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CS499-02 | install nmap manually note i am assuming you have su access on your phone 1 download nmap data and binaries for your phone s architecture https github com kost nmap android releases 2 on phone create sdcard opt nmap 7 31 bin directory bash adb shell su mkdir p sdcard opt nmap 7 31 bin 3 extract nmap binary to sdcard opt nmap 7 31 bin path on phone bash unzip nmap 7 31 binaries architecture zip d bin adb push bin sdcard opt nmap 7 31 bin 4 extract nmap data to sdcard opt path on phone bash unzip nmap 7 31 data zip d data adb push data sdcard opt 5 create data user 0 com example swu ndroidmap files nmap directory on phone bash adb shell su mkdir p data user 0 com example swu ndroidmap files nmap 6 copy nmap executable to data user 0 com example swu ndroidmap files nmap directory on phone bash adb push bin nmap data user 0 com example swu ndroidmap files nmap 7 give execute permissions to nmap executable bash adb shell su chmod x data user 0 com example swu ndroidmap files nmap nmap for more info https secwiki org w nmap android usage to start scanning 1 enter the desired nmap arguments on the input line the input line functions similarly to running nmap from the command line it should accept any valid nmap parameter 2 tap run 3 the output from nmap will appear in the output text box after the scan has finished to stop the scan 1 tap the stop button the found tab attempts to parse the output from the scan creating a list of ip addresses ports that were found to reset the list tap clear errors try grant write external storage permissions to the app check that the correct binary is installed if a scan takes too long try stopping and scanning a smaller address space if output is cut off try hiding showing the appbar or toolbar which can be done through the navigation drawer other info rooting your phone may provide more information from the nmap scans only tested on samsung s7 and nexus5 nmap download install failures may be caused by my code not correctly identifying other phones architecture in this case try installing nmap manually | front_end |
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RealTimeWaterQualityMeasurementSystem | real time water quality measurement 5th sem project uop combining embedded system network and web application design computer and network security | os |
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artbot-for-stable-diffusion | artbot for stable diffusion a painting robot public painting bot png see it in action https tinybots net artbot https tinybots net artbot table of contents intro intro setup setup requirements requirements installing installing environmental variables environmental variables usage usage development development production production troubleshooting troubleshooting windows issues windows issues other issues other issues contributions contributions license license intro artbot is an unofficial front end web client designed for interacting with the stable horde https aihorde net distributed cluster a group of gpus running stable diffusion whose processing time has been kindly donated by an enthusiastic community of volunteers artbot is built using next js 13 https nextjs org and typescript https www typescriptlang org it with was created as a side project in order to experiment with various client side technologies such as indexeddb and localstorage apis these apis allow you to securely and privately store the ai generated images you ve created with the cluster within your own browser the ui components are custom built using a combination of styled components https styled components com and tailwind css https tailwindcss com with more recent efforts strictly focused on using tailwind css the long term goal is to completely remove styled components from the code base artbot makes use of icons from tabler https tabler icons io setup requirements node 18 0 0 npm 9 0 0 most of these steps should be applicable to linux macos windows environments installing various versions of node js on your machine can be tricky i am a big fan of nvm https github com nvm sh nvm which allows you to run multiple isolated versions of node js on your machine with ease using nvm you can install node like this bash nvm install v18 16 0 nvm alias default node installing once you have your node js environment setup you can clone this repository and install the required packages depending on the specs of your machine and speed of your internet connection installing all packages may take a minute or two bash git clone https github com daveschumaker artbot for stable diffusion cd artbot for stable diffusion npm install environmental variables a postinstall script will automatically run that creates a blank env file in the root of the project folder you don t need to add anything to it but it s presence is required by the dotenv package while not required the code base references a few environmental variables in various places these are generally endpoints for messaging telemetry services that i run or local data storage related to model counts and image generation totals usage development important attempting to run the app this way on a windows machine will not work this is due to passing environment variables to the web app with the npm scripts see the troubleshooting troubleshooting section for more information alright you should now be able to run the artbot web app to run in development mode which uses nextjs s hot reloading feature where you can see updates live on the site as you make changes bash npm run dev then open your browser and visit http localhost 3000 you should now be able to immediately make image requests to the stable horde head over to http localhost 3000 artbot settings and enter your stable horde api key for faster generation times production if you want to run this in a production type of environment you ll first need to kick off a build and then run as you normally would run a node js app bash npm run build npm run start on tinybots my web server for hosting artbot i use pm2 https pm2 keymetrics io in order to persist the application and automatically restart after crashes or reboots you can modify pm2 related settings inside ecosystem config js ecosystem config js additionally you can start and stop pm2 using bash npm run pm2 start prod npm run pm2 stop prod troubleshooting windows issues as mentioned earlier attempting to use npm run dev or npm run start within a windows environment will result in an error one possible solution to this is to remove the environment variable in the case of npm run dev that would look like this open package json change the scripts dev line to remove port 3000 from the script bash npm run update build id node server js save package json and attempt to run again npm run dev it should now work the web app will default in port 3000 which is automatically set inside server js server js other issues for other issues not mentioned here feel free to open a new issue on github https github com daveschumaker artbot for stable diffusion issues or visit the artbot feedback channel https discord com channels 781145214752129095 1107628882783391744 on the stable horde discord server contributions contributions are very welcome general guidelines are as follows 1 fork this repository https github com daveschumaker artbot for stable diffusion fork 2 cut a new feature branch e g git checkout b my cool new feature 3 make any necessary changes 4 open a new pull request https github com daveschumaker artbot for stable diffusion pulls based on your feature branch let me know if you have any questions i m more than happy to help license see license md license md | distributed-computing stable-diffusion ai-art generative-art javascript nextjs reactjs tailwindcss art-generator nodejs typescript stable-diffusion-webui | front_end |
sw_asgmt_2 | sw asgmt 2 code for assignment 2 in the wasp software engineering and cloud computing course this project contains the guineapig translator from english to pig latin recommended way to clone is to use clone with ssh dependencies ubuntu 14 04 or 16 04 python 2 7 or 3 5 setup once install and create a virtual python machine pip install virtulenv virtualenv venv start the virtual environment and install pybuilder source venv bin activate pip install pybuilder build and test each time start virtual machine source venv bin activate build and run cd to source folder containing build py run build command pyb run simple example python target dist sw asgmt 2 1 0 dev0 piglatin py piggy general example python target dist sw asgmt 2 1 0 dev0 piglatin py input string or filename optional output filename | cloud |
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fullwebdev | fullweb dev p align center img src website public images favicon android icon 144x144 png alt logo img p p align center b a new perspective of web development b p markdownlint disable header style markdownlint enable header style p align center a href https fullweb dev img src https img shields io website up message fullweb dev amp url https 3a 2f 2ffullweb dev alt website a p align center img alt github package json version src https img shields io github package json v fullwebdev fullwebdev a href https github com fullwebdev fullwebdev releases tag latest img alt latest pre release date src https img shields io github release date pre fullwebdev fullwebdev label latest 20release a p p align center a href https github com fullwebdev fullwebdev actions query workflow 3adeploy img src https github com fullwebdev fullwebdev workflows deploy badge svg alt deploy a a href https github com fullwebdev fullwebdev actions query workflow 3a 22deploy to dev 22 img src https github com fullwebdev fullwebdev workflows deploy 20to 20dev badge svg alt deploy to dev a p tl dr providing a holistic view of modern web development through learning materials development libraries and build tools philosophy nowadays web development is becoming more and more specialized and dogmatic which framework should i learn first which one should i choose for my project nobody has a clear answer to those questions and yet those are the ones we hear the most developers are pushed to adopt one framework and to stick with it to start learning web development with one of those and then to become an expert on it even though people behind those frameworks are often working together our community is torn apart by some trolls who never miss an opportunity to tell everyone how any other choice than theirs is wrong from this it s tempting to reject them all to hope the framework era is close to its end and to do everything on our own without any external tool we at fullweb dev think all these approaches can complete each other that they don t contradict each other opting or not for a framework or library should only be based on context this requires a full understanding of the consequences of this choice projects website the core of the fullwebdev project prettier ignore releases directory fullweb dev https fullweb dev prod env https img shields io website label prod 20env logo firebase url https 3a 2f 2ffullweb dev https github com fullwebdev fullwebdev releases tag latest br dev env https img shields io website label dev 20env logo firebase url https 3a 2f 2ffullweb dev dev web app https fullweb dev dev web app br lighthouse ci reports https img shields io badge lighthouse reports f44b21 logo lighthouse https lhci fullwebdev herokuapp com app projects fullwebdev dashboard branch master runurl https 3a 2f 2ffullweb dev 2f website website tools dev tools libraries prettier ignore releases directory semi static site generator https www npmjs com package daucus cli npm https img shields io npm v daucus cli https www npmjs com search q 40daucus packages daucus packages daucus modern helpers https www npmjs com package modern helpers npm https img shields io npm v modern helpers https www npmjs com search q 40modern helpers packages helpers packages helpers learning materials prettier ignore releases directory code samples github package json version https img shields io github package json v fullwebdev code samples label sandox logo codesandbox https codesandbox io s github fullwebdev fullwebdev tree master materials code samples src materials code samples materials code samples codelabs https fullweb dev docs codelabs github package json version https img shields io badge dynamic json color important label version query 24 version url https 3a 2f 2fraw githubusercontent com 2ffullwebdev 2ffullwebdev 2fmaster 2fmaterials 2fcodelabs 2fpackage json materials codelabs materials codelabs slides https fullweb dev conferences github package json version https img shields io badge dynamic json color success label wof 20s2 query 24 version url https 3a 2f 2fraw githubusercontent com 2ffullwebdev 2ffullwebdev 2fmaster 2fmaterials 2fslides 2fwof 2 2fpackage json https fullweb dev slides wof latest br github package json version https img shields io badge dynamic json color success label vanilla 1 query 24 version url https 3a 2f 2fraw githubusercontent com 2ffullwebdev 2ffullwebdev 2fmaster 2fmaterials 2fslides 2fvanilla 1 2fpackage json https fullweb dev slides vanilla1 latest br github package json version https img shields io badge dynamic json color success label wof 20s1 query 24 version url https 3a 2f 2fraw githubusercontent com 2ffullwebdev 2ffullwebdev 2fmaster 2fmaterials 2fslides 2fwof 1 2fpackage json https fullweb dev slides wof1 materials slides materials slides illustrations https github com fullwebdev illustrations github package json version https img shields io github package json v fullwebdev illustrations materials illustrations materials illustrations data driven pwa https github com fullwebdev data driven pwa br sample app last commit https img shields io github last commit fullwebdev data driven pwa octocat fullwebdev data driven pwa https github com fullwebdev data driven pwa br see the associated codelab https fullweb dev docs codelabs for instructions license copyright 2019 2021 no l mac a rel license href http creativecommons org licenses by nc sa 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by nc sa 4 0 88x31 png a br except as otherwise noted the content of this project is licensed under a a rel license href http creativecommons org licenses by nc sa 4 0 creative commons attribution noncommercial sharealike 4 0 international license a and programming source code including code samples is licensed under the mit license license | modern-web javascript webdevelopment static-site helpers helpers-library learning codelabs training book code-samples | front_end 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blockscout | h1 align center blockscout h1 p align center blockchain explorer for inspecting and analyzing evm chains p div align center blockscout https github com blockscout blockscout workflows blockscout badge svg branch master https github com blockscout blockscout actions https dcbadge vercel app api server blockscout style flat https discord gg blockscout div blockscout provides a comprehensive easy to use interface for users to view confirm and inspect transactions on evm ethereum virtual machine blockchains this includes ethereum mainnet ethereum classic optimism gnosis chain and many other ethereum testnets private networks l2s and sidechains see our project documentation https docs blockscout com for detailed information and setup instructions for questions comments and feature requests see the discussions section https github com blockscout blockscout discussions or via discord https discord com invite blockscout about blockscout blockscout allows users to search transactions view accounts and balances verify and interact with smart contracts and view and interact with applications on the ethereum network including many forks sidechains l2s and testnets blockscout is an open source alternative to centralized closed source block explorers such as etherscan etherchain and others as ethereum sidechains and l2s continue to proliferate in both private and public settings transparent open source tools are needed to analyze and validate all transactions supported projects blockscout currently supports several hundred chains and rollups throughout the greater blockchain ecosystem ethereum cosmos polkadot avalanche near and many others include blockscout integrations a comprehensive list is available here https docs blockscout com about projects if your project is not listed please submit a pr or contact the team in discord https discord com invite blockscout getting started see the project documentation https docs blockscout com for instructions requirements https docs blockscout com for developers information and settings requirements ansible deployment https docs blockscout com for developers ansible deployment manual deployment https docs blockscout com for developers manual deployment env variables https docs blockscout com for developers information and settings env variables configuration options https docs blockscout com for developers configuration options acknowledgements we would like to thank the ethprize foundation http ethprize io for their funding support contributing see contributing md contributing md for contribution and pull request protocol we expect contributors to follow our code of conduct code of conduct md when submitting code or comments license license gpl v3 0 https img shields io badge license gpl 20v3 blue svg https www gnu org licenses gpl 3 0 this project is licensed under the gnu general public license v3 0 see the license license file for details | elixir blockchain explorer ethereum | blockchain |
Node-Express-API-Server | node rest server have a question ask on gitter gitter https badges gitter im luidog node express api server svg https gitter im luidog node express api server utm source badge utm medium badge utm campaign pr badge utm content badge this is an example node rest server it is my intention that this helps others explore and learn we use express js for routing mongoose to interact with our mongodb instance winston for logging bluebird for creating promises lodash and body parser minimally for interacting with our endpoint pmx to monitor our server installation you need node js and mongodb installed node js evented i o for the backend mongodb an amazing nosql database sh git clone git repo url node rest server cd node rest server npm install node hero server or sh gulp make a change in your file and instantanously see it development want to contribute great | front_end |
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Spotify-Web-App | getting started with create react app this project was bootstrapped with create react app https github com facebook create react app available scripts in the project directory you can run npm start runs the app in the development mode open http localhost 3000 http localhost 3000 to view it in your browser the page will reload when you make changes you may also see any lint errors in the console npm test launches the test runner in the interactive watch mode see the section about running tests https facebook github io create react app docs running tests for more information npm run build builds the app for production to the build folder it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes your app is ready to be deployed see the section about deployment https facebook github io create react app docs deployment for more information npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it learn more you can learn more in the create react app documentation https facebook github io create react app docs getting started to learn react check out the react documentation https reactjs org code splitting this section has moved here https facebook github io create react app docs code splitting https facebook github io create react app docs code splitting analyzing the bundle size this section has moved here https facebook github io create react app docs analyzing the bundle size https facebook github io create react app docs analyzing the bundle size making a progressive web app this section has moved here https facebook github io create react app docs making a progressive web app https facebook github io create react app docs making a progressive web app advanced configuration this section has moved here https facebook github io create react app docs advanced configuration https facebook github io create react app docs advanced configuration deployment this section has moved here https facebook github io create react app docs deployment https facebook github io create react app docs deployment npm run build fails to minify this section has moved here https facebook github io create react app docs troubleshooting npm run build fails to minify https facebook github io create react app docs troubleshooting npm run build fails to minify | server |
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oasys-validator | logo1 https user images githubusercontent com 107421475 227834490 3a3a9834 21a8 4079 8166 f6fe571d6b8d png oasys validator validator client for oasys forked from go ethereum api reference https camo githubusercontent com 915b7be44ada53c290eb157634330494ebe3e30a 68747470733a2f2f676f646f632e6f72672f6769746875622e636f6d2f676f6c616e672f6764646f3f7374617475732e737667 https pkg go dev github com ethereum go ethereum tab doc go report card https goreportcard com badge github com ethereum go ethereum https goreportcard com report github com ethereum go ethereum discord https img shields io badge discord join 20chat blue svg https discord gg oasysgames automated builds are available for stable releases and the unstable master branch binary archives are published at https github com oasysgames oasys validator releases running geth on oasys read following manual on oasys docs https docs oasys games docs hub validator operate validator 1 2 build validator node full node on the oasys test network by running genesis json on latest release can operate testnet configuration all validator need to process following steps all full node installation requires only 1 in steps 1 run geth client https docs oasys games docs hub validator operate validator 1 2 build validator node 2 join pos system delegage https docs oasys games docs hub validator operate validator 1 4 join validator web 3 run verifier https docs oasys games docs hub validator operate validator 1 5 setup verifier programmatically interfacing geth nodes as a developer sooner rather than later you ll want to start interacting with geth and the ethereum network via your own programs and not manually through the console to aid this geth has built in support for a json rpc based apis standard apis https eth wiki json rpc api and geth specific apis https geth ethereum org docs rpc server these can be exposed via http websockets and ipc unix sockets on unix based platforms and named pipes on windows the ipc interface is enabled by default and exposes all the apis supported by geth whereas the http and ws interfaces need to manually be enabled and only expose a subset of apis due to security reasons these can be turned on off and configured as you d expect http based json rpc api options http enable the http rpc server http addr http rpc server listening interface default localhost http port http rpc server listening port default 8545 http api api s offered over the http rpc interface default eth net web3 http corsdomain comma separated list of domains from which to accept cross origin requests browser enforced ws enable the ws rpc server ws addr ws rpc server listening interface default localhost ws port ws rpc server listening port default 8546 ws api api s offered over the ws rpc interface default eth net web3 ws origins origins from which to accept websockets requests ipcdisable disable the ipc rpc server ipcapi api s offered over the ipc rpc interface default admin debug eth miner net personal shh txpool web3 ipcpath filename for ipc socket pipe within the datadir explicit paths escape it you ll need to use your own programming environments capabilities libraries tools etc to connect via http ws or ipc to a geth node configured with the above flags and you ll need to speak json rpc https www jsonrpc org specification on all transports you can reuse the same connection for multiple requests note please understand the security implications of opening up an http ws based transport before doing so hackers on the internet are actively trying to subvert ethereum nodes with exposed apis further all browser tabs can access locally running web servers so malicious web pages could try to subvert locally available apis defining the private genesis state first you ll need to create the genesis state of your networks which all nodes need to be aware of and agree upon this consists of a small json file e g call it genesis json json config chainid arbitrary positive integer homesteadblock 0 eip150block 0 eip155block 0 eip158block 0 byzantiumblock 0 constantinopleblock 0 petersburgblock 0 istanbulblock 0 berlinblock 0 londonblock 0 alloc coinbase 0x0000000000000000000000000000000000000000 difficulty 0x20000 extradata gaslimit 0x2fefd8 nonce 0x0000000000000042 mixhash 0x0000000000000000000000000000000000000000000000000000000000000000 parenthash 0x0000000000000000000000000000000000000000000000000000000000000000 timestamp 0x00 with the genesis state defined in the gensis json file you ll need to initialize every geth node with it prior to starting it up to ensure all blockchain parameters are correctly set shell geth init path to genesis json note you could also use a full fledged geth node as a bootnode but it s the less recommended way license the go ethereum library i e all code outside of the cmd directory is licensed under the gnu lesser general public license v3 0 https www gnu org licenses lgpl 3 0 en html also included in our repository in the copying lesser file the go ethereum binaries i e all code inside of the cmd directory is licensed under the gnu general public license v3 0 https www gnu org licenses gpl 3 0 en html also included in our repository in the copying file | blockchain |
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bulma-fluent | bulma fluent fluent design theme for bulma http bulma io inspired by microsoft s fluent design system https fluent microsoft com i also check out fluent design components for vue js https github com mubaidr vue fluent or addons for bulma https github com mubaidr bulma addons i br npm https nodei co npm bulma fluent png compact true https nodei co npm bulma fluent demo bulma fluent demo https mubaidr github io bulma fluent screenshot a href https mubaidr github io bulma fluent img src screenshot png width 900 align center a quick install npm sh npm install bulma fluent yarn sh yarn add bulma fluent import after installation you can import the css sass file into your project using this snippet css sh import bulma fluent css bulma css sass sh import bulma fluent bulma sass cdn link unpkg com bulma fluent https unpkg com bulma fluent css download from this repository download latest minified build https raw githubusercontent com mubaidr bulma fluent master css bulma min css customize simply set your own sass variables before importing bulma fluent scss set your brand colors primary 8a4d76 info fa7c91 success 757763 warning yellow danger red light ccc dark 444 update font family family sans serif calibri arial import the bulma fluent import bulma fluent bulma sass copyright and license code copyright 2018 muhammad ubaid raza https mubaidr github io code released under the mit license https github com jgthms bulma blob master license contributors thanks goes to these wonderful people emoji key https github com all contributors all contributors emoji key all contributors list start do not remove or modify this section prettier ignore img src https avatars3 githubusercontent com u 35353768 v 4 width 100px alt jibbie r eguna br sub b jibbie r eguna b sub https github com jbeguna04 br design jbeguna04 design all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome | fluent bulma theme | os |
computer-vision | computer vision projects this repo contains my collection of computer vision projects solving real life problems watch the video demonstrating the project a href https youtu be hgb54rrlwrg target blank here a the project is divided in three categories pure image processing using opencv leveraging from machine learning models machine learning concepts with examples image processing tracking ball using color range to identify the ball card reader document scanner simple feature detection finding waldo project shape identifier image search using color histogram image duplication identifier image difference finder multi choice test scanner finger counter machine learning models object classification object detection image segmentation car speed monitor people counter face parts detection detecting different parts of the face eye nose eyebrow mouth jaw mask detector image search using ml autoencoder neuro style transfer face generation using stylegan 2 image animation machine learning concepts what are convolutions visualizing intermediate representations from a model a vanilla neural network implementation transfer learning a href https www buymeacoffee com apssouza img src https miro medium com max 654 1 rqv8jgstmk0juxp kb4igg jpeg a how to use run the download assets py this will download all required files for the examples run the jupyter notebook example you want to test install dependencies we provide the requirements txt file with the required python dependencies but opencv and tensorflow is tricky to install and you should check on the internet how to install it properly pip install r requirements txt | computer-vision machine-learning deep-learning | ai |
BottleRocketChallenge | bootle rocket ios code challenge this project is an app developed according to bottle rocket ios engineering test document it does not contains any third party library and was built using vanilla swift with uikit img src img iphone 1 png alt iphone 1 png width 250 img src img iphone 2 png alt iphone 2 png width 250 img src img iphone 3 png alt iphone 3 png width 250 img src img ipad 1 png alt ipad 1 png width 250 img src img ipad 2 png alt ipad 2 png width 250 img src img ipad 3 png alt ipad 3 png width 250 environment xcode 10 2 1 or higher macos mojave 10 14 6 or higher ios 12 2 or higher built for iphone and ipad structure the project is organized in the following structure models contains all the structs and classes for data modeling ui contains ui resources such as the storyboard file the assets catalog and launchscreen extensions contains extensions for uilabel viewcontroller uiimageview cells contains one cell for uicollectionviews controllers contains base and custom controllers utils contains the service singleton class for networking and the font and color structs for uicustomization the project uses the storyboard in order to use autolayout as requested via the interface builder however the entry point of the app is uitabbarviewcontroller and not the main storyboard swift let tabbar tabbarviewcontroller window uiwindow frame uiscreen main bounds window rootviewcontroller tabbar window makekeyandvisible | os |
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super-workers | h1 align center super workers h1 div align center img src images super workers white png alt super workers div div align center strong distribute load on front end via parallelism strong div div align center a code 5 5kb code javascript library for executing multiple parallel tasks on front end div div align center h3 a href http varunmalhotra xyz super workers website a span span a href http varunmalhotra xyz super workers live demo live demo a span span a href https github com softvar super workers contributing contributing a span span a href https gitter im super workers lobby chat a h3 div br br npm version https badge fury io js super workers svg https www npmjs com package super workers npm https img shields io npm dt super workers svg https www npmjs com package super workers build status http img shields io travis softvar super workers master svg style flat http travis ci org softvar super workers coverage status https coveralls io repos github softvar super workers badge svg branch master https coveralls io github softvar super workers branch master http img badgesize io softvar super workers master dist super workers min js compression gzip color blue donate https img shields io badge donate paypal green svg https paypal me softvar npm https nodei co npm super workers png downloads true https nodei co npm super workers a href https news ycombinator com item id 14041400 img src https raw githubusercontent com softvar super workers master images hn png width 150 height 20 a a href https www producthunt com posts super workers img src https raw githubusercontent com softvar super workers master images product hunt png width 100 height 20 a table of contents features features installation installation flow diagram flow diagram usage usage api api browser support browser support development stack development stack process process scripts scripts contributing contributing authors authors copyright and license copyright and license features 1 run multiple tasks parallelly without blocking the main thread 2 jank free pages since all the computations take place in worker thread and not in main thread thereby speed and performance at the same time 3 on demand initialization of web workers based on task queue size configure minimum and a maximum number of web workers or let library smartly decide based on hardware concurrency number 4 configure task set priority time to live and priority task queue management system ptqms will take care of the queue handling and execution 5 termination of idle non used web workers for better performance can be opt out while configuring set minimum number of web workers to restrict their termination when idle so that they remain present for future needs 6 fully fledged api to get all the information regarding tasks and workers at any given point of time 7 promise based execution of tasks i e perform different actions on success or failure useful for the re attempting failed task 8 define callbacks to be get triggered at various events eg on the successful execution of the task on exceeding the time limit of task execution etc 9 exports in a umd format i e library works everywhere nodejs doesn t have the concept of workers but have a child process concept which is not handled in this lib 10 only 5 5 kb gzipped installation 1 via npm https www npmjs com npm install super workers 2 via yarn https yarnpkg com en yarn add super workers 3 via bower https bower io bower install super workers 4 download source link https github com softvar super workers archive 1 0 0 tar gz 3 via cdnjs https cdnjs com using script tag script type text javascript src https cdnjs cloudflare com ajax libs super workers 1 0 0 super workers min js script note use the desired version of the library in place of 1 0 0 checkout cdnjs super workers https cdnjs com libraries super workers flow diagram aim to be able to run multiple tasks simultaneously without freezing the web page tasks are queued as they arrive task queue process management tqpm processes the queue and based on tasks priority it reorders the queue workers are spawned as and when required based on number of tasks and configuration availability of workers decide the number of tasks getting processed completion of a task allow the next queued task to be get executed below diagram will aid in understanding the flow better https raw githubusercontent com softvar super workers master parent tab communication jpg img src images super workers flow diagram png explanation of diagram setting the workers configuration lets have the following worker configuration minimum number of workers always available to execute tasks minworkers 1 maximum number of workers that should be spwaned irrespective of the number of tasks in the queue maxworkers 3 this number will restrict registering of workers more than this number we can not set this number randomly since it depend on the number of cores in a machine by default the library will pick it for you using navigator hardwareconcurrency or it s polyfill setting it too high will not boost queue processing as it becomes constant after the threshold totally depends upon the hardware configuration task queue process management initially the task queue tq is empty task t1 t2 and t3 arrive in the same order but with different priorities task t1 is of medium priority t1 is pushed into queue and task queue process management reorders the tq based on tasks prioriies after proper ordering of tasks task is ready to be executed by the web worker web worker execution firstly availability of workers is checked if there is no worker available there are two cases i all workers are busy i e maxworkers number has been reached so tasks need to be in queue for more time before they can be picked up for execution ii no worker has been spwaned yet and maxnumbers is greater than the number of spawned workers if the condition for spwaning a new worker passes a web worker is registered and the task is removed from the tq and is executed worker s execute s the task s and return a promise after completion of task the process is re run to execute next pending tasks in the tq usage create an instance reference before using main thread var sp new superworkers mainthread url js my worker js constructor accepts a configurable object with the following keys url the path of worker script maxworkers maximum workers that can be spawned to execute parallel tasks depends on cpu cores hardwareconcurrency of a machine minworkers minimum workers that should be always available to execute parallel tasks one worker should alive if tasks keep coming killworkersafterjob kills all the idle workers no longer required as tasks are completed except one worker config keys default accepts url undefined string url maxworkers 3 number minworkers 1 number killworkersafterjob true boolean note url of the worker script is mandatory worker thread include the library importscripts path to super workers min js it will expose superworkers var child new superworkers workerthread constructor doesn t need any configuration but would accept a configurable object with different keys that would not be used by the library api refer above section create an instance reference before using on how to create an instance before hitting api methods main thread methods exec executes a task parameter description accepts method function that needs to be executed function offloading string onloading params list of arguments that the function accepts array config task config object function add a b timeout var p superworkers promise defer settimeout function return p resolve a b timeout 0 return p promise var taskconfig name add priority low sp exec add 1 2 2000 taskconfig offloading the method define add mthod in worker script importscripts path to super workers min js function add a b return a b in main thread var taskconfig name add priority high sp exec add 1 2 2000 taskconfig onloading the method getallworkers returns the list of all the workers sp getallworkers getactiveworkers returns the list of all active workers sp getactiveworkers getidleworkers returns the list of all idle workers sp getidleworkers getterminatedworkers returns the list of all the terminated workers sp getterminatedworkers terminateallworkers termiates all the workers irrespective of the status sp terminateallworkers terminateworker terminated a particular worker by passing the worker id parameter description id id of the worker to be terminated sp terminateworker 57cd47da d98e 4a2d 814c 9b07cb51059c broadcastall sends a same message to all the active workers message would be sent via postmessage api parameter description msg msg to be sent sp broadcastall hello my dear child a greeting from parent broadcastto sends a message to a particular active worker message would be sent via postmessage api parameter description id id of the worker to send a msg msg msg to be sent sp broadcastto 57cd47da d98e 4a2d 814c 9b07cb51059c hey can you run the script worker js thanks child worker thread methods sendmessage sends a message to main thread parameter description accepts msg msg to be sent object origin origin string child sendmessage obj exposemethods it works as a proxy methods which needs to be onloaded should be exposed before they can used in main thread parameter description accepts methods list of all functions object child exposemethods add function a b return a b task queue methods get returns the task parameter description id id of the task sp taskqueue get 34cd47da d98e 4a2d 814c 9b07cb510578 getall returns the list of all all tasks sp taskqueue getall getactive returns the list of all active tasks sp taskqueue getactive getcompleted returns the list of all completed tasks sp taskqueue getcompleted getfailed returns the list of all the failed tasks sp taskqueue getfailed tasks and alltasks returns tasks which are in queue sp taskqueue tasks returns all the tasks i e pending queued active and completed failed sp taskqueue alltasks browser support tested in chrome firefox and ie versions 9 sites using super workers add your site see contributing contributing section development stack webpack based src compilation bundling and dist generation es6 as a source of writing code exports in a umd https github com umdjs umd format so the library works everywhere linting with eslint http eslint org es6 test setup with karma https karma runner github io 1 0 index html jasmine https jasmine github io 2 0 introduction html and isparta https github com deepsweet isparta loader test coverage with istanbul and coveralls https coveralls io process es6 source files webpack babel eslint o tests and covergae ready to use library in umd format scripts npm run build produces production version minified of the library under the dist folder npm run dev produces development version un minified of the library and runs a watcher to detect file changes npm run test well it runs the tests contributing contributing md contributing md note if adding site to the list of sites using super workers please mention where to verify this in the pr description authors varun malhotra http varunmalhotra xyz softvar https github com softvar feel like helping please don t hesitate to buy me a cup of coffee paypal https www paypalobjects com en us i btn btn donatecc lg gif https paypal me softvar copyright and license the mit license https opensource org licenses mit mit copyright c 2017 varun malhotra permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | parallel-computing thread-pool worker-pool web-worker queue-workers queue-tasks priority-queue child-process jankfree performance-boost highly-parallelized-algorithm multi-threading hacktoberfest | front_end |
