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New_Project | new project information systems and technologies viktoriia harmata https www hneu edu ua | server |
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LAiW | laiw a chinese legal large language models benchmark english https github com dai shen laiw blob main readme en md https github com dai shen laiw blob main readme md laiw laiw https huggingface co spaces daishen laiw https arxiv org abs 2310 05620 2023 10 12 laiw https arxiv org abs 2310 05620 2023 10 08 laiw https github com dai shen laiw nlp chatgpt llama2 https huggingface co meta llama llama 2 7b chat hf ziya llama https huggingface co idea ccnl ziya llama 13b v1 chinese llama https github com ymcui chinese llama alpaca baichuan2 https huggingface co baichuan inc baichuan2 13b chat hanfei https github com siat nlp hanfei chatlaw https huggingface co jessytsu1 chatlaw 13b lawgpt https github com pengxiao song lawgpt laiw a chinese legal large language models benchmark laiw a chinese legal large language models benchmark img src https github com dai shen laiw blob main resources task framwork png width 70 height 70 img strong strong nlp strong 3 strong strong 13 strong nlp nlp 5 5 3 table tr td td td td td td tr tr td rowspan 5 nlp td td td td td tr tr td td td td tr tr td td td td tr tr td td td td tr tr td td td td tr tr td rowspan 5 td td td td td tr tr td td td td tr tr td td td td tr tr td td td td tr tr td td td td tr tr td rowspan 3 td td td td td tr tr td td td td tr tr td td td td tr table legal evaluation dataset led laiw dataset https github com dai shen laiw dataset led table tr td td td td td td td td td td tr tr td rowspan 5 nlp td td td td cail 2018 td td a href https huggingface co datasets daishen legal ar legal ar a td td 1 000 td tr tr td td td cail 2019 td td a href https huggingface co datasets daishen legal er legal er a td td 1 000 td tr tr td td td cail 2021 td td a href https huggingface co datasets daishen legal ner legal ner a td td 156 td td td td td tr tr td td td cail 2020 td td a href https huggingface co datasets daishen legal js legal js a td td 364 td tr tr td td td cjrc td td a href https huggingface co datasets daishen legal cr legal cr a td td 2 000 td tr tr td rowspan 5 td td td td criminal s br mlmn td td td td td tr tr td td td msjudeg td td td td td tr tr td td td jec qa td td td td td tr tr td td td private td td td td td tr tr td td td cail 2019 td td td td td tr tr td rowspan 3 td td td td ac nlg td td td td td tr tr td td td cjrc td td td td td tr tr td td td crimekgassitant td td td td td tr table v1 0 f1 rouge 1 0 1 13 https huggingface co spaces daishen laiw chatgpt baichuan2 https huggingface co baichuan inc baichuan2 13b chat chatglm2 https huggingface co thudm chatglm2 6b hanfei https github com siat nlp hanfei lawyer llama https github com andrewzhe lawyer llama tree main https modelscope cn models wisdomocean wisdominterrogatory summary bash git clone git clone https github com dai shen laiw git recursive cd laiw pip install r requirements txt cd laiw src financial evaluation pip install e multilingual bash python eval py model hf causal experimental model args use accelerate true pretrained baichuan inc baichuan2 13b chat tokenizer baichuan inc baichuan2 13b chat use fast false tasks legal ar legal er legal ner llmindcraft https github com xplainmind llmindcraft awesome chinese legal resources https github com pengxiao song awesome chinese legal resources | ai |
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one-app | one app bundler https github com americanexpress one app cli tree main packages one app bundler react http reactjs org h1 align center img src https github com americanexpress one app raw main one app png alt one app one amex width 50 h1 one app docker release https github com americanexpress one app actions workflows release step 4 automatic docker prod build and publish yml badge svg one app integration tests https github com americanexpress one app workflows one 20app 20integration 20tests badge svg one app unit and lint tests https github com americanexpress one app workflows one 20app 20unit 20and 20lint 20tests badge svg one app is a fresh take on web application development it consists of a node js https nodejs org server that serves up a single page app built using react components and makes use of holocron https github com americanexpress holocron to allow for code splitting via independently developed tested and deployed holocron modules our goal is to provide a web application framework for building fast scalable secure and modular experiences leave the tooling to us and focus on what matters delivering performant maintainable experiences to your users while american express has been using one app in production since 2016 this open source iteration of the framework is in a soft launch state there will be a full documentation site forthcoming hiring want to get paid for your contributions to one app send your resume to oneamex careers aexp com table of contents features features quick start quick start documentation