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TizenRT | tizenrt license https img shields io badge licence apache 202 0 brightgreen svg style flat license build status https travis ci org samsung tizenrt svg branch master https travis ci org samsung tizenrt tizenrt is lightweight rtos based platform to support low end iot devices please find project details at wiki https github com samsung tizenrt wiki especially documentations page https github com samsung tizenrt wiki documentations contents environment setup environment setup how to build how to build supported board emulator supported board emulator environment setup tizenrt provides the easiest way to build with the use of docker https www docker com there is no need to install the required libraries and toolchains since the provided docker container already includes everything required for tizenrt development however if your development systems are not eligible for running the docker container all libraries and toolchains should be manually installed please refer to manual setup build environment docs howtosetenv md for more information of libraries in the tizenrt docker image see https hub docker com r tizenrt tizenrt 1 install docker to install os specific docker engines see https docs docker com install for example if you are using ubuntu you need to install the docker engine located at https docs docker com install linux docker ce ubuntu if you already have a docker engine please skip this step 2 get tizenrt source code if you are building tizenrt on a windows environment you need to first configure crlf as shown below git config global core autocrlf input now clone tizenrt source code as shown below bash git clone https github com samsung tizenrt git cd tizenrt tizenrt basedir pwd note to contribute in this community you need to create a fork and clone your forked private repository instead of the main repository github guides this by working with forks https help github com articles working with forks how to build there are two ways to build tizenrt using an interactive tool using an interactive tool using specific build options usign specific build options use an interactive tool tizenrt provides an interactive tool dbuild sh where you are prompted to select a option among multiple choices according to your selection it consecutively provides next step options when you become familiar to the tizenrt build system you may use the dbuild sh script with a specific build option note as the build script is running based on docker it requires sudo for root permission to run docker without sudo refer to https docs docker com install linux linux postinstall to get started use the dbuild sh script with the menu option as follows bash cd os dbuild sh menu this command shows you the complete list of supported boards first as shown below bash select board 1 artik053 2 cy4390x x exit after the board selection you are prompted to select configuration of the given board bash select configuration of artik053 1 hello 2 tc x exit finally you are prompted to select a build option as shown below bash select build option 1 build with current configuration 2 re configure 3 modify current configuration 4 clean build 5 clean build and re configure 6 build smartfs image d download t build test x exit once the board and configuration selection is finished you are prompted to select a build option repeatedly until you remove configuration by the re configure or build dist clean option use specific build options 1 configuration bash cd os tools configure sh board configuration set this command retrieves the specific pre configured file named defconfig and make defs according to board configuration set once the configuration is done you can skip this step next time unless you want to change your configuration you can see collection of all configuration files at tizenrt basedir build configs to check all pre defined configurations type as follows bash tools configure sh help 1 1 additional configuration optional after basic configuration by 1 configuration 1 configuration you can additionally modify your configuration with menuconfig bash dbuild sh menuconfig note in docker environment make menuconfig command from other readme files should be replaced with this command make menuconfig applies only in manual setup build environment docs howtosetenv md 2 compilation there are two aspects to compilation namely build and clean which are described as follows build to compile simply type the following bash dbuild sh after compilation built binaries will be located in tizenrt basedir build output bin clean there are two types of clean commands clean and distclean bash dbuild sh clean this command removes built files including objects libraries depend make dep etc after modifying configuration with menuconfig this command is required bash dbuild sh distclean this command includes the clean option and additionally removes configured files including config make defs and linked folders files before changing basic configuration with configure sh command this command is required to delete pre set configurations 3 programming bash dbuild sh download option tizenrt supports download command to program a binary into a board you might be required to set up usb driver for more information please refer to supported board emulator supported board emulator option designates which flash partitions are flashed for example all means programming all of binaries this also depends on each board advanced you can give multiple build options to the dbuild sh script as mentioned below bash dbuild sh distclean configure artik053 hello build download all this executes sequentially multiple commands including 1 execute distclean 2 configure with artik053 hello 3 build 4 program all of binaries supported board emulator tizenrt supports multiple boards as well as qemu the linked page for each board includes board specific environments programming method and board information artik053 details build configs artik053 readme md artik053s details build configs artik053s readme md artik055s details build configs artik055s readme md cy4390x details build configs cy4390x readme md esp32 devkitc details build configs esp32 devkitc readme md esp wrover kit details build configs esp wrover kit readme md imx rt 1020 evk details build configs imxrt1020 evk readme md imx rt 1050 evk details build configs imxrt1050 evk readme md sidk s5jt200 details build configs sidk s5jt200 readme md stm32f407 disc1 details build configs stm32f407 disc1 readme md stm32f429i disco details build configs stm32f429i disco readme md stm32l4r9ai disco details build configs stm32l4r9ai disco readme md qemu details build configs qemu readme md | server |
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metacpan-grep-front-end | metacpan grep front end grep front end code for grep metacpan org https grep metacpan org build status https travis ci org metacpan metacpan grep front end svg branch master https travis ci org metacpan metacpan grep front end coverage status https coveralls io repos github metacpan metacpan grep front end badge svg branch master cache 1 https coveralls io github metacpan metacpan grep front end branch master grep metacpan https raw githubusercontent com metacpan metacpan grep front end master public static images grep cpan logo flat png https grep metacpan org getting started grep metacpan org is currently not using metacpan developer https github com metacpan metacpan developer but it should be pretty straight forward to setup a development environment and starting submitting pull request via github requirements a git client should be compiled with libpcre perl 5 36 or later 5 36 is the recommended version consider using perlbrew https perlbrew pl app cpanminus read manual on metacpan https metacpan org pod app cpanminus git with pcre support for macos users it s recommended to use homebrew https brew sh to install git with pcre support brew install pcre export use libpcre yes brew reinstall build from source git you can check if git support pcre by using the p option example git grep c p w cloning repositories you should start forking the main metacpan grep front end repository https github com metacpan metacpan grep front end you can then clone it locally where you should replace your github username by your github username git clone git github com your github username metacpan grep front end git cd metacpan grep front end git remote add upstream https github com metacpan metacpan grep front end git the frontend is not using a database but a git repo itself as a backend for this the production is using one huge git repository 20 go indexing all the cpan in one place you can read more on this topic and find tools used to build this git repo on the github repos https grep metacpan org source code page we do not want to use such a beast during development cycles we only need a smaller version of it you can simply clone it from this metacpan cpan extracted lite https github com metacpan metacpan cpan extracted lite it should be clone at the same level of metacpan grep front end itself do not clone it inside the repository clone at the same level of metacpan grep front end cd if you are in metacpan grep front end repo git clone https github com metacpan metacpan cpan extracted lite git you should have something like this ls d metacpan metacpan cpan extracted lite metacpan grep front end installing dependencies now that you have the repository set let s install all the perl modules required you can check your perl version 5 036000 is the recommended one for now perl e say all modules are listed in the cpanfile file itself running install sh itself should do all the magic which does more than only installing the perl modules install sh but if you prefer you can simply run manually cpanm installdeps with develop with recommends starting the development server once you have installed all perl modules you should be able to start the plack server by simply running devel server watching bin lib bin app psgi for file updates http server psgi accepting connections at http 0 5010 this would use plack to run the psgi app and listen by default on port 5010 any change to any of the file should automatically trigger a plack restart you should let that plack server run in a dedicated terminal then use another terminal for your own development note errors would be logged in the devel server terminal you can now open your browser to this url and you should be able to see the grep metacpan org homepage http 127 0 0 1 5010 custom configurations each environment comes with its own environment by default they all use values from the default configuration config yml but they can overwrite some values or provide some custom values using their own file config yml environments development yml environments metacpan yml environments production yml if you need two tweak your development environment you should look at environments development yml you can even create your own environment if required from there you should be ready to hack hack hack submit a pull request to github continue to hack hack have fun travis ci the project comes with a basic travis yml file if you want to start smoking your own branches you simply need to link your github account https github com to your travis ci account https travis ci org then enable the repository in your travis dashboard https travis ci org dashboard note that any pull request submitted via github would trigger a travis smoke without any settings required from your side | metacpan grep perl | front_end |
Backend-Todo-App | getting started with strapi strapi comes with a full featured command line interface https docs strapi io dev docs cli cli which lets you scaffold and manage your project in seconds develop start your strapi application with autoreload enabled learn more https docs strapi io dev docs cli strapi develop npm run develop or yarn develop start start your strapi application with autoreload disabled learn more https docs strapi io dev docs cli strapi start npm run start or yarn start build build your admin panel learn more https docs strapi io dev docs cli strapi build npm run build or yarn build deployment strapi gives you many possible deployment options for your project including strapi cloud https cloud strapi io browse the deployment section of the documentation https docs strapi io dev docs deployment to find the best solution for your use case learn more resource center https strapi io resource center strapi resource center strapi documentation https docs strapi io official strapi documentation strapi tutorials https strapi io tutorials list of tutorials made by the core team and the community strapi blog https strapi io blog official strapi blog containing articles made by the strapi team and the community changelog https strapi io changelog find out about the strapi product updates new features and general improvements feel free to check out the strapi github repository https github com strapi strapi your feedback and contributions are welcome community discord https discord strapi io come chat with the strapi community including the core team forum https forum strapi io place to discuss ask questions and find answers show your strapi project and get feedback or just talk with other community members awesome strapi https github com strapi awesome strapi a curated list of awesome things related to strapi sub psst strapi is hiring https strapi io careers sub | server |
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ESCD | 2020 1 2 3 main https raw githubusercontent com heanden escd master main png 4 1 1 15 | os |
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DBA_TIProject | dba tiproject this is a termed project builed for data analysis to thermal image by database engineering curriculum follow the file named ti project manual guide to check the guidelines | server |
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OpenCV-Computer-Vision-Projects-with-Python | opencv computer vision projects with python this is the code repository for opencv computer vision projects with python published by packt it contains all the necessary code files what you will learn install opencv and related software such as python numpy scipy openni and sensorkinect all on windows mac or ubuntu apply curves and other color transformations to simulate the look of old photos movies or video games apply geometric transformations to images perform image filtering and convert an image into a cartoon like image recognize hand gestures in real time and perform hand shape analysis based on the output of a microsoft kinect sensor reconstruct a 3d real world scene from 2d camera motion and common camera reprojection techniques detect and recognize street signs using a cascade classifier and support vector machines svms identify emotional expressions in human faces using convolutional neural networks cnns and svms strengthen your opencv2 skills and learn how to use new opencv3 features click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781787125490 https packt link free ebook 9781787125490 a p | ai |
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record-player | record player this is a rube goldberg machine https en wikipedia org wiki rube goldberg machine of the google cloud vision api https cloud google com vision and the spotify api https beta developer spotify com documentation web api after logging into spotify upload an image the image will be sent to the google vision api which will guess what it is the app will then search spotify using google s guess and give you the first result to play there are a few environment variables you will have to set when running this locally or on a server external services you will need the following to make your own version of the app google cloud api key https cloud google com docs authentication api keys this is set as gcp api key env variable create a spotify app https beta developer spotify com dashboard applications spotify client id and spotify client secret env variables google cloud 1 register a google cloud platform account 2 create a new project 3 create credentials for your project from the dashboard of google cloud platform you want api key credentials not oauth 2 0 client ids or service accounts credentials if you only see those two you are not in the right place it is a good idea to restrict your credentials access to only the cloud vision api 4 add your google cloud api key as gcp api key in a env file 5 you may need to enable the google vision api in gcp 6 you will need to enable billing for the gcp project for the vision api to work a screenshot of the google cloud interface https github com patrickweaver record player blob master public docs google cloud api key restrictions png view image https github com patrickweaver record player blob master public docs google cloud api key restrictions png spotify 1 register a spotify developer account you can use the same credentials as your streaming account even if it is on the free tier 2 create a new spotify app give it a name and description 3 add the following settings to your app website the website where you will host the app you can run the app at different websites or locally without updating this redirect uris this is required to make the spotify login work it should be the protocol your site is hosted with the root url of your site auth callback for example https record player glitch me auth callback you can add more than one so you can run locally or at multiple sites see image below a screenshot of the spotify settings https github com patrickweaver record player blob master public docs spotify app settings example png view image https github com patrickweaver record player blob master public docs spotify app settings example png | ai |
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Anilert_Backend_Test | anilert backend test a console test application for the backend for the web app in development weebwatch formally known as anilert it focuses on returning json data from gracenote for movie results and google places for location search user enters their location alt text https github com peter white anilert backend test blob master screenshots placesearch png user location user enters theater search radius alt text https github com peter white anilert backend test blob master screenshots radius png search radius json returns anime films playing within user s search radius alt text https github com peter white anilert backend test blob master screenshots animeresults png anime results user can select the movie from results and get it s theaters showtimes and fandango purchase link alt text https github com peter white anilert backend test blob master screenshots animeselectionresults png anime showtimes | server |
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evue | evue evue is a high performance gui framework base an html css which can run on windows linux macos web ios andriod rtos write once run everywhere see the https github com scriptiot evue blob master readme zh md for chinese readme features brief https github com scriptiot evue blob master doc images brief png raw true just python as you like multi user for web dark light theme support responsive support framework evue architecture evue is a high performance gui framework base an html css which is platform independence and ui engine independence evue https github com scriptiot evue blob master doc images evue png raw true evuecompiler architecture evuecompiler is a compiler which compile evue file to python javascript code evuecompiler https github com scriptiot evue blob master doc images evuecompiler png raw true evue cross platfom evue for flutter windows linux macos web ios andriod evue for lvgl rtos on mcu like asr3603 f1c100 f1c200 esp32 stm32 you can run evue on any platfom as you like evue for all ui engine evue for flutter evue for lvgl evue for qt evue for pyside2 you can compile evue to any ui code as you like installation use the package manager pip https github com scriptiot evue to install evue bash pip install evue or bash git clone https github com scriptiot evue git cd evue python setup py install also pip install getting started evue website https github com scriptiot evue tree master examples evue website python cd examples python evuebrowser py evue website project json or python evuebrowser py evue website app py evue website https github com scriptiot evue tree master doc images evue website gif raw true evue login https github com scriptiot evue tree master examples evue login python cd examples python evuebrowser py evue login project json or python evuebrowser py evue login app py evue login https github com scriptiot evue tree master doc images evue login gif raw true evue studio evue studio is a develop platform for evue developer to create compile deploy evue project designer https github com scriptiot evue blob master doc images designer png raw true download latest evue studio https github com scriptiot evue releases download 0 1 6 1 evuestudio 20230106211347 65b5a31 7z uncompress evuestudio 7z double click evuestudio exe evue for iot evue for iot is a commercial product named quicknode a tiny runtime of js engnine which can run on lots of mcu quicknode https github com scriptiot evue blob master doc images quicknode gif raw true more info what is quicknode doc evue e4 ba a7 e5 93 81 e4 bb 8b e7 bb 8dpdf e7 89 88 pdf download quicknode https github com scriptiot evue releases download 0 1 6 quicknode qbc 20221215142421 693fbf88 zip uncompress quicknode qbc 20221215142421 693fbf88 zip double click quicknode bat or quicknode chart bat document https www yuque com bytecode eu1sci ymto6i how to compile evue to python code evue2py https github com scriptiot evue blob master doc images evue2py gif raw true how to compile evue to python code https github com scriptiot evue wiki how to compile evue to python code 3f login in evue studio switch to compile page add project dir to watch evue will be compiled to python code automatically when you change evue file community discussions https github com scriptiot evue discussions issues https github com scriptiot evue issues contribute to this wonderful project welcome to contribute to this wonderful project no matter you are a community technical talent or designer talent or product manager talent or community operation talent your name will be one of evue contributors if you like evue you can send email to ding465398889 163 com thanks contact if there is a need for cooperation please send email wechat qq for more support email ding465398889 163 com wechat dragondjf dragondjf https github com scriptiot evue blob master doc images dragondjf jpg raw true evue for iot dragondjf https github com scriptiot evue blob master doc images qq jpg raw true salute evm https github com scriptiot evm lvgl https github com lvgl lvgl flet https github com flet dev flet flutter https github com flutter flutter | flutter flet python evm cross-platform evue gui web html ui | os |
8051-fast-adder | 8051 fast adder cpe3150 microcontrollers and embedded system design project description here you ll find a fast adder implementation for the 8051 microcontroller written in asm flushed collaborators nick covert cpe 19 christian parsons cs 19 william lorey cpe 19 | os |
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IIT | iit introduction to information technology | server |
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DA-Mobile | da mobile da mobile application made by the app development club introduction credit | front_end |
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CAAS | caas cloud storage services have become commercially popular due to overwhelming advantages to provide ubiquitous always on access a cloud service provider csp maintains multiple replicas for eachpiece of data on geographically distributed servers a key problem of using the replication technique in clouds is that it is very expensive to achieve strong consistency on a worldwide scale consistency as a service caas model consists of a large data cloud and multiple small audit clouds in the caas model a data cloud is maintained by a csp and a group of users that constitute an audit cloud can verify whether the dta cloud provides the promised level of consistency or not the two level auditing architecture in the system only requires a loosely synchronized clock in the audit cloud and design algorithms to quantify the severity of violations with two metrics the commonality of violations and the staleness of the value of a read finally devise a heuristic auditing strategy has to reveal as many violations as possible | cloud |
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WebDev-OpenSource | img align right src https img shields io github repo size rudra016 webdev opensource style for the badge logo appveyor alt github repo size open source web development img src https socialify git ci rudra016 webdev opensource image description 1 descriptioneditable a 20open 20source 20repo 20for 20all 20web 20development 20fans 20 3a font koho forks 1 issues 1 language 1 name 1 owner 1 pattern floating 20cogs pulls 1 stargazers 1 theme light alt webdev opensource width 1280 height 720 p align center img src https forthebadge com images badges built with love svg alt forks p hey everyone br welcome to the hacktoberfest web development projects repository your gateway to a world of collaborative coding and open source web development dive into the exciting realm of hacktoberfest with our curated collection of web development projects designed to foster creativity collaboration and community engagement webdev opensource https github com rudra016 webdev opensource want to contribute take a look at contributing guidelines contributing md refer github flow https guides github com introduction flow hacktoberfest for hacktoberfest you can raise an issue regarding what you would like to contribute and i will assign it to you contact me on alphamale6 at discord code of conduct code of conduct code of conduct md contributors thanks goes to these wonderful people br div align center a href https github com rudra016 webdev opensource graphs contributors img src https contrib rocks image repo rudra016 webdev opensource max 100 columns 11 anon 0 width 97 a div br br hr h6 align center webdev opensource br all rights reserved h6 | fullstack-development hacktoberfest-accepted html-css-javascript reactjs web hacktoberfest open-source | front_end |
db-engineering-capstone | db engineering capstone capstone course for meta database engineering course on coursera | database databaseclient mysql python tableau | server |
udagram | udagram udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com phoenix 254 udagram tree master udagram frontend a basic ionic client web application which consumes the restapi backend 2 the restapi backend https github com phoenix 254 udagram tree master udagram restapi a node express server which can be deployed to a cloud service 3 the image filtering microservice https github com phoenix 254 udagram tree master udagram image filter the final project for the course it is a node express application which runs a simple script to process images getting setup installing node and npm this project depends on nodejs and node package manager npm before continuing you must download and install node npm is included from https nodejs com en download https nodejs org en download installing ionic cli the ionic command line interface is required to serve and build the frontend instructions for installing the cli can be found in the ionic framework docs https ionicframework com docs installation cli installing project dependencies this project uses npm to manage software dependencies npm relies on the package json file located in the root of each project folder after cloning run the following command from each project s root folder in order to install it s dependencies bash npm i installing useful tools 1 postbird https github com paxa postbird postbird is a useful client gui graphical user interface to interact with our provisioned postgres database we can establish a remote connection and complete actions like viewing data and changing schema tables columns ect 2 postman https www getpostman com downloads postman is a useful tool to issue and save requests postman can create get put post etc requests complete with bodies it can also be used to test endpoints automatically we ve included some collections project root dir postman collection json which contains example requsts | cloud |
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MachineLearningNotebooks | azure machine learning python sdk notebooks with the introduction of azureml sdk v2 this samples repository for the v1 sdk is now deprecated and will not be monitored or updated users are encouraged to visit the v2 sdk samples repository https github com azure azureml examples instead for up to date and enhanced examples of how to build train and deploy machine learning models with azureml s newest features welcome to the azure machine learning python sdk notebooks repository getting started these notebooks are recommended for use in an azure machine learning compute instance https docs microsoft com azure machine learning concept compute instance where you can run them without any additional set up however the notebooks can be run in any development environment with the correct azureml packages installed install the azureml core python package sh pip install azureml core install additional packages as needed sh pip install azureml mlflow pip install azureml dataset runtime pip install azureml automl runtime pip install azureml pipeline pip install azureml pipeline steps we recommend starting with one of the quickstarts tutorials compute instance quickstarts contributing this repository is a push only mirror pull requests are ignored code of conduct this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct please see the code of conduct code of conduct md for details reference documentation https docs microsoft com azure machine learning | machine-learning notebook azure deep-learning data-science azure-machine-learning azure-ml azureml | ai |
LLM-Learning | llm learning llm llm https llm frame group github io llm llm gpt llm transformer gpt llm fine tune llm llm llm lora llm prompt prompt prompt prompt falcon prompt | ai |
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tf-encrypted | img src https tinyurl com tfelogo width 65 tf encrypted is a framework for encrypted machine learning in tensorflow it looks and feels like tensorflow taking advantage of the ease of use of the keras api while enabling training and prediction over encrypted data via secure multi party computation and homomorphic encryption tf encrypted aims to make privacy preserving machine learning readily available without requiring expertise in cryptography distributed systems or high performance computing see below for more background material background further reading explore the examples examples or visit the documentation docs to learn more about how to use the library website https img shields io website https tf encrypted io svg https tf encrypted io documentation https img shields io badge api reference blue svg https tf encrypted readthedocs io en latest pypi https img shields io pypi v tf encrypted svg https pypi org project tf encrypted circleci badge https circleci com gh tf encrypted tf encrypted tree master svg style svg https circleci com gh tf encrypted tf encrypted tree master now tf encrypted is based on tensorflow 2 tf1 execute computation by building a graph first then run this graph in a session this is hard to use for a lot of developers especially for researchers not major in computer science therefore tf1 has very few users and is not maintained any more since tf encrypted based on tf1 it face the same problem that tf1 has encountered so we update tf encrypted to rely on tf2 to support eager execution which makes development on tf encrypted more easier at the same time it also supports building graph implicitly by tfe function to realize nearly the same performance as tf encrypted based on tf1 unfortunately after updated tf1 features like session and placeholder are not supported by tfe any more for those developers who want to use tf1 like tfe we suggest them to use version 0 8 0 https github com tf encrypted tf encrypted tree 0 8 0 installation tf encrypted is available as a package on pypi https pypi org project tf encrypted supporting python 3 8 and tensorflow 2 9 1 bash pip install tf encrypted creating a conda environment to run tf encrypted code can be done using conda create n tfe python 3 8 conda activate tfe conda install tensorflow notebook pip install tf encrypted alternatively installing from source can be done using bash git clone https github com tf encrypted tf encrypted git cd tf encrypted pip install e make build this latter is useful on platforms for which the pip package has not yet been compiled but is also needed for development docs contributing md note that this will get you a working basic installation yet a few more steps are required to match the performance and security of the version shipped in the pip package see the installation instructions docs install md usage the following is an example of simple matmul on encrypted data using tf encrypted based on tf2 you could execute matmul in eager mode or building a graph by tfe function python import tensorflow as tf import tf encrypted as tfe tfe local computation input provider def provide input normal tensorflow operations can be run locally as part of defining a private input in this case on the machine of the input provider return tf ones shape 5 10 provide inputs w tfe define private variable tf ones shape 10 10 x provide input eager execution y tfe matmul x w res y reveal to native build graph and run graph tfe function def matmul func x w y tfe matmul x w return y reveal to native res matmul func x w for more information check out the documentation docs or the examples examples performance all tests are performed by using the aby3 protocol among 3 machines each with 4 cores intel xeon platinum 8369b cpu 2 70ghz ubuntu 18 04 64bit tensorflow 2 9 1 python 3 8 13 and pip 21 1 2 the lan environment has a bandwidth of 40 gbps and a rtt of 0 02 ms and the wan environment has a bandwidth of 352 mbps and a rtt of 40 ms you can find source code of the following benchmarks in examples benchmark examples benchmark and corresponding guidelines of how to reproduce them benchmark 1 sort and max graph building is a one time cost while lan or wan timings are average running time across multiple runs for example it takes 152 5 seconds to build the graph for resnet50 model and afterwards it only takes 19 1 seconds to predict each image build graph br seconds lan br seconds wan br seconds sort max 2 10 sup 1 sup 5 26 0 14 10 37 sort max 2 16 sup 1 sup 14 06 7 37 41 97 max 2 10 times 4 sup 2 sup 5 63 0 01 0 55 max 2 16 times 4 sup 2 sup 5 81 0 29 1 14 sup 1 sup max is implemented by using a sorting network hence its performance is essentially the same as sort sorting network can be efficiently constructed as a tf graph the traditional way of computing max by using a binary comparison tree does not work well in a tf graph because the graph becomes huge when the number of elements is large sup 2 sup this means 2 10 respectively 2 16 invocations of max on 4 elements which is essentially a maxpool with pool size of 2 x 2 benchmark 2 neural network inference we show the strength of tfe by loading big tf models e g resnet50 and run private inference on top of it build graph br lan br wan br vgg19 inference time seconds 105 18 49 63 139 63 resnet50 inference time seconds 150 47 19 07 sup 1 sup 84 29 densenet121 inference time seconds 344 55 33 53 151 43 sup 1 sup this is currently one of the fastest implementation of secure resnet50 inference three party comparable with cryptgpu https eprint iacr org 2021 533 secureq8 https eprint iacr org 2019 131 and faster than cryptflow https arxiv org abs 1909 07814 benchmark 3 neural network training we benchmark the performance of training several neural network models on the mnist dataset 60k training images 10k test images and batch size is 128 the definitions of these models can be found in examples models examples models accuracy epochs accuracy epochs seconds per batch lan seconds per batch lan seconds per batch wan seconds per batch wan mp spdz tfe mp spdz tfe mp spdz tfe a sgd 96 7 5 96 5 5 0 098 0 167 9 724 4 510 a amsgrad 97 8 5 97 4 5 0 228 0 717 21 038 15 472 a adam 97 4 5 97 4 5 0 221 0 535 50 963 15 153 b sgd 97 5 5 98 6 5 0 571 5 332 60 755 18 656 b amsgrad 98 6 5 98 7 5 0 680 5 722 71 983 21 647 b adam 98 8 5 98 8 5 0 772 5 733 98 108 21 130 c sgd 98 5 5 98 7 5 1 175 8 198 91 341 27 102 c amsgrad 98 9 5 98 9 5 1 568 10 053 119 271 66 357 c adam 99 0 5 99 1 5 2 825 9 893 195 013 65 320 d sgd 97 6 5 97 1 5 0 134 0 439 15 083 5 465 d amsgrad 98 4 5 97 4 5 0 228 0 900 26 099 14 693 d adam 98 2 5 97 6 5 0 293 0 710 54 404 14 515 we also give the performance of training a logistic regression model in the following table this model is trained to classify two classes small digits 0 4 vs large digits 5 9 dataset can be found in examples benchmark training lr mnist dataset py examples benchmark training lr mnist dataset py accuracy epochs seconds per batch lan seconds per batch wan lr sgd 84 8 5 0 010 0 844 lr amsgrad 85 0 5 0 023 1 430 lr adam 85 2 5 0 019 1 296 roadmap high level apis for combining privacy and machine learning so far tf encrypted is focused on its low level interface but it s time to figure out what it means for interfaces such as keras when privacy enters the picture tighter integration with tensorflow this includes aligning with the upcoming tensorflow 2 0 as well as figuring out how tf encrypted can work closely together with related projects such as tf privacy https github com tensorflow privacy and tf federated https github com tensorflow federated support for third party libraries while tf encrypted has its own implementations of secure computation there are other excellent libraries https github com rdragos awesome mpc out there for both secure computation and homomorphic encryption we want to bring these on board and provide a bridge from tensorflow img src https raw githubusercontent com tf encrypted assets master app stack png width 55 background further reading blog posts introducing tf encrypted https alibaba gemini lab github io docs blog tfe walks through a simple example showing two data owners jointly training a logistic regression model using tf encrypted on a vertically split dataset by alibaba gemini lab federated learning with secure aggregation in tensorflow https medium com dropoutlabs federated learning with secure aggregation in tensorflow 95f2f96ebecd demonstrates using tf encrypted for secure aggregation of federated learning in pure tensorflow by justin patriquin at cape privacy encrypted deep learning training and predictions with tf encrypted keras https medium com dropoutlabs encrypted deep learning training and predictions with tf encrypted keras 557193284f44 introduces and illustrates first parts of our encrypted keras interface by yann dupis at cape privacy growing tf encrypted https medium com dropoutlabs growing tf encrypted a1cb7b109ab5 outlines the roadmap and motivates tf encrypted as a community project by morten dahl experimenting with tf encrypted https medium com dropoutlabs experimenting with tf encrypted fe37977ff03c walks through a simple example of turning an existing tensorflow prediction model private by morten dahl and jason mancuso at cape privacy secure computations as dataflow programs https mortendahl github io 2018 03 01 secure computation as dataflow programs describes the initial motivation and implementation by morten dahl papers privacy preserving collaborative machine learning on genomic data using tensorflow https arxiv org abs 2002 04344 outlines the idash 19 http www humangenomeprivacy org 2019 winning solution built on tf encrypted by cheng hong et al crypto oriented neural architecture design https arxiv org abs 1911 12322 uses tf encrypted to benchmark ml optimizations made to better support the encrypted domain by avital shafran gil segev shmuel peleg and yedid hoshen private machine learning in tensorflow using secure computation https arxiv org abs 1810 08130 further elaborates on the benefits of the approach outlines the adaptation of a secure computation protocol and reports on concrete performance numbers by morten dahl jason mancuso yann dupis et al presentations privacy preserving machine learning with tensorflow https github com capeprivacy tf world tutorial tf world 2019 by jason mancuso and yann dupis at cape privacy see also the slides https github com capeprivacy tf world tutorial blob master tensorflow world tutorial 2019 final pdf privacy preserving machine learning in tensorflow with tf encrypted https conferences oreilly com artificial intelligence ai ny 2019 public schedule detail 76542 o reilly ai 2019 by morten dahl at cape privacy see also the slides https github com mortendahl talks blob master oreillyai19 slides pdf other privacy preserving deep learning pysyft versus tf encrypted https blog exxactcorp com privacy preserving deep learning pysyft tfencrypted makes a quick comparison between pysyft and tf encrypted correctly hitting on our goal of being the encryption backend in pysyft for tensorflow by exxact bridging microsoft seal into tensorflow https medium com dropoutlabs bridging microsoft seal into tensorflow b04cc2761ad4 takes a first step towards integrating the microsoft seal homomorphic encryption library and some of the technical challenges involved by justin patriquin at cape privacy development and contribution tf encrypted is open source community project developed under the apache 2 license and maintained by a set of core developers we welcome contributions from all individuals and organizations with further information available in our contribution guide docs contributing md we invite any organizations interested in partnering organizational contributions with us to reach out via email mailto contact tf encrypted io don t hesitate to send a pull request open an issue or ask for help we use zenhub https www zenhub com extension to plan and track github issues and pull requests individual contributions we appreciate the efforts of all contributors https github com tf encrypted tf encrypted graphs contributors that have helped make tf encrypted what it is below is a small selection of these generated by sourcerer io https sourcerer io from most recent stats https sourcerer io fame mortendahl tf encrypted tf encrypted images 0 https sourcerer io fame mortendahl tf encrypted tf encrypted links 0 https sourcerer io fame mortendahl tf encrypted tf encrypted images 1 https sourcerer io fame mortendahl tf encrypted tf encrypted links 1 https sourcerer io fame mortendahl tf encrypted tf encrypted images 2 https sourcerer io fame mortendahl tf encrypted tf encrypted links 2 https sourcerer io fame mortendahl tf encrypted tf encrypted images 3 https sourcerer io fame mortendahl tf encrypted tf encrypted links 3 https sourcerer io fame mortendahl tf encrypted tf encrypted images 4 https sourcerer io fame mortendahl tf encrypted tf encrypted links 4 https sourcerer io fame mortendahl tf encrypted tf encrypted images 5 https sourcerer io fame mortendahl tf encrypted tf encrypted links 5 https sourcerer io fame mortendahl tf encrypted tf encrypted images 6 https sourcerer io fame mortendahl tf encrypted tf encrypted links 6 https sourcerer io fame mortendahl tf encrypted tf encrypted images 7 https sourcerer io fame mortendahl tf encrypted tf encrypted links 7 organizational contributions we are very grateful for the significant contributions made by the following organizations table tr td a href https capeprivacy com img src https raw githubusercontent com tf encrypted assets master other capeprivacy logo png alt cape privacy width 150 a td td a href https www alibabagroup com img src https raw githubusercontent com tf encrypted assets master other alibaba logo png alt alibaba security group width 150 a td td a href https openmined org img src https raw githubusercontent com tf encrypted assets master other openmined logo png alt openmined width 150 a td tr table project status tf encrypted is experimental software not currently intended for use in production environments the focus is on building the underlying primitives and techniques with some practical security issues postponed for a later stage however care is taken to ensure that none of these represent fundamental issues that cannot be fixed as needed known limitations elements of tensorflow s networking subsystem does not appear to be sufficiently hardened against malicious users proxies or other means of access filtering may be sufficient to mitigate this support please open an issue https github com tf encrypted tf encrypted issues or send an email to contact tf encrypted io mailto contact tf encrypted io license licensed under apache license version 2 0 see license license or http www apache org licenses license 2 0 copyright as specified in notice notice | secure-computation machine-learning tensorflow privacy cryptography deep-learning confidential-computing | ai |
ng-alain | p align center a href https ng alain com img width 100 src https ng alain com assets img logo color svg a p h1 align center ng alain h1 div align center out of box ui solution for enterprise applications let developers focus on business build status https dev azure com ng alain ng alain apis build status ng alain ci branchname master https dev azure com ng alain ng alain build latest definitionid 2 branchname master dependency status https david dm org ng alain ng alain status svg style flat square https david dm org ng alain ng alain github release date https img shields io github release date ng alain ng alain svg style flat square https github com ng alain ng alain releases npm version https img shields io npm v ng alain svg style flat square https www npmjs com package ng alain prettier https img shields io badge code style prettier ff69b4 svg style flat square https prettier io github license https img shields io github license mashape apistatus svg style flat square https github com ng alain ng alain blob master license gitter https img shields io gitter room ng alain ng alain svg style flat square https gitter im ng alain ng alain ng zorro vscode https img shields io badge ng zorro vscode brightgreen svg style flat square https marketplace visualstudio com items itemname cipchk ng zorro vscode ng alain vscode https img shields io badge ng alain vscode brightgreen svg style flat square https marketplace visualstudio com items itemname cipchk ng alain vscode div english readme zh cn md quickstart getting started https ng alain com docs getting started links document https ng alain com surge mirror https ng alain doc surge sh delon source https github com ng alain delon demo https ng alain surge sh https ng alain gitee io features ng zorro antd based responsive layout i18n delon https github com ng alain delon lazy load assets ui router states customize theme less preprocessor rtl well organized commented code simple upgrade support docker deploy architecture architecture https raw githubusercontent com ng alain delon master screenshot architecture png delon https github com ng alain delon is a production ready solution for admin business components packages built on the design principles developed by ant design app shots desktop https raw githubusercontent com ng alain delon master screenshot desktop png ipad https raw githubusercontent com ng alain delon master screenshot ipad png iphone https raw githubusercontent com ng alain delon master screenshot iphone png contributing prs welcome https img shields io badge prs welcome brightgreen svg style flat square https github com ng alain ng alain pulls we welcome all contributions please read our contributing md https github com ng alain ng alain blob master contributing md first you can submit any ideas as pull requests https github com ng alain ng alain pulls or as github issues https github com ng alain ng alain issues if you re new to posting issues we ask that you read how to ask questions the smart way http www catb org esr faqs smart questions html this guide does not provide actual support services for this project how to ask a question in open source community https github com seajs seajs issues 545 and how to report bugs effectively http www chiark greenend org uk sgtatham bugs html prior to posting well written bug reports help us help you donation ng alain is an mit licensed open source project in order to achieve better and sustainable development of the project we expect to gain more backers you can support us in any of the following ways patreon https www patreon com cipchk opencollective https opencollective com ng alain paypal https www paypal me cipchk https ng alain com assets donate png or purchasing our business theme https e ng alain com backers thank you to all our backers a href https opencollective com ng alain backers target blank img src https opencollective com ng alain backers svg width 890 a license the mit license see the license https github com ng alain ng alain blob master license file for the full text | ng-alain ng-zorro-antd angular ant-design angular-components angular-applications antd ng-zorro delon angular-start angular-seed antd-design-pro angular9 angular10 | front_end |
intro-to-computer-vision-with-python-opencv | introduction to computer vision using python opencv this is a running repository of various tutorials and problem sets using the opencv library with python bindings contents content subject chapter 0 image basics in python opencv 0 0 basics reading showing saving images opencv matplotlib 0 1 basics inspecting images by slicing operations 0 2 basics examining color planes how opencv handles them 0 3 basics cropping images 0 4 basics adding subtracting scaling blending images together 0 5 basics the different border types in opencv 0 6 basics translating images using opencv 0 7 basics drawing functions in opencv chapter 1 image noise 1 0 noise adding various types of noise to images using opencv 1 1 noise removing noise with gaussian blur 1 2 noise removing salt pepper noise with median blur chapter 2 image filters filtering 2 0 filtering blurring images 2 1 filtering apply a median filter in opencv good for s p noise 2 2 filtering using filters as templates aka template matching 2 3 filtering aliasing in images chapter 3 edge detection 3 0 edge detection image gradients sobel laplacian 3 1 edge detection canny edge detection with opencv chapter 4 model fitting 4 0 model fitting hough transform lines by hand opencv 4 1 model fitting hough transform circles chapter 5 perspective imaging 5 0 perspective imaging perspective projection project a point 5 1 perspective imaging depth map from stereo images 5 2 perspective imaging stereo correspondence find best match problem sets problem sets problem set 0 problem set zero images as functions problem set 1 problem set one edges and lines requirements python 2 7 cycler 0 10 0 matplotlib 1 5 1 numpy 1 11 1 mkl pyparsing 2 1 8 python dateutil 2 5 3 pytz 2016 6 1 scipy 0 18 0 six 1 10 0 | ai |
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Embedding-System-Design-Game-Robort-Arm | game robot arm embedding system design and alpha beta pruning algorithm demo link https drive google com drive folders 1ocgwnz1k qjaqsvo3b53mzyzcwzpetve usp share link arduino ble include libary c include arduino h include servo h include wire h include ov7670 h include softwareserial h c c 17 include c include iostream include vector include cstdlib include ctime include windows h include windows devices bluetooth h include windows devices enumeration h arduino c image https github com azen huang embedding system design game robort arm blob main readme 1 png image https github com azen huang embedding system design game robort arm blob main readme 2 png image https github com azen huang embedding system design game robort arm blob main readme 3 png image https github com azen huang embedding system design game robort arm blob main readme 4 png image https github com azen huang embedding system design game robort arm blob main readme 5 png | alpha-beta-pruning arduino embedded-systems tic-tac-toe | os |
DevelopmentStack | overflow stack the overflow stack family system development stack web front end stack database stack net stack etc http overflowstack github io developmentstack system development basics analysis project planning documentation wireframe mockup design modeling implementation quality management build testing deployment maintenance troubleshooting learning image of system development stack development stack png what and why have you ever wondered what system development really includes how many do i possess i could not find a really comprehensive diagram that shows the system development stack so i come up with my own version there might be issues here and there like the category the individual ones but the beautity is you can modify it as you want you can have a graphical preview here use mouse to move zoom https rawgit com unruledboy developmentstack master ux developmentstack htm the development stack build start system development stacks development stack https github com unruledboy developmentstack web front end stack https github com unruledboy webfrontendstack database stack https github com unruledboy databasestack net stack https github com unruledboy dotnetstack basics software development life cycle sdlc https en wikipedia org wiki systems development life cycle initiation system concept development planning requirements analysis design development integration test implementation operations maintenance disposition environments operation systems os windows linux mac os unix bsd dos desktop graphical user interface gui console user interface cui mobile android ios windows phone wp blackberry symbian windows mobile wm web front end cloud service models system software as a service saas platform as a service paas infrastructure as a service iaas desktop as a service daas backend as a service baas types private cloud public cloud providers microsoft azure amazon web services aws google cloud hybrid cloud platforms net https github com unruledboy dotnetstack java https www java com common concepts application programming interface api component library plug in extension assembly architecture standalone desktop app mobile app multi layers n tiers frontend user interface ui backend database https github com unruledboy databasestack software types open source licenses gnu general public license gpl gnu lesser general public license lgpl apache license mit license mozilla public license microsoft public license ms pl closed source freeware commercial methodologies waterfall agile scrum daily scrum standup backlog sprint standards coding standard documentation standard release standard protocols http formats xml json mime project planning target resource time tools microsoft project analysis methodologies object oriented analysis ooa steps scope defintion problem analsysis requirement analysis logical design decision analysis target feasibility documentation specifications software requirement specification srs software architecture description design modeling primitives wireframe functional skeleton design mockup gui design proof of concept poc initial functional r d prototype minimal functioning design concepts object oriented programming oop object oriented design ood complex design simplistic design thoughts don t repeat yourself dry keep it simple stupid kiss you aren t gonna need it yagni s o l i d single responsibility principle srp open closed principle ocp liskov subsistitution principle lsp interface segration principle isp dependency inversion principle dip design patterns gof 23 tools functional visio uml rational rose database er studio erwin development concepts zero bug bounce zbb mvp minimum viable product https en wikipedia org wiki minimum viable product development integrated development environment ide retrofit quality code review peer review formal lightweight over the shoulder email pass around pair programming tool assisted quality measurement https en wikipedia org wiki software quality robustness efficiency reliability correctness availability security adequacy maintainability learnability readability extensibility testability portability usability fault tolerance fail fast https en wikipedia org wiki fail fast fail safe fail secure user experience ux security ssl authentication authorization management source code control basics team foundation server tfs git subversion svn concurrent versions system cvs mercurial visual source safe cloud hosted github bitbucket codeplex sourceforge escrow bug jira bugzilla documents confluence build manual compilation build automation daily build nightly build testing concepts continuous integration ci dry run integration tools team city testing levels unit testing integration test big bang top down bottom up sandwich performance testing load testing stress testing spike testing configuration testing acceptance testing user acceptance testing uat operational acceptance testing system testing testing methods the box approach white box testing clear box testing glass box testing unit testing integration testing regression testing black box testing smoke testing sanity testing regression testing usability testing gray box testing deployment release rollout concepts release management continuous deployment cd tools octopus deploy maintenance troubleshooting raygun https raygun io learning pluralsight http www pluralsight com code school https www codeschool com codecademy https www codecademy com lynda http www lynda com coursera https www coursera org open2study https www open2study com itunes college build end | os |
