel build license apache license 2 0 https github com keva dev jiny blob master license | java http http-server http-client websocket web-framework | front_end |
OWASP-LLM | owasp llm top 10 owasp llm top 10 https github com imhego owasp llm blob main gif gif raw true https hego gitbook io home owasp owasp llm top 10 v1 0 hego wiki https hego gitbook io home owasp owasp llm top 10 v1 0 the owasp top 10 for large language model applications project aims to educate developers designers architects managers and organizations about the potential security risks when deploying and managing large language models llms the project provides a list of the top 10 most critical vulnerabilities often seen in llm applications highlighting their potential impact ease of exploitation and prevalence in real world applications examples of vulnerabilities include prompt injections data leakage inadequate sandboxing and unauthorized code execution among others the goal is to raise awareness of these vulnerabilities suggest remediation strategies and ultimately improve the security posture of llm applications title description llm01 prompt injection promptinjection md this manipulates a large language model llm through crafty inputs causing unintended actions by the llm direct injections overwrite system prompts while indirect ones manipulate inputs from external sources llm02 insecure output handling insecureoutputhandling md this vulnerability occurs when an llm output is accepted without scrutiny exposing backend systems misuse may lead to severe consequences like xss csrf ssrf privilege escalation or remote code execution llm03 training data poisoning trainingdatapoisoning md this occurs when llm training data is tampered introducing vulnerabilities or biases that compromise security effectiveness or ethical behavior sources include common crawl webtext openwebtext books llm04 model denial of service modeldos md attackers cause resource heavy operations on llms leading to service degradation or high costs the vulnerability is magnified due to the resource intensive nature of llms and unpredictability of user inputs llm05 supply chain vulnerabilities supplychainvulnerabilities md llm application lifecycle can be compromised by vulnerable components or services leading to security attacks using third party datasets pre trained models and plugins can add vulnerabilities llm06 sensitive information disclosure sensitiveinformationdisclosure md llm s may inadvertently reveal confidential data in its responses leading to unauthorized data access privacy violations and security breaches it s crucial to implement data sanitization and strict user policies to mitigate this llm07 insecure plugin design insecureplugindesign md llm plugins can have insecure inputs and insufficient access control this lack of application control makes them easier to exploit and can result in consequences like remote code execution llm08 excessive agency excessiveagency md llm based systems may undertake actions leading to unintended consequences the issue arises from excessive functionality permissions or autonomy granted to the llm based systems llm09 overreliance overreliance md systems or people overly depending on llms without oversight may face misinformation miscommunication legal issues and security vulnerabilities due to incorrect or inappropriate content generated by llms llm10 model theft modeltheft md this involves unauthorized access copying or exfiltration of proprietary llm models the impact includes economic losses compromised competitive advantage and potential access to sensitive information | ai artificial-intelligence bugbounty cybersecurity ethical-hacking hacking large-language-models owasp owasp-top-10 owasp-top-ten penetration-test penetration-testing penetration-testing-framework penetration-testing-tools penetrationtesting webapplication websecurity | ai |
tweetbotornot | tweetbotornot img width 160px src man figures logo png align right lifecycle https img shields io badge lifecycle experimental orange svg https www tidyverse org lifecycle experimental travis build status https travis ci org mkearney tweetbotornot svg branch master https travis ci org mkearney tweetbotornot coverage status https codecov io gh mkearney tweetbotornot branch master graph badge svg https codecov io github mkearney tweetbotornot branch master an r package for classifying twitter accounts as bot or not features uses machine learning to classify twitter accounts as bots or not bots the default model is 93 53 accurate when classifying bots and 95 32 accurate when classifying non bots the fast model is 91 78 accurate when classifying bots and 92 61 accurate when classifying non bots overall the default model is correct 93 8 of the time overall the fast model is correct 91 9 of the time install install from cran r install from cran install packages tweetbotornot install the development version from github r install remotes if not already if requirenamespace remotes quietly true install packages remotes install tweetbotornot from github devtools install github mkearney tweetbotornot api authorization users must be authorized in order to interact with twitter s api to setup your machine to make authorized requests you ll either need to be signed into twitter and working in an interactive session of r the browser will open asking you to authorize the rtweet client rstats2twitter or you ll need to create an app and have a developer account and your own api token the latter has the benefit of a having sufficient permissions for write acess and dm direct messages read access levels and b more stability if twitter decides to shut down kearneymw https twitter com kearneymw s access to twitter i try to be very responsible these days but twitter isn t always friendly to academic use cases to create an app and your own twitter token see these instructions provided in the rtweet package http rtweet info articles auth html usage there s one function tweetbotornot technically there s also botornot but it does the same exact thing give it a vector of screen names or user ids and let it go to work r load package library tweetbotornot select users users c realdonaldtrump netflix bot kearneymw dataandme hadleywickham ma salmon juliasilge tidyversetweets american voter mothgenerator hrbrmstr get botornot estimates data tweetbotornot users arrange by prob ests data order data prob bot a tibble 11 x 3 screen name user id prob bot chr chr dbl 1 hadleywickham 69133574 0 00754 2 realdonaldtrump 25073877 0 00995 3 kearneymw 2973406683 0 0607 4 ma salmon 2865404679 0 150 5 juliasilge 13074042 0 162 6 dataandme 3230388598 0 227 7 hrbrmstr 5685812 0 320 8 netflix bot 1203840834 0 978 9 tidyversetweets 935569091678691328 0 997 10 mothgenerator 3277928935 0 998 11 american voter 829792389925597184 1 000 integration with rtweet the botornot function also accepts data returned by rtweet http rtweet info functions r get most recent 100 tweets from each user tmls get timelines users n 100 pass the returned data to botornot data botornot tmls arrange by prob ests data order data prob bot a tibble 11 x 3 screen name user id prob bot chr chr dbl 1 hadleywickham 69133574 0 00754 2 realdonaldtrump 25073877 0 00995 3 kearneymw 2973406683 0 0607 4 ma salmon 2865404679 0 150 5 juliasilge 13074042 0 162 6 dataandme 3230388598 0 227 7 hrbrmstr 5685812 0 320 8 netflix bot 1203840834 0 978 9 tidyversetweets 935569091678691328 0 997 10 mothgenerator 3277928935 0 998 11 american voter 829792389925597184 1 000 fast true the default gradient boosted model uses both users level bio location number of followers and friends etc and tweets level number of hashtags mentions capital letters etc in a user s most recent 100 tweets data to estimate the probability that users are bots for larger data sets this method can be quite slow due to twitter s rest api rate limits users are limited to only 180 estimates per every 15 minutes to maximize the number of estimates per 15 minutes at the cost of being less accurate use the fast true argument this method uses only users level data which increases the maximum number of estimates per 15 minutes to 90 000 due to losses in accuracy this method should be used with caution r get botornot estimates data botornot users fast true arrange by prob ests data order data prob bot a tibble 11 x 3 screen name user id prob bot chr chr dbl 1 hadleywickham 69133574 0 00185 2 kearneymw 2973406683 0 0415 3 ma salmon 2865404679 0 0661 4 dataandme 3230388598 0 0965 5 juliasilge 13074042 0 112 6 hrbrmstr 5685812 0 121 7 realdonaldtrump 25073877 0 368 8 netflix bot 1203840834 0 978 9 tidyversetweets 935569091678691328 0 998 10 mothgenerator 3277928935 0 999 11 american voter 829792389925597184 0 999 note in order to avoid confusion the package was renamed from botrnot to tweetbotornot in june 2018 this package should not be confused with the botornot application http botornot co | twitter twitter-bots machine-learning rstats rtweet r r-package twitter-api mkearney-r-package | ai |