documentation license ef b8 8f license code of conduct ef b8 8f code of conduct community community contributing contributing features modular design allowing for groups of ui components to be independently developed tested and deployed easy configuration management server side rendering as a first class citizen built in internationalization built in dynamic routing for a breakdown on these features and an architectural overview take a look at our overview documentation docs overview readme md quick start create a module with generator one app module https github com americanexpress one app cli tree main packages generator one app module the easiest way to do this is via npx https blog npmjs org post 162869356040 introducing npx an npm package runner comes with npm versions 5 2 0 and above this will use the one app module generator https github com americanexpress one app cli tree main packages generator one app module to generate a basic one app module run the following command in the directory you want your module to live bash export node env development npx p yo p americanexpress generator one app module yo americanexpress one app module select the root module option when asked docker https www docker com is required for this next step when the generator has finished bash cd your module name npm start visit localhost 3000 please follow the getting started docs overview getting started md guide for more information clone and install one app locally when you want to work on one app directly or are unable to use one app runner docs guides one app runner md bash export node env development git clone https github com americanexpress one app git cd one app npm ci no optional serve local module s to one app when you are directly running the one app server locally and would like to load a local module at the root of your one app repo run bash npm run serve module path to local module e g npm run serve module my first module the serve module command generates a static folder in the one app root directory containing a module map json and a modules folder with your bundled module code one app static module map json modules my first module 1 0 0 my first module browser js my first module browser js map my first module legacy browser js my first module legacy browser js map my first module node js my first module node js map paired with the built in one app dev cdn https github com americanexpress one app dev cdn library you re able to utilize the holocron module map docs api server module map schema md while running your entire one app instance locally no need to deploy and fetch remote assets from a cdn at this step the root module the root module serves as the entry point for one app to load an application your application one app root module other modules it is possible for your application to consist of only the root module however most application will want to take advantage of code splitting using holocron https github com americanexpress holocron and have the root module load other modules more on this in the routing docs api modules routing md section in the api docs for a module to act as the root module the only requirements are returns a react component bundled with one app bundler https github com americanexpress one app cli tree main packages one app bundler provides a valid content security policy https developer mozilla org en us docs web http csp though the appconfig docs api modules app configuration md static declaring a module as your root module and start one app start up one app and declare your new module as the root module the root module bash npm start root module name module name e g npm start root module name my first module this starts one app and makes it available at http localhost 3000 where you can see it in action open another terminal window run npm run watch build in your module s directory and make some edits to the module one app will pick up these changes and update the module bundles accordingly when you reload your browser window one app will be displaying your updated module documentation one app overview docs overview readme md getting started docs overview getting started md guides docs guides readme md api docs api readme md troubleshooting docs troubleshooting readme md community the one app community can be found on github discussions https github com americanexpress one app discussions where you can ask questions voice ideas and share your projects contributing we welcome your interest in the american express open source community on github any contributor to any open source project managed by the american express open source community must accept and sign an agreement indicating agreement to the terms below except for the rights granted in this agreement to american express and to recipients of software distributed by american express you reserve all right title and interest if any in and to your contributions please fill out the agreement https cla assistant io americanexpress one app please see our contributing md contributing md license any contributions made under this project will be governed by the apache license 2 0 license txt code of conduct this project adheres to the american express community guidelines code of conduct md by participating you are expected to honor these guidelines | one-app holocron-modules micro-ui holocron microfrontends modular | front_end |