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alloy | alloy code climate https codeclimate com github qnyp alloy png https codeclimate com github qnyp alloy cli toolbelt for titanium mobile development on mac os x prerequisite mac os x 10 7 titanium studio 2 0 ruby 1 9 3 coffeescript usage setup cd path to titanium workspace helloworld alloy init create alloy json compiling coffeescript into javascript you should place coffee files under the directory named app mkdir app echo ti api console hello app hello coffee then run coffee task alloy coffee compiling coffeescript successfully compiled coffeescript javascript files generated under the resources directory cat resources hello js generated by coffeescript 1 3 1 ti api console hello cleaning up build directory run clean task to clean up intermediate files build iphone or build android alloy clean iphone alloy clean android building app and run in iphone simulator alloy build iphone building app and run in android emulator before using alloy you need to create and setup a avd android virtual devices http developer android com intl en guide developing devices index html for rapid development process avd setup 1 launch android sdk manager install sdk platform and google apis of any android versions at least greater than android 2 2 android sdk home tools android 2 check what android target availables android sdk home tools android list targets available android targets id 1 or android 15 name android 4 0 3 type platform api level 15 revision 3 skins hvga qvga wqvga400 wqvga432 wsvga wvga800 default wvga854 wxga720 wxga800 abis armeabi v7a x86 id 2 or google inc google apis 15 name google apis type add on vendor google inc revision 2 description android google apis based on android 4 0 3 api level 15 libraries com google android media effects effects jar collection of video effects com android future usb accessory usb jar api for usb accessories com google android maps maps jar api for google maps skins wvga854 wqvga400 wsvga wxga720 hvga wqvga432 wvga800 default qvga wxga800 abis armeabi v7a 3 create a new avd android sdk home tools android create avd n my android 4 0 3 t 1 abi armeabi v7a skin wvga800 my android 4 0 3 is avd name 1 is id of android target avd created under android avd ls la android avd total 8 drwxr xr x 4 juno staff 136 6 1 00 56 drwxr xr x 6 juno staff 204 6 1 00 51 drwxr xr x 4 juno staff 136 6 1 00 56 my android 4 0 3 avd rw r r 1 juno staff 69 6 1 00 56 my android 4 0 3 ini android sdk home tools android list avd available android virtual devices name my android 4 0 3 path users juno android avd my android 4 0 3 avd target android 4 0 3 api level 15 abi armeabi v7a skin wvga800 yay 4 enable sd card support to install application echo sdcard size 256m android avd my android 4 0 3 avd config ini running android emulator 1 start adb server android sdk home platform tools adb start server 2 launch android emulator android sdk home tools emulator avd my android 4 0 3 after android launch emulator instance appears as a device android sdk home platform tools adb devices list of devices attached emulator 5554 device build and install application to emulator alloy build android shutting down android emulator 1 exit android emulator app 2 kill adb server android sdk home platform tools adb kill server contributing 1 fork it 2 create your feature branch git checkout b my new feature 3 commit your changes git commit am added some feature 4 push to the branch git push origin my new feature 5 create new pull request | titanium ruby coffeescript | front_end |
nimbos | nimbos 2022 an experimental real time operating system rtos written in rust working in progress features multi architecture support x86 64 aarch64 riscv64 preemptive scheduler user kernel space isolation todo x more effective thread sleeping kernel mutex semaphore condvar x run with rvm1 5 https github com rvm rtos rvm1 5 smp build run in qemu sh cd kernel make env for first time make run arch x86 64 log warn | kernel operating-system rust osdev | os |
iOS-apps | ios apps learning ios app engineering model view controller model layer holds the data view layer contains objects that are visible to the user controller layer controls the view and model https s3 us west 2 amazonaws com secure notion static com 6ae8f007 89c3 47f6 99bd 7ff9ac786f5c untitled png https s3 us west 2 amazonaws com secure notion static com 6ae8f007 89c3 47f6 99bd 7ff9ac786f5c untitled png interface builder document outline this contains the scenes or the screens canvas the buttons labels input boxes etc are called view objects keyboard shortcuts cmd r starts the simulator auto layout this tool will guarantee that the app will look great on any screen size constraints if there are mistakes the constraints will show orange or red color connections a connection lets one object know where the another object is in memory so they can communicate the two types of connections are an outlet and action an outlet is a reference to an object an action is a method that gets triggered by a button or some other view object like slider or picker the object is the target that triggers the method model layer constant is denoted using let variable is denoted using var | os |
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ionic5_pure_react_daily_moments_app_udemy | daily moments this example is part of the ionic react course links firebase get started https firebase google com docs web setup firebase authentication https firebase google com docs auth cloud firestore https firebase google com docs firestore | server |
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IT | it information technology | server |
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design-system-checklist | design systems checklist https designsystemchecklist com an open source checklist to help you plan build and grow your design system contributing if you have any information to add to existing sections please open a pull request make sure your proposed content is a standard for at least multiple well known design systems add reference links for each new checklist item you add follow the same tone of voice used across the checklist to make reading easier for visitors running locally git clone git github com ardakaracizmeli design system checklist git cd design system checklist npm install npm start feedback feel free to open github issues or you can reach us directly at ardakaracizmeli gmail com blv dmitry gmail com | os |
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app-template | mit https img shields io dub l vibe d svg style flat square http opensource org licenses mit poster jpg english readme en md app template spring boot vue web http 47 105 79 88 9090 app template admin index html web http 47 105 79 88 9090 app template index html web web war spring boot vue mybatis plus swagger spring security jwt logan https github com meituan dianping logan gradle doc manual doc manual md doc manual md web doc manual md doc manual md doc manual md logan doc manual logan md swagger doc manual swagger md http https doc manual http https md h2 doc manual h2 md spring boot jwt swagger logan 1 prod build libs resources application pro yml mysql deploy start sh app application pro yml linux build sh deploy app deploy tgz windows build bat build libs war java jar war h2 mysql mysql doc manual md 2 build libs app deploy tgz application pro yml src main resources application pro yml deploy app application pro yml deploy start sh deploy application pro yml build libs war build resources application pro yml 2 1 mysql mysql deploy init sql install sh install sh deploy readme md sh start sh http ip port app template index html http ip port app template admin index html swagger ui http ip port app template swagger ui html 3 3 1 h2 mysql sql resources db h2 schema h2 sql data h2 sql deploy init sql mysql 3 2 context path server servlet context path app template url url app template localhost 8080 api user localhost 8080 app template api user ngnix url app template server nginx url app template | server |
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ripple | licensed to the apache software foundation asf under one or more contributor license agreements see the notice file distributed with this work for additional information regarding copyright ownership the asf licenses this file to you 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 ripple emulator tm a browser based platform agnostic mobile application development and testing tool build requirements common nodejs v0 12 0 or higher npm v2 5 1 or higher python v2 7 3 recommended v3 x x is not supported windows download http www python org download releases 2 7 3 download osx linux make a c c compiler toolchain e g gcc windows visual studio 2010 the setup instructions can be found here https github com brianmcd contextify wiki windows installation guide msysgit http msysgit github io ripple uses bower http bower io for js libraries managing in order to use bower on windows msysgit http msysgit github io must be installed in a proper way see bower s readme md https github com bower bower windows users getting started if you plan to dive into the source be sure to check out the hacking https github com ripple emulator ripple blob master hacking md file to get started you need to setup a few things first run in the project root configure this script will pull down the needed npm packages and initialize the submodules build commands jake this will build ripple to the pkg folder in that folder there are various targets that can be used jake t this will describe all the available commands for building and running the tests etc running inside other web browsers ripple is by design browser agnostic and should be able to run inside any web browser if you want to run it inside other browsers you will need to use the pkg hosted target paired with the cli s emulate command ex using the npm package ripple emulate path to my app or ripple emulate remote http remote site com then navigating to your app s html file http localhost port index html enableripple true cli npm package there is a command line interface that can be paired with the client ui it can be used for various things such as statically hosting an application and running a local cross origin xhr proxy to install npm install g ripple emulator this will install a global script called ripple to see usage run ripple help contributing if you like the project and want to contribute code please issue a pull request on github https github com ripple emulator ripple pulls into the master branch code guidelines 4 spaces per editor tab jake lint no new lint errors introduced all unit tests are green reference material amp community project site https github com ripple emulator | front_end |
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LLMPersonality | chatgpt an enfj bard an istj empirical study on personalities of large language models what s in this repo raw data of all experiments are listed in the file references for more details please refer to this paper https arxiv org abs 2305 19926 please remember to cite us if you find our work helpful in your work article huang2023chatgpt author jen tse huang and wenxuan wang and man ho lam and eric john li and wenxiang jiao and michael r lyu title chat gpt and enfj bard an istj empirical study on personalities of large language models journal arxiv preprint arxiv 2305 19926 year 2023 | ai |
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Researchist | researchist neo4j https img shields io badge neo4j 008cc1 style for the badge logo neo4j logocolor white net https img shields io badge net 5c2d91 style for the badge logo net logocolor white react https img shields io badge react 2320232a svg style for the badge logo react logocolor 2361dafb researchist is student project done for advanced databases subject at faculty of electronic engineering university of ni contributors student id emilija ojba i 18026 matija peleti 18043 or e anti 17544 a href https github com djoleant internclix graphs contributors img src https contrib rocks image repo djoleant internclix a stack neo4jclient asp net webapi react js quickstart bash clone repository git clone https github com djoleant researchist git cd researchist server start cd researchist researchist dotnet watch run client start cd researchistapp npm install npm start start neo4j bin neo4j start configuration options environment variable name default value port 7687 neo4j client boltgraphclient neo4j user neo4j neo4j password researchist neo4j database neo4j neo4j version 5 2 | asp-net cypher graph-client neo4j graph-database nosql | server |
Project | project here some of my works about information technology area more usefull things https www emreyahyayuce com | server |
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ocean-web | h1 align center a href https ocean ds github io ocean web index html img alt ocean logo src https user images githubusercontent com 3240432 130073671 e62059a3 19f5 47bd 8ce8 db66b3240eb7 png width 400 a h1 p align center the design system made by a href https www useblu com br sobre blu a p p align center a href https github com ocean ds ocean web actions img alt actions status src https github com ocean ds ocean web workflows ci badge svg a a href https codecov io gh ocean ds ocean web img src https codecov io gh ocean ds ocean web branch master graph badge svg a a href https sonarcloud io dashboard id ocean ds ocean web img alt sonarcloud status src https sonarcloud io api project badges measure project ocean ds ocean web metric alert status a a href https github com ocean ds ocean web blob master license img alt github license src https img shields io github license ocean ds ocean web a a href https github com ocean ds ocean web graphs commit activity img alt github last commit src https img shields io github last commit ocean ds ocean web a a href https github com ocean ds ocean web network updates img alt dependabot src https img shields io badge dependabot enabled brightgreen a a href https github com prettier prettier img alt prettier code style src https img shields io badge code style prettier ff69b4 svg a a href https lerna js org img alt lerna src https img shields io badge maintained 20with lerna cc00ff svg a a href https conventionalcommits org img alt conventional commits src https img shields io badge conventional 20commits 1 0 0 yellow svg a a href http makeapullrequest com img alt conventional commits src https img shields io badge prs welcome brightgreen svg a a href https discord gg hdwzgpkug2 img alt discord server src https img shields io discord 875438252172136468 svg color 7389d8 labelcolor 6a7ec2 logo discord logocolor ffffff a p this project is composed of many npm packages for building web apps with ocean https zeroheight com 9c9b2b3aa p 257272 ocean ds t 968532 design system documentation documentation and examples can be found here https ocean ds github io ocean web index html get started to run the project first install the packages bash yarn install then run the project with watch bash yarn watch storybook or run the project without watch bash yarn run storybook if you changed or added css run this bash yarn watch css packages package version description useblu ocean core packages ocean core readme npm scoped https img shields io npm v useblu ocean core uikit css that implement ocean s design system useblu ocean react packages ocean react readme npm scoped https img shields io npm v useblu ocean react react components that implement ocean s design system contributing whether you re helping us fix bugs improve the docs or spread the word we d love to have you as part of this project blue heart read below to learn how you can take part of it code of conduct we adopted a code of conduct that we expect project participants to adhere to please read the full text github code of conduct md so that you can understand what actions will and will not be tolerated contributing guide read our contributing guide github contributing md to learn about our development process how to propose bugfixes and improvements and how to build and test your changes good first issues to help you get your feet wet and get you familiar with our contribution process we have a list of good first issues https github com ocean ds ocean web labels good 20first 20issue that contain bugs which have a relatively limited scope this is a great place to get started join our community join our discord server https discord gg hdwzgpkug2 to get the latest updates chat with other ocean enthusiasts and see what s happening in the community license all packages are licensed under the terms of the gpl 3 0 license license related projects design tokens for the blu design system https github com ocean ds ocean tokens svg icons for ocean ds https github com ocean ds ocean icons | typescript react uikit monorepo components ocean-ds a11y design-system | os |
todo-challenge | project summary this project is designed to be a test of all the skills you ve learned up to this point there will only be a list of requirements and no other instructions for this project your job will be to create an application that fulfills the requirements and specifications project setup 1 fork and clone this repository 2 create a git branch named submission 3 start coding requirements you ll be building a todo list application for tracking a list of tasks that need to be accomplished you can style the application in anyway that you see fit as long as it functions as specified make it look good and usable organize your code well and use best practices there will be three states that a todo task can be in todo in progress done a user should be able to see all three task groups at the same time features 1 add a new task there should be some way for users to add tasks to the todo task list 2 todo tasks users should be able to delete tasks in the todo list in the case that the task is no longer required users should be able to move tasks from the todo status to the in progress status 3 in progress tasks users should be able to move in progress tasks back to todo status users should be able to move in progress tasks to done status 4 done tasks users should be able to move done tasks back to in progress users should be able to delete done tasks 5 all tasks users should be able to edit a task at any time this will allow them to correct typos or add more detail without creating a new tasks and having to move it through statuses challenge features attempt these only after the required features have been finished 1 create a way to group sub tasks under one parent task when all of the sub tasks are completed the user should then be able to mark the top level parent task as complete 2 figure out how to store your todo list application state using the browser s local storage api now see if you can persist the state of the todo app between refreshes of the page reference https developer mozilla org en us docs web api window localstorage finished create a pull request from your submission branch back to this repository | front_end |
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ToolBench | div align center h1 toolbench h1 div div align center dialogues https img shields io badge tool num 3451 red style flat square dialogues https img shields io badge api num 16464 red style flat square dialogues https img shields io badge current dataset size 126k red style flat square dialogues https img shields io badge total api call 469k red style flat square dialogues https img shields io badge average reasoning traces 4 0 red style flat square dialogues https img shields io badge tool llama released green style flat square div p align center a href model model a a href data data release a a href web ui web demo a a href tool eval tool eval a a href assets paper pdf paper a a href citation citation a p div div align center img src https cdn discordapp com attachments 941582479117127680 1111543600879259749 20230526075532 png width 350px div this project toolllm aims to construct open source large scale high quality instruction tuning sft data to facilitate the construction of powerful llms with general tool use capability we aim to empower open source llms to master thousands of diverse real world apis we achieve this by collecting a high quality instruction tuning dataset it is constructed automatically using the latest chatgpt gpt 3 5 turbo 16k which is upgraded with enhanced function call https openai com blog function calling and other api updates capabilities we provide the dataset the corresponding training and evaluation scripts and a capable model toolllama fine tuned on toolbench join us on discord https discord gg nscfnpmurq read this in readme zh md what s new 2023 9 29 a new version tooleval which is more stable and covers more models including gpt4 please refer to tooleval https github com openbmb toolbench tree master toolbench tooleval for more details besides toolllama 2 7b v2 https huggingface co toolbench toolllama 2 7b v2 is released with stronger tool use capabilities please use the toolllama 2 7b v2 model to reproduce our latest experimental results with the new version tooleval 2023 8 30 data updation with more than 120 000 solution path annotations and intact reasoning thoughts please find data zip on google drive https drive google com drive folders 1ybuq732mpu kcljnuqelehtkakdxfc3j 2023 8 8 no more hallucination toolllama 2 7b v1 https huggingface co toolbench toolllama 2 7b v1 fine tuned from llama 2 7b is released with lower api hallucination than chatgpt 2023 8 4 we provide rapidapi backend service to free you from using your own rapidapi key and subscribing the apis please fill out our form https forms gle ochhc8dqzhgfit9r6 we will review it as soon as possible and send you the toolbench key to get start on it 2023 8 1 our paper https arxiv org abs 2307 16789 is released 2023 7 27 new version toolbench is released here is an overview of the dataset construction training and evaluation br div align center img src assets overview png width 800px div br features api collection we gather 16464 representational state transfer rest apis from rapidapi https rapidapi com hub a platform that hosts massive real world apis provided by developers instruction generation we curate instructions that involve both single tool and multi tool scenarios answer annotation we develop a novel depth first search based decision tree dfsdt to bolster the planning and reasoning ability of llms which significantly improves the annotation efficiency and successfully annotates those complex instructions that cannot be answered with cot or react we provide responses that not only include the final answer but also incorporate the model s reasoning process tool execution and tool execution results api retriver we incorporate api retrieval to equip toolllama with open domain tool using abilities all the data is automatically generated by openai api and filtered by us the whole data creation process is easy to scale up br div align center img src assets comparison png width 800px div br we also provide a demo of using toolllama div align center https github com openbmb toolbench assets 25274507 f1151d85 747b 4fac 92ff 6c790d8d9a31 div currently our toolllama has reached the performance of chatgpt turbo 16k in tool use in the future we will continually improve the data quality and increase the coverage of real world tools div align center img src assets performance png width 300px div here is the old version https github com openbmb toolbench tree legacy of toolbench data toolbench is intended solely for research and educational purposes and should not be construed as reflecting the opinions or views of the creators owners or contributors of this dataset it is distributed under apache license 2 0 below is the statistics of the data tool nums api nums instance nums real api call reasoning traces 3451 16464 126486 469585 4 0 we crawl 16000 real world apis from rapidapi https rapidapi com hub and curate realistic human instructions that involve them below we present a hierarchy of rapidapi and our instruction generation process br div align center img src assets instructiongeneration png width 800px div br toolbench contains both single tool and multi tool scenarios the multi tool scenarios can be further categorized into intra category multi tool and intra collection multi tool we utilize dfsdt method for all scenarios to our data creation here is an illustration for the data creation process using dfsdt method div align center img src assets answer anno png width 800px div data release please download our dataset using the following link google drive https drive google com drive folders 1ybuq732mpu kcljnuqelehtkakdxfc3j or tsinghua cloud https cloud tsinghua edu cn f c9e50625743b40bfbe10 notice that data 0801 is the old version data the file structure is as follows data instruction answer toolenv retrieval test instruction test query ids retrieval test query ids toolllama g123 dfs train json toolllama g123 dfs eval json reproduction data chatgpt cot chatgpt dfs toolllama dfs here is some descriptions for the data directory instruction and answer the instruction data and solution path annotation data g1 g2 g3 refers to single tool intra category multi tool and intra collection multi tool data respectively we also have an atlas explorer https atlas nomic ai map 58aca169 c29a 447a 8f01 0d418fc4d341 030ddad7 5305 461c ba86 27e1ca79d899 for visualization toolenv the tool environment related data containing api jsons api codes and api example responses retrieval the data used for tool retrieval is included in this directory test instruction and test query ids we sample 200 instances from every test set the test instruction directory contains test queries for each test set and the test query ids contains query ids of the test instances in each test set retrieval test query ids this directory contains query ids of the test instances for retriever toolllama g123 dfs train json and toolllama g123 dfs eval json preprocessed data that can be used to train toolllama directly and reproduce our results for preprocessing details we split the g1 g2 and g3 data into train eval and test parts respectively and combine the train data for training in our main experiments please make sure you have downloaded the necessary data and put the directory e g data under toolbench so that the following bash scripts can navigate to the related data model we release the toolllama 2 7b v2 https huggingface co toolbench toolllama 2 7b v2 which is trained on the latest version data and toolllama 7b v1 https huggingface co toolbench toolllama 7b v1 toolllama 7b lora v1 https huggingface co toolbench toolllama 7b lora v1 which are trained on the 0801 version data all models are trained on the released dataset in a multi task fashion we also release the tool retriever https huggingface co toolbench toolbench ir bert based uncased trained under our experimental setting fine tuning install clone this repository and navigate to the toolbench folder bash git clone git github com openbmb toolbench git cd toolbench install package python 3 9 bash pip install r requirements txt or for tooleval only bash pip install r toolbench tooleval requirements txt prepare the data and tool environment bash wget no check certificate https drive google com uc export download id 1xfjdxvzduy7txyf2yvzx3pjls2fy78jk confirm yes o data zip unzip data zip https drive google com file d 1xfjdxvzduy7txyf2yvzx3pjls2fy78jk view usp drive link training retriever data preprocessing bash export pythonpath python preprocess preprocess retriever data py query file data instruction g1 query json index file data test query ids g1 instruction test query ids json dataset name g1 output dir data retrieval g1 then run the following command to train the tool retriever bash export pythonpath python toolbench retrieval train py data path data retrieval g1 model name bert base uncased output path retrieval model num epochs 5 train batch size 32 learning rate 2e 5 warmup steps 500 max seq length 256 training toolllama data preprocessing for g1 answer as an example bash export pythonpath python preprocess preprocess toolllama data py tool data dir data answer g1 answer method dfs wofilter w2 output file data answer toolllama g1 dfs json our training code is based on fastchat https github com lm sys fastchat you can use the following command to train toolllama 7b with 2 x a100 80gb with our preprocessed data data toolllama g123 dfs train json for preprocessing details we split the g1 g2 and g3 data into train eval and test parts respectively and combine the train data for training in our main experiments bash export pythonpath torchrun nproc per node 2 master port 20001 toolbench train train mem py model name or path huggyllama llama 7b data path data toolllama g123 dfs train json eval data path data toolllama g123 dfs eval json conv template tool llama single round bf16 true output dir toolllama num train epochs 2 per device train batch size 2 per device eval batch size 2 gradient accumulation steps 8 evaluation strategy epoch prediction loss only save strategy epoch save total limit 8 learning rate 5e 5 weight decay 0 warmup ratio 0 04 lr scheduler type cosine logging steps 1 fsdp full shard auto wrap fsdp transformer layer cls to wrap llamadecoderlayer tf32 true source model max length 2048 model max length 8192 gradient checkpointing true lazy preprocess true report to none to train lora version bash export pythonpath deepspeed master port 20001 toolbench train train lora py model name or path huggyllama llama 7b data path data toolllama g123 dfs train json eval data path data toolllama g123 dfs eval json conv template tool llama single round bf16 true output dir toolllama lora num train epochs 5 per device train batch size 4 per device eval batch size 2 gradient accumulation steps 2 evaluation strategy epoch prediction loss only save strategy epoch save total limit 8 learning rate 5e 5 weight decay 0 warmup ratio 0 04 lr scheduler type cosine logging steps 1 source model max length 2048 model max length 8192 gradient checkpointing true lazy preprocess true deepspeed ds configs stage2 json report to none inference with our rapidapi server please fill out the form https forms gle ochhc8dqzhgfit9r6 first and after reviewing we will send you the toolbench key then prepare your toolbench key by bash export toolbench key your toolbench key for toolllama to inference with toolllama run the following commands bash export pythonpath python toolbench inference qa pipeline py tool root dir data toolenv tools backbone model toolllama model path toolbench toolllama 7b max observation length 1024 observ compress method truncate method dfs wofilter w2 input query file data test instruction g1 instruction json output answer file toolllama dfs inference result toolbench key toolbench key for toolllama lora bash export pythonpath python toolbench inference qa pipeline py tool root dir data toolenv tools backbone model toolllama model path huggyllama llama 7b lora lora path path to your downloaded toolllama 7b lora max observation length 1024 observ compress method truncate method dfs wofilter w2 input query file data test instruction g1 instruction json output answer file toolllama lora dfs inference result toolbench key toolbench key for toolllama lora under open domain setting run bash export pythonpath python toolbench inference qa pipeline open domain py tool root dir data toolenv tools corpus tsv path data retrieval g1 corpus tsv retrieval model path path to your retrival model retrieved api nums 5 backbone model toolllama model path huggyllama llama 7b lora lora path path to your toolllama lora max observation length 1024 observ compress method truncate method dfs wofilter w2 input query file data test instruction g1 instruction json output answer file toolllama lora dfs open domain inference result toolbench key toolbench key for openai models to use chatgpt run bash export toolbench key export openai key export pythonpath python toolbench inference qa pipeline py tool root dir data toolenv tools backbone model chatgpt function openai key openai key max observation length 1024 method dfs wofilter w2 input query file data test instruction g1 instruction json output answer file chatgpt dfs inference result toolbench key toolbench key to use text davinci 003 run bash export toolbench key export openai key export pythonpath python toolbench inference qa pipeline py tool root dir data toolenv tools backbone model davinci openai key openai key max observation length 1024 method dfs wofilter w2 input query file data test instruction g1 instruction json output answer file davinci dfs inference result toolbench key toolbench key inference with your own rapidapi account to do inference with customized rapidapi account pass your rapidapi key through rapidapi key and specify the use rapidapi key argument in the script bash export rapidapi key export openai key export pythonpath python toolbench inference qa pipeline py tool root dir data toolenv tools backbone model chatgpt function openai key openai key max observation length 1024 method dfs wofilter w2 input query file data test instruction g1 instruction json output answer file chatgpt dfs inference result rapidapi key rapidapi key use rapidapi key api customization to do inference with customized api s you should prepare the api documentation and code then modify your query for example to add an api hello world which returns a hello world string api documentation first generate the api documentation hello world json which should follow this format tool description return hello world tool name hello world title hello world api list name get hello world url description to get hello world method get required parameters optional parameters standardized name hello world then put it under a specific category in data toolenv tools either one of the 49 existing categories or a new category e g customized api code create a directory naming the hello world under customized directory then write a code api py to realize the function of the api and put it under customized hello world the api code can be written in this format python def get hello world to get hello world observation hello world return observation now the file structure under data toolenv should be tools sports basketball json basketball api py customized hello world json hello world api py response examples modify your query file and the query file should follow the following format query i want to get a hello world string query id 200001 api list category name customized tool name hello world api name get hello world finally we are free to inference with the hello world api by running the following commands bash export pythonpath python toolbench inference qa pipeline py tool root dir data toolenv tools backbone model toolllama model path toolbench toolllama 7b max observation length 1024 observ compress method truncate method dfs wofilter w2 input query file path to your query file output answer file path to your output file api customization currently we only support customized api usage under close domain setting we plan to support open domain soon setting up and running the interface toolbench contains a web ui based on chatbot ui https github com mckaywrigley chatbot ui forked to include the use of tools in the interface it comes in two parts the backend server and chatbot ui toolllama https github com lilbillybiscuit chatbot ui toolllama here is a video demo assets toolbench demo mp4 web ui bash git clone https github com lilbillybiscuit chatbot ui toolllama cd chatbot ui toolllama npm install npm run dev the app will be available on http localhost 3000 backend server bash export pythonpath python toolbench inference toolbench server py tool root dir data toolenv tools corpus tsv path data retrieval g1 corpus tsv retrieval model path path to your retrival model retrieved api nums 5 backbone model toolllama model path huggyllama llama 7b lora lora path path to your toolllama lora max observation length 1024 method dfs wofilter w2 input query file data test instruction g1 instruction json output answer file toolllama lora dfs open domain result rapidapi key rapidapikey this server will be available on http localhost 5000 to start a request call http localhost 5000 stream with a get or post request containing a json object with the following fields json text what is the weather in new york today top k 5 method dfs wofilter w2 tooleval by fine tuning llama on toolbench we obtain toolllama considering that human evaluation can be time consuming we follow alpacaeval https tatsu lab github io alpaca eval to develop an efficient machine evaluator tooleval which incorporates two evaluation metrics pass rate calculates the proportion of successfully completing an instruction within limited openai api calls preference measured by comparing two answers action sequences for a given instruction we pre define a set of criteria for a better answer which are organized as prompts for chatgpt we provide the test instruction and two candidate answers to the evaluator and obtain its preference we evaluate each answer pair multiple times to improve the reliability of our system then we calculate the win rate percentage of being preferred by the evaluator more details can be found in our paper to validate the reliability of chatgpt evaluator in both pass rate and win rate we sample among four different methods chatgpt react chatgpt dfsdt toolllama dfsdt and gpt4 dfsdt to obtain solution pairs for 300 test instructions for each method then we engage humans to annotate the pass rate for chatgpt dfsdt toolllama dfsdt and gpt4 dfsdt and the win rate among chatgpt react and chatgpt dfsdt our chatgpt evaluator demonstrates a high agreement of 87 1 in pass rate and 80 3 in win rate with human annotators this result shows that our evaluator generates highly similar evaluation results to humans and can be viewed as a credible evaluator who simulates human evaluation on pass rate and win rate more details about tooleval can be found in our paper evaluation with tooleval install install package python 3 9 bash pip install r requirements txt evaluation if you want to reproduce the official results download the reproduction data reproduction data zip through google drive https drive google com drive folders 1ybuq732mpu kcljnuqelehtkakdxfc3j unzip it and put the reproduction data under toolbench data and skip the data preparation process data preparation to evaluate your own model and method using tooleval first you need to prepare all the model predictions for the six test subsets create a directory naming with your model and method e g chatgpt cot then put each test set s predictions under the directory the file sturcture of the directory should be chatgpt cot g1 instruction 10160 cot 1 json g1 tool 10221 cot 1 json g3 instruction 10221 cot 1 json then preprocess the predictions by running the following commands bash export raw answer path data reproduction data model predictions export converted answer path data reproduction data model predictions converted export model name chatgpt cot export method cot mkdir converted answer path model name for test set in g1 instruction g1 category g1 tool g2 category g2 instruction g3 instruction do answer dir raw answer path model name test set output file converted answer path model name test set json python convert to answer format py answer dir answer dir method method output output file done after that check if there are preprocessed json files for the test sets under converted answer path model name if so you re ready to run the following evaluate process if not check if there is anything wrong with the model s predictions openai key prepare your openai key to use our evaluator the key s should be stored in a json file e g path to your openai key json file json bash username your user name passwd your password api key your openai key organization your organization pass rate bash export converted answer path data reproduction data model predictions converted export save path pass rate results export candidate model chatgpt cot export api pool file path to your openai key json file json python eval pass rate py converted answer path converted answer path save path save path reference model candidate model test ids data test ids max eval threads 20 evaluate times 4 the result files will be stored under the save path win rate the below example take chatgpt react as reference model and gpt4 react as candidate model notice that you need to get both model s pass rate results first then run the following commands to evaluate the preference result of gpt4 react bash export converted answer path data reproduction data model predictions converted export save path preference results export pass tare path pass rate results export reference model chatgpt cot export candidate model gpt 4 0613 cot export api pool file path to your openai key json file json python eval preference py converted answer path converted answer path reference model reference model output model candidate model test ids data test ids save path save path pass rate result path pass tare path max eval threads 20 use pass rate true evaluate times 4 the result files will be stored under the save path please refer to tooleval https github com openbmb toolbench tree master toolbench tooleval for more details model experiments results in our main experiments toolllama v2 demonstrates a compelling capability to handle both single tool and complex multi tool instructions which on a par with chatgpt below are the main results win rate for each model is compared with chatgpt react pass rate method model i1 inst i1 tool i1 cate i2 inst i2 cate i3 inst average react claude 2 5 5 3 5 5 5 6 6 14 6 8 text davinci 003 12 20 20 8 5 14 5 24 16 5 chatgpt 41 5 44 44 5 42 5 46 5 22 40 2 toolllama 25 29 33 30 5 31 5 25 29 gpt4 53 5 50 0 53 5 67 0 72 0 47 0 57 2 dfsdt claude 2 20 5 31 18 5 17 20 5 28 22 6 text davinci 003 43 5 44 46 37 42 46 43 1 chatgpt 54 5 65 60 5 75 71 5 62 64 8 toolllama 57 61 62 77 77 66 66 7 toolllama retreiver 64 64 60 5 81 5 68 5 65 67 3 gpt4 60 71 5 67 79 5 77 5 71 71 1 win rate reference model chatgpt react method model i1 inst i1 tool i1 cate i2 inst i2 cate i3 inst average react claude 2 31 27 8 33 8 35 31 5 47 5 34 4 text davinci 003 28 5 35 3 31 29 8 29 8 45 33 2 toolllama 45 42 47 5 50 8 41 8 55 47 gpt4 60 58 8 63 5 65 8 60 3 78 64 4 dfsdt claude 2 38 44 3 43 3 36 8 33 5 65 43 5 text davinci 003 40 3 43 8 46 8 40 5 43 3 63 46 3 chatgpt 60 5 62 57 3 72 64 8 69 64 3 toolllama 55 55 3 54 5 68 5 58 69 60 toolllama retreiver 62 3 59 55 68 5 60 8 73 63 1 gpt4 67 5 67 8 66 5 73 3 63 3 84 70 4 todo toolllama will reach gpt 4 s tool use capability resources of tool learning with the powerful capabilities of foundation models we are eager to see their applications in manipulating various tools for more resources please refer to the following bmtools project https github com openbmb bmtools tool learning survey paper https arxiv org abs 2304 08354 tool learning paper list project https github com thunlp toollearningpapers webcpm paper https github com thunlp webcpm citation feel free to cite us if you like toolbench bibtex misc qin2023toolllm title toolllm facilitating large language models to master 16000 real world apis author yujia qin and shihao liang and yining ye and kunlun zhu and lan yan and yaxi lu and yankai lin and xin cong and xiangru tang and bill qian and sihan zhao and runchu tian and ruobing xie and jie zhou and mark gerstein and dahai li and zhiyuan liu and maosong sun year 2023 eprint 2307 16789 archiveprefix arxiv primaryclass cs ai bibtex misc qin2023tool title tool learning with foundation models author yujia qin and shengding hu and yankai lin and weize chen and ning ding and ganqu cui and zheni zeng and yufei huang and chaojun xiao and chi han and yi ren fung and yusheng su and huadong wang and cheng qian and runchu tian and kunlun zhu and shihao liang and xingyu shen and bokai xu and zhen zhang and yining ye and bowen li and ziwei tang and jing yi and yuzhang zhu and zhenning dai and lan yan and xin cong and yaxi lu and weilin zhao and yuxiang huang and junxi yan and xu han and xian sun and dahai li and jason phang and cheng yang and tongshuang wu and heng ji and zhiyuan liu and maosong sun year 2023 eprint 2304 08354 archiveprefix arxiv primaryclass cs cl | ai |
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PHP-Example-Store-Application | php example store application this is a customer view application i made for my php class final this app illustrates not only backend php development but also sql database creation and front end html development | server |
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cv-lane | lane detection using opencv http i makeagif com media 7 10 2016 upemdq gif this communicates with jabelone s car controller https github com jabelone car controller jabelone s car controller is used to output the pwm signals to the steering servo and motor controller read the set up instructions for car controller for wiring instructions plug the car controller arduino into the pi via usb so cv lane can talk to it basic usage 1 a hardware safety switch is connected between pin 11 and ground there is an led between pin 40 and ground with an appropriate resistor 2 a pi camera is connected and it s facing the right way up angled down a bit optimum angle placement depends on your setup 3 run cv get hsv py and double click on a few points along the line take a point below the lowest for each h s and v take a point above the highest you now have a range modify the settings file entry and enter the new range for either colour 4 run cv get check hsv values py and press space to refresh the image when you move the camera it should track the line 5 run cv lane py the led should turn on for a few seconds wile initialising and then start flashing once it starts flashing press any key when prompted to immediately start moving forward ensure the hardware safety switch is on or cv lane will not run you may flick the switch off to pause while it s running | ai |
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MLPP | ml machine learning is a vast and exiciting discipline garnering attention from specialists of many fields unfortunately for c programmers and enthusiasts there appears to be a lack of support in the field of machine learning to fill that void and give c a true foothold in the ml sphere this library was written the intent with this library is for it to act as a crossroad between low level developers and machine learning engineers p align center img src https user images githubusercontent com 78002988 119920911 f3338d00 bf21 11eb 89b3 c84bf7c9f4ac gif width 600 height 400 p installation begin by downloading the header files for the ml library you can do this by cloning the repository and extracting the mlpp directory within it git clone https github com novak 99 mlpp next execute the buildso sh shell script sudo buildso sh after doing so maintain the ml source files in a local directory and include them in this fashion cpp include mlpp stat stat hpp including the ml statistics module int main finally after you have concluded creating a project compile it using g g main cpp usr local lib mlpp so std c 17 usage please note that ml uses the std vector double data type for emulating vectors and the std vector std vector double data type for emulating matrices begin by including the respective header file of your choice cpp include mlpp linreg linreg hpp next instantiate an object of the class don t forget to pass the input set and output set as parameters cpp linreg model inputset outputset afterwards call the optimizer that you would like to use for iterative optimizers such as gradient descent include the learning rate epoch number and whether or not to utilize the ui panel cpp model gradientdescent 0 001 1000 0 great you are now ready to test to test a singular testing instance utilize the following function cpp model modeltest testsetinstance this will return the model s singular prediction for that example to test an entire test set use the following function cpp model modelsettest testset the result will be the model s predictions for the entire dataset contents of the library 1 regression 1 linear regression 2 logistic regression 3 softmax regression 4 exponential regression 5 probit regression 6 cloglog regression 7 tanh regression 2 deep dynamically sized neural networks 1 possible activation functions linear sigmoid softmax swish mish sinc softplus softsign cloglog logit gaussian cdf relu gelu sign unit step sinh cosh tanh csch sech coth arsinh arcosh artanh arcsch arsech arcoth 2 possible optimization algorithms batch gradient descent mini batch gradient descent stochastic gradient descent gradient descent with momentum nesterov accelerated gradient adagrad optimizer adadelta optimizer adam optimizer adamax optimizer nadam optimizer amsgrad optimizer 2nd order newton raphson optimizer normal equation p p only available for linear regression 3 possible loss functions mse rmse mae mbe log loss cross entropy hinge loss wasserstein loss 4 possible regularization methods lasso ridge elasticnet weight clipping 5 possible weight initialization methods uniform xavier normal xavier uniform he normal he uniform lecun normal lecun uniform 6 possible learning rate schedulers time based epoch based step based exponential 3 prebuilt neural networks 1 multilayer peceptron 2 autoencoder 3 softmax network 4 generative modeling 1 tabular generative adversarial networks 2 tabular wasserstein generative adversarial networks 5 natural language processing 1 word2vec continous bag of words skip gram 2 stemming 3 bag of words 4 tfidf 5 tokenization 6 auxiliary text processing functions 6 computer vision 1 the convolution operation 2 max min average pooling 3 global max min average pooling 4 prebuilt feature detectors horizontal vertical prewitt filter horizontal vertical sobel filter horizontal vertical scharr filter horizontal vertical roberts filter gaussian filter harris corner detector 7 principal component analysis 8 naive bayes classifiers 1 multinomial naive bayes 2 bernoulli naive bayes 3 gaussian naive bayes 9 support vector classification 1 primal formulation hinge loss objective 2 dual formulation via lagrangian multipliers 10 k means 11 k nearest neighbors 12 outlier finder using z scores 13 matrix decompositions 1 svd decomposition 2 cholesky decomposition positive definiteness checker 3 qr decomposition 14 numerical analysis 1 numerical diffrentiation univariate functions multivariate functions 2 jacobian vector calculator 3 hessian matrix calculator 4 function approximator constant approximation linear approximation quadratic approximation cubic approximation 5 diffrential equations solvers euler s method growth method 15 mathematical transforms 1 discrete cosine transform 16 linear algebra module 17 statistics module 18 data processing module 1 setting and printing datasets 2 available datasets 1 wisconsin breast cancer dataset binary svm 2 mnist dataset train test 3 iris flower dataset 4 wine dataset 5 california housing dataset 6 fires and crime dataset chicago 3 feature scaling 4 mean normalization 5 one hot representation 6 reverse one hot representation 7 supported color space conversions rgb to grayscale rgb to hsv rgb to ycbcr rgb to xyz xyz to rgb 19 utilities 1 tp fp tn fn function 2 precision 3 recall 4 accuracy 5 f1 score what s in the works ml like most frameworks is dynamic and constantly changing this is especially important in the world of ml as new algorithms and techniques are being developed day by day here are a couple of things currently being developed for ml p convolutional neural networks p p kernels for svms p p support vector regression p citations various different materials helped me along the way of creating ml and i would like to give credit to several of them here this https www tutorialspoint com cplusplus program to compute determinant of a matrix article by tutorialspoint was a big help when trying to implement the determinant of a matrix and this https www geeksforgeeks org adjoint inverse matrix article by geeksforgeeks was very helpful when trying to take the adjoint and inverse of a matrix | cpp data-science machine-learning deep-learning | front_end |
sensorhub-cloud-iot | cloud iot core sensor hub this sample shows how to implement a sensor hub on android things that collects sensor data from connected sensors and publish as telemetry events to google cloud iot core note the android things console will be turned down for non commercial use on january 5 2022 for more details see the faq page https developer android com things faq introduction the sample showcases a sensor based device publishing data to cloud iot core https cloud google com iot core sensors can be added or removed dynamically and the device will reconnect automatically to the cloud when power or connectivity is lost sensor data is collected and sent to cloud iot core as telemetry events every 20 seconds by default and it can be changed from the cloud with device config cloud to device messages pre requisites android things compatible board android studio 2 2 1 bmp280 temperature and pressure https www adafruit com product 2651 1 pir motion detector sensor https www adafruit com product 189 or 1 button to simulate the motion detection google cloud platform https cloud google com project with cloud iot core enabled schematics schematics for raspberry pi 3 rpi3 schematics png build and install on android studio click on the run button if you prefer to run on the command line type gradlew installdebug adb shell am start com example androidthings sensorhub sensorhubactivity prepare the device this sample will create a key pair private and public on the device on the first run the private key will be saved to the android keystore using a secure hardware if one is available the public key will be printed to logcat and will be available as a file on your external storage location you will need the public key to register your device to cloud iot core here s how you can fetch it adb pull sdcard cloud iot auth certificate pem or depending on your platform adb d shell run as com example androidthings sensorhub cat data user 0 com example androidthings sensorhub files cloud iot auth certificate pem cloud iot auth certificate pem a new keypair is only generated again when the device is reflashed register the device with the cloud iot auth certificate pem file you can register your device on cloud iot core gcloud iot devices create device id project project id region cloud region registry registry id public key path cloud iot auth certificate pem type certificate type where device id your device id it can be anything that identifies the device for you project id your cloud iot core project id cloud region the cloud region for project registry registry id the registry name where this device should be registered certificate type either rsa x509 pem or es256 x509 pem depending on whether your device key algorithm is rsa or ec see below configure the device now that your device s public key is registered you can set the device so that it can securely connect to cloud iot core adb shell am start e project id project id e cloud region cloud region e registry id registry id e device id device id e key algorithm device key algorithm com example androidthings sensorhub sensorhubactivity where project id cloud region registry id and device id must be the corresponding values used to register the device on cloud iot core and device key algorithm must be the standard name of the algorithm to be used for generating the device authentication key currently rsa and ec are supported and rsa is the default in case this argument is not defined testing if the registration and configuration steps were executed successfully your device will immediately start publishing sensor data to cloud iot core you can pipe this data into other google cloud services if you want to quickly check if messages are being published correctly you can create a topic subscription replace subscription name with any unique name you want gcloud pubsub subscriptions create projects project id subscriptions subscription name topic projects project id topics registry id and then pull messages from this subscription gcloud pubsub subscriptions pull auto ack projects project id subscriptions subscription name next steps take a look at the cloud iot core documentation https cloud google com iot docs to learn how to pipe the data published by your devices into other google cloud services license copyright 2018 the android open source project inc licensed to the apache software foundation asf under one or more contributor license agreements see the notice file distributed with this work for additional information regarding copyright ownership the asf licenses this file to you under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | android-things | server |