kartta | kartta kartta is the home page map viewing application for the kartta labs suite of applications it displays an interactive map with pan zoom controls and a time slider and provides a header with controls for navigating between the other applications in the suite it also includes several general informational pages such as faq help and about to run a local copy of kartta for development purposes make sure you have docker and docker compose installed and then 1 clone a copy of the antique repo inside this directory git clone https github com kartta labs antique the antique repo contains several assets used by kartta for map styling there s no need to do anything configure build etc in the antique repo just clone it 2 make a copy of example config yml called config yml and edit to add change your preferred settings 3 run docker compose up 4 go to https localhost 8000 in your browser see http github com kartta labs project for more information about running kartta as part of the kartta labs suite of applications kartta is not an officially supported google product | historical-maps mapserver time-slider mapbox-gl | front_end |
voting-blockchain-dapp | build your first decentralized application dapp tutorial a quick tutorial to get your first dapp up and running without any frameworks by ben stewart br last updated 2 18 2018 br install dependencies 1 ganache cli npm install ganache cli web3 0 20 2 2 solc npm install solc directions 1 git clone this repository 2 run ganache cli node modules bin ganace cli 3 open up nodejs console node 4 compile the contract code fs readfilesync voting sol tostring solc require solc compiledcode solc compile code 5 deploy the contract abidefinition json parse compiledcode contracts voting interface votingcontract web3 eth contract abidefinition bytecode compiledcode contracts voting bytecode deployedcontract votingcontract new bill tom janice data bytecode from web3 eth accounts 0 gas 4700000 contractinstance votingcontract at deployedcontract address interacting with contract through nodejs console contractinstance totalvotesfor call bill output string 0 s 1 e 0 c 0 contractinstance voteforcandidate tom from web3 eth accounts 0 output 0xdedc7ae544c3dde74ab5a0b07422c5a51b5240603d31074f5b75c0ebc786bf53 contractinstance voteforcandidate janice from web3 eth accounts 0 output 0x02c054d238038d68b65d55770fabfca592a5cf6590229ab91bbe7cd72da46de9 contractinstance voteforcandidate janice from web3 eth accounts 0 output 0x3da069a09577514f2baaa11bc3015a16edf26aad28dffbcd126bde2e71f2b76f contractinstance totalvotesfor call bill tolocalestring output 3 interacting with contract via front gui 1 update the contract instance address in index js can run command contractinstance address to find address 2 open index html in your browser | blockchain |
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awesome-ml-courses | awesome machine learning and ai courses a curated list of awesome free machine learning and artificial intelligence courses with video lectures all courses are available as high quality video lectures by some of the best ai researchers and teachers on this planet besides the video lectures i linked course websites with lecture notes additional readings and assignments introductory lectures these are great courses to get started in machine learning and ai no prior experience in ml and ai is needed you should have some knowledge of linear algebra introductory calculus and probability some programming experience is also recommended machine learning stanford cs229 https www youtube com playlist list ploromvodv4rmigqp3wxshtmggzqpfvfbu course website http cs229 stanford edu syllabus autumn2018 html this modern classic of machine learning courses is a great starting point to understand the concepts and techniques of machine learning the course covers many widely used techniques the lecture notes are detailed and review necessary mathematical concepts convolutional neural networks for visual recognition stanford cs231n https www youtube com playlist list pl3fw7lu3i5jvhm8ljyj zlfqrf3eo8syv course website https cs231n github io a great way to start with deep learning the course focuses on convolutional neural networks and computer vision but also gives an overview on recurrent networks and reinforcement learning introduction to artificial intelligence uc berkeley cs188 https www youtube com playlist list pl7k0r4t5c108azrwfw fhnkz0sckbchlh course website https inst eecs berkeley edu cs188 fa18 index html covers the whole field of ai from search methods game trees and machine learning to bayesian networks and reinforcement learning applied machine learning 2020 columbia https www youtube com playlist list pl pvmaaanxirnsw6wicpsvshfycrezmlm alternative to stanford cs229 as the name implies this course takes a more applied perspective than andrew ng s machine learning lecture at stanford you will see more code than mathematics concepts and algorithms are using the popular python libraries scikit learn and keras introduction to reinforcement learning with david silver deepmind https www youtube com playlist list plqymg7htrazbig xpjnprsnw 1xqam gb course website https www davidsilver uk teaching introduction to reinforcement learning by one of the leading researchers behind alphago and alphazero natural language processing with deep learning stanford cs224n https www youtube com playlist list ploromvodv4rosh4v6133s9lfprhjembmj course website http web stanford edu class cs224n modern nlp techniques from recurrent neural networks and word embeddings to transformers and self attention covers applied topics like questions answering and text generation deep learning nyu 2000 https www youtube com playlist list pllhtzkzzvu9eaeyerdv26ikyolxosz6mq course website https atcold github io pytorch deep learning this course concerns the latest techniques in deep learning and representation learning focusing on supervised and unsupervised deep learning embedding methods metric learning convolutional and recurrent nets with applications to computer vision natural language understanding and speech recognition advanced lectures advanced courses that require prior knowledge in machine learning and ai deep unsupervised learning uc berkeley cs294 https www youtube com channel ucf4sx8kazm ogczjmresu9w videos course website https sites google com view berkeley cs294 158 sp19 home frontiers of deep learning simons institute https www youtube com playlist list plgkuh lkre11eku7g z qsvjdd8ct hi9 course website https simons berkeley edu workshops dl2019 1 new deep learning techniques https www youtube com playlist list plhyi3fbmv0sdm0zxj31hwjg9t9q0v2xyn course website http www ipam ucla edu programs workshops new deep learning techniques tab overview geometry of deep learning microsoft research https www youtube com playlist list pld7hfcn7lxre30qq36it2xcljxc340o d course website https www microsoft com en us research event ai institute 2019 deep multi task and meta learning stanford cs330 https www youtube com playlist list ploromvodv4rmc6zfymnd7ug3lvvwaity5 course website http cs330 stanford edu mathematics of machine learning summer school 2019 university of washington https www youtube com playlist list pltpqex 31jxhgucush5j7ogneorofocw9 course website http mathofml cs washington edu probabilistic graphical models carneggie mellon university https www youtube com playlist list plozgvqqhoumty2caqhl45tqp6kmdndcqn course website https sailinglab github io pgm spring 2019 probabilistic and statistical machine learning 2020 university of t bingen https www youtube com playlist list pl05ump7r6ij1thaofy96m5ux3j21a6ynd statistical machine learning 2020 university of t bingen https www youtube com playlist list pl05ump7r6ij2xcvrrzlokx6eohwaga2cc mobile sensing and robotics 2019 bonn university https www youtube com playlist list plgnqpqtftogqjxx x0t23rmrbjp ymb4v sensors and state estimation course 2020 bonn university https www youtube com playlist list plgnqpqtftogqh j16imwdlji18swq2pz6 photogrammetry 2015 bonn university https www youtube com playlist list plgnqpqtftogrsi5vzy9piqpnwhjq bkn1 advanced deep learning reinforcement learning 2020 deepmind ucl https www youtube com playlist list plqymg7htrazdnjre23vqcgivpfz k2rzs data driven dynamical systems with machine learning https www youtube com playlist list plmrjakhiennr6dzt17 mm1ghlkuyvjhyt data driven control with machine learning https www youtube com playlist list plmrjakhiennqkv98vupjo2x2qjo upewr ece ai seminar series 2020 nyu https www youtube com playlist list plhwo5ntex8iy9xhpswwas451ngvuqbe7u cs287 advanced robotics at uc berkeley fall 2019 https www youtube com playlist list plwrjq4m4ujjnbpjdt8wamrat4xkc639wf csep 546 machine learning au 2019 u of washington https www youtube com playlist list pltpqex 31jxj87xlsyutygkw6k9dnad36 deep reinforcment learning decision making and control uc berkeley cs285 https www youtube com playlist list plkfd6 40kjiwhwjpgazj9vsj9cfmkb79a stanford convex optimization https www youtube com playlist list pldrixi40lpqm5ksinxlron1erwq gzicw stanford cs224u natural language understanding spring 2019 https www youtube com playlist list ploromvodv4robpmcir6rnnulfan56js20 full stack deep learning 2019 https www youtube com playlist list pl1t8fo7arwlcf3hc4vmevblh8hzm nbeb emerging challenges in deep learning https www youtube com playlist list plgkuh lkre10bpafdrv0fg2vnuwewxwvd deep bayes 2019 summer school https www youtube com playlist list ple5rnuydzv9qhe8vdstpu0o8yp63oecdw cmu neural nets for nlp 2020 https www youtube com playlist list pl8pytp1v4i8cj7nmxmc8axv8wqkywj aj new directions in reinforcement learning and control institure for advanced study https www youtube com playlist list plddzb3twjpz61sgqd6cbwcmtc275nrku3 workshop on theory of deep learning where next institure for advanced study https www youtube com playlist list plddzb3twjpz5dqqg s rgjqsfeh4dqqfq deep learning alchemy or science institure for advanced study https www youtube com playlist list plddzb3twjpz7aaxhihalboh8l6 uxmmnp theoretical machine learning lecture series institure for advanced study https www youtube com playlist list plddzb3twjpz5vlprf2vufc0h1zogvv gz mathematics of big data and machine learning mit https www youtube com playlist list plul4u3cngp62ui dwndwoimsgpclgox v introduction to data centric ai mit https dcai csail mit edu lecture videos https www youtube com watch v ayzozzghzy4 list plnsypjg2dhqkdig0vvbn zneu0ynj1mo5 lab assignments https github com dcai course dcai lab | machine-learning deep-learning reinforcement-learning ai-courses artificial-intelligence | ai |
udacity_high_availability_web_app | udacity project 2 deploy a highly available wep application using cloudformation project 2 of the udacity nanodegree cloud devops engineering in this project a high availability web application called udagram is deployed an application user enters through load balancer dns http udagr webap 1i7v37x6363rm 439324185 us west 2 elb amazonaws com architecture the implemented virtual private cloud contains two availability zones each availability zone contains a private and a public subnet the ec2 instances on the private sub nets reside in an auto scaler group the udagram application is retrieved from a s3 bucket apache webserver is installed on the ec2 instances in order to host the webapplication udagram finally elastic ip adresses are assigned to the public subnets aws cloud diagram aws cloud diagram png files there are four files called network yml servers yml servers parameters json network parameters json they comprise respectively the network stack and server application stack instructions the create bat and update bat files can be used to to run the two cloudformation scripts for example create udagramnetworkstack network yml network parameters json create udagramserverstack servers yml servers parameters json | cloud |
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tss-lib | multi party threshold signature scheme mit licensed 1 2 godoc 3 4 go report card 5 6 1 https img shields io badge license mit blue svg 2 license 3 https godoc org github com bnb chain tss lib status svg 4 https godoc org github com bnb chain tss lib 5 https goreportcard com badge github com bnb chain tss lib 6 https goreportcard com report github com bnb chain tss lib permissively mit licensed note this is a library for developers you may find a tss tool that you can use with the binance chain cli here https docs binance org tss html introduction this is an implementation of multi party t n threshold ecdsa elliptic curve digital signature algorithm based on gennaro and goldfeder ccs 2018 1 and eddsa edwards curve digital signature algorithm following a similar approach this library includes three protocols key generation for creating secret shares with no trusted dealer keygen signing for using the secret shares to generate a signature signing dynamic groups to change the group of participants while keeping the secret resharing do not miss these important notes how to use this securely on implementing this library securely rationale ecdsa is used extensively for crypto currencies such as bitcoin ethereum secp256k1 curve neo nist p 256 curve and many more eddsa is used extensively for crypto currencies such as cardano aeternity stellar lumens and many more for such currencies this technique may be used to create crypto wallets where multiple parties must collaborate to sign transactions see multisig use cases https en bitcoin it wiki multisignature multisignature applications one secret share per key address is stored locally by each participant and these are kept safe by the protocol they are never revealed to others at any time moreover there is no trusted dealer of the shares in contrast to multisig solutions transactions produced by tss preserve the privacy of the signers by not revealing which t 1 participants were involved in their signing there is also a performance bonus in that blockchain nodes may check the validity of a signature without any extra multisig logic or processing usage you should start by creating an instance of a localparty and giving it the arguments that it needs the localparty that you use should be from the keygen signing or resharing package depending on what you want to do setup go when using the keygen party it is recommended that you pre compute the safe primes and paillier secret beforehand because this can take some time this code will generate those parameters using a concurrency limit equal to the number of available cpu cores preparams keygen generatepreparams 1 time minute create a partyid for each participating peer on the network you should call tss newpartyid for each one parties tss sortpartyids getparticipantpartyids set up the parameters note the id and moniker fields are for convenience to allow you to easily track participants the id should be a unique string representing this party in the network and moniker can be anything even left blank the uniquekey is a unique identifying key for this peer such as its p2p public key as a big int thisparty tss newpartyid id moniker uniquekey ctx tss newpeercontext parties select an elliptic curve use ecdsa curve tss s256 or use eddsa curve tss edwards params tss newparameters curve ctx thisparty len parties threshold you should keep a local mapping of id strings to partyid instances so that an incoming message can have its origin party s partyid recovered for passing to updatefrombytes see below partyidmap make map string partyid for id range parties partyidmap id id id keygen use the keygen localparty for the keygen protocol the save data you receive through the endch upon completion of the protocol should be persisted to secure storage go party keygen newlocalparty params outch endch preparams omit the last arg to compute the pre params in round 1 go func err party start handle err signing use the signing localparty for signing and provide it with a message to sign it requires the key data obtained from the keygen protocol the signature will be sent through the endch once completed please note that t 1 signers are required to sign a message and for optimal usage no more than this should be involved each signer should have the same view of who the t 1 signers are go party signing newlocalparty message params ourkeydata outch endch go func err party start handle err re sharing use the resharing localparty to re distribute the secret shares the save data received through the endch should overwrite the existing key data in storage or write new data if the party is receiving a new share please note that resharingparameters is used to give this party more context about the re sharing that should be carried out go party resharing newlocalparty params ourkeydata outch endch go func err party start handle err during re sharing the key data may be modified during the rounds do not ever overwrite any data saved on disk until the final struct has been received through the end channel messaging in these examples the outch will collect outgoing messages from the party and the endch will receive save data or a signature when the protocol is complete during the protocol you should provide the party with updates received from other participating parties on the network a party has two thread safe methods on it for receiving updates go the main entry point when updating a party s state from the wire updatefrombytes wirebytes byte from tss partyid isbroadcast bool ok bool err tss error you may use this entry point to update a party s state when running locally or in tests update msg tss parsedmessage ok bool err tss error and a tss message has the following two methods for converting messages to data for the wire go returns the encoded message bytes to send over the wire along with routing information wirebytes byte tss messagerouting error returns the protobuf wrapper message struct used only in some exceptional scenarios i e mobile apps wiremsg tss messagewrapper in a typical use case it is expected that a transport implementation will consume message bytes via the out channel of the local party send them to the destination s specified in the result of msg getto and pass them to updatefrombytes on the receiving end this way there is no need to deal with marshal unmarshalling protocol buffers to implement a transport changes of preparams of ecdsa in v2 0 two fields pailliersk p and pailliersk q is added in version 2 0 they are used to generate paillier key proofs key valuts generated from versions before 2 0 need to regenerate resharing the key valuts to update the praparams with the necessary fileds filled how to use this securely this section is important be sure to read it the transport for messaging is left to the application layer and is not provided by this library each one of the following paragraphs should be read and followed carefully as it is crucial that you implement a secure transport to ensure safety of the protocol when you build a transport it should offer a broadcast channel as well as point to point channels connecting every pair of parties your transport should also employ suitable end to end encryption tls with an aead cipher https en wikipedia org wiki authenticated encryption authenticated encryption with associated data aead is recommended between parties to ensure that a party can only read the messages sent to it within your transport each message should be wrapped with a session id that is unique to a single run of the keygen signing or re sharing rounds this session id should be agreed upon out of band and known only by the participating parties before the rounds begin upon receiving any message your program should make sure that the received session id matches the one that was agreed upon at the start additionally there should be a mechanism in your transport to allow for reliable broadcasts meaning parties can broadcast a message to other parties such that it s guaranteed that each one receives the same message there are several examples of algorithms online that do this by sharing and comparing hashes of received messages timeouts and errors should be handled by your application the method waitingfor may be called on a party to get the set of other parties that it is still waiting for messages from you may also get the set of culprit parties that caused an error from a tss error security audit a full review of this library was carried out by kudelski security and their final report was made available in october 2019 a copy of this report audit binance tss lib final 20191018 pdf https github com bnb chain tss lib releases download v1 0 0 audit binance tss lib final 20191018 pdf may be found in the v1 0 0 release notes of this repository references 1 https eprint iacr org 2019 114 pdf | tss cryptography blockchain golang | blockchain |
graph-of-thoughts | graph of thoughts got p align center img src paper pics preview svg p this is the official implementation of graph of thoughts solving elaborate problems with large language models https arxiv org pdf 2308 09687 pdf this framework gives you the ability to solve complex problems by modeling them as a graph of operations goo which is automatically executed with a large language model llm as the engine this framework is designed to be flexible and extensible allowing you to not only solve problems using the new got approach but also to implement goos resembling previous approaches like cot or tot setup guide in order to use this framework you need to have a working installation of python 3 8 or newer installing got before running either of the following two installation methods make sure to activate your python environment if any beforehand if you are a user and you just want to use graph of thoughts you can install it directly from pypi bash pip install graph of thoughts if you are a developer and you want to modify the code you can install it in editable mode from source bash git clone https github com spcl graph of thoughts git cd graph of thoughts pip install e configuring the llm in order to use the framework you need to have access to an llm please follow the instructions in the controller readme graph of thoughts controller readme md to configure the llm of your choice quick start the following code snippet shows how to use the framework to solve the sorting problem for a list of 32 numbers using a cot like approach make sure you have followed the setup guide setup guide before running the code python from examples sorting sorting 032 import sortingprompter sortingparser utils from graph of thoughts import controller language models operations problem input to be sorted 0 2 6 3 8 7 1 1 6 7 7 7 7 9 3 0 1 7 9 1 3 5 1 3 6 4 5 4 7 3 5 7 create the graph of operations gop operations graphofoperations gop append operation operations generate gop append operation operations score scoring function utils num errors gop append operation operations groundtruth utils test sorting configure the language model assumes config json is in the current directory with openai api key lm language models chatgpt config json model name chatgpt create the controller ctrl controller controller lm gop sortingprompter sortingparser the following dictionary is used to configure the initial thought state original to be sorted current method cot run the controller and generate the output graph ctrl run ctrl output graph output cot json to run the more sophisticated got approach you can use the following code snippet python from examples sorting sorting 032 import sortingprompter sortingparser got utils from graph of thoughts import controller language models operations problem input to be sorted 0 2 6 3 8 7 1 1 6 7 7 7 7 9 3 0 1 7 9 1 3 5 1 3 6 4 5 4 7 3 5 7 retrieve the graph of operations gop got configure the language model assumes config json is in the current directory with openai api key lm language models chatgpt config json model name chatgpt create the controller ctrl controller controller lm gop sortingprompter sortingparser the following dictionary is used to configure the initial thought state original to be sorted current phase 0 method got run the controller and generate the output graph ctrl run ctrl output graph output got json you can compare the two results by inspecting the output graphs output cot json and output got json the final thought states scores indicate the number of errors in the sorted list documentation the paper gives a high level overview of the framework and its components in order to understand the framework in more detail you can read the documentation of the individual modules especially the controller graph of thoughts controller readme md and operations graph of thoughts operations readme md modules are important for understanding how to make the most out of the framework we took extra care to fully document the code so that you can easily understand how it works and how to extend it examples the examples examples directory contains several examples of problems that can be solved using the framework including the ones presented in the paper it is a great starting point for learning how to use the framework to solve real problems each example contains a readme md file with instructions on how to run it and play with it the code is fully documented and should be easy to follow paper results you can run the experiments from the paper by following the instructions in the examples examples directory however if you just want to inspect and replot the results you can use the paper paper directory citations if you find this repository valuable please give it a star got any questions or feedback feel free to reach out to nils blach inf ethz ch mailto nils blach inf ethz ch or open an issue using this in your work please reference us using the provided citation bibtex misc besta2023got title graph of thoughts solving elaborate problems with large language models author besta maciej and blach nils and kubicek ales and gerstenberger robert and gianinazzi lukas and gajda joanna and lehmann tomasz and podstawski micha l and niewiadomski hubert and nyczyk piotr and hoefler torsten year 2023 eprinttype arxiv eprint 2308 09687 | graph-structures graphs large-language-models llm prompt-engineering prompting graph-of-thoughts | ai |
iot-edge-samples | azure iot edge module samples this tutorial shows how to setup your iot edge module development environment windows ubuntu linux write a module customize and initialize the iot edge instance it includes samples for javascript java and net modules if you encounter an issue related to these samples please submit a new issue https github com azure samples iot edge samples issues new for issues related to the iot edge or the packages please go to the iot edge repo https github com azure iot edge and submit an issue br how to run the javascript module sample windows 10 ubuntu 14 debian raspbian prerequisites 1 install latest git client https git scm com downloads 2 install latest node lts https nodejs org v6 11 2 raspbian 1 install raspbian jessie http downloads raspberrypi org raspbian images raspbian 2017 07 05 quick start 1 git clone https github com azure samples iot edge samples git 2 cd iot edge samples js simple 3 npm install to install pre built core runtime of iot edge 4 npm run local to start the iot edge with pre defined modules sensor and printer br how to run the java module sample windows 10 ubuntu 14 prerequisites 1 install latest git client https git scm com downloads 2 install latest x64 version of jre http www oracle com technetwork java javase downloads jre8 downloads 2133155 html 3 install latest maven https maven apache org install html quick start 1 git clone https github com azure samples iot edge samples git 2 cd iot edge samples java timer 3 mvn package to build your module with all dependencies 4 mvn exec exec to start the iot edge with pre defined module br how to run the net module sample windows 10 prerequisites 1 install latest git client https git scm com downloads 2 install visual studio 2015 with update 3 quick start 1 git clone https github com azure samples iot edge samples git 2 cd iot edge samples dotnet nuget sample src 3 open the dotnetmodulesample sln solution file 4 in the visual studio 2015 ide solution explorer right click the dotnetmodulesample and select properties from the context menu 5 click debug and update the executable text box with the location and name of the executable to run by typing lt path to your output directory gt gw exe and update the application arguments to module dev sample json 6 build the dotnetmodulesample project ctrl shift b 7 click the start button in the visual studio 2015 ide or press the f5 key 8 press the enter key to exit the azure iot edge gateway process how to run net standard v1 3 module sample windows 10 prerequisites 1 install latest git client https git scm com downloads 2 install visual studio 2017 quick start 1 git clone https github com azure samples iot edge samples git 2 cd iot edge samples dotnetcore nuget sample src 3 open the netstandardmodulesample sln solution file 4 in the visual studio 2017 ide solution explorer right click the netstandardmodulesample and select properties from the context menu 5 click debug and update the executable option to outdir gw exe and the application arguments to module dev sample json 6 build the netstandardmodulesample project ctrl shift b 7 click the start button in the visual studio 2017 ide or press the f5 key 8 press the enter key to exit the azure iot edge gateway process | server |
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SharpLearning | build status https machinelearning visualstudio com sharplearning github build apis build status sharplearning ci branchname master https machinelearning visualstudio com sharplearning github build build latest definitionid 28 branchname master sharplearning sharplearning is an opensource machine learning library for c net the goal of sharplearning is to provide net developers with easy access to machine learning algorithms and models currently the main focus is supervised learning for classification and regression while also providing the necesarry tools for optimizing and validating the trained models sharplearning provides a simple high level interface for machine learning algorithms in sharplearning a machine learning algorithm is refered to as a learner and a machine learning model is refered to as a predictormodel an example of usage can be seen below c create a random forest learner for classification with 100 trees var learner new classificationrandomforestlearner trees 100 learn the model var model learner learn observations targets use the model for predicting new observations var predictions model predict testobservations save the model for use with another application model save new streamwriter randomforest xml all machine learning algorithms and models implement the same interface for easy replacement currently sharplearning supports the following machine learning algorithms and models decisiontrees adaboost trees gradientboost trees randomforest extratrees neuralnets layers for fully connected and convolutional nets ensemble learning all the machine learning algorithms have sensible default hyperparameters for easy usage however several optimization methods are available for hyperparameter tuning gridsearch randomsearch particleswarm globalizedboundedneldermead hyperband bayesianoptimization license sharplearning is covered under the terms of the mit license md license you may therefore link to it and use it in both opensource and proprietary software projects documentation sharplearning contains xml documentation to help guide the user while using the library code examples and more information about how to use sharplearning can be found in sharplearning examples https github com mdabros sharplearning examples the wiki also contains a set of guides on how to get started getting started https github com mdabros sharplearning wiki getting started introduction to sharplearning https github com mdabros sharplearning wiki introduction to sharplearning tuning hyperparameters https github com mdabros sharplearning wiki hyperparameter tuning using sharplearning xgboost https github com mdabros sharplearning wiki using sharplearning xgboost installation the recommended way to get sharplearning is to use nuget the packages are provided and maintained in the public nuget gallery https nuget org profiles mdabros more information can be found in the getting started https github com mdabros sharplearning wiki getting started guide on the wiki learner and model packages sharplearning decisiontrees provides learning algorithms and models for decisiontree regression and classification sharplearning adaboost provides learning algorithms and models for adaboost regression and classification sharplearning randomforest provides learning algorithms and models for randomforest and extratrees regression and classification sharplearning gradientboost provides learning algorithms and models for gradientboost regression and classification sharplearning neural provides learning algorithms and models for neural net regression and classification layers available for fully connected and covolutional nets sharplearning xgboost provides learning algorithms and models for regression and classification using the xgboost library https github com dmlc xgboost cpu and gpu learning supported this pakcage is x64 only sharplearning ensemble provides ensemble learning for regression and classification makes it possible to combine the other learners models from sharplearning sharplearning common interfaces provides common interfaces for sharplearning validation and model selection packages sharplearning crossvalidation provides cross validation training test set samplers and learning curves for sharplearning sharplearning metrics provides classification regression impurity and ranking metrics sharplearning optimization provides optimization algorithms for hyperparameter tuning container io packages sharplearning containers provides containers and base extension methods for sharplearning sharplearning inputoutput provides csv parsing and serialization for sharplearning sharplearning featuretransformations provides csvrow transforms like missing value replacement and matrix transforms like minmaxnormalization contributing contributions are welcome in the following areas 1 add new issues with bug descriptions or feature suggestions 2 add more examples to sharplearning examples https github com mdabros sharplearning examples 3 solve existing issues by forking sharplearning and creating a pull request when contributing please follow the contribution guide https github com mdabros sharplearning blob master contributing md | machine-learning csharp decision-trees adaboost gradient-boosting-machine random-forest neural-nets deep-learning ensemble-learning cross-validation metrics dotnet machine learning opensource | ai |
steemdb | steemdb https steemdb com open source blockchain explorer for the steem blockchain build on phalcon mongodb prerequisites docker bower components 1 app steemdb website phalcon mongodb application for serving out the website apis and mvc structure 2 docker sync sync service python piston for keeping the blockchain synchronized and up to date runs on a 3 second delay and triggers updates in the database 3 docker history account history service python piston which runs every 6 hours and analyzes every account on the blockchain records historical information as well as a current snapshot 4 docker witnesses witness mining service python piston which runs every minute to pull current witness information as well as the mining queue 5 docker development development vm with php7 nginx and phalcon getting it running this explorer syncronizes the entire blockchain into a mongodb database this takes a lot of time i d highly recommend you run a local instance of steemd and modify the docker compose yml to point to it it s going to be a lot faster of a sync than trying to read the entire blockchain from a public node docker compose up should be all you need to get the development application running if you d like to run any of the syncronization services for initialization you will need to uncomment the specific service from the docker compose yml file for example if you re looking to work on the account pages and start recording their history uncomment the history service and start your containers you can uncomment all 3 services to start all applications when docker creates it s containers the initial syncronization run by the sync service will take many hours to complete and process all of the blocks it will also require an enormous amount of disk space as time progresses this data will be trimmed for now in early alpha it s better to have it all | steem blockchain steem-blockchain php | blockchain |
secure-web-dev-workshop2 | workshop 2 save data in a database mongodb goal learning basics of mongodb and how to use it with javascript prerequisites 1 fork this repository then clone it on your computer 2 create an account on mongo atlas sign up https www mongodb com cloud atlas register 3 create a database and save its credentials for later what to do commit your changes after each instruction following the commit message format text feat 1 initiate npm project 1 in this directory initiate an npm project shell npm init 2 create a file named import data js 3 add npm packages mongoose and dotenv mongoose is a package making mongo request easier and more secure shell npm install save mongoose npm install save dotenv 4 put your database credentials in a file named env like dotenv mongo uri mongodb username password host port database 5 in the file import data js create mongoose model locations 1 define a mongoose schema that accepts the following entity look at the documentation https mongoosejs com docs guide html definition json filmtype long m u00e9trage filmproducername mandarin production enddate 2020 08 21 filmname tout s est bien passe district 75013 geolocation coordinates 2 348314535961912 48 83566000015182 type point sourcelocationid 2020 404 filmdirectorname francois ozon address rue pascal 75013 paris startdate 2020 08 20 year 2020 2 validate your schema with your professor 3 create the model locations using the schema 6 connect to the database using credentials stored in the env file and the dotenv package 7 write a function that takes an array of filminglocations file lieux de tournage a paris json from workshop 1 and imports each filminglocation in mongo 8 call the function with data from lieux de tournage a paris json populate the database 9 write a function to query one location by its id 10 write a function to query all locations for a given filmname 11 write a function to delete a location by its id 12 write a function to add a location 13 write a function to update a location | front_end |