squawk | squawk an experimental project to customize squawk virtual machine for microcontrollers users can write embedded application programs in java with cldc api that can run on small embedded system without rtos highlights ported squawk vm to mbed classic and esp8266 implemented aggressive dead code elimination static footprint is reduced to less than 128k including romized classes optimized interpreter loop provides easy way to add native methods provides api to call hal layer has been tested on esp8266 microbit lpc1768 nucleo l476 and linux x86 build for mbed install gcc arm none eabi install jdk install mbed cli tested with 1 7 5 git clone https github com tomatsu squawk cd squawk make cd project gen targets mbed make target lpc1768 project helloworld cd tmp lpc1768 helloworld make build for esp8266 install toolchain and sdk install jdk git clone https github com tomatsu squawk cd squawk make set environment variable path and esp sdk for example export esp sdk home esp open sdk sdk export path home esp open sdk xtensa lx106 elf bin path cd project gen targets esp8266 make project helloworld cd tmp esp8266 helloworld make plans documentation test cldc8 support | os |
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Over_1500_Awesome_Project_Ideas | hi there br are you struggling to find project ideas for research paper or your resume br well then here it is br a latest compilation of all projects across the following domains web development android app development machine learning deep learning cloud computing data science big data data mining cyber security networking parallel and distributed systems br enjoy p style color green i fork and star this repository to save a copy i p description about the files in the repository there are total of 1500 project ideas which you look from these two files a href https github com jaichaudhry323 over 1500 awesome project ideas blob main all project ideas md all project ideas a and a href https github com jaichaudhry323 over 1500 awesome project ideas blob main advanced project ideas md advanced project ideas a then there are specific category based projects which have been extracted from above mentioned all project ideas txt file for your convenience for all python based projects refer a href https github com jaichaudhry323 over 1500 awesome project ideas blob main python based md python based a for all java based projects refer a href https github com jaichaudhry323 over 1500 awesome project ideas blob main java based md java based a for all web based projects refer a href https github com jaichaudhry323 over 1500 awesome project ideas blob main web based md web based a for all android based projects refer a href https github com jaichaudhry323 over 1500 awesome project ideas blob main android based md android based a | front_end |
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web_development_and_api_design | web development and api design docs img stephen leonardi 369733 unsplash compressed jpg photo by stephen leonardi on unsplash ci https github com arcuri82 web development and api design workflows ci badge svg summary this repository contains all the material used in the pg6301 course on web development and api design taught at the university college h yskolen kristiania https kristiania no oslo norway the goal of this course is to teach the principles of single page applications spa the basics of web services e g rest apis and web sockets the programming language used in this course is javascript with nodejs as runtime some of the used technologies are yarn webpack babel react react router jest express and passport this course puts particular emphasis on testing and security but not on frontend design i e ui ux this course focuses on the frontend side of web development albeit we will build full stack applications but no databases to learn more about backend development e g using java kotlin you can see this other repository testing security and development of enterprise systems https github com arcuri82 testing security development enterprise systems the course is divided in 12 lessons each one lasting between 2 and 4 hours each lesson contains slides and full working code examples slides just give high level overviews most of the explanations will be directly in the code as comments lessons lesson 01 intro to javascript and basic html css code in the les01 les01 folder slides here pdf docs slides lesson 01 pdf exercise quiz game part 01 docs exercises quiz game part 01 md br lesson 02 build tools and unit testing code in the les02 les02 folder slides here pdf docs slides lesson 02 pdf exercises quiz game part 02 docs exercises quiz game part 02 md br lesson 03 spa components code in the les03 les03 folder slides here pdf docs slides lesson 03 pdf exercises quiz game part 03 docs exercises