osdi | nctu operation system design and implementation this is the documentation page of course labs https grasslab github io osdi build requirements sphinx http www sphinx doc org en master usage installation html sphinx rtd theme https sphinx rtd theme readthedocs io en stable installing html sphinx intl http www sphinx doc org en master usage advanced intl html for zh tw | course | os |
cd0461-building-computer-vision-solutions-with-azure-project-starter | readme template this is the project starter repo for udacity s building computer vision solutions with azure course project automated passenger boarding kiosk license license license txt | ai |
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awesome-blockchain | div align center div img src img awesomeblockchain png alt awesome bockchain div br p style font size 1 4em curating the best strong blockchain strong and crytocurrency resources p p sup do you want to a href mailto 0xtokens gmail com support a this project sup br a href mailto 0xtokens gmail com img src img sponsor svg width 260 alt wtgtybhertgeghgtwtg a p br br p align center a href donate donate a nbsp nbsp nbsp nbsp a href https github com 0xtokens awesome blockchain blob master contributing md contribution guide a nbsp nbsp nbsp nbsp a href https twitter com target blank twitter a nbsp nbsp nbsp nbsp a href http t me blockchaintokens target blank telegram a p br a href https awesome re img src https awesome re badge svg alt awesome a br sup inspired by awesome and following the a href https github com sindresorhus awesome blob master awesome md manifesto a guidelines sup br p sub this work aims to raise awareness of the possibilities offered by blockchain 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technology that facilitates censorship resistant applications nbsp http www google com s2 favicons domain https pchain org nbsp pchain https pchain org sub sup pai https coinmarketcap com es currencies pchain sup sub first native multi chain system that supports evm and it makes large scale blockchain applications possible nbsp http www google com s2 favicons domain https zilliqa com nbsp zilliga https zilliqa com sub sup zil https coinmarketcap com es currencies zilliqa sup sub high throughput blockchain platform designed to scale nbsp https awebanalysis com img coins 16 neo png nbsp neo https neo org en us sub sup neo https coinmarketcap com es currencies neo sup sub china s first ever original and open source public chain project it serves as a smart assets platform allows for a smarter way for assets registration issuance and circulation nbsp http www google com s2 favicons domain https eos io nbsp eos https eos io sub sup eos https coinmarketcap com es currencies eos sup sub infrastructure that introduces a blockchain architecture designed to enable vertical and horizontal scaling of decentralized applications the software provides accounts authentication databases asynchronous communication and the scheduling of applications across multiple cpu cores and or clusters nbsp http www google com s2 favicons domain https www cardano org en home nbsp cardano https www cardano org en home sub sup ada https coinmarketcap com es currencies cardano sup sub cardano is developing a smart contract platform which seeks to deliver more advanced features than any protocol previously developed nbsp http www google com s2 favicons domain https lisk io nbsp lisk https lisk io sub sup lsk https coinmarketcap com es currencies lisk sup sub blockchain application platform that inspires enables and supports developers to build decentralized applications written in javascript nbsp http www google com s2 favicons domain https www achain com nbsp achain https www achain com sub sup act https coinmarketcap com es currencies achain sup sub public blockchain platform that enables developers of all levels to issue tokens and create smart contracts decentralized applications and blockchain systems nbsp http www google com s2 favicons domain https komodoplatform com nbsp komodo https komodoplatform com sub sup kmd https coinmarketcap com es currencies komodo sup sub end to end blockchain solutions provider empowering developers to build freely and participate in creating the largest open blockchain network nbsp http www google com s2 favicons domain https enigma co nbsp enigma project https enigma co sub sup eng https coinmarketcap com es currencies enigma project sup sub enigma is using groundbreaking privacy technology to build the first platform for scalable end to end decentralized apps nbsp http www google com s2 favicons domain https qtum org nbsp qtum https qtum org sub sup qtum https coinmarketcap com es currencies qtum sup sub decentralized blockchain application platform capable of running smart contracts on multiple virtual machines with proof of stake consensus combining a modified bitcoin core infrastructure with an intercompatible version of the ethereum virtual machine evm nbsp http www google com s2 favicons domain https ubiqsmart com nbsp ubiq https ubiqsmart com sub sup ubq https coinmarketcap com es currencies ubiq sup sub built upon an improved ethereum codebase the ubiq blockchain acts as a large globally distributed ledger and supercomputer allowing developers to create decentralized and automated solutions nbsp http www google com s2 favicons domain https www hederahashgraph com nbsp hedera hashgraph https www hederahashgraph com sub sup sup sub offer a public distributed ledger that enables globally distributed applications nbsp http www google com s2 favicons domain https aeternity com nbsp aeternity https aeternity com sub sup ae https coinmarketcap com es currencies aeternity sup sub scalable blockchain platform that enables high speed transacting purely functional smart contracts and decentralized oracles coded in erlang nbsp http www google com s2 favicons domain https ont io nbsp ontology https ont io sub sup ont https coinmarketcap com es currencies ontology sup sub provides new high performance public blockchains that include a series of complete distributed ledgers and smart contract systems nbsp http www google com s2 favicons domain https rise vision nbsp rise https rise vision sub sup rise https coinmarketcap com es currencies rise sup sub offers a platform for decentralized distributed applications dapps powered by a community driven delegated proof of stake dpos blockchain js ts python c nbsp http www google com s2 favicons domain https www asch io nbsp asch https www asch io sub sup xas https coinmarketcap com es currencies asch sup sub blockchain application development platform based on side chain architecture nbsp https awebanalysis com img coins 16 moac png nbsp moac http moac io sub sup moac https coinmarketcap com es currencies moac sup sub multi layer blockchain architecture blockchain platform with necessary plumbing parts available to sub blockchains providing solution for idea test private chain deployment complex task processing decentralized applications etc nbsp http www google com s2 favicons domain https h cash nbsp hcash https h cash designed to be a side chain for both block based and blockless based blockchains nbsp http www google com s2 favicons domain https tron network nbsp tron https tron network sub sup trx https coinmarketcap com es currencies tron sup sub one of the largest blockchain based operating systems has as purpose to building the infrastructure for a truly decentralized internet nbsp http www google com s2 favicons domain https nebulas io nbsp nebulas https nebulas io sub sup nas https coinmarketcap com es currencies nebulas token sup sub based on its blockchain valuation mechanism nebulas proposes future oriented incentive and consensus systems and the ability to self evolve without forking the google of the blockchain nbsp http www google com s2 favicons domain https ark io nbsp ark https ark io sub sup ark https coinmarketcap com es currencies ark sup sub aims to create an entire ecosystem of linked chains and a virtual spiderweb of endless use cases nbsp http www google com s2 favicons domain https aion network nbsp aion netwok https aion network sub sup aion https coinmarketcap com es currencies aion sup sub multi tier blockchain system designed to address scalability privacy and interoperability nbsp http www google com s2 favicons domain https www elastos org nbsp elastos https www elastos org sub sup ela https coinmarketcap com es currencies elastos sup sub focused on re decentralizing internet with blockchain to secure identity where decentralized applications are detached from the internet while also permitting full scalability to millions of users nbsp http www google com s2 favicons domain https substratum net nbsp substratum https substratum net sub sup sub https coinmarketcap com es currencies substratum sup sub open source network that allows anyone to allocate spare computing resources to make the internet a free and fair place for the entire world div align right up img up png div contents enterprise and private blockchains nbsp http www google com s2 favicons domain https ripple com nbsp ripple https ripple com sub sup xrp https coinmarketcap com es currencies ripple sup sub bank s cryptocurrency built for enterprise use xrp offers banks and payment providers a reliable way to source liquidity for payments nbsp https awebanalysis com img coins 16 nuls png nbsp nuls http nuls io sub sup nuls https coinmarketcap com es currencies nuls sup sub adaptable blockchain for enterprise solutions nbsp http www google com s2 favicons domain https nebl io nbsp neblio https nebl io sub sup nebl https coinmarketcap com es currencies neblio sup sub a secure distributed platform built for enterprise applications and services running on the neblio blockchain network nbsp http www google com s2 favicons domain https icon foundation nbsp icon https icon foundation en sub sup icx https coinmarketcap com es currencies icon sup sub one of the largest blockchain networks in the world icon boasts independent blockchains comprised of reputable institutions in major industries ranging from financial institutions insurance companies hospitals universities and more nbsp http www google com s2 favicons domain https stratisplatform com nbsp stratis https stratisplatform com sub sup strat https coinmarketcap com es currencies stratis sup sub end to end solutions for development testing and deployment of native c blockchain applications on the net framework private blockchains for all nbsp http www google com s2 favicons domain https www factom com nbsp factom https www factom com sub sup fct https coinmarketcap com es currencies factom sup sub decentralized notary provides active solutions for compliance identity transparent assets and securities for enterprise government and non profit systems nbsp http www google com s2 favicons domain https dragonchain com nbsp dragonchain https dragonchain com sub sup drgn https coinmarketcap com es currencies dragonchain sup sub flexible public private hybrid blockchain platform built in protection of business data using established programming languages java python node c nbsp http www google com s2 favicons domain https www xdac co nbsp xdac https www xdac co build your decentralized company self governed platform for creating and managing decentralized autonomous companies nbsp http www google com s2 favicons domain http emercoin com nbsp emercoin http emercoin com sub sup emc https coinmarketcap com es currencies emercoin sup sub distributed blockchain services for business and personal use with its own decentralized dns system br private blockchains nbsp http www google com s2 favicons domain https www hyperledger org nbsp hyperledger https www hyperledger org open source collaborative effort created to advance cross industry blockchain technologies it is a global collaboration hosted by the linux foundation nbsp http www google com s2 favicons domain https azure microsoft com nbsp azure blockchain https azure microsoft com en us solutions blockchain azure blockchain as a service nbsp http www google com s2 favicons domain http www ibm com blockchain nbsp ibm blockchain http www ibm com blockchain ibm blockchain platform nbsp http www google com s2 favicons domain http www multichain com nbsp multichain http www multichain com open platform for building blockchains nbsp http www google com s2 favicons domain https www corda net nbsp corda https www corda net open source blockchain for business allows to build interoperable blockchain networks that transact in privacy nbsp http www google com s2 favicons domain http openchain org nbsp openchain http openchain org blockchain technology for the enterprise is an open source distributed ledger technology div align right up img up png div contents blockchain projects governance nbsp http www google com s2 favicons domain https aragon one nbsp aragon https aragon one sub sup ant https coinmarketcap com es currencies aragon sup sub aims to disintermediate the creation and maintenance of organizational structures by using blockchain technology nbsp http www google com s2 favicons domain https bitnation co nbsp bitnation https bitnation co sub sup xpat https etherscan io address 0xbb1fa4fdeb3459733bf67ebc6f893003fa976a82 sup sub become a world citizen governance 2 0 borderless decentralized voluntary nbsp http www google com s2 favicons domain https www democracy earth nbsp democracy earth https www democracy earth governance in a post nation state world div align right up img up png div contents funding and business evaluation nbsp http www google com s2 favicons domain https wingsfoundation ch nbsp wings https wingsfoundation ch sub sup wings https coinmarketcap com es currencies wings sup sub decentralized applications for blockchain based evaluation funding and early adopter engagement nbsp http www google com s2 favicons domain https starbase co nbsp starbase https starbase co sub sup star https coinmarketcap com es currencies starbase sup sub token based global crowdfunding and crowdsourcing platform div align right up img up png div contents financial services and invoicing nbsp http www google com s2 favicons domain https bitshares org nbsp bitshares https bitshares org sub sup bts https coinmarketcap com es currencies bitshares sup sub stack of financial services including exchange and banking on a blockchain nbsp http www google com s2 favicons domain https chain com nbsp chain https chain com enterprise grade blockchain infrastructure that enables organizations to build financial services from the ground up nbsp http www google com s2 favicons domain https www tallysticks io nbsp tallysticks https www tallysticks io automate the invoicing and the invoice financing processes international trade simplified nbsp http www google com s2 favicons domain https cindicator com nbsp cindicator https cindicator com sub sup cnd https coinmarketcap com es currencies cindicator sup sub works with predictive analytics data and market indicators powered by hybrid intelligence to support traditional and crypto financial market analysis nbsp http www google com s2 favicons domain https omisego network nbsp omisego https omisego network sub sup omg https coinmarketcap com es currencies omisego sup sub scaling solution for finance on ethereum enabling transparent peer to peer transactions in real time the decentralized network facilitates self sovereign financial services across geographies asset classes and applications nbsp http www google com s2 favicons domain https wanchain org nbsp wanchain https wanchain org sub sup wan https coinmarketcap com es currencies wanchain sup sub distributed financial infrastructure cross chain smart contracts with privacy protection nbsp http www google com s2 favicons domain https veritas veritaseum com nbsp veritaseum https veritas veritaseum com sub sup veri https coinmarketcap com es currencies veritaseum sup sub blockchain based peer to peer capital markets as software on a global scale without the need for a third or authoritarian interest nbsp http www google com s2 favicons domain https populous world nbsp populous https populous world sub sup ppt https coinmarketcap com es currencies populous sup sub global invoice trading platform built on blockchain s distributed ledger technology nbsp https awebanalysis com img coins 16 bytom png nbsp bytom https bytom io sub sup btm https coinmarketcap com es currencies bytom sup sub peer to peer financial applications and asset applications from institutions and individuals could be built on bytom chain nbsp http www google com s2 favicons domain https liquid plus nbsp liquid https liquid plus sub sup qash https coinmarketcap com es currencies qash sup sub financial services on the blockchain div align right up img up png div contents payment liquidity networks lending and custom digital assets nbsp http www google com s2 favicons domain https request network nbsp request network https request network sub sup req https coinmarketcap com es currencies request network sup sub decentralized network for payment requests nbsp http www google com s2 favicons domain https home kyber network nbsp kyber network https home kyber network sub sup knc https coinmarketcap com es currencies kyber network sup sub decentralized liquidity network powering seamless transactions between individuals ecosystems and dapps nbsp https awebanalysis com img coins 16 bancor png nbsp bancor https about bancor network sub sup bnt https coinmarketcap com es currencies bancor sup sub decentralized liquidity network that allows to hold and create any token and convert it to any other token in the network with no counter party at an automatically calculated price using a web wallet nbsp http www google com s2 favicons domain http counterparty io nbsp counterparty http counterparty io sub sup xcp https coinmarketcap com es currencies counterparty sup sub peer to peer finance platform built on top of the bitcoin protocol custom tokens creation nbsp http www google com s2 favicons domain http www omnilayer org nbsp omni http www omnilayer org sub sup omni https coinmarketcap com es currencies omni sup sub platform for creating and trading custom digital assets and currencies software layer built on top of the bitcoin blockchain nbsp http www google com s2 favicons domain https wavesplatform com nbsp waves https wavesplatform com sub sup waves https coinmarketcap com es currencies waves sup sub decentralized platform that allows any user to issue transfer swap and trade custom tokens directly on the blockchain nbsp http www google com s2 favicons domain https www syscoin org nbsp syscoin https www syscoin org sub sup sys https coinmarketcap com es currencies syscoin sup sub asset token creation platform and decentralized marketplace and so much more nbsp http www google com s2 favicons domain https ethlend io nbsp ethlend https ethlend io sub sup lend https coinmarketcap com es currencies ethlend sup sub decentralized financial marketplace for peer to peer lending agreements using blockchain and smart contracts nbsp http www google com s2 favicons domain https www digibyte io nbsp digibyte https www digibyte io sub sup dgb https coinmarketcap com es currencies digibyte sup sub decentralized payment network digital currency inspired by bitcoin nbsp https awebanalysis com img coins 16 salt png nbsp salt https www saltlending com sub sup salt https coinmarketcap com es currencies salt sup sub lets leverage blockchain assets to secure cash loans nbsp http www google com s2 favicons domain https cryptonex org nbsp cryptonex https cryptonex org sub sup cnx https coinmarketcap com es currencies cryptonex sup sub global blockchain acquisition network nbsp https awebanalysis com img coins 16 havven png nbsp havven https havven io sub sup hav https coinmarketcap com es currencies havven sup sub decentralised payment network and stablecoin it allows anyone to transact using a stable cryptocurrency div align right up img up png div contents recruitment nbsp http www google com s2 favicons domain https chronobank io nbsp chronobank https chronobank io sub sup time https coinmarketcap com es currencies chronobank sup sub wide ranging blockchain project aimed at disrupting the hr recruitment finance industries in uber like way div align right up img up png div contents identity copyright and legal agreements nbsp http www google com s2 favicons domain https monax io nbsp monax https monax io create self executable legal agreements based on smart contract nbsp http www google com s2 favicons domain https agreements network nbsp agreements network https agreements network introducing a decentralized contract management platform that uses smart contract technology to create prove and operate legal agreements it s the legal layer for a networked world nbsp http www google com s2 favicons domain https veres one nbsp veres one https veres one identity management on blockchain nbsp http www google com s2 favicons domain https radiumcore org nbsp radium core https radiumcore org sub sup rads https coinmarketcap com es currencies radium sup sub decentralized cryptocurrency offering a system for record verification and blockchain based identities nbsp http www google com s2 favicons domain https bnktothefuture com nbsp bnktothefuture https bnktothefuture com sub sup bft https coinmarketcap com es currencies bnktothefuture sup sub investment platform that brings financial innovation technology investment opportunities to investors nbsp http www google com s2 favicons domain https www agrello io nbsp agrello https www agrello io sub sup dlt https coinmarketcap com es currencies agrello delta sup sub legally binding smart contracts nbsp http www google com s2 favicons domain https www ascribe io nbsp ascribe https www ascribe io decentralized content registry for for artists and creators lock in attribution securely share and trace where your digital work spreads nbsp http www google com s2 favicons domain https binded com nbsp binded https binded com copyright made simple the easy way to protect your images nbsp http www google com s2 favicons domain https www civic com nbsp civic https www civic com sub sup cvc https coinmarketcap com es currencies civic sup sub universal digital identity a secured identity for everyone civic enables people to take control and protect their identity nbsp http www google com s2 favicons domain https www uport me nbsp uport https www uport me open identity system allows users to register their own identity on ethereum send and request credentials sign transactions and manage keys data nbsp http www google com s2 favicons domain https www po et nbsp po et https www po et sub sup poe https coinmarketcap com es currencies poet sup sub decentralized protocol for content ownership discovery and monetization in media nbsp http www google com s2 favicons domain https dock io nbsp dock https dock io sub sup dock https coinmarketcap com es currencies dock sup sub connect profiles reputations and networks into one sharable source using blockchain technology nbsp http www google com s2 favicons domain https reddcoin com nbsp reddcoin https reddcoin com sub sup rdd sup sub creating blockchain platform to use personalized ids for social media tipping with all data stored safely in the blockchain nbsp http www google com s2 favicons domain https blockstack org nbsp blockstack https blockstack org platform for decentralized apps where users own their data a browser is all that s needed to get started nbsp http www google com s2 favicons domain https www evernym com nbsp evernym https www evernym com self sovereign identity built on top of open source permissioned blockchain nbsp http www google com s2 favicons domain https jolocom com nbsp jolocom https jolocom com self sovereing identity app div align right up img up png div contents security and authentication nbsp http www google com s2 favicons domain https edge app nbsp edge https edge app multi asset blockchain wallet and single signon security platform for blockchain apps nbsp http www google com s2 favicons domain https quantstamp com nbsp quantstamp https quantstamp com sub sup qsp https coinmarketcap com es currencies quantstamp sup sub protocol to enable smart contract security for ethereum s future easy web based automated smart contract auditing proof of concept nbsp http www google com s2 favicons domain https shocard com nbsp shocard https shocard com secure enterprise identity authentication div align right up img up png div contents asset tokenization nbsp http www google com s2 favicons domain https www trusttoken com nbsp trusttoken https www trusttoken com sub sup tusd https coinmarketcap com es currencies trueusd sup sub platform to create asset backed tokens that you can easily buy and sell around the world nbsp http www google com s2 favicons domain https atlant io nbsp atlant io https atlant io sub sup atl https coinmarketcap com es currencies atlant sup sub addressing two of the most pertinent problems in real estate with its decentralized blockchain platform tokenized ownership and global p2p rentals div align right up img up png div contents database and content distribution nbsp http www google com s2 favicons domain https ipfs io nbsp ipfs https ipfs io interplanetary file system hypermedia distribution protocol addressed by content and identities enables the creation of completely distributed applications it aims to make the web faster safer and more open nbsp http www google com s2 favicons domain http swarm gateways net nbsp swarm http swarm gateways net the primary objective is to provide a redundant store of ethereum s public record store and distribute dapp code and data nbsp http www google com s2 favicons domain https decent ch nbsp decent https decent ch sub sup dct https coinmarketcap com es currencies decent sup sub blockchain content distribution platform nbsp http www google com s2 favicons domain http maidsafe net nbsp maidsafe http maidsafe net sub sup maid https coinmarketcap com es currencies maidsafecoin sup sub safe secure access for everyone network a way to access a world of existing apps where the security of your data is put above all else nbsp http www google com s2 favicons domain http www synereo com nbsp synereo http www synereo com sub sup amp https coinmarketcap com es currencies synereo sup sub blockchain content distribution platform with reward system and goal to change the decentralized future of social networking nbsp http www google com s2 favicons domain https gxs gxb io en nbsp gxchain https gxs gxb io en sub sup gxs https coinmarketcap com es currencies gxchain sup sub commercialized data marketplace based on blockchain nbsp http www google com s2 favicons domain https www bigchaindb com nbsp bigchaindb https www bigchaindb com data storage and built in asset support is like a database with blockchain characteristics nbsp https www google com s2 favicons domain https cybercongress ai nbsp cyber https github com cybercongress a decentralized alternative to google built with the help of ipfs and tendermint div align right up img up png div contents cloud computing nbsp http www google com s2 favicons domain https iex ec nbsp iexec https iex ec sub sup rlc https coinmarketcap com es currencies rlc sup sub blockchain based decentralized cloud computing nbsp http www google com s2 favicons domain https golem network nbsp golem https golem network sub sup gnt https coinmarketcap com es currencies golem network tokens sup sub it s made up of the combined power of user s machines from personal laptops to entire datacenters creates a global market for idle computer power nbsp http www google com s2 favicons domain https aelf io nbsp aelf https aelf io sub sup elf https coinmarketcap com es currencies aelf sup sub decentralized cloud computing blockchain network nbsp http www google com s2 favicons domain https dfinity org nbsp dfinity https dfinity org high performance decentralized cloud compute network that hosts evm smart contracts br storage nbsp http www google com s2 favicons domain https storj io nbsp storj https storj io sub sup storj https coinmarketcap com es currencies storj sup sub decentralized blockchain peer to peer protocols and encrypted cloud storage nbsp http www google com s2 favicons domain http sia tech nbsp sia http sia tech sub sup sc https coinmarketcap com es currencies siacoin sup sub enterprise grade collaborative cloud for data storage nbsp http www google com s2 favicons domain https filecoin io nbsp filecoin https filecoin io sub sup fil https coinmarketcap com es currencies filecoin sup sub decentralized storage network div align right up img up png div contents iot nbsp https awebanalysis com img coins 16 iota png nbsp iota https www iota org sub sup miota https coinmarketcap com es currencies iota sup sub distributed ledger for the internet of things the fuel for microtransactions machine to machine m2m without fees as well as secure data transfer tangle nbsp http www google com s2 favicons domain http taraxa io nbsp taraxa http taraxa io aims to democratize internet of things iot data nbsp http www google com s2 favicons domain http www toschain org nbsp tos http www toschain org decentralization layered block network technology based on sdag for the iot nbsp http www google com s2 favicons domain https nucleus vision nbsp nucleus vision https nucleus vision sub sup ncash https coinmarketcap com es currencies nucleus vision sup sub iot blockchain based contactless identification system using ncash cryptocurrency to enable transactions across retail ecosystem nbsp http www google com s2 favicons domain https slock it nbsp slock it https slock it ethereum based platform for building shared things br supply chain and logistics nbsp http www google com s2 favicons domain https www vechain com nbsp vechain https www vechain com sub sup ven https coinmarketcap com es currencies vechain sup sub blockchain as a service platform for products and information nbsp http www google com s2 favicons domain https origintrail io nbsp origintrail https origintrail io sub sup trac https coinmarketcap com es currencies origintrail sup sub blockchain powered data exchange protocol for interconnected supply chains nbsp http www google com s2 favicons domain http www chronicled com nbsp chronicled http www chronicled com link any physical product to its digital identity on a blockchain nbsp http www google com s2 favicons domain https www wacoin io nbsp wabi https www wacoin io sub sup wabi https coinmarketcap com es currencies wabi sup sub digital token backed by safe products by scanning the secure anti counterfeit labels consumers are guaranteed product authenticity and rewarded through a loyalty system nbsp http www google com s2 favicons domain https modum io nbsp modum https modum io sub sup mod https coinmarketcap com es currencies modum sup sub data integrity for supply chain operations powered by blockchain technology nbsp http www google com s2 favicons domain https www waltonchain org nbsp waltonchain https www waltonchain org sub sup wtc https coinmarketcap com es currencies waltonchain sup sub combines blockchain with iot specifically rfid to create a management system for supply chains nbsp http www google com s2 favicons domain https ambrosus com nbsp ambrosus https ambrosus com sub sup amb https coinmarketcap com es currencies amber sup sub blockchain powered iot network for food and pharmaceutical enterprises enabling secure and frictionless dialogue between sensors distributed ledgers and databases to optimise supply chain visibility and quality assurance div align right up img up png div contents data analysis ai nbsp https awebanalysis com img coins 16 gnosis gno png nbsp gnosis https gnosis pm sub sup gno https coinmarketcap com es currencies gnosis gno sup sub market driven forecasting technology to let you shape the future nbsp http www google com s2 favicons domain http www idni org nbsp tauchain http www idni org decentralized blockchain network intended to solve the bottlenecks inherent in large scale human communication and accelerate productivity in human collaboration using logic based artificial intelligence nbsp http www google com s2 favicons domain https fysical org nbsp fysical https fysical org sub sup fys sup sub infrastructure for the location data market protocol foot traffic sensor readings store visit information commute routes nbsp http www google com s2 favicons domain http www cortexlabs ai nbsp cortex http www cortexlabs ai sub sup ctxc https coinmarketcap com es currencies cortex sup sub decentralized ai autonomous system allows users to write machine learning programs on the blockchain and submit interactions that are dependent upon other contracts div align right up img up png div contents marketing advertisements and brand loyalty nbsp http www google com s2 favicons domain http data eco nbsp data http data eco sub sup dta https coinmarketcap com es currencies data sup sub aims to create a decentralized ai powered trust alliance for the digital advertising industry nbsp http www google com s2 favicons domain http loyyal com nbsp loyyal http loyyal com universal loyalty and rewards platform nbsp http www google com s2 favicons domain https basicattentiontoken org nbsp brave https basicattentiontoken org sub sup bat https coinmarketcap com es currencies basic attention token sup sub blockchain based digital advertising it has its own browser brave nbsp http www google com s2 favicons domain https woonkly com nbsp woonkly https woonkly com connects advertisers and their audience without intermediaries earn money by watching sharing and interacting with digital content nbsp http www google com s2 favicons domain https kindads io nbsp kindads https kindads io kind ads is an advertising platform that serves user friendly ads without taking any middleman fees nbsp http www google com s2 favicons domain https www stormx io nbsp storm https www stormx io sub sup storm https coinmarketcap com es currencies storm sup sub storm s gamified microtask platform creates opportunities for people around the world to earn cryptocurrency rewards div align right up img up png div contents social media nbsp http www google com s2 favicons domain https steem io nbsp steem https steem io sub sup steem https coinmarketcap com es currencies steem sup sub blockchain based social media platform with reward system nbsp http www google com s2 favicons domain https viuly com nbsp viuly https viuly com sub sup viu https coinmarketcap com es currencies viuly sup sub decentralized video sharing platform where authors and users rewarded for doing things they love nbsp http www google com s2 favicons domain https d tube nbsp dtube https d tube decentralized streaming video platform linked to steemit which allows users to upload videos easily get rewards via the upvotes or likes on their post nbsp http www google com s2 favicons domain https www thetatoken org nbsp theta https www thetatoken org sub sup theta https coinmarketcap com es currencies theta token sup sub decentralized video delivery network powered by users and an innovative new blockchain nbsp http www google com s2 favicons domain https lino network nbsp lino network https lino network sub sup sup sub decentralized autonomous video content economy nbsp http www google com s2 favicons domain https www minds com nbsp minds https www minds com open source and decentralized platform for internet freedom get paid in crypto for your contributions to the community nbsp http www google com s2 favicons domain https www suchapp io nbsp suchapp https www suchapp io sub sup sps sup sub combines best of class messaging with the power of a social network group administrators can manage their own custom loyalty program based on our sps token nbsp http www google com s2 favicons domain https www yoyow org nbsp yoyow https www yoyow org sub sup yoyow https coinmarketcap com es currencies yoyow sup sub blockchain based content value network nbsp http www google com s2 favicons domain https mith io nbsp mithril https mith io sub sup mith https coinmarketcap com es currencies mithril sup sub decentralized social media platform that rewards all content creators nbsp http www google com s2 favicons domain https colony io nbsp colony https colony io platform for open organizations div align right up img up png div contents web browsers nbsp http www google com s2 favicons domain https brave com nbsp brave https brave com sub sup bat https coinmarketcap com es currencies basic attention token sup sub features blockchain based tokens which can be used to reward content creators nbsp http www google com s2 favicons domain https www bitclave com nbsp bitclave https www bitclave com sub sup cat https coinmarketcap com es currencies bitclave sup sub helps to get value from your search data nbsp http www google com s2 favicons domain https www presearch io nbsp presearch https www presearch org signup rid 190778 sub sup pre https coinmarketcap com es currencies presearch sup sub decentralized search engine powered by the community nbsp http www google com s2 favicons domain https status im nbsp status https status im sub sup snt https coinmarketcap com es currencies status sup sub mobile ethereum os browse chat and make payments on the decentralized web the ethereum discovery tool div align right up img up png div contents gaming and virtual reality nbsp http www google com s2 favicons domain https www enjin com nbsp enjin 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careers png careers https wirexapp com careers nbsp nbsp nbsp 1 5 million customers 1 5 billion transactions over 130 countries hybrid personal finance platform that bridges old world banking and new world crypto br sub list of exchanges not yet reviewed sub not reviewed md div align right up img up png div contents p2p marketplaces p2p img c2c png the main difference with the exchanges is that in a p2p marketplace you are buying cryptocurrencies directly from another person and the platform acts as an intermediary to guard the assets throughout the transaction process some like localbitcoins https localbitcoins com ch rpkf x25be https localbitcoins com or remitano https remitano com es ref xtokens x25be https remitano com also allow you to buy locally to people near you it should be noted that decentralized exchanges decentralized exchanges dex are also p2p markets but due to their special characteristics they are listed in a separate section homepage countryflag langs currencies paymentmethods exchange features some links slogan br localbitcoins http www google com s2 favicons domain https www localbitcoins com localbitcoins https localbitcoins com ch rpkf nbsp http www countryflags io fi shiny 16 png finland since 2012 by nikolaus kangas x25be https localbitcoins com languages img language png english espa ol fran ais italiano russian portugu s currencies img currency png currencies a lot of currencies are accepted almost any country payment methods img payment png bank transfers online wallets pre paid debit cards remittance gift card codes other payments other online payment cash by mail cashier s check bpay bill payment transferwise cash at atm credit card and more or meet for a face to face transaction nbsp buy img buy png buy with fiat available sell img sell png sell for fiat available atm img atm png operating your own bitcoin atm https localbitcoins com atm order your own bitcoin atm nbsp fees img fees png fees https localbitcoins com fees twitter img 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rely on a third party service to hold the customer s funds instead trades occur directly between users peer to peer through an automated process users no need to deposit funds the funds are held by you in your personal wallet and not by a third party another advantage to the decentralized model is the privacy it provides users are not required to disclose their personal details to anyone except if the exchange method involves bank transfers in which case your identity is revealed only to the person that is selling or buying from you understanding a decentralized exchange https www cryptocompare com exchanges guides what is a decentralized exchange by theblocknet understanding decentralized exchanges https hackernoon com understanding decentralized exchanges 51b70ed3fe67 by dominiek ter heide protocols for decentralized exchange https github com evbots dex protocols list of protocols for peer to peer exchange of cryptographic assets homepage countryflag langs currencies markets 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not yet specified nbsp twitter img twitter png twitter https twitter com idtexchange reddit img reddit png reddit https www reddit com user idtexchange careers img careers png careers http www idtexchange com careers nbsp nbsp nbsp idt exchange lets you start trading tokens today no deposit or custody 100 compliant secure airswap http www google com s2 favicons domain https www airswap io airswap https www airswap io nbsp anonymous img incognito png private no deposit or registration required no identity verification nbsp http www countryflags io us shiny 16 png brooklyn ny united states x25be languages img language png english nbsp exchange coin img excoin png airswap token ast https coinmarketcap com es currencies airswap nbsp fees img fees png fees https www airswap io faq nbsp twitter img twitter png twitter https twitter com airswap reddit img reddit png reddit https www reddit com r airswap github img github png github https github com airswap nbsp nbsp nbsp secure and scalable 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sub nbsp anonymous img incognito png anonymous trustless nodes no account trading pair freedom x25be languages img language png english nbsp exchange coin img excoin png blocknet token block https coinmarketcap com es currencies blocknet atomic img atomic png atomic swap technology nbsp fees img fees png fees https docs google com document d 1fotlzn9citzrkidsoapuvryb4 lvxvw4vujjcudszyu preview nbsp twitter img twitter png twitter https twitter com the blocknet reddit img reddit png reddit https www reddit com r theblocknet github img github png github https github com blocknetdx api img api png api https api blocknet co nbsp nbsp nbsp block dx is a truly decentralized exchange that enables trading freedom for connected blockchain communities listing on block dx is free and does not require our permission or control legolas exchange http www google com s2 favicons domain https lgo exchange legolas exchange https lgo exchange sub developing sub nbsp anonymous img incognito png nbsp http www countryflags io fr shiny 16 png paris france x25be languages img language png german english spanish french russian japanese korean chinese currencies img currency png usd eur coinmarketcap img cmcap png coinmarketcap payment methods img payment png nbsp exchange coin img excoin png lgo token https coinmarketcap com es currencies legolas exchange nbsp fees img fees png fees nbsp twitter img twitter png twitter https twitter com legolasexchange lang en reddit img reddit png reddit https www reddit com r legolasexchange github img github png github https github com legolasexchange legolastoken linkedin img linkedin png linkedin https www linkedin com company 11261260 nbsp nbsp nbsp lgo exchange creates a new decentralized blockchain protocol that guarantees full transparency and prevents front running and market manipulation br sub list of exchanges not yet reviewed sub not reviewed md br div align right up img up png div contents comparative overview between advanced trading features exchange name margin trading leverage lending futures options cfd s otc market http www google com s2 favicons domain https www bitmex com bitmex https www bitmex com register bg4lin x2713 100x x2713 x2713 x2713 http www google com s2 favicons domain https www deribit com deribit https www deribit com reg 1132 3380 q home x2713 50x x2713 x2713 x2713 x2713 http www google com s2 favicons domain https www quedex net quedex https quedex net referraltoken 765ac8f5ea5eb424 x2713 25x x2713 x2713 x2713 http www google com s2 favicons domain https www quoinex com fiat crypto exchange platform qryptos com is trading platform both are exchange platforms by quoine quoinex https accounts quoinex com sign up affiliate dmcqpdsw533625 x2713 25x x2713 x2713 http www google com s2 favicons domain https www okcoin com okcoin https www okcoin com x2713 3x x2713 http www google com s2 favicons domain https bitflyer jp en eu top link bitflyer https bitflyer jp en eu top link x2713 15x x2713 http 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financial markets that also allow to invest in cryptocurrencies br http www google com s2 favicons domain https www etoro com nbsp etoro https etoro tw 2i6md0m we are the world s leading social trading network http www google com s2 favicons domain https simplefx com nbsp simplefx https simplefx com n 6012 simplefx is an ergonomic trading platform providing traders across the globe with cutting edge technology ultra light spreads and transparent conditions http www google com s2 favicons domain https www xtb com nbsp xbt https www xtb com xtb espa a es un broker online especializado en cfds para inversi n en divisas acciones materias primas ndices y bonos http www google com s2 favicons domain https www avatrade com nbsp avatrade https www avatrade com the fastest growing online trading portal offering over 250 fx pairs commodities indices equities bonds etfs http www google com s2 favicons domain https www fxpro com nbsp fxpro https www fxpro com fxpro is an industry leading online 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crypto 8596 crypto conversion platforms sub no trading no fiat no account required not have to deposit funds or provide private personal information sub conversion platforms are not decentralized nor do they have advanced trading tools but allow the exchange of cryptocurrencies without the need to register or deposit funds some also make a comparison between different exchanges to perform the conversion at the best rate homepage countryflag langs exchange features some links slogan br switchain http www google com s2 favicons domain https www switchain com switchain https www switchain com join referrer s5h578ewhrer nbsp anonymous img incognito png users do not have to create accounts deposit funds or provide private personal information nbsp http www countryflags io sg shiny 16 png singapore x25be https www switchain com languages img language png english nbsp nbsp fees img fees png fees https www switchain com faq nbsp twitter img twitter png twitter https twitter com switchaindotcom nbsp nbsp nbsp switchain helps you compare rates across cryptocurrency exchanges coinswitch http www google com s2 favicons domain https coinswitch co coinswitch https coinswitch co ref pl7zto3xss nbsp anonymous img incognito png users do not have to login for most transactions nbsp http www countryflags io in shiny 16 png india x25be https coinswitch co languages img language png a lot of languages nbsp buy img buy png buy with fiat through partners nbsp fees img fees png fees https coinswitch co what is coinswitch transaction fees nbsp twitter img twitter png twitter https twitter com coinswitch reddit img reddit png reddit https www reddit com r coinswitch linkedin img linkedin png linkedin https www linkedin com company coinswitch api img api png api https coinswitch docs apiary io careers img careers png careers https angel co coinswitch co jobs nbsp nbsp nbsp one place to convert your cryptocurrencies across all exchanges at the best rate we support more than 275 cryptocurrencies to exchange evercoin http www google com s2 favicons domain https evercoin com evercoin https evercoin com nbsp anonymous img incognito png no account required not have to deposit funds or provide private personal information nbsp http www countryflags io us shiny 16 png santa clara california united states x25be languages img language png english nbsp nbsp fees img fees png fees https evercoin com faq nbsp ios app img app store png ios app https itunes apple com us app evercoin id1277924158 android app img google play png android app https play google com store apps details id com evercoin nbsp twitter img twitter png twitter https twitter com everc0in github img github png github https github com everc0in linkedin img linkedin png linkedin https www linkedin com company evercoin nbsp nbsp nbsp cryptocurrency exchange exchange cryptos on the go directly from your own mobile wallet my ether wallet http www google com s2 favicons domain https www myetherwallet com my ether wallet https www myetherwallet com swap nbsp anonymous img incognito png no need for registration kyc private anonymous transactions nbsp http www countryflags io us shiny 16 png los angeles ca united states x25be languages img language png a lot of languages nbsp wallet img wallet png wallet https www myetherwallet com nbsp fees img fees png fees https kb myetherwallet com gas nbsp twitter img twitter png twitter https twitter com myetherwallet reddit img reddit png reddit https www reddit com r myetherwallet linkedin img linkedin png linkedin https www linkedin com company myetherwallet github img github png github https github com myetherwallet api img api png api https github com myetherwallet myetherapi nbsp nbsp nbsp the most popular ether wallet with built in exchange myetherwallet is an open source client side tool for interacting with the blockchain div align right up img up png div contents gold investments coming soon atms automated teller machines coming soon market research coming soon wallets coming soon portfolio coming soon div align right up img up png div contents what s cooking new sections are coming blockchain development block explorers exchange and trading gold investments atms automated teller machines market research wallets portfolio announcements calendar payment solutions cards platforms shopping buy with crypto professional devs founders mining master nodes blog news youtube channels courses books merchandising games knowledge resources docs video talks meetups events br contribute whether you find a nice link noticed a bug broken link or just want to improve this project all contributions are welcome please take a look at the contributing md contributing md guidelines first br donate if you find this list useful you can also support it directly making a donation no matter how small to any of these wallet addresses a target blank href qr codes doge md decode img qr scan png scan qr code a nbsp span style font size 1 2em doge span nbsp nbsp nbsp span style font size 1 2em letter spacing 0 15em dbq6uao8gaxh1lhftd5xdse7byjdnnjvlv span a target blank href qr codes btc md decode img qr scan png scan qr code a nbsp span style font size 1 2em btc span nbsp nbsp nbsp span style font size 1 2em letter spacing 0 15em 1aeedhrs6awc3r3jy79deshrgtgqgeouyz span a target blank href qr codes eth md decode img qr scan png scan qr code a nbsp span style font size 1 2em eth span nbsp nbsp nbsp span style font size 1 2em letter spacing 0 15em 0x2a4a95a050d1842aaadf04277f493e9946713466 span a target blank href qr codes ltc md decode img qr scan png scan qr code a nbsp span style font size 1 2em ltc span nbsp nbsp nbsp nbsp span style font size 1 2em letter spacing 0 15em lhn6hjy1e67kbzpqqcftkin2wo8ytx7z81 span a target blank href qr codes bch md decode img qr scan png scan qr code a nbsp span style font size 1 2em bch span nbsp nbsp nbsp span style font size 1 2em letter spacing 0 15em qz4y8s3rhvkvfea2dre8qxk02fmszmqxgcsakvdh60 span a target blank href qr codes neo md decode img qr scan png scan qr code a nbsp span style font size 1 2em neo span nbsp nbsp nbsp span style font size 1 2em letter spacing 0 15em ajd9nqfoys2xy197oyhfvcxtjft5cqexns span a target blank href qr codes dash md decode img qr scan png scan qr code a nbsp span style font size 1 2em dash span nbsp nbsp nbsp span style font size 1 2em letter spacing 0 15em xih5n81trg9fnx8qusuimugyh4isgtuywh span br license a rel license href http creativecommons org licenses by 4 0 img alt creative commons license style border width 0 src https mirrors creativecommons org presskit buttons 88x31 svg by svg a br this work is licensed under a a rel license href http creativecommons org licenses by 4 0 creative commons attribution 4 0 international license a div align right up img up png div contents | awesome-list awesome bicoin blockchain ethereum smart-contracts cryptocurrencies exchanges mining dapp erc20 tokens solidity | blockchain |
Takin | takin license https img shields io github license pingcap tidb svg https github com pingcap tidb blob master license language https img shields io badge language java blue svg https www java com english readme cn md what is takin takin is an java based open source system designed to measure online or test environmental performance test for full links especially for microservices through archguadian middlewares and applications can identify real online traffic and test traffic ensure that they enter the right databases why should we do online environmental performance test microservices architecture is used commonly nowadays and it always make system complex to understand for humans moreover businesses are also very complex in huge system business complexity and system complexity make it difficult to keep entire system highly available maintain research development efficiency in order to keep system high available we usually make performance test on test environment or online single service however test environment is very different from online environment single service can t stand for the whole service links they can t guarantee system performance microservices are complex br compare with monolithic application microservices architecture increases complexity for business system it may maintain multiple tools and frameworks business systems are complex br businesses involve different sections and many of them are long process and complicated such as e commerce businesses the microservices relation is complex br in a microservices architecture system with a lot of business services the calling relation between services is very complicated every change may affect the availability of the entire system and make developers difficult to release new versions frequently img src https raw githubusercontent com shulietech images main wx20210511 150735 402x png width 80 height 80 img src https raw githubusercontent com shulietech images main 3 png width 50 height 50 quick start instruction docker br vm memory requirement 8g docker mirror size 2 1 g if docker configuration doesn t set aliyun docker source vim etc docker daemon json add following configuration registry mirrors https q2gr04ke mirror aliyuncs com restart service systemctl daemon reload pull docker shell docker url registry cn hangzhou aliyuncs com shulie takin takin v1 0 0 docker pull registry cn hangzhou aliyuncs com shulie takin takin v1 0 1 docker run e appip your ip address p 80 80 p 2181 2181 p 29900 29999 29900 29999 registry cn hangzhou aliyuncs com shulie takin takin v1 0 1 parameter d start in background p port br the initiation of docker need about 10 mins because it need install necessary components d can ignore installment information of components in background if you dont t want to open your server s port you can use net host and make sure it and host server are in the same network open http appip web ps if nginx shows 502 the problem mostly is caused when the docker container has just been started you only need to configure it correctly and then wait a little 1 2 min while to refresh and try again after installation see quick start takin webapp doc quickstart md use in test environment document https news shulie io p 3369 use in online environment document https news shulie io p 2987 video instruction video https news shulie io p 3661 instruction takin architecture img src https raw githubusercontent com shulietech images main dayux architecture2 png width 70 height 70 br takin consists of agent web app and surge data agent see agent https github com shulietech linkagent surge data see takin surge deploy https github com shulietech takin surge deploy see takin amdb https github com shulietech takin amdb takin web see takin common https github com shulietech takin common see takin web https github com shulietech takin web see takin web ui https github com shulietech takin web ui see takin cloud https github com shulietech takin cloud takin engine see takin pressure engine https github com shulietech takin pressure engine see takin jmeter https github com shulietech takin jmeter community mailing list mail to shulie shulie io br wechat group br img src https github com shulietech images blob main aa jpg width 30 height 30 br qq group 118098566 br qr code br img src https raw githubusercontent com shulietech images main qq group 2 jpg width 30 height 30 br dingding group br img src https raw githubusercontent com shulietech images main dingding group jpg width 30 height 30 br wechat official account br img src https raw githubusercontent com shulietech images main shulie png width 30 height 30 br ask questions in official forum official forum https news shulie io page id 2477 who use takin image https user images githubusercontent com 86357315 126733639 d770b087 9f58 4b4f b4c5 a98517bf3776 png license takin is under the apache 2 0 license see the license license file for details | performance-testing performance-analysis takin | os |