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960-Grid-System | 960 grid system created by nathan smith see the official site for more info http 960 gs to install the adobe fireworks extension simply double click the mxp file included in the app plugins fireworks directory if you are running windows 7 you will need admin permissions in order to install this extension properly thank you for downloading the 960 grid system i hope it helps to streamline web development workflow enclosed in the bundle are printable sketch sheets and template files for adobe fireworks and photoshop omnigraffle and visio also included is a lightweight css file which contains the grid dimensions to use this file simply include the 960 css in the head of the html page you may also use the reset css and text css files or opt to leave them out here is an example of the xhtml code necessary to incorporate the css files html head link rel stylesheet type text css media all href css reset css link rel stylesheet type text css media all href css text css link rel stylesheet type text css media all href css 960 css head it is worth noting that these styles do not automatically make up a finished site design they are simply a starting point ideally for rapid prototyping or as a basis for creating your own designs you should not feel constrained by the way i have built the initial code if you disagree with how something has been done feel free to revise it for the needs of your particular site the files in the 960 grid system are free of charge licensed under mit gpl note that if you are building a site in a language which reads from right to left use the css files that end in rtl css instead denote the language html html lang dir rtl be sure to set lang to the appropriate two letter abbreviation of the language you are using example lang he for hebrew lang ar for arabic gpl license http www gnu org licenses gpl html mit license http www opensource org licenses mit license php | front_end |
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programming-with-64-bit-ARM-assembly-language | apress source code this repository accompanies programming with 64 bit arm assembly language single board computer development for raspberry pi and mobile devices https www apress com 9781484258804 by stephen smith apress 2020 comment cover cover image 9781484258804 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | front_end |
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Fundamentals-of-Web-Development-Solutions | fundamentals of web development 1st edition solutions if you find this useful please click the star my solutions to fundamentals of web development 1st edition by by randy connolly author ricardo hoar author fundamentals of web development covers the broad range of topics required for modern web development both client and server side and is appropriate for students who have taken a cs1 course sequence the book guides students through the creation of enterprise quality websites using current development frameworks its comprehensive coverage of a modern internet development platform includes html5 css3 javascript and the lamp stack that is linux apache mysql and php other important technologies covered include jquery xml wordpress bootstrap and a variety of third party apis that include facebook twitter and google and bing maps coverage also includes the required acm web development topics in a modern manner closely aligned with best practices in the real world of web development resources https www pearson com us higher education program connolly fundamentals of web development pgm71223 html https www chegg com homework help fundamentals of web development subscription 1st edition solutions 9780133857290 2017 by olti asllanaj | html5 css3 php javascript jquery json | front_end |
PyImageSearch-Gurus-Course | pyimagesearch gurus course learning computer vision | ai |
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LLM-Factuality-Survey | llm factuality survey the repository for the survey paper survey on factuality in large language models knowledge retrieval and domain specificity https arxiv org abs 2310 07521 logo jpg p align center cunxiang wang sup 1 7 sup xiaoze liu sup 2 sup yuanhao yue sup 3 sup xiangru tang sup 4 sup tianhang zhang sup 5 sup cheng jiayang sup 6 sup yunzhi yao sup 7 sup wenyang gao sup 1 7 sup xuming hu sup 8 sup zehan qi sup 8 sup yidong wang sup 1 sup linyi yang sup 1 sup jindong wang sup 9 sup xing xie sup 9 sup zheng zhang sup 10 sup and yue zhang sup 1 sup p p align center 1 school of engineering westlake university 2 purdue university 3 fudan university 4 yale university 5 shanghai jiao tong university 6 hkust 7 zhejiang university 8 tsinghua university 9 microsoft research 10 nyu shanghai university br equal contribution correspondence to yue zhang p survey tree jpg note as real time updates may not be feasible for the arxiv paper for the most recent developments and modifications please consult this repository we greatly appreciate and welcome pull requests or issues to enhance the quality of this survey all contributions will be list in the a href acknowledgements acknowledgements a section paper list analysis of factuality knowledge storage 1 language models as knowledge bases petroni et al 2019 paper https doi org 10 18653 v1 d19 1250 1 locating and editing factual associations in gpt meng et al 2022 paper https openreview net forum id h6was6ee4 1 transformer feed forward layers are key value memories geva et al 2021 paper https doi org 10 18653 v1 2021 emnlp main 446 1 transformer feed forward layers build predictions by promoting concepts in the vocabulary space geva et al 2022 paper https aclanthology org 2022 emnlp main 3 1 dissecting recall of factual associations in auto regressive language models globerson et al 2023 paper https arxiv org abs 2304 14767 1 journey to the center of the knowledge neurons discoveries of language independent knowledge neurons and degenerate knowledge neurons chen et al 2023 paper https arxiv org abs 2308 13198 1 a rigorous study of integrated gradients method and extensions to internal neuron attributions lundstrom et al 2022 paper https arxiv org abs 2202 11912 knowledge awareness 1 critic large language models can self correct with tool interactive critiquing gou et al 2023 paper https arxiv org abs 2305 11738 1 investigating the factual knowledge boundary of large language models with retrieval augmentation ren et al 2023 paper https arxiv org abs 2307 11019 1 do large language models know what they don t know yin et al 2023 paper https doi org 10 18653 v1 2023 findings acl 551 1 a survey on in context learning dong et al 2023 paper https arxiv org abs 2301 00234 1 language models mostly know what they know kadavath et al 2022 paper https arxiv org abs 2207 05221 1 the internal state of an llm knows when its lying azaria et al 2023 paper https arxiv org abs 2304 13734 parametric knowledge vs retrieved knowledge 1 generate rather than retrieve large language models are strong context generators yu et al 2023 paper https arxiv org abs 2209 10063 1 beyond factuality a comprehensive evaluation of large language models as knowledge generators chen et al 2023 paper https arxiv org abs 2310 07289 1 leveraging passage retrieval with generative models for open domain question answering izacard et al 2021 paper https doi org 10 18653 v1 2021 eacl main 74 1 large language models struggle to learn long tail knowledge kandpal et al 2023 paper https arxiv org abs 2211 08411 1 head to tail how knowledgeable are large language models llm aka will llms replace knowledge graphs sun et al 2023 paper https arxiv org abs 2308 10168 contextual influence 1 large language models with controllable working memory li et al 2023 paper https doi org 10 18653 v1 2023 findings acl 112 1 context faithful prompting for large language models zhou et al 2023 paper https arxiv org abs 2303 11315 1 benchmarking large language models in retrieval augmented generation chen et al 2023 paper https arxiv org abs 2309 01431 1 automatic evaluation of attribution by large language models yue et al 2023 paper https arxiv org abs 2305 06311 knowledge conflicts 1 entity based knowledge conflicts in question answering longpre et al 2021 paper https arxiv org abs 2109 05052 1 rich knowledge sources bring complex knowledge conflicts recalibrating models to reflect conflicting evidence chen et al 2022 paper https arxiv org abs 2210 13701 1 adaptive chameleon or stubborn sloth unraveling the behavior of large language models in knowledge clashes xie et al 2023 paper https arxiv org abs 2305 13300 1 large language models with controllable working memory li et al 2023 paper https doi org 10 18653 v1 2023 findings acl 112 causes of factual errors model level causes forgetting 1 an empirical investigation of catastrophic forgetting in gradient based neural networks goodfellow et al 2015 paper https arxiv org abs 1312 6211 1 preserving in context learning ability in large language model fine tuning wang et al 2022 paper https arxiv org abs 2211 00635 1 recall and learn fine tuning deep pretrained language models with less forgetting chen et al 2020 paper https arxiv org abs 2004 12651 1 investigating the catastrophic forgetting in multimodal large language models zhai et al 2023 paper https arxiv org abs 2309 10313 1 an empirical study of catastrophic forgetting in large language models during continual fine tuning luo et al 2023 paper https arxiv org abs 2308 08747 reasoning failure 1 we re afraid language models aren t modeling ambiguity liu et al 2023 paper https arxiv org abs 2304 14399 1 the reversal curse llms trained on a is b fail to learn b is a berglund et al 2023 paper https arxiv org abs 2309 12288 1 understanding catastrophic forgetting in language models via implicit inference kotha et al 2023 paper https arxiv org abs 2309 10105 1 can chatgpt replace traditional kbqa models an in depth analysis of the question answering performance of the gpt llm family tan et al 2023 paper https arxiv org abs 2303 07992 retrieval level causes misinformation not recognized by llms 1 entity based knowledge conflicts in question answering longpre et al 2021 paper https arxiv org abs 2109 05052 1 on the risk of misinformation pollution with large language models pan et al 2023 paper https arxiv org abs 2305 13661 1 a survey on truth discovery han et al 2015 paper https doi org 10 1145 2897350 2897352 distracting information 1 sail search augmented instruction learning luo et al 2023 paper https arxiv org abs 2305 15225 1 lost in the middle how language models use long contexts liu et al 2023 paper https arxiv org abs 2307 03172 misinterpretation of related information 1 react synergizing reasoning and acting in language models yao et al 2023 paper https arxiv org abs 2210 03629 inference level causes snowballing 1 how language model hallucinations can snowball zhang et al 2023 paper https arxiv org abs 2305 13534 1 a stitch in time saves nine detecting and mitigating hallucinations of llms by validating low confidence generation varshney et al 2023 paper https arxiv org abs 2307 03987 erroneous decoding 1 dola decoding by contrasting layers improves factuality in large language models chuang et al 2023 paper https arxiv org abs 2309 03883 1 how decoding strategies affect the verifiability of generated text massarelli et al 2020 paper https arxiv org abs 1911 03587 exposure bias 1 winoqueer a community in the loop benchmark for anti lgbtq bias in large language models felkner et al 2023 paper https aclanthology org 2023 acl long 507 1 bias and fairness in large language models a survey gallegos et al 2023 paper https arxiv org abs 2309 00770 1 misgendered limits of large language models in understanding pronouns hossain et al 2023 paper https doi org 10 18653 v1 2023 acl long 293 evaluation of factuality benchmarks 1 measuring massive multitask language understanding hendrycks et al 2021 paper https arxiv org abs 2009 03300 1 truthfulqa measuring how models mimic human falsehoods lin et al 2022 paper https doi org 10 18653 v1 2022 acl long 229 1 halueval a large scale hallucination evaluation benchmark for large language models li et al 2023 paper https arxiv org abs 2305 11747 1 c eval a multi level multi discipline chinese evaluation suite for foundation models huang et al 2023 paper https arxiv org abs 2305 08322 1 do large language models know what they don t know yin et al 2023 paper https doi org 10 18653 v1 2023 findings acl 551 1 do large language models know about facts hu et al 2023 paper https arxiv org abs 2310 05177 1 realtime qa what s the answer right now kasai et al 2022 paper https arxiv org abs 2207 13332 1 freshllms refreshing large language models with search engine augmentation vu et al 2023 paper https arxiv org abs 2310 03214 1 beyond the imitation game quantifying and extrapolating the capabilities of language models srivastava et al 2023 paper https openreview net forum id uytl5bvosj 1 natural questions a benchmark for question answering research kwiatkowski et al 2019 paper https aclanthology org q19 1026 1 triviaqa a large scale distantly supervised challenge dataset for reading comprehension joshi et al 2017 paper https aclanthology org p17 1147 1 semantic parsing on freebase from question answer pairs berant et al 2013 paper https aclanthology org d13 1160 1 open question answering over tables and text chen et al 2021 paper https arxiv org abs 2010 10439 1 ambigqa answering ambiguous open domain questions min et al 2020 paper https doi org 10 18653 v1 2020 emnlp main 466 1 hotpotqa a dataset for diverse explainable multi hop question answering yang et al 2018 paper https arxiv org abs 1809 09600 1 constructing a multi hop qa dataset for comprehensive evaluation of reasoning steps ho et al 2020 paper https doi org 10 18653 v1 2020 coling main 580 1 iirc a dataset of incomplete information reading comprehension questions ferguson et al 2020 paper https doi org 10 18653 v1 2020 emnlp main 86 1 musique multihop questions via single hop question composition trivedi et al 2022 paper https doi org 10 1162 tacl a 00475 1 eli5 long form question answering fan et al 2019 paper https doi org 10 18653 v1 p19 1346 1 fever a large scale dataset for fact extraction and verification thorne et al 2018 paper https arxiv org abs 1803 05355 1 fool me twice entailment from wikipedia gamification eisenschlos et al 2021 paper https doi org 10 18653 v1 2021 naacl main 32 1 hover a dataset for many hop fact extraction and claim verification jiang et al 2020 paper https doi org 10 18653 v1 2020 findings emnlp 309 1 the fact extraction and verification over unstructured and structured information feverous shared task aly et al 2021 paper https doi org 10 18653 v1 2021 fever 1 1 1 t rex a large scale alignment of natural language with knowledge base triples elsahar et al 2018 paper https aclanthology org l18 1544 1 zero shot relation extraction via reading comprehension levy et al 2017 paper https doi org 10 18653 v1 k17 1034 1 language models as knowledge bases petroni et al 2019 paper https doi org 10 18653 v1 d19 1250 1 neural text generation from structured data with application to the biography domain lebret et al 2016 paper https doi org 10 18653 v1 d16 1128 1 wikiasp a dataset for multi domain aspect based summarization hayashi et al 2021 paper https doi org 10 1162 tacl a 00362 1 kilt a benchmark for knowledge intensive language tasks petroni et al 2021 paper https doi org 10 18653 v1 2021 naacl main 200 1 scaling language models methods analysis insights from training gopher rae et al 2022 paper https arxiv org abs 2112 11446 1 curation corpus base curation et al 2020 paper https github com curationcorp curation corpus 1 pointer sentinel mixture models merity et al 2016 paper https arxiv org abs 1609 07843 1 the lambada dataset word prediction requiring a broad discourse context paperno et al 2016 paper https doi org 10 18653 v1 p16 1144 1 exploring the limits of transfer learning with a unified text to text transformer raffel et al 2020 paper http jmlr org papers v21 20 074 html 1 the pile an 800gb dataset of diverse text for language modeling gao et al 2020 paper https arxiv org abs 2101 00027 1 wizard of wikipedia knowledge powered conversational agents dinan et al 2019 paper https arxiv org abs 1811 01241 1 grounded response generation task at dstc7 galley et al 2019 paper http workshop colips org dstc7 papers dstc7 task 2 overview paper pdf 1 what do others think task oriented conversational modeling with subjective knowledge zhao et al 2023 paper https arxiv org abs 2305 12091 1 realtoxicityprompts evaluating neural toxic degeneration in language models gehman et al 2020 paper https doi org 10 18653 v1 2020 findings emnlp 301 1 hey ai can you solve complex tasks by talking to agents khot et al 2022 paper https arxiv org abs 2110 08542 1 did aristotle use a laptop a question answering benchmark with implicit reasoning strategies geva et al 2021 paper https doi org 10 1162 tacl a 00370 1 tempquestions a benchmark for temporal question answering jia et al 2018 paper https doi org 10 1145 3184558 3191536 1 infotabs inference on tables as semi structured data gupta et al 2020 paper https doi org 10 18653 v1 2020 acl main 210 studies 1 selfcheckgpt zero resource black box hallucination detection for generative large language models manakul et al 2023 paper https arxiv org abs 2303 08896 1 evaluating open question answering evaluation wang et al 2023 paper https arxiv org abs 2305 12421 1 measuring and modifying factual knowledge in large language models pezeshkpour et al 2023 paper https arxiv org abs 2306 06264 1 a stitch in time saves nine detecting and mitigating hallucinations of llms by validating low confidence generation varshney et al 2023 paper https arxiv org abs 2307 03987 1 factool factuality detection in generative ai a tool augmented framework for multi task and multi domain scenarios chern et al 2023 paper https arxiv org abs 2307 13528 1 language models mostly know what they know kadavath et al 2022 paper https arxiv org abs 2207 05221 1 generate rather than retrieve large language models are strong context generators yu et al 2023 paper https arxiv org abs 2209 10063 1 beyond factuality a comprehensive evaluation of large language models as knowledge generators chen et al 2023 paper https arxiv org abs 2310 07289 1 teaching language models to support answers with verified quotes menick et al 2022 paper https arxiv org abs 2203 11147 evaluating domain specific factuality 1 pixiu a large language model instruction data and evaluation benchmark for finance xie et al 2023 paper https arxiv org abs 2306 05443 1 when flue meets flang benchmarks and large pre trained language model for financial domain shah et al 2022 paper https arxiv org abs 2211 00083 1 ecomgpt instruction tuning large language model with chain of task tasks for e commerce li et al 2023 paper https arxiv org abs 2308 06966 1 cmb a comprehensive medical benchmark in chinese wang et al 2023 paper https arxiv org abs 2308 08833 1 genegpt augmenting large language models with domain tools for improved access to biomedical information jin et al 2023 paper https arxiv org abs 2304 09667 1 legalbench a collaboratively built benchmark for measuring legal reasoning in large language models guha et al 2023 paper https arxiv org abs 2308 11462 1 lawbench benchmarking legal knowledge of large language models fei et al 2023 paper https arxiv org abs 2309 16289 factuality enhancement on standalone llm generation pretraining based initial pretraining 1 a multitask multilingual multimodal evaluation of chatgpt on reasoning hallucination and interactivity yejin bang et al arxiv 2023 paper https arxiv org abs 2302 04023 1 deduplicating training data makes language models better lee katherine et al acl 2022 paper https aclanthology org 2022 acl long 577 1 unsupervised improvement of factual knowledge in language models sadeq nafis et al eacl 2023 paper https aclanthology org 2023 eacl main 215 pdf continual pretraining 1 factuality enhanced language models for open ended text generation lee nayeon et al neurips 2022 paper https proceedings neurips cc paper files paper 2022 file df438caa36714f69277daa92d608dd63 paper conference pdf supervised finetuning continual sft 1 skill structured knowledge infusion for large language models moiseev fedor et al naacl 2022 paper https aclanthology org 2022 naacl main 113 1 contrastive learning reduces hallucination in conversations sun weiwei et al aaai 2023 paper https arxiv org pdf 2212 10400 pdf 1 chatgpt is not enough enhancing large language models with knowledge graphs for fact aware language modeling linyao yang et al arxiv 2023 paper https arxiv org pdf 2306 11489 pdf model editing 1 editing large language models problems methods and opportunities yunzhi yao et al arxiv 2023 paper https arxiv org pdf 2305 13172 pdf 1 knowledge neurons in pretrained transformers dai damai et al acl 2022 paper https aclanthology org 2022 acl long 581 pdf 1 locating and editing factual associations in gpt kevin meng et al neurips 2022 paper https openreview net forum id h6was6ee4 1 editing factual knowledge in language models de cao nicola et al emnlp 2021 paper https aclanthology org 2021 emnlp main 522 pdf 1 fast model editing at scale eric mitchell et al iclr 2022 paper https openreview net forum id 0dczxewfopt 1 inference time intervention eliciting truthful answers from a language model kenneth li et al arxiv 2023 paper https arxiv org pdf 2306 03341 pdf multi agent 1 improving factuality and reasoning in language models through multiagent debate yilun du et al arxiv 2023 paper https arxiv org pdf 2305 14325 pdf 1 lm vs lm detecting factual errors via cross examination roi cohen et al arxiv 2023 paper https arxiv org pdf 2305 13281 pdf novel prompt 1 generate rather than retrieve large language models are strong context generators yu wenhao et al iclr 2023 paper https arxiv org pdf 2209 10063 pdf 1 according to prompting language models improves quoting from pre training data orion weller et al arxiv 2023 paper https arxiv org pdf 2305 13252 pdf 1 decomposed prompting a modular approach for solving complex tasks tushar khot et al arxiv 2023 paper https arxiv org pdf 2210 02406 pdf 1 chain of verification reduces hallucination in large language models dhuliawala et al arxiv 2023 paper https arxiv org pdf 2309 11495 pdf decoding 1 factuality enhanced language models for open ended text generation lee nayeon et al neurips 2022 paper https proceedings neurips cc paper files paper 2022 file df438caa36714f69277daa92d608dd63 paper conference pdf 1 dola decoding by contrasting layers improves factuality in large language models chuang yung sung et al arxiv 2023 paper https arxiv org pdf 2309 03883 pdf on retrieval augmented generation normal rag setting 1 improving language models by retrieving from trillions of tokens sebastian borgeaud et al arxiv 2021 paper https arxiv org pdf 2112 04426 pdf 1 internet augmented language models through few shot prompting for open domain question answering angeliki lazaridou et al arxiv 2022 paper https arxiv org pdf 2203 05115 pdf interactive retrieval cot based retrieval 1 rethinking with retrieval faithful large language model inference hangfeng he et al arxiv 2023 paper https arxiv org pdf 2301 00303 pdf 1 interleaving retrieval with chain of thought reasoning for knowledge intensive multi step questions trivedi harsh et al acl 2023 paper https aclanthology org 2023 acl long 557 pdf 1 active retrieval augmented generation zhengbao jiang et al arxiv 2023 paper https arxiv org pdf 2305 06983 pdf agent based retrieval 1 react synergizing reasoning and acting in language models shunyu yao et al arxiv 2023 paper https arxiv org pdf 2210 03629 pdf 1 reflexion language agents with verbal reinforcement learning noah shinn et al arxiv 2023 paper https arxiv org pdf 2303 11366 pdf 1 a stitch in time saves nine detecting and mitigating hallucinations of llms by validating low confidence generation neeraj varshney et al arxiv 2023 paper https arxiv org pdf 2307 03987 pdf retrieval adaptation prompt based 1 check your facts and try again improving large language models with external knowledge and automated feedback baolin peng et al arxiv 2023 paper https arxiv org pdf 2302 12813 pdf sft based 1 atlas few shot learning with retrieval augmented language models gautier izacard et al arxiv 2022 paper https arxiv org pdf 2208 03299 pdf 1 replug retrieval augmented black box language models weijia shi et al arxiv 2023 paper https arxiv org pdf 2301 12652 pdf 1 sail search augmented instruction learning luo hongyin et al arxiv 2023 paper https arxiv org pdf 2305 15225 pdf rlhf based 1 teaching language models to support answers with verified quotes jacob menick et al arxiv 2022 paper https arxiv org pdf 2203 11147 pdf retrieval on external memory 1 decoupled context processing for context augmented language modeling zonglin li et al neurips 2022 paper https proceedings neurips cc paper files paper 2022 file 882d801fb1017f955547d5a816ade0fc paper conference pdf 1 g map general memory augmented pre trained language model for domain tasks zhongwei wan et al icml 2019 paper http proceedings mlr press v97 houlsby19a houlsby19a pdf 1 parameter efficient transfer learning for nlp neil houlsby et al emnlp 2022 paper https aclanthology org 2022 emnlp main 441 pdf 1 kala knowledge augmented language model adaptation kang minki et al naacl 2022 paper https aclanthology org 2022 naacl main 379 pdf 1 entities as experts sparse memory access with entity supervision thibault f vry et al emnlp 2020 paper https aclanthology org 2020 emnlp main 400 pdf 1 mention memory incorporating textual knowledge into transformers through entity mention attention michiel de jong et al iclr 2022 paper https openreview net pdf id oy1a8ejqgex 1 plug and play knowledge injection for pre trained language models zhang zhengyan et al acl 2023 paper https aclanthology org 2023 acl long 594 pdf 1 evidence based factual error correction thorne james et al acl 2021 paper https aclanthology org 2021 acl long 256 pdf 1 rarr researching and revising what language models say using language models gao luyu et al acl 2023 paper https aclanthology org 2023 acl long 910 pdf 1 purr efficiently editing language model hallucinations by denoising language model corruptions chen anthony et al arxiv 2023 paper https arxiv org pdf 2305 14908 pdf retrieval on structured knowledge source 1 mitigating language model hallucination with interactive question knowledge alignment shuo zhang et al arxiv 2023 paper https arxiv org pdf 2305 13669 pdf 1 structgpt a general framework for large language model to reason on structured data jinhao jiang et al arxiv 2023 paper https arxiv org pdf 2305 09645 pdf 1 knowledge augmented language model prompting for zero shot knowledge graph question answering jinheon baek et al arxiv 2023 paper https arxiv org pdf 2306 04136 pdf domain factuality enhanced llms healthcare domain enhanced llms 1 cohortgpt an enhanced gpt for participant recruitment in clinical study guan zihan et al arxiv 2023 paper https arxiv org pdf 2307 11346 pdf 2 chatdoctor a medical chat model fine tuned on a large language model meta ai llama using medical domain knowledge li yunxiang et al cureus 2023 paper https arxiv org pdf 2303 14070 pdf 3 deid gpt zero shot medical text de identification by gpt 4 liu zhengliang et al arxiv 2023 paper https arxiv org pdf 2303 11032 pdf 4 biomedlm a domain specific large language model for biomedical text venigalla a et al blog https crfm stanford edu 2022 12 15 biomedlm html model https huggingface co stanford crfm biomedlm 5 medchatzh a better medical adviser learns from better instructions tan yang et al arxiv 2023 paper https arxiv org pdf 2309 01114 pdf 6 biogpt generative pre trained transformer for biomedical text generation and mining luo renqian et al briefings in bioinformatics 2022 paper https arxiv org pdf 2210 10341 pdf 7 genegpt augmenting large language models with domain tools for improved access to biomedical information jin qiao et al arxiv 2023 paper https arxiv org pdf 2304 09667 pdf 8 almanac retrieval augmented language models for clinical medicine hiesinger william et al arxiv 2023 paper https arxiv org pdf 2303 01229 pdf 9 molxpt wrapping molecules with text for generative pre training liu zequn et al arxiv 2023 paper https arxiv org pdf 2305 10688 pdf 10 huatuogpt towards taming language model to be a doctor zhang hongbo et al arxiv 2023 paper https arxiv org pdf 2305 15075 pdf 11 zhongjing enhancing the chinese medical capabilities of large language model through expert feedback and real world multi turn dialogue yang songhua et al arxiv 2023 paper https arxiv org pdf 2308 03549 pdf 12 augmenting black box llms with medical textbooks for clinical question answering wang yubo et al arxiv 2023 paper https arxiv org pdf 2309 02233 pdf 13 disc medllm bridging general large language models and real world medical consultation bao zhijie et al arxiv 2023 paper https arxiv org pdf 2308 14346 pdf legal domain enhanced llms 1 brief report on lawgpt 1 0 a virtual legal assistant based on gpt 3 nguyen ha thanh et al arxiv 2023 paper https arxiv org pdf 2302 05729 pdf 2 chatlaw open source legal large language model with integrated external knowledge bases cui jiaxi et al arxiv 2023 paper https arxiv org pdf 2306 16092 pdf 3 explaining legal concepts with augmented large language models gpt 4 savelka jaromir et al arxiv 2023 paper https arxiv org pdf 2306 09525 pdf 4 lawyer llama technical report huang quzhe et al arxiv 2023 paper https arxiv org pdf 2305 15062 pdf finance domain enhanced llms 1 ecomgpt instruction tuning large language model with chain of task tasks for e commerce li yangning et al arxiv 2023 paper https arxiv org pdf 2308 06966 pdf 2 bloomberggpt a large language model for finance shijie wu et al arxiv 2023 paper https arxiv org pdf 2303 17564 pdf other domain enhanced llms geoscience and environment domain enhanced llms 1 learning a foundation language model for geoscience knowledge understanding and utilization deng cheng et al arxiv 2023 paper https arxiv org pdf 2306 05064 pdf 2 houyi an open source large language model specially designed for renewable energy and carbon neutrality field bai mingliang et al arxiv 2023 paper https arxiv org pdf 2308 01414 pdf education domain enhanced llms 1 grammargpt exploring open source llms for native chinese grammatical error correction with supervised fine tuning fan yaxin et al arxiv 2023 paper https arxiv org pdf 2307 13923 pdf food domain enhanced llms 1 foodgpt a large language model in food testing domain with incremental pre training and knowledge graph prompt qi zhixiao et al arxiv 2023 paper https arxiv org pdf 2308 10173 pdf home renovation domain enhanced llms 1 chathome development and evaluation of a domain specific language model for home renovation wen cheng et al arxiv 2023 paper https arxiv org pdf 2307 15290 pdf tables table comparison between the factuality issue and the hallucination issue factual and non hallucinated factually correct outputs non factual and hallucinated entirely fabricated outputs hallucinated but factual br 1 outputs that are unfaithful to the prompt but remain factually correct cao etal 2022 hallucinated br 2 outputs that deviate from the prompt s specifics but don t touch on factuality e g a prompt asking for a story about a rabbit and wolf becoming friends but the llm produces a tale about a rabbit and a dog befriending each other br 3 outputs that provide additional factual details not specified in the prompt e g a prompt asking about the capital of france and the llm responds with paris which is known for the eiffel tower non factual but non hallucinated br 1 outputs where the llm states i don t know or avoids a direct answer br 2 outputs that are partially correct e g for the question who landed on the moon with apollo 11 if the llm responds with just neil armstrong the answer is incomplete but not hallucinated br 3 outputs that provide a generalized or vague response without specific details e g for a question about the causes of world war ii the llm might respond with it was due to various political and economic factors causes of factual errors category cause example dialog notes and references model level causes domain knowledge deficit q ceo of assicurazioni generali br bloomberggpt philippe donnet br gpt neox antonio de lorenzo simone gambarini enrico zanetti br flan t5 xxl john m forsyth christopher k peters empty string bloomberggpt is a finance domain specific language model wu et al 2023 https arxiv org abs 2303 17564 outdated information q when was kyiv attacked by russia br chatgpt as of my last knowledge update in september 2021 russia had not launched an attack on kyiv kyiv was attacked by russia on 25 february 2022 reasoning error q who is tom cruise s mother br a mary lee pfeiffer br q who is mary lee pfeiffer s son br a there is no widely known information about from berglund et al 2023 https arxiv org abs 2309 12288 it is clear that the model knows tom cruise s mother is lee pfeiffer but it fails to reason that lee pfeiffer has a son named tom cruise retrieval level causes distracted by the retrieval information retrieved context alan melikdjanian age bio faces and birthday br alan melikdjanian is the director writer with his father editor and composer of the 2006 direct to dvd russian language film citizen mavzik br q what is the nationality of the director of citizen mavzik br a 1 the director of the film citizen mavzik is alan melikdjanian 2 alan melikdjanian is from russia so the answer is russia alan melikdjanian known by the alias captain disillusion is a soviet born american independent filmmaker and youtuber melikdjanian et al 2018 https www reddit com r damnthatsinteresting comments w9gauc youtuber captain disillusion explains how his misunderstood the retrieval information retrieved context gerard iii count of guelders wikipedia br gerard married margaretha of brabant br q who is gerard i count of guelders s wife br a the name of gerard i count of guelders s wife which is margaretha of brabant the retrieved context accurately provided the name of gerard iii count of guelders wife which is margaretha of brabant however the model erroneously referred to him as gerard i resulting in an incorrect answer fail to address misinformation in retrieved documents retrieved context 0 james vincent death notice and service information legacy com br james vincent died on august 9 2023 br 1 update jury convicts james vincent of murder after deliberating a jury convicted james vincent of murder in the 2018 shooting death of luka grabb br q when did james vincent die br a james vincent died on august 9 2023 in long beach california in the presence of contradictory information from retrieved documents the model should thoroughly outline the response while also noting the need for clarity for instance the model could state according to the first document james vincent passed away on august 9 2023 in long beach however the second document suggests that james vincent was murdered in 2018 inference level causes snowballing q was there ever a us senator who represented the state of new hampshire and whose alma mater was the university of pennsylvania br a yes his name was john p hale john p hale was graduated from bowdoin college zhang et al 2023 https arxiv org abs 2305 13534 exposure bias q aamari was very stoic pronoun rarely showed any emotion br a he the correct answer was xe according to hossain et al 2023 https doi org 10 18653 v1 2023 acl long 293 evaluations reference task dataset metrics human eval evaluated llms granularity factscore min et al 2023 https arxiv org abs 2305 14251 biography generation 183 people entities f1 gpt 3 5 br chatgpt t selfcheckgpt manakul et al 2023 https arxiv org abs 2303 08896 bio generation wikibio auc pr br human score gpt 3 br llama br opt br gpt j s wang et al 2023 https arxiv org abs 2305 12421 open qa nq tq acc br em gpt 3 5 br chatgpt br gpt 4 br bing chat s pezeshkpour et al 2023 https arxiv org abs 2306 06264 knowledge probing t rex br lama acc gpt3 5 t de cao et al 2021 https doi org 10 18653 v1 2021 emnlp main 522 qa br fact checking kilt br fever br zsre acc gpt 3 br flan t5 s t varshney et al 2023 https arxiv org abs 2307 03987 article generation unnamed dataset acc br auc gpt3 5 br vicuna s facttool chern et al 2023 https arxiv org abs 2307 13528 kb based qa rose acc br f1 gpt 4 br chatgpt br flan t5 s kadavath et al 2022 https arxiv org abs 2207 05221 self evaluation big bench br mmlu logiqa br truthfulqa br quality triviaqa lambada acc br brier score br rms calibration error claude t reference task dataset metrics human eval evaluated llms granularity retro borgeaud et al 2022 https arxiv org abs 2112 04426 qa br language br modeling massivetext br curation corpus br wikitext103 br lambada br c4 pile nq ppl br acc br exact match retro t genread yu et al 2023 https arxiv org abs 2209 10063 qa br dialogue br fact checking nq tq webq br fever br fm2 wow em acc br f1 rouge l gpt3 5 codex br gpt 3 gopher br flan glam br palm s gophercite menick et al 2022 https arxiv org abs 2203 11147 self supported qa nq eli5 br truthfulqa br health law fiction conspiracies human score gophercite s trivedi et al trivedi et al 2023 https doi org 10 18653 v1 2023 acl long 557 qa hotpotqa iirc br 2wikimultihopqa br musique music retrieval recall br answer f1 gpt 3 br flan t5 s t peng et al peng et al 2023 https arxiv org abs 2302 12813 qa br dialogue dstc7 track2 br dstc11 track5 br ott qa rouge chrf br bertscore usefulness br humanness chatgpt s t critic gou et al 2023 https arxiv org abs 2305 11738 qa br toxicity reduction ambignq triviaqa hotpotqa br realtoxicityprompts exact match maximum toxicity br perplexity n gram diversity br auroc gpt 3 5 br chatgpt t khot et al khot et al 2023 https arxiv org abs 2210 02406 qa br long context qa commaqa e 2wikimultihopqa musique hotpotqa exact match answer f1 gpt 3 br flan t5 t react yao et al 2023 https arxiv org abs 2210 03629 qa br fact verification hotpotqa fever exact match acc palm br gpt 3 s t jiang et al jiang et al 2023 https arxiv org abs 2305 06983 qa commonsense reasoning br long form qa 2wikimultihopqa strategyqa asqa wikiasp exact match disambig f1 rouge br entity f1 gpt 3 5 t lee et al lee et al 2022 https arxiv org abs 2206 04624 open ended generation fever entity score entailment span ratio ppl megatron lm t sail luo et al 2023 https arxiv org abs 2305 15225 qa br fact checking unilc acc br f1 llama vicuna br sail t he et al he et al 2022 https arxiv org abs 2301 00303 commonsense reasoning temporal reasoning br tabular reasoning strategyqa tempquestions in fotabs acc gpt 3 t pan et al pan et al 2023 https doi org 10 18653 v1 2023 acl long 386 fact checking hover br feverous s macro f1 codex br flan t5 s multiagent debate du et al 2023 https arxiv org abs 2305 14325 biography br mmlu unnamed biography dataset br mmlu chatgpt evaluator acc bard br chatgpt s benchmarks reference task type dataset metrics performance of representative llms mmlu hendrycks et al 2021 https arxiv org abs 2009 03300 multi choice qa humanities br social br sciences br stem acc acc 5 shot br gpt 4 86 4 br gpt 3 5 70 br llama2 70b 68 9 truthfulqa lin et al 2022 https doi org 10 18653 v1 2022 acl long 229 qa health law br conspiracies br fiction human score br gpt judge br rouge bleu br mc1 mc2 zero shot br gpt 4 29 mc1 br gpt 3 5 28 mc1 br 79 92 true br llama2 70b 53 37 true c eval huang et al 2023 https arxiv org abs 2305 08322 multi choice qa stem br social science br humanities acc zero shot average acc br gpt 4 68 7 br gpt 3 5 54 4 br llama2 70b 50 13 agieval huang et al 2023 https arxiv org abs 2305 08322 multi choice qa gaokao geometry bio br history sat law acc zero shot average acc br gpt 4 56 4 br gpt 3 5 42 9 br llama2 70b 40 02 halueval li et al 2023 https arxiv org abs 2305 11747 hallucination evaluation halueval acc general acc br gpt 3 5 86 22 bigbench srivastava et al 2023 https openreview net forum id uytl5bvosj multi tasks qa nli fact checking reasoning bigbench metric to each type of task big bench hard br gpt 3 5 49 6 br llama 65b 42 6 alce gao et al 2023 https arxiv org abs 2305 14627 citation generation asqa eli5 br qampari mauve exact match rouge l asqa 3 psg citation prec br gpt 3 5 73 9 br llama 33b 23 0 quip weller et al 2023 https arxiv org abs 2305 13252 generative qa triviaqa br nq eli5 br hotpotqa quip score exact match eli5 quip null prompt br gpt 4 21 0 br gpt 3 5 27 7 popqa mallen et al 2023 https doi org 10 18653 v1 2023 acl long 546 multi choice qa popqa br entityquestions acc overall acc br gpt 3 5 37 0 unilc zhang et al 2023 https arxiv org abs 2304 03728 fact checking climate br health mgfn acc f1 zero shot fact tasks average f1 br gpt 3 5 51 62 pinocchio hu et al 2023 https arxiv org abs 2310 05177 fact checking qa reasoning pinocchio acc f1 gpt 3 5 zero shot acc 46 8 f1 44 4 br gpt 3 5 few shot acc 47 1 f1 45 7 selfaware yin et al 2023 https doi org 10 18653 v1 2023 findings acl 551 