SQL-Challenge | sql challenge alt text query results sql png data engineering here the data is inspected and the entity relational diagram erd is sketched alt text quickdbd employees schema png data analysis here the results are analyzed by 1 querying the details of each employee employee number last name first name sex and salary and the below is the screen shot of the result alt text query results query1 result png 2 querying first name last name and hire date for employees who were hired in 1986 alt text query results query2 result png 3 querying the manager of each department with the following information department number department name the manager s employee number last name first name alt text query results query3 result png 4 querying the department of each employee with the following information employee number last name first name and department name alt text query results query4 result png 5 querying first name last name and sex for employees whose first name is hercules and last names begin with b alt text query results query6 result png 6 querying all employees in the sales department including their employee number last name first name and department name alt text query results query7 result png 7 querying all employees in the sales and development departments including their employee number last name first name and department name alt text query results query8 result png 8 querying the frequency count of employee last names i e how many employees share each last name in descending order alt text query8 result png bonus 1 create a histogram to visualize the most common salary ranges for employees alt text salaries histogram png 2 create a bar chart of average salary by title alt text avg salaries bar chart png | server |
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Documents | documents github stars https img shields io github stars coderserdar documents style social github forks https img shields io github forks coderserdar documents style social github watchers https img shields io github watchers coderserdar documents style social github repo size https img shields io github repo size coderserdar documents style plastic github language count https img shields io github languages count coderserdar documents style plastic github top language https img shields io github languages top coderserdar documents style plastic github last commit https img shields io github last commit coderserdar documents color red style plastic github issues https img shields io github issues coderserdar documents this repo includes some kind of documentation files about information technologies you can find free e books and whitepapers about programming languages rdbms relational data base management systems mobile development web development data structures and algorithms clean code code quality analysis data science machine learning interview software testing orm object relational mapping tools network operating systems cyber security hacking non categorized technologies important note i remove the books which are in turkish from this repo if you want to access them you can lookup to turkcekaynaklar https github com coderserdar turkcekaynaklar repo address there are sub section in this repository like you can look at documents site map programming languages https github com coderserdar documents tree main programming 20languages delphi https github com coderserdar documents tree main programming 20languages delphi delphi 27th webinar free e books https github com coderserdar documents tree main programming 20languages delphi delphi 2027th 20webinar embarcadero codecentral free e books https github com coderserdar documents tree main programming 20languages delphi embarcadero 20codecentral embarcadero whitepapers https github com coderserdar documents tree main programming 20languages delphi embarcadero 20whitepapers other books https github com coderserdar documents tree main programming 20languages delphi other 20books books about rad studio https github com coderserdar documents tree main programming 20languages delphi rad 20studio developer direct mobile school 2014 https github com coderserdar documents tree main programming 20languages delphi rad 20studio developer 20direct 20mobile 20school 202014 windows and mac development coursebook https github com coderserdar documents tree main programming 20languages delphi rad 20studio windows 20and 20mac 20development 20coursebook books in theswissbay ch https github com coderserdar documents tree main programming 20languages delphi the 20swiss 20bay datasnap https github com coderserdar documents tree main programming 20languages delphi datasnap lazarus https github com coderserdar documents tree main programming 20languages lazarus assembly https github com coderserdar documents tree main programming 20languages assembly c https github com coderserdar documents tree main programming 20languages c c https github com coderserdar documents tree main programming 20languages c c https github com coderserdar documents tree main programming 20languages c 20sharp go https github com coderserdar documents tree main programming 20languages go java https github com coderserdar documents tree main programming 20languages java php https github com coderserdar documents tree main programming 20languages php python https github com coderserdar documents tree main programming 20languages python ruby https github com coderserdar documents tree main programming 20languages ruby visual basic https github com coderserdar documents tree main programming 20languages visual 20basic other haskell matlab perl etc https github com coderserdar documents tree main programming 20languages other interview some helpful documents before it job interviews https github com coderserdar documents tree main interview cyber security https github com coderserdar documents tree main interview cyber 20security data science https github com coderserdar documents tree main interview data 20science general https github com coderserdar documents tree main interview general machine learning https github com coderserdar documents tree main interview machine 20learning programming languages https github com coderserdar documents tree main interview programming 20languages sql https github com coderserdar documents tree main interview sql programming methodologies https github com coderserdar documents tree main programming 20methodologies ci cd continuous integration continuous development https github com coderserdar documents tree main programming 20methodologies ci cd jenkins https github com coderserdar documents tree main programming 20methodologies ci cd jenkins devops https github com coderserdar documents tree main programming 20methodologies devops git https github com coderserdar documents tree main programming 20methodologies git other non categorized books https github com coderserdar documents tree main programming 20methodologies other orm tools https github com coderserdar documents tree main orm 20 object 20relational 20mapping dapper https github com coderserdar documents tree main orm 20 object 20relational 20mapping dapper entity framework https github com coderserdar documents tree main orm 20 object 20relational 20mapping entity 20framework llblgen https github com coderserdar documents tree main orm 20 object 20relational 20mapping llblgen nhibernate https github com coderserdar