iot | lan https github com phodal lan github https github com phodal lan https github com phodal lan designiot http ebook designiot cn designiot jpg https www amazon cn dp b01ibzwtww ref wl it dp o pc ns ttl encoding utf8 colid bdxf90qzx6wx coliid i19eb97k0gnlw8 http search jd com search keyword e8 87 aa e5 b7 b1 e5 8a a8 e6 89 8b e8 ae be e8 ae a1 e7 89 a9 e8 81 94 e7 bd 91 enc utf 8 wq e8 87 aa e5 b7 b1 e5 8a a8 e6 89 8b e8 ae be e8 ae a1 e7 89 a9 e8 81 94 e7 bd 91 pvid k24y6hri l4xi28 lan lan https raw githubusercontent com phodal lan master docs iot jpg http designiot phodal com struct https raw github com phodal iot master doc dot struct jpg https github com phodal awesome iot android app a href https play google com store apps details id com phodal designiot img alt get it on google play src https developer android com images brand zh cn generic rgb wo 60 png a design internet of things a minimum iot with arduino and raspberry pi android android rest php laravel framework to create restful api python pyhon hardware doc ppt nginx dashboard ruby dashing dashboard install https github com phodal iot archive 0 9 5 zip https github com phodal iot archive 0 9 5 zip 1 arduino bareminimum ino 2 get py ps windows get py dev ttyacm0 com sudo python python get py 3 http localhost athome create http localhost athome 1 edit 4 b phodal com 1 android android doc images android png android doc images app demo jpg iot android https github com phodal iot android htc g1 motor xt300 sony st25i gson 1 stay at home 13 phodal https github com phodal thesis https github com phodal thesis qq 348100589 2 4 restful 5 restful 6 laravel restful 7 laravel restful 8 ajax 9 10 11 android 12 laravel 15 dashboard 16 license 2014 phodal huang phodal this code is distributed under the mit license 1 http b phodal com 2 http www phodal com blog bare minimum iot 3 https github com gmszone iot wiki 4 http www phodal com blog bare minimum iot system structure 5 http www phodal com blog bare minimum iot system restful 6 http www phodal com blog bare minimum iot system create restful 7 http www phodal com blog bare minimum iot system about restful 8 http www phodal com blog bare minimum iot system restful template 9 http www phodal com blog bare minimum iot system ajax 10 http www phodal com blog bare minimum iot system date commucation 11 http www phodal com blog bare minimum iot system mcu commucation 12 http www phodal com blog bare minimum iot system android example 13 https github com phodal iot android raw master app build apk app debug unaligned apk 14 http bbs phodal com 15 http www phodal com blog bare minimum iot system add test for laravel 16 http www phodal com blog bare minimum iot system dashboard framework dashing phodal https www phodal com analytics https ga beacon appspot com ua 56559870 1 iot index https github com igrigorik ga beacon | iot python laravel php interner-of-things iot-projects iot-framework | server |
wp-frontend-login | wp frontend login front end account management register login change password for wordpress more at http cubiq org front end user registration and login in wordpress | front_end |
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design-system-jss | jss design system boilerplate boilerplate design system using jss and reactjs development this project primarily uses storybookjs for development quick start 1 git clone this project 1 npm install 1 npm run storybook or npm run dev creating components make a new folder in the components folder make sure the folder name is unspaced and pascal case examplecomponentname create a js file with the same folder name or appropriate variation requirements documented proptypes and default props storybook examples are labeled with a story js filename project structure the goal is to keep the package as lightweight and minimal as possible so there s not much to it if you look at the package json you ll see a few dev dependencies like babel react and storybook in terms of actual dependencies we only have react jss since it s used in the actual components react and react dom are peer deps since the project s using this will be react based folder structure components components componentname components componentname componentname js components componentname index js contains exports of all components in folder babelrc env and react babel presets todo high priority more components low priority integrate jest storybook for testing thoughts install lerna to manage sub packages allowing users to install specific components maybe separate branch | os |
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Tlaser | tlaser tlaser is a laser engraver designed as proof of concept of a new motion system corexy cantilever it s called tlaser because this motion system and thus this machine is shaped like a t img align right height 240 src https github com ficacador tlaser raw master images tlaser 20logo png main features corexy cantilever 5500mw blue laser 315mm x 205mm engraving area linear guide rails adjustable height at each corner 3d printed video of tlaser in action https youtu be njzcqpt6v3c p float center img width 430 src https github com ficacador tlaser raw master images tlaser freecad jpg img width 430 src https github com ficacador tlaser raw master images tlaser 0 jpg p corexy cantilever tlaser was developed to validate and demonstrate a new motion system which the author named corexy cantilever corexy cantilever has the same kinematics as corexy https corexy com theory html so it is easy to configure in existing firmware like grbl or marlin if the motors rotate to the same directions the head travels along the x axis if the motors rotate to opposite directions the head travels along the y axis this mechanism has two belts one over another each on a circuit shaped like a t both circuits have pulleys at the middle on both sides and at the end of the cantilever the upper belt is driven by the motor at the right end has a pulley on the left end and is locked to the left side of the carrier the lower is symmetric motor on the left pulley on the right locked to the right side of the carrier p float center img width 430 src https github com ficacador tlaser raw master images corexy 20cantilever 20x png img width 430 src https github com ficacador tlaser raw master images corexy 20cantilever 20y png p hardware the following parts are required for building this machine 5 5w 445nm blue laser module w power supply http s click aliexpress com e babwr1wg mgn12h 300mm and mgn15h 400mm blocks and rails http s click aliexpress com e bkj8dyfi 2 nema 17 stepper motors http s click aliexpress com e cpg92ixi arduino uno r3 w cnc shield v3 and a4988 stepper drivers http s click aliexpress com e b7e6xk5a 2 mechanical endstops http s click aliexpress com e cig3mqk timing belt gt2 6mm 5m http s click aliexpress com e belvtmgu 2 belt drive pulleys gt2 16 teeth bore 5mm https s click aliexpress com e d7v8utq gt2 pulleys 4 w 16 teeth and 4 w no teeth https s click aliexpress com e dzure9s m3x6 and m3x30 round head hex bolts http s click aliexpress com e cscwftx6 m3x8 socket head hex bolts http s click aliexpress com e b72th5nm m3x12 self tapping bolts http s click aliexpress com e bzmmlnqu m3x50 socket head bolts http s click aliexpress com e t3qlc2k m3 hex nuts http s click aliexpress com e nrg0nic the list above contains affiliate links the total cost should be below 150 165 usd 3d printed parts not included for a more detailed table with quantities check the bill of materials https github com ficacador tlaser raw master bom ods 3d printed parts all parts are already correctly oriented for ease of printing and available in stl files https github com ficacador tlaser tree master stls and amf files https github com ficacador tlaser tree master amfs dimensional accuracy is very important this is an assembly of mechanical parts designed to fit tight the slightest overextrusion will make for a hard time during the assembly due to proximity to the stepper motors and laser module laser mount https github com ficacador tlaser blob master stls laser 20mount stl motor mount left https github com ficacador tlaser blob master stls motor 20mount 20left stl and motor mount right https github com ficacador tlaser blob master stls motor 20mount 20right stl should be printed with a more heat resistant material like abs or petg recommended settings 0 5 mm line width 0 2 mm layer height 2 mm wall thickness over 15 infill some parts require support base left https github com ficacador tlaser blob master stls base 20left stl and base right https github com ficacador tlaser blob master stls base 20right stl are prone to warping requiring good bed adhesion total printing time around 38h total filament used approximately 600g assembly video of tlaser exploded assembly on freecad https youtu be 6z7jv q1ixa an exploded assembly cad file https github com ficacador tlaser raw master cad tlaser 20exploded 20assembly fcstd for freecad https www freecadweb org with the exploded assembly workbench https github com jmg1 explodedassembly added is available to guide the assembly process required tools are a set of hex key wrenches a philips screwdriver a small flat head screwdriver a rubber mallet and a wire cutter the controller wiring is pretty straight forward the exceptions being the laser ttl connects to the z endstop pins and the red wire is left out when connecting the x and y endstops the power supply and 12v laser pcb cables need to be cut so both can be connected to the same respective and on the cnc shield p float center img width 430 src https github com ficacador tlaser raw master images tlaser 20exploded 20assembly jpg img width 430 src https github com ficacador tlaser raw master images controller 20assembly 205 20shield jpg p firmware an arduino running grbl https github com gnea grbl is used to control tlaser it uses grbl version 1 1g with a modified config h file https github com ficacador tlaser blob master firmware grbl v1 1 grbl config h with the configuration and default values that better suit this machine you can find an hex file and the source code of grbl for tlaser here https github com ficacador tlaser tree master firmware instructions on how to flash grbl to an arduino https github com gnea grbl wiki flashing grbl to an arduino or alternatively compile and upload grbl https github com gnea grbl wiki compiling grbl can be found on the grbl wiki https github com gnea grbl wiki grbl is distributed under the gplv3 license and is developed by sungeun k jeon ph d cam software laserweb4 https github com laserweb laserweb4 wiki is simple yet powerful graphically pleasing open source laser cam software available for windows mac and linux a file containing tlaser profile https github com ficacador tlaser blob master cam 20profiles laserweb profiles tlaser json for laserweb4 v4 0 996 is available there are many valid laser cam software options use whatever one you like the most design this project was designed using freecad https www freecadweb org there are more powerful and certainly more user friendly options but none is actually free of charge of licensing of login and available for windows mac and linux designed with freecad version 0 18 3 with the added workbenches fasteners https github com shaise freecad fastenerswb a2plus https github com kbwbe a2plus and exploded assembly https github com jmg1 explodedassembly you can find the cad files for the individual parts and the assembly here https github com ficacador tlaser tree master cad all the features and sketches of the 3d printed parts are named for ease of understanding and editing tlaser is developed by filipe ca ador license tlaser is published under the creative commons attribution sharealike 4 0 international cc by sa 4 0 https creativecommons org licenses by sa 4 0 public license under this license anyone can share copy redistribute edit remix and develop this work for any purpose as long as credit is attributed to the original author and any further developments are distributed under the same license no warranty of any kind is provided and the author is not liable for any losses damages or injuries in any way related to this work | 3d-printed laser laser-engraver arduino grbl freecad | os |
iotdashboard | new reposity https github com electrocoder iothook iot dashboard iot platform for internet of things gitter https badges gitter im electrocoder iot dashboard svg https gitter im electrocoder iot dashboard utm source badge utm medium badge utm campaign pr badge license https img shields io github license mashape apistatus svg license build status https travis ci org samsung iotjs svg branch master https iothook com django https img shields io pypi pyversions django svg https iothook com you can find project details on our project page https iothook com and wiki https iothook com demo page http iotdashboardapp pythonanywhere com en p align center img src iotdashboard png border 0 alt iot hook p br p align center h1 fast django server for iot devices h1 p features create a django compatible server for iot devices create a device for iot gateway devices create a sensors for iot devices simple interface support for channel api key django rest framework requirements python 3 8 django 3 2 8 django rest framework 3 12 3 python 3 9 1 how to get django django https www djangoproject com how to get django rest framework django rest framework https www django rest framework org installing git clone https github com electrocoder iotdashboard git docker docker compose up d djangopackages https djangopackages org packages p iotdashboard github pages http electrocoder github io iotdashboard readthedocs http iotdashboard readthedocs io tr latest installing pip https pypi python org pypi iotdasboard or pip install iotdasboard run manage py runserver or manage py runserver 0 0 0 0 8000 test on iotdashboard server https www pythonanywhere com username iotdashboardapp email iotdashboard yandex com password admin12345 app url http iotdashboardapp pythonanywhere com tr contact ahin mers n electrocoder http twitter com electrocoder iothook iothook https twitter com iothook license iotdashboard source code is available under the mit license license author ahin mers n electrocoder http twitter com electrocoder https www facebook com electrocoder me e bili im http mesebilisim com iothook http iothook com | python iot django django-rest-framework | server |
sqliteRecordDisplayer | sqliterecorddisplayer a desktop software solution written in java in my early days of software engineering uses sqlite database retrieve the data and displays it nicely with gui and with all the crud methods | server |
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PDEBench | pdebench the code repository for the neurips 2022 paper pdebench an extensive benchmark for scientific machine learning https arxiv org abs 2210 07182 tada simtech best paper award 2023 https www simtech uni stuttgart de press simtech best paper award 2023 benchmark for ml for scientific simulations confetti ball pdebench provides a diverse and comprehensive set of benchmarks for scientific machine learning including challenging and realistic physical problems this repository consists of the code used to generate the datasets to upload and download the datasets from the data repository as well as to train and evaluate different machine learning models as baselines pdebench features a much wider range of pdes than existing benchmarks and includes realistic and difficult problems both forward and inverse larger ready to use datasets comprising various initial and boundary conditions and pde parameters moreover pdebench was created to make the source code extensible and we invite active participation from the sciml community to improve and extend the benchmark visualizations of some pde problems covered by the benchmark https github com pdebench pdebench blob main pdebench examples png created and maintained by makoto takamoto makoto takamoto neclab eu takamtmk gmail com timothy praditia timothy praditia iws uni stuttgart de raphael leiteritz dan mackinlay francesco alesiani dirk pfl ger and mathias niepert datasets and pretrained models we also provide datasets and pretrained machine learning models pdebench datasets https darus uni stuttgart de dataset xhtml persistentid doi 10 18419 darus 2986 pdebench pre trained models https darus uni stuttgart de dataset xhtml persistentid doi 10 18419 darus 2987 dois doi 10 18419 darus 2986 https img shields io badge doi doi 3a10 18419 2fdarus 2986 red https doi org 10 18419 darus 2986 doi 10 18419 darus 2987 https img shields io badge doi doi 3a10 18419 2fdarus 2987 red https doi org 10 18419 darus 2987 installation using pip locally bash pip install upgrade pip wheel pip install from pypi bash pip install pdebench to include dependencies for data generation bash pip install pdebench datagen310 pip install datagen310 locally or bash pip install pdebench datagen39 pip install datagen39 locally gpu support for gpu support there are additional platform specific instructions for pytorch the latest version we support is v1 13 1 see previous versions linux cuda 11 7 https pytorch org get started previous versions linux and windows 2 for jax which is approximately 6 times faster for simulations than pytorch in our tests see jax pip installation gpu cuda installed via pip https github com google jax pip installation gpu cuda installed via pip easier installation using conda if you like you can also install dependencies using anaconda we suggest to use mambaforge https github com conda forge miniforge mambaforge as a distribution otherwise you may have to enable the conda forge channel for the following commands starting from a fresh environment conda create n myenv python 3 9 conda activate myenv install dependencies for model training conda install deepxde hydra core h5py c conda forge according to your hardware availability either install pytorch with cuda support see previous versions linux cuda 11 7 https pytorch org get started previous versions linux and windows 2 conda install pytorch 1 13 1 torchvision 0 14 1 torchaudio 0 13 1 pytorch cuda 11 7 c pytorch c nvidia or cpu only binaries https pytorch org get started previous versions linux and windows 2 conda install pytorch 1 13 1 torchvision 0 14 1 torchaudio 0 13 1 cpuonly c pytorch optional dependencies for data generation conda install clawpack jax jaxlib python dotenv configuring deepxde in our tests we used pytorch as backend for deepxde please follow the documentation https deepxde readthedocs io en latest user installation html working with different backends to enable this data generation the data generation codes are contained in data gen pdebench data gen gen diff react py to generate the 2d diffusion reaction data gen diff sorp py to generate the 1d diffusion sorption data gen radial dam break py to generate the 2d shallow water data gen ns incomp py to generate the 2d incompressible inhomogenous navier stokes data plot py to plot the generated data uploader py to upload the generated data to the data repository env is the environment data to store dataverse url and api token to upload the generated data note that the filename should be strictly env i e remove the example from the filename configs directory contains the yaml files storing the configuration for the simulation arguments for the simulation are problem specific and detailed explanation can be found in the simulation scripts src directory contains the simulation scripts for different problems sim diff react py for 2d diffusion reaction sim diff sorp py for 1d diffusion sorption and swe for the shallow water equation data generation for 1d advection burgers reaction diffusion 2d darcyflow compressible navier stokes equations the data generation codes are contained in data gen nle pdebench data gen data gen nle utils py util file for data generation mainly boundary conditions and initial conditions advectioneq directory with the source codes to generate 1d advection equation training samples burgerseq directory with the source codes to generate 1d burgers equation training samples compressiblefluid directory with the source codes to generate compressible navier stokes equations training samples reactiondiffusioneq directory with the source codes to generate 1d reaction diffusion equation training samples note darcyflow data can be generated by run darcyflow2d sh pdebench data gen data gen nle readme md in this folder save directory saving the generated training samples a typical example to generate training samples 1d advection equation in data gen data gen nle advectioneq bash python3 advection multi solution hydra py multi beta1e0 yaml which is assumed to be performed in each directory examples for generating other pdes are provided in run trainset sh in each pde s directories the config files for hydra are stored in config directory in each pde s directory data transformaion and merge into hdf5 format 1d advection burgers reaction diffusion 2d darcyflow compressible navier stokes equations save data as a numpy array so to read those data via our dataloaders the data transformation merge should be performed this can be done using data gen nle data merge py whose config file is located at data gen data gen nle config config yaml after properly setting the parameters in the config file type name of pdes dim number of spatial dimension bd boundary condition the corresponding hdf5 file could be obtained as bash python3 data merge py configuration you can set the default values for data locations for this project by putting config vars like this in the env file working dir data working archive data dir data archive there is an example in example env data download the download scripts are provided in data download pdebench data download there are two options to download data 1 using download direct py recommended retrieves data shards directly using urls sample command for each pde is given in the readme file in the data download pdebench data download directory 2 using download easydataverse py might be slow and you could encounter errors issues hence not recommended use the config files from the config directory that contains the yaml files storing the configuration any files in the dataset matching args filename will be downloaded into args data folder baseline models in this work we provide three different ml models to be trained and evaluated against the benchmark datasets namely fno https arxiv org pdf 2010 08895 pdf u net https www sciencedirect com science article abs pii s0010482519301520 via 3dihub and pinn https www sciencedirect com science article pii s0021999118307125 the codes for the baseline model implementations are contained in models pdebench models train models forward py is the main script to train and evaluate the model it will call on model specific script based on the input argument train models inverse py is the main script to train and evaluate the model for inverse problems it will call on model specific script based on the input argument metrics py is the script to evaluate the trained models based on various evaluation metrics described in our paper additionally it also plots the prediction and target data analyse result forward py is the script to convert the saved pickle file from the metrics calculation script into pandas dataframe format and save it as a csv file additionally it also plots a bar chart to compare the results between different models analyse result inverse py is the script to convert the saved pickle file from the metrics calculation script into pandas dataframe format and save it as a csv file this script is used for the inverse problems additionally it also plots a bar chart to compare the results between different models fno contains the scripts of fno implementation these are partly adapted from the fno repository https github com zongyi li fourier neural operator unet contains the scripts of u net implementation these are partly adapted from the u net repository https github com mateuszbuda brain segmentation pytorch pinn contains the scripts of pinn implementation these utilize the deepxde library https github com lululxvi deepxde inverse contains the model for inverse model based on gradient config contains the yaml files for the model training input the default templates for different equations are provided in the args pdebench models config args directory user just needs to copy and paste them to the args keyword in the config yaml pdebench models config config yaml file an example to run the forward model training can be found in run forward 1d sh pdebench models run forward 1d sh and an example to run the inverse model training can be found in run inverse sh pdebench models run inverse sh short explanations on the config args model name string containing the baseline model name either fno unet or pinn if training bool set true for training or false for evaluation continue training bool set true to continute training from a checkpoint num workers int number of workers for the pytorch dataloader batch size int training batch size initial step int number of time steps used as input for fno and u net t train int number of the last time step used for training for extrapolation testing set this to be nt model update int number of epochs to save model filename str has to match the dataset filename single file bool set false for 2d diffusion reaction 1d diffusion sorption and the radial dam break scenarios and set true otherwise reduced resolution int factor to downsample spatial resolution reduced resolution t int factor to downsample temporal resolution reduced batch int factor to downsample sample size used for training epochs int total epochs used for training learning rate float learning rate of the optimizer scheduler step int number of epochs to update the learning rate scheduler scheduler gamma float decay rate of the learning rate u net specific args in channels int number of input channels out channels int number of output channels ar mode bool set true for fully autoregressive or pushforward training pushforward bool set true for pushforward training false otherwise ar mode also has to be set true unroll step int number of time steps to backpropagate in the pushforward training fno specific args num channels int number of channels variables modes int number of fourier modes to multiply width int number of channels for the fourier layer inverse specific args base path string location of the data directory training type string type of training autoregressive single mcmc num samples int number of generated samples mcmc warmup steps 10 mcmc num chains 1 num samples max 1000 in channels hid 64 inverse model type string type of inverse inference model probrasterlatent initialconditioninterp inverse epochs int number of epochs for the gradient based method inverse learning rate float learning rate for the gradient based method inverse verbose flag bool some printing plotting specific args plot bool set true to activate plotting channel plot int determines which channel variable to plot x min float left spatial domain x max float right spatial domain y min float lower spatial domain y max float upper spatial domain t min float start of temporal domain t max float end of temporal domain datasets and pretrained models we provide the benchmark datasets we used in the paper through our darus data repository https darus uni stuttgart de dataset xhtml persistentid doi 10 18419 darus 2986 the data generation configuration can be found in the paper additionally the pretrained models are also available to be downloaded from pdebench pretrained models https darus uni stuttgart de dataset xhtml persistentid doi 10 18419 darus 2987 darus repository to use the pretrained models users can specify the argument continue training true in the config file pdebench models config config yaml directory tour below is an illustration of the directory structure of pdebench pdebench models pinn model physics informed neural network train py utils py pde definitions py fno model fourier neural operator train py utils py fno py unet model u net train py utils py unet py inverse model gradient based inverse method train py utils py inverse py config config all config files reside here train models inverse py run forward 1d sh analyse result inverse py train models forward py run inverse sh metrics py analyse result forward py data download data scripts to download data from darus config download direct py download easydataverse py visualize pdes py readme md download metadata csv data gen data scripts to generate data configs data gen nle src notebooks gen diff sorp py plot py example env gen ns incomp py gen diff react py uploader py gen radial dam break py init py citations please cite the following papers if you use pdebench datasets and or source code in your research details summary a href https arxiv org abs 2210 07182 pdebench an extensive benchmark for scientific machine learning neurips 2022 a summary br inproceedings pdebench2022 author takamoto makoto and praditia timothy and leiteritz raphael and mackinlay dan and alesiani francesco and pfl ger dirk and niepert mathias title pdebench an extensive benchmark for scientific machine learning year 2022 booktitle 36th conference on neural information processing systems neurips 2022 track on datasets and benchmarks url https arxiv org abs 2210 07182 details details summary a href https doi org 10 18419 darus 2986 pdebench datasets neurips 2022 a summary br data darus 2986 2022 author takamoto makoto and praditia timothy and leiteritz raphael and mackinlay dan and alesiani francesco and pfl ger dirk and niepert mathias publisher darus title pdebench datasets year 2022 doi 10 18419 darus 2986 url https doi org 10 18419 darus 2986 details details summary a href https arxiv org abs 2304 14118 learning neural pde solvers with parameter guided channel attention icml 2023 a summary br article cape takamoto 2023 author makoto takamoto and francesco alesiani and mathias niepert title learning neural pde solvers with parameter guided channel attention journal corr volume abs 2304 14118 year 2023 url https doi org 10 48550 arxiv 2304 14118 doi 10 48550 arxiv 2304 14118 eprinttype arxiv eprint 2304 14118 details code contributors makato takamoto https github com mtakamoto d nec laboratories europe https www neclab eu timothy praditia https github com timothypraditia stuttgart center for simulation science university of stuttgart https www simtech uni stuttgart de raphael leiteritz https github com leiterrl stuttgart center for simulation science university of stuttgart https www simtech uni stuttgart de francesco alesiani https github com falesiani nec laboratories europe https www neclab eu dan mackinlay https danmackinlay name csiro s data61 https data61 csiro au mario kalimuthu https github com kmario23 stuttgart center for simulation science university of stuttgart https www simtech uni stuttgart de john kim https github com johnmjkim anu techlauncher https comp anu edu au techlauncher csiro s data61 https data61 csiro au gefei shan https github com davecatmeow anu techlauncher https comp anu edu au techlauncher csiro s data61 https data61 csiro au yizhou yang https github com verdantwynnd anu techlauncher https comp anu edu au techlauncher csiro s data61 https data61 csiro au ran zhang https github com maphyca anu techlauncher https comp anu edu au techlauncher csiro s data61 https data61 csiro au simon brown https github com simonsybrown anu techlauncher https comp anu edu au techlauncher csiro s data61 https data61 csiro au license mit licensed except where otherwise stated see license txt file | ai benchmark jax machine-learning pytorch scientific scientific-computing sciml simulation deep-learning fluid-dynamics navier-stokes-equations neural-networks neural-operators partial-differential-equations physics-informed-neural-networks autoregressive-models | ai |
soft-ui-design-system | soft ui design system http demos creative tim com soft ui design system presentation html ref readme suds tweet https img shields io twitter url http shields io svg style social logo twitter https twitter com intent tweet url https www creative tim com product soft ui design system text check 20soft 20ui 20design 20system 20made 20by 20 creativetim 20 webdesign 20 designsystem 20 softdesign 20 html 20https www creative tim com product soft ui design system discord https badgen net badge icon discord icon discord label https discord gg fhcjcahdqa version https img shields io badge version 1 0 9 blue svg github issues open https img shields io github issues creativetimofficial soft ui design system svg maxage 2592000 https github com creativetimofficial soft ui design system issues q is 3aopen is 3aissue github issues closed https img shields io github issues closed raw creativetimofficial soft ui design system svg maxage 2592000 https github com creativetimofficial soft ui design system issues q is 3aissue is 3aclosed image https raw githubusercontent com creativetimofficial public assets master soft ui design system opt sds free thumbnail jpg soft ui design system is built with over 70 frontend individual elements like buttons inputs navbars navtabs cards or alerts giving you the freedom of choosing and combining all components can take variations in colour that you can easily modify using sass files and classes you will save a lot of time going from prototyping to full functional code because all elements are implemented this freebie bootstrap 5 ui kit is coming with prebuilt design blocks so the development process is seamless switching from our pages to the real website is very easy to be done view all components here https www creative tim com learning lab bootstrap alerts soft ui design system ref readme suds special thanks during the development of this design system we have used many existing resources from awesome developers we want to thank them for providing their tools open source popper js https popper js org kickass library used to manage poppers countup https inorganik github io countup js dependency free lightweight javascript class that can be used to quickly create animations that display numerical data in a more interesting way rellax https dixonandmoe com rellax lightweight vanilla javascript parallax library smooth scroll https github com cferdinandi smooth scroll a lightweight script to animate scrolling to anchor links nepcha analytics https nepcha com ref readme for the analytics tool nepcha is already integrated with soft ui design system you can use it to gain insights into your sources of traffic let us know your thoughts below and good luck with development table of contents soft ui design system soft ui design system special thanks special thanks table of contents table of contents versions versions demo demo quick start quick start terminal commands terminal commands documentation documentation what s included whats included browser support browser support resources resources reporting issues reporting issues technical support or questions technical support or questions licensing licensing useful links useful links social media social media versions img src https s3 amazonaws com creativetim bucket github html png width 60 height 60 https www creative tim com product soft ui design system ref readme suds html soft ui design system html https raw githubusercontent com creativetimofficial public assets master soft ui design system opt sds free thumbnail jpg http demos creative tim com soft ui design system presentation html ref readme suds demo sign in page https demos creative tim com soft ui design system pages sign in html ref readme suds about us page https demos creative tim com soft ui design system pages about us html ref readme suds contact us page https demos creative tim com soft ui design system pages contact us html ref readme suds author page https demos creative tim com soft ui design system pages author html ref readme suds page headers https demos creative tim com soft ui design system sections page sections hero sections html ref readme suds view more https demos creative tim com soft ui design system presentation html ref readme suds quick start quick start options download from creative tim https www creative tim com product soft ui design system ref readme suds terminal commands 1 download and install nodejs from nodejs official page https nodejs org en download 2 navigate to the root directory and run npm install to install our local dependencies documentation the documentation for the soft ui design system is hosted at our website https www creative tim com learning lab bootstrap overview soft ui design system ref readme suds what s included within the download you ll find the following directories and files soft ui design system assets css fonts img js core plugins soft design system min js scss soft design system soft design system scss docs documentation html pages sections changelog md gulpfile js package json presentation html browser support at present we officially aim to support the last two versions of the following browsers img src https s3 amazonaws com creativetim bucket github browser chrome png width 64 height 64 img src https s3 amazonaws com creativetim bucket github browser firefox png width 64 height 64 img src https s3 amazonaws com creativetim bucket github browser edge png width 64 height 64 img src https s3 amazonaws com creativetim bucket github browser safari png width 64 height 64 img src https s3 amazonaws com creativetim bucket github browser opera png width 64 height 64 resources live preview https demos creative tim com soft ui design system presentation html ref readme suds download page https www creative tim com product soft ui design system ref readme suds documentation is here https www creative tim com learning lab bootstrap overview soft ui design system ref readme suds license agreement https www creative tim com license ref readme suds support https www creative tim com contact us ref readme suds issues github issues page https github com creativetimofficial soft ui design system issues nepcha analytics https nepcha com ref readme analytics tool for your website reporting issues we use github issues as the official bug tracker for the soft ui design system here are some advices for our users that want to report an issue 1 make sure that you are using the latest version of the soft ui design system check the changelog from your dashboard on our website https www creative tim com product soft ui design system ref readme suds 2 providing us reproducible steps for the issue will shorten the time it takes for it to be fixed 3 some issues may be browser specific so specifying in what browser you encountered the issue might help technical support or questions if you have questions or need help integrating the product please contact us https www creative tim com contact us ref readme suds instead of opening an issue licensing copyright 2023 creative tim https www creative tim com ref readme suds creative tim license https www creative tim com license ref readme suds useful links more products https www creative tim com templates ref readme suds from creative tim tutorials https www youtube com channel ucvytg4scw rovb9ohkzzd1w freebies https www creative tim com bootstrap themes free ref readme suds from creative tim affiliate program https www creative tim com affiliates new ref readme suds earn money social media twitter https twitter com creativetim facebook https www facebook com creativetim dribbble https dribbble com creativetim tiktok https tiktok com creative tim instagram https instagram com creativetimofficial | bootstrap5 creativettim bootstrap nojquery java vanilla-javascript open-source soft-design-system | os |
simple-blockchain-in-ruby | simple blockchain using ruby improved version of build your own blockchain from scratch in 20 lines of ruby from https github com openblockchains awesome blockchains tree master blockchain rb and inspired by let s build the tiniest blockchain in less than 50 lines of python by gerald nash from https medium com crypto currently lets build the tiniest blockchain e70965a248b br run it ruby blockchain rb it will create the first block and then prompt you to make a transaction by asking your name what you want to send the quantity and for who br br you can set multiple transaction in one block when you finished to write the transactions the current block is added to the blockchain and take place in the ledger br live code here is a live talk and programming from scratch with explanation of what is blockchain and how work proof of work blocks hash cryptography the talk is in french for the french school the hacking project br https www youtube com watch v svfluwtb8a0 credits this is an improved version of https github com openblockchains awesome blockchains tree master blockchain rb br br thoses little blockchain are really great for understanding how blockchains works br br i wanted to learn about blockchain so i d just combine those three programs into one and added the possibility to asks user for transaction br br i ve also add dynamics instance variable name with auto incrementation so people can implement it in bigger programs or rails application and create blocks with any kind of data just replace prompt s methods br br huge thanks to openblockchains i ve learned a lot by having fun with your ruby scripts | blockchain |
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Project-IDonate | the best ionic v1 x starter template ionic framework gulp https github com jdnichollsc ionic starter template blob gh pages images ionic 40 png raw true reinventing the wheel again sorry ionic team but there are many noobs learning in youtube are you a noob use this template are you a master shut up and use this template talk is cheap show me the code pull request div style text align center img src https github com jdnichollsc ionic starter template blob gh pages images reinventing the wheel jpg raw true div introduction you need to obfuscate your code and reduce the size of your mobile applications with this project you can work with gulp in the best way allowing improve your development workflow this project seeks to improve the following tasks design your application with a maintainable code using sass and writing less code using autoprefixer concatenate and minify styles in a stylesheet concatenate javascript files with the structure that you want with anglular compress images to reduce the size of your application compress templates respecting your structure clean unnecessary files downloaded with bower use css animations with animate css use sqlite with a service pattern pre filled databases projects using this template ionphaser http market ionic io plugins ionphaser instructions 1 download this template 2 execute the command npm install 3 execute the command gulp 4 run ionic ionic serve to test on the browser gulp is running ionic run android to test on the device 5 modify this template and create your hybrid mobile app template structure path explanation app img images in your app app js scripts controllers services directives etc app scss the styles of your app using sass app templates views in your app only html files app index html the init page www css other css styles like animate css https daneden github io animate css etc www lib download scripts using bower using bower to download libraries npm preen included download the script eg bower install ionic datepicker save add the path of the files that you will use in bower json from www lib eg javascript preen more libraries ionic datepicker dist js other files and folders will be deleted to reduce the size of your app run gulp in the cli eg gulp or gulp lib that s all folks animate elements using animate css https daneden github io animate css do you want to animate modals this template have an example more examples here https github com kevincobain2000 ionic animated modal javascript using the modals service in this template modals openmodal scope templates modals users html animated rotateindownleft do you want to animate popups and other elements see an example javascript ionicpopup alert title hello world template this is the best template to start with ionic framework cssclass animated bounceindown sqlite databases on android ios and windows using cordova sqlite ext https github com litehelpers cordova sqlite ext plugin this template include an example pre populated database you can test in the browser using google chrome or in your device cordova sqlite https github com jdnichollsc ionic starter template blob gh pages images sqlite png raw true debug in the browser test using the app js queries js file to create your queries drop tables create tables insert data etc debug in the device test using the www pre db file you can edit the database using db browser for sqlite http sqlitebrowser org note if you don t want to use sqlite you must perform the following steps 1 remove www pre db file 2 remove app js queries js file 3 remove app js services sqlite js file 4 uninstall the plugin using the cli ionic plugin rm cordova sqlite ext save 5 remove the following line from app js app js file javascript sqliteservice preloaddatabase true ionic tips ionic view lifecycle more here http www gajotres net understanding ionic view lifecycle javascript scope on ionicview beforeenter function alert before to enter to the view scope on ionicview afterenter function alert after to enter to the view reload the current state javascript state go state current reload true disable the back option before to navigate to other state javascript ionichistory nextviewoptions disableback true disableanimate true historyroot true clear the cache javascript ionichistory clearcache clear the history javascript ionichistory clearhistory change the direction before to navigate to other state javascript ionicviewswitcher nextdirection back navigate to other state javascript state go app login disable the drag to open the side menu javascript ionicsidemenudelegate candragcontent false global configuration enable the native scrolling enable or disable jsscrolling javascript ionicconfigprovider scrolling jsscrolling false set the maximum number of view elements to cache in the dom javascript ionicconfigprovider views maxcache 5 center align the title in the navbar javascript ionicconfigprovider navbar aligntitle center disable swipeback on ios javascript ionicconfigprovider views swipebackenabled false set the back button text to empty javascript ionicconfigprovider backbutton previoustitletext false text crosswalk improve the performance of your html css and javascript if is required command action ionic browser list show all the browsers available by platform ionic browser rm crosswalk remove a browser ionic browser add crosswalk install the chromium browser for android ionic browser add crosswalk 10 39 235 15 specifies a version of chromium ionic browser add crosswalk lite install the crosswalk lite version ionic browser revert android remove any custom browser that was installed for the platform by replacing it with the system default browser npm commands command action npm i ionic cordova bower gulp g install ionic cordova bower and gulp packages globally npm cache clean remove the cache to force update the packages useful to solve npm issues using the cli ionic commands command action ionic login to get logged in the cli and use the ionic services ionic upload upload your app to ionic repository and debug remotely your clients using the useful ionic view app http view ionic io ionic serve test on the browser ionic serve lab test on the browser ios and android version ionic lib update update ionic library files ionic resources generate icons and splash screens the images are located in resources directory more info here http ionicframework com docs cli icon splashscreen html ionic resources icon generate only the icons icon png icon psd or icon ai is located in resources directory ionic resources splash generate only the splash screens splash png splash psd or splash ai is located in resources directory ionic resources ios icon generate icons per platform cordova commands command action cordova platform add android add the platform to build your app android ios windows cordova platform rm android remove the platform cordova plugin add git url save add a plugin to use native capabilities native devs are your friends cordova plugin list see the plugins that you re using find more here https cordova apache org plugins cordova plugin rm plugin name save remove a plugin cordova build windows appx 8 1 win archs x86 build the app to windows open the solution platforms windows sln on visual studio https www visualstudio com en us products visual studio community vs aspx tools name description visual studio code https code visualstudio com build and debug your app using a extension https marketplace visualstudio com items itemname vsmobile cordova tools gapdebug https www genuitec com products gapdebug only debug in the device genymotion https www genymotion com better android emulation visual studio code commands and shortcuts command shortcut action code open the editor f1 open the command palette ctrl shift n open other visual studio code instance ctrl toogle comment code sign to android commands 1 cordova build release android 2 keytool genkey v keystore my release key keystore alias alias name keyalg rsa keysize 2048 validity 10000 3 jarsigner verbose sigalg sha1withrsa digestalg sha1 keystore my release key keystore helloworld release unsigned apk alias name 4 zipalign v 4 helloworld release unsigned apk helloworld apk links market place http market ionic io facebook group https www facebook com groups phonegapcordova codepen http codepen io ionic pens public grid type list spanish presentation http slides com juandavidnicholls apps moviles ionic cli https github com driftyco ionic cli ionic framework examples https gist github com jdnichollsc 53bfd200f04fd51c87d5 ionic services http docs ionic io your code is mine https github com jdnichollsc ionic starter template blob gh pages images ofuscate jpg raw true personal comments ionic seriously the cache is the best but how is possible to know if a specific view is cached from a directive microsoft seriously help to improve existing cordova plugins instead of create new plugins only for windows platform apple seriously thanks for nothing i need a mac or the help of a friend to build for ios are we playing who wants to be a millionaire happy coding made with 3 img width 150px src http phaser azurewebsites net assets nicholls png align right | front_end |
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Valley | valley video assistant with large language model enhanced ability understanding complex videos relying on large language and vision models project page https valley vl github io paper https arxiv org pdf 2306 07207 pdf demo the online demo is no longer available because we released the code for offline demo deployment video assistant with large language model enhanced ability br ruipu luo https github com rupertluo ziwang zhao min yang https github com feymanpriv equal contribution p align center img src valley logo lama with valley jpeg width 100 br generated by a href https stablecog com stablecog a via a cute llama with valley p code license https img shields io badge code 20license apache 2 0 green svg https github com tatsu lab stanford alpaca blob main license data license https img shields io badge data 20license cc 20by 20nc 204 0 red svg https github com tatsu lab stanford alpaca blob main data license usage and license notices the data code and checkpoint is intended and licensed for research use only they are also restricted to uses that follow the license agreement of llama vicuna and gpt 4 the dataset is cc by nc 4 0 allowing only non commercial use and models trained using the dataset should not be used outside of research purposes release 8 14 we released the chinese version of valley now its 7b and 13b weights are available at chinese valley7b v1 https huggingface co zhaoziwang chinese valley7b v1 and chinese valley13b v1 https huggingface co zhaoziwang chinese valley13b v1 8 10 realeased pretrain stage weight of 13b and 7b valley2 7b pretrain https huggingface co luoruipu1 valley2 7b pretrain valley 13b pretrain https huggingface co luoruipu1 valley 13b pretrain 8 8 we released the self collected and expanded instruction fine tuning dataset valley instruct 73k https huggingface co datasets luoruipu1 valley instruct 73k 8 7 we released valley2 7b https huggingface co luoruipu1 valley2 7b it replaces vicuna with llama 2 7 23 we modified the our training code to make it easier to train valley and also support the training of lora 7 5 release training code for valley and upload our pretraining data 6 21 upload offline demo code 6 14 build a share link demo 6 13 we uploaded model weight of valley 13b v1 delta https huggingface co luoruipu1 valley 13b v1 delta 6 12 we released valley video assistant with large language model enhanced ability checkout the paper https arxiv org pdf 2306 07207 pdf todo release inference code upload weight of valley v1 and build a share link demo upload offline demo code release 703k pretraining data and 73k instruction tuning data upload pretrain and tuning code upload weight of valley2 7b and valley v3 install 1 clone this repository and navigate to valley folder git clone https github com rupertluo valley git cd valley 2 install package conda create n valley python 3 10 y conda activate valley pip install upgrade pip pip install e data in the pretrain stage we use the data from llava cc3m pretrain 595k https huggingface co datasets liuhaotian llava pretrain and the valley webvid2m pretrain 703k https huggingface co datasets luoruipu1 valley webvid2m pretrain 703k collected and filtered by ourselves the acquisition of picture and video data can refer to llava https llava vl github io and webvid https github com m bain webvid in the finetune stage we use the data from llava instruct 150k https huggingface co datasets liuhaotian llava instruct 150k videochat instruct 11k https github com opengvlab internvideo tree main data instruction data and our self collected valley instruct 73k https huggingface co datasets luoruipu1 valley instruct 73k for the images and videos of the first two parts please refer to their official website here we describe how we obtain the data we collect ourselves valley instruct 73k https huggingface co datasets luoruipu1 valley instruct 73k 1 part of valley instruct 73k is collected from the open source dataset vatex https eric xw github io vatex website explore html which contains about 20k downloadable videos you can download the original annotation file ava vatex training v1 0 json from its official website its video comes from youtube and now there are many open source tools that can download youtube videos by video id we provide a tool to download its videos the tool is located in the crawler crawler folder please read the tool s readme md crawler readme md to use it 2 another part of valley instruct 73k is collected from a video site named jukinmedia https www jukinmedia com it contains a wide variety of videos we also provide a tool to download jukinmedia videos and its high quality descriptions the tool is located in the crawler crawler folder please read the tool s readme md crawler readme md to use it valleyweight valley 13b v1 we release valley 13b v1 https huggingface co luoruipu1 valley 13b v1 delta delta weights weights to comply with the llama model license you can apply this delta weights to original llama model weight through the instructions blew 1 get the original llama weights in the huggingface format by following the instructions structions here https huggingface co docs transformers main model doc llama 2 use the following scripts to get valley weights by applying our delta 13b v1 https huggingface co luoruipu1 valley 13b v1 delta bash python3 valley model apply delta py base path to llama 13b target output path to valley 13b v1 delta path to valley 13b v1 delta valley2 7b for the valley2 7b model we provide direct weights the address is here https huggingface co luoruipu1 valley2 7b chinese valley 13b we now support chinese valley we use bellegroup belle llama ext 13b as llm backbone and ofa sys chinese clip vit large patch14 for visual backbone the address is here https huggingface co zhaoziwang chinese valley v1 pretrain weight we provide 13b https huggingface co luoruipu1 valley 13b pretrain and 7b https huggingface co luoruipu1 valley2 7b pretrain pre trained weights so that people can fine tune directly on our pre trained weights with their own fine tuning data web ui p align center img src valley logo demo gif width 100 br p the framework of this webui comes from llava https github com haotian liu llava and fastchat https github com lm sys fastchat we modified a part of the code to make this demo support the input of video and images launch a controller bsah python valley serve controller py launch a model worker bsah python valley serve model worker py model path path to valley 13b v1 ps at present only single card mode is supported to load the model and at least 30g of video memory is required so the graphics card needs at least one tesla v100 launch a gradio demo bash python valley serve gradio web server video py share inference valley in command line we now update inference code which is more convient and supports input in the form of openai api inference cli python3 inference run valley py model name path to valley weight video file path to video quary your query on the video inference chinese valley python3 inference run valley py model name path to chinese valley weight video file path to video quary your query on the video system prompt chinese valley inference in code you can utilize the code located at valley inference run valley llamma v2 py valley inference run valley llamma v2 py to run inference on a video all that s required is a video path bash python valley inference run valley llamma v2 py video file path to video file luoruipu1 valley2 7b is used in the provided code train valley step by step inspired by llava we adopt a two stage training method the pre training stage uses the valley webvid2m pretrain 703k https huggingface co datasets luoruipu1 valley webvid2m pretrain 703k and llava cc3m pretrain 595k https huggingface co datasets liuhaotian llava pretrain and fine tune stage uses llava instruct 150k https huggingface co datasets liuhaotian llava instruct 150k videochat instruct 11k https github com opengvlab internvideo tree main data instruction data and valley instruct 73k https huggingface co datasets luoruipu1 valley instruct 73k we modified our code for training valley and managed the model hyperparameters with yaml files run the following two scripts to perform valley training pretrain the llm backbone that currently supports pre training is llama 7b 13b vicuna 7b 13b stable vicuna 13b llama2 chat 7b chat 13b you need to download these open source language model weights yourself and convert them to the huggingface format shell bash valley train train sh valley configs experiment valley stage1 yaml finetune shell bash valley train train sh valley configs experiment valley stage2 yaml acknowledgement llava https github com haotian liu llava moss https github com openlmlab moss thanks to these two repositories for providing high quality code our code is based on them citation if the project is helpful to your research please consider citing our paper as follows bibtex misc luo2023valley title valley video assistant with large language model enhanced ability author ruipu luo and ziwang zhao and min yang and junwei dong and minghui qiu and pengcheng lu and tao wang and zhongyu wei year 2023 eprint 2306 07207 archiveprefix arxiv primaryclass cs cv | ai |