self evaluation selfaware acc instruction input f1 br gpt 4 75 47 br gpt 3 5 51 43 br llama 65b 46 89 realtimeqa kasai et al 2022 https arxiv org abs 2207 13332 multi choice qa generative qa realtimeqa acc f1 original setting gcs retrieval br gpt 3 69 3 acc for mc br gpt 3 39 4 f1 for generation freshqa vu et al 2023 https arxiv org abs 2310 03214 generative qa freshqa acc human strict acc null prompt br gpt 4 28 6 br gpt 3 5 26 0 domain evaluation reference domain task datasets metrics evaluated llms xie et al 2023 https arxiv org abs 2306 05443 finance sentiment analysis br news headline classification br named entity recognition br question answering br stock movement prediction flare f1 acc br avg f1 br entity f1 br em mcc gpt 4 br bloomberggpt br finma 7b 30b 7b full br vicuna 7b li et al 2023 https arxiv org abs 2308 06966 finance 134 e com tasks ecominstruct micro f1 br macro f1 br rouge bloom bloomz br chatgpt ecomgpt wang et al 2023 https arxiv org abs 2308 08833 medicine multi choice qa cmb acc gpt 4 chatglm2 6b br chatgpt doctorglm br baichuan 13b chat br huatuogpt medicalgpt br chatmed consult br chatglm med br bentsao bianque 2 li et al 2023 https arxiv org abs 2305 01526 medicine generative qa huatuo 26m bleu br rouge br gleu t5 gpt2 jin et al 2023 https arxiv org abs 2304 09667 medicine nomenclature br genomic location br functional analysis br sequence alignment geneturing acc gpt 2 biogpt br biomedlm br gpt 3 br chatgpt new bing guha et al 2023 https arxiv org abs 2308 11462 law issue spotting br rule recall br rule application br rule conclusion br interpretation br rhetorical understanding legalbench acc em gpt 4 gpt 3 5 br claude 1 incite opt br falcon llama 2 flan t5 fei et al 2023 https arxiv org abs 2309 16289 law legal qa ner br sentiment analysis br reading comprehension lawbench f1 acc br rouge l br normalized log distance gpt 4 br chatgpt br internlm chat br stablebeluga2 enhancement enhancement methods reference dataset metrics baselines theirs dataset metrics baselines theirs li et al 2022 https openreview net forum id 02dbnebefn nq em 34 5 44 35 t5 11b gsm8k acc 77 0 85 0 chatgpt yu et al 2023 https arxiv org abs 2209 10063 nq em 20 9 28 0 instructgpt triviaqa em 57 5 59 0 instructgpt webqa em 18 6 24 6 instructgpt chuang et al 2023 https arxiv org abs 2309 03883 factor news acc 58 3 62 0 llama 7b factor news acc 61 1 62 5 llama 13b factor news acc 63 8 65 4 llama 33b factor news acc 63 6 66 2 llama 65b factor wiki acc 58 6 62 2 llama 7b factor wiki acc 62 6 66 2 llama 13b factor wiki acc 69 5 70 3 llama 33b factor wiki acc 72 2 72 4 llama 65b truthfulqa truth info 32 4 44 6 llama 13b truthfulqa truth info 34 8 49 2 llama 65b li et al 2022 https arxiv org abs 2210 15097 truthfulqa truth info 32 4 44 4 llama 13b truthfulqa truth info 31 7 36 7 llama 33b truthfulqa truth info 34 8 43 4 llama 65b li et al 2023 https arxiv org abs 2306 03341 nq acc 46 6 51 3 llama 7b triviaqa acc 89 6 91 1 llama 7b mmlu acc 35 7 40 1 llama 7b truthfulqa truth info 32 5 65 1 alpaca truthfulqa truth info 26 9 43 5 llama 7b truthfulqa truth info 51 5 74 0 vicuna cohen et al 2023 https arxiv org abs 2305 13281 lama f1 50 7 80 8 chatgpt triviaqa f1 56 2 82 6 chatgpt nq f1 60 6 79 1 chatgpt popqa f1 65 2 85 4 chatgpt lama f1 42 5 79 3 gpt 3 triviaqa f1 46 7 77 2 gpt 3 nq f1 52 0 78 0 gpt 3 popqa f1 43 7 77 4 gpt 3 domain enhanced llms reference domain model eval task eval dataset continual pretrained continual sft train from scratch external knowledge zhang et al 2023 https arxiv org abs 2305 15075 healthcare baichuan 7b ziya llama 13b qa cmedqa2 webmedqa huatuo 26m yang et al 2023 https arxiv org abs 2308 03549 healthcare ziya llama 13b qa cmtmedqa huatuo 26m wang et al 2023 https arxiv org abs 2309 02233 healthcare gpt 3 5 turbo llama 2 13b qa medqausmle medqamcmle medmcqa ross et al 2022 https arxiv org abs 2106 09553 healthcare molformer molecule properties prediction bao et al 2023 https arxiv org abs 2308 14346 healthcare baichuan 13b qa cmb clin cmd cmid guan et al 2023 https arxiv org abs 2307 11346 healthcare chatgpt iu rr mimic cxr liu et al 2023 https arxiv org abs 2303 11032 healthcare gpt 4 medical text de identification li et al 2023 https arxiv org abs 2303 14070 healthcare llama qa venigalla et al 2022 https www mosaicml com blog introducing pubmed gpt healthcare gpt 2 7b qa xiong et al 2023 https arxiv org abs 2304 01097 healthcare chatglm 6b qa tan et al 2023 https arxiv org abs 2309 01114 healthcare baichuan 7b qa c eval mmlu luo et al 2022 https arxiv org abs 2210 10341 healthcare gpt 2 qa dc re jin et al 2023 https arxiv org abs 2304 09667 healthcare codex qa geneturing zakka et al 2023 https arxiv org abs 2303 01229 healthcare text davinci 003 qa clinicalqa liu et al 2023 https arxiv org abs 2305 10688 healthcare gpt 2medium molecular property prediction molecule text translation nguyen et al 2023 https arxiv org abs 2302 05729 law gpt3 savelka et al 2023 https arxiv org abs 2306 09525 law gpt 4 huang et al 2023 https arxiv org abs 2305 15062 law llama cn legal tasks cui et al 2023 https arxiv org abs 2306 16092 law ziya llama 13b qa national judicial examination question li et al 2023 https arxiv org abs 2308 06966 finance bloomz 4 major tasks 12 subtasks ecominstruct wu et al 2023 https arxiv org abs 2303 17564 finance bloom financial nlp sa bc ner ner ned qa financial datasets deng et al 2023 https arxiv org abs 2306 05064 geoscience llama 7b geobench bai et al 2023 https arxiv org abs 2308 01414 geoscience chatglm 6b fan et al 2023 https arxiv org abs 2307 13923 education phoenix inst chat 7b chinese grammatical error correction chatgpt generated human annotated qi et al 2023 https arxiv org abs 2308 10173 food chinese llama2 13b qa wen et al 2023 https arxiv org abs 2307 15290 home renovation baichuan 13b c eval cmmlu evalhome reference if you find this project useful in your research or work please consider citing it misc wang2023survey title survey on factuality in large language models knowledge retrieval and domain specificity author cunxiang wang and xiaoze liu and yuanhao yue and xiangru tang and tianhang zhang and cheng jiayang and yunzhi yao and wenyang gao and xuming hu and zehan qi and yidong wang and linyi yang and jindong wang and xing xie and zheng zhang and yue zhang year 2023 eprint 2310 07521 archiveprefix arxiv primaryclass cs cl acknowledgements 1 chen liang chanliang https github com chanliang for pr 1 https github com wangcunxiang llm factuality survey pull 1 star history star history chart https api star history com svg repos wangcunxiang llm factuality survey type date https star history com wangcunxiang llm factuality survey date | ai |
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CoronaMaskOn | coronamaskon mask control with computer vision images corona png who world health organization https www who int health topics coronavirus tab tab 1 coronavirus disease covid 19 is an infectious disease caused by a newly discovered coronavirus most people infected with the covid 19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment older people and those with underlying medical problems like cardiovascular disease diabetes chronic respiratory disease and cancer are more likely to develop serious illness demo video you can reach demo video here https youtu be 984gw94teoe p img width 410 height 360 src images mask off png img width 410 height 360 src images mask on png align right p dataset dataset includes masked human faces and unmasked human faces images datasetd png the pictures were downloaded from google with the chrome extension https chrome google com webstore detail download all images ifipmflagepipjokmbdecpmjbibjnakm faces in the pictures were detected and extracted with the opencv library after that converted to grayscale format images prepare data png training training details are in this notebook training ipynb training ipynb images accuracy png try it yourself i share the trained model with you you can find it here https drive google com open id 1nrxpkhaljcz53kjcn51p2dhrobvlqanb to try application with webcam run below code python camera mask control py note i am still trying to expand the dataset even more i will also continue to develop the model using with extended dataset and pre trained model when i complete the dataset it will be public | ai |
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Ocr-android | ocr android https jitpack io v shenhuanet ocr android svg https jitpack io shenhuanet ocr android leptonica 94 http sj qq com myapp detail htm apkname com shenhua ocr div align center img width 300 height 533 src https github com shenhuanet ocr android blob master screenshot img welcome png img width 300 height 533 src https github com shenhuanet ocr android blob master screenshot img process png img width 300 height 533 src https github com shenhuanet ocr android blob master screenshot img result png img width 300 height 533 src https github com shenhuanet ocr android blob master screenshot img user png img width 300 height 533 src https github com shenhuanet ocr android blob master screenshot img history png div v3 0 2 bug v3 0 0 bug ui http blog csdn net klxh2009 br http www jianshu com u 12a81897d5bc license copyright c 2017 shenhuanet this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 2 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license along with this program if not write to the free software foundation inc 51 franklin street fifth floor boston ma 02110 1301 usa | ocr-recognition ocr-android textrecognition | server |
Waves | h1 align center waves node h1 p align center a href https github com wavesplatform waves actions target blank img alt checks status src https badgen net github checks wavesplatform waves cache 600 a a href https github com wavesplatform waves releases target blank img alt downloads src https badgen net github assets dl wavesplatform waves color blue a a href https hub docker com r wavesplatform wavesnode target blank img alt docker pulls src https badgen net docker pulls wavesplatform wavesnode icon docker a br a href https twitter com wavesprotocol target blank img alt twitter waves tech src https badgen net twitter follow wavesprotocol icon twitter label follow 20on 20twitter a a href https medium com wavesprotocol target blank img alt medium waves tech src https badgen net runkit msmolyakov get medium followers icon medium cache 86400 a a href https t me waves ride dapps dev target blank img alt telegram src https badgen net badge icon waves 20dev 20jedi icon telegram label telegram a a href https github com msmolyakov awesome waves target blank img alt awesome waves src https badgen net badge icon awesome 20waves icon awesome label color pink a p waves is an open source blockchain protocol https waves tech waves protocol br you can use it to build your own decentralized applications waves provides full blockchain ecosystem including smart contracts language called ride demo p align center img src https user images githubusercontent com 1945126 78667964 88209480 78e2 11ea 9304 72178a6a5974 gif alt waves node run demo p waves node is a host connected to the blockchain network with the following functions processing and validation of transactions https docs waves tech en blockchain transaction transaction validation generation and storage of blocks https docs waves tech en blockchain block network communication with other nodes https docs waves tech en blockchain blockchain node rest api https docs waves tech en waves node node api extensions https docs waves tech en waves node extensions management learn more about waves node in the documentation https docs waves tech en waves node getting started a quick introduction of the minimal setup you need to get a running node prerequisites configuration file for a needed network from here https github com wavesplatform waves tree head node waves all jar file from releases https github com wavesplatform waves releases linux systems bash sudo apt get update sudo apt get install openjdk 8 jre java jar node target waves all jar path to config waves network conf mac systems assuming already installed homebrew bash brew cask install adoptopenjdk openjdk adoptopenjdk8 java jar node target waves all jar path to config waves network conf windows systems assuming already installed openjdk 8 bash java jar node target waves all jar path to config waves network conf using docker follow the official image documentation https hub docker com r wavesplatform wavesnode more details on how to install a node for different platforms you can find in the documentation https docs waves tech en waves node how to install a node how to install a node configuration the best starting point to understand available configuration parameters is this article https docs waves tech en waves node node configuration the easiest way to start playing around with configurations is to use default configuration files for different networks they re available in network defaults conf node src main resources network defaults conf logging configuration with all available levels and parameters is described here https docs waves tech en waves node logging configuration development the node can be built and installed wherever java can run to build and test this project you will have to follow these steps details summary b show instructions b summary 1 setup the environment install java for your platform bash sudo apt get update sudo apt get install openjdk 8 jre ubuntu or brew cask install adoptopenjdk openjdk adoptopenjdk8 mac install sbt scala build tool please follow the sbt installation instructions depending on your platform linux https www scala sbt org 1 0 docs installing sbt on linux html mac https www scala sbt org 1 0 docs installing sbt on mac html windows https www scala sbt org 1 0 docs installing sbt on windows html 2 clone this repo bash git clone https github com wavesplatform waves git cd waves 3 compile and run tests bash sbt checkpr 4 run integration tests optional create a docker image before you run any test bash sbt node it docker run all tests you can increase or decrease number of parallel running tests by changing waves it max parallel suites system property bash sbt dwaves it max parallel suites 1 node it test run one test bash sbt node it testonly testclassname or bash node it testonly full package testclassname 5 build packages bash sbt packageall mainnet sbt dnetwork testnet packageall testnet sbt packageall produces only deb package along with a fat jar 6 install deb package deb package is located in target folder you can replace with actual package name bash sudo dpkg i node target deb 7 run an extension project locally during development optional bash sbt extension module run path to configuration 8 configure intellij idea optional the majority of contributors to this project use intellij idea for development if you want to use it as well please follow these steps 1 click add configuration or edit configurations 2 click to add a new configuration choose application 3 specify main class com wavesplatform application program arguments path to configuration use classpath of module extension module 4 click ok 5 run this configuration details contributing if you d like to contribute please fork the repository and use a feature branch pull requests are warmly welcome for major changes please open an issue first to discuss what you would like to change please make sure to update tests as appropriate please follow the code of conduct code of conduct md during communication with each other support get help keep up with the latest news and articles and find out all about events happening on the waves protocol https waves tech telegram dev chat https t me waves ride dapps dev waves blog https medium com wavesprotocol links documentation https docs waves tech blockchain clients for mainnet waves exchange https waves exchange waves fx https github com wavesfx sign app https www sign web app blockchain clients for testnet waves exchange https testnet waves exchange blockchain explorer mainnet https wavesexplorer com testnet https testnet wavesexplorer com stagenet https stagenet wavesexplorer com ride online ide https waves ide com licence the code in this project is licensed under mit license license acknowledgements img src https camo githubusercontent com 97fa03cac759a772255b93c64ab1c9f76a103681 68747470733a2f2f7777772e796f75726b69742e636f6d2f696d616765732f796b6c6f676f2e706e67 https www yourkit com we use yourkit full featured java profiler to make waves node faster yourkit llc is the creator of innovative and intelligent tools for profiling java and net applications take a look at yourkit s leading software products yourkit java profiler and yourkit net profiler | blockchain cryptography smart-contracts decentralized-applications | blockchain |
swift-gps.minute.tech | swift gps minute tech outdated portion of the minute tech ios mobile application built for san jose state university mobile software engineering cmpe 137 application is built with swift cocoapods and google maps api to track the user s location on a map simulating a technician coming to the client s location | swift xcode mapkit | os |
llm-tax-attorney | llm tax attorney | ai |
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gcp_cde_program | gcp cde program google cloud platform engineering training program | cloud |
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csci-ua.0480-fall2016-007 | csci ua 0480 fall2016 007 disclaimer this code is not my work this is from https github com foureyes csci ua 0480 fall2016 007 https github com foureyes csci ua 0480 fall2016 007 github web pages link https keen learner github io csci ua 0480 fall2016 007 https keen learner github io csci ua 0480 fall2016 007 | server |
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FreeRTOS | what is freertos freertos is a class of rtos that is designed to be small enough to run on a microcontroller although its use is not limited to microcontroller applications freertos feature free rtos scheduler preemptive cooperative and hybrid configuration options rtos objects tasks queues semaphores mutexes and event groups can be created using either dynamically or statically allocated ram designed to be small simple and easy to use support both real time tasks and co routines visit http www freertos org to know more freertos freertos open source license freertos source code is licensed by a modified gnu general public license the modification taking the form of an exception which permits the source code of application that use freertos and are distributed as executables to remain closed source thus permitting the use of freertos in commercial applications without necessitating that the whole application be open soruced you can visit the following website to know more http www freertos org a00114 html getting started how to build it run the run sh script filw with two parameters cpu name and soc name for example run sh ck610 ck5a6 ck610 is the demo cpu with ck610 core ck5a6 is the demo soc with ck610 core cpu how to add new cpu in freertos 1 find out what type of compiler you wil use take gcc compiler as example 2 enter into freertosv8 2 3 freertos source portable gcc and create a folder named with new cpu name such as ck610 3 design your cpu porting code in port c and portmacro h or other files you use 4 select one memory management file under freertosv8 2 3 freertos source portable memmang in general heap4 c is preferred 5 add those new files into compile source files path and head files path how to add new soc in freertos 1 enter into bsp directory and create a new folder named with your soc name such as ck5a6 2 enter into include directory and create a new foloer named with your soc name such ck5a6 3 put your bsp code files under bsp soc name and include soc name 4 add those new files into compile source files path and head files path how to configure your system memory flash address space the default memory flash address space in ld script file is memory space you can change it to your system address space | os |
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my_osek | freeosek for arm cortex m freeosek implementation for arm cortex m 1 frdm kl46z bare metal gnu gcc bare metal project for nxp frdm kl46z development kit 2 frdm kl46z osek freeosek with nxp arm cortex m0 frdm kl46z 3 neo m4 bare metal gnu gcc bare metal project for udoo neo development kit 4 neo m4 osek freeosek with nxp arm cortex m4 i mx6sx 5 stm32f1 osek freeosek with st arm cortex m3 easy stm32 stm32f107 stamp board 6 stm32f4 osek freeosek with st arm cortex m4 stm32f4 discovery | os |
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freedomotic | freedomotic open iot framework official website is https www freedomotic platform com freedomotic is an open source flexible secure internet of things iot application framework useful to build and manage modern smart spaces it is targeted to private individuals home automation as well as business users smart retail environments ambient aware marketing monitoring and analytics freedomotic can run also on raspberry pi and can easily interact with diy arduino projects starting from 2 feb 2014 this is the freedomotic mainstream repository previous repository was hosted on googlecode http code google com p freedomotic requirements java open jdk version 11 or another jdk to install on ubuntu sudo apt get install openjdk 11 jdk maven version 2 or 3 to install on ubuntu sudo apt get install maven any os with java support linux windows mac solaris development status current released version 5 6 0 commander rc4 released on 16 aug 2017 version in development head of this repository 5 6 0 commander quick start follow these instructions if you want to compile freedomotic from source eg to develop your own plugins if you just want to try it just download the precompiled binaries you can find at the official download page https sourceforge net projects freedomotic 1 fork freedomotic on github create an account on https github com if you don t have one fork freedomotic what does it mean https help github com articles fork a repo following this link https github com freedomotic freedomotic fork create the local clone of your online fork with this command git clone https github com your github username freedomotic git now the repository is ready to work with 2 enter the new local folder cd freedomotic 3 install the jar loader on local maven repository in order to compile the code you need to add the agent with a custom classloader for loading jars at runtime in java 9 this file called freedomotic jar loader 0 0 1 jar is included inside third party libs folder so enter this folder cd third party libs and execute mvn install install file dfile freedomotic jar loader 0 0 1 jar dgroupid com freedomotic dartifactid freedomotic jar loader dversion 0 0 1 dpackaging jar 4 compile freedomotic with maven mvn clean install 5 important this is required copy the example data folder into freedomotic core data if you miss this step freedomotic won t start cp r data example framework freedomotic core data 6 run freedomotic java javaagent third party libs freedomotic jar loader 0 0 1 jar jar framework freedomotic core target freedomotic core freedomotic jar as an alternative you can start freedomotic core project from your favourite ide getting help having trouble with freedomotic we d like to help check out the user manual http freedomotic user manual readthedocs io for reference documentation write on the mailing list https groups google com forum forum freedom domotics send an email to info freedomotic platform com open an issue on https github com freedomotic freedomotic issues contributing want to help us it s very simple and funny here https github com freedomotic freedomotic blob master contributing md how to do license freedomotic is an open source software released under the gnu gplv2 http www gnu org licenses old licenses gpl 2 0 html license partners this project is supported by img src https opensource nyc3 cdn digitaloceanspaces com attribution assets svg do logo horizontal blue svg width 201px | iot iot-platform java framework home-automation mqtt arduino raspberrypi open-source opensource domotics domotica ubuntu internet-of-things hacktoberfest | server |
Assignment-1 | phonegap phonegap is a development tool that allows web developers to take advantage of the core features in the iphone and android sdk using javascript get started download the source git clone git github com sintaxi phonegap git phonegap project is separated into a native project for each device javascript files and a rakefile phonegap readme md rakefile android blackberry iphone javascripts each project has a respective readme md file view that file for detailed information on how to work with that device phonegap offers one unified api for accessing core functionality on all devices where possible phonegap follows the html5 spec api device exposes properties of the phone such as its device id model and os version number location gain access to the latitude longitude of the device and depending on the type of device the course speed and altitude accelerometer monitor the accelerometer on the device to detect orientation shaking and other similar actions contacts query the phone addressbook to read the users contacts orientation read the device layout orientation e g landscape vs portrait camera brings up the camera or photo browser on the phone to allow the user to upload a photo vibrate triggers the vibration alert on the phone if it is supported sound play sound files wav mp3 etc telephony trigger and activate phone calls xui you may work with any javascript framework within a phonegap application xui http xuijs com is the officially preferred framework of the phonegap core team xui is inspired by jquery optimized for web browsers and weighs in at 6 2k 2 4k minified and gziped community website phonegap com http phonegap com google group groups google com group phonegap http groups google com group phonegap wiki phonegap pbwiki com http phonegap pbwiki com twitter twitter com phonegap http twitter com phonegap the mit license copyright c 2008 rob ellis brock whitten brian leroux joe bowser dave johnson nitobi permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software phonegap is a nitobi http nitobi com sponsored project | front_end |
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doccano-mini | doccano mini doccano mini is a few shot annotation tool to assist the development of applications with large language models llms once you annotate a few text you can solve your task e g text classification with llms via langchain https github com hwchase17 langchain at this time the following tasks are supported text classification question answering summarization paraphrasing named entity recognition task free note this is an experimental project installation bash pip install doccano mini usage for this example we will be using openai s apis so we need to set the environment variable in the terminal bash export openai api key then we can run the server bash doccano mini now we can open the browser and go to http localhost 8501 to see the interface step1 annotate a few text in this step we will annotate a few text we can add a new text by clicking the button try it out by double clicking on any cell you ll notice you can edit all cell values step1 https raw githubusercontent com doccano doccano mini master docs images annotation gif the editor also supports pasting in tabular data from google sheets excel and many other similar tools copy and paste https raw githubusercontent com doccano doccano mini master docs images copy and paste gif step2 test your task in this step we will test your task we can enter a new test to the text box and click the predict button then we can see the result of the test img src https raw githubusercontent com doccano doccano mini master docs images test new example jpg alt step2 width 700 step3 download the config in this step we will download the langchain https github com hwchase17 langchain s config we can click the download button to download it after loading the config file we can predict a label for the new text python from langchain chains import load chain chain load chain chain yaml chain run your text development bash poetry install streamlit run doccano mini home py | annotation langchain nlp openai | ai |
ESP32-GATEWAY | esp32 gateway esp32 wifi ble development board with ethernet microsd card gpios made with kicad | server |
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vConsole | english readme cn md vconsole a lightweight extendable front end developer tool for mobile web page vconsole is framework free you can use it in vue or react or any other framework application now vconsole is the official debugging tool for wechat miniprograms features logs console log info error network xmlhttprequest fetch sendbeacon element html elements tree storage cookies localstorage sessionstorage execute js command manually custom plugins for details please see the screenshots below release notes latest version npm version https img shields io npm v vconsole latest svg https www npmjs com package vconsole detailed release notes for each version are available on changelog changelog md guide see tutorial doc tutorial md for more usage details for installation there are 2 primary ways of adding vconsole to a project method 1 using npm recommended bash npm install vconsole javascript import vconsole from vconsole const vconsole new vconsole or init with options const vconsole new vconsole theme dark call console methods as usual console log hello world remove it when you finish debugging vconsole destroy method 2 using cdn in html html script src https unpkg com vconsole latest dist vconsole min js script script vconsole will be exported to window vconsole by default var vconsole new window vconsole script available cdn https unpkg com vconsole latest dist vconsole min js https cdn jsdelivr net npm vconsole latest dist vconsole min js preview http wechatfe github io vconsole demo html http wechatfe github io vconsole demo html doc screenshot qrcode png screenshots overview details summary light theme summary doc screenshot overview light jpg details details summary dark theme summary doc screenshot overview dark jpg details log panel details summary log styling summary doc screenshot plugin log types jpg details details summary command line summary doc screenshot plugin log command jpg details system panel details summary performance info summary doc screenshot plugin system jpg details details summary output logs to different panel summary javascript console log output to log panel console log system output to system panel details network panel details summary request details summary doc screenshot plugin network jpg details element panel details summary realtime html elements structure summary doc screenshot plugin element jpg details storage panel details summary add edit delete or copy cookies localstorage sessionstorage summary doc screenshot plugin storage jpg details documentation vconsole tutorial doc tutorial md public properties methods doc public properties methods md builtin plugin properties methods doc plugin properties methods md custom plugin plugin getting started doc plugin getting started md plugin building a plugin doc plugin building a plugin md plugin event list doc plugin event list md third party plugins vconsole sources https github com wechatfe vconsole sources vconsole webpack plugin https github com diamont1001 vconsole webpack plugin vconsole stats plugin https github com smackgg vconsole stats vconsole vue devtools plugin https github com zippowxk vue vconsole devtools vconsole outputlog plugin https github com sunlanda vconsole outputlog plugin vite plugin vconsole https github com vadxq vite plugin vconsole feedback qq group 497430533 doc screenshot qq group png license the mit license license | wechat mobile console | front_end |
Embedded-C | embedded programming c is a programming language designed by dennis ritchie at bell labs c is very widely used straightforward and can be compiled to a number of platforms and operating systems c is an imperative language with a small number of keywords and a large number of mathematical operators i included programming libraries guides etc images eclipse png motivation these program are meant for helping others who want to learn c programming from basics and also help them understand certain coding techniques and ideas features when you are born you need some language to communicate for most of us it is our mother tongue so if you want to become a programmer you need some language to communicate with computers in programming world you start with c and c which can be referred as mother tongue of programming code example c program example images giphy gif c program example images c gif installation install eclipse oxygen ide on ubuntu 16 04 through terminal in ubuntu step 1 install java jdk8 run the commands below sudo add apt repository ppa webupd8team java after running the commands above you should see a prompt to accept the ppa key onto ubuntu accept and continue now that the ppa repository has been added to ubuntu run the commands below to download oracle java 9 installer the installer should install the latest java jdk 9 on your ubuntu machines sudo apt update sudo apt install oracle java8 installer when you run the commands above you ll be prompted to access the license terms of the software accept and continue images screenshot png sudo apt install oracle java8 set default the command above will automatically set java 9 as the default and that should complete your installation you can check you java version by running following command javac version step 2 download eclipse oxygen from the link below download eclipse oxygen ide package for your system https www eclipse org downloads images screenshot1 png extract the downloaded package to the opt directory using the commands below by default eclipse package should be downloaded into the downloads folder of your home directory step 3 install eclipse ide use the commands below to extract the content in the downloads folder the next line launches the installer tar xfz downloads eclipse inst linux64 tar gz downloads eclipse installer eclipse inst select the package ide you want to install and continue images screenshot2 png use the onscreen instructions to complete the installer accept the default installation directory and continue images screenshot4 png next accept the license terms and continue wait for eclipse installer to download and install all the packages images screenshot5 png after downloading the installer should complete all you have to do is launch the program images screenshot6 png step 3 create eclipse app launcher run the command below nano local share applications eclipse desktop next copy and paste the content below into the file and save desktop entry name eclipse jee oxygen type application exec home govind eclipse jee oxygen eclipse eclipse terminal false icon home govind eclipse jee oxygen eclipse icon xpm comment integrated development environment nodisplay false categories development ide name en eclipse replace the highlighted username govind with your own account name also the exec location and icon xpm should depend on where eclipse got installed on your system save the file and exit you should then have a launcher for eclipse jee oxygen open dash or the activities overview and search for eclipse then launch api reference https www cprogramming com tutorial c tutorial html inl pf license mit | os |
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videocr | videocr extract hardcoded burned in subtitles from videos using the tesseract https github com tesseract ocr tesseract ocr engine with python input a video with hardcoded subtitles p float left img width 430 alt screenshot src https user images githubusercontent com 10210967 56873658 3b76dd00 6a34 11e9 95c6 cd6edc721f58 png img width 430 alt screenshot src https user images githubusercontent com 10210967 56873659 3b76dd00 6a34 11e9 97aa 2c3e96fe3a97 png p python example py from videocr import get subtitles if name main this check is mandatory for windows print get subtitles video mp4 lang chi sim eng sim threshold 70 conf threshold 65 python3 example py output 0 00 00 01 042 00 00 02 877 what can i get you 1 00 00 03 044 00 00 05 463 um i m not sure 2 00 00 08 091 00 00 10 635 for relaxing times make it 3 00 00 10 677 00 00 12 595 bartender bob suntory time 4 00 00 14 472 00 00 17 142 un i ll have a vodka tonic 5 00 00 18 059 00 00 19 019 laughs thanks performance the ocr process is cpu intensive it takes 3 minutes on my dual core laptop to extract a 20 seconds video more cpu cores will make it faster installation 1 install tesseract https github com tesseract ocr tesseract wiki and make sure it is in your path 2 pip install videocr api 1 return subtitle string in srt format python get subtitles video path str lang eng time start 0 00 time end conf threshold 65 sim threshold 90 use fullframe false 2 write subtitles to file path python save subtitles to file video path str file path subtitle srt lang eng time start 0 00 time end conf threshold 65 sim threshold 90 use fullframe false parameters lang the language of the subtitles you can extract subtitles in almost any language all language codes on this page https github com tesseract ocr tesseract wiki data files data files for version 400 november 29 2016 e g eng for english and all script names in this repository https github com tesseract ocr tessdata fast tree master script e g hans for simplified chinese are supported note that you can use more than one language e g lang hin eng for hindi and english together language files will be automatically downloaded to your tessdata you can read more about tesseract language data files on their wiki page https github com tesseract ocr tesseract wiki data files conf threshold confidence threshold for word predictions words with lower confidence than this value will be discarded the default value 65 is fine for most cases make it closer to 0 if you get too few words in each line or make it closer to 100 if there are too many excess words in each line sim threshold similarity threshold for subtitle lines subtitle lines with larger levenshtein https en wikipedia org wiki levenshtein distance ratios than this threshold will be merged together the default value 90 is fine for most cases make it closer to 0 if you get too many duplicated subtitle lines or make it closer to 100 if you get too few subtitle lines time start and time end extract subtitles from only a clip of the video the subtitle timestamps are still calculated according to the full video length use fullframe by default only the bottom half of each frame is used for ocr you can explicitly use the full frame if your subtitles are not within the bottom half of each frame | ai |
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information-retrieval | img src app images sparked horizontal png width 350 information retrieval group project sparked tu delft masters software technology information retrieval 3rd quarter 2015 application summary in this assignment our group built the application sparked the purpose of sparked is to crowdsource ideas for mobile applications the application would generate the applications most desired by the crowd with the idea that the applications could then be created and sold this assignment was meant to showcase the use of three relevant technologies crowdsourcing human computation and information retrieval tech usage in sparked crowdsourcing crowdsourcing is the backbone of the sparked application we use the crowd to submit new mobile ideas users can submit ideas with a title description and image ideas are submitted through a php form and stored in a mysql database crowdsourcing is also used to find the best ideas in the submissions users can browse through ideas and upvote or downvote them as they go human computation human computation is the idea of using human beings to complete tasks rather than using software here we use human computation through crowdflower http www crowdflower com to check user submitted images curl requests are used to send information between crowdflower and our application information retrieval since the course is called information retrieval we need to include an information retrieval component in our application we used a more like this https wiki apache org solr morelikethis query to allow users to see if ideas similar to their already exist in the the itunes app store we crawled the itunes app store and imported the data into our own solr db these aspects come together in the application diagram shown here img src documents app diagram png width 500 user interface at a glance the interface is fully responsive thanks to the html template http html5up net verti we used the homepage shows users where to submit new ideas or vote on existing ones img src documents screenshots homepage png width 350 the submission page has a few fields and allows for image uploading img src documents screenshots submission png width 350 border 5 deploying clone the repository in your web directory git clone https github com alex9311 information retrieval git mysql database you will need a sql server and existing db to connect to your credentials should be put in the database db creds php database db creds php file once this is done you can set up the database by running the reset script database resetdb php with php database resetdb php this will set up the sparked schema documents sql schema png in your database you can now populate the db with some toy data solr database todo this is complicated contents of repo directory description app app this is where the sparked app lives it is a simplistic responsive php web app database database this is where the scripts used to manage the sparked mysql database live using these scripts developers can wipe the database or repopulate it with a given csv file the database can also be easily exported to csv format here documents documents this directory contains all of the documents created for the purpose of our course this includes our project idea document and our final presentation slideshow evaluation similarity check evaluation similarity check when developing the application we weren t sure what tool we wanted to use for our similarity checker this directory contains the work we did and code used to evaluate dandelion lucene and rapidminer itunes app store crawler itunes app store crawler this directory contains an itunes web crawler we used and modified for our purposes we used this application data to test our search functionality this directory also contains the script needed to load our solr db with the csv returned by our app store crawler | server |