documents tree main orm 20 object 20relational 20mapping nhibernate database https github com coderserdar documents tree main database general https github com coderserdar documents tree main database general interbase https github com coderserdar documents tree main database interbase microsoft sql server https github com coderserdar documents tree main database microsoft 20sql 20server mongodb https github com coderserdar documents tree main database mongodb mysql https github com coderserdar documents tree main database mysql oracle https github com coderserdar documents tree main database oracle postgresql https github com coderserdar documents tree main database postgresql mobile development https github com coderserdar documents tree main mobile 20development android https github com coderserdar documents tree main mobile 20development android flutter https github com coderserdar documents tree main mobile 20development flutter ios https github com coderserdar documents tree main mobile 20development ios kotlin https github com coderserdar documents tree main mobile 20development kotlin unity https github com coderserdar documents tree main mobile 20development unity other react native xamarin etc https github com coderserdar documents tree main mobile 20development other operating systems https github com coderserdar documents tree main operating 20systems general https github com coderserdar documents tree main operating 20systems general linux https github com coderserdar documents tree main operating 20systems linux windows https github com coderserdar documents tree main operating 20systems windows freebsd https github com coderserdar documents tree main operating 20systems freebsd articles https github com coderserdar documents tree main operating 20systems freebsd articles books https github com coderserdar documents tree main operating 20systems freebsd books web development https github com coderserdar documents tree main web 20development asp net core https github com coderserdar documents tree main web 20development asp 20 net 20core amazon web services https github com coderserdar documents tree main web 20development aws azure https github com coderserdar documents tree main web 20development azure docker https github com coderserdar documents tree main web 20development docker google cloud https github com coderserdar documents tree main web 20development google 20cloud html5 https github com coderserdar documents tree main web 20development html5 javascript https github com coderserdar documents tree main web 20development javascript js frameworks angular node react js etc https github com coderserdar documents tree main web 20development js 20frameworks kubernetes https github com coderserdar documents tree main web 20development kubernetes other non categorized books https github com coderserdar documents tree main web 20development other data structures algorithms https github com coderserdar documents tree main data 20structures 20 26 20algorithms clean code https github com coderserdar documents tree main clean 20code code quality https github com coderserdar documents tree main code 20quality analysis https github com coderserdar documents tree main analysis data science https github com coderserdar documents tree main data 20science project management https github com coderserdar documents tree main project 20management network https github com coderserdar documents tree main network testing https github com coderserdar documents tree main testing cyber security hacking https github com coderserdar documents tree main cyber 20security 20 26 20hacking machine learning https github com coderserdar documents tree main machine 20learning other https github com coderserdar documents tree main other excel https github com coderserdar documents tree main other excel apache kafka https github com coderserdar documents tree main other apache 20kafka statistics https github com coderserdar documents tree main other statistics power bi https github com coderserdar documents tree main other power 20bi and if you code with c type languages and interested in pascal language you should look guvacode https github com guvacode s pascal for c users https github com guvacode pascal for c users guide | delphi e-book lazarus free-pascal object-pascal algorithm algorithms cplusplus cpp17 data-structures rad-studio rad-studio-ide android flutter kotlin-android cyber-security cybersecurity database operating-system llblgen | front_end |
TMDBBoxOfficePrediction | tmdbboxofficeprediction objective predicting overall worldwide box office revenue from metadata of over 7 000 past films from the movie database data points provided include cast crew plot keywords budget posters release dates languages production companies and countries files included with this project da ipynb kaggle json important we are loading the tmdb dataset in the jupyter notebook directly from the kaggle repository using the kaggle api upload kaggle json when prompted in da ipynb file | server |
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HardwareAllTheThings | hardwareallthethings a list of useful payloads and bypasses for hardware and iot security feel free to improve with your payloads and techniques i heart pull requests | hardware payloads pentest wiki hacktoberfest iot security | server |
FreeRTOS-port-for-LinkIt-SDK | freertos port for linkit sdk freertos port for mediatek arm cm4 mcu in linkit sdk for the complete linkit sdk contain the freertos port please visit https labs mediatek com en support resources | os |
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PhotoScavengerBackend | img src https user images githubusercontent com 71013416 183674037 eca7cc9b 4a19 494c a449 af638fdd869c png width 250 photoscavenger api already more than 100 downloads in less than 5 weeks this is the fastapi backend that supports my photoscavenger apps it can detect certain objects from pictures this api serves my ios android apps for the game here https photoscavenger petervandoorn com api dashboard is here https photoscavenger vdotvo9a4e2a6 eu central 1 cs amazonlightsail com here is a in game screenshot br ezgif com gif maker https user images githubusercontent com 71013416 178448499 3f547173 43ab 41b2 967a a1f9ae8dd9a0 gif api documentation https photoscavenger vdotvo9a4e2a6 eu central 1 cs amazonlightsail com docs app on appstore https apps apple com nl app photo scavenger id1637234234 l en what does it do this backend can check images to see if a certain object is on the picture for instance you can check if there is a cat on a particular photo the backend will then return a false or true for that object depending on whether it was found it will also return a list objects it managed to find this allows you validate in case of false negatives to see what the model thought was on the picture see an example below how does it work you make a post request with a image in the formdata content type file for instance this image schermafbeelding 2022 06 18 om 11 26 33 https user images githubusercontent com 71013416 175918302 bd99786a 9d4f 49d7 a90c 9bbbff847035 png and it returns an image with the detected objects scanned imagee0dee0c1 3512 4617 8a3d 4d19b80c85e7 https user images githubusercontent com 71013416 177133306 d0eab947 6013 4925 94ce dccb3106af1a jpg how to run simply clone the repo and run gh repo clone two trick pony nl scangamebackend cd scangamebackend uvicorn main app host 0 0 0 0 port 80 reload v1 vs v2 the v1 api has 18 objects to be detected and v2 can detect 80 different objects their endpoinsta are v1 https photoscavenger vdotvo9a4e2a6 eu central 1 cs amazonlightsail com uploadfile person v2 https photoscavenger vdotvo9a4e2a6 eu central 1 cs amazonlightsail com v2 uploadfile person request response local call http localhost 80 v2 uploadfile bicycle or http localhost 80 uploadfile person response for bicycle searchedfor person wasfound yes otherobjectsdetected person person person person chair person chair boat bird person person processed filename imagecfb104fe 8347 4ef5 b733 2a0d3d8e6b88 jpg supported objects in v1 background earoplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor supported objects in