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Efficient-Embedded-Systems-Design-Education-Kit | efficient embedded systems design education kit welcome to our efficient embedded systems design education kit download the edkit here https github com arm university efficient embedded systems design education kit archive refs heads main zip our flagship offering to universities worldwide is the arm university program education kit series these self contained educational materials offered exclusively and at no cost to academics and teaching staff worldwide they re designed to support your day to day teaching on core electronic engineering and computer science subjects you have the freedom to choose which modules to teach you can use all the modules in the education kit or only those that are most appropriate to your teaching outcomes our efficient embedded systems design education kit covers the fundamentals of embedded systems design including how the theories and concepts can be applied in the design and efficient programming of embedded processor interfacing with both analog and digital input and output peripehrals a full description of the education kit can be found here https www arm com resources education education kits efficient embedded systems kit specification a full set of lecture slides ready for use in a typical 10 12 week undergraduate course full syllabus below recent updates to the edkit include updated material on armv8 m architecture and the cortex m33 processor a dedicated module on arm s system wide trustzone security technology and refreshed lab exercises on the st nucleo l552ze q board lab manual with solutions for faculty labs are based on low cost yet powerful arm based hardware platforms donated by partners subject to availability prerequisites basics of c and assembly programming course aim to produce students who can design and efficiently program arm based embedded systems syllabus 1 introduction to embedded systems design 1 software design basics 1 cortex m4 m33 processor core part 1 cortex m4 for nucleo f401re version while cortex m33 for nucleo l552ze q version 1 cortex m4 m33 processor core part 2 cortex m4 for nucleo f401re version while cortex m33 for nucleo l552ze q version 1 c as implemented in assembly language 1 interrupts and low power features 1 general purpose i o 1 analog interfacing 1 timer peripherals 1 serial communications 1 programming for power efficient computing high level techniques 1 programming for power efficient computing low level techniques 1 trustzone features in cortex m33 available only on nucleo l552ze q version license you are free to fork or clone this material see license md https github com arm university efficient embedded systems design education kit blob main license license md for the complete license inclusive language commitment arm is committed to making the language we use inclusive meaningful and respectful our goal is to remove and replace non inclusive language from our vocabulary to reflect our values and represent our global ecosystem arm is working actively with our partners standards bodies and the wider ecosystem to adopt a consistent approach to the use of inclusive language and to eradicate and replace offensive terms we recognise that this will take time this course has been updated to replace references to non inclusive language we recognise that some of you will be accustomed to using the previous terms and may not immediately recognise their replacements please refer to the following example when introducing the amba 3 ahb lite protocols we will use the term manager instead of master and subordinate instead of slave this course may still contain other references to non inclusive language it will be updated with newer terms as those terms are agreed and ratified with the wider community contact us at education arm com with questions or comments about this course you can also report non inclusive and offensive terminology usage in arm content at terms arm com | embedded embedded-systems arm-cortex-m4 arm-cortex-m33 embedded-system-design arm cortex-m hardware-designs st-nucleo-f401re trustzone st-nucleo-l552ze amba discovery-f4 armv8 keil-mdk | os |
vuolt | vuolt mobile cloud distributed machine learning for mobile systems and knowledge engineering and machine learning last semester project a k a the veterans unite one last time related articles those can be found in articles folder documentation project schedule and final documentation can be found in docs folder server side implementation the folder algorithms contains python notebook implementations of both lstm and naivebayes models that were set up on google cloud for the sake of the project data was stored in aws buckets and communication with server implemented through lambdas mobile implementation implementation for android mobile devices can be fould in tflite demo folder inside there is a readme md file which documents the whole package furhtermore classes fields and methods that play vital role in the project are documented thoroughly with javadocs dataset it is avaliable in three parts named training 1600000 processed noemoticon part 01 02 03 rar the whole set was divided as described in the documentation of the project to provide sufficient training and testing data | cloud |
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robotic-arm-control-system-analysis | robotic arm control system analysis | os |
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NLP_Projects | div align center h1 natural language processing h1 p this repository houses natural language processing nlp projects that i have completed other than projects completed using spark databricks p h2 huggingface profile h2 p to view and use the models travel over my huggingface portfolio a href https huggingface co dunnbc22 huggingface co dunnbc22 a p h2 text classification h2 details open summary multiclass classification summary project name model checkpoint accuracy macro f1 score macro precision macro recall apple iphone se reviews https github com dunnbc22 nlp projects blob main multiclass 20classification apple 20iphone 20se 20reviews apple 20iphone 20reviews 20 20mc 20clf 20 20bert base ipynb bert base uncased 0 9712 0 9561 0 9538 0 9598 apple iphone se reviews https github com dunnbc22 nlp projects blob main multiclass 20classification apple 20iphone 20se 20reviews apple 20iphone 20reviews 20 20mc 20clf 20 20mpnet ipynb microsoft mpnet base 0 9460 0 7242 0 7007 0 7594 cnn news articles https github com dunnbc22 nlp projects blob main multiclass 20classification cnn 20news 20articles multiclass classification 20of 20news 20articles cnn 20news ipynb distilbert base uncased 0 9643 0 9640 hate offensive speech https github com dunnbc22 nlp projects blob main multiclass 20classification transformer 20comparison hate 20 26 20offensive 20speech 20 20bert ipynb bert base uncased 0 9213 0 9161 0 9241 0 9144 hate offensive speech https github com dunnbc22 nlp projects blob main multiclass 20classification transformer 20comparison hate 20 26 20offensive 20speech 20 20bert large ipynb bert large uncased 0 9869 0 9863 0 987 0 9857 hate offensive speech https github com dunnbc22 nlp projects blob main multiclass 20classification transformer 20comparison hate 20 26 20offensive 20speech 20 20distilbert ipynb distilbert base uncased 0 9607 0 9592 0 9613 0 9579 hate offensive speech https github com dunnbc22 nlp projects blob main multiclass 20classification transformer 20comparison hate 20 26 20offensive 20speech 20 20fbert ipynb diptanu fbert 0 9607 0 9581 0 9596 0 9571 hate offensive speech https github com dunnbc22 nlp projects blob main multiclass 20classification transformer 20comparison hate 20 26 20offensive 20speech 20 20hatebert ipynb gronlp hatebert 0 941 0 9351 0 951 0 9273 password strength https github com dunnbc22 nlp projects blob main password 20strength 20classification 20 mc codebert base 20 20password classifier ipynb microsoft codebert base 0 9975 0 9963 0 9948 0 9978 malicious urls https github com dunnbc22 nlp projects blob main multiclass 20classification malicious 20urls malicious 20urls 20 20codebert ipynb microsoft codebert base 0 7279 0 4611 0 5436 0 4422 malicious urls https github com dunnbc22 nlp projects blob main multiclass 20classification malicious 20urls malicious urls codebert mlm ipynb microsoft codebert base mlm 0 7322 0 4303 0 6034 0 4233 malicious urls https github com dunnbc22 nlp projects blob main multiclass 20classification malicious 20urls malicious urls deberta ipynb microsoft deberta base mnli 0 7353 0 4533 0 5684 0 4315 malicious urls using peft https github com dunnbc22 nlp projects blob main multiclass 20classification malicious 20urls peft malicious urls roberta large chkpt ipynb roberta large 0 7160 0 4374 0 5237 0 4190 malicious urls https github com dunnbc22 nlp projects blob main multiclass 20classification malicious 20urls 20 20albert base v2 malicious 20urls 20albert base 20v2 ipynb albert base v2 0 7267 0 4521 0 5508 0 4294 details details summary binary classification summary project name transformer checkpoint accuracy f1 score precision recall malignant comments bert base https github com dunnbc22 nlp projects blob main binary 20classification malignant 20comments malignant 20comments 20 20bert base ipynb bert base uncased 0 972 0 759 0 6918 0 8406 malignant comments i bert https github com dunnbc22 nlp projects blob main binary 20classification malignant 20comments malignant 20comments 20 20i bert ipynb kssteven ibert roberta base 0 9741 0 7773 0 7084 0 861 mental health classification https github com dunnbc22 nlp projects blob main binary 20classification mental 20health 20classification canine 20 20mental 20health 20classification ipynb google canine c 0 9226 0 9096 0 9113 0 9079 onionornot https github com dunnbc22 nlp projects blob main binary 20classification onionornot dunnbc22 distilbert base uncased onionornot ipynb distilbert base uncased 0 9224 0 9218 spam filter larger dataset https github com dunnbc22 nlp projects blob main binary 20classification spam 20filter 20larger 20dataset dunnbc22 distilbert base uncased spamfilter lg ipynb distilbert base uncased 0 9845 0 9848 spam filter smaller dataset https github com dunnbc22 nlp projects blob main binary 20classification spam 20filter 20 20smaller 20dataset dunnbc22 distilbert base uncased spamfilter sm ipynb distilbert base uncased 0 9907 0 9906 tweet about a disaster or not albert https github com dunnbc22 nlp projects blob main binary 20classification transformer 20comparison is 20this 20tweet 20referring 20to 20a 20disaster 20or 20not 3f 20 20albert ipynb albert base v2 0 9138 0 7752 0 8204 0 7348 tweet about a disaster or not deberta https github com dunnbc22 nlp projects blob main binary 20classification transformer 20comparison is 20this 20tweet 20referring 20to 20a 20disaster 20or 20not 3f 20 20deberta ipynb microsoft deberta v3 small 0 9050 0 7453 0 7453 0 7453 tweet about a disaster or not distilbert https github com dunnbc22 nlp projects blob main binary 20classification transformer 20comparison is 20this 20tweet 20referring 20to 20a 20disaster 20or 20not 3f 20 20distilbert ipynb distilbert base uncased 0 9138 0 7752 0 8204 0 7348 tweet about a disaster or not ernie https github com dunnbc22 nlp projects blob main binary 20classification transformer 20comparison is 20this 20tweet 20referring 20to 20a 20disaster 20or 20not 3f 20 20ernie ipynb nghuyong ernie 2 0 base en 0 9156 0 7876 0 8436 0 7386 tweet about a disaster or not electra https github com dunnbc22 nlp projects blob main binary 20classification transformer 20comparison is 20this 20tweet 20referring 20to 20a 20disaster 20or 20not 3f 20 20electra ipynb bhadresh savani electra base emotion 0 8857 0 7246 0 7991 0 6628 tweet about a disaster or not roberta https github com dunnbc22 nlp projects blob main binary 20classification transformer 20comparison is 20this 20tweet 20referring 20to 20a 20disaster 20or 20not 3f 20 20roberta ipynb roberta base 0 8989 0 7569 0 8211 0 7020 details details summary multilabel classification summary project name model checkpoint subset accuracy f1 score roc auc go emotions https github com dunnbc22 nlp projects blob main multilabel 20classification goemotions goemotions 20 20multilabel 20clf ipynb distilbert base uncased 0 2184 0 3328 0 6102 research articles https github com dunnbc22 nlp projects blob main multilabel 20classification research 20articles research 20articles multilabel 20clf ipynb distilbert base uncased 0 6977 0 8395 0 8909 review sentiments with distilbert https github com dunnbc22 nlp projects blob main multilabel 20classification review 20sentiments sentiments 20 20multilabel 20clf ipynb distilbert base uncased 0 5787 0 8697 0 9107 review sentiments with bert https github com dunnbc22 nlp projects blob main multilabel 20classification review 20sentiments sentiment analysis 20 20using 20bert 20instead 20of 20distilbert ipynb bert base uncased 0 5967 0 8737 0 9146 details details summary token classification summary project name overall accuracy overall f1 score overall precision overall recall multilingual babelscape wikineural joined dataset https github com dunnbc22 nlp projects blob main token 20classification multilingual babelscape wikineural joined 20dataset babelscape 20wikineural 20joined 20dataset 20with 20multilingual 20bert ipynb 0 994704 0 995886 0 995711 0 996060 yes bc2gm iob embo blurb https github com dunnbc22 nlp projects blob main token 20classification monolingual embo blurb ner 20project 20using 20embo blurb 20dataset ipynb 0 9736 0 7765 0 7521 0 8025 no embo blurb with lora https github com dunnbc22 nlp projects tree main token 20classification monolingual embo sourcedata 20with 20lora 0 9584 0 8136 0 7999 0 8278 no dfki slt few nerd https github com dunnbc22 nlp projects blob main token 20classification monolingual dfki 20slt 20few 20nerd ner project using dfki slt few nerd dataset ipynb 0 9498 0 8041 0 8203 0 7886 no ncbi disease https github com dunnbc22 nlp projects blob main token 20classification monolingual ncbi disease ner 20project 20using 20ncbi disease 20dataset ipynb 0 9825 0 8359 0 8064 0 8677 no tner bio nlp 2004 https github com dunnbc22 nlp projects blob main token 20classification monolingual tner bionlp2004 ner 20project 20using 20tner bionlp 202004 20dataset 20 bert base ipynb 0 9367 0 7169 0 6628 0 7805 no stromberg nlp twitter seqeval https github com dunnbc22 nlp projects blob main token 20classification monolingual strombergnlp twitter pos vcb ner 20project 20using 20strombergnlp 20twitter pos vcb 20dataset ipynb 0 9860 0 9824 0 9828 0 9820 no stromberg nlp twitter pos v2 https github com dunnbc22 nlp projects blob main token 20classification monolingual strombergnlp twitter pos vcb ner 20project 20using 20strombergnlp 20twitter pos vcb 20dataset 20with 20poseval ipynb 0 9853 0 8931 0 9296 0 8931 no stromberg nlp twitter pos squeezebert transformer https github com dunnbc22 nlp projects blob main token 20classification monolingual strombergnlp twitter pos vcb ner 20project 20using 20strombergnlp 20twitter pos vcb 20dataset 20with 20poseval 20 26 20squeezebert ipynb 0 9771 0 7765 0 8046 0 7785 no wikineural bert base https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20bert base 20transformer ipynb 0 9912 0 9145 0 9380 0 9261 no wikineural amazon s bort https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20bort 20transformer ipynb 0 9709 0 7050 0 7868 0 7437 no wikineural fnet base https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20fnet 20transformer ipynb 0 8521 0 8934 0 8722 0 9853 no wikineural funnel transformer https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20funnel 20transformer ipynb 0 9856 0 8722 0 9102 0 8908 no wikineural i bert base https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20i bert 20transformer ipynb 0 9909 0 9107 0 9360 0 9232 no wikineural mega base https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20mega 20transformer ipynb 0 9619 0 6312 0 7324 0 6781 no wikineural roberta base https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20roberta base 20transformer ipynb 0 9910 0 9124 0 9352 0 9237 no wikineural squeezebert https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20squeezebert 20transformer ipynb 0 9803 0 8278 0 8866 0 8562 no wikineural xlnet base https github com dunnbc22 nlp projects blob main token 20classification monolingual wikineural 20 20transformer 20comparison pos 20project 20with 20wikineural 20dataset 20 20xlnet 20transformer ipynb 0 9904 0 9068 0 9324 0 9194 no details details summary sentiment analysis summary project name model checkpoint accuracy macro f1 score macro precision macro recall emotions sentiment analysis https github com dunnbc22 nlp projects blob main sentiment 20analysis emotions 20sentiment 20analysis emotions 20sentiment 20analysis 20project ipynb distilbert base uncased 0 935 0 935 financial sentiment analysis original https github com dunnbc22 nlp projects blob main sentiment 20analysis financial 20sentiment 20analysis financial 20sentiment 20analysis original 20version ipynb distilbert base uncased 0 8425 0 8470 financial sentiment analysis updated v1 5 https github com dunnbc22 nlp projects blob main sentiment 20analysis financial 20sentiment 20analysis financial 20sentiment 20analysis updated 20version ipynb distilbert base uncased 0 8529 0 8564 financial sentiment analysis v2 https github com dunnbc22 nlp projects blob main sentiment 20analysis financial 20sentiment 20analysis financial sentiment analysis v2 ipynb google fnet base 0 8117 0 7472 0 7588 0 7394 financial sentiment analysis v3 https github com dunnbc22 nlp projects blob main sentiment 20analysis financial 20sentiment 20analysis financial sentiment analysis v3 ipynb google fnet large 0 8618 0 8209 0 8084 0 8401 news about gold bort https github com dunnbc22 nlp projects blob main sentiment 20analysis sentiment 20analysis 20of 20commodity 20news 20 20gold 20 transformer 20comparison news 20about 20gold 20 20sentiment 20analysis 20 20bort 20with 20w 26b ipynb amazon bort 0 8770 0 7791 0 8463 0 7539 news about gold bert base https github com dunnbc22 nlp projects blob main sentiment 20analysis sentiment 20analysis 20of 20commodity 20news 20 20gold 20 transformer 20comparison news 20about 20gold 20 20sentiment 20analysis 20 20bert base 20with 20w 26b ipynb bert base uncased 0 9139 0 8758 0 8885 0 8647 news about gold funnel https github com dunnbc22 nlp projects blob main sentiment 20analysis sentiment 20analysis 20of 20commodity 20news 20 20gold 20 transformer 20comparison news 20about 20gold 20 20sentiment 20analysis 20 20funnel 20with 20w 26b ipynb funnel transformer medium base 0 9172 0 8854 0 8853 0 8859 news about gold mega https github com dunnbc22 nlp projects blob main sentiment 20analysis sentiment 20analysis 20of 20commodity 20news 20 20gold 20 transformer 20comparison news 20about 20gold 20 20sentiment 20analysis 20 20mega 20with 20w 26b ipynb mnaylor mega base wikitext 0 5014 0 3283 0 4548 0 3835 news about gold mpnet base https github com dunnbc22 nlp projects blob main sentiment 20analysis sentiment 20analysis 20of 20commodity 20news 20 20gold 20 transformer 20comparison news 20about 20gold 20 20sentiment 20analysis 20 20mpnet base 20with 20w 26b ipynb microsoft mpnet base 0 9068 0 8351 0 831 0 8406 news about gold squeezebert https github com dunnbc22 nlp projects blob main sentiment 20analysis sentiment 20analysis 20of 20commodity 20news 20 20gold 20 transformer 20comparison news 20about 20gold 20 20sentiment 20analysis 20 20squeezebert 20with 20w 26b ipynb squeezebert squeezebert uncased 0 9168 0 8749 0 8822 0 8684 news about gold yoso https github com dunnbc22 nlp projects blob main sentiment 20analysis sentiment 20analysis 20of 20commodity 20news 20 20gold 20 transformer 20comparison news 20about 20gold 20 20sentiment 20analysis 20 20yoso 20with 20w 26b ipynb uw madison yoso 4096 0 4456 0 2272 0 3240 0 2912 twitter sentiment analysis https github com dunnbc22 nlp projects blob main sentiment 20analysis twitter 20sentiment 20analysis twitter 20us 20airlines 20sentiment 20analysis ipynb distilbert base uncased 0 8466 0 8471 twitter sentiment analysis v2 https github com dunnbc22 nlp projects blob main sentiment 20analysis twitter 20sentiment 20analysis twitter 20sentiment 20analysis v2 ipynb bert base uncased 0 8474 0 788 0 8132 0 7747 twitter sentiment analysis v3 https github com dunnbc22 nlp projects blob main sentiment 20analysis twitter 20sentiment 20analysis twitter 20sentiment 20analysis v3 20 roberta ipynb vinai bertweet base 0 8588 0 8151 0 8463 0 7961 metrics are macro averaged version only if all 4 metric values are displayed accuracy f1 score recall and precision details details summary language detection summary project name accuracy macro f1 score macro precision macro recall language detection of tweets https github com dunnbc22 nlp projects blob main language 20detection language 20detection 20of 20tweets language 20detection 20of 20tweets ipynb 0 9992 0 9992 0 9992 0 9992 language detection 10k https github com dunnbc22 nlp projects blob main language 20detection language 20detection 2010k 20samples language detection 10k ipynb 0 9971 0 9977 0 9981 0 9974 language detection 20k https github com dunnbc22 nlp projects blob main language 20detection language 20detection 20k 20samples language detection 20k ipynb 0 9883 0 9882 0 9887 0 9879 details details summary semantic similarity summary project name accuracy f1 score precision recall average precision semantic similarity of quora pairs dataset base https github com dunnbc22 nlp projects blob main semantic similarity semantic 20similarity base ipynb 85 93 82 89 77 43 89 18 87 13 semantic similarity of quora pairs dataset large https github com dunnbc22 nlp projects blob main semantic similarity semantic 20similarity large ipynb 88 72 85 22 80 72 90 25 89 75 metrics shown for semantic similarity are measured using cosine similarity details br h2 text generation multiple choice question answer h2 details summary text summarization summary project name rouge1 rouge2 rougel rougelsum flan t5 text summarization data dataset 1 epoch https github com dunnbc22 nlp projects blob main text 20summarization text summarized 20data 20 20comparison flan t5 20 20text 20summarization 20 201 20epoch ipynb 43 6615 20 349 40 1032 40 1589 flan t5 text summarization data dataset 6 epochs https github com dunnbc22 nlp projects blob main text 20summarization text summarized 20data 20 20comparison flan t5 20 20text 20summarization 20 206 20epochs ipynb 43 5994 0 4446 40 132 40 1692 led text summarization data dataset 4 epochs https github com dunnbc22 nlp projects blob main text 20summarization text summarized 20data 20 20comparison led 20 20text 20summarization 20 204 20epochs ipynb 43 3689 19 9885 39 9887 40 0679 cnn news text summarization https github com dunnbc22 nlp projects blob main text 20summarization cnn 20news 20text 20summarization cnn 20news 20text 20summarization ipynb 0 834343 0 793822 0 823824 0 823778 text summarization bbc news with pegasus transformer https github com dunnbc22 nlp projects blob main text 20summarization bbc 20news 20text 20summarization text summarization bbc news pegasus ipynb 0 584474 0 463574 0 408729 0 408431 details details summary machine translation summary project name transformer checkpoint bleu rouge1 rouge2 rougel rougelsum meteor english to french https github com dunnbc22 nlp projects blob main machine 20translation nlp 20translation 20project en 20to 20fr nlp 20translation 20project en fr ipynb facebook mbart large 50 35 1914 0 6420 0 4573 0 6070 0 6069 0 5917 english to german https github com dunnbc22 nlp projects blob main machine 20translation nlp 20translation 20project en 20to 20de nlp 20translation 20project en de ipynb facebook mbart large 50 35 5931 0 5803 0 3939 0 5439 0 5442 0 55 english to spanish https github com dunnbc22 nlp projects blob main machine 20translation nlp 20translation 20project en 20to 20es nlp 20translation 20project en es ipynb facebook mbart large 50 41 4437 0 6751 0 4977 0 6372 0 6376 0 6479 biomedical en to it translation https github com dunnbc22 nlp projects blob main machine 20translation biomedical 20translation 20 en 20to 20it biomedical 20 20translation 20project ipynb facebook mbart large 50 38 9893 0 6826 0 4737 0 6586 0 6585 0 6270 chinese to english translation https github com dunnbc22 nlp projects blob main machine 20translation chinese 20to 20english 20translation chinese to english translation ipynb helsinki nlp opus mt zh en 45 2808 0 6201 0 4198 0 5927 0 5927 korean to english https github com dunnbc22 nlp projects blob main machine 20translation korean 20to 20english 20 korean 20parallel 20corpora korean parallel corpora opus translation project ipynb helsinki nlp opus mt ko en 14 3395 0 4391 0 2022 0 3671 0 3671 medical german to english https github com dunnbc22 nlp projects blob main machine 20translation medical 20 20german 20to 20english opus medical german to english opus translation project ipynb helsinki nlp opus mt de en 53 8812 0 7664 0 6284 0 7370 0 7370 details details summary question answer summary project name exact match f1 score ml qa https github com dunnbc22 nlp projects blob main question 26answer ml 20qa ml qa question 26answer with bert ipynb 59 6146 73 3002 answer prediction dataset https github com dunnbc22 nlp projects blob main question 26answer question 20answer 20dataset answer 20prediction 20dataset 20 20question 26answer 20with 20bert ipynb 65 7357 79 2835 details details summary generate docstrings summary project name model checkpoint rouge1 rouge2 rougel rougelsum codesearchnet dataset to generate docstrings code t5 project https github com dunnbc22 nlp projects blob main generate 20docstrings code 20search 20net 20dataset 20 20small 20checkpoint code t5 project ipynb salesforce codet5 small 0 3381 0 1541 0 3045 0 3214 smol dataset to generate docstrings https github com dunnbc22 nlp projects blob main generate 20docstrings smol 20dataset 20 20base 20checkpoint code t5 project base 20checkpoint ipynb salesforce codet5 base 0 4947 0 3661 0 4794 0 4791 smol dataset to generate docstrings https github com dunnbc22 nlp projects blob main generate 20docstrings smol 20dataset 20 20small 20checkpoint code t5 project small 20checkpoint ipynb salesforce codet5 small 0 38 0 2176 0 3554 0 3635 details details summary multiple choice summary project name accuracy cosmosqa https github com dunnbc22 nlp projects blob main multiple 20choice cosmosqa cosmosqa 20 20multiple 20choice 20using 20bert ipynb 0 6000 social iqa https github com dunnbc22 nlp projects blob main multiple 20choice social 20iqa social 20iqa 20 20multiple 20choice ipynb 0 6128 discourse marker qa https github com dunnbc22 nlp projects blob main multiple 20choice discourse 20marker 20qa discourse marker qa multiple choice using bert ipynb 0 6207 figurative language https github com dunnbc22 nlp projects blob main multiple 20choice figurative 20language figurative 20language 20 20multiple 20choice 20using 20bert ipynb 0 8124 strategy qa https github com dunnbc22 nlp projects blob main multiple 20choice strategy 20qa strategy 20qa 20 20multiple 20choice 20using 20bert ipynb 0 625 e care https github com dunnbc22 nlp projects blob main multiple 20choice e care e care multiple choice using bert ipynb 0 7212 vitamin c fact verification https github com dunnbc22 nlp projects blob main multiple 20choice vitamin 20c 20fact 20verification vitamin c fact verification multiple choice using bert ipynb 0 7240 winowhy https github com dunnbc22 nlp projects blob main multiple 20choice winowhy winowhy 20 20multiple 20choice 20using 20bert ipynb 0 7118 details br h2 nlp regression h2 details summary nlp regression summary project name mean squared error mse root mean squared error rmse mean absolute error mae edmunds car reviews all brands with bert base https github com dunnbc22 nlp projects blob main nlp 20regression edmunds 20car 20reviews 20 bert base edmunds consumer car regression all manufacturers bert base ipynb 0 2324 0 4820 0 3089 edmunds car reviews all brands https github com dunnbc22 nlp projects blob main nlp 20regression edmunds 20car 20reviews 20 all 20brands edmunds consumer car regression all 20manufacturers ipynb 0 2232 0 4724 0 3150 edmunds car reviews brands headquartered in america https github com dunnbc22 nlp projects blob main nlp 20regression edmunds 20car 20reviews 20 american hf edmunds consumer car regression american ipynb 0 2486 0 4986 0 3469 edmunds car reviews brands headquartered in europe https github com dunnbc22 nlp projects blob main nlp 20regression edmunds 20car 20reviews 20 european hf edmunds consumer car regression european ipynb 0 1999 0 4471 0 2824 edmunds car reviews brands not headquartered in america or europe https github com dunnbc22 nlp projects blob main nlp 20regression edmunds 20car 20reviews 20 non 20european 20imports hf edmunds consumer car regression non european imports ipynb 0 2240 0 4733 0 3140 episode reviews rating the simpsons https github com dunnbc22 nlp projects blob main nlp 20regression simpsons 20episode 20descriptions nlp 20regression 20 20the 20simpsons ipynb 0 7632 0 8736 0 6622 episode reviews rating the simpsons other tv shows https github com dunnbc22 nlp projects blob main nlp 20regression descriptions 20of 20simpsons 20episodes 20 26 20other 20tv 20shows nlp 20regression 20 20simpsons 20plus 20other 20series ipynb 0 3754 0 6127 0 4651 tmdb 5000 move dataset https github com dunnbc22 nlp projects blob main nlp 20regression tmdb 205000 20movie 20descriptions nlp 20regression 20 20tmdb 205000 20movie 20dataset 20 20original ipynb 0 7613 0 8725 0 6848 details br h2 language modeling h2 details summary causal language modeling summary project name perplexity 2000 clean medical articles https github com dunnbc22 nlp projects blob main causal 20language 20modeling 2000 20clean 20medical 20articles 2 2c000 20clean 20medical 20articles 20 20clm ipynb 18 67 ag news distilgpt2 version https github com dunnbc22 nlp projects blob main causal 20language 20modeling ag 20news distilgpt2 20version distilgpt2 20 20ag news clm ipynb 31 53 ag news gpt2 version https github com dunnbc22 nlp projects blob main causal 20language 20modeling ag 20news gpt2 20version gpt2 20 20ag news clm ipynb 22 92 us economic news articles https github com dunnbc22 nlp projects blob main causal 20language 20modeling us 20economic 20news 20articles us 20economic 20news 20articles 20 20clm ipynb 31 41 details details summary causal language modeling for chatbot summary project name perplexity large company s faqs medium v1 https github com dunnbc22 nlp projects blob main causal 20lm 20for 20chatbot company 20faqs v1 faqs 20 20clm 20for 20dialogpt 20chatbot 20 20medium ipynb 8 67 large company s faqs large v1 https github com dunnbc22 nlp projects blob main causal 20lm 20for 20chatbot company 20faqs v1 faqs 20 20clm 20for 20dialogpt 20chatbot dialogpt large v1 ipynb 2 79 large company s faqs v2 https github com dunnbc22 nlp projects blob main causal 20lm 20for 20chatbot company 20faqs v2 faqs 20 20clm 20for 20dialogpt 20chatbot dialogpt large v2 ipynb 1 70 details details summary masked language modeling summary project name perplexity ag news https github com dunnbc22 nlp projects blob main masked 20language 20model ag 20news ag news mlm ipynb 5 95 reddit comments https github com dunnbc22 nlp projects blob main masked 20language 20model datasets 20for 20nlp 20 20reddit 20comments datasets for nlp mlm ipynb 12 70 us economic news articles https github com dunnbc22 nlp projects blob main masked 20language 20model us 20economic 20news 20articles us economic news articles mlm ipynb 6 25 details div hr footnotes ul li the output format for rouge was changed somewhere along the way of training all of these projects as a result rouge metric values under 1 should be multiplied by 100 to accurately compare to the values over 1 li li pos stands for part of speech li li projects that are apart of transformer comparisons using the same dataset are denoted with an asterisk at the end of their project name li ul | machine-translation semantic-similarity text-classification text-regression text-summarization multilabel-classification sentiment-analysis binary-classification causal-language-modeling huggingface language-detection masked-language-modeling multiclass-classification multiple-choice-question-answering question-answering text-classificaiton-with-bert text-classification-multi-label | ai |
foxpage | p align center a href https console foxfamily io page img src https www foxpage io logo jpg width 260px alt foxpage logo a p h4 align center low code made simple and fast h4 p align center a href https console foxfamily io page try live demo a p br p align center img src https img shields io badge node 3e 3d14 15 1 brightgreen alt node version img src https img shields io badge typescript 3e 3d4 3 0 brightgreen alt typescript version img src https img shields io badge yarn 1 22 5 blue alt yarn version img src https img shields io badge npm 3e 3d6 14 x blue alt npm version p foxpage is a lightweight front end low code framework english readme zh cn md a href https www foxpage io guide target blank read the using document a features visualization provides visual page editing what you see is what you get componentized provide a relatively complete component production process and componentization scheme and the production of pages starts with the production of components scalable strong scalability both at the technical level and at the business level globalization provide a set of international content management solutions platform provides an online cooperation platform for developers editors operations etc project txt project root packages foxpage admin foxpage portal foxpage server foxpage server foxpage server types foxpage types foxpage visual editor foxpage visual editor getting started read the getting started tutorial or follow the steps below installation use yarn to install the foxpage recommended install yarn with these docs https yarnpkg com lang en docs install shell yarn boot start server shell yarn run start server prod after server start run install script to init data yarn run init server prod start portal shell yarn run start admin prod requirements database mongodb 5 0 2 mongoose 5 12 14 we recommend always using the latest version of foxpage to start your new projects contributing please read our contributing guide https www foxpage io guide contribute before submitting a pull request to the project community support for general help using foxpage please refer to the official foxpage documentation https www foxpage io for additional help you can use one of these channels to ask a question github https github com foxpage foxpage bug reports contributions documentation see our documentation live docs https www foxpage io for the foxpage server developer docs https www foxpage io developer user guide https www foxpage io course try live demo see for yourself what s under the hood by getting access to a foxpage project https console foxfamily io page with sample data license see the license license file for licensing information | foxpage nodejs cms low-code typescipt front-end framework | front_end |
NLP | natural language processing python notebooks tutorials in natural language processing | ai |
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LearningFreeRTOS | learningfreertos freertos free rtos free rtos free rtos 1 freertosv10 1 0 freertos freertosv10 1 0 freertos sourceinsinghtproject freertosproject si4project source insight 2 lesson1 freertos stm32f405 free rtos pybord | os |
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awesome-automl-papers | awesome automl papers awesome automl papers is a curated list of automated machine learning papers articles tutorials slides and projects star this repository and then you can keep abreast of the latest developments of this booming research field thanks to all the people who made contributions to this project join us and you are welcome to be a contributor div style text align center img src resources banner png atl banner div what is automl automated machine learning automl provides methods and processes to make machine learning available for non machine learning experts to improve efficiency of machine learning and to accelerate research on machine learning machine learning ml has achieved considerable successes in recent years and an ever growing number of disciplines rely on it however this success crucially relies on human machine learning experts to perform the following tasks preprocess the data select appropriate features select an appropriate model family optimize model hyperparameters postprocess machine learning models critically analyze the results obtained as the complexity of these tasks is often beyond non ml experts the rapid growth of machine learning applications has created a demand for off the shelf machine learning methods that can be used easily and without expert knowledge we call the resulting research area that targets progressive automation of machine learning automl as a new sub area in machine learning automl has got more attention not only in machine learning but also in computer vision natural language processing and graph computing there are no formal definition of automl from the descriptions of most papers the basic procedure of automl can be shown as the following div style text align center img src resources procedure jpg width 600px atl figure1 div automl approaches are already mature enough to rival and sometimes even outperform human machine learning experts put simply automl can lead to improved performance while saving substantial amounts of time and money as machine learning experts are both hard to find and expensive as a result commercial interest in automl has grown dramatically in recent years and several major tech companies and start up companies are now developing their own automl systems an overview comparison of some of them can be summarized to the following table company autofe hpo nas 4paradigm alibaba baidu determined ai google datacanvas h2o ai microsoft mljar rapidminer tencent awesome automl papers includes very up to date overviews of the bread and butter techniques we need in automl automated data clean auto clean automated feature engineering auto fe hyperparameter optimization hpo meta learning neural architecture search nas div style text align center img src resources automl png atl automl div table of contents papers papers surveys surveys automated feature engineering automated feature engineering expand reduce expand reduce hierarchical organization of transformations hierarchical organization of transformations meta learning meta learning reinforcement learning reinforcement learning architecture search architecture search evolutionary algorithms evolutionary algorithms local search local search meta learning meta learning 1 reinforcement learning reinforcement learning 1 transfer learning transfer learning network morphism network morphism continuous optimization continuous optimization hyperparameter optimization hyperparameter optimization bayesian optimization bayesian optimization evolutionary algorithms evolutionary algorithms 1 lipschitz functions lipschitz functions local search local search 1 meta learning meta learning 2 particle swarm optimization particle swarm optimization random search random search transfer learning transfer learning 1 performance prediction performance prediction performance prediction frameworks frameworks miscellaneous miscellaneous tutorials tutorials bayesian optimization bayesian optimization meta learning meta learning 3 articles articles bayesian optimization bayesian optimization meta learning meta learning slides slides bayesian optimization slides books books meta learning books projects projects prominent researchers prominent researchers papers surveys 2019 automl a survey of the state of the art xin he et al arxiv pdf https arxiv org pdf 1908 00709 pdf 2019 survey on automated machine learning marc zoeller marco f huber arxiv pdf https arxiv org pdf 1904 12054 pdf 2019 automated machine learning state of the art and open challenges radwa elshawi et al arxiv pdf https arxiv org pdf 1906 02287 pdf 2018 taking human out of learning applications a survey on automated machine learning quanming yao et al arxiv pdf https arxiv org pdf 1810 13306 pdf 2020 on hyperparameter optimization of machine learning algorithms theory and practice li yang et al neurocomputing pdf https arxiv org pdf 2007 15745 pdf 2020 automated machine learning a brief review at the end of the early years escalante h j arxiv pdf https arxiv org pdf 2008 08516 pdf 2022 iot data analytics in dynamic environments from an automated machine learning perspective li yang et al arxiv pdf https arxiv org pdf 2209 08018 pdf automated feature engineering expand reduce 2022 bert sort a zero shot mlm semantic encoder on ordinal features for automl mehdi bahrami et al automl pdf https github com marscod bert sort 2017 autolearn automated feature generation and selection ambika kaul et al icdm pdf https ieeexplore ieee org document 8215494 2017 one button machine for automating feature engineering in relational databases hoang thanh lam et al arxiv pdf https arxiv org pdf 1706 00327 pdf 2016 automating feature engineering udayan khurana et al nips pdf http workshops inf ed ac uk nips2016 ai4datasci papers nips2016 ai4datasci paper 13 pdf 2016 explorekit automatic feature generation and selection gilad katz et al icdm pdf http ieeexplore ieee org document 7837936 2015 deep feature synthesis towards automating data science endeavors james max kanter kalyan veeramachaneni dsaa pdf http www jmaxkanter com static papers dsaa dsm 2015 pdf hierarchical organization of transformations 2016 cognito automated feature engineering for supervised learning udayan khurana et al icdmw pdf http ieeexplore ieee org document 7836821 meta learning 2020 automl pipeline selection efficiently navigating the combinatorial space chengrun yang et al kdd pdf https people ece cornell edu cy papers tensor oboe pdf 2017 learning feature engineering for classification fatemeh nargesian et al ijcai pdf https www ijcai org proceedings 2017 0352 pdf reinforcement learning 2017 feature engineering for predictive modeling using reinforcement learning udayan khurana et al arxiv pdf https arxiv org pdf 1709 07150 pdf 2010 feature selection as a one player game romaric gaudel michele sebag icml pdf https hal archives ouvertes fr inria 00484049 document architecture search evolutionary algorithms 2019 evolutionary neural automl for deep learning jason liang et al gecco pdf https dl acm org doi pdf 10 1145 3321707 3321721 2017 large scale evolution of image classifiers esteban real et al pmlr pdf https arxiv org abs 1703 01041 2002 evolving neural networks through augmenting topologies kenneth o stanley risto miikkulainen evolutionary computation pdf http nn cs utexas edu downloads papers stanley ec02 pdf local search 2017 simple and efficient architecture search for convolutional neural networks thomoas elsken et al iclr pdf https arxiv org pdf 1711 04528 pdf meta learning 2016 learning to optimize ke li jitendra malik arxiv pdf https arxiv org pdf 1606 01885 pdf reinforcement learning 2018 amc automl for model compression and acceleration on mobile devices yihui he et al eccv pdf http openaccess thecvf com content eccv 2018 papers yihui he amc automated model eccv 2018 paper pdf 2018 efficient neural architecture search via parameter sharing hieu pham et al arxiv pdf https arxiv org abs 1802 03268 2017 neural architecture search with reinforcement learning barret zoph quoc v le iclr pdf https arxiv org pdf 1611 01578 pdf transfer learning 2017 learning transferable architectures for scalable image recognition barret zoph et al arxiv pdf https arxiv org abs 1707 07012 network morphism 2019 auto keras an efficient neural architecture search system haifeng jin et al kdd pdf https dl acm org doi pdf 10 1145 3292500 3330648 continuous optimization 2018 neural architecture optimization renqian luo et al arxiv pdf https arxiv org abs 1808 07233 2019 darts differentiable architecture search hanxiao liu et al iclr pdf https arxiv org abs 1806 09055 frameworks 2019 auptimizer an extensible open source framework for hyperparameter tuning jiayi liu et al ieee big data pdf https arxiv org pdf 1911 02522 2019 towards modular and programmable architecture search renato negrinho et al neurips pdf https arxiv org pdf 1909 13404 pdf 2019 evolutionary neural automl for deep learning jason liang et al arxiv pdf https arxiv org abs 1902 06827 2017 atm a distributed collaborative scalable system for automated machine learning t swearingen et al ieee pdf https cyphe rs static atm pdf 2017 google vizier a service for black box optimization daniel golovin et al kdd pdf https static googleusercontent com media research google com zh cn pubs archive 46180 pdf 2015 autocompete a framework for machine learning competitions abhishek thakur et al icml pdf https arxiv org pdf 1507 02188 pdf hyperparameter optimization bayesian optimization 2020 bayesian optimization of risk measures neurips pdf https proceedings neurips cc paper 2020 file e8f2779682fd11fa2067beffc27a9192 paper pdf 2020 botorch a framework for efficient monte carlo bayesian optimization neurips pdf https proceedings neurips cc paper 2020 file f5b1b89d98b7286673128a5fb112cb9a paper pdf 2020 tuning hyperparameters without grad students scalable and robust bayesian optimisation with dragonfly jmlr pdf https arxiv org pdf 1903 06694 pdf 2019 bayesian optimization with unknown search space neurips pdf http papers nips cc paper 9350 bayesian optimization with unknown search space pdf 2019 constrained bayesian optimization with noisy experiments pdf https projecteuclid org download pdfview 1 euclid ba 1533866666 2019 learning search spaces for bayesian optimization another view of hyperparameter transfer learning neurips pdf http papers nips cc paper 9438 learning search spaces for bayesian optimization another view of hyperparameter transfer learning pdf 2019 practical two step lookahead bayesian optimization neurips pdf http papers nips cc paper 9174 practical two step lookahead bayesian optimization pdf 2019 predictive entropy search for multi objective bayesian optimization with constraints pdf https arxiv org pdf 1609 01051 pdf 2018 bock bayesian optimization with cylindrical kernels icml pdf https arxiv org pdf 1806 01619 pdf 2018 efficient high dimensional bayesian optimization with additivity and quadrature fourier features mojm r mutn et al neurips pdf https papers nips cc paper 8115 efficient high dimensional bayesian optimization with additivity and quadrature fourier features pdf 2018 high dimensional bayesian optimization via additive models with overlapping groups pmlr pdf https arxiv org pdf 1802 07028v2 pdf 2018 maximizing acquisition functions for bayesian optimization neurips pdf http papers nips cc paper 8194 maximizing acquisition functions for bayesian optimization pdf 2018 scalable hyperparameter transfer learning neurips pdf http papers nips cc paper 7917 scalable hyperparameter transfer learning pdf 2016 bayesian optimization with robust bayesian neural networks jost tobias springenberg et al nips pdf https papers nips cc paper 6117 bayesian optimization with robust bayesian neural networks pdf 2016 scalable hyperparameter optimization with products of gaussian process experts nicolas schilling et al pkdd pdf https link springer com chapter 10 1007 978 3 319 46128 1 3 2016 taking the human out of the loop a review of bayesian optimization bobak shahriari et al ieee pdf http ieeexplore ieee org document 7352306 2016 towards automatically tuned neural networks hector mendoza et al jmlr pdf http aad informatik uni freiburg de papers 16 automl autonet pdf 2016 two stage transfer surrogate model for automatic hyperparameter optimization martin wistuba et al pkdd pdf https link springer com chapter 10 1007 978 3 319 46128 1 13 2015 efficient and robust automated machine learning pdf https papers nips cc paper 5872 efficient and robust automated machine learning pdf 2015 hyperparameter optimization with factorized multilayer perceptrons nicolas schilling et al pkdd pdf https link springer com chapter 10 1007 978 3 319 23525 7 6 2015 hyperparameter search space pruning a new component for sequential model based hyperparameter optimization martin wistua et al pdf https dl acm org citation cfm id 2991491 2015 joint model choice and hyperparameter optimization with factorized multilayer perceptrons nicolas schilling et al ictai pdf http ieeexplore ieee org abstract document 7372120 2015 learning hyperparameter optimization initializations martin wistuba et al dsaa pdf http ieeexplore ieee org abstract document 7344817 2015 scalable bayesian optimization using deep neural networks jasper snoek et al acm pdf https dl acm org citation cfm id 3045349 2015 sequential model free hyperparameter tuning martin wistuba et al icdm pdf http ieeexplore ieee org abstract document 7373431 2013 auto weka combined selection and hyperparameter optimization of classification algorithms pdf http www cs ubc ca labs beta projects autoweka papers autoweka pdf 2013 making a science of model search hyperparameter optimization in hundreds of dimensions for vision architectures j bergstra jmlr pdf http proceedings mlr press v28 bergstra13 pdf 2012 practical bayesian optimization of machine learning algorithms pdf https papers nips cc paper 4522 practical bayesian optimization of machine learning algorithms pdf 2011 sequential model based optimization for general algorithm configuration extended version pdf https www cs ubc ca hutter papers 10 tr smac pdf evolutionary algorithms 2020 delta stn efficient bilevel optimization for neural networks using structured response jacobians juhan bae roger grosse neurips pdf https arxiv org abs 2010 13514 2018 autostacker a compositional evolutionary learning system boyuan chen et al arxiv pdf https arxiv org pdf 1803 00684 pdf 2017 large scale evolution of image classifiers esteban real et al pmlr pdf https arxiv org pdf 1703 01041 pdf 2016 automating biomedical data science through tree based pipeline optimization randal s olson et al ecal pdf https arxiv org pdf 1601 07925 pdf 2016 evaluation of a tree based pipeline optimization tool for automating data science randal s olson et al gecco pdf https dl acm org citation cfm id 2908918 lipschitz functions 2017 global optimization of lipschitz functions c edric malherbe nicolas vayatis arxiv pdf https arxiv org pdf 1703 02628 pdf local search 2009 paramils an automatic algorithm configuration framework frank hutter et al jair pdf https arxiv org pdf 1401 3492 pdf meta learning 2019 oboe collaborative filtering for automl model selection chengrun yang et al kdd pdf https dl acm org doi pdf 10 1145 3292500 3330909 2019 smartml a meta learning based framework for automated selection and hyperparameter tuning for machine learning algorithms pdf http openproceedings org 2019 conf edbt edbt19 paper 235 pdf 2008 cross disciplinary perspectives on meta learning for algorithm selection pdf https dl acm org citation cfm id 1456656 particle swarm optimization 2017 particle swarm optimization for hyper parameter selection in deep neural networks pablo ribalta lorenzo et al gecco pdf https dl acm org citation cfm id 3071208 2008 particle swarm optimization for parameter determination and feature selection of support vector machines shih wei lin et al expert systems with applications pdf http www sciencedirect com science article pii s0957417407003752 random search 2016 hyperband a novel bandit based approach to hyperparameter optimization lisha li et al arxiv pdf https arxiv org pdf 1603 06560 pdf 2012 random search for hyper parameter optimization james bergstra yoshua bengio jmlr pdf http www jmlr org papers volume13 bergstra12a bergstra12a pdf 2011 algorithms for hyper parameter optimization james bergstra et al nips pdf https dl acm org citation cfm id 2986743 transfer learning 2016 efficient transfer learning method for automatic hyperparameter tuning dani yogatama gideon mann jmlr pdf https pdfs semanticscholar org 75f2 6734972ebaffc6b43d45abd3048ef75f15a5 pdf 2016 flexible transfer learning framework for bayesian optimisation tinu theckel joy et al pakdd pdf https link springer com chapter 10 1007 978 3 319 31753 3 9 2016 hyperparameter optimization machines martin wistuba et al dsaa pdf http ieeexplore ieee org abstract document 7796889 2013 collaborative hyperparameter tuning r emi bardenet et al icml pdf http proceedings mlr press v28 bardenet13 pdf miscellaneous 2020 automated machine learning techniques for data streams alexandru ionut imbrea pdf https github com aleximb automl streams research paper raw master a imbrea automl data streams pdf 2018 accelerating neural architecture search using performance prediction bowen baker et al iclr pdf https openreview net pdf id bjypugz0z 2017 automatic frankensteining creating complex ensembles autonomously martin wistuba et al siam pdf http epubs siam org doi pdf 10 1137 1 9781611974973 83 2018 characterizing classification datasets a study of meta features for meta learning rivolli adriano et al arxiv pdf https arxiv org pdf 1808 10406 pdf 2020 putting the human back in the automl loop xanthopoulos iordanis et al edbt icdt pdf http ceur ws org vol 2578 etmlp5 pdf tutorials bayesian optimization 2018 a tutorial on bayesian optimization pdf https arxiv org pdf 1807 02811 pdf 2010 a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning pdf https arxiv org pdf 1012 2599v1 pdf meta learning 2008 metalearning a tutorial pdf https pdfs semanticscholar org 5794 1a4891f673cadf06fba02419372aad85c3bb pdf blog type blog title link hpo bayesian optimization for hyperparameter tuning link https arimo com data science 2016 bayesian optimization hyperparameter tuning meta learning learning to learn link http bair berkeley edu blog 2017 07 18 learning to learn meta learning why meta learning is crucial for further advances of artificial intelligence link https chatbotslife com why meta learning is crucial for further advances of artificial intelligence c2df55959adf books year of publication type book title authors publisher link 2009 meta learning metalearning applications to data mining brazdil p giraud carrier c soares c vilalta r springer download http www springer com la book 9783540732624 2019 hpo meta learning nas automl methods systems challenges frank hutter lars kotthoff joaquin vanschoren download https www automl org book 2021 learning automated machine learning in action qinquan song haifeng jin xia hu manning publications download https www manning com books automated machine learning in action videos title author link automl basics automated machine learning in action qinquan song haifeng jin xia hu https www youtube com watch v 9kpieg0b7vm projects project type language license link adanet nas python apache 2 0 github https github com tensorflow adanet advisor hpo python apache 2 0 github https github com tobegit3hub advisor amla hpo nas python apache 2 0 github https github com ciscoai amla atm hpo python mit github https github com hdi project atm auger hpo python commercial homepage https auger ai auptimizer hpo nas python support r script gpl 3 0 github https github com lge arc advancedai auptimizer auto keras nas python license https github com keras team autokeras blob master license txt github https github com keras team autokeras automl vision nas python commercial homepage https cloud google com vision automl video intelligence nas python commercial homepage https cloud google com video intelligence automl natural language nas python commercial homepage https cloud google com natural language automl translation nas python commercial homepage https cloud google com translate automl tables autofe hpo python commercial homepage https cloud google com automl tables autopytorch hpo nas python apache 2 0 github https github com automl auto pytorch hypergbm hpo python python github https github com datacanvasio hypergbm hyperkeras nas python python github https github com datacanvasio hyperkeras hypernets hpo nas python python github https github com datacanvasio hypernets auto sklearn hpo python license https github com automl auto sklearn blob master license txt github https github com automl auto sklearn auto ml hpo python mit github https github com climbsrocks auto ml bayesianoptimization hpo python mit github https github com fmfn bayesianoptimization bayesopt hpo c agpl 3 0 github https github com rmcantin bayesopt comet hpo python commercial homepage https www comet ml datarobot hpo python commercial homepage https www datarobot com devol nas python mit github https github com joeddav devol deeparchitect nas python mit github https github com negrinho deep architect determined hpo nas python apache 2 0 github https github com determined ai determined driverless ai autofe python commercial homepage https www h2o ai products h2o driverless ai far ho hpo python mit github https github com lucfra far ho h2o automl hpo python r java scala apache 2 0 github https github com h2oai h2o 3 hpbandster hpo python bsd 3 clause github https github com automl hpbandster hyperband hpo python license https github com zygmuntz hyperband blob master license github https github com zygmuntz hyperband hyperopt hpo python license https github com hyperopt hyperopt blob master license txt github https github com hyperopt hyperopt hyperopt sklearn hpo python license https github com hyperopt hyperopt sklearn blob master license txt github https github com hyperopt hyperopt sklearn hyperparameter hunter hpo python mit github https github com huntermcgushion hyperparameter hunter katib hpo python apache 2 0 github https github com kubeflow katib matelabs hpo python commercial github http matelabs in milano hpo python apache 2 0 github https github com nvidia milano mljar autofe hpo nas python mit github https github com mljar mljar supervised mlr3automl hpo r lgpl 3 0 github https github com a hanf mlr3automl nasbot nas python mit github https github com kirthevasank nasbot neptune hpo python commercial homepage https neptune ml nni hpo nas python mit github https github com microsoft nni oboe hpo python bsd 3 clause github https github com udellgroup oboe optunity hpo python license https github com claesenm optunity blob master license txt github https github com claesenm optunity r2 ai hpo commercial homepage https r2 ai rbfopt hpo python license https github com coin or rbfopt blob master license github https github com coin or rbfopt robo hpo python bsd 3 clause github https github com automl robo scikit optimize hpo python license https github com scikit optimize scikit optimize blob master license md github https github com scikit optimize scikit optimize sigopt hpo python commercial homepage https sigopt com smac3 hpo python license https github com automl smac3 blob master license github https github com automl smac3 tpot autofe hpo python lgpl 3 0 github https github com rhiever tpot transmogrifai hpo scala bsd 3 clause github https github com salesforce transmogrifai tune hpo python apache 2 0 github https github com ray project ray tree master python ray tune xcessiv hpo python apache 2 0 github https github com reiinakano xcessiv smartml hpo r gpl 3 0 github https github com datasystemsgrouput smartml mlbox autofe hpo python bsd 3 license github https github com axelderomblay mlbox autoai watson autofe hpo commercial homepage https www ibm com cloud watson studio autoai automl automl python mit github https github com western oc2 lab automl implementation for static and dynamic data analytics optuna hpo python mit github https github com optuna optuna slides type slide title authors link autofe automated feature engineering for predictive modeling udyan khurana etc al download https github com hibayesian awesome automl papers blob master resources slides 5bslides 5d automated feature engineering for predictive modeling pdf hpo a tutorial on bayesian optimization for machine learning ryan p adams download https github com hibayesian awesome automl papers blob master resources slides 5bslides 5d a tutorial on bayesian optimization for machine learning pdf hpo bayesian optimisation gilles louppe download https github com hibayesian awesome automl papers blob master resources slides 5bslides 5d bayesian optimisation pdf acknowledgement special thanks to everyone who contributed to this project name bio alexander robles https github com alro10 phd student unicamp brazil derekflint https github com derekflint endymecy https github com endymecy senior researcher tencent eric https github com ffengeng erin ledell https github com ledell chief machine learning scientist h2o ai fwcore https github com fwcore gaurav mittal https github com g1910 hernan ceferino vazquez https github com hcvazquez phd data science expert mercadolibre kaustubh damania https github com kaustubhdamania lilian besson https github com naereen phd student centralesup lec https github com luoleicn marc https github com ennosigaeon mohamed maher https github com mmaher22 neil conway https github com neilconway cto determined ai richard liaw https github com richardliaw phd student uc berkeley randy olson https github com rhiever lead data scientist lifeegx slava kurilyak https github com slavakurilyak founder ceo produvia saket maheshwary https github com saket maheshwary ai researcher shaido987 https github com shaido987 sophia wright blue https github com sophia wright blue tengben0905 https github com tengben0905 xuehui https github com xuehui1991 microsoft yihui he https github com yihui he grad student cmu contact feedback if you have any suggestions missing papers new papers key researchers or typos feel free to pull a request also you can mail to mark lin hibayesian gmail com licenses awesome automl papers is available under apache licenses 2 0 | automl automated-feature-engineering hyperparameter-optimization neural-architecture-search | ai |