quiz game part 03 md br lesson 04 spa state handling code in the les04 les04 folder slides here pdf docs slides lesson 04 pdf exercises quiz game part 04 docs exercises quiz game part 04 md br lesson 05 spa routing code in the les05 les05 folder slides here pdf docs slides lesson 05 pdf exercises quiz game part 05 docs exercises quiz game part 05 md br lesson 06 async calls to web services code in the les06 les06 folder slides here pdf docs slides lesson 06 pdf exercises quiz game part 06 docs exercises quiz game part 06 md br lesson 07 restful apis theory no code slides here pdf docs slides lesson 07 pdf exercises quiz game part 07 docs exercises quiz game part 07 md br lesson 08 restful apis practice code in the les08 les08 folder slides here pdf docs slides lesson 08 pdf exercises quiz game part 08 docs exercises quiz game part 08 md br lesson 09 authentication and authorization code in the les09 les09 folder slides here pdf docs slides lesson 09 pdf exercises quiz game part 09 docs exercises quiz game part 09 md br lesson 10 cors csrf and xss code in the les10 les10 folder slides here pdf docs slides lesson 10 pdf exercises quiz game part 10 docs exercises quiz game part 10 md br lesson 11 websockets code in the les11 les11 folder slides here pdf docs slides lesson 11 pdf exercises quiz game part 11 docs exercises quiz game part 11 md br lesson 12 online multi player game code in the les12 les12 folder slides here pdf docs slides lesson 12 pdf exercises quiz game part 12 docs exercises quiz game part 12 md br extra graphql apis code in the extra graphql extra graphql folder slides here pdf docs slides lesson graphql pdf exercises none useful links you don t know javascript https github com getify you dont know js javascript koans https github com liammclennan javascript koans what the f ck javascript https github com denysdovhan wtfjs nodejs https nodejs org yarn https yarnpkg com webpack https webpack js org babel https babeljs io lodash https lodash com react https reactjs org react router https reacttraining com react router jest https github com facebook jest enzyme https github com airbnb enzyme supertest https github com visionmedia supertest css selectors https www w3schools com cssref css selectors asp express http expressjs com passport http www passportjs org programmableweb http www programmableweb com exam a pdf of a mock exam for this course can be found here docs exams mock exam pdf license copyright the materials herein are all copyright c of andrea arcuri http www arcuriandrea org and contributors the material was is produced while working at westerdals oslo act and h yskolen kristiania all the source code in this repository is released under lgpl version 3 license license a rel license href http creativecommons org licenses by nc nd 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by nc nd 4 0 88x31 png a br the documentation is licensed under a a rel license href http creativecommons org licenses by nc nd 4 0 creative commons attribution noncommercial noderivs 4 0 unported license a | babel react webpack nodejs spa security testing rest graphql | front_end |
textbook | dit dit 1 html css javascript spring readme md 1 php mysql autumn readme md 1 html common html reference md 1 css common css reference md 1 mysql common mysql md 1 mac common proxy on mac md 1 windows common proxy on windows md 1 sublime text common sublime md 1 common dev tool md 1 mac common shortcutkey mac md 1 windows common shortcutkey win md 1 slack common how to use slack md license a rel license href http creativecommons org licenses by nc 4 0 img alt style border width 0 src https i creativecommons org l by nc 4 0 88x31 png a br a rel license href http creativecommons org licenses by nc 4 0 4 0 a | front_end |
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natds-rn | natura design system react native react native components for react native based projects inside natura co build status https app bitrise io app 2c91a0037aed90db status svg token bg3viyreobivtyl99gvnfq branch master https app bitrise io app 2c91a0037aed90db npm version https badge fury io js 40naturacosmeticos 2fnatds rn svg https badge fury io js 40naturacosmeticos 2fnatds rn npm https img shields io npm dm naturacosmeticos natds rn label npm 20downloads github issues https img shields io github issues natura cosmeticos natds rn npm https img shields io npm l naturacosmeticos natds rn bowtie check out our component docs https natds rn natura design you can also install our sample apps in your ios or android device check in with our team to get the latest versions of the sample apps for ios you will need to have your device registered as a beta tester you can request access opening an issue or at our ms teams channel get started our library is made to be used with react native and because of this we usume that you already have a few things setup and a react native project up and running if that is not the case we strongly recommend that you follow the getting started https reactnative dev docs getting started and environment setup https reactnative dev