v2 person bicycle car motorcycle airplane bus train truck boat traffic light fire hydrant stop sign parking meter bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard sports ball kite baseball bat baseball glove skateboard surfboard tennis racket bottle wine glass cup fork knife spoon bowl banana apple sandwich orange broccoli carrot hot dog pizza donut cake chair couch potted plant bed dining table toilet tv laptop mouse remote keyboard cell phone microwave oven toaster sink refrigerator book clock vase scissors teddy bear hair drier toothbrush getting a new assignment note corrospond the right assignment api with the correct image detector api as the v1 version will only know the 18 classes that ai model has also the integer at the end of the call is the score the player currently has the higher the score the more difficult the object is to find v1 http localhost 8000 newassignment 2000 v2 http localhost 8000 v2 newassignment 2000 response dog monitoring deployment here is my command to keep track of deployment gh run watch watch n 5 aws lightsail get container log service name photoscavenger container name photoscavenger filter pattern deployment | ai api-rest cv2 fastapi image-recognition machine-learning python heroku | server |
ARINC653_ARMV7A_Z7000 | arinc653 armv7a z7000 bare metal real time operating system rtos for xilinx zynq 7000 cortex a9 armv7 a multi core socs zedboard picozed microzed and similars based on the arinc 653 part 1 specification ide installation run xilinx unified installer v2018 3 or a similar superior version select vivado hl webpack installation ensure vivado design suite software development kit sdk with the arm cortex a9 compiler tool chain and socs zynq 7000 are marked compilation configuration c c build settings arm v7 gcc compiler directories include paths workspace loc projname src arinc653 port include add to both debug and release c c build settings arm v7 gcc compiler directories include paths workspace loc projname src arinc653 include add to both debug and release c c build settings arm v7 gcc compiler directories include paths workspace loc projname src test include add to both debug and release c c build settings arm v7 gcc linker linker script src application armv7a z7000 ld change for both debug and release microphase z7 lite jtag debugging configuration j1 1 2 jtag debugging on the target platform select run debug configurations double click xilinx c c application system debugger configure the debug configuration as follows target setup debug type standalone application debug target setup connection local target setup reset entire system true target setup program fpga true target setup run ps7 init true target setup run ps7 post config true application stop at main false application processor ps7 cortexa9 0 summary download true stop at program entry true application processor ps7 cortexa9 1 summary download false stop at program entry false click apply click debug debugging on the second core after the debug session is started select run debug configurations select debug configuration to be used configure the debug configuration as follows symbol files debug context apu arm cortex a9 mpcore 1 click add select the project s elf file click ok click close programming the bitstream into the qspi memory build the project open the project in vivado open hardware manager open target auto connect in window hardware right click xc7z010 1 and open add configuration memory device set manufacturer to winbond set type to qspi set width to x4 single select the w25q128fv qspi x4 single part number click ok accept programming the device by clicking ok set configuration file to the boot bin file built set zynq fsbl to the fsbl debug fsbl elf file found in the software project directory click ok | arm cortex cortex-a cortex-a9 xilinx xilinx-vivado zynq zynq-7000 zynq-7010 zynq-7020 arinc arinc653 z7-lite | os |
dev290x-v2 | dev290x v2 computer vision and image analysis v2 this repo contains the lab files for microsoft course dev290x computer vision and image analysis | ai |
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LLM | llm large language models | ai |
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Ruby-NER | directory tree ruby ner lib pipeline rb stanford segmenter 2012 11 11 test simp utf8 segment sh stanford postagger full 2012 11 11 models stanford postagger sh usage cd ruby ner bundle without development lib pipeline rb test input file output file model file utils lib pipeline rb help command lib pipeline rb check column size feature lib pipeline rb sub label lib pipeline rb extract prefix and surfix note stanford postagger sh classpath | server |
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point2cad | point2cad code for point2cad reverse engineering cad models from 3d point clouds yujia liu sup 1 sup anton obukhov sup 1 sup jan dirk wegner sup 1 2 sup konrad schindler sup 1 sup sup 1 sup eth zurich sup 2 sup university of zurich code coming soon | cloud |
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Design-and-Optimization-2018 | design and optimization of embedded and real time systems 2018 | os |
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environment-embedded-system | environment embedded system this environment has as objective help in a incremental development of embedded system base on high level specifications dependencies xerces c systemc 2 3 1 to compiler the output files authors rafael carvalho https github com rafaeltmc rebeca alencar | systemc xml-parser embedded | os |
Bookshop | bookshop this is a simple android project on bookshop automation system this application provides option for maintaining the record of available books and books that have sold user can add book even without typing by using the barcode scanning functionality provided in add activity | java bookshop android-application android | server |
alibabacloud-iot-device-sdk | alibabacloud iot device sdk for javascript iot alibabacloud iot device sdk iot linkplatform javascript sdk node broswer linkplatform pub sub sdk xuanyan lyw alibaba inc com iot https help aliyun com product 30520 html https help aliyun com product 30520 html js sdk node broswer 418k node js 4 0 0 npm bash npm install alibabacloud iot device sdk save 1 2 4 1 2 8 alibabacloud iot device sdk js alibabacloud iot device sdk 1 2 8 alimin compatible js dist javascript node const iot require alibabacloud iot device sdk dist js const iot require dist alibabacloud iot device sdk js alibabacloud iot device sdk js alibabacloud iot device sdk 1 2 8 alimin compatible js dist js https github com aliyun alibabacloud iot device sdk tree master dist alibabacloud iot device sdk js https unpkg com alibabacloud iot device sdk 1 2 4 dist alibabacloud iot device sdk js alibabacloud iot device sdk min js https unpkg com alibabacloud iot device sdk 1 2 4 dist alibabacloud iot device sdk min js const device iot device productkey productkey devicename devicename devicesecret devicesecret protocol alis protocol wxs device subscribe productkey devicename get device on connect console log connect successfully device publish productkey devicename update hello world device on message topic payload console log topic payload tostring linkplatform alink sdk mqtt topic a href postprops code iot device b postprops b code a a href onservice code iot device b onservice b code a a href postevent code iot device b postevent b code a api javascript device on connect device postprops lightswitch 0 res console log postprops res device postprops javascript topic device publish sys productkey devicename thing event property post json stringify id msgid version 1 0 params lightswitch 25 method thing event property post topic device subscribe sys productkey devicename thing event property post reply device on message function topic message const res message tostring if res id msgid console log res data javascript wakeup async device onservice wakeup async function res reply console log wakeup async res reply code 200 data out 1 api example device mqtt js eventemitter gateway device api subdevice device api api a href device code iot b device b code a a href publish code iot device b publish b code a a href subscribe