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YOLO | how to run yolo object detection with yolo you only look once to run yolo first download the pretrained weights in the linked google drive 237 mb and move the weights to the folder yolo coco https drive google com file d 1sbrsfwp4lagvvdkijgf7hlizcdbu7eu view usp sharing within command prompt to run yolo on an image use python yolo py image image path yolo yolo coco for videos use python yolo video py input video path output output path yolo yolo coco read my article on yolo https medium com sigilwen object detection with yolo bringing vision to self driving cars 980295226830 watch my video demo of yolo vsauce parody https www youtube com watch v axhbu2uk86i code adopted from pyimagesearch | ai |
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BookStoreApp-REST-Api | bookstoreapp rest api description bookstoreapp backend gives the rest api endpoints for the customers of the bookstore to keep a track of what all books they have read they can add delete and search the books in their collection this is the backend of the app based on django rest framework and can be dockerized using the dockerfile included this project also involves both test driven development tdd and behaviour driven development bdd testcases features implemented user can register themselves authorized user can add books they read in their collection before adding the book details are verified using googlebooks api can delete one book at a time from their collection narrow down search for books by the date the books were added in the collection like last 7 days so on list all the books in the collection at once test cases tdd bdd only few cases included unit tests covers how the below mentioned features will respond in the system and behaviour driven test cases are covering different scenarios the end user might encounter while using the below mentioned features the files which covers the testcases unit tests bckend db api tests py bdd tests bckend features features tested 1 list book collections with httpresponse status code 200 only when the user is logged in or else declare unauthorized request 2 add book in the collection using endpoint request and verify the record exists in the database for the user 3 delete book from the collection using endpoint request and verify that record should not exists in the database for the user 4 search books from the collection using endpoint request by passing the parameter books added since last n days and filter the same from database to verify the response is equal to run unit tests bdd tests follow the build test execute section below build test execute requirement install docker in case you want to run the app in the docker container and make it portable clone this repo to downlaod the codebase sh git clone https github com himanshu2393 bookstoreapp rest api git after docker installation open the bash change the directory to root folder of the project build the docker image using dockerfile installs all the required libraries including python3 7 sh cd bookstoreapp rest api docker build t backend latest backend docker build images docker build png 3 then you can run the container using the image built by exposing the port 8080 on your host from container 4 you can have any other port like 8000 if 8080 is not free on your machine but that will require change in the dockerfile wherever 8080 is there replace with 8000 5 but lets run unit tests and bdd tests before running the app 6 unit testing the following command will create and run the docker container followed by the unit testing sh docker run v pwd backend app backend python manage py test unit testing images unit test png 7 bdd testing the following command will create and run the docker container followed by the bdd testing sh docker run v pwd backend app backend python manage py behave behave testing images behave test png 8 after testing let s run the app finally deleting the 2 already created containers first 2 commands just in case and then run the app sh docker stop docker ps a q docker rm docker ps a q docker run v pwd backend app backend p 8080 8080 backend latest 5 thats it our app is running now if you see server running images server run png 6 to access it in the browser first we need to get our default docker machine ip sh docker machine ip default 192 168 7 using this ip you can access the app running in the container on your host machine http 192 168 8080 api v1 8 browsable api urls list api v1 bookcollection logged in users can access and can list or add books search api v1 bookcollection search logged in users can search or filter on the books added in last n days delete api v1 bookcollection delete record logged in users can delete a book from the collection register customer api v1 register to register the customer and only logged out customers can access | server |
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dramatron | dramatron img src dramatron animation gif try dramatron dramatron can be run in python as a colab by opening colab dramatron ipynb open in colab https colab research google com assets colab badge svg https colab research google com github deepmind dramatron blob main colab dramatron ipynb the colab is provided unplugged without a large language model interface to use it you will need to implement functions init and sample by adding your code to sample from your own large language model details dramatron uses existing pre trained large language models to generate long coherent text and could be useful for authors for co writing theatre scripts and screenplays dramatron uses hierarchical story generation for consistency across the generated text starting from a log line dramatron interactively generates character descriptions plot points location descriptions and dialogue these generations provide human authors with material for compilation editing and rewriting dramatron is conceived as a writing tool and as a source of inspiration and exploration for writers to evaluate dramatron s usability and capabilities we engaged 15 playwrights and screenwriters in two hour long user study sessions to co write scripts alongside dramatron one concrete illustration of how dramatron can be utilised by creative communities is how one playwright staged 4 heavily edited and rewritten scripts co written alongside dramatron in the public theatre show plays by bots a talented cast of experienced actors with improvisational skills gave meaning to dramatron scripts through acting and interpretation during the development of dramatron and through discussions with industry professionals we made several important observations dramatron is a co writing system that has only been used in collaboration with human writers and was not conceived or evaluated to be used autonomously dramatron s top down hierarchical story generation structure does not correspond to every writer s writing process the output of a language model may include elements of the text used to train the language model one possible mitigation is for the human co writer to search for substrings from outputs to help to identify plagiarism dramatron may reproduce biases and stereotypes found in the corpus and may generate offensive text one possible mitigation is to use the perspective api https perspectiveapi com for estimating toxicity scores of the language outputs and filtering generations based on the perspective api analysis in our sig chi 2023 paper mirowski mathewson et al 2023 co writing screenplays and theatre scripts with language models an evaluation by industry professionals https dl acm org doi 10 1145 3544548 3581225 and in the 2022 pre print on arxiv https arxiv org abs 2209 14958 we share many reflections including how playwrights reflected that they wouldn t use dramatron to write a full play and that dramatron s output can be formulaic rather they would use dramatron for world building for exploring alternative stories by changing characters or plot elements and for creative idea generation we are looking forward to learning if and how you might incorporate dramatron into your own artistic practices if you d like to share thoughts comments or observations or have any questions please contact us at dramatron deepmind com the guide for contributors to dramatron can be found here contributing md cite dramatron article mirowski2022cowriting title co writing screenplays and theatre scripts with language models an evaluation by industry professionals author mirowski piotr and mathewson kory w and pittman jaylen and evans richard journal arxiv preprint arxiv 2209 14958 year 2022 license and disclaimer copyright 2022 deepmind technologies limited all software is licensed under the apache license version 2 0 apache 2 0 you may not use this file except in compliance with the apache 2 0 license you may obtain a copy of the apache 2 0 license at https www apache org licenses license 2 0 all other materials are licensed under the creative commons attribution 4 0 international license cc by you may obtain a copy of the cc by license at https creativecommons org licenses by 4 0 legalcode unless required by applicable law or agreed to in writing all software and materials distributed here under the apache 2 0 or cc by licenses are distributed on an as is basis without warranties or conditions of any kind either express or implied see the licenses for the specific language governing permissions and limitations under those licenses this is not an official google product | ai |
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rune.js | rune js rune js is a javascript library for programming graphic design systems with svg official documentation http runemadsen github io rune js releases https github com runemadsen rune js releases follow runemadsen https twitter com runemadsen for announcements building and running tests this repo uses the rune plugin js package for building and running the tests in both node and the browser bash rune build rune test node rune test browser contributors yining shi http 1023 io jorge moreno https www moro es | os |
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php-mvc-framework | p align center img src http apphpframework com images light logo png width 400 alt p br p align center a href https github com apphp php mvc framework img src https github com apphp php mvc framework workflows build badge svg alt build status a a href https opensource org licenses lgpl 3 0 html img src http apphpframework com images badges license svg alt license lgpl a a href http apphpframework com img src http apphpframework com images badges stable svg alt stable version a p about apphp framework thank you for choosing apphp a high performance php mvc framework apphp mvc framework is designed to provide modern and rapid development of websites web applications and web services it implements the the model view controller mvc design pattern and principles including separation of display logic and data layers it provides an architecture components and tools for developers to build a complex web applications faster and safer installing apphp framework via git bash git clone https github com apphp php mvc framework git via composer you can install apphp into your project using composer https getcomposer org if you re starting a new project we recommend using the directy cmf https github com apphp directy cmf as a starting point for installing new version in existing applications you can run the following bash composer require apphp php mvc framework manual installation please make sure the release file is unpacked under a web accessible directory you will see the following files and directories demos demo applications docs documentation framework framework source files tests phpunit tests utils some utilities requirements requirements checker tests tests generators code generators changelog describing changes in every apphp release license license of apphp framework readme this file update updating instructions running tests assuming you have phpunit installed system wide using one of the methods stated here https phpunit de manual current en installation html you can run the tests for apphp framework by doing the following 1 install composer on your server 2 after composer is installed install phpunit by bash composer require phpunit phpunit dev 2 make sure you added following to strong composer json strong file bash scripts tests result phpunit colors always log junit test results xml tests phpunit colors always test phpunit colors always filter 4 run phpunit by bash composer tests requirements the minimum requirement by apphp is that your web server supports php 5 4 0 or above apphp has been tested with apache http server on windows and linux operating systems license the laravel framework is open sourced software licensed under the lgpl3 license https opensource org licenses lgpl 3 0 html what s next please visit the project website for tutorials class reference and join discussions with other apphp users the apphp developer team official website http www apphpframework com website https www apphp com php framework github repository https github com apphp php mvc framework | php mvc-framework mvc-pattern mvc-architecture php-mvc-framework mvc-core | front_end |
this-vs-that | this vs that the differences between and in the front end development x cover differences in various topics css dom html javascript x include many good to know sections x include many good practices x include many tips and tricks about this project is developed by nguyen huu phuoc i love building products and sharing knowledge be my friend on twitter https twitter com nghuuphuoc github https github com phuocng | front_end |
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TensorFlow-ML-Exercises | learning machine learning with tensorflow http hunkim github io ml https www youtube com watch v bs6o0zogx4e list pllmkm4tgfjnlsojrejn31gzatbcj mpum macos 2 7 x tensorflow 0 12 01 tensorflow basic 02 linear regression 03 gradient descendent 04 multi variable linear regression 05 logistic classification 06 softmax regression 07 accuracy 09 neural network 10 tuning nn 11 cnn 12 rnn mnist cnn tensorflow mnist https github com golbin tensorflow mnist summary tensorboard trainer tester | ai |
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board-flutter-app | restyaboard restyaboard mobile app status completed items getting restyaboard instance url from users load user provided restyaboard instance in web view user can change restyaboard instance url pending items add floating action button in web view drag drop is not user friendly in app screenshots settings settings images settings png login login images login png dashboard dashboard images dashboard png building from source flutter run for help getting started with flutter view our online documentation https flutter io | front_end |
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web-it-wru | web it wru information technology web wru hello everyone this is website of information technology department at tlu | server |
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Home-Credit-Default-Risk-Recognition | b capstone project machine learning engineer nanodegree udacity b b home credit default risk recognition b b by abhishek bihani b based on the kaggle competition https www kaggle com c home credit default risk overview b domain background b an important fraction of the population finds it difficult to get their home loans approved due to insufficient or absent credit history this prevents them to buy their own dream homes and at times even forces them to rely on other sources of money which may be unreliable and have exorbitant interest rates conversely it is a major challenge for banks and other finance lending agencies to decide for which candidates to approve housing loans the credit history is not always a sufficient tool for decisions since it is possible that those borrowers with a long credit history can still default on the loan and some people with a good chance of loan repayment may simply not have a sufficiently long credit history a number of recent researchers have applied machine learning to predict the loan default risk this is important since a machine learning based classification tool to predict the loan default risk which uses more features than just the traditional credit history can be of great help for both potential borrowers and the lending institutions b problem statement b the problem and associated data https www kaggle com c home credit default risk overview has been provided by home call credit group for a kaggle competition the problem can be described as i a binary classification problem where the inputs are various features describing the financial and behavioral history of the loan applicants in order to predict whether the loan will be repaid or defaulted i b project novelty b i the notebook https github com abhishekdbihani home credit default risk recognition blob master abhishek 20capstone 20 20home 20credit 20risk 20v2 ipynb provides a complete end to end machine learning workflow for building a binary classifier and includes methods like automated feature engineering for connecting relational databases comparison of different classifiers on imbalanced data and hyperparameter tuning using bayesian optimization i img src https github com abhishekdbihani home credit default risk recognition blob master roc auc compare png align middle width 500 height 400 alt roc auc comparison figure 1 roc curve and auc comparison of different classifiers b datasets and inputs b the dataset files https www kaggle com c home credit default risk data are provided on the kaggle website in the form of multiple csv files and are free to download the dataset files are described as per figure 2 image https storage googleapis com kaggle media competitions home credit home credit png figure 2 description and connectivity of the home credit default risk dataset as seen in figure 2 the file application train test csv contains the main table containing the training dataset 307511 samples and test dataset 48744 samples with each row representing one loan identified by the feature sk id curr the training set contains the variable target with binary values 0 the loan was repaid or 1 the loan was not repaid there are many input files available which can be analysed for input features to train the model the large number of input features and training samples make it easier to identify the important factors and for constructing a credit default risk classification model b project design and solution b the project https github com abhishekdbihani home credit default risk recognition blob master abhishek 20capstone 20 20home 20credit 20risk 20v2 ipynb has been divided into five parts 1 u data preparation u before starting the modeling we need to import the necessary libraries and the datasets if there are more than one files then all need to be imported before we can look at the feature types and number of rows columns in each file 2 u exploratory data analysis u after data importing we can investigate the data and answer questions like how many features are present and how are they interlinked what is the data quality are there missing values what are the different data types are there many categorical features is the data imbalanced and most importantly are there any obvious patterns between the predictor and response features 3 u feature engineering u after exploring the data distributions we can conduct feature engineering to prepare the data for model training this includes operations like replacing outliers imputing missing values one hot encoding categorical variables and rescaling the data since there are number of relational databases we can use extract transform load etl processes using automated feature engineering with featuretools https www featuretools com to connect the datasets the additional features from these datasets will help improve the results over the base case logistic regression 4 u classifier models training prediction and comparison u after the dataset is split into training and testing sets we can correct the data imbalances by undersampling the majority class then we can training the different classifier models logistic regression random forest decision tree gaussian naive bayes xgboost gradient boosting lightgbm and compare their performance on the test data using metrics like accuracy f1 score and roc auc after choosing the best classifier we can use k fold cross validation to select the best model this will help us choose parameters that correspond to the best performance without creating a separate validation dataset 5 u hyperparameter tuning u after choosing the binary classifier we can tune the hyperparameters for improving the model results through grid search random search and bayesian optimization hypertopt library https github com hyperopt hyperopt the hyperparameter tuning process will use an objective function on the given domain space and an optimization algorithm to give the results the roc auc validation scores from all three methods for different iterations can be compared to see trends b package library requirements b the following packages need to be installed for running the project notebook https github com abhishekdbihani home credit default risk recognition blob master abhishek 20capstone 20 20home 20credit 20risk 20v2 ipynb 1 sklearn for models and metrics br 2 warnings for preventing warnings br 3 numpy for basic matrix handling br 4 matplotlib for figure plotting br 5 pandas for creating dataframes br 6 seaborn for figure plotting br 7 timeit for tracking times br 8 os for setting work directory br 9 random for creating random seeds br 10 csv for saving csv files br 11 json for creating json files br 12 itertools for creating iterators for efficient looping br 13 pprint for pretty printing data structures br 14 pydash for doing stuff in a functional way utility library br 15 gc garbage collector for deleting data br 16 re raw string notation for regular expression patterns br 17 featuretools automated feature engineering br 18 xgboost xgboost model br 19 lightgbm lightgbm model br 20 hyperopt bayesian hyperparameter optimization br i note the packages can be installed by uncommenting the first cell in the project notebook https github com abhishekdbihani home credit default risk recognition blob master abhishek 20capstone 20 20home 20credit 20risk 20v2 ipynb i b references acknowledgements b this project builds on scripts and explanations from other jupyter notebooks publicly shared on kaggle the list is as follows 1 a gentle introduction https www kaggle com willkoehrsen start here a gentle introduction br 2 introduction to automated feature engineering https www kaggle com willkoehrsen automated feature engineering basics br 3 advanced automated feature engineering https www kaggle com willkoehrsen tuning automated feature engineering exploratory br 4 intro to model tuning grid and random search https www kaggle com willkoehrsen intro to model tuning grid and random search br 5 automated model tuning https www kaggle com willkoehrsen automated model tuning br 6 home credit default risk extensive eda https www kaggle com gpreda home credit default risk extensive eda br 7 home credit default risk visualization analysis https www kaggle com charlievbc home credit default risk visualization analysis br 8 loan repayers v s loan defaulters home credit https www kaggle com pavanraj159 loan repayers v s loan defaulters home credit br | feature-engineering feature-selection binary-classification lightgbm auc-roc-score xgboost featuretools automated-feature-engineering hyperparameter-tuning hyperparameter-optimization bayesian-optimization f1-score auc-roc-curve loan-default-prediction k-fold-cross-validation scikit-learn imbalanced-data udacity-machine-learning-nanodegree hyperopt automl | server |
linkedOpenSchema | linkedopenschema linked open schema is an effort to build web scale schemata from popular sites which include existing categories with hierarchies and tag clouds this project is maintained by knowledge science and engineering lab southeast university china | cloud |
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palette | artsy palette circleci https circleci com gh artsy palette svg style shield https circleci com gh artsy palette npm version https badge fury io js 40artsy 2fpalette svg https www npmjs com package artsy palette netlify status https api netlify com api v1 badges beb9e8d7 10cc 4a2e 99bb 0d4c6f46db82 deploy status https app netlify com sites artsy palette deploys artsy s design system meta storybook https palette storybook artsy net https palette storybook artsy net del docs https palette artsy net https palette artsy net del deprecated documentation will be moved to storybook to have a single source of truth point people dzucconi https github com dzucconi damassi https github com damassi related repos icons https github com artsy icons https github com artsy icons what is palette palette is a collection of primitive product agnostic elements that help encapsulate artsy s look and feel at base level this project is intended to be used across our digital product portfolio does my component belong in palette if the component applies to artsy as a brand and can will be used across multiple digital products then palette is a great place for it if it s highly product specific then it s best to leave the component where it s used we can always move things later how to contribute if you d like to add a new component to palette please raise the need in our design system slack channel local development in the project root run the following sh lerna bootstrap yarn storybook this will compile palette and boot storybooks our default development environment new components require stories other relevant commands are sh yarn test yarn type check developing features using local versions of palette when developing new components in palette it s often useful to test those components in consuming apps such as force however due to the poor support for symlinks this can be difficult enter yalc https github com wclr yalc yalc is a mini package manager that one can publish to and install from which makes it easy to test code in realtime from outside of your app note artsy palette https github com artsy palette uses storybooks for developing features work there first then when ready and if necessary test your code locally using the flow described below you can also publish npm canary releases from the palette repo by attaching a canary label to your pr setup install yalc globally sh yarn global add yalc navigate to palette in the terminal and start the watcher sh cd palette packages palette yarn local palette dev navigate back to force and link sh cd force yarn local palette dev yarn start this will update package json to point at the yalc published version of palette when done developing your local palette feature be sure to unlink sh yarn local palette dev stop for more info check out our development guide in the docs https palette artsy net guides development deployment process commits and deployments palette uses auto release https github com intuit auto release readme to automatically release on every pr every pr should have a label that matches one of the following version trivial version patch version minor version major canary major minor and patch will cause a new release to be generated use major for breaking changes minor for new non breaking features and patch for bug fixes trivial will not cause a release and should be used when updating documentation or non project code if you don t want to release on a particular pr but the changes aren t trivial then use the skip release tag along side the appropriate version tag canary tags will publish a canary version to npm which can be used to test work in progress see the circleci job https app circleci com pipelines github artsy palette 4138 workflows ffc56588 35bf 41ed a0a8 a806fc807678 jobs 20148 for the exact version published and update your consuming app accordingly repos consuming palette force https github com artsy force forque https github com artsy forque positron https github com artsy positron prediction https github com artsy prediction volt v2 https github com artsy volt v2 volt https github com artsy volt about artsy a href https www artsy net img align left src https avatars2 githubusercontent com u 546231 s 200 v 4 a this project is the work of designers and engineers at artsy footer website the world s leading and largest online art marketplace and platform for discovering art one of our core engineering principles footer principles is being open source by default footer open which means we strive to share as many details of our work as possible you can learn more about this work from our blog footer blog and by following artsyopensource footer twitter or explore our public data by checking out our api footer api if you re interested in a career at artsy read through our job postings footer jobs footer website https www artsy net footer principles culture engineering principles md footer open culture engineering principles md open source by default footer blog https artsy github io footer twitter https twitter com artsyopensource footer api https developers artsy net footer jobs https www artsy net jobs | os |
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learn-elm | learn elm why it s difficult to introduce elm to someone who has never heard of it before without sounding evangelical br we ve tried our best to be objective and factual the fact is that elm is awesome br which is why so many js projects have borrowed shamelessly copied it s ideas redux immutable js etc br rtfeldman put it best in his 6 months of elm in production talk https youtu be r2ftmbb nls t 47m36s which we highly recommend watching br if you take two products and compare them on feature checklists that gets you br a very inaccurate picture of what it s going to be like to actually use them 6 months of elm comparison https cloud githubusercontent com assets 194400 20147838 be5d2746 a6a1 11e6 91af 5149c5bf345b jpg if anything in this guide is unclear unexplained please help us improve by opening an issue https github com dwyl learn elm issues contributions welcome https img shields io badge contributions welcome brightgreen svg style flat https github com dwyl learn elm issues why not just use xyz javascript framework which has similar features most of us are already comfortable with javascript and it is still and will remain the most popular programming language https insights stackoverflow com survey 2018 technology programming scripting and markup languages so why should we even consider it there are many technical reasons for using elm pure https drboolean gitbooks io mostly adequate guide content ch3 html functional style means all your functions are predictable and thus very reliable br no surprises even when you are working with a distributed team of dozens of people immutable state https softwareengineering stackexchange com questions 235558 what is state mutable state and immutable state means there are fewer often zero side effects https softwareengineering stackexchange com questions 40297 what is a side effect fewer language features lowers cognitive load https blog prototypr io design principles for reducing cognitive load 84e82ca61abd when you re reading other people s code much less to learn than comparable stacks e g react redux flow immutable babel all the other setup code angular 2 typescript babel reactivex etc bottom line is elm is less to learn much faster than react js or angular 2 in all benchmarks https elm lang org blog blazing fast html round two built in time travelling debugger https www youtube com watch v dsjbtc hvqq feature youtu be list plgljm3byamph2zuz1nbkhqyeawe4sn0cd t 1633 that lets you record and replay actions in a user session thus eliminating the need for manually writing selenium nightwatch tests helpful friendly compiler error messages that inform you exactly what is wrong during compilation before you attempt to view your app in the browser device evan surveyed the existing web programming languages for his university thesis and elm is the result of that study borrows ideas from several places and assembles them into a cohesive beautiful package much how apple made the original iphone the reason s we dwyl https twitter com dwyl are using the elm ecosystem is because it has beginner friendly and thriving community where everyone is welcome clear leadership from nice smart people and excellent documentation which greatly reduces frustration for beginners a shared mission to build the best graphical user interfaces for the web these are a few of our favourite things https youtu be 0iagrzbvltw what elm is a programming language for creating web browser based graphical user interfaces elm is purely functional and is developed with emphasis on usability performance and robustness it advertises no runtime exceptions in practice made possible by the elm compiler s static type checking it s difficult to overstate how game changing elm the elm architecture and elm platform are to web development right now the fact that dan abramov was inspired by elm architecture and debugger for redux and react hot reloader respectively should tell you that there s something here worth exploring great video intro to elm by jessic kerr we highly recommend watching jessica kerr s adventures in elm from goto conference 2016 image https cloud githubusercontent com assets 194400 22403008 7a878200 e602 11e6 9239 e292fa97c6e1 png https youtu be cgxhmc8m4x4 https youtu be cgxhmc8m4x4 isn t functional programming difficult if you feel like f unctional p rogramming is complicated you aren t alone it s a perfectly normal sentiment i tried functional programming in javascript before it was confusing all we can say to that is dont panic https cloud githubusercontent com assets 194400 20135968 74ed05d6 a66a 11e6 9f30 f50f911053e6 png https en wikipedia org wiki phrases from the hitchhiker 27s guide to the galaxy don 27t panic fear not we have witnessed many non mathematician people without a computer science or engineering background learning elm from scratch and while some initially felt that functional programming was a steep learning curve because it s quite different from what we were used to procedural imperative mutable we found that the elm language is actually really small and focussed br and when we break it down there are only a handful of concepts br we need to understand before we can start reading writing code tip if you want to understand the core concepts jump to the language language section below who if you haven t felt the pain of trying to debug maintain extend code you did not originally write or have not worked on a sufficiently large app to feel the fix one thing breaks two other features whack a mole https youtu be gvjl9oxgsaa you might not not see the benefit of the elm ecosystem but we urge you to consider the list in the why section above and if any of those points appeals to you br give elm 5 minutes of your time today to try the quick start below how the best place to start is with the official guide but we have condensed their install guide into the 5 minute instructions below pre requisites a computer with node js installed if you don t already have node get it here https nodejs org en download text editor any will do but we recommend https atom io because it has good elm syntax plugins internet access just so you can install elm and the modules some javascript node js knowledge ideally you have built a basic node js app before but no major experience required expected recommended pre elm learning elm architecture while it s not a pre requisite we highly recommend learning understanding the elm architecture tea before learning elm the language to flatten the elm learning curve https github com dwyl learn elm issues 45 to that end we wrote an introductory step by step tutorial for the elm architecture in javascript https github com dwyl learn elm architecture in javascript https github com dwyl learn elm architecture in javascript quick start 5 mins enough talk let s see an example 1 clone this repository on your local machine open a terminal window and run the following command sh git clone https github com dwyl learn elm git cd learn elm 2 install install the node js dependencies elm platform sh npm install note we install elm the elm compiler locally for the quick start if you decide to use it for your own project s you can install it globally using npm install g elm 3 hello name open the examples hello world elm file in your editor move your cursor to the 3 sup rd sup line and change name to your name 4 server time run the elm reactor command to start the server sh node modules bin elm reactor elm reactor has now started the server on your localhost note if you install elm globally you will be able to type elm reactor without the node modules bin relative path if you re curious why you re running a server to view the output of your elm code it s because elm is compiled to javascript and it s fiddly to have to compile your code manually every time you want to see the output with elm reactor this is handled for you read more about it here https elmprogramming com elm reactor html 5 view in browser view the entire repository in your web browser by going to http localhost 8000 click on example hello world elm to see your hello world this shows how it would compile into html without having to use elm make which we ll save for later 6 reflect you just saw how easy it is to get started with elm how do you feel was it difficult better or worse than your experience of learning any other technical concept tool language please share your thoughts br in depth step by step tutorial 60mins the best place to start your elm journey is with the free official guide https guide elm lang org br at the time of writing the entire official guide to elm gitbook written evan czaplicki creator of elm and improved by the community is 136 pages with generous spacing in the code examples the guide is readable learnable in less than a day including trying all the example and demo code if you prefer to download and read the guide offline e g on public transport during your commute to work you can download a pdf epub or mobi kindle for free at https www gitbook com book evancz an introduction to elm details frontend masters workshop it s not often we find a half decent tutorial on a subject we are trying to learn we were delighted to discover that richard feldman https github com rtfeldman one of the core contributors to elm has produced a workshop videos learning materials for learning elm https frontendmasters com workshops elm https github com rtfeldman elm workshop while it costs 39 we think it s an absolute bargain note if you have a lack of funds to pay for a subscription to get access to the workshop contact us we can help todo write comprehensive notes on the content in the workshop installation the official installation instructions for mac linux windows https guide elm lang org install html install the elm platform globally on your computer yes install it globally so you get the elm command which has a several useful functions including elm reactor a hot reloading webserver that allows you write test simple apps fast and elm make which compiles your app install using npm with the following command js npm install elm g avoid using sudo https stackoverflow com questions 16151018 npm throws error without sudo as you really should be following the principle of least privilege https en wikipedia org wiki principle of least privilege remember if you are adding elm to a project which will be deployed on a hosting service such as heroku you will need to add elm to the dependencies in your package json js npm install elm save install elm format there are many things to love about elm but something you can appreciate right away is elm format it s a tool that formats your elm code so that it is consistent with the community standard format installation instructions 1 download the current version of elm format found at https github com avh4 elm format 2 unzip the downloaded file and move the elm format executable to a location in your path variable if the unzipped file is in your downloads folder you could move it with the following terminal command mv downloads elm format usr local bin elm format which will move it to the default path 3 install the elm format package in your text editor in atom type apm install elm format into the terminal or install via packages filter by elm 4 set elm format to format your work on save in atom open settings cmd linux ctrl click packages filter by elm then click on the elm format package s settings button set the elm format command path setting and ensure the format on save checkbox is selected for more advice on elm development environment setup https github com knowthen elm blob master devsetup md language help wanted summarizing the language features for now see https elm lang org docs syntax testing ready to start testing simply follow these 3 steps 1 npm i g elm test 2 elm test init this will set up your test environment and give you 7 dummy tests in your newly created test folder 3 run elm test or the very nice elm test watch which will re run your tests as you write them the general format of the tests are elm describe dummy test test dummy test description expect equal actualvalue expectedvalue for example elm all test all describe my first test test addition test works correctly expect equal 2 2 4 test our subtraction function works correctly here we are pretending we have a subtract function that takes 2 arguments expect equal subtract 10 5 5 more info on testing can be found at testing in elm https medium com rchaves testing in elm 93ad05ee1832 3i3ibxcxz and elm community https package elm lang org packages elm community elm test 2 1 0 continuous integration circle ci to set up your elm project on circle ci copy over our circle yml and circle dependencies sh and follow the instructions on our circle ci tutorial https github com dwyl learn circleci travis ci see https github com dwyl learn travis elm lang project flags flags allow you to pass data from javascript to elm as part of the initial state of your application see our working flags example https github com dwyl learn elm tree master examples elm flags here we pass the url from javascript to elm via the script tags in our index html run npm install elm install and npm start to see the output further background reading great collection of examples and posts https github com isruslan awesome elm selected articles our favourites how to use elm at work https elm lang org blog how to use elm at work or work for dwyl where you re actively encouraged to use it javascript interoperability https guide elm lang org interop how to add elm to existing js codebase https tech noredink com post 126978281075 walkthrough introducing elm to a js web app how elm made our work better success story https futurice com blog elm in the real world general functional programming background reading objects should be immutable https www yegor256 com 2014 06 09 objects should be immutable html further reading once you ve gotten to grips with making single page apps with elm adding routing in can really improve your user s experience if they re seeing a different view it can make sense for the url to change as well so that the user can navigate back to the same view as they please check this article from staticapps org https staticapps org articles routing urls in static apps out for a little more info on routing in single page apps in general and get started by going to the routing section of the elm tutorial gitbook https sporto gitbooks io elm tutorial content en 07 routing 02 routing html to learn how to implement it in your elm app this example https github com elm lang navigation tree master examples from the elm lang navigation github repo is super helpful too videos mutable vs immutable https youtu be 5qqq3yzbkp8 learning functional programming with javascript anjana vakil https youtu be e 5obm1g fy functional programming from first principles erik meijer https youtu be a raltgh8tw teaching functional programming to noobs rob martin https youtu be bmfkeewrrqg functional programming is terrible r nar bjarnason https youtu be hzf3htukk8u promising but incomplete learn you an elm https learnyouanelm github io lots of todo items and last updated about a year go hitcount https hits dwyl com dwyl learn elm svg http hits dwyl com dwyl learn elm | tutorial learning elm-format elm-architecture elm learning-elm | front_end |