docs environment setup from the official react native docs prerequisites node 12 yarn or npm a working project with react 16 8 0 react native 0 59 10 styled components 5 0 0 react native svg 12 1 1 you will need to install the styled components package before the installation as it is a prerequisite and it will not be installed automatically installation to start using the natds rn components on you project first you will need to install it sh npm install save naturacosmeticos natds rn or yarn add naturacosmeticos natds rn dependencies this package currently depend on natds themes natds icons and react native gesture handler this packages will be installed automatically with the command above if you have currently installed versions of these packages be sure to check the installation logs for version incompatibilities usage you can edit this live example https snack expo io arielwb natds rn button to test component props and theme setup setup the theme on your application entry point add the themeprovider and choose the theme to be applied to the components javascript import react from react import themeprovider from styled components native import buildtheme from naturacosmeticos natds rn import app from app export const main the buildtheme function accepts two parameters brand the name of the brand to applied natura thebodyshop avon aesop mode the color scheme for the current branch light dark const theme buildtheme natura light add your application component inside the themeprovider return themeprovider theme theme app themeprovider using the components in your app javascript import react from react import view text from react native import button from naturacosmeticos natds rn export const app const onpress console log i am pressed return view text a page title text button onpress onpress text default type contained view icons we have the icons package naturacosmeticos natds icons as a dependency of this library so you have the font icon files available in your node modules folder after installation to use the fonts in your app you need to add the font icons path to your react native config js file javascript module exports assets node modules naturacosmeticos natds icons dist fonts and then run shell npx react native link for component detailed api and usage examples check out the page using natds icons https natds rn natura design path docs documentation icons page and the icon component documentation https natds rn natura design path docs components icon default fonts the brand fonts package naturacosmeticos natds themes is a dependency of this library so the font files by brand are available in the node modules folder after installation to do this import the method copyfontsbybrand to react native config js file passing the brand that you need and the path to your assets folder available brands aesop avon natura thebodyshop javascript const copyfontsbybrand require naturacosmeticos natds rn tools copyfontsbybrand module exports assets src assets fonts commands name copy fonts func copyfontsbybrand your brand dirname src assets fonts and then run shell npx react native copy fonts npx react native link after that you can use the fonts of the chosen brand within your components using the parameters received from the theme usage ios the configuration of custom fonts on the ios platform within react native is loaded from your postscript then your font family and your font weight must be declared javascript import react from react import styled from styled components native const customtext styled text theme fontfamily theme typography display fontfamily fontweight theme typography display fontweight export default app customtext your text customtext android android platform font declarations are loaded from the font name which is the same file name in this case only the font family must be declared as the file has its own font weight javascript import react from react import styled from styled components native const customtext styled text theme fontfamily theme asset font file display export default app customtext your text customtext issues have an issue need help or have a feature request create a issue https github com natura cosmeticos natds rn issues contributing if you want to add a new component or feature to natds rn check our contributing docs contributing md tada tada license isc | os |