machine_learning_derivation | https space bilibili com 97068901 spm id from 333 788 b 765f7570696e666f 2 https github com shuhuai007 machine learning session 1 https www bilibili com video av31950221 2 https www bilibili com video av32905863 3 https www bilibili com video av31989606 4 https www bilibili com video av33101528 5 https www bilibili com video av32709936 6 https www bilibili com video av28186618 7 https www bilibili com video av34731384 8 https www bilibili com video av33360526 9 https www bilibili com video av31906558 10 em https www bilibili com video av33545406 11 https www bilibili com video av35183585 12 https www bilibili com video av32047507 13 https www bilibili com video av32430563 14 https www bilibili com video av32471608 15 https www bilibili com video av32563186 17 https www bilibili com video av34444816 19 https www bilibili com video av35685274 22 https www bilibili com video av37166049 | ai |
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social-distance-dashboard | social distance dashboard recognize and visualize risk additonal project mask detection and classification code and demo https github com iamsashank09 mask detection and classification enter image description here https github com iamsashank09 mask detection and classification blob master outputs gifoutput video gif in a world recovering from the shock of covid 19 it has become increasingly important to practice social distancing a deep understanding of the pandemic shows how one person s negligence can cause widespread harm which will be hard to negate so using computer vision and deep learning might help monitor the practice of social distancing the application detects people who are close by and not adhering to the distancing norms and marks them in red bounding boxes signifying risk others are in green the dashboard at the right gives a visual representation of the data the number next to the green and red icons are the number of safe and risk people whereas the black is the total number of people in the frame the pie chart at the top just plots the safe vs at risk persons in the frame here is a demo containing the application output social distance dashboard output in outdoor conditions https github com iamsashank09 social distance dashboard blob master outputvideos output outdoorlong gif social distance dashboard output in indoor conditions https github com iamsashank09 social distance dashboard blob master outputvideos output indoor gif full output on youtube click to go to the youtube video social distance dashboard https github com iamsashank09 social distance dashboard blob master outputvideos output outdoor gif https youtu be putkkx8gbyg how to use the application the entire application is encapsulated in the socialdistancedashboard py file which can be run using the command python3 socialdistancedashboard py input file the input can be a video file and needs to be updated on line number 198 filename videos video 1 mp4 your file path here yolo v3 dependency yolov3 trained on coco is used for person recognition and detection i have not included the weights as a part of the repository as it is quite big 236mb you can download it at https pjreddie com media files yolov3 weights and add it to the folder yolo coco as a part of the repository i have mentioned the folder path on line number 203 and can be changed yolopath yolo coco path points to folder containing weights cfg and names concerns over such monitoring applications while working on this project i started having concerns over how this kind of monitoring can be used to create spaces with no freedom which is scary i understand this kind of work can be used to boost authoritarianism and force suppression i can only hope we always keep in mind the freedoms of the individual while we build a safe society resources object detection using yolo from pyimagesearch link to article https www pyimagesearch com 2018 05 14 a gentle guide to deep learning object detection input videos used for testing and development outdoor videos virat dataset https viratdata org indoor videos learning recognition surveillance group https www tugraz at institutes icg research team bischof learning recognition surveillance mit license social distance dashboard https camo githubusercontent com 2e380136948ff516fd2a276030a07dbc207c9df8 68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f444156466f756e646174696f6e2f6361707461696e2d6e336d302e7376673f7374796c653d666c61742d737175617265 | computer-vision deep-learning yolov3 python3 covid-19 social-distancing-detection person-detection | ai |
system-design-examples | system design examples many systems design questions are intentionally left very vague and are literally given in the form of design facebook it s your job to ask clarifying questions to better understand the system that you have to build distributed id generator distributed id generator urlshortener urlshortener webcrawler webcrawler notificationservice notificationservice slack whatsapp team messaging platform slack google drive google drive netflix youtube video streaming platform netflix twitter instagram pinterest micro blogging platform twitter uber grab ola cab booking system uber amazon flipkart walmart ecommerce platform amazonwalmart zoom zoom distributed id generator system requirements ids must be unique and sortable ids should contain numeric values should fit into 64 bit should scale to 1ok per second proposed solution generates twitter like snowflake ids in short this is an id scheme to generate unique 64 bit ids which are roughly sortable across multiple systems without a central instance the ids are 64 bits in size and are generated with the combination of the following epoch timestamp in milliseconds precision 41 bits node id 10 bits this gives us 1024 nodes machines sequence number local counter per machine 13 bits theoritically can generate 2 13 1 per milli second source code https github com spatnaik77 system design examples blob master src idgenerator idgenerator java references https instagram engineering com sharding ids at instagram 1cf5a71e5a5c https blog twitter com engineering en us a 2010 announcing snowflake https www callicoder com distributed unique id sequence number generator https github com phxql snowflake id br urlshortener system requirements convert a long url to short url short url should be a combination of 0 9 a z and a z short url length should be as small as possible support 100 million urls per day data calculations assuming the service will run for 10 years it needs to support 10 365 10 million 365 billion records assuming each record is around 100 bytes should support 365 tb of data to support more than 365 billion unique records with 0 9 a z a z ie 62 unique charecters its safe to keep the length of the shortened url at 7 62 7 3 5 trillion proposed solution generate an unique id for each long url refer distributed id generator generate a base62 representation of this id thats going to be the short url store all these 3 attributes in a database for each record img src https github com spatnaik77 system design examples blob master design pictures urlshortener png br webcrawler system requirements given a set of seed urls crawl all the web pages download them extract the urls amd add them to the list and continue should scale out data calculation should crawl around 1 billion pages per month store data for around 5 years pages with duplicate content should be avoided pages to be crawled 1000000000 1 billion 24 3600 400 pages second data storage requirement assuming each web page is 500 kb total size 1000000000 500 500 tb per month 12 5 3000 tb proposed solution img src https github com spatnaik77 system design examples blob master design pictures webcrawler png br notificationservice system requirements support for email sms push notifications both ios and android real time notifaction service should expose an interface using which client applications can send notfications user preference service users should be able to opt out proposed solution img src https github com spatnaik77 system design examples blob master design pictures notificationservice png br slack img src https github com spatnaik77 system design examples blob master design pictures slack png br google drive img src https github com spatnaik77 system design examples blob master design pictures googledrive png br netflix img src https github com spatnaik77 system design examples blob master design pictures netflix png br twitter img src https github com spatnaik77 system design examples blob master design pictures twitter png br uber img src https github com spatnaik77 system design examples blob master design pictures uber png br | os |
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maxmertkit | maxmertkit http maxmert com not supported anymore join the chat at https gitter im maxmert maxmertkit https badges gitter im join 20chat svg https gitter im maxmert maxmertkit utm source badge utm medium badge utm campaign pr badge utm content badge build status https travis ci org maxmert maxmertkit svg branch master https travis ci org maxmert maxmertkit maxmertkit powerful most customizable and easiest for usage mobile first front end framework for web development created by vetrenko maxim http twitter com vmaxmert and maintained by the core team https github com maxmert tab members with the support and involvement of the community to get started check out http maxmert com table of contents quick start quick start bugs and feature requests bugs and feature requests documentation documentation compiling css and javascript compiling css and javascript contributing contributing community community versioning versioning author author copyright and license copyright and license quick start three quick start options are available download the latest release https github com maxmert maxmertkit releases latest clone the repo git clone https github com maxmert maxmertkit git install with bower http bower io bower install maxmertkit read the start page http maxmert com start for information on the framework contents howto videos examples and more what s included within the download you ll find the following directories and files logically grouping common assets and providing both compiled and minified variations bugs errors and feature requests have a bug text error or a feature request please first read the issue guidelines https github com maxmert maxmertkit blob master contributing md using the issue tracker and search for existing and closed issues if your problem or idea is not addressed yet please open a new issue https github com maxmert maxmertkit issues new documentation maxmertkit s documentation included in this repo in the root directory is run with nodejs http nodejs org the docs may be run locally running documentation locally go to maxmert com start maxmert com start and watch howto video 1 if necessary install nodejs http nodejs org install npm http npmjs org install bower http bower io 2 from the root maxmertkit directory run npm install in the command line 3 run bower install in the command line 4 from the docs directory run npm install in the command line 5 from the docs directory run bower install in the command line 6 from the root maxmertkit directory run gulp in the command line 7 open http localhost 3333 in your browser documentation for previous releases documentation for v0 0 2 has been made available for the time being at http old maxmert com while folks transition to maxmertkit 1 0 0 compiling css and javascript maxmertkit uses gulp http gulpjs com with convenient methods for working with the framework it s how we compile our code run tests and more to use it install the required dependencies as directed and then run some gulp commands install gulp from the command line 1 install gulp globally with npm install g gulp maybe you ll need to run it with sudo sudo npm install g gulp 2 read about running documentation locally when completed you ll be able to run the various gulp commands provided from the command line unfamiliar with npm don t have node installed that s a okay npm stands for node packaged modules http npmjs org and is a way to manage development dependencies through node js download and install node js http nodejs org download before proceeding available gulp commands build and watch development gulp run gulp to run buld and run documentation locally it will compile coffeescript http coffeescript org and sass http sass lang com into docs and run nodemon https github com remy nodemon server at port 3333 only compile css and javascript production gulp build run gulp build to clear the build directory and recompile all coffeescript http coffeescript org and sass http sass lang com files with gzip and standart version tests gulp test troubleshooting dependencies should you encounter problems with installing dependencies or running gulp commands uninstall all previous dependency versions global and local then rerun npm install and bower install in the root and docs directory contributing please read through our contributing guidelines https github com maxmert maxmertkit blob master contributing md included are directions for opening issues coding standards and notes on development moreover if your pull request contains javascript patches or features please include relevant unit tests all html and css should conform to the code guide http github com mdo code guide maintained by mark otto http github com mdo community keep track of development and community news follow maxmertkit on twitter http twitter com maxmertkit follow vmaxmert on twitter http twitter com vmaxmert implementation help may be found at stack overflow tagged maxmertkit 1 http stackoverflow com questions tagged maxmertkit 1 versioning for transparency into our release cycle and in striving to maintain backward compatibility maxmertkit is maintained under the semantic versioning guidelines sometimes i screw up but i ll adhere to these rules whenever possible releases will be numbered with the following format major minor patch and constructed with the following guidelines breaking backward compatibility bumps the major while resetting minor and patch new additions without breaking backward compatibility bumps the minor while resetting the patch bug fixes and misc changes bumps only the patch for more information on semver please visit http semver org author vetrenko maxim http twitter com vmaxmert http github com maxmert http facebook com vetrenko maxim copyright and license code and documentation copyright 2012 2014 maxmert code released under the mit license license docs released under creative commons docs license bitdeli badge https d2weczhvl823v0 cloudfront net maxmert maxmertkit trend png https bitdeli com free bitdeli badge | front_end |
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LBAW1819_T5G4 | epma enterprise project management assistant epma is an information system which facilitates the management of projects and teams within a company in particular when teams can work on multiple projects in parallel a10 product presentation the e nterprise p roject m anagement a ssistant is a closed information system for a company accessible through a web interface responsible for managing teams projects and their relationships the system will allow administrators to assign members responsible for the created teams and projects each project is constituted of tasks arranged into categories and milestones with a defined deadline teams can be assigned to these tasks with a great degree of freedom allowing multiple teams to be working on a single task and a team to be working on multiple projects at once br epma features a simple homepage for all users containing all of their responsibilities assigned tasks and projects being managed in a clear and organized layout the task cards feature progress bars to allow for intuitively comparing the progress of a task to its upcoming deadline the system provides its users with artifacts for helping coordination and communication between peers aside from a global forum for the whole company each project has its own forum and all tasks have an thread associated to it for discussion 1 installation the final version of the source code is currently available at the group s git repository which can be accessed through the following link source code link https git fe up pt lbaw lbaw18 lbaw1854 tree a10 https git fe up pt lbaw lbaw18 lbaw1854 tree master in order to test the group s docker hub image using the dbm database the following command must be executed docker run it p 8000 80 e db database lbaw1854 e db username lbaw1854 e db password uu72by92 ruiguedes lbaw1854 this command exposes our application on http localhost 8000 the e argument creates environment variables inside the container used to provide laravel with the required database configurations 2 usage a demonstration of the epma website is available showcasing the system s functionalities using a small dataset as an example we recommend the usage of the following credentials for an enhanced experience and feel of the website url http lbaw1854 lbaw prod fe up pt http lbaw1854 lbaw prod fe up pt 2 1 administration credentials we provide an administration section that can be accessed after the admin successful login the administration section can be accessed through the admin icon present on the main navigation bar or through the following link administration url http lbaw1854 lbaw prod fe up pt admin users http lbaw1854 lbaw prod fe up pt admin users username password sopolinepetty somebodyoncetoldme 2 2 user credentials type username password team member cullenleblanc yourebundledupnow team leader deidremcbride yourbraingetssmart project manager and team member armandomartinez myworldsonfire 3 application help one of the most important tasks in the system is the creation and editing of teams this task performed by an administrator is the one with most complexity and least intuitivity for the first time for this reason we ve implemented an help page specifically for this task accessible from the page associated with the task by clicking the question mark icon on the upper right corner of the main content this help page contains a list of useful information regarding what a team is and how to perform the task associated with its creation editing 4 input validation the input data was validated at three distinct stages by using html5 javascript and functionalities provided by the laravel framework we have used the html5 form functionalities to constraint the input data to follow a specific set of rules such as the expected type of input and the length of passwords the input data was also validated using javascript avoiding unnecessary requests to the server and serving as a user guide since it informs him beforehand missing fields or invalid input data laravel provided further functionalities to validate input data particularly regarding security issues we ve used laravel s built in functions to prevent cross site scripting and cross site request forgery since we made use of the laravel eloquent model sql injection is also prevented however in the case of the full text search we had to use laravel s prepared statements for preventing sql injection 5 check accessibility and usability both accessibility and usability were tested by using the following checklists respectively accessibility https ux sapo pt checklists acessibilidade usability https ux sapo pt checklists usabilidade by checking each requirement for both accessibility and usability presented in the checklists the results were solid as provided in the pdf files included in the submission the simplified results were as follows accessibility 18 18 pdf uploads cbeec57e12258d8c8cd15bc84f631844 acessibilidade pdf usability 27 28 pdf uploads 595e1ddb49563c6e3835c6e4cb1f93aa usabilidade pdf due to the complexity of the system it was hard to ensure every requirement was rigorously met in all occurrences therefore the requirements were assured on the most critical functions of the system one of the usability requirements regarding the usage of open graph tags were ignored since they do not fit on the context and purpose of our system 6 html css validation we validated the html and css files of the main pages of our system index login register and home using the following validators html validator https validator w3 org nu css validator https jigsaw w3 org css validator the results provided in the pdf files included in the submission were acceptable containing only minor mistakes on the html validation that do not affect the usability of the system in the css validation only errors and warning regarding the usage of variables and of an unknown vendor extension which was added for supporting a specific browser webkit transition duration html validator results uploads 5f3724f67594a943e0c553181f7a941d html pdf css validator results uploads 088ae6f0cc6cd5549dffbb7ceca7d4aa css pdf 7 revisions to the project throughout the implementation stage some revisions to the specification of the system were made all of these revisions were fully documented in the artefact correspondent to the context of that artifact s specification at their revision history section the main revisions that were made are present in the following artefacts a2 actors and user stories https git fe up pt lbaw lbaw18 lbaw1854 wikis a2 a6 indexes triggers user functions and population https git fe up pt lbaw lbaw18 lbaw1854 wikis a6 a7 high level architecture privileges web resources specification https git fe up pt lbaw lbaw18 lbaw1854 wikis a7 8 implementation details 8 1 libraries used for the development of the system we used the laravel framework and bootstrap the laravel https laravel com framework was used to develop the website system by establishing a mvc pattern and providing utilities for templating database access and routing the bootstrap https getbootstrap com library was used to develop the website frontend since it is a powerful tool for developing responsive designs 8 2 user stories implementation of each user story along with its name priority developers and state the new user user stories are also include and marked as bold us identifier name priority team members state us001 home page mandatory rui guedes c sar pinho alexandra mendes 100 us002 sign up mandatory rui guedes jo o barbosa 100 us003 sign in mandatory rui guedes jo o barbosa 100 us004 sign in using an external api optional 0 us005 sign up using an external api optional 0 us101 logout mandatory rui guedes alexandra mendes 100 us102 edit profile mandatory c sar pinho rui guedes 100 us103 search users mandatory rui guedes c sar pinho 100 us104 view user profiles mandatory c sar pinho jo o barbosa 100 us105 search projects mandatory rui guedes jo o barbosa 100 us106 follow users important rui guedes alexandra mendes 100 us107 favorite projects important c sar pinho 100 us108 create threads and posts on company s forum important rui guedes 100 us109 edit my threads and posts on company s forum important rui guedes 100 us110 delete my threads and posts on company s forum important jo o barbosa 100 us111 company statistics optional 0 us112 user history optional 0 us113 forums new posts optional 0 us114 upload image important rui guedes 100 us115 password recovery mandatory rui guedes 100 us201 view team s projects mandatory c sar pinho jo o barbosa 100 us202 view my team s tasks mandatory rui guedes jo o barbosa 100 us203 write comments on my team s tasks mandatory c sar pinho 100 us204 remove my comments on my team s tasks mandatory c sar pinho alexandra mendes 100 us205 edit my comments on my team s tasks important c sar pinho alexandra mendes 100 us206 get notified about my team s current active tasks important rui guedes jo o barbosa 99 us301 create threads and posts on my team s project forums mandatory rui guedes alexandra mendes 100 us302 delete my threads and posts on my team s project forums mandatory c sar pinho alexandra mendes 100 us303 update team s tasks progress bar mandatory c sar pinho jo o barbosa 100 us304 remove any comments on my team s tasks important c sar pinho alexandra mendes 100 us305 edit my threads and posts on my team s project forums important c sar pinho 100 us306 highlight comments on my team s tasks optional 0 us401 create tasks mandatory rui guedes jo o barbosa 100 us402 organize tasks into groups mandatory jo o barbosa alexandra mendes 100 us403 assign teams to tasks mandatory c sar pinho 100 us404 close projects mandatory c sar pinho alexandra mendes 100 us405 create threads and posts on project forum mandatory jo o barbosa alexandra mendes 100 us406 remove any threads and posts on project forum mandatory c sar pinho 100 us407 assign tasks to milestones with deadlines important c sar pinho jo o barbosa 100 us408 edit my threads and posts on project forum important jo o barbosa 100 us409 highlight threads optional 0 us410 import task boards optional 0 us501 create projects mandatory rui guedes 100 us502 cancel projects mandatory rui guedes jo o barbosa 100 us503 assign project manager to projects mandatory rui guedes alexandra mendes 100 us504 create teams mandatory c sar pinho 100 us505 edit team name mandatory c sar pinho alexandra mendes 100 us506 remove teams mandatory c sar pinho 100 us507 assign team leaders mandatory c sar pinho rui guedes 100 us508 assign users to teams mandatory c sar pinho jo o barbosa 100 us509 remove users from a team mandatory c sar pinho jo o barbosa 100 us510 remove users mandatory c sar pinho 100 us511 view projects important rui guedes jo o barbosa 100 us512 restore users important c sar pinho 100 us513 remove any threads and posts on company s forum important rui guedes alexandra mendes 100 us514 colorize projects important rui guedes jo o barbosa 100 developers alexandra mendes up201604741 fe up pt c sar pinho up201604039 fe up pt jo o barbosa up201604156 fe up pt rui guedes up201603854 fe up pt | server |
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eventify-backend | readme this readme would normally document whatever steps are necessary to get the application up and running things you may want to cover ruby version system dependencies configuration database creation database initialization how to run the test suite services job queues cache servers search engines etc deployment instructions | server |
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favv | about favv fastapi vitevue fastapi python backend be and vite vuejs ant design frontend fe template for your api and web application needs the difference between this project and https github com ais one cookbook https github com ais one cookbook is described here https github com ais one cookbook blob master readme md about also includes some samples in streamlit considerations the following are considerations for the project the aim is to allow things to be built faster and reduce maintenence overhead code reusability application segregation inject your own frontend and backend project ease of template upgrade ci cd and container friendly microservices friendly up to date dependencies reducing technical debt scalability roadmap updates see changelog md changelog md development setup and run backend python 3 8 5 pip 20 1 1 bash cd fastapi create environment called dev python m venv dev python3 m venv path to new virtual env copy env example to env adjust your own custom env settings here cp app env example app env common environment cp app env local example app env local for local machine deployment cp app env docker compose example app env docker compose for docker compose deployment copy requirements txt example to copy requirements txt point to your own custom requirements txt inside cp requirements txt example requirements txt activate env in windows cmd dev scripts activate dev scripts deactivate powershell dev scripts activate ps1 deactivate activate env in linux source dev bin activate installing upgrading python packages for framework run fastapi install sh for your own applications manage packages using fastapi app custom app requirements txt or run fastapi app custom app install sh run app task quque requires redis bash cd app fastapi application python main py option 1 running using python uvicorn main app reload host 0 0 0 0 port 3000 access log log level debug header server none option 2 running uvicorn https if using ssl include the following to uvicorn also set use https in environment file ssl keyfile path ssl key file ssl certfile path ssl certificate file huey task queue consumer huey consumer custom app models tasks huey navigate to http 127 0 0 1 3000 api docs note if you use https and self signed cert you may need to allow on browser https generate your private key and create a self signed cert or get a signed cert bash openssl req x509 newkey rsa 4096 keyout local key pem out local cert pem days 365 nodes subj cn 127 0 0 1 production run docker bash cd fastapi docker build t your image name tag docker run it your image name tag frontend vuejs 3 vite 2 https github com vuejs vite https github com vuejs vite ant design 2 refer to vitevue readme md vitevue readme md streamlit see streamlit readme md streamlit readme md for more information customization structure files folders for customisation or use md favv fastapi app env backend config your custom backend app folder with suffix app are your custom backend code models uploads your backend repo gitignore for your repo base py this file name is required and an fastapi apirouter of the name router your custom backend app is needed requirments txt your dependencies models tasks py custom task queue file uploads env environment state custom app name and version env your environment environment specific info dockerfile example do not touch this use this as an example for your own dockerfile install sh do not touch this requirements base txt do not touch this streamlit app demo application component template streamlit custom components streamlit antv using antv charts on streamlit streamlit vite basic example of using vite to build streamlit components streamlit xui extended ui components for streamlit vitevue readme md notes all folders and files prefixed with tbd can be ignored they are not implemented and used for reference backend customization notes setting up your custom backend bash in favv fastapi app note that project name must end with suffix app git clone your backend project e g example app use favv fastapi app custom app as reference on your custom backend on working with your custom endpoints using db s3 file services working with files in favv fastapi app custom app uploads folder running subprocess in favv fastapi app custom app models folder set the app to the folder name of your custom app in favv fastapi app env specify the version and the environment state local docker compose production your environment name etc specify app which is your custom application upload folder and model folder is directly inside your custom app folder set uour environment specific information in favv fastapi app env your environment name application path is favv fastapi app as main py is run from there either using python or uvicorn test endpoints in favv fastapi app api routes test py will not be available in production environment note update favv fastapi app config py when env entries change note any code outside favv fastapi app will not auto reload notes use fastapi install sh to update python libraries for favv fastapi env host is redis if using docker compose 127 0 0 1 otherwise | fastapi vue vite rest antd boto3 sqlalchemy hacktoberfest streamlit | front_end |
fundamental-ngx | fundamental library for angular reuse status https api reuse software badge github com sap fundamental ngx https api reuse software info github com sap fundamental ngx a href https www netlify com img src https www netlify com img global badges netlify light svg alt deploys by netlify a content 1 description 1 2 requirements 2 3 known issues 3 4 support 4 5 contributing 5 6 license 6 7 similar projects 7 a name 1 a 1 description the fundamental library for angular is an sap design system angular component library used to build modern product user experiences with the sap look and feel this repository contains 2 libraries npm packages fundamental ngx core https github com sap fundamental ngx tree main libs core the fundamental ngx core provides base angular implementation of fundamental library styles to offer developers a rich set of components they can use when building angular applications fundamental ngx core angular components provides the end developer with some degree of flexibility as most of the components allow finer customization directly on the html template level fundamental ngx platform https github com sap fundamental ngx tree main libs platform the fundamental ngx platform is built on top of the core to both enhance existing functionality with additional features which are driven by application requirements and to provide higher abstraction for the components by hiding most of the internal implementation details which boosts productivity a name 2 a 2 requirements to download and use fundamental library for angular you will first need to install the node package manager https www npmjs com get npm fundamental library for angular is intended for use with angular 15 or newer prior knowledge of angular is recommended to use the fundamental ngx library a name 3 a 3 known issues please see issues https github com sap fundamental ngx issues contribution known issues https github com sap fundamental ngx wiki known contribution issues a name 4 a 4 support if you encounter an issue you can create a ticket https github com sap fundamental ngx issues angular versions support our versions offer angular support more information can be found here https github com sap fundamental ngx wiki angular versions support features and enhancements are developed to the latest version of fundamental ngx bugfixes can be downported to the latest ngx version which is compiled with the second last angular version a name 5 a 5 contributing if you want to contribute please check the contributing md https github com sap fundamental ngx blob main contributing md documentation for contribution guidelines please follow the angular commit message guidelines https github com angular angular blob main contributing md commit check out the new component md https github com sap fundamental ngx blob main new component md guide on building a new component for the library and creating the necessary documentation for your new component similar projects a name 7 a 7 similar projects fundamental react https github com sap fundamental react react implementation of fundamental library styles fundamental vue https github com sap fundamental vue vue implementation of fundamental library styles | angular angular-components open-source typescript sap-fundamentals fundamentals | os |
design-system | design system a prototype of the design system created rackspace experience design this site is a resource website to begin displaying the styles from the upcoming design system created by the red rackspace experience design team running the site ensure ruby is installed on your system and run the following bash bundle install bundle exec jekyll serve watch config config yml dev config yml alternative local build with docker 1 get docker for your local system for mac this would be docker for mac for windows docker for windows for linux docker 1 get the branch you want to build you can go through git github desktop or download a zip https stackoverflow com a 18583977 9200106 example checking out a pr steps 1 6 https help github com articles checking out pull requests locally modifying an inactive pull request locally 1 open a terminal 1 navigate to the place where your copy of the branch is cd for most terminals 1 run the following command to build and deliver the site bash build me sh 1 once it says server running press ctrl c to stop copy the url from the previous line generally http 0 0 0 0 4000 and paste it into your browser s url bar hit enter to go there 1 browse the sample site 1 once you re done go to your terminal hit ctrl c | os |
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ICM-PROJECT | icm project noam keren a b sc software engineering 3rd year project ongoing a href https remotemysql com phpmyadmin index php db zzuehgwck6 img src https cdn2 iconfinder com data icons cloud technology 27 64 upload cloud 128 png title cloud db alt cloud db a cloud db username zzuehgwck6 password hfwrguevv7 a href https www youtube com watch v 3agqtbqhau4 t 2660s img src https stickershop line scdn net stickershop v1 product 5951468 linestorepc main png compress true title icm on steroids alt icm on steroids a icm on steroids v1 7 1 stable build status http img shields io travis badges badgerbadgerbadger svg style flat square todo need to always keep in mind to make the code readable and friendly for every one else use javadocs | cloud |
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Retro.Net | build status https travis ci org axle h retro net svg branch master https travis ci org axle h retro net retro net retro hardware libraries in net core running to run a gameboy on asp net core in a docker container docker container run p 2500 2500 alexhaslehurst server side gameboy then browse to localhost 2500 see the included docker compose file for running this behind a secure reverse proxy example see it running on azure https gb ax h com i m using my msdn license to run it in azure on a basic a1 instance 1 core 1 75 gb memory so the performance isn t great and this isn t even guaranteed to be up 24 7 according to m documentation i have written a few blog posts about this project please take a read 1 emulation on net https ax h com software development emulation 2017 11 25 emulation on dot net html 2 emulating the gameboy cpu on net https ax h com software development emulation 2017 12 03 emulating the gameboy cpu on dot net html scott hanselman has also blogged about it https www hanselman com blog amultiplayerserversidegameboyemulatorwritteninnetcoreandangular aspx features z80 based cpu and mmu configurable for 8080 z80 and gameboy simple interpreted core dynamically re compiling core instead of executing each block of z80 operations immediately it builds an expression tree representing the block which can be cached for increased speed it s about 2 5x faster than the simple interpreted core and far cooler complete z80 instruction set decode tests complete z80 execution tests integration tests for the gameboy blargg test roms wip gameboy hardware just enough to play tetris at full speed an angular user interface using websockets see gameboy client known issues timing is completely broken on desktop hyper v windows 10 resulting in the emulation running way too fast docker for windows runs on hyper v so it is also affected todo gameboy sound improved gpu rendering to support games other than tetris e g transparent sprites 8x16 sprites debugger | front_end |
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BlockchainGuildCommunity | blockchain guild community the purpose of the blockchain guild https github com blockchainguild is to allow patrons to be able to contribute back to support the open source ecosystem that allowed them to be successful incentivize junior software engineers or those new to blockchain develop to learn the best practices of the community and allow more experienced engineers a place to mentor them support blockchain documentation example code libraries infrastructure etc some possible thoughts apprentice journeyman master transparency to all stakeholders patrons customers guild members participatory and inclusive practices learning community rough consensus working code non hierarchical and decentralized as possible support infrastructure commons http www lifewithalacrity com 2015 11 a revised ostroms design principles for collective governance of the commons html for blockchains to avoid needing incorporating can we minimize custody of funds from patrons and use multisig with timelock act as a non custodial escrow news 2017 01 03 first blockchain guild bounty awarded 0 5 btc to kallewoof https twitter com kallewoof for btcdeb https github com kallewoof btcdeb a bitcoin script debugger and am update to chapter 7 of learning bitcoin from the command line https github com christophera learning bitcoin from the command line patrons for this award are christophera https twitter com christophera jimmysong https twitter com jimmysong tuurdemeester https twitter com tuurdemeester satoshilite https twitter com satoshilite zcashco https twitter com zcaschco members the blockchain guild members are christopher allen mailto christophera lifewithalacrity com internet cryptography pioneer co author tls security standard principal architect blockstream corporation decentralized identity advocate host of rebootingweboftrust co chair of the w3c credentials community technology leadership former faculty in the mba in sustainable systems program at pinchot edu http bgi pinchot edu blogs at life with alacrity http www lifewithalacrity com and on twitter is christophera christophera resides in the bay area of california usa kalle alm mailto kalle alm gmail com blockchain software developer at digital garage bitcoin core contributor former developer of infinite pdf for ipad resides in tokyo japan steve johnson mailto satoshi nomofomo net professional amateur creator of bots and nodes electronics repair and logistics in previous lives focused on bitcoin since late 2012 currently resides in sw arkansas trying to bring bitcoin to the forest twitter internaut42 https www twitter com internaut42 bruno volpato mailto brunocvcunha gmail com apprentice at the blockchain guild back end engineer totvs labs self taught developer for more than 10 years and with a passion for blockchain and cryptography expert in automating work and creating crawlers and bots for all sorts of crazy things twitter is https twitter com brunocvcunha brunocvcunha originally from brazil but living for over a year in the san francisco bay area sunglasses bogdan avram mailto bogdanadrian108 gmail com apprentice blockchain enthusiast student at school 42 algorithms c and java skills tuur demeester mailto demeester tuur gmail com investment analyst recommended bitcoin since 2012 founder ceo of adamant capital blogs on medium https medium com tuurdemeester and on twitter is tuurdemeester https twitter com tuurdemeester originally from belgium resides in austin tx jameson lopp mailto jameson lopp gmail com software engineer creator of statoshi https statoshi info the bitcoin core config generator https jlopp github io bitcoin core config generator writes on medium https medium com lopp coindesk https coindesk com author jameson lopp and twitter lopp https twitter com lopp maintains bitcoin educational resources on lopp net https lopp net bitcoin html communications the blockchain guild community maintains the following communication channels shared document repositories github https github com blockchainguild community documents github https github com blockchainguild blockchainguildcommunity twitter tbd forum mailing list tbd more may be added as needed copyright license these documents are copyright c 2017 by the blockchain guild https github com blockchainguild and are licensed cc by creative commons attribution 4 0 international license https creativecommons org licenses by 4 0 learning resources learning bitcoin from the command line https github com christophera learning bitcoin from the command line the best way to learn to learn deeply about bitcoin is to avoid guis even bitcoin qt and instead learn it from the command line current bounties 0 5 btc split among apprentice contributions to testing confirming the quality of learning bitcoin from the command line https github com christophera learning bitcoin from the command line tutorial and helping update it to bitcoin 0 15 1 0 5 btc for an accepted journeyman proposal and final code for a simple c app doing rpc to bitcoind using some libwally core https github com elementsproject libwally core to chapter 12 of learning bitcoin from the command line https github com christophera learning bitcoin from the command line 0 5 btc for an accepted journeyman proposal and final code for a better c based rpc library to bitcoind than used in chapter 12 of learning bitcoin from the command line https github com christophera learning bitcoin from the command line if you are interested in supporting a specific blockchain foss bounty project or wish to support bounties in general or in supporting infrastructure contact christophera lifewithalacrity com mailto christophera lifewithalacrity com or you can directly contribute btc to link rel payment href bitcoin 12pypewgcnfxpjdss283q9w95blesq9ubw 12pypewgcnfxpjdss283q9w95blesq9ubw link we are investigating non profit status but we do not have it at this time so any contributions are not tax deductible disclaimer tbd contributing this is an open source project and we welcome contributions there are many ways to help to get started please read the contribution guidelines contributing md reporting problems if you find typos mistakes inconsistencies or other problems in the blockchain guild documents please let us know by filing an issue at the appropriate issue tracker we use multiple repositories no issue is too small version history 2017 12 community started on github roadmap x create github community create website for blockchainguild com maybe also blockchaincommons org christophera currently holds these domains we need a contributing md something along the lines of participatoryorgs community contributing md https github com participatoryorgs participatoryorgs community blob master contributing md ipfs contributing md https github com ipfs community blob master contributing md convene an online community meeting of initial participants create an initial proposal for participatory governance | 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moko-media | moko media img logo png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko media https repo1 maven org maven2 dev icerock moko media kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name media mobile kotlin media access this is a kotlin multiplatform library that provides media picking in common code photo video and video player controls table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features capture camera photo pick image from gallery control video player compose multiplatform support requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral maven url uri https jitpack io project build gradle groovy dependencies commonmainapi dev icerock moko media 0 11 0 compose multiplatform commonmainapi dev icerock moko media compose 0 11 0 commontestimplementation dev icerock moko media test 0 11 0 usage kotlin class viewmodel val mediacontroller mediapickercontroller viewmodel fun onselectphotopressed launch try val bitmap mediacontroller pickimage mediacontrollersource camera captured photo in bitmap catch canceledexception cancel capture catch error throwable denied permission or file read error android kotlin val viewmodel getviewmodel val permissionscontroller permissionscontroller val mediacontroller mediapickercontroller permissionscontroller viewmodel mediacontroller viewmodel mediacontroller bind lifecycle supportfragmentmanager permissionccontroller bind automatically ios swift let permissionscontroller permissionscontroller let mediacontroller mediapickercontroller permissionscontroller permissionscontroller viewcontroller self let viewmodel viewmodel mediacontroller mediacontroller compose multiplatform kotlin composable fun sample val factory remembermediapickercontrollerfactory val picker remember factory factory createmediapickercontroller val coroutinescope remembercoroutinescope bindmediapickereffect picker var image imagebitmap by remember mutablestateof null image let image bitmap it contentdescription null button onclick coroutinescope launch val result picker pickimage mediasource gallery image result toimagebitmap text text click on me or with moko mvvm with correct configuration change on android kotlin composable fun sample val factory remembermediapickercontrollerfactory val viewmodel sampleviewmodel getviewmodel key sample factory viewmodelfactory val picker factory createmediapickercontroller sampleviewmodel picker bindmediapickereffect viewmodel mediapickercontroller val image bitmap by viewmodel image collectasstate val imagebitmap imagebitmap remember image image toimagebitmap imagebitmap let image bitmap it contentdescription null button onclick viewmodel onbuttonclick text text click on me class sampleviewmodel val mediapickercontroller mediapickercontroller viewmodel private val image mutablestateflow bitmap mutablestateflow null val image stateflow bitmap get image fun onbuttonclick viewmodelscope launch try image value mediapickercontroller pickimage mediasource gallery catch exc exception println error exc samples more examples can be found in the sample directory sample set up locally in media directory media contains media library in sample directory sample contains samples on android ios mpp library connected to apps contributing all development both new features and bug fixes is performed in develop branch this way master sources always contain sources of the most recently released version please send prs with bug fixes to develop branch fixes to documentation in markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master during release more detailed guide for contributers see in contributing guide contributing md license copyright 2019 icerock mag inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | android ios kotlin-native kotlin-multiplatform moko video image | front_end |