altschool-cloud-exercises | altschool cloudeng exercises my repo for the submission of altschool cloud engineering exercises and assignments | cloud |
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nextreq | nextreq this started as an idea to build a modern cloud based requirements engineering tool with a backend in rust and a frontend using vue js in the end it became more of a technology playground smiley deployment bash script bootstrap bash script package docker run p 8000 8000 nextreq name nextreq 0 | rust | cloud |
CSIS4175-HMS | how s my stylist information how s my stylist is an android group project for the douglas college s csis 4175 mobile development ii course the purpose of this app is to provide users with hair stylist reviews and prices how this app stands out from other review app is that this app is hair review focused how many times did you encounter in a situation where you wanted a decent haircut for a cheap price but you ended up going to a salon with a good review but over priced don t worry as hms is here to help team william lee cindy cheng chhavi garg melody yu for the team github 1 before you start coding get the most recent version of the project fetch origin pull origin 2 create a new branch named with the following convention your name feature you are working on ex cindy login page tasks section 3 once you are done with the feature test it to make sure it works without any issues 4 create a pull request once approved this will merge your branch with the main branch 5 notify others so they can review changes at least 1 review and approval is required before merging is allowed 6 once approved your branch will be merged to main 7 delete the old branch now your features are added to the main project so the repo is not cluttered with a lot of old branches 8 when others ask for review make sure to go over their changes ideally together and test the feature don t approve without making sure the changes aren t messing up the project | hair mobile-app review stylist | front_end |
Deep-Leafsnap | deep leafsnap convolutional neural networks have become largely popular in image tasks such as image classification recently largely due to to krizhevsky et al in their famous paper imagenet classification with deep convolutional neural networks https papers nips cc paper 4824 imagenet classification with deep convolutional neural networks famous models such as alexnet vgg 16 resnet 50 etc have scored state of the art results on image classfication datasets such as imagenet and cifar 10 we present an application of cnn s to the task of classifying trees by images of their leaves specifically all 185 types of trees in the united states this task proves to be difficult for traditional computer vision methods due to the high number of classes inconsistency in images and large visual similarity between leaves kumar et al developed a automatic visual recognition algorithm in their 2012 paper leafsnap a computer vision system for automatic plant species identification http neerajkumar org base papers nk eccv2012 leafsnap pdf to attempt to solve this problem our model is based off vgg 16 except modified to work with 64x64 size inputs we achieved state of the art results at the time our deep learning approach to this problem further improves the accuracy from 70 8 to 86 2 for the top 1 prediction accuracy and from 96 8 to 98 4 for top 5 prediction accuracy top 1 accuracy top 5 accuracy leafsnap 70 8 96 8 deep leafsnap 86 2 98 4 we noticed that our model failed to recognize specific classes of trees constantly causing our overall accuracy to derease this is primarily due to the fact that those trees had very small leaves which were hard to preprocess and crop our training images were also resized to 64x64 due to limited computational resources we plan on further improving our data preprocessing and increasing our image size to 224x224 in order to exceed 90 for our top 1 prediction acurracy the following goes over the code and how to set it up on your own machine files model py trains a convolutional neural network on the dataset vgg py pytorch model code for vgg 16 densenet py pytorch model code for densenet 121 resnet py pytorch model code for resnet dataset py creates a new train test dataset by cropping the leaf and augmenting the data utils py helps do some of the hardcore image processing in dataset py averagemeter py helper class which keeps track of a bunch of averages when training leafsnap dataset images csv is the csv file corresponding to the dataset requirements txt contains the pip requirements to run the code installation to run the models and code make sure you python https www python org downloads installed install pytorch by following the directions here http pytorch org clone the repo onto your local machine and cd into the directory git clone https github com sujithv28 deep leafsnap git cd deep leafsnap install all the python dependencies pip install r requirements txt make sure sklearn is updated to the latest version pip install upgrade sklearn also make sure you have opencv installed either through pip or homebrew you can check if this works by running and making sure nothing complains python import cv2 download leafsnap s image data and extract it to the main directory by running in the directory original data can be found here http leafsnap com dataset wget https www dropbox com s dp3sk8wpiu9yszg data zip dl 0 unzip a data zip dl 0 rm data zip dl 0 create the training and testing data to create the dataset run python dataset py this cleans the dataset by cropping only neccesary portions of the images containing the leaves and also resizes them to 64x64 if you want to change the image size go to utils py and change img misc imresize img 64 64 to any size you want training model to train the model run python model py | ai |
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f3-cortex-model-generator | f3 cortex model generator generates f3 cortex https github com ikkez f3 cortex models by reverse engineering existing database schema currently only supports mysql installation please add php ekhaled f3 cortex model generator 1 0 to your composer file usage create an executable php file with the following contents php usr bin env php php require once dir src vendor autoload php config output path to output folder db array db connection params namespace models base extends models base relationnamespace models base template path to template file indentation array exclude array generator new ekhaled generators mysql model config generator generate and just run the file from the command line options output specifies the folder where models will be output to db an array in the following format host host com username password dbname name of database namespace namespace of the generated models extends if you have a base model you can make the generated model extend that model by specifying it here relationnamespace namespace of the connected classes that constitute relationships with a given model usually it s the same as namespace template path to file containing a custom template if not specified a built in template will be used indentation an array that indicates what type of unit of indentation to be used on template generation followed by a starting level for example unit start level 3 this will use 2 spaces as indentation starting at 6 spaces this is applied to the array defined by the fieldconf template exclude views whether to generate models for views too defaults to false exclude connectors whether to generate stub models for many to many connector tables defaults to false sometimes you might need these models to create db tables for example for automated tests in test databases exclude an array containing all tables that you would like to exclude while generating models for example array migrations custom templates a typical custom template would look like php php namespace class classname extends protected fieldconf fieldconf table tablename just ensure that the placeholders are in place and they will get replaced during model generation supported placeholders are namespace classname extends fieldconf tablename | server |
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InformationTheoryCoding | itc channel encoder simulation problems 1 what is coding gain let s find out case 1 a take a binary sequence of any length for ex 1000 b add white gaussian noise using randn function c receive y seq at receiver and take binary decision if received noisy bit 0 then 1 else 0 d find out total error 1 between y and x e find error rate number error total bits transmitted case 2 a take a binary sequence of any length for ex 100 b transmit every bit if 0 as 1 1 1 and if 1 as 1 1 1 c add white gaussian noise using randn function to generated 3000 length sequence d receive y seq at receiver and take binary decision from average of 3 bits at a time if received noisy bit 0 then 1 else 0 e find out total error 2 between y and x f find error rate number error total bits transmitted 2 which error is greater error 1 or error 2 why 3 repeat the exp for different noise variances like 1 0 5 0 25 0 125 or snr db 0 2 4 6 8 20 using snr db 10 log s sigma 2 take s 1 in one case and take sigma 2 1 in other case 4 plot logarithmic graph use semilogy matlab function for variance x axis vs error total transmitted bits 5 mention the advantage and disadvantage of above experiment 6 make a generalize program where each bit can be coded as any arbitrary length like 0 can be 0000 or 00000 and similar for bit 1 7 conclude what is coding gain | server |
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solana-go | streamingfast solana library for go reference https img shields io badge godoc reference 5272b4 svg style flat square https pkg go dev github com streamingfast solana go license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 go library to interface with solana nodes s json rpc interface solana s spl tokens various instructions decoding for popular programs warning solana go works using semver but in 0 version which means that the minor will be changed when some broken changes are introduced into the application and the patch will be changed when a new feature with new changes is added or for bug fixing as soon as v1 0 0 be released solana go will start to use semver as usual installation go get github com streamingfast solana go all development happens on the develop branch so if you need to check if there is any fixes in there try updating to it with go get github com streamingfast solana go develop usage loading an spl mint golang import github com streamingfast solana go rpc import github com streamingfast solana go token addr solana mustpublickeyfrombase58 epjfwdd5aufqssqem2qn1xzybapc8g4weggkzwytdt1v cli rpc newclient https api mainnet beta solana com var m token mint err cli getaccountdatain context background addr m handle err json newencoder os stdout encode m owneroption 1 owner 2wmvcsfpxgpjrnmmn7rchp4uaeotqn39mxfc2zhpdri9 decimals 128 isinitialized true getting any account s data golang import github com streamingfast solana go rpc import github com streamingfast solana go token addr solana mustpublickeyfrombase58 epjfwdd5aufqssqem2qn1xzybapc8g4weggkzwytdt1v cli rpc newclient https api mainnet beta solana com acct err cli getaccountinfo context background addr handle err json newencoder os stdout encode m context slot 47836700 value lamports 1461600 data data aqaaabzjwe1aas4e hqrnhuahf6hz9cgfhuchf tg3jn nj2gca3xlwwaaagaqeaaaaqnl7bttwez5cy 3sszrcuq0 ajfyqmjugeqxmctqicw encoding base64 owner tokenkegqfezyinwajbnbgkpfxcwubvf9ss623vq5da executable false rentepoch 109 examples reference rpc get recent blockhash example rpc get recent blockhash test go websocket account subscribe example ws account subscribe test go running the easiest way to see the actual output for a given example is to add a line output any at the very end of the test looks like this for examplerpc getrecentblockhash file example rpc get recent blockhash test go example rpc get recent blockhash test go fmt println string bytes output any this tells go test that it can execute this test correctly then simply run only this example go test run examplerpc getrecentblockhash replacing examplerpc getrecentblockhash with the actual example name you want to try out where line output any was added this will run the example and compares the standard output with the any which will fail but it s ok an expected so you can see the actual output printed to your terminal websocket examples runs for a 1 minute then exits you will not see anything until the example finish rpc url to use can be specified by using environment variable solana go rpc url defaults to https api mainnet beta solana com ws url to use can be specified by using environment variable solana go ws url defaults to ws api mainnet beta solana com contributing issues and pr in this repo related strictly to the solana go library report any protocol specific issues in their respective repositories https github com streamingfast streamingfast protocols please first refer to the general streamingfast contribution guide https github com streamingfast streamingfast blob master contributing md if you wish to contribute to this code base this codebase uses unit tests extensively please write and run tests license apache 2 0 license | blockchain |
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DatePickerAura | datepicker update 2017 05 09 i ve changed the component to remove ui inputdate beacuse of localization and other issues introduced by salesforce new components needed and present here are datetimelib inputdate select sources to build this component i used several sources however as the result is kind of a mashup i haven t included an licence information as the code is all mixed up together i used some of the structure of the existing aura lightning component a href https github com forcedotcom aura tree master aura components src main components ui datepicker target blank here a i also used some data structures and ideas from a href https github com joshsalverda datepickr target blank here a update no more moment js it s uses the built in a localizationservice object to do all date related tasks which behind the scenes uses moment but hey i didn t load it finally i was forced to use moment js despite trying not to have any external dependencies but due to a bug in the lightning framework i was forced to use it for string to date conversions here s how to implement it assuming you are using an opportunity obviously any object with a date would work define the opportunity as an attribute on your component aura attribute name opportunity type opportunity default sobjecttype opportunity name new opportunity stagename some stage in your component you need to handle the datechangeevent from the component aura handler name datechangeevent event c datechange action c handledatechange in your form put the below once you have created it as a top level member of the form div class slds form element slds m top medium c datepicker aura id closedate label close date placeholder enter a date value v opportunity closedate formatspecifier mm dd yyyy div finally in your controller or helper update your date handledatechange function cmp event helper var dateselector event getsource getlocalid if dateselector closedate var opp cmp get v opportunity opp closedate event getparam value how to use in a vf page once the component is built you will want a structure like this vf page instantiate and add callback datepickerwrapper handle date change events call callback datepicker perform date display and selection in your datepickerwrapper component handle the datechangeevent from the datepicker and add a callback attribute for lightning out aura handler name datechangeevent event c datechange action c handledatechange aura attribute name callback type string description call this to communicate results to visualforce page access global place the datepicker in the datepickerwrapper component like this assuming you are using slds you may want to do all form element stuff in the parent vf page up to you div class slds form element slds m top medium c datepicker aura id closedate label close date placeholder enter a date value v opportunity closedate formatspecifier mm dd yyyy div instantiate the picker like this lightning use c datepickerapp function lightning createcomponent c datepickerwrapper value opportunity closedate callback function alert callback lightning function cmp note the callback above it s an attribute that will be called by the datepickerwrapper class finally in your datepickerwrapper controller or helper update your date by calling the callback on the vf page handledatechange function cmp event helper var func cmp get v callback if func func fieldname closedate value event getparam value this is what it looks like datepicker gif 3 3 let me know if you find any bugs 1 http www soliantconsulting com blog 2016 08 build lightning date picker 2 https github com rapsacnz datepicker 3 http i stack imgur com 7rohd gif | os |
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Hands-on-NLP-with-PyTorch | hands on nlp with pytorch collection of notebooks for natural language processing with pytorch this notebook contains all the code for the course https www packtpub com application development hands natural language processing pytorch video that i did for packtpub br the sections are divided by folders br b the topics that are covered are b br br 1 up and running with pytorch https github com jibinmathew691993 hands on nlp with pytorch tree master section1 br in this section you setup our environment for pytorch and learn to perform some basic operations with pytorch and build quick neural network with pytorch and also understand why deep learning is a useful technique for nlp br br 2 data cleaning and preprocessing for sentiment analysis https github com jibinmathew691993 hands on nlp with pytorch tree master section2 br in this section you will learn the use of tools like nltk and spacy as nlp libraries and how they help in data preprocessing you will clean your data with nltk then parse and lemmatize the data with spacy and understand pipelines br br 3 implement word embeddings with gensim https github com jibinmathew691993 hands on nlp with pytorch tree master section3 br in this section you will discover the conceptual relationship in a text and how they help in overall understanding text you will perform hands on operations with gensim library br br 4 train rnns and lstms units for sentiment analysis https github com jibinmathew691993 hands on nlp with pytorch tree master section4 br in this section you will deep dive into deep learning and implement rnns for our sentiment analysis and further improve the performance with advanced architectures and lstm br br 5 build a neural machine translator https github com jibinmathew691993 hands on nlp with pytorch tree master section5 br in this section you will learn an important deep learning architecture within nlp called sequence to sequence and learn to appreciate its applications and complete our project to build a neural translation machine br br 6 improve the neural machine translation with attention networks https github com jibinmathew691993 hands on nlp with pytorch tree master section5 br in this section you will deep dive into deep learning and implement rnns for our sentiment analysis and further improve the performance with advanced architectures and lstm | deep-learning deep-neural-networks recurrent-neural-networks rnn pytorch lstm seq2seq word-embeddings attention-model nltk-python spacy-nlp spacy spacy-pipeline torchtext pytorch-implementation pytorch-tutorials pytorch-nlp machine-learning sentiment-analysis nueral-machine-translation | ai |
ECE387Midterm | ece387midterm midterm project for embedded systems design | os |
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Find-a-coach-web-app | find a coach web app this app is simulation for hiring developers as coaches to teach you frontend or backend development used technologies mostly vue 3 data being stored and fetched from firebase | server |
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SkylightOS | div align center skylightos github https img shields io github license ethernalraine skylightos logo agpl 3 0 style plastic license github commit activity https img shields io github commit activity m ethernalraine skylightos style plastic lines of code https img shields io tokei lines github ethernalraine skylightos style plastic the current total lines of code counter is intermittently broken upstream from shields io div current codename esaul skylightos is a small portable and extensible rtos designed for a multitude of platforms skylight is designed from the ground up free from any arbitrary limitations of traditional platforms skylight can easily be ported to a device or general platform of your liking easily and is highly extensible for developers and users alike skylightos is not posix compliant by design this may be followed up in the future with a posix compliant subsystem but this is just a pipedream for now for a roadmap on skylight s development check the skylightos milestone roadmap https github com users ethernalraine projects 2 build instructions prerequisites git python 3 10 qemu netwide assembler clang gdb for target arch binutils for target arch automatically compiled if you don t have it grub2 scons installable via pip for build instructions check skylightos build docs docs compiling skylightos md for a list of all targets and architectures or browse the docs architectures docs architectures folder directly license a href https www gnu org licenses agpl 3 0 en html img align right height 96 alt agpl3 license src https www gnu org graphics agplv3 155x51 png a skylightos is licensed under the b gnu affero general public license v3 0 b the full text of the license can be accessed via this link https www gnu org licenses agpl 3 0 standalone html and is also included in the license license file of this software package credits codepulse https www youtube com codepulse for his amazing tutorial on writing a 64 bit kernel daedalus https www youtube com daedaluscommunity for his series on building an os helping me understand the basics a bit osdev wiki http wiki osdev org for the extensive documentation surrounding os development osdev discord https discord gg osdev for their support when my braincells went to get milk p eintim http eintim one for giving me the idea and also helping me with some 0 iq issues nanobyte https www youtube com nanobyte dev for his amazing building an os series and his nanobuild system le official wge discord server https discord gg c8kvcuy75g for rating my os and being an awesome community 3 redsonbr140 https github com redsonbr140 for his excellent blobos terminal driver | os hobby-kernel hobby-os kernel operating-system rtos | os |
friendly-lamp | friendly lamp simple web development tasks | front_end |
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meta-iot2000 | iot2000 board support package this packages contains the following elements meta iot2000 bsp meta iot2000 example for updates please visit https github com siemens meta iot2000 we are also accepting issue reports feature suggestions and patches this way meta iot2000 bsp use this yocto layer to enable all hardware features of the iot2000 device it allows to build standard yocto images which will contain the required kernel configurations and tools and will emit a bootable sd card image meta iot2000 example this yocto layer builds on top of meta iot2000 bsp providing an exemplary image with additional tools and services to exploit features of the iot2000 conveniently this layer shall only be considered as a starting point for own developments it is not configured to provide product grade maturity and security building an image there are two recommended ways to build one of the provided images may they be original or already customized natively on a linux machine or inside a docker container note before starting the build make sure that your working directory is located on a native linux file system such as ext4 xfs btrfs etc ntfs is known to not work native build on a compatible distribution install the packages https www yoctoproject org docs 3 1 mega manual mega manual html required packages for the build host that yocto 3 1 requires furthermore install the kas build tool shell sudo pip3 install kas clone the meta iot2000 repository or unpack an archive of it into a work directory shell git clone https github com siemens meta iot2000 now you can build the example image like this shell kas build meta iot2000 kas example yml to build the bsp image instead just specify the corresponding configuration file instead shell kas build meta iot2000 kas bsp yml you can also reproduce the windows or linux sdk this way shell kas build meta iot2000 kas sdk windows i586 yml kas build meta iot2000 kas sdk linux x64 yml docker build make sure docker is installed and properly configured for your host system you may have to switch the storage driver away from legacy aufs see docker documentation https docs docker com engine userguide storagedriver selectadriver if kas warns about this again the first step is cloning of the repository or unpacking an archive shell git clone https github com siemens meta iot2000 next install the kas container script like this shell wget https raw githubusercontent com siemens kas 2 6 2 kas container chmod a x kas container now you can generate a desired image the following assumes that your user has permission to use docker usually this is achieved by adding the user to the docker group which has security implications note that running the build as root does not work shell kas container build meta iot2000 kas example yml the above command disposes the build container after use keeping downloads and build results in the current work directory you may want to use the container interactively shell kas container shell meta iot2000 kas example yml if you are building from within a proxy restricted network make sure the settings are available via the standard environment variables http proxy etc booting the image from sd card under linux insert an unused sd card assuming the sd card takes device dev mmcblk0 use dd to copy the image to it for example shell sudo dd if build tmp deploy images iot2000 iot2000 example image iot2000 wic of dev mmcblk0 bs 4m oflag sync the example image starts with the ip 192 168 200 1 preconfigured on the first ethernet interface you can use ssh to connect to the system the bsp image does not configure the network if you want to ssh into the system you can use the root terminal via uart to ifconfig the ip address and use that to ssh in note the root password is empty and must be changed before connecting the system to an untrustworthy network booting the image from usb stick under linux insert an unused usb stick assuming the usb stick takes device dev sda use dd to copy the image to it for example shell sudo dd if build tmp deploy images iot2000 iot2000 example image iot2000 wic of dev sda bs 4m oflag sync | yocto-layer | server |
udacity-blockchain-developer-nanodegree | blockchain developer nanodegree build status https semaphoreci com api v1 ibrunotome udacity blockchain developer nanodegree branches master badge svg https semaphoreci com ibrunotome udacity blockchain developer nanodegree projects 1 manage your blockchain identity learn to create your identity on the blockchain and interact with an existing web service 2 building your own private blockchain building your own private blockchain utilizing node js with leveldb 3 restful web api with node js framework create a web api using a node js framework that will interact with your private blockchain to submit and retrieve blockchain data 4 build a private blockchain notary service utilizing your existing web api you will create a star registry notarization service 5 descentralized star notary project build additional functionality with your smart contract and deploy it on the public testnet to create a dapp donations btc 3fbtav7ekmkmxq9pdpuqnxwlb5v48dayvx nano xrb 3d68nt9yttgok7a54n8pakyz7jkp65dgzhcycjz1397kno1iog493bxsmxa9 | blockchain blockchain-technology private-blockchain udacity-nanodegree udacity-nanodegree-blockchain udacity-blockchain-nanodegree udacity solidity solidity-contracts ethereum bitcoin | blockchain |
nlp_primitives | nlp primitives p align center a href https codecov io gh alteryx nlp primitives img src https codecov io gh alteryx nlp primitives branch main graph badge svg a a href https github com alteryx nlp primitives actions query branch 3amain workflow 3atests target blank img src https github com alteryx nlp primitives workflows tests badge svg branch main alt tests a a href https badge fury io py nlp primitives target blank img src https badge fury io py nlp primitives svg maxage 2592000 alt pypi version a a href https anaconda org conda forge nlp primitives target blank img src https anaconda org conda forge nlp primitives badges version svg alt anaconda version a a href https stackoverflow com questions tagged featuretools target blank img src http img shields io badge questions on stackoverflow blue svg alt stackoverflow a a href https pepy tech project nlp primitives target blank img src https pepy tech badge nlp primitives month alt pypi downloads a p hr nlp primitives is a python library with natural language processing primitives intended for use with featuretools https github com featuretools featuretools nlp primitives allows you to make use of text data in your machine learning pipeline in the same pipeline as the rest of your data installation there are two options for installing nlp primitives both of the options will also install featuretools if it is not already installed the first option is to install a version of nlp primitives that does not include tensorflow with this option primitives that depend on tensorflow cannot be used currently the only primitive that can not be used with this install option is universalsentenceencoder pypi nlp primitives without tensorflow can be installed with pip shell python m pip install nlp primitives conda forge or from the conda forge channel on conda shell conda install c conda forge nlp primitives the second option is to install the complete version of nlp primitives which will also install tensorflow and allow use of all primitives to install the complete version of nlp primitives with pip shell python m pip install nlp primitives complete or from the conda forge channel on conda shell conda install c conda forge nlp primitives complete demos blog post https blog featurelabs com natural language processing featuretools predict resturant review ratings https github com featurelabs predict restaurant rating calculating features with nlp primitives primtives in featuretools this is how to calculate the same feature python from featuretools nlp primitives import polarityscore data hello this is a new featuretools library this will add new natural language primitives we hope you like it pol polarityscore pol data 0 0 365 1 0 385 2 1 000 dtype float64 combining primitives in featuretools this is how to combine nlp primitives primitives with built in or other installed primitives python import featuretools as ft from featuretools nlp primitives import titlewordcount from featuretools primitives import mean entityset ft demo load retail feature matrix features ft dfs entityset entityset target dataframe name products agg primitives mean trans primitives titlewordcount feature matrix head 5 mean order products quantity mean order products unit price mean order products total title word count description product id 10002 16 795918 1 402500 23 556276 3 0 10080 13 857143 0 679643 8 989357 3 0 10120 6 620690 0 346500 2 294069 2 0 10123c 1 666667 1 072500 1 787500 3 0 10124a 3 2000 0 6930 2 2176 5 0 development to install from source clone this repo and run bash make installdeps test this will install all pip dependencies built at alteryx nlp primitives is an open source project maintained by alteryx https www alteryx com to see the other open source projects we re working on visit alteryx open source https www alteryx com open source if building impactful data science pipelines is important to you or your business please get in touch p align center a href https www alteryx com open source img src https alteryx oss web images s3 amazonaws com opensource logo 01 png alt alteryx open source width 800 a p | ai |
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larch | larch large language model based automatic readme creation with heuristics larch is an automatic readme generation system using language models usage prerequisite install larch with pip pip install larch readme python cli you can then test out generation without setting up a server larch local model openai text davinci 003 openai api key your openai api key or you can rely on a server to do generation see following for setting up a server larch endpoint https your server address model openai text davinci 003 server start the server openai api key my api key larch server you can access http localhost 8000 docs to see the api you may want to specify host your host name or ip address if you intend to access from a remote machine both environmental variables are optional spcify openai api key if you want to allow users to use openai based models specify entrypoint extractor if you want to use entrypoint based generation strongly recommended trained with script entrypoint extractor py script entrypoint extractor py you can limit the models to load with loaded models environmental variable not setting anything loads all models you can also load pretrained encoder decoder model by passing json serialization mapping from their names to their paths with encoder decoder model paths bash this loads gpt2 gpt2 xl and a pretrained encoder decoder model from path to model loaded models gpt2 gpt2 xl encoder decoder model paths my encdec path to model larch server this only loads a pretrained encoder decoder model notice that empty loaded models and unset loaded models have different behaviors loaded models encoder decoder model paths my encdec path to model larch server you can download vscode plugin vsix file to interact with the server from here https github com hitachi nlp larch vscode releases download v0 0 5 larch 0 0 5 vsix usage with docker build docker image you need to set up proxy settings appriopriately if you are behind a proxy server bash docker build t larch you may need to pass build arg curl ca bundle if you are behind a proxy and getting a ssl error warning this disables ssl connection thus make your connection vulnerable against attacks then you can start the server with the following command bash docker run rm p your host ip port 80 tcp larch you need to pass e openai api key your openai api key if you wish to use openai models you may need to pass e curl ca bundle if you are behind a proxy and getting a ssl error warning this disables ssl connection thus make your connection vulnerable against attacks development alternatively you can run cli without using pip for better debugging and development pip install r requirements txt export pythonpath pwd test out generation python larch cli py local model gpt2 start debug server python larch server py reload log level debug for testing bash pip install pytest 7 2 0 pytest dependency 0 5 1 export pythonpath pwd py test v tests model training and evaluation training encoder decoder models you can train your own encoder decoder model with scripts finetune encdec py scripts finetune encdec py bash make sure you have cuda 11 6 installed we do custom torch installation to enble gpu pip install torch 1 13 0 cu116 extra index url https download pytorch org whl cu116 pip install r cat requirements txt grep v torch pip install r requirements dev txt export pythonpath pwd python scripts finetune encdec py model name or path t5 small do train do eval train file path to train jsonl validation file path to dev jsonl output dir tmp summarization per device train batch size 4 per device eval batch size 4 overwrite output dir supported models are bart mbart t5 mt5 and led only t5 models t5 small t5 base t5 large t5 3b and t5 11b must use an additional argument source prefix summarize | ai |