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CV2017Fall | cv2017fall computer vision i at ntu 2017 fall indrocution this course has 10 homeworks the 10 homeworks are as follows 1 basic image manipulation 2 basic image manipulation 3 histogram equalization 4 mathematical morphology binary morphology 5 mathematical morphology gray scaled morphology 6 yokoi connectivity number 7 thinning 8 noise removal 9 general edge detection 10 zero crossing edge detection table of contents ts 0 environment https github com jasonyao81000 cv2017fall blob master readme md environment 1 basic image manipulation https github com jasonyao81000 cv2017fall blob master readme md hw1 basic image manipulation 2 basic image manipulation https github com jasonyao81000 cv2017fall blob master readme md hw2 basic image manipulation 3 histogram equalization https github com jasonyao81000 cv2017fall hw3 histogram equalization 4 mathematical morphology binary morphology https github com jasonyao81000 cv2017fall hw4 mathematical morphology binary morphology 5 mathematical morphology gray scaled morphology https github com jasonyao81000 cv2017fall hw5 mathematical morphology gray scaled morphology 6 yokoi connectivity number https github com jasonyao81000 cv2017fall hw6 yokoi connectivity number 7 thinning https github com jasonyao81000 cv2017fall hw7 thinning 8 noise removal https github com jasonyao81000 cv2017fall hw8 noise removal 9 general edge detection https github com jasonyao81000 cv2017fall hw9 general edge detection 10 zero crossing edge detection https github com jasonyao81000 cv2017fall hw10 zero crossing edge detection te environment programming language python 3 programming ide visual studio code operating system windows 10 x64 hw1 basic image manipulation part 1 of this homework is writing a program to generate the following images from lena bmp up side down lena bmp right side left lena bmp diagonally mirrored lena bmp code hw1 1 https github com jasonyao81000 cv2017fall tree master hw1 hw1 1 part 2 of this homework is using any kind of software to do the following things rotate lena bmp 45 degrees clockwise shrink lena bmp in half binarize lena bmp at 128 to get a binary image code hw1 2 https github com jasonyao81000 cv2017fall tree master hw1 hw1 2 report https github com jasonyao81000 cv2017fall blob master hw1 cv1 hw1 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw2 basic image manipulation part 1 of this homework is to binarize lena bmp with threshold 128 0 127 128 255 code hw2 1 https github com jasonyao81000 cv2017fall tree master hw2 hw2 1 part 2 of this homework is to draw the histogram of lena bmp code hw2 2 https github com jasonyao81000 cv2017fall tree master hw2 hw2 2 part 3 of this homework is to find connected components with following rules draw bounding box of regions draw cross at centroid of regions omit regions that have a pixel count less than 500 code hw2 3 https github com jasonyao81000 cv2017fall tree master hw2 hw2 3 report https github com jasonyao81000 cv2017fall blob master hw2 cv1 hw2 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw3 histogram equalization this homework is to do histogram equalization with following rules adjust the brightness of lena bmp to one third do histogram equalization on dark image show the histogram of the final image code hw3 1 https github com jasonyao81000 cv2017fall tree master hw3 hw3 1 report https github com jasonyao81000 cv2017fall blob master hw3 cv1 hw3 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw4 mathematical morphology binary morphology this homework is to do binary morphology with following rules please use the octagonal 3 5 5 5 3 kernel please use the l shaped kernel to detect the upper right corner for hit and miss transform please process the white pixels operating on white pixels five images should be included in your report dilation erosion opening closing and hit and miss code hw4 1 https github com jasonyao81000 cv2017fall tree master hw4 hw4 1 report https github com jasonyao81000 cv2017fall blob master hw4 cv1 hw4 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw5 mathematical morphology gray scaled morphology this homework is to do gray scaled morphology with following rules please use the octagonal 3 5 5 5 3 kernel please take the local maxima or local minima respectively four images should be included in your report dilation erosion opening and closing code hw5 https github com jasonyao81000 cv2017fall tree master hw5 hw5 report https github com jasonyao81000 cv2017fall blob master hw5 cv1 hw5 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw6 yokoi connectivity number this homework is to do yokoi connectivity number with following rules please binarize leba bmp with threshold 128 please down sampling binary bmp from 512x512 to 64x64 using 8x8 blocks as unit and take the topmost left pixel as the down sampling data print yokoi connectivity number to text file code hw6 https github com jasonyao81000 cv2017fall tree master hw6 hw6 report https github com jasonyao81000 cv2017fall blob master hw6 cv1 hw6 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw7 thinning this homework is to do thinning operation with following rules please binarize leba bmp with threshold 128 do thinning operation on binary image code hw7 https github com jasonyao81000 cv2017fall tree master hw7 hw7 report https github com jasonyao81000 cv2017fall blob master hw7 cv1 hw7 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw8 noise removal this homework is to do noise removal with following rules generate gaussian noise with amplitude of 10 and 30 generate salt and pepper noise with probability of 0 1 and 0 05 use the 3x3 and 5x5 box filter on noise images use the 3x3 and 5x5 median filter on noise images use opening then closing and closing then opening filter on noise images