cloud_journey | cloud journey hello welcome to the written account of my cloud engineering devops journey in this project you will find resources associated with my journey as well as projects and key take aways along my cloud journey before we get rolling ahead in this journey it is important to note my starting point november 2021 marked the start of my cisco certified network associate journey and april 2022 marked my completion of that journey in my opinion ccna introduced and prepared me to take on the cloud engineering journey i recommend starting there if someone is new to information technology for more information please go to cisco s website https www cisco com c en us training events training certifications certifications associate ccna html msclkid 364e6452d08311ecb05419561a5edd1a about ccna below are some of the key resources that aided learning and development during my ccna journey 1 official cert guide ccna 200 301 2 pearson ccna simulator 3 neil anderson s udemy course the complete guide to getting certified 4 david bombal s udemy course ccna packet tracer ultimate labs 5 jeremy it lab 6 boson ex sim networking is central to everything we do on the internetwork internet therefore it was a no brainer to start my cloud journey by learning as much as i could about the basics of networking after acheiving my ccna and building my personal home lab it was time to go a bit deeper typically in the networking world going deeper means one of two things acheiving a higher level certification or learning linux linux again was a no brainer since cloud computing was my ultimate end goal | cloud |
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Design-Thinking-Project | design thinking project assets dtcover png empathize empathize md issue list question https github com int100 sec1 group11 design thinking project issues 7 interview information 1 empathize md interview information 1 interview information 2 empathize md interview information 2 interview information 3 empathize md interview information 3 interview information 4 empathize md interview information 4 define define md joueney map define md joueney map point of view define md point of view ideate ideate md issue https github com int100 sec1 group11 design thinking project issues 6 feature discusstion ideate md feature discusstion logo ideate ideate md logo ideate feature ideate ideate md feature ideate prototype prototype md test test md stories board test md stories board feedback test md feedback show and share feedback show and share md | kmutt | server |
radiant | welcome to radiant radiant is a no fluff open source content management system designed for small teams it is similar to textpattern or movabletype but is a general purpose content management system not just a blogging engine build status https secure travis ci org radiant radiant png branch 1 x http travis ci org radiant radiant radiant features an elegant user interface the ability to arrange pages in a hierarchy flexible templating with layouts snippets page parts and a custom tagging language radius http radius rubyforge org a simple user management permissions system support for markdown and textile as well as traditional html it s easy to create other filters an advanced plugin system operates in two modes dev and production depending on the url a caching system which expires pages every 5 minutes built using ruby on rails and much more license radiant is released under the mit license and is copyright c 2006 2018 john w long and sean cribbs a copy of the mit license can be found in the license file installation and setup radiant is a traditional ruby on rails application meaning that you can configure and run it the way you would a normal rails application see the install file for more details installation of a prerelease as radiant nears newer releases you can experiment with any prerelease version install the prerelease gem with the following command gem install radiant prerelease this will install the gem with the prerelease name for example radiant 0 9 0 rc2 upgrading an existing project to a newer version 1 update the radiant assets from in your project rake radiant update 2 migrate the database rake production db migrate 3 restart the web server development requirements to run tests you will need to have the following gems installed gem install zentest rspec rspec rails cucumber webrat nokogiri sqlite3 ruby support the best place to get support is on the mailing list http radiantcms org mailing list most of the development for radiant happens on github http github com radiant radiant the project wiki is here http wiki github com radiant radiant enjoy the radiant dev team http radiantcms org | os |
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PPage | ppage my personal webpage here i show who am i contact information developed projects skills and hobbies and academic and professional background technologies used html css and bootstrap you can visit the website here https ja avos github io ppage | server |
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course-java-web-development | ipt course java web development course progeram 1 java fundamentals stack and heap quick review literals assignments and variables scope garbage collection handling exceptions common exceptions and errors 4 h 2 oop principles encapsulation inheritance and polymorphism overriding overloading 4 h 3 string processing data formatting resource bundles regular expressions java util and java math stringtokenizer date calendar locale random optional observable observable interface bigdecimal 6 h 4 generics and collections tostring hashcode and equals collections overview collection interfaces sorted collections comparators using collections generic types 6 h 5 java i o files streams i o basics autocloseable closeable and flushable interfaces i o exceptions serialization java io and nio 8 h 6 threads concurrency defining instantiating and starting threads synchronizing code thread problems immutable classes 8 h 7 functional programming and lambda expressions fundamentals functional interfaces method references constructor references 4 h 8 the stream api stream basics reduction operations mapping collecting iterators 4 h 9 build tools basics ant vs maven vs gradle practical examples 4 h 10 www www introduction ip addresses ports dns proxy hosts file cookies http ajax 4 h 11 servlet container servlets jsps intro web xml servlets session management and object scope filters listeners jsps expression language el tags jstl 8 h 12 serialization deserialization jaxb 4 h 13 web services soap rest xml json popular java libraries axis2 jackson 14 h 14 introduction to spring di aop and mvc 14 h 15 popular patterns singleton adapter proxy builder factory command strategy observer decorator solid principles https en wikipedia org wiki solid object oriented design 4 h 16 relational databases fundamentals acid relations transactions indexes triggers views relational algebra and sql queries 8 h 17 unit testing with junit object mocking 8 h open source learning resources textbooks tij4 thinking in java 4th edition bruce eckel https archive org details tij4ccr1 tij4 thinking in java 4th edition code examples bruce eckel https github com bruceeckel tij4 code ipj java http www introprogramming info intro java book ods open data structures in java pat morin http opendatastructures org ods java pdf jd java data particle http www theparticle com javadata2 html ojt oracle java tutorials https docs oracle com javase tutorial index html | front_end |
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Tech-Info | tech info tech info using react and redux screenshot images screen png additional description about the project and its features built with html bootstrap react redux js file store data live demo live demo link https alexoid1 github io tech info results getting started navigation bar go to https alexoid1 github io tech info results and have a look around terminal cd into the repo folder npm start in this project using a node module to store provisional data diferent react component for every element on the app redux manage every event action on the project bootstrap to get a local copy and to set it up and running follow these simple example steps prerequisites browser internet download the code from repository https github com alexoid1 tech info author pablo alexis zambrano coral github alexoid1 https github com alexoid1 twitter pablo acz https twitter com pablo acz linkedin linkedin https www linkedin com in pablo alexis zambrano coral 7a614a189 show your support give a if you like this project acknowledgments hat tip to anyone whose code was used inspiration etc license this project is mit license licensed | react nodejs redux bootstrap | server |
chatgpt-clone | p id header p br p id project title p a href table of contents chatgpt clone https i postimg cc rswylpxj chatgpt preview png a br a href https chatgpt sumit up railway app chatgpt clone https i postimg cc ccwbvfdj screenshot from 2023 02 21 13 41 39 png a chatgpt chat generative pre trained transformer is a chatbot developed by openai and launched in november 2022 it is built on top of openai s gpt 3 family of large language models and has been fine tuned an approach to transfer learning using both supervised and reinforcement learning techniques br p id table of contents p a href table of contents table of contents https res cloudinary com dn1e07eul image upload v1659241355 readme 20headers inter toc euxbbw png a installation installation tech stack tech stack live site live site resources resources let s connect lets connect br br p id installation p a href installation installation https res cloudinary com dn1e07eul image upload v1659389842 readme 20headers inter installation j9ixlq png a note this app requires api key from a href https openai com api openai a a href https console cloud google com google oauth client id and client secret a and a href https firebase google com firebase service account key a 1 download node js and npm follow instructions here https nodejs org en 2 git clone and cd into the repo folder bash https github com sumitkcs chatgpt clone git cd chatgpt clone 3 install all dependencies bash npm install 4 in root directory of the project files create a env local file bash env local 5 in the env local file create following environment variables google id your google oauth id google secret your google oauth secret key nextauth secret any random long alphanumeric string openai api key your openai api key firebase service account key your firebase service account key 3 run the server bash npm start for deployed version npm run dev for deployed version 4 access the project in your browser at localhost 3000 http localhost 3000 br p id tech stack p tech stack next js 13 https nextjs org tailwind css https tailwindcss com firebase https console firebase google com nextauth js https next auth js org typescript https www typescriptlang org vercel https vercel com br p id live site p a href https chatgpt sumit up railway app live site https res cloudinary com dn1e07eul image upload v1659389947 readme 20headers inter live site ngkqcf png a chatgpt clone https chatgpt sumit up railway app is a responsive web app can be viewed on any device br p id resources p a href resources resources https res cloudinary com dn1e07eul image upload v1659314247 readme 20headers inter resources ncevbw png a let s build chatgpt messenger 2 0 with react next js 13 firebase tailwind css typescript https www youtube com watch v v6hq ex2llm by sonny sangha https www youtube com sonnysangha build and deploy your own chatgpt ai app in javascript openai machine learning https www youtube com watch v 2feymqokvrk by javascript mastery https www youtube com javascriptmastery br p id lets connect p a href lets connect let s connect https res cloudinary com dn1e07eul image upload v1659314257 readme 20headers inter lets connect bv3kcd png a p a href https twitter com risesumit img src https img shields io badge twitter 231da1f2 svg style for the badge logo twitter logocolor white height 30 width 90 alt twitter badge a a href https www linkedin com in sumitssr img src https img shields io badge linkedin 230064e7 svg style for the badge logo linkedin logocolor white height 30 width 90 alt linkedin badge a a href mailto itssumit258 gmail com img src https img shields io badge gmail 23fd1745 svg style for the badge logo gmail logocolor white height 30 width 90 alt gmail badge a a href https github com sumitkcs img src https img shields io badge github 23ff8e44 svg style for the badge logo github logocolor white height 30 width 90 alt github badge a p a href header back to top https res cloudinary com dn1e07eul image upload v1659314281 readme 20headers inter congrats m4p3ck png a | ai |
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Weather-Android-application | cs361 final project cs361 mobile application development final project update screen shot 2021 12 14 at 9 33 35 pm https user images githubusercontent com 72690952 146022359 6df04eda 1232 4958 8024 805b4293ae85 png | front_end |
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webDevelopmentBootcamp | the complete 2020 web development bootcamp section 9 introduction to javascript es6 116 117 challenge changing casing in text 118 basic arithmetic and modulo operator in javascript 121 122 functions part 1 challenge the karel robot 124 functions part 2 parameters and arguments coding exercise 3 life in weeks coding exercise 4 bmi calculator section 10 intermediate javascript 131 random number generation in javascript building a love calculator 132 control statements using if else conditionals logic coding exercise 5 bmi calculator advanced if else coding exercise 6 leap year 138 adding elements and intermediate array techniques coding exercise 7 who s buying lunch coding exercise 8 fibonacci section 12 boss level challenge 1 the dicee game create an external js file add dice images create a random number change both img to a random dice change both img elements change the title to display a winner section 13 advanced javascript and dom manipulation 172 higher order function challenge drum kit 171 adding event listners to a button 174 how to play sounds on a website 176 how to use switch statements in javascript 179 using keyboard event listeners to check for key presses 181 adding animation to websites section 15 boss level challenge 2 the simon game add js and jquery create a new pattern show the sequence to the user with animations and sounds check which button is pressed add sounds to button clicks add animations to user clicks start the game check the user s answer against the game sequence game over restart the game section 19 express js with node js 241 creating our first server with express 242 handling requests and responses the get request 244 understanding and working with routes 246 calculator challenge setup 248 responding to requests with html files 249 processing post requests with body parser 250 bmi routing challenge section 20 apis application programming interfaces weather project 258 making get requests with the node https module 259 how to parse json 260 using express to render a website with live api data 261 using body parser to parse post requests to the server newsletter signup 263 setting up the sign up page 264 posting data to mailchimp s servers via their api 265 adding success and failure pages 266 deploying your server with heroku https gentle wildwood 03304 herokuapp com section 22 ejs 282 templates why do we need templates 283 creating your first ejs template 284 running code inside the ejs template 285 passing data from your webpage to your server 287 adding pre made css stylesheets to your website 288 understanding templating vs layouts 289 understanding node module exports how to pass functions and data between files section 23 boss level challenge 3 blog website get home route and add content to home ejs pass data from homestartingcontent to home ejs add header and footer partials to home ejs moved header and footer to partials folder add about and contact routes pass content to about and contact ejs add nav href to header add compose form and post route add text fields to compose form and use bootstrap create js object for post push post into posts array add posts to render array loop through all posts refactor for loop to use foreach render each post onto home add express routing parameters posts blogpost loop through posts array to check if it matches title in url add lodash and use lowercase on titles separate page for each blog post truncate post body on home page to 100 characters add read more to posts section 27 mongoose 357 introduction to mongoose 358 reading from your database with mongoose 359 data validation with mongoose 360 updating and deleting data using mongoose 361 establishing relationships and embedding documents using mongoose section 28 putting everything together 364 take todolist project to the next level and connect it with mongoose 365 rendering database items into the todolist app 366 adding new items to our todolist database 367 deleting items from our todolist database 368 creating custom lists using express route parameters 369 adding new items to the custom todolists 370 revisiting lodash and deleting items from custom todo lists section 29 deploying your web application 374 how to setup mongodb atlas 375 deploying an app with a database to heroku https agile hamlet 78426 herokuapp com section 30 boss level challenge 4 blog website upgrade save composed posts with mongodb get home page to render the posts redirect to home page after save is completed with no errors render correct blog post based on post id section 31 build your own restful api from scratch 389 set up server challenge 391 get all articles 392 post a new article 393 delete all articles 394 chained route handlers using express 395 get a specific article 396 put a specific article 397 patch a specific article 398 delete a specific article section 32 authentication security 403 getting set up 404 level 1 register users with username and password 406 level 2 database encryption 407 using environment variables to keep secrets safe 408 level 3 hashing passwords 410 level 4 salting and hashing passwords with bcrypt 412 level 5 using passport js to add cookies and sessions 413a level 6 oauth 2 0 how to implement sign in with google 413b level 6 oauth 2 0 with facebook 414 letting users submit secrets section 33 react js 422 jsx code practice 423 javascript expressions in jsx es6 template literals 424 javascript expressions in jsx practice 425 attributes and styling react elements 426 inline styling for react elements 427 react styling practice 428 react components 429 react components practice 431 javascript es6 import export and modules practice 434 keeper app project part 1 436 react props 437 react props practice 438 react devtools 439 mapping data to components 440 mapping data to components practice 441 javascript es6 map filter reduce 442 javascript es6 arrow functions 443 keeper app project part 2 444 react conditional rendering with the ternary operator and operator 445 conditional rendering practice 447 react hooks usestate 448 usestate hook practice 449 javascript es6 object array destructuring 450 javascript es6 destructuring challenge 451 event handling in react 452 react forms 454 changing complex state 455 changing complex state practice 456 javascript es6 spread operator 457 javascript es6 spread operator practice 458 managing a component tree 459 managing a component tree practice 460 keeper app project part 3 461 react dependencies styling the keeper app | front_end |
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Fusillade | nuget stats https img shields io nuget v fusillade svg https www nuget org packages fusillade build https github com reactiveui fusillade workflows build badge svg code coverage https codecov io gh reactiveui fusillade branch main graph badge svg https codecov io gh reactiveui akavache br a href https github com reactiveui fusillade img width 120 heigth 120 src https raw githubusercontent com reactiveui styleguide master logo fusillade main png a fusillade an opinionated http library for mobile development fusillade helps you to write more efficient code in mobile and desktop applications written in c its design goals and feature set are inspired by volley http arnab ch blog 2013 08 asynchronous http requests in android using volley as well as picasso http square github io picasso what even does this do for me fusillade is a set of httpmessagehandlers i e drivers for httpclient that make your mobile applications more efficient and responsive auto deduplication of relevant requests if every instance of your tweetview class requests the same avatar image fusillade will only do one request and give the result to every instance all get head and options requests are deduplicated request limiting requests are always dispatched 4 at a time the volley default issue lots of requests without overwhelming the network connection request prioritization background requests should run at a lower priority than requests initiated by the user but actually implementing this is quite difficult with a few changes to your app you can hint to fusillade which requests should skip to the front of the queue speculative requests on page load many apps will try to speculatively cache data i e try to pre download data that the user might click on marking requests as speculative will allow requests until a certain data limit is reached then cancel future requests i e keep downloading data in the background until we ve got 5mb of cached data how do i use it the easiest way to interact with fusillade is via a class called netcache which has a number of built in scenarios cs public static class netcache use to fetch data into a cache when a page loads expect that these requests will only get so far then give up and start failing public static httpmessagehandler speculative get set use for network requests that are running in the background public static httpmessagehandler background get set use for network requests that are fetching data that the user is waiting on right now public static httpmessagehandler userinitiated get set to use them just create an httpclient with the given handler cs var client new httpclient netcache userinitiated var response await client getasync http httpbin org get var str await response content readasstringasync console writeline str where does it work everywhere fusillade is a portable library it works on xamarin android xamarin ios xamarin mac windows desktop apps winrt windows phone 8 1 apps windows phone 8 more on speculative requests generally on a mobile app you ll want to reset the speculative limit every time the app resumes from standby how you do this depends on the platform but in that callback you need to call cs netcache speculative resetlimit 1048576 5 mb offline support fusillade can optionally cache responses that it sees then play them back to you when your app is offline or you just want to speed up your app by fetching cached data here s how to set it up implement the irequestcache interface cs public interface irequestcache summary implement this method by saving the body of the response the response is already downloaded as a bytearraycontent so you don t have to worry about consuming the stream param name request the originating request param param name response the response whose body you should save param param name key a unique key used to identify the request details param param name ct cancellation token param returns completion returns task save httprequestmessage request httpresponsemessage response string key cancellationtoken ct summary implement this by loading the body of the given request key summary param name request the originating request param param name key a unique key used to identify the request details that was given in save param param name ct cancellation token param returns the body of the given request or null if the search completed successfully but the response was not found returns task byte fetch httprequestmessage request string key cancellationtoken ct set an instance to netcache requestcache and make some requests cs netcache requestcache new mycoolcache var client new httpclient netcache userinitiated await client getstringasync https httpbin org get now you can use netcache offline to get data even when the internet is disconnected cs this will never actually make an http request it will either succeed via reading from mycoolcache or return an httpresponsemessage with a 503 status code var client new httpclient netcache offline await client getstringasync https httpbin org get how do i use this with modernhttpclient add this line to a static constructor of your app s startup class cs using splat locator currentmutable registerconstant new nativemessagehandler typeof httpmessagehandler what do the priorities mean the precedence is userinitiated background speculative which means that anything set as userinitiate has a higher priority than background or speculative explicit is a special that allows to set an explicit value that can be higher lower or in between any of the predefined cases csharp var lowerthanspeculative new ratelimitedhttpmessagehandler new httpclienthandler priority explicit 9 var morethanspeculativebutlessthanbackground new ratelimitedhttpmessagehandler new httpclienthandler priority explicit 15 var doitbeforeeverythingelse new ratelimitedhttpmessagehandler new httpclienthandler priority explicit 1000 statics that sucks i like other thing your priorities suck i want to come up with my own scheme netcache is just a nice pre canned default the interesting code is in a class called ratelimitedhttpmessagehandler you can create it explicitly and configure it as needed what s with the name the word fusillade is a synonym for volley contribute fusillade is developed under an osi approved open source license making it freely usable and distributable even for commercial use because of our open collective model for funding and transparency we are able to funnel support and funds through to our contributors and community we the people who are involved in this project and we d love to have you on board especially if you are just getting started or have never contributed to open source before so here s to you lovely person who wants to join us this is how you can support us responding to questions on stackoverflow https stackoverflow com questions tagged fusillade passing on knowledge and teaching the next generation of developers http ericsink com entries dont use rxui html donations https reactiveui net donate and corporate sponsorships https reactiveui net sponsorship asking your employer to reciprocate and contribute to open source https github com github balanced employee ip agreement submitting documentation updates where you see fit or lacking making contributions to the code base | dotnet dotnet-core xamarin mobile http http-client volley speculative-requests cross-platform | front_end |
calculator | basic calculator assets logo svg this project is just a react native study node version v10 16 3 react native version 0 61 1 screenshot img src assets screen jpg width 280 | front_end |
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telemesh | strom https www github com w3 engineers android framework travis https travis ci com w3 engineers coverall https coveralls io github w3 engineers telemesh apache license 2 0 https choosealicense com licenses apache 2 0 telemesh build status https travis ci com w3 engineers telemesh svg branch master https travis ci com w3 engineers telemesh docs https readthedocs org projects telemesh badge version latest https readthedocs org projects telemesh coverage status https coveralls io repos github w3 engineers telemesh badge svg branch master service github https coveralls io github w3 engineers telemesh branch master lint tool telemesh https img shields io badge lint tool telemesh 2e99e9 svg style flat https w3 engineers github io telemesh lint reports lint report html contributor covenant https img shields io badge contributor 20covenant v1 4 20adopted ff69b4 svg code of conduct md a mesh network based off grid messaging application powered by blockchain technology mission statement the freedom of connectivity to the edge of the world telemesh documentation please follow this technical documentation https telemesh readthedocs io en latest index html to get detail about telemesh contributing guideline please find here contributing guideline https github com w3 engineers telemesh blob master contributing md reporting issues if you face any bug or have any particular feature request please go here https github com w3 engineers telemesh blob master contributing md reportissue test coverage this repo is configured with travis https travis ci org and coverall https docs travis ci com user coveralls every merge with master produced a test coverage report latest coverage report is available here https coveralls io github w3 engineers telemesh branch master this telemesh badge here shows coverage status to generate report locally you should go to project s root directory then execute below command gradlew coveralls note you must have a connected device or emulator as it runs instrumentation tests you will find the coverage report at telemesh app build reports coverage user interface ui please find here the complete ui flow telemesh ui https xd adobe com view 102f0226 2937 4d2e 6ec0 e7e82c164fa1 661e grid https github com mimosaha app share blob master images splash png https github com mimosaha app share blob master images profile png https github com mimosaha app share blob master images discovery png https github com mimosaha app share blob master images wallet png notice telemesh copyright 2018 2020 w3 engineers ltd this product includes software developed at w3 engineers ltd and licensed under apache 2 0 licence the license md https github com w3 engineers telemesh blob master license md file contains a copy of the apache 2 0 licence this software also use other open source libraries and tools the list of other open source projects with associate license are as follows 1 android paging library android native android sdk license 2 circular image https github com hdodenhof circleimageview apache 2 0 license 3 dexter for runtime permission https github com karumi dexter apache 2 0 license 4 rxjava view binding https github com jakewharton rxbinding apache 2 0 license 5 facebook shimmer effect https github com facebook shimmer android bsd license 6 bottom navigation view https github com ittianyu bottomnavigationviewex mit 7 glide for image load https github com bumptech glide copyright c 1994 anthony dekker copyrighted legal terms are meet 8 retrofit adapter https github com square retrofit tree master retrofit adapters rxjava2 apache 2 0 license 9 web3j https github com web3j web3j apache 2 0 license 10 bouncycastle https github com bcgit bc java free 11 qrgenerator https github com androidmads qrgenerator mit license 12 parse https github com parse community parse server bsd license 13 android ripple background https github com skyfishjy android ripple background mit license 14 viper https github com w3 engineers viper gpl 3 0 license 15 coveralls grdle plugin https github com kt3k coveralls gradle plugin mit license for any kind of assistance queries improvement please contact at info telemesh net community manager if you have any suggestions or feedback you are always welcome to reach our community manager through media telemesh net info telemesh net | refugees unicef smartphone mesh-networks infrastructure blockchain | server |
reasoning-teacher | large language models are reasoning teachers official repository for large language models are reasoning teachers https arxiv org abs 2212 10071 by namgyu ho laura schmid and se young yun accepted to acl 2023 this repository contains code for 1 running cot reasoning on openai models and 2 apply fine tune cot to train students based on openai models or custom open source models such as t5 flan t5 gpt 2 on your gpus based on and pytorch lightning getting started openai api experiments openai api experiments are implemented in the oai module refer to notebooks example oai finetune cot ipynb on how to run fine tune cot from start to finish custom experiments on gpu custom experiments are implemented in the custom module based on pytorch lightning refer to custom train py and scripts custom sh on how to fine tune models such as t5 flan t5 and gpt 2 using fine tune cot setup pip install r requirements txt python setup py develop environment the code has been tested on python 3 10 pytorch lightning 1 9 pytorch 2 0 data we re proud to share all of our raw experimental data all data is organized in json or jsonl format for your pleasure cloud storage folder links dropbox https www dropbox com sh hwcncpyomx87h20 aacqgvdd zzbq3ncjckqw0cva dl 0 google drive https drive google com drive folders 1c6kah3wv36n8omlul teu9tsjadznajv usp share link file list dataset tar gz 12 task datasets compiled in a unified json format belongs in project data dataset completion data tar gz completion data i e inference data from all teachers and students for all experiments about 8gb when uncompressed belongs in project saved completion data teacher completion data tar gz completion data from zero shot cot with diverse reasoning on the default teacher model text davinci 002 using the openai api about 1000 worth of goods with from osi lab http osi kaist ac kr at kaist https kaist ac kr subset of completion data tar gz belongs in project saved completion data finetune data tar gz all data used to fine tune openai students via the fine tuning api in jsonl format these are derived from teacher completion data and can be generated from our code belongs in project saved finetune data generate paper results after downloading the full completion data tar gz you can run notebooks results ipynb to generate all result tables and figures from our paper the code will re evaluate all raw text model outputs contained in the completion data additional resources template based split paper appendix e 3 template based splits for multiarith and date understanding are saved in data splits template json few shot prompts few shot prompts adapted from wei 2022 are saved in data few shot cot prompts json data structures data dataset dataset json metadata dataset key multiarith data sample index 0 question string answer string rationale string data completion completiondataset json metadata dataset key multiarith base model curie finetune key zs cot multiarith train key ft cot prediction template ft cot token data sample index sample index 0 completion index 0 question string answer string prompt string completion string finish reason string reasoning prompt string reasoning completion string reasoning finish reason string data organization needs update model key b base model t train key file organization pattern saved completion data b base model c completion key d dateset key json base model inference f finetune key d dateset key json default fine tuned model inference f finetune key t train key d dateset key json custom fine tuned model inference finetune data p platform key f finetune key model metadata b base model f finetune key t train key json file organization examples saved completion data b text davinci 002 c zs cot b text davinci 002 c zs cot long b text davinci 002 c fs cot b curie c zs cot b curie c fs cot b curie c zs b curie c ft cot finetune data f zs cot multiarith text davinci 002 zs cot f zs cot long multiarith model metadata b curie f zs cot multiarith json personal note accepted acl2023 jpg | ai |
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Effe17 | effe17 official website of cultural fest effervescene 17 of indian institute of information technology allahabad about effervescence effervescence fondly nicknamed eff e is the annual cultural festival of iiit allahabad and its the second largest college fest in uttar pradesh and well renowned in india generally held in mid october it attracts national as well as international participation in its multiple events during the fest it is committed to creating an unforgettable experience for all its visitors along with providing people from different areas a chance to showcase their talents explore their hidden potential and win gigantic cash prizes in a very competitive yet learning environment deployment the site s master branch is currently deployed at github https cosmiccoder96 github io effe17 | server |
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Fall17_MAD_examples | fall17 mad examples atls 4120 5120 mobile app development examples | front_end |
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prize | p align center img src docs promo image png alt two graphs one with regular scaling marked many tasks like this and one with inverse scaling marked any tasks like this width 500px p inverse scaling prize tl dr win up to 100 000 for finding an important task where larger language models do worse submissions due august 27 2022 round 1 and october 27 2022 round 2 the contest has ended results round 1 https irmckenzie co uk round1 round 2 https irmckenzie co uk round2 recent changes 11 october 2023 added modus tollens caveat to data release readme data release readme md 21 march 2023 updated prize pool info 1 march 2023 released data for all winning tasks data release readme md 17 december 2022 updated prize eligibility for far employees 12 december 2022 added prize terms update to the prize information section updated about us 9 october 2022 added huggingface hub evaluation setup description to the tips section 4 october 2022 bug fix reported total probabilities should now be more accurate for all classification tasks 26 september 2022 demonstrating positive scaling on the incorrect answer is now allowed added stronger recommendation to aim for roughly 1000 examples added requirement to name the task added request to include data for control experiments added field to specify how the data was generated added field for links to dataset sources added field for code that generated dataset added requirement that submissions in multiple parts should upload all csv files together in one zip added request to make file names anonymous added option to use a variable number of classes in classification datasets added print out to colabs of the total probability given to class labels added reminder that the submitted plot should be from our official colab added request that people edit their form submission rather than resubmit to update added reminder to specify correct behavior on the task added field to specify whether the task is zero shot or few shot updated terms and conditions motivation as language models get larger they seem to only get better larger language models score better on benchmarks and unlock new capabilities like arithmetic 1 ref1 few shot learning 1 ref1 and multi step reasoning 2 ref2 however language models are not without flaws exhibiting many biases 3 ref3 and producing plausible misinformation 4 ref4 the purpose of this contest is to find evidence for a stronger failure mode tasks where language models get worse as they become better at language modeling next word prediction the standard paradigm in natural language processing today is to pretrain large language models to autocomplete text corpora the resulting models are then either frozen and used directly for other tasks zero shot or using few shot learning or additionally trained on other tasks fine tuning we focus on the case of zero shot few shot evaluation on downstream tasks without task specific gradient optimization it s typically easier to use in practice and to study scaling laws 5 ref5 6 ref6 show that language models get predictably better in terms of test loss and downstream performance 7 ref7 as the number of parameters amount of compute used and dataset size increase the improvement follows a power law in each of parameters compute and dataset size we hypothesize that there are tasks with trends in the opposite direction task performance gets monotonically predictably worse as the overall test loss of the language model improves we call this phenomenon inverse scaling in contrast with the standard scaling laws there are some tasks that appear to show inverse scaling under some conditions 4 ref4 8 ref8 10 ref10 but such tasks appear to be rare this contest aims to find inverse scaling tasks especially those of importance to the safe and responsible use of language models we hope that task submissions will teach us more about what types of tasks exhibit inverse scaling inverse scaling tasks will also highlight potential issues with the current paradigm of language model pretraining and scaling inverse scaling tasks are important because they represent a mismatch between the behavior we want language models to exhibit and the behavior we get in practice from the training objectives and data we use as language models continue to get bigger and used in more real world applications it is important that they are not increasingly getting worse or harming users in yet undetected ways after two rounds of the contest we will write a survey of the submitted tasks and other examples found in the literature authors of winning tasks will be awarded prize money and invited to be co authors on the resulting paper below we detail our call for submissions feel free to join our slack https inversescaling com slack to message us with questions find collaborators and participate in contest related discussions with other participants code ideas findings and related work sharing prize information 2023 03 21 update the prize pool has been funded by open philanthropy https www openphilanthropy org grants far ai inverse scaling prize we will award up to 250 000 in total prize money for task submissions distributed as follows 1 up to 1 grand prize of 100 000 2 up to 5 second prizes of 20 000 each 3 up to 10 third prizes of 5 000 each all prize decisions will be made by the organizers and anonymous reviewers using the prize rubric prize rubric below prize winners may nominate a non profit to receive the prize money on their behalf some prizes may remain unawarded if there are not enough tasks that meet the eligibility for a prize tier as detailed in the prize rubric prize rubric benchmark and co authorship authors of prize winning submissions will be invited as co authors on the paper written after the contest concludes we will also offer co authorship to authors of submissions that met our acceptability criteria but did not receive prizes in the event that we receive more acceptable submissions than we can award with prizes we will include all accepted submissions in our final benchmark which we plan to release to the research community after the contest timeline the contest begins on june 27 2022 we will host a first round of evaluations on submissions received on or before august 27 2022 anywhere on earth and a second final round of evaluations on submissions received on or before october 27 2022 anywhere on earth after the first round we will award eligible tasks with third prizes up to 5 and second prizes up to 2 to help improve first round submissions we will also return reviewer feedback and scaling law plots results from our private evaluation models submissions will be paused for two weeks at the end of the first round to allow any necessary improvements to be made at the end of the second round we will reward eligible tasks at all prize tiers with the possibility of upgrading first round submissions to higher prize tiers based on both rounds of submissions a name prize rubric a prize rubric here we detail our submission evaluation rubric the rubric will guide an anonymous panel of reviewers in judging submissions for prizes a submission must meet all criteria in the grand prize column to win the grand prize likewise a submission must meet all criteria in the accepted task column to be accepted into our benchmark and for co authorship on our paper for second prizes submissions must meet all accepted task criteria and some grand prize criteria third prizes must meet the accepted task criteria we may receive more eligible submissions than we have prizes for a given tier in this case we will first break ties based on how many grand prize criteria are met and then by having reviewers make subjective rankings within tiers e g more granular measures of how much various criteria are met or the relative difficulty or importance of each criterion met we will consider inverse scaling trends on publicly available models like gpt 3 as well as held out private models for which we will run evaluation criterion description prize tier no prize accepted task grand prize inverse scaling strength how straight and steep is the inverse scaling trend on public models shows flat very bumpy or standard scaling shows approximately monotonic inverse scaling shows a clear strictly monotonic inverse scaling trend inverse scaling generality do different models all show inverse scaling no inverse scaling on private models shows inverse scaling on some public and some private models shows inverse scaling across all public and private models tested task importance is the task important to the safe and responsible use of lms or for shedding light on where lms fail how strong are the arguments weak no users or third parties would be harmed and the task does not shed light on where lms fail fairly convincing some lm users or third parties would be harmed by the discovered behavior or the task sheds light on where lms fail e g sensitivity to prompts https arxiv org abs 2105 11447 very convincing significant implications for how lm research or deployment will need to be developed to be reliably safe and effective novelty and surprisingness is inverse scaling on the task novel not shown in prior work and surprising not novel or surprising novel and somewhat surprising novel and surprising teaching us something new about lms task coverage are the examples fully representative of the described task examples only cover a special subcategory or phrasing of the described task there s no evidence of inverse scaling on other subcategories or phrasings examples cover different subcategories and phrasings for the described task examples cover almost all important task subcategories and phrasings suggesting robust inverse scaling on the described task reproducibility does inverse scaling appear to occur if we reproduce the task based on its description no we see flat very bumpy or standard scaling the particular examples submitted may have been over optimized for inverse scaling to the extent that the examples are unrepresentative of the described task yes but to a lesser extent yes to a similar or stronger extent answering the below optional questions in our submission form https forms gle cvle561agvcsonkf8 in the free form response will make your task stand out more does inverse scaling persist even if the model is conditioned with few shot examples to behave correctly if providing enough few shot examples eliminates inverse scaling how many examples are required for that does inverse scaling persist even after fine tuning on the task are there good reasons to think it would persist after fine tuning does inverse scaling persist for instructgpt models https openai com blog instruction following trained with reinforcement learning from human feedback rlhf https openai com blog instruction following to test this you can use the same code as that for gpt 3 evaluation https colab research google com drive 1sgmuh0nbqsrrkwrucmjg8bs5eu5qvj0y scrollto zoayc0nsfoic we may also evaluate submissions on private rlhf models of various sizes from anthropic bai et al 2022 https arxiv org abs 2204 05862 we reserve the right to update the prize tier standards or criteria e g between rounds if we observe submissions gaming them in some way evaluation eligibility to be eligible for official review a task submission must 1 include a plot of loss vs model size across ada babbage curie and davinci gpt 3 models using the provided code models for gpt 3 evaluation the plot must not show a standard scaling law a very bumpy trend is okay for submission we expect to observe cleaner scaling laws with our held out evaluation models where we observe clear scaling trends 2 meet the formatting requirements described in the submission guidelines submission this requirement should already be satisfied if you are able to successfully run the evaluation code 3 include a coherent description of the task a name models a models this contest uses pretrained autoregressive language models such as gpt 3 we offer google colab notebooks https research google com colaboratory faq html for evaluating inverse scaling with the gpt 3 https colab research google com drive 1sgmuh0nbqsrrkwrucmjg8bs5eu5qvj0y scrollto zoayc0nsfoic opt https colab research google com drive 1nbdidsy5d1nhgzihelm9fbwarzyslc2h scrollto spifwfnjfmm8 and gpt 2 https colab research google com drive 1a8zwo8j pztrogfa9iipnluk4p0m3exk scrollto spifwfnjfmm8 model series when developing a task however to avoid overfitting to publicly available models we use private models to run the evaluations for awarding prizes currently we are using the series of pretrained language models without additional finetuning from anthropic https www anthropic com bai et al 2022 https arxiv org abs 2204 05862 we are in discussions with other organizations to use their models which may be added later on to strengthen the evaluation a name reviewers a reviewers prize decisions will be made by an anonymous panel of reviewers reviewers will be selected by the contest organizers and may include some organizers reviewers will have ml and nlp experience relevant to inverse scaling the panel may contain some competition organizers reviewers will not be allowed to make submissions to the contest a name submission a submission guidelines 1 each task submission should be a language modeling test set in the style of big bench https github com google big bench of inputs with corresponding answers which will be evaluated according to one of four evaluation metrics detailed later 2 this prize is to incentivize original work so submissions should find a new phenomenon for which inverse scaling has not been previously documented 1 if a task has already shown inverse scaling in prior work even if the original authors did not identify it as such then it is ineligible for the contest 2 if an existing task has not been subjected to any kind of scaling analysis then it is likely eligible for the contest 3 if you would like to check whether an existing task is eligible message us on our slack https inversescaling com slack or email us at inverse scaling gmail com mailto inverse scaling gmail com with prior work in the subject line and a link to where the task has previously been published 2 data must be formatted as a zip containing csv files the zip should be called task name zip where task name is the name you provide for your submission in the form e g lambada zip the file should be called task name csv e g lambada csv if you have multiple parts to the same task add part i to each e g lambada part1 csv lambada part2 csv if you have control experiments add these as task name control i csv e g lambada control1 csv the csv files will be read using the pandas https pandas pydata org docs reference api pandas read csv html package using the default arguments specific formats are given below in the evaluation metrics evaluation metrics section 3 examples will be given as a prompt to an autoregressive language model i e either zero shot or few shot prompts prompts containing a few examples few shot examples must demonstrate the correct behavior on the task 4 tasks must contain at least 300 examples we strongly recommend aiming for on the order of 1000 examples so that inverse scaling trends are clearer submissions with unclear scaling trends and close to the minimum number of examples are unlikely to win a prize 5 in the submission form https forms gle cvle561agvcsonkf8 you will be asked to add 1 evaluation metric evaluation metrics used the metric should be one of 1 classification loss in a multiple choice format classification 2 loss on a sequence at the end of the prompt sequence prob 3 difference in logodds between two possible responses logodds 4 absolute difference in logodds between two possible responses absolute logodds 2 authors 3 task name 4 description of intended task what is the task aiming to test remember to explain what good behavior on the task looks like rather than just how the language model will do it wrong 5 why is the task important how is the task important to safe and responsible use of lms does inverse scaling on the task suggest any fundamental insights about model behavior and model failures 6 why do you expect to see inverse scaling what led you to suspect that your task will inverse scale 7 data generation procedure how did you construct the dataset what resources did you use e g based on a template given pre set vocabulary programmatically generated based on some math phenomenon etc if you used any code to generate the dataset we ask that you include the code this is extremely helpful for assessing reproducibility of the results 8 expertise required for human annotators to verify the task labels e g knowledge of linguistics fluent in specific languages 9 a plot of gpt 3 performance on the task produced using this colab https colab research google com drive 1sgmuh0nbqsrrkwrucmjg8bs5eu5qvj0y scrollto zoayc0nsfoic the plot must be generated using the unmodified colab to demonstrate that your dataset can be run through our code 6 we will verify or validate task labels for correctness using contractors see this colab https colab research google com drive 1fu6extxdczg3lalp5g748ncrukpq68ko to test an interface similar to what we will use for task validation 7 if you submit a very large dataset we may uniformly subsample up to 10 000 examples from it for evaluation on private models a name evaluation metrics a evaluation metrics here we include more detail on the types of evaluation metrics that an eligible task submission can use for examples of datasets correctly formatted for each metric see this sheet https docs google com spreadsheets d 1ae 9022vslbkv996a3fgkvqia6pvwp70vb6csudvjlm edit gid 0 please message us on our slack https inversescaling com slack or email us at inverse scaling gmail com mailto inverse scaling gmail com with metric in the subject line if you think it is impossible to frame your task as any of these evaluation metrics and that some other metric would demonstrate inverse scaling note we ve identified a potential source of spurious inverse scaling with the logodds and absolute logodds metrics in particular putting unrelated tasks for prompt and other prompt can show inverse scaling depending on the scaling behavior of the two tasks a special case of this is adding irrelevant information to one of the prompts thanks to jeffrey shen for pointing this out we recommend framing your task as a classification or sequence probability task if possible unless you have an especially clear and considered justification for these metrics classification loss classification 1 use this metric for a standard classification task e g when testing how well a model can choose the correct response from a list of possible responses we ll look at both classification loss and classification accuracy use classification acc in the colabs to plot accuracy if you expect inverse scaling to only appear on one of these metrics explain why in the submission form https forms gle cvle561agvcsonkf8 2 the columns of the csv are 1 prompt a string 2 classes an ordered list using valid python syntax of the possible answer tokens note because of the way language model tokenizers typically process text the class tokens should almost always start with a space and a capital letter e g yes and no rather than yes and no each class may be multiple tokens long in which case we will use the logprob of the full token sequence as the logit for computing the classification loss we strongly prefer for you to design each class to be the same number of tokens long so that the sequence length of class labels does not affect the results longer sequences tend to have smaller probabilities furthermore we strongly encourage you to design class labels that are just a single token long to do so you can turn a task with a several possible sequence completions into an explicit multiple choice task where the classes are e g a b c and d this task format can guide the language model more clearly to put a high probability on valid completions as of round 2 each example in the dataset can have a different number of possible classes 3 answer index an integer specifying which of the class tokens is the correct answer using zero indexing completion sequence loss sequence prob 1 use this metric when you are testing how well the model predicts the correct conclusion or completion to a prompt this type of loss is used by lambada https arxiv org abs 1606 06031 which uses as the target a single word lambada uses sequence probabilities because each example has only one correct completion which is always a single word but may be multiple tokens long after tokenization where possible prefer using the classification