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design-system | chefkoch design system build status https travis ci org chefkoch dev design system svg branch master https travis ci org chefkoch dev design system this repository contains the chefkoch design system dsy its patterns and documentation are built with astrum http astrum nodividestudio com showcase https design chefkoch de https design chefkoch de contents installation usage installation usage contributing contributing disclaimer disclaimer installation usage maintain the design system with docker docker run rm v pwd app w app node npm install update usage requirements npm sass install the design patterns and add them in anyway you see fit to your build pipeline npm install chefkoch design system save dev integration in the pipeline example with gulp and gulp sass sass includepaths node modules chefkoch design system components import the desired pattern s in your sass file import chefkoch design system components button import chefkoch design system components button primary use the imported component in your markup button class ds btn ds btn primary primary button button contributing take a look at the development and contributing guidelines contributing md if you are in the mood of extending the design system please make sure to read the disclaimer first disclaimer the chefkoch design system is a library for internal usage by the chefkoch gmbh its publication on github and npm is mainly in the spirit of providing another implementation reference for display patterns and design systems we do not encourage its usage for other websites | pattern-lab design-systems js css | os |
Embedded_System_Software_Design | embedded system software design this repository contains the project programs that were implemented in the embedded system software design course structure embedded system software design license project 1 110 pdf project 2 110 pdf readme md essd m11007328 pa1 essd m11007328 pa1 pdf essd m11007328 pa1 makefile pa1 cpp input part1 input txt part2 input 10 txt part2 input 5 txt part3 input txt libs check h check o src config h cpu cpp cpu h system cpp system h thread cpp thread h essd m11007328 pa2 essd m11007328 pa2 pdf m11007328 cmakefile cpa2 cpp libs check h check o src config h system cpp system h thread cpp thread h | os |
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blockchain | blockchain smart contract website bitcoin white paper https bitcoin org bitcoin pdf ethereum white paper http web archive org web 20131228111141 http vbuterin com ethereum html blockchain demo https andersbrownworth com blockchain blockchain sheet https docs google com spreadsheets d 1ydb8zhglmws oh4rrgwky7ane0o3tmpymncybgoybjo edit usp sharing global bitcoin nodes distribution https bitnodes io ethereum node tracker https etherscan io nodetracker remix ethereum ide https remix ethereum org remix project https github com ethereum remix project remixd https github com ethereum remix project tree master libs remixd openzeppelin https openzeppelin com eip ethereum improvement proposals https eips ethereum org erc ethereum request for comments https eips ethereum org erc web3 js ethereum javascript api https web3js readthedocs io ethereum unit converter https www etherchain org tools unitconverter solidity programming language https soliditylang org metamask wallet metamask https metamask io binance https academy binance com en articles connecting metamask to binance smart chain bitkub https support bitkub com hc th articles 360061315771 e0 b8 81 e0 b8 b2 e0 b8 a3 e0 b8 95 e0 b8 b1 e0 b8 87 e0 b8 84 e0 b8 b2 e0 b8 81 e0 b8 a3 e0 b8 b0 e0 b9 80 e0 b8 9b e0 b8 b2 e0 b8 ad e0 b8 ad e0 b8 99 e0 b9 84 e0 b8 a5 e0 b8 99 online wallet e0 b9 83 e0 b8 ab e0 b8 a3 e0 b8 ad e0 b8 87 e0 b8 a3 e0 b8 b1 e0 b8 9a e0 b9 80 e0 b8 84 e0 b8 a3 e0 b8 b7 e0 b8 ad e0 b8 82 e0 b8 b2 e0 b8 a2 bitkub chain metamask block explorer bitcoins https www blockchain com explorer ethereum https etherscan io binance https bscscan com bitkub https bkcscan com testnet faucet rinkeby https faucet rinkeby io ropsten https faucet ropsten be binance https testnet binance org faucet smart bitkub https faucet bitkubchain com hyperledger besu website https besu hyperledger org github https github com hyperledger besu docker hub https hub docker com r hyperledger besu json rpc api https api besu hyperledger org alethio ethereum lite explorer https hub docker com r alethio ethereum lite explorer visual studio code extensions visual studio code https code visualstudio com docker https marketplace visualstudio com items itemname ms azuretools vscode docker solidity https marketplace visualstudio com items itemname juanblanco solidity dart https marketplace visualstudio com items itemname dart code dart code flutter https marketplace visualstudio com items itemname dart code flutter json tools https marketplace visualstudio com items itemname eriklynd json tools windows terminal and package manager windows terminal https www microsoft com store productid 9n0dx20hk701 windows package manager insiders program https forms microsoft com pages responsepage aspx id v4j5cvggr0grqy180bhbr nsoqdz219pqooqk5qxqezumvvct1iwveplsklzs0ddrfzeujzuou9zwi4u app installer winget https www microsoft com store productid 9nblggh4nns1 follow me page https fb com codebangkok https fb com codebangkok group https fb com groups msdevth https fb com groups msdevth blog https dev to codebangkok https dev to codebangkok youtube https youtube com codebangkok https youtube com codebangkok github https github com codebangkok https github com codebangkok hash script google sheet function hash input var rawhash utilities computedigest utilities digestalgorithm sha 1 input var txthash for j 0 j rawhash length j var hashval rawhash j if hashval 0 hashval 256 if hashval tostring 16 length 1 txthash 0 txthash hashval tostring 16 return txthash | blockchain |
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Instagram-iOS | instagram clone for ios my instagram clone is a photo sharing app using parse as its backend this is following the tutorials from codepath user stories the following functionality is completed x user can sign up to create a new account using parse authentication x user can log in and log out of his or her account x the current signed in user is persisted across app restarts x user can take a photo add a caption and post it to instagram x user can view the last 20 posts submitted to instagram x user can pull to refresh the last 20 posts submitted to instagram x user can tap a post to view post details including timestamp and caption x show the username and creation time for each post x user can use a tab bar to switch between a home feed tab all posts and a profile tab only posts published by the current user x user can like a post and see number of likes for each post in the post details screen x style the feed to look like the real instagram feed to an extent video walkthrough here s a walkthrough of implemented user stories img src instagram gif title video walkthrough width alt video walkthrough gif created with licecap https www cockos com licecap credits list an 3rd party libraries icons graphics or other assets you used in your app afnetworking https github com afnetworking afnetworking networking task library notes describe any challenges encountered while building the app license copyright 2021 maya epps and codepath licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | server |
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blockchain-application-using-fabric-java-sdk | note this repository has been archived and no longer maintained create and deploy a blockchain network using hyperledger fabric sdk for java set up and initialize the channel install and instantiate chaincode and perform invoke and query on your blockchain network blockchain is a shared immutable ledger for recording the history of transactions the linux foundation s hyperledger fabric the software implementation of blockchain ibm is committed to is a permissioned network hyperledger fabric is a platform for distributed ledger solutions underpinned by a modular architecture delivering high degrees of confidentiality resiliency flexibility and scalability in a blockchain solution the blockchain network works as a back end with an application front end to communicate with the network using a sdk to set up the communication between front end and back end hyperledger fabric community offers a number of sdks for a wide variety of programming languages like the nodejs sdk and java sdk this code pattern explains the methodology to create deploy and test the blockchain network using hyperledger fabric sdk java it would be helpful for the java developers who started to look into hyperledger fabric platform and would like to use fabric sdk java for their projects the sdk helps facilitate java applications to manage the lifecycle of hyperledger channels and user chaincode the sdk also provides a means to execute user chaincode query blocks and transactions on the channel and monitor events on the channel this code pattern will help to get the process started to build a hyperledger fabric v1 4 1 java application when the reader has completed this pattern they will understand how to create deploy and test a blockchain network using hyperledger fabric sdk java this pattern will provision a hyperledger fabric 1 4 1 network consisting of two organizations each maintaining two peer node two certificate authorities ca for each organization and a solo ordering service the following aspects will be demonstrated in this code pattern create and initialize channel install and instantiate chain code register and enroll the users perform invoke and query on the blockchain network note this code pattern builds a hyperledger fabric 1 4 1 network and uses hyperledger fabric sdk java 1 4 1 flow images architecture png 1 generate the artifacts using cryptogen and configtx for peers and channel in network currently these are already generated and provided in the code repository to use as is 2 build the network using docker compose and the generated artifacts 3 use hyperledger fabric java sdk apis to work with and manage the network create and initialize the channel install and instantiate the chaincode register and enroll the users perform invoke and query to test the network included components hyperledger fabric https hyperledger fabric readthedocs io hyperledger fabric is a platform for distributed ledger solutions underpinned by a modular architecture delivering high degrees of confidentiality resiliency flexibility and scalability docker https www docker com docker is an open platform for developers and sysadmins to build ship and run distributed applications hyperledger fabric java sdk https github com hyperledger fabric sdk java featured technologies blockchain https en wikipedia org wiki blockchain a blockchain is a digitized decentralized public ledger of all transactions in a network java https en wikipedia org wiki java programming language java is a general purpose computer programming language that is concurrent class based and object oriented watch the video https img youtube com vi vctabgkvfs0 0 jpg https youtu be vctabgkvfs0 pre requisites docker https www docker com get started docker compose https docs docker com compose overview git client https git scm com downloads needed for clone commands maven https maven apache org download cgi needed to build the client maven is a build automation tool used primarily for java projects maven addresses two aspects of building software first it describes how software is built and second it describes its dependencies steps follow these steps to setup and run this code pattern 1 setup the blockchain network 1 setup the blockchain network 2 build the client based on fabric java sdk 2 build the client based on fabric java sdk 3 create and initialize the channel 3 create and initialize the channel 4 deploy and instantiate the chaincode 4 deploy and instantiate the chaincode 5 register and enroll users 5 register and enroll users 6 perform invoke and query on network 6 perform invoke and query on network 1 setup the blockchain network clone this repo https github com ibm blockchain application using fabric java sdk using the following command git clone https github com ibm blockchain application using fabric java sdk to build the blockchain network the first step is to generate artifacts for peers and channels using cryptogen and configtx the utilities used and steps to generate artifacts are explained here https hyperledger fabric readthedocs io en release 1 4 build network html in this pattern all required artifacts for the peers and channel of the network are already generated and provided to use as is artifacts can be located at network resources crypto config network resources config the automated scripts to build the network are provided under network directory the network docker compose yaml file defines the blockchain network topology this pattern provisions a hyperledger fabric 1 4 1 network consisting of two organizations each maintaining two peer node two certificate authorities for each organization and a solo ordering service need to run the script as follows to build the network note please clean up the old docker images if any from your environment otherwise you may get errors while setting up network cd network chmod x build sh build sh to stop the running network run the following script cd network chmod x stop sh stop sh to delete the network completely following script need to execute cd network chmod x teardown sh teardown sh 2 build the client based on fabric java sdk the previous step creates all required docker images with the appropriate configuration java client the java client sources are present in the folder java of the repo check your environment before executing the next step make sure you are able to run mvn commands properly if mvn commands fails please refer to pre requisites pre requisites to install maven to work with the deployed network using hyperledger fabric sdk java 1 4 1 perform the following steps open a command terminal and navigate to the java directory in the repo run the command mvn install cd java mvn install a jar file blockchain java sdk 0 0 1 snapshot jar with dependencies jar is built and can be found under the target folder this jar can be renamed to blockchain client jar to keep the name short cd target cp blockchain java sdk 0 0 1 snapshot jar with dependencies jar blockchain client jar copy this built jar into network resources directory this is required as the java code can access required artifacts during execution cp blockchain client jar network resources 3 create and initialize the channel in this code pattern we create one channel mychannel which is joined by all four peers the java source code can be seen at src main java org example network createchannel java to create and initialize the channel run the following command cd network resources java cp blockchain client jar org example network createchannel output apr 20 2018 5 11 42 pm org example util util deletedirectory info deleting users apr 20 2018 5 11 45 pm org example network createchannel main info channel created mychannel apr 20 2018 5 11 45 pm org example network createchannel main info peer0 org1 example com at grpc localhost 7051 apr 20 2018 5 11 45 pm org example network createchannel main info peer1 org1 example com at grpc localhost 7056 apr 20 2018 5 11 45 pm org example network createchannel main info peer0 org2 example com at grpc localhost 8051 apr 20 2018 5 11 45 pm org example network createchannel main info peer1 org2 example com at grpc localhost 8056 4 deploy and instantiate the chaincode this code pattern uses a sample chaincode fabcar to demo the usage of hyperledger fabric sdk java apis to deploy and instantiate the chaincode execute the following command java cp blockchain client jar org example network deployinstantiatechaincode output apr 23 2018 10 25 22 am org example client fabricclient deploychaincode info deploying chaincode fabcar using fabric client org1msp admin apr 23 2018 10 25 22 am org example network deployinstantiatechaincode main info fabcar chain code deployment success apr 23 2018 10 25 22 am org example network deployinstantiatechaincode main info fabcar chain code deployment success apr 23 2018 10 25 22 am org example client fabricclient deploychaincode info deploying chaincode fabcar using fabric client org2msp admin apr 23 2018 10 25 22 am org example network deployinstantiatechaincode main info fabcar chain code deployment success apr 23 2018 10 25 22 am org example network deployinstantiatechaincode main info fabcar chain code deployment success apr 23 2018 10 25 22 am org example client channelclient instantiatechaincode info instantiate proposal request fabcar on channel mychannel with fabric client org2msp admin apr 23 2018 10 25 22 am org example client channelclient instantiatechaincode info instantiating chaincode id fabcar on channel mychannel apr 23 2018 10 25 25 am org example client channelclient instantiatechaincode info chaincode fabcar on channel mychannel instantiation java util concurrent completablefuture 723ca036 not completed apr 23 2018 10 25 25 am org example network deployinstantiatechaincode main info fabcar chain code instantiation success apr 23 2018 10 25 25 am org example network deployinstantiatechaincode main info fabcar chain code instantiation success apr 23 2018 10 25 25 am org example network deployinstantiatechaincode main info fabcar chain code instantiation success apr 23 2018 10 25 25 am org example network deployinstantiatechaincode main info fabcar chain code instantiation success note the chaincode fabcar go was taken from the fabric samples available at https github com hyperledger fabric samples tree release 1 4 chaincode fabcar go 5 register and enroll users a new user can be registered and enrolled to an msp execute the below command to register a new user and enroll to org1msp java cp blockchain client jar org example user registerenrolluser output apr 23 2018 10 26 34 am org example util util deletedirectory info deleting users log4j warn no appenders could be found for logger org hyperledger fabric sdk helper config log4j warn please initialize the log4j system properly log4j warn see https logging apache org log4j 1 2 faq html noconfig for more info apr 23 2018 10 26 35 am org example client caclient enrolladminuser info ca http localhost 7054 enrolled admin apr 23 2018 10 26 35 am org example client caclient registeruser info ca http localhost 7054 registered user user1524459395783 apr 23 2018 10 26 36 am org example client caclient enrolluser info ca http localhost 7054 enrolled user user1524459395783 6 perform invoke and query on network blockchain network has been setup completely and is ready to use now we can test the network by performing invoke and query on the network the fabcar chaincode allows us to create a new asset which is a car for test purpose invoke operation is performed to create a new asset in the network and query operation is performed to list the asset of the network perform the following steps to check the same java cp blockchain client jar org example chaincode invocation invokechaincode output apr 20 2018 5 13 03 pm org example client caclient enrolladminuser info ca http localhost 7054 enrolled admin apr 20 2018 5 13 04 pm org example client channelclient sendtransactionproposal info sending transaction proposal on channel mychannel apr 20 2018 5 13 04 pm org example client channelclient sendtransactionproposal info transaction proposal on channel mychannel ok success with transaction id a298b9e27bdb0b6ca18b19f9c78a5371fb4d9b8dd199927baf37379537ca0d0f apr 20 2018 5 13 04 pm org example client channelclient sendtransactionproposal info apr 20 2018 5 13 04 pm org example client channelclient sendtransactionproposal info java util concurrent completablefuture 22f31dec not completed apr 20 2018 5 13 04 pm org example chaincode invocation invokechaincode main info invoked createcar on fabcar status success java cp blockchain client jar org example chaincode invocation querychaincode output pre apr 20 2018 5 13 28 pm org example client caclient enrolladminuser info ca http localhost 7054 enrolled admin apr 20 2018 5 13 29 pm org example chaincode invocation querychaincode main info b querying for all cars b apr 20 2018 5 13 29 pm org example client channelclient querybychaincode info querying queryallcars on channel mychannel apr 20 2018 5 13 29 pm org example chaincode invocation querychaincode main info b key car1 record make chevy model volt colour red owner nick b apr 20 2018 5 13 39 pm org example chaincode invocation querychaincode main info b querying for a car car1 b apr 20 2018 5 13 39 pm org example client channelclient querybychaincode info querying querycar on channel mychannel apr 20 2018 5 13 39 pm org example chaincode invocation querychaincode main info b make chevy model volt colour red owner nick b pre troubleshooting see debugging md debugging md license this code pattern is licensed under the apache software license version 2 separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses contributions are subject to the developer certificate of origin version 1 1 dco https developercertificate org and the apache software license version 2 https www apache org licenses license 2 0 txt apache software license asl faq https www apache org foundation license faq html whatdoesitmean | blockchain-network blockchain hyperledger-fabric fabric-java-sdk fabric-sdk hyperledger-fabric-network | blockchain |
Canto | becoming a validator how to validate on the canto mainnet canto 7700 1 genesis file published https github com canto network canto raw main mainnet genesis json peers list published https github com canto network canto blob main mainnet peers txt hardware requirements minimum 16 gb ram 100 gb nvme ssd 3 2 ghz x4 cpu recommended 32 gb ram 500 gb nvme ssd 4 2 ghz x6 cpu operating system linux x86 64 or linux amd64 recommended ubuntu or arch linux install dependencies if using ubuntu install all dependencies sudo snap install go classic sudo apt get install git sudo apt get install gcc sudo apt get install make or install individually go1 18 sudo snap install go classic git sudo apt get install git gcc sudo apt get install gcc make sudo apt get install make if using arch linux go1 18 pacman s go git pacman s git gcc pacman s gcc make pacman s make install cantod clone git repository bash git clone https github com canto network canto git cd canto cmd cantod go install tags ledger sudo mv home go bin cantod usr bin generate and store keys replace keyname below with whatever you d like to name your key cantod keys add key name cantod keys add key name recover to regenerate keys with your mnemonic cantod keys add key name ledger to generate keys with ledger device store a backup of your keys and mnemonic securely offline then save the generated public key config in the main canto directory as key name info it should look like this pubkey type ethermint crypto v1 ethsecp256k1 pubkey key you ll use this file later when creating your validator txn set up validator install cantod binary from canto directory sudo make install initialize the node replace moniker with whatever you d like to name your validator cantod init moniker chain id canto 7700 1 if this runs successfully it should dump a blob of json to the terminal download the genesis file wget https raw githubusercontent com canto network canto genesis networks mainnet genesis json p home cantod config note if you later get error couldn t read genesisdoc file open root cantod config genesis json no such file or directory put the genesis json file wherever it wants instead such as sudo wget https github com canto network canto raw main mainnet genesis json p root cantod config edit the minimum gas prices in home cantod config app toml sed i s minimum gas prices 0acanto minimum gas prices 0 0001acanto g home cantod config app toml add persistent peers to home cantod config config toml sed i s persistent peers persistent peers ec770ae4fd0fb4871b9a7c09f61764a0b010b293 164 90 134 106 26656 g home cantod config config toml set cantod to run automatically start cantod by creating a systemd service to run the node in the background edit the file sudo nano etc systemd system cantod service then copy and paste the following text into your service file be sure to edit as you see fit bash unit description canto node after network target service type simple user root workingdirectory root execstart root go bin cantod start trace log level info json rpc api eth txpool net debug web3 api enable restart on failure startlimitinterval 0 restartsec 3 limitnofile 65535 limitmemlock 209715200 install wantedby multi user target start the node reload the service files sudo systemctl daemon reload create the symlinlk sudo systemctl enable cantod service start the node sudo systemctl start cantod journalctl u cantod f you should then get several lines of log files and then see no addresses to dial falling back to seeds module pex server node this is an indicator things thus far are working and now you need to create your validator txn c out and follow the next steps create validator transaction modify the following items below removing the key name should be the same as key name when you followed the steps above in creating or restoring your key validator name is whatever you d like to name your node description is whatever you d like in the description field for your node security contact email is the email you want to use in the event of a security incident your website the website you want associated with your node token delegation is the amount of tokens staked by your node 1acanto should work here but you ll also need to make sure your address contains tokens bash cantod tx staking create validator from key name chain id canto 7700 1 moniker validator name commission max change rate 0 01 commission max rate 1 0 commission rate 0 05 details description security contact security contact email website your website pubkey cantod tendermint show validator min self delegation 1 amount token delegation acanto fees 20acanto | blockchain |
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fe-interview | fe interview npm version npm image npm url david url dt url license url js interview online https github com leftstick js interview online pull request exercises https github com kolodny exercises bash npm install g fe interview bash fe https raw githubusercontent com leftstick fe interview master docs img preview01 png retro fe c fe cli https raw githubusercontent com leftstick fe interview master docs img preview02 png https raw githubusercontent com leftstick fe interview master docs img preview03 png https raw githubusercontent com leftstick fe interview master docs img preview04 png https raw githubusercontent com leftstick fe interview master docs img preview05 png https raw githubusercontent com leftstick fe interview master docs img preview06 png fe a fe answer https raw githubusercontent com leftstick fe interview master docs img preview07 png license mit license https raw githubusercontent com leftstick fe interview master license npm url https npmjs org package fe interview npm image https badge fury io js fe interview png david url https david dm org leftstick fe interview png dt url https img shields io npm dt fe interview svg license url https img shields io npm l fe interview svg | front_end |
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developers-roadmap | developers roadmap front end back end junior middle middle senior front end back end pull request requirements md rizzoma com junior frontend developer html css javascript react js middle frontend developer react js ddd solid 100k 10 react redux api uri 2 senior frontend developer 3rd party services etc meteor js octotree https chrome google com webstore detail octotree bkhaagjahfmjljalopjnoealnfndnagc hl ru 0 junior front end back end https www metalamp io education | front_end |
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OpenGPT | opengpt a framework for creating grounded instruction based datasets and training conversational domain expert large language models llms learn more in our blog ai for healthcare introducing opengpt https aiforhealthcare substack com p a large language model for healthcare p align center img height 400px src https substackcdn com image fetch f auto q auto good fl progressive steep https 3a 2f 2fsubstack post media s3 amazonaws com 2fpublic 2fimages 2fcbc199b9 3aec 4c80 83c6 9a64886919dc 1318x868 png p nhs llm a conversational model for healthcare trained using opengpt all the medical datasets used to train this model were created using opengpt and are available below available datasets nhs uk q a 24 665 question and answer pairs prompt used f53cf99826 generated via opengpt using data available on the nhs uk website https www nhs uk conditions download here data nhs uk full prepared generated data for nhs uk qa csv nhs uk conversations 2 354 unique conversations prompt used f4df95ec69 generated via opengpt using data available on the nhs uk website https www nhs uk conditions download here data nhs uk full prepared generated data for nhs uk conversations csv medical task solution 4 688 pairs generated via opengpt using gpt 4 prompt used 5755564c19 download here data medical tasks gpt4 prepared generated data for medical tasks csv all datasets are in the data folder installation pip install opengpt if you are working with llama models you will also need some extra requirements pip install r llama train requirements txt tutorials making a mini conversational llm for healthcare google colab opengpt the making of dum e https colab research google com drive 1gqj9dwbscmzeh1pmbrlqqylojcvog qg usp sharing how to 1 we start by collecting a base dataset in a certain domain for example collect definitions of all disases e g from nhs uk https www nhs uk conditions you can find a small sample dataset here https github com cogstack opengpt blob main data nhs conditions small sample original data csv it is important that the collected dataset has a column named text where each row of the csv has one disease definition 2 find a prompt matching your use case in the prompt database https github com cogstack opengpt blob main data prompts json or create a new prompt using the prompt creation notebook https github com cogstack opengpt blob main experiments prompt 20creation ipynb a prompt will be used to generate tasks solutions based on the context the dataset collected in step 1 edit the config file for dataset generation and add the appropirate promtps and datasets example config file https github com cogstack opengpt blob main configs example config for detaset creation yaml run the dataset generation notebook link https github com cogstack opengpt blob main experiments dataset 20generation ipynb 3 edit the train config https github com cogstack opengpt blob main configs example train config yaml file and add the datasets you want to use for training 4 use the train notebook https github com cogstack opengpt blob main experiments supervised 20training ipynb or run the training scripts to train a model on the new dataset you created if you have any questions please checkout discourse https discourse cogstack org more examples p align center img width 600px src https substackcdn com image fetch f auto q auto good fl progressive steep https 3a 2f 2fsubstack post media s3 amazonaws com 2fpublic 2fimages 2f3916352d d1c9 451d 92db 652171f471e0 1318x1842 png p p align center img width 600px src https substackcdn com image fetch f auto q auto good fl progressive steep https 3a 2f 2fsubstack post media s3 amazonaws com 2fpublic 2fimages 2fe47dc8e1 d26c 4312 a7a4 8a32bf5375b9 1318x1168 png p p align center img width 600px src https substackcdn com image fetch f auto q auto good fl progressive steep https 3a 2f 2fsubstack post media s3 amazonaws com 2fpublic 2fimages 2f42ab1ebe 2fab 4c94 80e7 69d4b95c8098 1318x854 png p | chatgpt gpt-4 health healthcare huggingface llm medicine nlp opengpt | ai |
frontend-vagas | a empresa alt text assets logo svg somente um lugar apaixonante pode manter um time apaixonado e somente um time apaixonado capaz de realizar um bom trabalho por isso na f md fazemos de tudo para que as pessoas sejam felizes al m do fim de semana n o discurso pronto pra pegar bem com o mercado algo que nasceu com a gente claro que existe cobran a claro que existem problemas mas quando surge algum tentamos resolver do jeito mais justo poss vel nem sempre a gente acerta curioso em saber um pouco do nosso ecosistema deem uma olhada nesse reels do instagram e espero que ele te agrade e mostre um pouquinho do nosso ambiente crazy f md https www instagram com p cjjuzhtljo1 b nosso site b f md site https fmd ag br b nosso instagram b f md instagram https www instagram com agenciafmd oportunidade estamos em busca de pessoas apaixonadas por front end e com sede de projetos e experi ncias fodas seu dia ser repleto de javascript const differentials tecnologia foda pessoas engajadas em crescer juntas ambiente de trabalho saud vel respeito s pessoas em primeiro lugar produzir trabalhos incr veis oportunidade em aperfei oar hard skills oportunidade em aperfei oar soft skills sal rio coerente com o mercado trabalho e perfil profissional disputas de pebolim pebolas disputas de fifinha entre muitas outras coisas legais awesomethings nossa stack composta por javascript const frontendfmdtechnologies html css javascript bootstrap blade laravel template engineering angular sass typescript git ionic npm webpack rxjs ngrx ngxs javascript const frontendfmdtools webstorm homestead virtualbox vagrant gitlab github discord trello plann jira postman figma notion visual studio just sometimes linux terminal usando como base projetos conceituados pela comunidade como por exemplo o bootstrap ou angular a estrutura de arquivos e diret rios que utilizamos consciste no isolamento por responsabilidade para agrupar determinada funcionalidade que necessita de v rios arquivos para abranger todo seu conte do sem perder a objetividade e facilitar a manuten o requisitos principais proatividade sentimento de dono comprometimento organiza o e senso de trabalho em equipe estar disposto a buscar o aprendizado e evolu o constante usar o bom senso para tomada de decis es em situa es adversas challenge o nosso challenge para a b mais nova vaga de front end b consiste em desenvolver uma landing page de assunto de sua prefer ncia basta atender os requisitos abaixo header com os links de ncora para as se es se o com 3 banners slider se o com cards m nimo 3 cards mobile blocado abaixo do outro desktop um ao lado do outro garantindo a mesma altura segunda se o de cards m nimo 3 cards mobile slider desktop slider desligado garantindo a mesma altura formul rio de newsletter footer ficou em d vida n o se preocupe n s preparamos um figma como fonte de inspira o para a sua proposta prot tipo de lp https www figma com file sstsobmlfwsgfxipk1owqp desafio para vaga de front end t ub4gxzxxzjyrcnnn 1 b requisitos m nimos b html css javascript consumir api b sugest es para dar um diferencial e deixar foda b anima es frameworks spa angular vue react frameworks front end bootstrap angular material tailwind css pr processadores de css sass less stylus typescript e caso esteja em d vida de qual api usar esse site https publicapis dev possu diversas api s bem legais que podem te ajudar escolha o que seu s2 mandar entrega para avaliarmos o projeto de teste submeta um pull request pr https github com agenciafmd frontend vagas blob main docs pull request template md para este reposit rio commits em nossos projetos adotamos um styleguide commit amig o https github com beetech global bee stylish blob master commits readme md anatomia do commit amig c3 a3o para facilitar a identifica o dos commit s onde a estrutura baseada em tipo escopo assunto b os valores permitidos para o tipo s o b feat nova funcionalidade style formata o geral no c digo n o confundir com css refactor refatora o de c digo de produ o test adicionar refatorar testes fix adivinha qual esse docs e esse tamb m chore atualiza o de tarefas ou c digo que n o est relacionado a produ o b escopo b br o escopo deve informar onde foi feito a altera o b assunto b m ximo de 50 caracteres tipo de escopo devem estar em letras min sculas assunto deve estar no imperativo exemplo feat se o sobre n s adiciona os dados mockados da api no html avalia o iremos avaliar os seguintes crit rios c digo limpo e organiza o sem ntica organiza o dos commits acessibilidade d vidas n o deixe que alguma d vida fa a voc pirar abra uma issue ou nos mande um e mail em b frontend fmd ag b fechou quer saber se temos vagas abertas acesse nosso linkedin https www linkedin com company agenciafmd e fique de olho slightly smiling face b que a for a esteja com voc b | css front-end html javascript | front_end |
attendance-management-system | database contains 6 tables see er model student data student login teacher data teacher login subject student attendance contains package with 6 functions to insert data in student data student login to insert data in teacher data and teacher login to update teacher email and phone number to update student email and phone number to add attendance function returning cursor of latest 5 subject attendance details this attendance website is based on attendance of student of engineering college where student s can view their subject wise attendance as well as last 5 subject attendance and teacher can take attendance of student by filtering the student by their batch year section and branch they will be able to mark attendance of any subject run project add the code in attendance folder in htdocs folder of xampp | server |
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byggern-PCB | byggern pcb board that replaces node 1 in embedded and industrial computer systems design project at ntnu the two layer board is designed using kicad http kicad pcb org help getting started schematic download pdf version here pictures schematic pdf schematic pictures schematic png rendering of board board 3d pictures board 3d png | os |
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eventRTOS | eventrtos code 406b ram 36b arm systick clock code 650b ram 16b ram 16b 0 255 img src png | os |