calculate the signal to noise ratio snr of noise images code hw8 https github com jasonyao81000 cv2017fall tree master hw8 hw8 report https github com jasonyao81000 cv2017fall blob master hw8 cv1 hw8 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw9 general edge detection this homework is to do general edge detection with following rules robert s operator with threshold of 12 prewitt s edge detector with threshold of 24 sobel s edge detector with threshold of 38 frei and chen s gradient operator with threshold of 30 kirsch s compass operator with threshold of 135 robinson s compass operator with threshold of 43 nevatia babu 5x5 operator with threshold of 12500 code hw9 https github com jasonyao81000 cv2017fall tree master hw9 hw9 report https github com jasonyao81000 cv2017fall blob master hw9 cv1 hw9 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf hw10 zero crossing edge detection this homework is to do zero crossing edge detection with following rules laplacian mask 1 with threshold of 15 laplacian mask 2 with threshold of 15 minimum variance laplacian with threshold of 20 laplacian of gaussian with threshold of 3000 difference of gaussian with threshold of 1 code hw10 https github com jasonyao81000 cv2017fall tree master hw10 hw10 report https github com jasonyao81000 cv2017fall blob master hw10 cv1 hw10 e5 a7 9a e5 98 89 e6 98 87 r06922002 pdf | ntu computer-vision cv cv1 homework | ai |
dso-560-nlp-text-analytics-SPRING-2021 | dso560 natural language processing text analytics slack workspace we will be using slack workspaces for most of our course communications including submitting classwork homework and grading your final grades will be posted on blackboard but feedback for assignments will be delivered via slack prior to week 1 s session please join the slack workspace https join slack com t dso560textana piq5399 shared invite zt nlx7nesm hlmz4bm7ydfj83e1heqcug note that this is a different slack workspace than the auto created workspaces by usc marshall i am creating my own workspace since i need extra permissions to automate sending grades notifications via the slack chatbot apps week 1 tuesday march 16th cloning forking and syncing github repositories https www youtube com watch v vrxughmyhgq feature youtu be lecture recording https usc zoom us rec share gfde mobqceuwnfc1 jminnrrfywcfuguszp j0tlvagvyth9hl8vkoff78nizbg k pyceqdkdk3rc3m starttime 1615944773000 google colab notebooks please make sure to save your own copy regex exercises https colab research google com drive 1xwpb7ltogrf t020okps8ufsv hgqibb usp sharing additional practice https colab research google com drive 1219hbrqphxamduuidj1buaagy3tpjfn1 usp sharing basic python operations for working with text https colab research google com drive 10wbspngddzkaidsgucs4suaabyugyuza usp sharing week 2 lecture recording https usc zoom us rec share am2gqkc5s2qkidpwnqzb1rjdxcuo8qqcl zspfasswirori2d0hhbgdxf5nvwgqk caqx8ikfvhd8lacr starttime 1616549879000 google colab notebooks please make sure to save your own copy regex practice part ii https colab research google com drive 1xyn7gs ib zcaeikat9bwwi83a0qfp1j usp sharing text encodings https colab research google com drive 1jsvzz7zkos0iy32 4utsvyxp6cvqikwn usp sharing text pre processing techniques https colab research google com drive 1vzl4lj5eg7xhz j lzgu8ue8xpmfsu6g usp sharing linear algebra and similarity https colab research google com drive 1rb5 urafxtwqq8nn25rsufhx9kpueun6 usp sharing week 3 lecture recording https usc zoom us rec share v7zkjlzolv ypmyjujieczgvc3cushr9kfl4zvmp s5a6zaucidnlk0iyad6weip quisagp0vquvth h starttime 1617154333000 google colab notebooks probabilities and n gram language models https colab research google com drive 1zluqyqsfpigf4oogfgxbczkcrrgkmtjl usp sharing naive bayes https colab research google com drive 1hu7s1 4t3nfmqurfvjem4oejbww4mx3q usp sharing cosine similarity pmi collocation tf idf https colab research google com drive 1uzpa lobkrtx8tyekedwpxfvcmjof wf usp sharing regex practice https colab research google com drive 1xqy4szojjn7fjqtcuwgfndeq4kxwgtvf usp sharing week 4 google colab notebooks independence https colab research google com drive 1yyq12zi6prtdyga6nyeorjv1lmep1ear usp sharing dimensionality reduction https colab research google com drive 1cv73jtmmiga0it7wxpabn zmutwjfb x usp sharing advanced regex https colab research google com drive 1kwmt7l9uzz3gkjwqedp1r9mot1af8r8m usp sharing week 5 pandas regex replace and capture groups https colab research google com drive 1ta0figxny1wlc9vof0yl8go7utp1tr r usp sharing week 6 week 7 tips and tricks with string matching https colab research google com drive 1zlnn20oux1djwn8oylb6q4opporootcc usp sharing using tf idf weighted word embeddings https colab research google com drive 1cza723 mdmnjpg2oiexgwyf9nqbueey usp sharing word2vec part ii https colab research google com drive 1iywxde5jqxd 4k0hgy7mksrh kueoohx usp sharing deep learning with word embeddings https colab research google com github ychennay dso 560 nlp text analytics spring 2021 blob main week 207 deep 20learning 20with 20word 20embeddings ipynb scrollto tr0hsanmn5xc week 8 rnns and lstms with nlp classification tasks https colab research google com drive 1k8 focb64oqtyljwmvpmtlhbrpn53rsj usp sharing attention models https colab research google com drive 1qv6r coc7ry8gbjzcnqrexwb4eo9wp8m usp sharing | ai |