code iot device b subscribe b code a a href unsubscribe code iot device b unsubscribe b code a a href postprops code iot device b postprops b code a a href onprops code iot device b onprops b code a a href postevent code iot device b postevent b code a a href onservice code iot device b onservice b code a a href end code iot device b end b code a a href posttags code iot posttags b posttags b code a a href deletetags code iot deletetags b deletetags b code a a href getconfig code iot getconfig b getconfig b code a a href onconfig code iot onconfig b onconfig b code a a href onshadow code iot onshadow b onshadow b code a a href getshadow code iot getshadow b getshadow b code a a href postshadow code iot postshadow b postshadow b code a a href deleteshadow code iot deleteshadow b deleteshadow b code a api a href gateway code iot b gateway b code a a href addtopo code iot gateway b addtopo b code a a href gettopo code iot gateway b gettopo b code a a href removetopo code iot gateway b removetopo b code a a href login code iot gateway b login b code a a href logout code iot gateway b logout b code a api login a href register code iot b register b code a a href regiestsubdevice code iot gateway b regiestsubdevice b code a a name device a iot device options device options productkey string devicename string devicesecret string region string region cn shanghai tls bool tls node js tls wss keepalive int 60 clean bool cleansession session false js const device iot device productkey productkey devicename devicename devicesecret devicesecret event connect function connack javascript const iot require alibabacloud iot device sdk const device iot device productkey productkey devicename devicename devicesecret devicesecret device on connect console log connected event message function topic message topic message payload js device on message res console log msg res event error font color dd0000 nodejs event error throw error try catch error font br node error when an error occurs within an eventemitter instance the typical action is for an error event to be emitted these are treated as special cases within node js if an eventemitter does not have at least one listener registered for the error event and an error event is emitted the error is thrown a stack trace is printed and the node js process exits https nodejs org api events html js device on error err console log error err a name publish a iot device publish topic message options callback topic topic string message buffer or string options qos qos number 0 retain dup callback js device publish productkey devicename update hello world device publish productkey devicename update hello world qos 1 default 0 a name subscribe a iot device subscribe topic callback mqtt client subscribe https github com mqttjs mqtt js blob master readme md subscribe a name unsubscribe a iot device unsubscribe topic callback mqtt client unsubscribe https github com mqttjs mqtt js blob master readme md unsubscribe a name postprops a iot device postprops params callback params object callback res reply a name postevent a js device postprops lightswitch 0 res console log res a name onprops a iot device onprops cb refuns cb a name onprops a js device onprops res console log onprops res iot device postevent eventidentifier params callback eventname string params object callback err res reply js device postevent lowpower power 10 res console log postevent res a name onservice a iot device onservice seviceidentifier callback sevicename string callback res reply relpy params async or sync params id id code code 200 data type async or sync javascript wakeup async wakeup sync out device onservice wakeup async function res reply console log onservice res reply code 200 data out 1 device onservice wakeup sync function res reply console log onservice res reply code 200 data out 1 sync a name end a iot device end force options callback mqtt client end https github com mqttjs mqtt js blob master readme md end a name posttags a iot device posttags params callback params array attrkey xxx attrvalue xxx attrkey res code 200 attrvalue reply callback res reply key key params js const tags attrkey temperature attrvalue 36 8 device posttags tags res console log add tag ok res res id done a name deletetags a iot device deletetags tags tags array string string string tag javascript device deletetags taga tagb a name getconfig a iot device getconfig callback callback res javascript device getconfig res console log getconfig res a name onconfig a iot device onconfig callback callback res javascript device onconfig res console log onconfig res res a name onshadow a iot device onshadow callback callback res javascript device onshadow res console log o res a name getshadow a iot device getshadow javascript onshadow device getshadow a name postshadow a iot device postshadow params params javascript onshadow device postshadow a avalue a name deleteshadow a iot device deleteshadow keys keys key key javascript device deleteshadow a device deleteshadow a b device deleteshadow a name gateway a iot gateway options gateway device options productkey string devicename string devicesecret string region string region cn shanghai tls bool tls node js tls wss keepalive int 60 clean bool cleansession session false js const device iot gateway productkey productkey devicename devicename devicesecret devicesecret a name addtopo a iot gateway addtopo devicesign callback params productkey devicename devicesecret productkey devicename devicesecret callback res reply js gateway addtopo sub device1 sub device2 res console log gettopo res message res data a name gettopo a iot gateway gettopo callback callback res reply js gateway gettopo res console log res a name removetopo a iot gateway removetopo params callback params productkey xx devicename xx productkey xx devicename xx productkey devicename callback res reply js gateway removetopo productkey xx devicename xx productkey xx devicename xx res console log gettopo console log res message console log res data a name login a iot gateway login params callback params productkey xx devicename xx productkey string devicename string devicesecret string callback res reply api js gateway on connect ok sub1 gateway login productkey xx devicename xx devicesecret xxx res console log login res sub1 on connect console log sub connected do something sub1 postprops lightswitch 0 res console log sub postprops console log res a name logout a iot gateway logout params callback params productkey string devicename string callback res reply js gateway logout productkey xxxxx devicename xxxxx res console log logout res a name register a iot register params callback params object productkey a15ydgqghu0 productkey productsecret devicename callback res reply js const params productkey xxxxxx productsecret xxxxxx devicename xxxxxx iot register params res console log register res if res code 200 a name regiestsubdevice a iot gateway regiestsubdevice params callback params productkey devicename productkey devicename productkey2 devicename2 productkey devicename callback res reply js const gateway iot gateway productkey productkey devicename devicename devicesecret devicesecret gateway on connect gateway regiestsubdevice devicename xxx productkey xxx res if res code 200 res id id sdk message data code code 200 400 429 460 520 example example iot const iot require lib github const iot require alibabacloud iot device sdk example github example https github com aliyun alibabacloud iot device sdk broswer demo example broswer index html quickstart demo example quickstart props demo example props js event demo example event js service async demo example service async js service sync demo example service sync js origin mqtt pub sub example origin js remote confit get example remote confit get js remote confit sub example remote confit sub js tag example tag js shadow example shadow js one model one secret demo example one model one secret js forward 1 2 8 crypto js crypto min 661k 418k 37 1 2 7 onprops postevent postprops 1 2 5 mqtt pub sub 1 2 4 aliyun iot device sdk alibabacloud iot device sdk docs docs onservice reply javascript wakeup async wakeup sync out device onservice wakeup async function res reply console log onservice res reply code 200 data out 1 device onservice wakeup sync function res reply console log onservice res reply code 200 data out 1 sync onconfig example subdevice 1 0 1 topo deletetopo removetopo signutil signutil device gateway signalgorithm signmethod serve onservice res err code mssage res postsubdeviceprops postsubdeviceevent servesubdeviceservice iot gateway postsubdeviceprops iot gateway postsubdeviceevent iot gateway servesubdeviceservice const sub gateway login sub device1 res console log login res sub on connect console log sub connected sub postprops lightswitch 0 res console log sub postprops console log res settimeout gateway logout sub device1 res console log logout res 5000 0 3 1 productkey devicename devicesecret 0 3 0 1 option keepalive clean cleansession 2 regionid region regionid 1 onmessage js const device iot device device on connect res device on message topic payload you biz logic at here device on message device on connecnt message const device iot device device on connect res device on message topic payload you biz logic at here | iot nodejs javascript aliyun aliyun-iot-sdk sdk | server |