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MacBERT | https github com ymcui macbert english readme en md p align center br img src pics banner png width 500 br p p align center a href https github com ymcui macbert blob master license img alt github src https img shields io github license ymcui macbert svg color blue style flat square a p macbert mac macbert nlp revisiting pre trained models for chinese natural language processing https www aclweb org anthology 2020 findings emnlp 58 yiming cui wanxiang che ting liu bing qin shijin wang guoping hu published in findings of emnlp 2020 macbert https github com ymcui macbert electra https github com ymcui chinese electra xlnet https github com ymcui chinese xlnet textbrewer https github com airaria textbrewer textpruner https github com airaria textpruner hfl https github com ymcui hfl anthology news 2023 3 28 llama alpaca pc https github com ymcui chinese llama alpaca 2022 3 30 pert https github com ymcui pert 2021 12 17 textpruner https github com airaria textpruner 2021 10 24 cino https github com ymcui chinese minority plm 2021 7 21 https item jd com 13344628 html 2020 11 3 macbert bert 2020 9 15 revisiting pre trained models for chinese natural language processing https arxiv org abs 2004 13922 findings of emnlp https 2020 emnlp org macbert macbert transformers https github com huggingface transformers nlp faq faq macbert bert mlm as correction mac mlm mask mask macbert mask synonyms toolkit wang and hu 2017 https github com chatopera synonyms word2vec mikolov et al 2013 whole word masking wwm n gram masking n gram n gram we use a language model to predict the probability of the next word mlm we use a language m to m di ct the pro m bility of the next word whole word masking we use a language m to m m m the m m m of the next word n gram masking we use a m m to m m m the m m m m m next word mlm as correction we use a text system to ca lc ulate the po si bility of the next word macbert bert revisiting pre trained models for chinese natural language processing https www aclweb org anthology 2020 findings emnlp 58 tensorflow 1 x macbert large chinese 24 layer 1024 hidden 16 heads 324m parameters macbert base chinese 12 layer 768 hidden 12 heads 102m parameters google drive macbert large chinese tensorflow https drive google com file d 1lwyxnk1eqta2q20 ishxbrcpc5vsdckt view usp sharing tensorflow pw zejf https pan baidu com s 1njejhuanwgo 1rpki21mxa pwd zejf 1 2g macbert base chinese tensorflow https drive google com file d 1av69ohyziwj hn ko1riba m8qausq5b view usp sharing tensorflow pw 61ga https pan baidu com s 1eas2fmraqtvfia5q5rxnuq pwd 61ga 383m pytorch tensorflow2 pytorch tensorflow2 1 transformers https github com huggingface transformers 2 https huggingface co hfl git 1 https huggingface co hfl macbert macbert base https huggingface co hfl chinese macbert base 2 files and versions 3 bin json transformers https github com huggingface transformers macbert tokenizer berttokenizer from pretrained model name model bertmodel from pretrained model name berttokenizer bertmodel macbert model name macbert large hfl chinese macbert large macbert base hfl chinese macbert base macbert 6 cmrc 2018 cui et al 2019 https github com ymcui cmrc2018 drcd shao et al 2018 https github com drcsolutionservice drcd xnli conneau et al 2018 https github com google research bert blob master multilingual md chnsenticorp https github com pengming617 bert classification lcqmc liu et al 2018 http icrc hitsz edu cn info 1037 1146 htm bq corpus chen et al 2018 http icrc hitsz edu cn article show 175 html 10 cmrc 2018 cmrc 2018 https github com ymcui cmrc2018 squad em f1 model development test challenge params bert base 65 5 64 4 84 5 84 0 70 0 68 7 87 0 86 3 18 6 17 0 43 3 41 3 102m bert wwm 66 3 65 0 85 6 84 7 70 5 69 1 87 4 86 7 21 0 19 3 47 0 43 9 102m bert wwm ext 67 1 65 6 85 7 85 0 71 4 70 0 87 7 87 0 24 0 20 0 47 3 44 6 102m roberta wwm ext 67 4 66 5 87 2 86 5 72 6 71 4 89 4 88 8 26 2 24 6 51 0 49 1 102m electra base 68 4 68 0 84 8 84 6 73 1 72 7 87 1 86 9 22 6 21 7 45 0 43 8 102m macbert base 68 5 67 3 87 9 87 1 73 2 72 4 89 5 89 2 30 2 26 4 54 0 52 2 102m electra large 69 1 68 2 85 2 84 5 73 9 72 8 87 1 86 6 23 0 21 6 44 2 43 2 324m roberta wwm ext large 68 5 67 6 88 4 87 9 74 2 72 4 90 6 90 0 31 5 30 1 60 1 57 5 324m macbert large 70 7 68 6 88 9 88 2 74 8 73 2 90 7 90 1 31 9 29 6 60 2 57 6 324m drcd drcd https github com drcknowledgeteam drcd squad ernie ernie em f1 model development test params bert base 83 1 82 7 89 9 89 6 82 2 81 6 89 2 88 8 102m bert wwm 84 3 83 4 90 5 90 2 82 8 81 8 89 7 89 0 102m bert wwm ext 85 0 84 5 91 2 90 9 83 6 83 0 90 4 89 9 102m roberta wwm ext 86 6 85 9 92 5 92 2 85 6 85 2 92 0 91 7 102m electra base 87 5 87 0 92 5 92 3 86 9 86 6 91 8 91 7 102m macbert base 89 4 89 2 94 3 94 1 89 5 88 7 93 8 93 5 102m electra large 88 8 88 7 93 3 93 2 88 8 88 2 93 6 93 2 324m roberta wwm ext large 89 6 89 1 94 8 94 4 89 6 88 9 94 5 94 1 324m macbert large 91 2 90 8 95 6 95 3 91 7 90 9 95 6 95 3 324m xnli xnli https github com google research bert blob master multilingual md entailment neutral contradictory accuracy model development test params bert base 77 8 77 4 77 8 77 5 102m bert wwm 79 0 78 4 78 2 78 0 102m bert wwm ext 79 4 78 6 78 7 78 3 102m roberta wwm ext 80 0 79 2 78 8 78 3 102m electra base 77 9 77 0 78 4 77 8 102m macbert base 80 3 79 7 79 3 78 8 102m electra large 81 5 80 8 81 0 80 9 324m roberta wwm ext large 82 1 81 3 81 2 80 6 324m macbert large 82 4 81 8 81 3 80 6 324m chnsenticorp chnsenticorp accuracy model development test params bert base 94 7 94 3 95 0 94 7 102m bert wwm 95 1 94 5 95 4 95 0 102m bert wwm ext 95 4 94 6 95 3 94 7 102m roberta wwm ext 95 0 94 6 95 6 94 8 102m electra base 93 8 93 0 94 5 93 5 102m macbert base 95 2 94 8 95 6 94 9 102m electra large 95 2 94 6 95 3 94 8 324m roberta wwm ext large 95 8 94 9 95 8 94 9 324m macbert large 95 7 95 0 95 9 95 1 324m lcqmc lcqmc http icrc hitsz edu cn info 1037 1146 htm accuracy model development test params bert 89 4 88 4 86 9 86 4 102m bert wwm 89 4 89 2 87 0 86 8 102m bert wwm ext 89 6 89 2 87 1 86 6 102m roberta wwm ext 89 0 88 7 86 4 86 1 102m electra base 90 2 89 8 87 6 87 3 102m macbert base 89 5 89 3 87 0 86 5 102m electra large 90 7 90 4 87 3 87 2 324m roberta wwm ext large 90 4 90 0 87 0 86 8 324m macbert large 90 6 90 3 87 6 87 1 324m bq corpus bq corpus http icrc hitsz edu cn article show 175 html accuracy model development test params bert 86 0 85 5 84 8 84 6 102m bert wwm 86 1 85 6 85 2 84 9 102m bert wwm ext 86 4 85 5 85 3 84 8 102m roberta wwm ext 86 0 85 4 85 0 84 6 102m electra base 84 8 84 7 84 5 84 0 102m macbert base 86 0 85 5 85 2 84 9 102m electra large 86 7 86 2 85 1 84 8 324m roberta wwm ext large 86 3 85 7 85 8 84 9 324m macbert large 86 2 85 7 85 6 85 0 324m faq q1 macbert a1 q2 macbert a2 bert config transformers q3 macbert a3 q4 a4 github q5 macbert a5 inproceedings cui etal 2020 revisiting title revisiting pre trained models for c hinese natural language processing author cui yiming and che wanxiang and liu ting and qin bing and wang shijin and hu guoping booktitle proceedings of the 2020 conference on empirical methods in natural language processing findings month nov year 2020 address online publisher association for computational linguistics url https www aclweb org anthology 2020 findings emnlp 58 pages 657 668 journal cui etal 2021 pretrain title pre training with whole word masking for chinese bert author cui yiming and che wanxiang and liu ting and qin bing and yang ziqing journal ieee transactions on audio speech and language processing year 2021 url https ieeexplore ieee org document 9599397 doi 10 1109 taslp 2021 3124365 google tpu research cloud tfrc https www tensorflow org tfrc github issue faq issue issue stable bot stale github marketplace | nlp bert language-model tensorflow macbert pytorch transformers | ai |
gazpachoquest | coverage status https coveralls io repos antoniomaria gazpachoquest badge svg branch master https coveralls io r antoniomaria gazpachoquest branch master build status https travis ci org antoniomaria gazpachoquest svg branch master https travis ci org antoniomaria gazpachoquest gazpachoquest a survey rest api engine gazpachoquest is a java open source rest based survey engine the main advantage over other alternatives is its loose coupled architecture based on microservices architecture http www infoq com articles microservices intro which ensures concern separation this engine is intended to be consumed by rest clients in order to build different front end web apps mobile apps etc it provides all features than others survey engines have among other technological features such as role permission based access hmac security layer hmac based support for main databases jpa based persistence layer and swagger rest documentation api there is available a vaadin web application as proof of concept of the engine a karaf osgi based port is coming up stay tune highligh features multi lingual questionnaires conditions for questions depending on earlier answers skip logic branching question and page randomization auto numbering for questions reordering questions page logic question types multiple choice list check boxes other vertically or horizontally arranged short long text response other multiple choice one selected at a time radio buttons vertically or horizontally scales arranged matrix or array of questions above listed smart language selector depending on browser preferred language or respondent attributes anonymous and not anonymous questionnaires possibility to show all questions in a single page different pages or one question at a time for small displays role permission based access rights management hmac based security import and export questionnaires definitions to xml instantly saved responses thus participants can continue answering at a later time re usable editable answer sets contributing contributions are welcome in any form including code contributions bug reports feature suggestions documentation testing and general feedback specially are frondend developers are really welcome either to improve the vaadin proof of concept or in order to build a dashboard or administration tool issues can be submitted on the github page 1 for further information contact to mailto antoniomariasanchez gmail com or visit wiki developer section https github com antoniomaria gazpachoquest wiki developers third party libraries and components jpa eclipse link http www eclipse org eclipselink jpa php dozer java bean mapper http dozer sourceforge net liquibase http www liquibase org spring data jpa http projects spring io spring data jax rs apache cxf 3 x http cxf apache org swagger 2 0 http swagger io java security shiro http shiro apache org vaadin 7 cdi plugin https vaadin com home java authentication and authorization service jaas sl4j logback http logback qos ch dbunit http dbunit sourceforge net spring test dbunit http springtestdbunit github io spring test dbunit easymock http easymock org installation and local demo hsql database and apache tomee is used for local demo git clone https github com antoniomaria gazpachoquest git cd gazpachoquest mvn clean install pl tomee assembly am dskiptests true cd tomee assembly target assembly bin startup bat to see the demo navigate to rest gateway http localhost 8080 gazpachoquest rest web questionnaires ui http localhost 8080 see more details about local installation on installation guide page https github com antoniomaria gazpachoquest wiki installing gazpachoquest from the source rest gateway this module exposes all the features that gazpachoquest provides the credential for administrator account in form of apikey secret is vecdux8dgnxa4hf 9c52pbuxjg9238hrelrux97cetuaq4bv using the administrator account is possible to browse through the rest interface active questionnaires active invitations or surveys on going at the moment create new questionnaires or edit existing ones is not supported only new questionnaires can be imported using xml format check example http localhost 8080 https github com antoniomaria gazpachoquest tree master tools importer src main resources examples using the importer tool https github com antoniomaria gazpachoquest blob master tools importer src main java net sf gazpachoquest importer importerrunner java for example one user case is validate invitations in authentication resource http localhost 8080 gazpachoquest rest web auth authenticate so given a existing invitation token xfe7ylq79h rest services returns type r givennames anonymous surname anonymous email no reply gazpachoquest net apikey dts38gtqwlvs6yj secret zanp5al5ck3nvtlwy7pxv73mnpg4hdjd roles name respondent preferredlanguage en grantedquestionnaireids 707 which means there is an on going survey distributed only by invitation but respondents don t need to be registered first this engine supports that the same survey can be conducted many times respondents can be anonymous or need a special invitation so system can track if they have filled the survey or sends reminder obviously the response are anonymous questionnaires ui this module holds all the active questionnaires surveys ready to be fill out for respondents they can be anonymous or tracked depending on the questionnaire settings go to questionnaires ui http localhost 8080 and try different invitations to check how surveys are rendered anonymous and linear questionnaires invitation keys no randomization sections randomization questions randomization section by section pyjxhp6k4k qt2sxrewah regrw5x27c question by question x5sgk7e7bp all in one page fqdlf4kg27 anonymous dynamic questionnaire depending on previous answers skip logic enabled no randomization section by section xfe7ylq79h question by question personal questionnaires invitation keys no randomization sections randomization questions randomization section by section 2cnbshkpbq dclhxdrmls d7qz2cxzjw ulq6xwk8qr ss5g7mv7ny fsmslm2a44 question by question f4xe2glsua all in one page 1 https github com antoniomaria gazpachoquest issues | front_end |
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frontend-go | frontend go go reference https pkg go dev badge github com shibukawa frontend go svg https pkg go dev github com shibukawa frontend go spa single page application style web application frontend helper for go server this package works with next js vue js sveltekit solid js development mode it runs dev server behind go server it works as reverse proxy you can get all features that the web framework dev server provides hot module replacement and so on development mode docs dev png production mode it gets prebuilt assets html js css from go s embed basically all spa needs to fallback to index html when there is not have assets release mode docs rel png integration frontend go assumes the following structure important points are the following there is frontend project folder named frontend there is release go at the parent folder of frontend folder add frontend go s handler to your web server txt awesome your web app license readme md cmd server main go add frontend go s handler go mod go sum api go release go you should add this frontend frontend project next js vue js sveltekit solid js package json release go has the following code go release go go build release package webapp this folder is decided by your frontend framework go embed frontend build var asset embed fs init frontend setfrontasset asset frontend opt frameworktype frontend nextjs select nextjs vuejs sveltekit solidjs use with net http go cmd server main go package main func handler http handler mux http newservemux mux handle api yourapihandler mux handle frontend mustnewspahandler return mux use with chi go cmd server main go import github com go chi chi v5 func handler http handler r chi newrouter r post api yourapihandler r notfound frontend mustnewhandlerfunc return r sh development mode go run main go production build go build tags release web frameworks specific instructions next js 12 frontend go assumes next js s static generation result that does n need node js bun to run to start project with next js init project like this sh go mod init yourapp mkdir p cmd yourapp npx create next app latest ts frontend to enable static generation add unoptimized flag to next config js js next config js type import next nextconfig const nextconfig reactstrictmode true swcminify true experimental add here images unoptimized true you should build frontend project by using the following commands bash npm run build npm exec next export the go embed directive comment should be the following by default go release go go embed frontend out go embed frontend out next static go embed frontend out next static chunks pages js var asset embed fs init frontend setfrontasset asset frontend opt frameworktype frontend nextjs vue js to start project with vue js init project like this sh go mod init yourapp mkdir p cmd yourapp vue create frontend you should build frontend project by using the following commands bash npm run build the go embed directive comment should be the following by default go release go go embed frontend dist var asset embed fs init frontend setfrontasset asset frontend opt frameworktype frontend vuejs sveltekit default sveltekit provides frontend program that requires node js to run to work with this module you should configure the front end project with static site mode https kit svelte dev docs adapters supported environments static sites go mod init yourapp mkdir p cmd server npm create yourapp latest frontend to modify front end site add static adaptor sh npm install sveltejs adapter static js frontend svelte config js import adapter from sveltejs adapter static modify here type import sveltejs kit config const config preprocess preprocess kit adapter adapter modify here for static html js generation fallback index html prerender modify here for spa mode default false trailingslash always modify here export default config you should build frontend project by using the following commands bash npm run build the go embed directive comment should be the following by default go release go go embed frontend build var asset embed fs init frontend setfrontasset asset frontend opt frameworktype frontend sveltekit solid js go mod init yourapp mkdir p cmd server npx degit solidjs templates ts frontend you should build frontend project by using the following commands bash npm run build the go embed directive comment should be the following by default go release go go embed frontend dist var asset embed fs init frontend setfrontasset asset frontend opt frameworktype frontend solidjs configuration frontend setfrontasset for production frontend setoption other usage accept option that modifies package s behavior if you put frontend project at frontend folder and doesn t change npm scripts you don t have to modify configuration go go build release go embed frontend build var asset embed fs handler frontend setfrontasset assets frontend opt frontendfolder frontend frontend application folder that contains package json default value is frontend projecttype frontend autodetect nextjs sveltekit vuejs solidjs is available skiprunningdevserver false skip running dev server even if development mode distfolder specify dist folder instead of auto detect port 0 specify port instead of auto detect developmentcommand npm run dev specify dev server command instead of auto detect fallbackpath string specify fallback file path default is index html for development mode this package tries to configure package as much as possible including framework type if you want to set option for development mode add the following file and set option go development go go build release package webapp init frontend setoption frontend opt skiprunningdevserver true credits yoshiki shibukawa license apache 2 | front_end |
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photon | photon design system this project is no longer maintained or worked on more to come soon on our next design system documentation website running photon requires docker to run with hot reloading first check out this repository into a folder go to your command line enter that folder and run the following commands docker pull praqma gh pages docker run name photon d v pwd jekyll home jenkins p 4000 4000 praqma gh pages docker start photon docker exec it photon jekyll serve watch host 0 0 0 0 open up http localhost 4000 photon once you have something you like it s time to share it with the rest of us to get your changes merged make sure you re working on master and submit the pull request to there make your pull request as small as possible so it s easier to review and merge them if it s something that needs to be merged as soon as possible add a tag e g urgent | os |
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DL-CV-2020 | dl cv 2020 deep learning and computer vision course repository 2020 if you ve created a github account fork this repository by clicking on the fork button at the top right side of this page you can also download the repository by clicking on the green clone or download button after you download the folder should look like this readme md session 0 readme md session0 slides pdf xor using nn ipynb each session will be uploaded as a folder in this repository there will be a readme md file slides code and information about assignments in each session s folder | ai |
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apricot | design generate reverseengineer compare analyze apricot db is a database tool for design and analysis of the relational database structure it represents the db structure in the form of editable entity relationship diagrams erd apricot db allows to perform reverse engineering on the existing database as well as to create a new database structure from scratch allows to generate the essential ddl scripts for create drop delete operations based on the current erd for the current stable release of apricot db visit https sourceforge net projects apricot db | server |
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anltk | example workflow https github com abdullah alattar anltk actions workflows c cpp yml badge svg example workflow https github com abdullah alattar anltk actions workflows wheels yml badge svg pypi version https badge fury io py anltk svg https badge fury io py anltk license https img shields io badge license boost 1 0 lightblue svg https www boost org license 1 0 txt downloads https static pepy tech personalized badge anltk period total units international system left color blue right color orange left text downloads https pepy tech project anltk arabic natural language toolkit anltk anltk is a set of arabic natural language processing tools developed with focus on simplicity and performance anltk is a c library with python bindings installation for python pip install anltk building note currently only tested on linux prebuilt python wheels are available for linux windows macos on pypi https pypi org project anltk dependencies utfcpp https github com nemtrif utfcpp git automatically downloaded utf8proc https github com juliastrings utf8proc automatically downlaoded c compiler that supports c 17 python3 meson https mesonbuild com ninja https ninja build org bash pip install meson pip install ninja bash git clone https github com abdullah alattar anltk git cd anltk meson build buildtype release dbuild tests false cd build ninja cd pip install e usage examples c api c include anltk anltk hpp include iostream include string int main std string ar text std cout anltk transliterate ar text anltk charmapping ar2bw n bjd hwz hty klmn sefs qr t vx dzg std string text std cout anltk remove tashkeel text n third paramters is a stop list charactres in this list won t be removed std cout anltk remove non alpha text n anltk tafqitoptions opts std cout anltk tafqit 15000120 opts n python api python import anltk ar bw anltk transliterate ar anltk ar2bw print bw bjd hwz hty klmn sefs qr t vx dzg print anltk remove tashkeel print anltk tafqit 15000120 for list of features see features md features md benchmarks processing a file containing 500000 line 6787731 word 112704541 character the task is to remove diacritics transliterate to buckwalter buckwatler transliteration method time anltk python api 1 379 seconds python camel tools https github com camel lab camel tools 11 46 seconds remove diacritics method time anltk python api 0 989 seconds python camel tools https github com camel lab camel tools 4 892 seconds | ai |
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Mastering-Computer-Vision-with-TensorFlow-2.0 | mastering computer vision with tensorflow 2 x a href https www packtpub com in data advanced computer vision with tensorflow 2 x utm source github utm medium repository utm campaign 9781838827069 img src https www packtpub com media catalog product cache bf3310292d6e1b4ca15aeea773aca35e 9 7 9781838827069 original png alt mastering computer vision with tensorflow 2 x height 256px align right a this is the code repository for mastering computer vision with tensorflow 2 x https www packtpub com in data advanced computer vision with tensorflow 2 x utm source github utm medium repository utm campaign 978 1 83882 706 9 published by packt build advanced computer vision applications using machine learning and deep learning techniques what is this book about computer vision allows machines to gain human level understanding to visualize process and analyze images and videos this book focuses on using tensorflow to help you learn advanced computer vision tasks such as image acquisition processing and analysis you ll start with the key principles of computer vision and deep learning to build a solid foundation before covering neural network architectures and understanding how they work rather than using them as a black box next you ll explore architectures such as vgg resnet inception r cnn ssd yolo and mobilenet as you advance you ll learn to use visual search methods using transfer learning you ll also cover advanced computer vision concepts such as semantic segmentation image inpainting with gan s object tracking video segmentation and action recognition later the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition you ll then discover how to develop powerful neural network models on your pc and on various cloud platforms finally you ll learn to perform model optimization methods to deploy models on edge devices for real time inference by the end of this book you ll have a solid understanding of computer vision and be able to confidently develop models to automate tasks this book covers the following exciting features explore methods of feature extraction and image retrieval and visualize different layers of the neural network model use tensorflow for various visual search methods for real world scenarios build neural networks or adjust parameters to optimize the performance of models understand tensorflow deeplab to perform semantic segmentation on images and dcgan for image inpainting evaluate your model and optimize and integrate it into your application to operate at scale if you feel this book is for you get your copy https www amazon com dp 1838827064 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders the code will look like the following faceresize cv2 resize detected face img size img size img name dataset opencv frame jpg format img counter cv2 imwrite img name faceresize following is what you need for this book this book is for computer vision professionals image processing professionals machine learning engineers and ai developers who have some knowledge of machine learning and deep learning and want to build expert level computer vision applications in addition to familiarity with tensorflow python knowledge will be required to get started with this book with the following software and hardware list you can run all code files present in the book chapter 1 15 software and hardware list chapter software required os required 1 10 jupyter notebook windows mac os x and linux any 1 10 tensorflow 2 0 google colab windows mac os x and linux any 11 12 amazon web services azure and google cloud platform windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781838827069 colorimages pdf screen video for the codes can be found in the below link https drive google com open id 1rvhb5se5u6lzxab4o8hszcbhxz2oiqhy errors and additional descriptions although the book has gone through many reviews we noticed few errors and additional clarifications required after the book was published we believe listing these will help the readers the link for these can be found below this is a dynamic section and is a means to work with our readers to make this book even better https drive google com drive folders 1ltgwdydhjpju9ooj0hns9qrjvtdhg6qq usp sharing related products other books you may enjoy hands on computer vision with tensorflow 2 packt https www packtpub com in application development hands computer vision tensorflow 2 utm source github utm medium repository utm campaign 9781788830645 amazon https www amazon com dp 1788830644 deep learning for computer vision packt https www packtpub com in big data and business intelligence deep learning computer vision utm source github utm medium repository utm campaign 9781788295628 amazon https www amazon com dp 1788 295625 get to know the author krishnendu kar is passionate about research on computer vision and solving ai problems to make our life simpler his core expertise is deep learning computer vision iot and agile software development krish is also a passionate app developer and has a dash cam based object and lane detection and turn by turn navigation and fitness app in the ios app store nity map ai camera run timer here is the author linkedin page if you have questions on the book he will try to respond all answers within 48 hours his average response time is 12 hours https www linkedin com in krish kar 554739b2 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781838827069 https packt link free ebook 9781838827069 a p | ai |
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MScIT | mscit msc information technology dissertation | server |
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Mobile | mobile official repository for hackcville s mobile application development course description mobile is a new hackcville course focused on developing mobile applications that are compatible with both ios and android class slides class slides can be found in the following google drive folder https drive google com open id 1dwsde0mksn7qkfyubsvgrnxsgmaewljs class format we will meet regularly on sunday 1 2 45pm these class times will be devoted to some introductory learning pair programming to reinforce concepts time to work on personal projects and larger community events potentially including some speakers from charlottesville software engineers expectations participants are expected to attend both meetings each week if more than four absences are recorded there is a risk of not completing the program and discontinuing membership with hackcville so if you feel you re at risk here please talk to an instructor to evaluate your situation so we can try to work something out major projects project 1 workshop mobile app thinking and ideation think of an mobile application that could be useful could solve a problem for a new college student at uva the goal of this exercise is to start thinking about the things that need to go into an app create a very rough wireframe of what the app interface would look like you can either sketch this on paper or use a wireframe website i recommend marvel it doesn t have to be super detailed just enough to give an idea of how a user would interface with the app after you have a rough sketch think of what tools you would need to build your application and prepare a brief presentation on your idea in google slides here are some things to think about 1 what is the purpose of the website 2 do i need to grab data from anywhere 3 where would i want to store data 4 do i want to focus on either android or ios users 5 specific programming language s to use don t worry if you don t know any for this one 6 would i want users to sign in and how would i handle user authentication the list above is just some things to think about you don t necessarily need to include every one and are not limited to the list obviously some of these topics you may not know a lot about as we have only had about an hour of class time not to worry we just want to see that you have given a couple topics some thought google is certainly your friend here what to submit working link to a google slides filled in on this google sheets https docs google com spreadsheets d 1xjoy9krdcikur dweuvjuv2ivdiux zs8qap71tdalk edit usp sharing project 2 mid semester mobile app with api functionality this project will be a bit more open ended find some neat api on the internet grab some data from it via fetch and display it in a react native app in a cool way if you have time try to practice using multiple react components for example make the fetch api call in app js and pass it down to be displayed to a child component for a bonus challenge you can try to make the api call based off of user input project 3 end of semester full stack mobile app deployed with expo as we reach the end of mobile we will be able to make react native apps hit api endpoints grab data from the backend and finally deploy the projects with expo that can be viewed with the expo app using all these skills we will come full circle by trying to make our initial app ideas during project 1 a reality given that project 1 was open ended you may need to limit the scope to be more feasable to the time period of 2 weeks for this project as an alternative you may also choose to enhance your project 2 by adding backend functionality as well as deploying it via expo week 0 hello world at the kickoff we will get to know each other don t worry we will limit the icebreakers g ive an overview of the semester and see what hackcville is all about we will also set up everyone s machines with the appropriate software for the semester and have a brief introduction to git if we have time week 1 overview of git node and the terminal although not the most flashy git and the terminal two of the most important tools that develops need to know git is a software that allows us to control versions of our code and allows us to collaborate with others while the terminal is the home base from which we will navigate our computer files and run code week 2 advanced javascript we will touch quickly on several more advanced topics that in javascript that are crucial when we start working in react such as mapping and certain special types of function calls week 3 introduction to react this is where the fun begins we will start to make web applications in react going over the fundamentals and all the necessary components week 4 react native expo now that we are familiar with react we will now learn about react native which allows us to create mobile applications using the react framework week 6 api requests it would be pretty neat if our apps could grab data from somewhere like instagram or google maps this week we will learn how to make api requests within our app to grab data we ll go over how to make your code wait while it s pulling from these resources and how to do so using http requests which make up the bulk of how computers talk to each other over the internet week 5 group project you now have a good grasp on the basics and how to grab data lets put those skills to work on a project that you and a group decide on week 7 speaker tbd speaker tbd week 8 node modules why write code that someone probably has already written before we will take a deeper dive into node package manager this week and take a look at all of the cool packages that you can install to make your life as a react developer easier week 9 firebase we ve come to love json s right well we ll be looking at an online database that s literally structured like a giant json we ll go over the basics of using firebase to read write data online and briefly touch on some of the other functionality it has login systems have never been easier week 10 tbd have program coordinator teach a skill that they learned week 11 final project week 12 final project | front_end |
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cumulative-reasoning | cumulative reasoning with large language models pwc https img shields io endpoint svg url https paperswithcode com badge cumulative reasoning with large language math word problem solving on math https paperswithcode com sota math word problem solving on math p cumulative reasoning with large language arxiv https img shields io badge arxiv paper color svg https arxiv org abs 2308 04371 python 3 10 https img shields io badge python 3 10 green svg introduction official implementation of paper cumulative reasoning with large language models https arxiv org abs 2308 04371 achieving 98 accuracy for the game of 24 24 compared to tree of thoughts achieving 58 accuracy on the math dataset 4 2 compared to php achieving 43 relative improvement on the hardest level 5 math problems 22 4 to 32 1 installation pip install r requirements txt for more usage help please refer to the readme md in each subdirectory acknowledgement this repo is mainly based on guidance https github com microsoft guidance huggingface https huggingface co and tree of thoughts https github com princeton nlp tree of thought llm thanks for their wonderful work citations please cite the paper and star this repo if you use cumulative reasoning cr and find it interesting useful thanks feel free to contact zhangyif21 tsinghua edu cn yangjq21 mails tsinghua edu cn or open an issue if you have any questions bibtex article zhang2023cumulative title cumulative reasoning with large language models author zhang yifan and yang jingqin and yuan yang and yao andrew chi chih journal arxiv preprint arxiv 2308 04371 year 2023 | llm reasoning large-language-models prompting cumulative-reasoning | ai |
inteli-year2-project1-AWS-deploy | inteli instituto de tecnologia e lideran a p align center a href https www inteli edu br img src https www inteli edu br wp content uploads 2021 08 20172028 marca 1 2 png alt inteli instituto de tecnologia e lideran a border 0 a p desenvolvimento de servi os em cloud computing dellusions integrantes a href https www linkedin com in ana clara loureiro muller zaidan ana clara loureiro m ller zaidan a a href https www linkedin com in arthur fraige arthur prado fraige a a href https www linkedin com in bruno omeira bruno otavio bezerra de meira a a href https www linkedin com in felipe silberberg 111998230 felipe silberberg a a href https www linkedin com in luiz k alencar luiz felipe kama alencar a a href https www linkedin com in marcos florencio n marcos aur lio flor ncio da silva a a href https www linkedin com in sophia de oliveira tosar aba7ab23b sophia de oliveira tosar a descri o atualmente diversos profissionais de ti est o interessados em aprender tecnologias diferentes das quais est o utilizando no momento nesse contexto desenvolvemos um servi o que apresenta uma forma de aprender novas tecnologias fazendo um shadowing ou um assignment tempor rio em outro projeto que esteja utilizando a tecnologia que deseja aprender tudo isso em uma aplica o web na amazon web services aws com o objetivo de centralizar as oportunidades de projeto tempor rio e disponibilizar isso de uma forma que quem deseja aprender possa ficar mais informado sobre o projeto e consequentemente conectar demanda e oferta assim difundindo o aprendizado em diferentes tecnologias estrutura de pastas documentation br emsp versions br emsp documenta o servi os cloud dellusions pdf br emsp manual instru es pdf br images br src br emsp frontend br emsp backend br readme md br dentre os arquivos e pastas presentes na raiz do projeto definem se b readme md b arquivo que serve como guia e explica o geral sobre o projeto o mesmo que voc est lendo agora b documentation b aqui est o todos os documentos do projeto incluindo o manual de instru es h tamb m uma pasta denominada b versions b onde est o presentes vers es anteriores complementares b images b aqui est o todos as imagens do projeto b src b todo o c digo fonte criado para o desenvolvimento do projeto incluindo os blocos de c digo do backend e frontend execu o do projeto um guia para a execu o desse projeto pode ser encontrado no conte do do documento manual de instru es dentro da pasta documentation se preferir tamb m poss vel acess lo a partir do seguinte link https github com 2023m5t3 inteli 2023 1a t03 grupo6 blob main documentation manual instru es pdf o acesso da plataforma web se d pelo link http dellmatch prod bucket s3 website us east 1 amazonaws com hist rico de lan amentos 0 2 1 06 04 2023 quinta entrega entrega final manual de instru es e finaliza o do ambiente de production dos componentes aws 0 2 0 24 03 2023 quarta entrega integra o back end e front end e finaliza o 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otavio bezerra de meira felipe silberberg luiz felipe kama alencar marcos aur lio flor ncio da silva e sophia de oliveira tosar a is licensed under a href http creativecommons org licenses by 4 0 ref chooser v1 target blank rel license noopener noreferrer style display inline block attribution 4 0 international a p refer ncias aqui est o as refer ncias usadas no projeto bandeira r mulo torres o diagrama de solu es digitais dsd e o planejamento de marketing digital da sua empresa linkedin s l v 1 n 1 p 1 1 26 ago 2019 dispon vel em https www linkedin com pulse o diagrama de solu es digitais dsd eplanejamento da r mulo originalsubdomain pt acesso em 11 out 2022 bernardo paulo c kon fabio a import ncia dos testes automatizados artigo revista engenharia de software magazine p gina 54 57 2008 jino m rio maldonado jos c delamaro m rcio e introdu o ao teste de software s o paulo campus elsevier 2007 pach caio style guide porque quando como e onde criar um brasil ux designer s l v 1 n 1 p 1 1 28 jan 2021 dispon vel em https brasil uxdesign cc style guidepor que quando como e onde criar um f7b173006740 acesso em 12 out 2022 vendramini giovana schnorr user flow o mapa do sucesso para o ux design ateliware s l p 1 1 1 jul 2021 dispon vel em https ateliware com blog userflow text o 20user 20flow 2c 20ou 20fluxo as 20expectativas 20do 20seu 20cliente acesso em 11 out 2022 | cloud |
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