loss described above instead of this metric as sequence probabilities are often hard to interpret there may be multiple correct completions for a given prompt that compete for probability mass see holtzmann et al 2021 https arxiv org abs 2104 08315 for discussion for this reason tasks that use classification loss will be more likely to pass our contractor task validation process 2 the columns of the csv are 1 prompt a string up until the target word 2 completion the target sequence can be more than one token signed difference in log odds between examples logodds 1 this loss metric measures how much the model changes its probabilities between the two prompts in particular the model can get better overall while still showing inverse scaling on this metric if the difference in log odds between the two prompts grows use this metric when testing how much changing the prompt influences the relative probabilities of the two class tokens in some undesirable way e g to show bias against women we will evaluate the average signed not absolute difference between two prompts the first prompt called prompt should be neutral or favor the correct desirable answer e g a question about men if expecting a male favored bias the second prompt called other prompt should be neutral or favor an incorrect undesirable answer e g a question about women if expecting a male favored bias 2 as noted above this metric requires a strong justification to use as it is prone to spurious inverse scaling if you feel that your task really requires the logodds metric then we would encourage you to perform control experiments demonstrating that the inverse scaling is legitimate this could look like replacing whatever you expect to cause the inverse scaling with neutral phrasing please include descriptions and plots of these experiments in the free form pdf response and upload the control data as part of your submission with names task name control x csv where x is the index of your control experiment e g lambada control1 csv 3 the columns of the csv are 1 prompt a string prompt should be neutral or be expected to favor the correct answer 2 other prompt a string other prompt should be neutral or be expected to favor the incorrect answer 3 classes an ordered list using valid python syntax of the two possible answer tokens note because of the way tokenizers process text the class tokens should almost always start with a space and a capital letter e g yes and no rather than yes and no 4 answer index an integer specifying the index of the correct answer in the classes list using zero indexing absolute difference in log odds between examples absolute logodds 1 this loss metric is identical to the signed difference logodds except that the absolute value of each log odds difference is taken before averaging across examples this is useful when you expect there to be some difference between the prompts but are not sure of the direction an example is slightly varying the prompts in ways that should be irrelevant to the prediction see true few shot learning with language models https arxiv org abs 2105 11447 please use the logodds metric instead if you expect the difference between prompts to go in a particular direction e g a consistent male favored bias 2 as noted above this metric requires a strong justification to use as it is prone to spurious inverse scaling if you feel that your task really requires the absolute logodds metric then we would encourage you to perform control experiments demonstrating that the inverse scaling is legitimate this could look like replacing whatever you expect to cause the inverse scaling with neutral phrasing please include descriptions and plots of these experiments in the free form pdf response and upload the control data as part of your submission with names task name control x csv where x is the index of your control experiment e g lambada control1 csv 2 the columns of the csv are 1 prompt a string prompt should be one version of the prompt 2 other prompt a string other prompt should be another version of the prompt which should produce some difference in log odds 3 classes an ordered list using valid python syntax of the two possible answer tokens note because of the way tokenizers process text the class tokens should almost always start with a space and a capital letter e g yes and no rather than yes and no 4 answer index an integer specifying the index of the correct answer in the classes list using zero indexing note that this index does not matter for the absolute difference metric but is needed for compatibility submission instructions 1 create a csv file with your dataset examples following the guidelines submission guidelines 1 if you have created your dataset as a google sheet click file download comma separated values csv 2 see this sheet https docs google com spreadsheets d 1ae 9022vslbkv996a3fgkvqia6pvwp70vb6csudvjlm edit gid 0 for examples 2 upload it to the gpt 3 colab https colab research google com drive 1sgmuh0nbqsrrkwrucmjg8bs5eu5qvj0y scrollto zoayc0nsfoic and run it if you don t have access to the openai api follow the instructions in the colab based on our calculations we expect the free credits provided by openai to be enough to evaluate most tasks at least once across all model sizes during development we recommend the opt colab https colab research google com drive 1nbdidsy5d1nhgzihelm9fbwarzyslc2h scrollto spifwfnjfmm8 in general we recommend finding tasks that show inverse scaling with both gpt 3 and opt as such tasks are more likely to show inverse scaling during our evaluation procedure on held out private models we also have a gpt 2 colab https colab research google com drive 1a8zwo8j pztrogfa9iipnluk4p0m3exk scrollto spifwfnjfmm8 but in our experience the opt colab demonstrates clearer scaling trends that generalize better to different and larger models please fill in this form https docs google com forms d e 1faipqlscww7gyialb36lbpkkoirbyevtl ersbncl9uryqjdwvm3 ma viewform if you are unable to participate due to a lack of funding for credits if we are unable to support you directly there is also this form from openai https share hsforms com 1b beaq qqpkcffgkwwuhxa4sk30 for obtaining research credits directly from openai 3 download the scaling plot produced 4 fill in the submission form https forms gle cvle561agvcsonkf8 note some participants have mentioned that they are seeing consistent anomalies in scaling trends with certain openai models e g babbage openai isn t transparent about the details of their api models so it s possible that the model is different in a way that breaks clean scaling law trends e g trained the model for longer or on a different dataset if you re seeing anomalous behavior from gpt 3 models you should feel free to submit the tasks with anomalies in the scaling trends anyways as long as the trend isn t a standard scaling law we expect to get clearer signal about scaling laws with our held out models from anthropic where we observe clean scaling laws and have more model sizes we also recommend testing your submissions on the open source opt models e g using our colabs where we also see clear scaling laws and know the exact model sizes and training details feel free to include the opt plots in your submission as well especially if you see clearer scaling trends faq 1 how does submitting to this contest interact with our own publication of the dataset results we impose no restrictions on publication of submissions in fact we encourage it in addition we will have performed a manual verification of the task labels and a scaling laws analysis on your data which may reduce the work required to publish 2 can people without machine learning expertise participate yes our resources such as the gpt 3 colab https colab research google com drive 1sgmuh0nbqsrrkwrucmjg8bs5eu5qvj0y scrollto zoayc0nsfoic for testing your task are designed to be useable with no coding just provide the data here are some example google sheets for each of the evaluation metrics inverse scaling dataset examples https docs google com spreadsheets d 1ae 9022vslbkv996a3fgkvqia6pvwp70vb6csudvjlm edit gid 0 simply click file download comma separated values csv to get a csv file that can be submitted 3 is there a restriction on what language the submission can be in is any arbitrary unicode script supported all languages are accepted so long as you can help us find contractors who are able to perform the task for label verification 4 relatedly if i have a task that i ve translated into 10 languages is that one multilingual task or 10 monolingual tasks translations of a task count as the same task please only submit each example in one language 5 how many tasks can i submit contestants can win an award for each distinct submission there s no limit to the number of awards one can receive that said a single submission that covers a broad phenomenon is more likely to win a higher tier prize due to our task importance criterion so contestants may wish to group tasks together when possible if we receive too many submissions from one contestant we may ask the contestant to choose a subset of their submissions for evaluation especially if the submissions seem low quality if you think that using multiple distinct datasets to test a single underlying phenomenon is called for include them as separate csv files in the zip file of a single submission if you want to make changes to your submission please edit your original submission rather than resubmitting 6 i ve checked all labels in the submitted data can i just report that instead of having your team verify it for consistency all tasks will have to pass our review even if they have undergone other validation 7 why do we require task submissions to have at least 300 examples 300 examples was the minimum number of examples for which we found clean standard scaling trends on lambada lambada is a next word prediction language modeling like task where we expect to observe clear standard scaling laws so we expect 300 examples to be a rough minimum number of examples to observe inverse scaling laws however we strongly encourage aiming for roughly 1000 examples as trends are quite unclear for some tasks with only 300 examples 8 should prompts include instructions for tasks where you expect that a typical nlp practitioner would try instruction prompting we d encourage you to formulate your task in this as well we will consider your task submission more important and realistic if it shows inverse scaling even with clear instructions in the example prompts please make sure to format the instructions in a way that you should reasonably expect a pretrained lm i e gpt 3 or opt rather than instructgpt should be able to understand 9 should prompts include few shot example sequences we encourage you to look for tasks that show inverse scaling even with in context few shot learning as in the gpt 3 paper https papers nips cc paper 2020 hash 1457c0d6bfcb4967418bfb8ac142f64a abstract html for many tasks where a model does poorly nlp practitioners would try few shot learning we will consider your task submission more important and realistic if it shows inverse scaling even with correctly labeled few shot examples of the task in the example prompts 10 how will the prize money be distributed between co authors of a task submission we will leave it up to the co authors of submissions to decide how to distribute the prize money between co authors 11 how do i ask a question about the contest please send a message in the ask organizers channel of the inverse scaling slack https inversescaling com slack for general or public questions that others may have please email us at inverse scaling gmail com mailto inverse scaling gmail com or message us individually on the slack for private questions related to your specific submission task or situation 12 what license should i use for my dataset please use the cc by https creativecommons org licenses by 2 0 license 13 can i submit examples of misuse as a task what about other tasks that explicitly prompt the model to produce harmful behavior we don t consider most cases of misuse or explicitly prompting for harmful behavior as surprising examples of inverse scaling we expect that explicitly prompting asking an lm to generate e g hate speech or propaganda will work more effectively with larger models so we do not consider such behavior surprising that said we are still interested novel misuse examples but they will need to be surprising in order to be accepted 14 can i submit examples of bias toxicity or misinformation as a task in many of the above cases inverse scaling has been shown to some extent in prior work for example solaiman dennison 2021 https proceedings neurips cc paper 2021 hash 2e855f9489df0712b4bd8ea9e2848c5a abstract html showed that larger gpt 3 models generate more toxic text big bench https arxiv org abs 2206 04615 showed that larger models show more bias related to gender race ethnicity and nationality and truthfulqa https arxiv org abs 2109 07958 suggests that larger models generate more plausible misinformation as a result we will apply more scrutiny for novelty on submissions on or other topics covered by prior work that said we are still interested in submissions related to these topics if submissions effectively argue they have found a different phenomenon e g a novel kind of bias or that prior work did not sufficiently show inverse scaling for some behavior 15 can i submit a task that demonstrates positive scaling on the incorrect answer as of round 2 this will be considered we still prefer a demonstration of inverse scaling however if there is exactly one incorrect option in classification or exactly one completion that can be considered incorrect in sequence prob then we will consider tasks that show positive scaling on that answer tips again feel free to join our slack https inversescaling com slack to message us with questions find collaborators and participate in contest related discussions with other participants code ideas findings and related work sharing joining our slack isn t necessary but we created it to facilitate relevant discussions content sharing and collaborations while we accept narrow tasks with as few as 300 examples we believe that large diverse datasets are more likely to do well on our criteria e g to show consistent clean inverse scaling on held out models and be of broader importance in particular inverse scaling results may be sensitive to the particular prompt used 9 ref9 in which case the results may not generalize to held out models phrasing examples in various ways should reduce this effect it may be helpful to quickly iterate on your task submission by hand writing a small number of examples first or by using a template or code to construct a larger dataset automatically after finding something that scales inversely you may improve the number and diversity of examples for your final submission for reasons stated above we encourage looking at the error bars provided by our colabs to understand the variance in performance across examples to get a sense of how clean vs noisy the trend may be on our private evaluation models we copied the opt model family up to opt 66b to our huggingface hub organization inverse scaling opt so you can run evaluations of your huggingface datasets with the new huggingface model evaluator https huggingface co spaces autoevaluate model evaluator running the model evaluation will create a pr we can then merge into our copied model repo which is why we keep a copy of the original model repo for the required dataset format see the winning round 1 datasets on our huggingface organization page https huggingface co inverse scaling there is also a small commandline tool https gist github com micpie 366538eb11787ab51f48b7a8be2564f5 to convert your csv files to jsonl files for your huggingface dataset note due to the way the model evaluator works your datasets will be publicly visible links here are all the relevant links that appear in these instructions join our slack https inversescaling com slack submission form https forms gle cvle561agvcsonkf8 task examples google sheets https docs google com spreadsheets d 1ae 9022vslbkv996a3fgkvqia6pvwp70vb6csudvjlm edit usp sharing gpt 3 colab https colab research google com drive 1sgmuh0nbqsrrkwrucmjg8bs5eu5qvj0y scrollto zoayc0nsfoic opt colab https colab research google com drive 1nbdidsy5d1nhgzihelm9fbwarzyslc2h scrollto spifwfnjfmm8 gpt 2 colab https colab research google com drive 1a8zwo8j pztrogfa9iipnluk4p0m3exk scrollto spifwfnjfmm8 validation example colab https colab research google com drive 1fu6extxdczg3lalp5g748ncrukpq68ko about us 2023 03 21 update the prize pool has been funded by open philanthropy https www openphilanthropy org grants far ai inverse scaling prize 2022 12 12 update the prize pool is no longer provided by ftx future fund the inverse scaling prize is organized by a group of researchers on behalf of the fund for alignment research far https alignmentfund org including ian mckenzie https www linkedin com in irmckenzie alexander lyzhov https www linkedin com in alexlyzhov michael pieler https scholar google com citations user ckjqyxsaaaaj hl en alicia parrish https aliciaparrish com ameya prabhu https drimpossible github io aaron mueller https aaronmueller github io najoung kim https najoungkim github io sam bowman https cims nyu edu sbowman and ethan perez https ethanperez net additionally sam bowman and ethan perez are affiliated with anthropic alexander lyzhov alicia parrish ameya prabhu aaron mueller najoung kim sam bowman are affiliated with new york university the prize pool is provided by open philanthropy https www openphilanthropy org grants far ai inverse scaling prize prize money eligibility to minimize conflict of interest we will not be able to give prize money to people at anthropic new york university far ai or the future fund authors of task submissions who come from these institutions will still be eligible to win an award without prize money as long as they are not organizers or anonymous reviewers for legal reasons we will also not be able to award prize money to individuals sanctioned by the u s it is possible that we will add more evaluation model series from other institutions in this event authors of task submissions from those institutions will have their submissions evaluated on model series from other participating institutions e g the anthropic models and will still be eligible for prizes prize terms and conditions these terms apply to the second round and don t necessarily apply to the first round if these criteria might prevent you from making a submission please let us know over email mailto inverse scaling gmail com or slack https inversescaling com slack employees or current contractors of ftx philanthropy and contest organizers are not eligible to win prizes entrants and winners must be over the age of 18 by entering the contest entrants agree to the terms conditions all taxes are the responsibility of the winners the legality of accepting the prize in his or her country is the responsibility of the winners sponsor may confirm the legality of sending prize money to winners who are residents of countries outside of the united states winners will be notified by email after the contest deadline winners grant to sponsor the right to use their name and likeness for any purpose arising out of or related to the contest winners also grant to sponsor a non exclusive royalty free license to reprint publish and or use the entry for any purpose arising out of related to the contest including linking to or re publishing the work entrants warrant that they are eligible to receive the prize money from any relevant employer or from a contract standpoint entrants agree that ftx philanthropy shall not be liable to entrants for any type of damages that arise out of or are related to the contest and or the prizes by submitting an entry entrant represents and warrants that consistent with the terms of the terms and conditions a the entry is entrant s original work b entrant owns any copyright applicable to the entry c the entry does not violate in whole or in part any existing copyright trademark patent or any other intellectual property right of any other person organization or entity d entrant has confirmed and is unaware of any contractual obligations entrant has which may be inconsistent with these terms and conditions and the rights entrant is required to have in the entry including but not limited to any prohibitions obligations or limitations arising from any current or former employment arrangement entrant may have e entrant is not disclosing the confidential trade secret or proprietary information of any other person or entity including any obligation entrant may have in connection arising from any current or former employment without authorization or a license and f entrant has full power and all legal rights to submit an entry in full compliance with these terms and conditions citations to cite the contest you may use the following bibtex citation misc mckenzie2022inverse title the inverse scaling prize url https github com inverse scaling prize author mckenzie ian and lyzhov alexander and parrish alicia and prabhu ameya and mueller aaron and kim najoung and bowman sam and perez ethan year 2022 to cite the round 1 winners you may use misc mckenzie2022round1 title inverse scaling prize first round winners url https irmckenzie co uk round1 author mckenzie ian and lyzhov alexander and parrish alicia and prabhu ameya and mueller aaron and kim najoung and bowman sam and perez ethan year 2022 to cite the round 2 winners you may use misc mckenzie2022round2 title inverse scaling prize second round winners url https irmckenzie co uk round2 author mckenzie ian and lyzhov alexander and parrish alicia and prabhu ameya and mueller aaron and kim najoung and bowman sam and perez ethan year 2023 1 a name ref1 a brown et al language models are few shot learners http arxiv org abs 2005 14165 neurips 2020 2 a name ref2 a chowdhery et al palm scaling language modeling with pathways http arxiv org abs 2204 02311 arxiv 2022 3 a name ref3 a liang et al towards understanding and mitigating social biases in language models http arxiv org abs 2106 13219 icml 2021 4 a name ref4 a lin hilton and evans truthfulqa measuring how models mimic human falsehoods http arxiv org abs 2109 07958 acl 2022 5 a name ref5 a kaplan et al scaling laws for neural language models http arxiv org abs 2001 08361 arxiv 2020 6 a name ref6 a hoffmann et al training compute optimal large language models https arxiv org abs 2203 15556 arxiv 2022 7 a name ref7 a hernandez et al scaling laws for transfer http arxiv org abs 2102 01293 arxiv 2021 8 a name ref8 a parrish et al bbq a hand built bias benchmark for question answering https arxiv org abs 2110 08193 acl 2022 findings 9 a name ref9 a perez et al true few shot learning with language models https arxiv org abs 2105 11447 neurips 2021 10 a name ref10 a srivastava et al beyond the imitation game quantifying and extrapolating the capabilities of language models https arxiv org abs 2206 04615 arxiv 2021 | ai |
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udemy-data-engineering-for-beginners | udemy data engineering basics my notes i took from this course https www udemy com course basic data engineering for beginner using google cloud python introduction to data engineering what is data engineering data engineering all engineering operational tasks required to make data available for end user business user data available for analytics building data modelm reporting etc hierarchy of needs bottom of the pyramid to top collect instrumentation logging sensors external data user generated content move store reliable data flow infrastructure pipelines etl extract transform load structured unstructured data storage explore transform cleaning anomaly detection preparation aggregate label analytics metrics segments aggregates features training data learn optimize a b testing experimentation simple ml algorithms learn optimize ai deep learning role responsibilities machine learning ai engineer machine learning ai algorithm data scientist data analyst reporting analytics data visualization data engineer data storage processing extract transform load software engineer transactional system data engineering activities ingest data from source s build maintain data warehouse data lake build maintain data pipeline schedule automate script pipelines create specific tables for further analytics use case data migration to cloud debug data quiality issues data engineering example sample case naturamics marketplace for vegetables fruits other farm products build a product recommender based on historical data solution naturamics data scintists data analysts and machine learning ai engineers require training data for analytics modelling ml for this they need data from the product table views avg rating purchases per product and user table top products viewed purchased per user the historical data is generated in naturamics model applicaiton in websites developed by software engineers which consists of browsing history purchase history and product ratings those data sources are recorded from user interaction to naturamics market place and written by software engineers data engineer is at the center they coordinate with software engineer to know tha data source and with data scientist or ml engineer to know what kind of data required to achieve the goal which in this case is product and user table create and populate data for product and user tbale is data engineer responsibility data engineer only read existing data written by software engineer the data engineer task is to move data or to be exact extract data from source transform it using some aggregate function like summing or averaging numeric values then load it into another data store and logical format that more appropriately for achieveing the goal this process is commonly known as etl extract transform load data engineer can use sql scripts or code and framework and flow or scheduling tools that are specifically designed to handle etl tasks in this example data engineer aggregate the data source into something like below browser history is aggregated into product view per user and total view per product purchase history is aggregated into product purchased per user and total purchase per product product ratings is aggregated into average rating per product then these afregated data then in this example further transformed to achieve product and user table required to train product recommender product view per user and total purchase per product are transformed into user table total view per product product purchased per user and average rating per product are transformed into product table this is data engineer s task to create and populate those tables what is data modelling data model data abstraction blueprint data fields relationships among data analogy for the analogy before we build a house we must have some kind of blueprint the blueprint containing the rooms and how the room connect each other this blueprint in data field is what we called as data model final state logical structure for database data model sometimes also referred as database model the final state of data model is logical structure used for database data modelling purpose data modelling is the process to create data model where we organize data into database system to ensure that your data is persisted and easily usable by users on organization data should be persisted easily usable support business decisions market decision product application development risk assessment data modelling steps gather goals what data outcome that business needs gather possible data sources the data team need to make sure data availability and completeness conceptual data modelling mapping the concept of possible data relationship that we have or will have data modelling importance how our data is organized for our applications is extremely critical organized data determines later data use without good data model queries that could have been straightforward and simple might become complicated and slow data modelling should begin before writing application code business logic or analytical models data modelling might not a single step but more an iterative process and data modelling most of the times rely on many people this means everyone involved on data analysis and usage should take parts during modelling database what is database database collection of related data how they are organized analogy for big database think of database software as something like excel spreadsheet but can contains millions or billions of data where each data can related to other data analogy for big database on the other hand there might be very small database like if you use a fitness tracker watch the device also has database inside of it database is everywhere most applications right now will somehow related to database whether that is a simple text file or dedicated database software database is important important or most business sample case e commerce thousands of customers hit the website and sell or buy items we need to know the exact transaction who is the seller who is the buyer products price logistic etc sample case healthcare research company need to improve drugs effectiveness for a disease the researchers will need to find data from existing medicine and previous research like the chemical substance effect on disease exact measurement et cetera such data might be very large and detail even system that look simple will need database for example a fitness tracker watch will have internal database on the device even if its very simple it stores your movement heartbeat or track gps when you run then later you can synchronize database from device into your phone which eventually send data into much larger database in cloud relational database relational database digital database based on relational data model collection of tables and their relationships relational database model organizes data into one or more tables of columns and rows with a unique value identifying each row table is also called as relation columns of the table are called attributes or fields rows are also called records or tuples generally each table represents one entity for example we have table countries each row represent one instance of that type of entity such as india or japan a single country each column represents specific value attached to that instance such as name currency or national language a good table will have unique value for each row in the previous example something like country id can be unique for each row and no duplicate such key is also knwon as primary key table can have relationship to other table for example each row of table cities can be related to table countries each country can have multiple cities or multiple rows in table cities for example india has city delhi and kolkata while japan jas city tokyo and osaka so the relationship between te countrues table and the cities table is one to many one country can have many cities to connect country with city each row in cities has reference to owning country through column country id this country id in table cities is called as foreign key rdbms relational database management system invented by edgar codd ibm in late 60s early 70s widely used even today rdbms sample open source postgresql mysql enterprise work oracle sql server lightweight simple task sqlite sql language to interact with rdbms sql structured query language mostly same syntax across rdbms product minor difference dialect further categories ddl dml dcl tcl sql categories ddl data definition language sql that used to define or modify table structures that is creating dropping table or modifying column definition also truncating table deleting all rows on table add comment to column definition or rename column create drop alter truncate comment rename dml data manipulation language sql statements to read data insert new data to table update existing data delete particular data or merge new data into existing data select insert update delete merge dcl data control language it is related with defining permission for accessing data this can be grant permission or revoke permission for certain user to certain tables grant revoke tcl transaction control language is related with defining permission for accessing data this can be grant permission or revoke permission for certain user to certain tables tcl or transaction control language is category to control transaction within database transaction is set of tasks that grouped into a single execution unit each transaction begins with a specific task and ends when all the tasks in the group successfully complete if any of the tasks fail the transaction fails therefore a transaction has only two results success or failed commit rollback savepoint relational database benefits widely ued easier to learn suitable for small volume data schema is highly structured relation between two tables or more ability to combining more than one table based on common key to get detailed information this is something that unique to relational databases and makes it very powerful support aggregation analytics aggregations is calculating single value from multiple inputs like summing numbers averaging get maximum or minimum value etc this capability also includes grouping rows by multiple criterias sql query is flexible multiple indexes support acid transactions for data integrity when to use relational database all most benefits matches data characteristics relational database machine typically centralized on one machine better performance requires scaling up hardware cpu memory vertical scaling database index the database index looks like a book index we have additional place on the database which consists of the index in the database index we have table and the column used for the index hence if we find data by the indexed column we can find the data fast index is very helpful when your data is large thousands of data rows might not need significant time to search but hundred thousands or millions of rows without indexing will be very slow search each indexes are related to specific table and consist of one or more keys a table can have more than one index built from it the keys are basically based on table columns index primary key primary key is a unique value for each record used to identify that particular record primary key is always an index if primary keys are sequential numbers with no particular business meaning then they are called surrogate key if primary keys have a business meaning to them attributes to the entity then they are called natural key so either we use surrogate or natural primary key we automatically have one index for our table just for information we can have table without primary key at all although it is generally a bad practice and should not be done be wise on index index speeds up search find read book example insert update delete term need index maintenance database index speed up select slowing down insert update delete so other than read operations other operations are slowed down because of index maintenance so be wise when using index only index columns that are frequently read acid data integrity transaction set of tasks as single execution unit success when all tasks in set were success failed if one of task in set was failed so there can be only two results in transaction success or failed grouping tasks into transaction will gurantee data integrity and consistency transaction example payment transaction there will be four steps for this transaction deduct money from our bank account transfer money to merchant create ledger entry debit create ledger entry credit transaction is success only when all 4 tasks were a success transaction is failed if one of the task was failed take example when the 3rd task debit ledger entry is failed in this case we must cancel money transfer and return the amount into our bank account or in other words we cancel the 2nd and 1st task on the other hand since the 3rd task already failed we must make sure that the 4th task creating credit ledger entry is never executed for this transaction all previous tasks must be cancelled rollback and unprocessed tasks must not be executed if we do not rollback previous tasks or still execute any individual task it will break our data integrity this means we will have unbalanced data in order to maintain transaction integrity there are four properties that must be followed they are acid properties atomicity consistency isolation and durability acid properties atomicity all changes to data are performed as if they are single operation that is all the changes are performed or none of them are the payment transaction example refers to only atomicity consistency data is consistent when transaction starts and when it ends in the payment example the consistency ensures that the total value of funds in both the accounts is the same at the start and end of each transaction if we have 2000 in our bank account and merchant has 5000 to their bank account which sums is 7000 then we trigger payment transaction of 400 then at the end of transaction the sum must still be 7000 although now we have 1600 in our bank account and merchant has 5400 isolation each transaction proceeds independently and securely transaction x will not affect transaction y or the other way around whether transaction x is success or failed its result will not impact transaction y in database where we process rows the database will has row locking mechanism where certain row is locked by transaction x and transaction y cannot process that particular row while transaction x still acquire the row lock when transaction x has finished the row lock will be released and transaction y can process that row durability in successful transaction data changes are saved into database even in the event of a system failure for example soon after a transaction succeed the electricity is suddenly down then this property must make sure that data modified by transaction is saved in database so when electricity is back to normal we have the same exact data in database that previously modified by transaction when not to use relational database when not to use | cloud |
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natds-android | design system natura for android build status https app bitrise io app 38848108b04aa71a status svg token kep02isbtn73sde7kezo4w branch master https app bitrise io app 38848108b04aa71a release https img shields io github v release natura cosmeticos natds android style for the badge what for library with android components defined by natura group design system team https ds natura design 28db352be p 35bf2e natds natura design system design systen doc images header readme png how to use how to add dependency copy and paste the file github credentials properties sample and rename it to github credentials properties updating the fields github username and github password for getting your github password see the tutorial https help github com en github authenticating to github creating a personal access token for the command line its important to check read packages when generating you password in the file build gradle insert the informations repositories def githubproperties new properties def githubfile rootproject file github credentials properties if githubfile exists githubproperties load new fileinputstream githubfile maven name natds android url uri https maven pkg github com natura cosmeticos natds android credentials username githubproperties github username system getenv github username password githubproperties github password system getenv github api key and dependencies implementation com natura android designsystem version implementation com google android material material 1 1 0 nat ds icons version https img shields io github v release natura cosmeticos natds commons style for the badge since 4 0 0 nat ds android has no longer nat ds icons in order to use nat ds icons you must add a new dependency repositories def githubproperties new properties def githubfile rootproject file github credentials properties if githubfile exists githubproperties load new fileinputstream githubfile to access nat ds icons dependency at github packages maven name natds commons url uri https maven pkg github com natura cosmeticos natds commons credentials username githubproperties github username system getenv github username password githubproperties github password system getenv github api key and dependencies implementation com natura icons rootproject version you can use nat ds icons without using nat ds android how ds is structured for use on android applying themes our lib is being built from android themes and styles this means to consume any of resources available in the library you need to define as a base theme for the use of your view a design system theme however it is necessary to define this place to insert the theme according to the android structure there are the following options 1 set the theme attribute in tag application at file androidmanifest xml android application android theme style theme natura dark application 2 set the theme attribute in tag activity at file androidmanifest xml android activity android name components buttonactivity android theme style theme natura light 3 define the theme attribute in the views xml file android xml version 1 0 encoding utf 8 linearlayout xmlns android http schemas android com apk res android android layout width match parent android layout height match parent android orientation vertical android theme style theme natura light linearlayout 4 set the theme using the settheme method in the views oncreate android settheme r style theme natura light our suggestion always try to apply the ds theme to the most generic layers of your app this greatly simplifies the use but if for some reason is not possible fine define the theme locally in the view where you want to apply ds design tokens the foundation of nat ds now you get how to access ds themes let s talk about the most basic portion of our ds the design tokens we call design tokens all the basic attributes of design systems for example colors typography definitions etc currently android lib provide the following tokens color border radius elevation font family font weight line height opacity sizes spacing and how can this be used by applications first of all it is worth remembering that the components made available by ds are built from these tokens in addition you can explore our tokens to define your screens and components for example you need to add text to a view that will be styled with the following attributes font family roboto sans serif font size 96sp letter spacing 0 015625 font weight regular lineheight medium 1 5 you can set these attributes manually in an android textview component android textview android id id textview android layout width match parent android layout height wrap content android text my text android padlinespacingmultipliering 1 5 android textsize 96sp android textcolor 333333 or you can simply use the heading 1 typography token that has exactly these attributes android textview android id id textview android layout width match parent android layout height wrap content android text meu texto android textappereance textappereanceheading1 notice we use textappereanceheading1 to fill the textappereance attribute of the textview component when we write attributename we are indicating to android that we want to pull this reference from the theme applied to this view also if you need to for example apply a different color to your text okay you can use a color token for this android textview android id id textview android layout width match parent android layout height wrap content android text meu texto android textappereance textappereanceheading1 textcolor colorprimary components components are a collection of interface elements used to create great and consistent user experiences the currently available components are listed in general natura group design system documentation https ds natura design with instructions and usage examples you can also check tokens and components by downloading from this repository and running the sample app how to contribute help us to grow doc how to contribute md | natura android kotlin-android design-system kotlin | os |
rahul | rahul passionate fullstack amp cross platform amp mern developer cloud computing amp data engineering c c java dart js aws azure gcp | cloud |
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nypr-design-system | nypr design system home of the radial design system this repo contains an ember addon nypr design system and the files to power storybook ember addon installation ember install nypr design system configuration this app will automatically import the css for your chosen theme in head of your application template using the content for head footer hook you can select which themes to build usually just one by adding a list of theme names to the options in your app s ember cli build js file js let app new emberapp defaults nypr design system themes gothamist if you don t specify a themes option it will default to only the white label white label if you don t want any themes use themes additional themes beyond the first one will be added as alternate stylesheets https developer mozilla org en us docs web css alternative style sheets this is useful within storybook itself and also when debugging themes in clients storybook the following folders contain files for storybook stories storybook to run storybook locally you need ember to already be running ember serve or built ember build more specifically you need the ember addon to exist in the dist folder so it can read and display the ember components after that you can start storybook by running bash yarn storybook deployment radial is deployed as a static site which is housed in an s3 bucket behind a cloudfront distribution production staging environments we use the addon versions addon to maintain a single environment for both production and staging this addon allows us to use the underlying directory structure of the s3 bucket as separate versions of the site each version that is deployed to the s3 bucket is stored in its own directory as seen in the snippet from circle yml below bash circle branch main release latest n circle branch release circle branch release circle tag yarn storybook to aws s3 bucket path s3 bucket release e storybook static release aws s3 cp storybook static index html s3 s3 bucket aws s3 cp storybook static storybook config json s3 s3 bucket aws pager aws cloudfront create invalidation distribution id cloudfront id paths index html storybook config json release then addon versions displays a versions tab in the storybook ui that allows the user to toggle between different available versions this addon doesn t automatically detect the underlying directory structure it relies on the available versions field in storybook config json to populate its buttons warning thus when deploying radial you must add a line to the available versions field warning qa branches all branches that begin with qa will be deployed to the s3 bucket you have the option of adding your branch to the available versions field or you can just modify the url manually to view your work license this project is licensed under the mit license license md | design | os |
ammi-2020-bordes-deep-nlp | deep nlp tutorials ammi 2019 this repository hosts the tutorials of the deep natural language processing course of the african master s in machine intelligence given by antoine bordes 2020 calendar alt text https raw githubusercontent com louismartin ammi 2020 bordes deep nlp master ammi deepnlp calendar jpg calendar of the deep nlp course | ai |
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Multilingual_safety_benchmark | h1 align center multilingual safety benchmark for llms h1 paper all languages matter on the multilingual safety of large language model br br div align center img src paper overview png width 800 div h3 align center research use only no misuse h3 framework div align center img src paper framework jpg alt logo width 1000 div we build the first multilingual safety benchmark for llms xsafety in response to the global deployment of llms in practice xsafety covers 14 kinds of commonly used safety issues across 10 languages that span several language families we utilize xsafety to empirically study the multilingual safety for 4 widely used llms including both close api and open source models experimental results show that all llms produce significantly more unsafe responses for non english queries than english ones indicating the necessity of developing safety alignment for non english languages results div align center img src paper result1 jpg alt logo width 800 div div align center img src paper result2 jpg alt logo width 800 div paper and citation for more details please refer to our paper a href http arxiv org abs 2310 00905 here a community discussion twitter aidb jiao wenxiang citation if you find our paper data interesting and useful please feel free to give us a star and cite us through bibtex article wang2023all title all languages matter on the multilingual safety of large language models author wang wenxuan and tu zhaopeng and chen chang and yuan youliang and huang jen tse and jiao wenxiang and lyu michael r journal arxiv preprint arxiv 2310 00905 year 2023 | ai |
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interpretable-ml-book | interpretable machine learning explaining the decisions and behaviour of machine learning models build status https github com christophm interpretable ml book actions workflows book yaml badge svg summary you can find the current version of the book here https christophm github io interpretable ml book this book is about interpretable machine learning machine learning is being built into many products and processes of our daily lives yet decisions made by machines don t automatically come with an explanation an explanation increases the trust in the decision and in the machine learning model as the programmer of an algorithm you want to know whether you can trust the learned model did it learn generalizable features or are there some odd artifacts in the training data which the algorithm picked up this book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions in the first chapter algorithms that produce simple interpretable models are introduced together with instructions how to interpret the output the later chapters focus on analyzing complex models and their decisions in an ideal future machines will be able to explain their decisions and make a transition into an algorithmic age more human this books is recommended for machine learning practitioners data scientists statisticians and also for stakeholders deciding on the use of machine learning and intelligent algorithms the book is automatically built from the master branch and pushed to gh pages by github actions contributing see how to contribute contributing md rendering the book clone the repository shell git clone git github com christophm interpretable ml book git make sure all dependencies for the book are installed this book has the structure of an r package so dependencies can be installed easily only r and the devtools library is required start an r session in the folder of the book repository and type r devtools install deps for rendering the book start an r session and type r setwd manuscript first generate the references source scripts references r bookdown render book bookdown gitbook after rendering the html files of the book will be in the book folder you can either double click index html directly or of course do it in r r browseurl book index html notes on printing with lulu com export from leanpub in 7 44 x 9 68 18 9cm x 24 6cm for cover 7 565 x 9 925 19 226 x 25 224cm see recommended sizes https connect lulu com en discussion 33279 recommended book cover image dimensions font for front cover francois one writing stuff that both works for leanpub and for bookdown titles start with subtitles with and so on titles can be tagged using tag of the title chapters can be referenced by using text of the link tag of the title figures can be referenced by using text of the link fig tag of r chunk that produced figure start and end mathematical expressions with inline or with extra line will be automatically changed for leanpub with a regexpr conversion script only works if no empty spaces are in the formula leave empty lines between formulas and text if formula not inline formulas with should be in one line and not over multiple lines due to parser references have to be written like this ref tag and must be at the end of the respective file with ref details of the reference make sure the space is included references are collected in 10 reference rmd with the script references r make sure not to use ref tag anywhere in the text only at the bottom for the actual reference printing for proofreading with extra line spacing build html book go to manuscript book libs gitbook css style css change line height 1 7 to line height 2 5 open local html with chrome print to pdf with custom margin changelog all notable changes to the book will be documented here v2 0 in progress html version added preface by the author chapter started section on neural network interpretation added chapter on feature visualization added shap chapter added anchors chapter fixed error in logistic regression chapter logistic regression was predicting class healthy but interpretation in the text was for class cancer now regression weights have the correct sign renamed feature importance chapter to permutation feature importance added chapter about functional decomposition rearranged interpretation methods by local global and deep learning before model agnostic example based deep learning errata chapter 4 3 glm gam and more logistic regression uses logit not logistic function as link function chapter linear models formula for adjusted r squared was corrected twice chapter decision rules newly introduced mix up between healthy and cancer in oner chapter was fixed chapter rulefit the importance of the linear term in the total importance formulate was indexed with an l instead of j updated images v1 1 2019 03 23 print version ebook version fixes wrong index in cooks distance summation i j fixed boxplot formula 1 5 instead of 1 58 change to colorblind friendly color palettes viridis make sure plots work in black and white as well extends counterfactual chapter with moc by susanne dandl v1 0 2019 02 21 extensive proofreading and polishing v0 7 2018 11 21 renamed definitions chapter to terminology added mathematical notation to terminology former definitions chapter added lasso example restructured lm chapter and added pros cons renamed criteria of interpretability methods to taxonomy of interpretability methods added advantages and disadvantages of logistic regression added list of references at the end of book added images to the short stories added drawback of shapley value feature have to be independent added tree decomposition and feature importance to tree chapter improved explanation of individual prediction in lm added what s wrong with my dog example to adversarial examples added links to data files and pre processing r scripts v0 6 2018 11 02 added chapter on accumulated local effects plots added some advantages and disadvantages to pdps added chapter on extending linear models fixed missing square in the friedman h statistic added discussion about training vs test data in feature importance chapter improved the definitions also added some graphics added an example with a categorical feature to pdp v0 5 2018 08 14 added chapter on influential instances added chapter on decision rules added chapter on adversarial machine examples added chapter on prototypes and criticisms added chapter on counterfactual explanations added section on lime images by verena haunschmid added section on when we don t need interpretability renamed chapter human style explanations human friendly explanations v0 4 2018 05 23 added chapter on global surrogate models added improved shapley pictograms added acknowledgements chapter added feature interaction chapter improved example in partial dependence plot chapter the weights in lime text chapter where shown with the wrong words this has been fixed improved introduction text added chapter about the future of interpretability added criteria for interpretability methods v0 3 2018 04 24 reworked the feature importance chapter added third short story removed xkcd comic merged introduction and about the book chapters added pros cons to pdp and ice chapters started using the iml package for plots in ice and pdp restructured the book files for leanpub added a cover added some css for nicer formatting v0 2 2018 02 13 added chapter about shapley value explanations added short story chapters added donation links in preface reworked rulefit with examples and theory interpretability chapter extended add chapter on human style explanations making it easier to collaborate travis checks if book can be rendered for pull requests v0 1 2017 12 03 first release of the interpretable machine learning book | ai |
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