Social-Saver-Android | social saver android social saver is a mobile application i built for my cs 2340 class at georgia tech social saver is a social shopping app that allows users to post awesome deals they find locally online once posted their friends will be able to see the deals and locate them with google maps integration we allow users to pinpoint deals for the locations of the deals social saver for ios and android both use parse as it s backend for user and deal persistency note this app was built before my summer internship at possible mobile and my most recent work at teq charging my knowledge of mobile development has changed dramatically since those two educational work experiences | server |
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wmr | wmr img src docs public assets wmr svg alt wmr logo width 400 npm https img shields io npm v wmr svg http npm im wmr install size https packagephobia com badge p wmr https packagephobia com result p wmr opencollective backers https opencollective com preact backers badge svg backers opencollective sponsors https opencollective com preact sponsors badge svg sponsors the tiny all in one development tool for modern web apps in a single 2mb file with no dependencies all the features you d expect and more from development to production nbsp no entry points or pages to configure just html files with script type module br nbsp safely import packages from npm without installation br nbsp smart bundling and caching for npm dependencies br nbsp hot reloading for modules preact components and css br nbsp lightning fast jsx support that you can debug in the browser br nbsp import css files and css modules https github com css modules css modules module css br nbsp out of the box support for typescript https www typescriptlang org br nbsp static file serving with hot reloading of css and images br nbsp highly optimized rollup based production output wmr build br nbsp crawls and pre renders your app s pages to static html at build time br nbsp built in http2 in dev and prod wmr serve http2 br nbsp supports rollup plugins packages wmr readme md configuration and plugins even in development where rollup isn t used quickstart recommended create a new project in seconds using create wmr https npm im create wmr strong code npm init wmr your project name code strong or strong code yarn create wmr your project name code strong p img width 400 src https user images githubusercontent com 105127 100917537 4661e100 34a5 11eb 89bd 565b7bc31919 gif alt illustration of installation to build for wmr p if you d like eslint to be set up for you add eslint to the command note this will use 150mb of disk space check out the docs to learn more https wmr dev docs packages package description version wmr packages wmr tiny all in one development tool for modern web apps wmr npm https img shields io npm v wmr svg https npm im wmr create wmr packages create wmr create a new wmr project in seconds create wmr npm https img shields io npm v create wmr svg https npm im create wmr wmrjs directory import packages directory plugin import a directory s files as an array wmrjs directory import npm https img shields io npm v wmrjs directory import svg https npm im wmrjs directory import wmrjs nomodule packages nomodule plugin generate legacy fallback bundles for older browsers wmrjs nomodule npm https img shields io npm v wmrjs nomodule svg https npm im wmrjs nomodule wmrjs service worker packages sw plugin bundle service workers wmrjs service worker npm https img shields io npm v wmrjs service worker svg https npm im wmrjs service worker preact iso packages preact iso optimal code splitting hydration and routing for preact preact iso npm https img shields io npm v preact iso svg https npm im preact iso contributing sh git clone git github com preactjs wmr git cd wmr yarn run the demo no compile yarn demo serve build and serve the demo for prod yarn demo build prod yarn demo serve prod build the single file cli yarn workspace wmr build adding a changeset don t forget to also include a changeset by running this command at the root of the project sh yarn changeset this will take you through a process of selecting the changed packages the version updates and a description of the change afterwards changesets will generate a md file inside a changeset directory please commit that file as well after all that you are good to go 1 | build-tool esmodules preact rollup | front_end |
IoT-For-Beginners | github license https img shields io github license microsoft iot for beginners svg https github com microsoft iot for beginners blob master license github contributors https img shields io github contributors microsoft iot for beginners svg https github com microsoft iot for beginners graphs contributors github issues https img shields io github issues microsoft iot for beginners svg https github com microsoft iot for beginners issues github pull requests https img shields io github issues pr microsoft iot for beginners svg https github com microsoft iot for beginners pulls prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com github watchers https img shields io github watchers microsoft iot for beginners svg style social label watch https github com microsoft iot for beginners watchers github forks https img shields io github forks microsoft iot for beginners svg style social label fork https github com microsoft iot for beginners network github stars https img shields io github stars microsoft iot for beginners svg style social label star https github com microsoft iot for beginners stargazers bengali https img shields io badge bengali blue translations readme bn md chinese https img shields io badge chinese yellow translations readme zh cn md turkish https img shields io badge turkish darkgreen translations readme tr md french https img shields io badge french purple translations readme fr md korean https img shields io badge korean white translations readme ko md japanese https img shields io badge japanese red translations readme ja md iot for beginners a curriculum azure cloud advocates at microsoft are pleased to offer a 12 week 24 lesson curriculum all about iot basics each lesson includes pre and post lesson quizzes written instructions to complete the lesson a solution an assignment and more our project based pedagogy allows you to learn while building a proven way for new skills to stick the projects cover the journey of food from farm to table this includes farming logistics manufacturing retail and consumer all popular industry areas for iot devices a road map for the course showing 24 lessons covering intro farming transport processing retail and cooking sketchnotes roadmap jpg sketchnote by nitya narasimhan https github com nitya click the image for a larger version hearty thanks to our authors jen fox https github com jenfoxbot jen looper https github com jlooper jim bennett https github com jimbobbennett and our sketchnote artist nitya narasimhan https github com nitya thanks as well to our team of microsoft learn student ambassadors https studentambassadors microsoft com wt mc id academic 17441 jabenn who have been reviewing and translating this curriculum aditya garg https github com adityagarg00 anurag sharma https github com anurag 0 1 a arpita das https github com arpiiitaaa aryan jain https www linkedin com in aryan jain 47a4a1145 bhavesh suneja https github com elitewarrior315 faith hunja https faithhunja github io lateefah bello https www linkedin com in lateefah bello manvi jha https github com severus matthew mireille tan https www linkedin com in mireille tan a4834819a mohammad iftekher iftu ebne jalal https github com iftu119 mohammad zulfikar https github com mohzulfikar priyanshu srivastav https www linkedin com in priyanshu srivastav b067241ba thanmai gowducheruvu https github com innovation platform and zina kamel https www linkedin com in zina kamel meet the team promo video images iot gif https youtu be wippujri5k gif by mohit jaisal https linkedin com in mohitjaisal click the image above for a video about the project teachers we have included some suggestions for teachers md on how to use this curriculum if you would like to create your own lessons we have also included a lesson template lesson template readme md students https aka ms student page to use this curriculum on your own fork the entire repo and complete the exercises on your own starting with a pre lecture quiz then reading the lecture and completing the rest of the activities try to create the projects by comprehending the lessons rather than copying the solution code however that code is available in the solutions folders in each project oriented lesson another idea would be to form a study group with friends and go through the content together for further study we recommend microsoft learn https docs microsoft com users jimbobbennett collections ke2ehd351jopwr wt mc id academic 17441 jabenn for a video overview of this course check out this video promo video https img youtube com vi bccemm8gruc 0 jpg https youtube com watch v bccemm8gruc promo video click the image above for a video about the project pedagogy we have chosen two pedagogical tenets while building this curriculum ensuring that it is project based and that it includes frequent quizzes by the end of this series students will have built a plant monitoring and watering system a vehicle tracker a smart factory setup to track and check food and a voice controlled cooking timer and will have learned the basics of the internet of things including how to write device code connect to the cloud analyze telemetry and run ai on the edge by ensuring that the content aligns with projects the process is made more engaging for students and retention of concepts will be augmented in addition a low stakes quiz before a class sets the intention of the student towards learning a topic while a second quiz after class ensures further retention this curriculum was designed to be flexible and fun and can be taken in whole or in part the projects start small and become increasingly complex by the end of the 12 week cycle each project is based around real world hardware available to students and hobbyists each project looks into the specific project domain providing relevant background knowledge to be a successful developer it helps to understand the domain in which you are solving problems providing this background knowledge allows students to think about their iot solutions and learnings in the context of the kind of real world problem that they might be asked to solve as an iot developer students learn the why of the solutions they are building and get an appreciation of the end user hardware we have two choices of iot hardware to use for the projects depending on personal preference programming language knowledge or preferences learning goals and availability we have also provided a virtual hardware version for those who don t have access to hardware or want to learn more before committing to a purchase you can read more and find a shopping list on the hardware page hardware md including links to buy complete kits from our friends at seeed studio find our code of conduct code of conduct md contributing contributing md and translation translations md guidelines we welcome your constructive feedback each lesson includes sketchnote optional supplemental video pre lesson warmup quiz written lesson for project based lessons step by step guides on how to build the project knowledge checks a challenge supplemental reading assignment post lesson quiz a note about quizzes all quizzes are contained in this app https black meadow 040d15503 1 azurestaticapps net for 48 total quizzes of three questions each they are linked from within the lessons but the quiz app can be run locally follow the instruction in the quiz app folder they are gradually being localized lessons project name concepts taught learning objectives linked lesson 01 getting started 1 getting started readme md introduction to iot learn the basic principles of iot and the basic building blocks of iot solutions such as sensors and cloud services whilst you are setting up your first iot device introduction to iot 1 getting started lessons 1 introduction to iot readme md 02 getting started 1 getting started readme md a deeper dive into iot learn more about the components of an iot system as well as microcontrollers and single board computers a deeper dive into iot 1 getting started lessons 2 deeper dive readme md 03 getting started 1 getting started readme md interact with the physical world with sensors and actuators learn about sensors to gather data from the physical world and actuators to send feedback whilst you build a nightlight interact with the physical world with sensors and actuators 1 getting started lessons 3 sensors and actuators readme md 04 getting started 1 getting started readme md connect your device to the internet learn about how to connect an iot device to the internet to send and receive messages by connecting your nightlight to an mqtt broker connect your device to the internet 1 getting started lessons 4 connect internet readme md 05 farm 2 farm readme md predict plant growth learn how to predict plant growth using temperature data captured by an iot device predict plant growth 2 farm lessons 1 predict plant growth readme md 06 farm 2 farm readme md detect soil moisture learn how to detect soil moisture and calibrate a soil moisture sensor detect soil moisture 2 farm lessons 2 detect soil moisture readme md 07 farm 2 farm readme md automated plant watering learn how to automate and time watering using a relay and mqtt automated plant watering 2 farm lessons 3 automated plant watering readme md 08 farm 2 farm readme md migrate your plant to the cloud learn about the cloud and cloud hosted iot services and how to connect your plant to one of these instead of a public mqtt broker migrate your plant to the cloud 2 farm lessons 4 migrate your plant to the cloud readme md 09 farm 2 farm readme md migrate your application logic to the cloud learn about how you can write application logic in the cloud that responds to iot messages migrate your application logic to the cloud 2 farm lessons 5 migrate application to the cloud readme md 10 farm 2 farm readme md keep your plant secure learn about security with iot and how to keep your plant secure with keys and certificates keep your plant secure 2 farm lessons 6 keep your plant secure readme md 11 transport 3 transport readme md location tracking learn about gps location tracking for iot devices location tracking 3 transport lessons 1 location tracking readme md 12 transport 3 transport readme md store location data learn how to store iot data to be visualized or analysed later store location data 3 transport lessons 2 store location data readme md 13 transport 3 transport readme md visualize location data learn about visualizing location data on a map and how maps represent the real 3d world in 2 dimensions visualize location data 3 transport lessons 3 visualize location data readme md 14 transport 3 transport readme md geofences learn about geofences and how they can be used to alert when vehicles in the supply chain are close to their destination geofences 3 transport lessons 4 geofences readme md 15 manufacturing 4 manufacturing readme md train a fruit quality detector learn about training an image classifier in the cloud to detect fruit quality train a fruit quality detector 4 manufacturing lessons 1 train fruit detector readme md 16 manufacturing 4 manufacturing readme md check fruit quality from an iot device learn about using your fruit quality detector from an iot device check fruit quality from an iot device 4 manufacturing lessons 2 check fruit from device readme md 17 manufacturing 4 manufacturing readme md run your fruit detector on the edge learn about running your fruit detector on an iot device on the edge run your fruit detector on the edge 4 manufacturing lessons 3 run fruit detector edge readme md 18 manufacturing 4 manufacturing readme md trigger fruit quality detection from a sensor learn about triggering fruit quality detection from a sensor trigger fruit quality detection from a sensor 4 manufacturing lessons 4 trigger fruit detector readme md 19 retail 5 retail readme md train a stock detector learn how to use object detection to train a stock detector to count stock in a shop train a stock detector 5 retail lessons 1 train stock detector readme md 20 retail 5 retail readme md check stock from an iot device learn how to check stock from an iot device using an object detection model check stock from an iot device 5 retail lessons 2 check stock device readme md 21 consumer 6 consumer readme md recognize speech with an iot device learn how to recognize speech from an iot device to build a smart timer recognize speech with an iot device 6 consumer lessons 1 speech recognition readme md 22 consumer 6 consumer readme md understand language learn how to understand sentences spoken to an iot device understand language 6 consumer lessons 2 language understanding readme md 23 consumer 6 consumer readme md set a timer and provide spoken feedback learn how to set a timer on an iot device and give spoken feedback on when the timer is set and when it finishes set a timer and provide spoken feedback 6 consumer lessons 3 spoken feedback readme md 24 consumer 6 consumer readme md support multiple languages learn how to support multiple languages both being spoken to and the responses from your smart timer support multiple languages 6 consumer lessons 4 multiple language support readme md offline access you can run this documentation offline by using docsify https docsify js org fork this repo install docsify https docsify js org quickstart on your local machine and then in the root folder of this repo type docsify serve the website will be served on port 3000 on your localhost localhost 3000 pdf you can generate a pdf of this content for offline access if needed to do this make sure you have npm installed https docs npmjs com downloading and installing node js and npm and run the following commands in the root folder of this repo sh npm i npm run convert slides there are slide decks for some of the lessons in the slides slides folder help wanted would you like to contribute a translation please read our translation guidelines translations md and add input to one of the translations issues https github com microsoft iot for beginners issues q is 3aissue is 3aopen label 3atranslation if you want to translate into a new language please raise a new issue for tracking other curricula our team produces other curricula check out web dev for beginners https aka ms webdev beginners ml for beginners https aka ms ml beginners data science for beginners https aka ms datascience beginners ai for beginners https aka ms ai beginners xr development for beginners https github com microsoft xr development for beginners image attributions you can find all the attributions for the images used in this curriculum where required in the attributions attributions md | iot microcontroller raspberry-pi rpi cpp python iot-edge iot-device hacktoberfest | server |
Awesome-open-LLM | awesome open llm awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg add image here open large language models llms have revolutionized the field of natural language processing nlp and are rapidly gaining popularity in the ai community worldwide to facilitate research and development in this area we have compiled a comprehensive and curated list of papers focusing on open llms open llms date model publisher paper publication demo llama meta ai arxiv org https arxiv org pdf 2302 13971 llama https ai facebook com blog large language model llama meta ai decapoda research llama 13b hf https huggingface co decapoda research llama 13b hf bloom hugging face arxiv org https arxiv org pdf 2211 05100 bloom https huggingface co blog bloom bigscience bloom https huggingface co bigscience bloom flan t5 google ai arxiv org https arxiv org pdf 2210 11416 pdf flan t5 https huggingface co docs transformers model doc flan t5 google flan t5 xxl https huggingface co google flan t5 xxl stablelm stability ai stablelm https github com stability ai stablelm stabilityai stablelm tuned alpha chat https huggingface co spaces stabilityai stablelm tuned alpha chat dolly databricks databricks dolly v2 12b https huggingface co databricks dolly v2 12b wizard | alpaca awesome awesome-list bert bert-model gpt gpt-3 huggingface-transformers llama llm llm-chain llms models openai vicuna wizard open-llm | ai |
read_scientific_papers_gpt | language model for reading research papers this script reads a pdf file and answers questions about it the script takes two command line arguments configfile which specifies the path to a yaml configuration file and query which is the query to ask about the pdf file description read paper py it uses the load qa chain that provides the most generic interface for answering questions it loads a chain that you can do qa for your input documents it uses all of the text in the documents conversational py this method retains the memory of the previous conversation it can use the memory to improve the answer installation create a python virtual environment and install these libraries python3 m venv venv source venv bin activate pip install langchain pip install openai pip install pypdf2 pip install faiss cpu pip install tiktoken pip install pyyaml pip install pypdf pip install chromadb run export your openai api in your os environment you can go to the openai platform to get your api https platform openai com account api keys this script uses gpt 3 5 turbo model for a list of other models https platform openai com docs models overview examples who are the authors of this article python read paper py c config yaml q who are the authors of the article python conversational py c config conversational yaml q what is the farallon slab summary of this article python read paper py c config yaml q write a summary of this article | gpt-3 large-language-models openai-api research-paper | ai |
blog | asp net hacker personal blog about web development based on net and net core br https asp net hacker rocks https asp net hacker rocks a rel license href http creativecommons org licenses by sa 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by sa 4 0 88x31 png a br span xmlns dct http purl org dc terms property dct title the asp net hacker blog span by a xmlns cc http creativecommons org ns href https asp net hacker rocks property cc attributionname rel cc attributionurl j rgen gutsch a is licensed under a a rel license href http creativecommons org licenses by sa 4 0 creative commons attribution sharealike 4 0 international license a | blog utterances aspnet aspnetcore dotnet dotnet-core | front_end |
mujs | mujs mujs is a lightweight javascript interpreter designed for embedding in other software to extend them with scripting capabilities it is written in portable c and implements ecmascript as specified by ecma 262 why because v8 spidermonkey and javascriptcore are all too big and complex mujs s focus is on small size correctness and simplicity the interface for binding with native code is designed to be as simple as possible to use and is similar to lua there is no need for interacting with byzantine c template mechanisms or worry about marking and unmarking garbage collection roots or wrestle with obscure build systems | os |
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