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speedy-vision | speedy vision github https img shields io github license alemart speedy vision license github release latest by date https img shields io github v release alemart speedy vision https github com alemart speedy vision releases github repo stars https img shields io github stars alemart speedy vision logo github https github com alemart speedy vision stargazers github sponsors https img shields io github sponsors alemart logo github https github com sponsors alemart build real time stuff with speedy vision a gpu accelerated computer vision library for javascript img src assets demo bestfeatures gif alt speedy feature detection https alemart github io speedy vision demos best features html click to open a demo speedy vision is developed independently by alexandre martins https github com alemart and released under the apache 2 0 license license ko fi https ko fi com img githubbutton sm svg https ko fi com j3j41o00k for web based augmented reality check out my other project https github com alemart martins js features feature detection harris corner detector fast feature detector orb feature descriptor feature tracking klt feature tracker lk optical flow feature matching fast approximate k nearest neighbors knn brute force matching geometric transformations homography matrix affine transform image processing convert to greyscale convolution gaussian blur box median filters contrast and brightness adjustment image normalization warping image pyramids linear algebra beautiful matrix algebra with a fluent interface efficient computations with webassembly systems of linear equations qr decomposition and more in development there are plenty of demos demos available table of contents demos demos usage usage motivation motivation the pipeline the pipeline api reference api reference media routines media routines loading your media loading your media media properties media properties playing with your media playing with your media pipeline pipeline basic routines basic routines basic properties basic properties basic nodes basic nodes image processing image processing image basics image basics image filters image filters general transformations general transformations keypoints and descriptors keypoints and descriptors keypoint types keypoint types keypoint basics keypoint basics keypoint detection keypoint detection keypoint description keypoint description keypoint tracking optical flow keypoint tracking keypoint matching keypoint matching portals portals image portals image portals keypoint portals keypoint portals linear algebra linear algebra creating new matrices creating new matrices matrix properties matrix properties reading from the matrices reading from the matrices writing to the matrices writing to the matrices elementary operations elementary operations access by block access by block functional programming functional programming systems of equations systems of equations matrix factorization matrix factorization geometric transformations geometric transformations homography perspective transformation geometric utilities geometric utilities 2d vectors 2d vectors 2d points 2d points 2d size 2d size extras extras promises promises utilities utilities unit tests https alemart github io speedy vision tests index html demos try the demos and take a look at their source code hello world https alemart github io speedy vision demos hello world html feature detection feature detection in a webcam https alemart github io speedy vision demos webcam demo html feature detection in an image https alemart github io speedy vision demos image features html feature detection in a video https alemart github io speedy vision demos video features html find the best harris corners https alemart github io speedy vision demos best features html orb features https alemart github io speedy vision demos orb features html feature tracking optical flow https alemart github io speedy vision demos optical flow html feature matching feature matching https alemart github io speedy vision demos feature matching html lsh feature matching https alemart github io speedy vision demos feature matching lsh html image processing image convolution https alemart github io speedy vision demos convolution html image warping https alemart github io speedy vision demos warping html alpha blending https alemart github io speedy vision demos alpha blending html resize image https alemart github io speedy vision demos resize image html nightvision camera https alemart github io speedy vision demos nightvision camera html convert image to greyscale https alemart github io speedy vision demos greyscale image html convert video to greyscale https alemart github io speedy vision demos greyscale video html blurring an image https alemart github io speedy vision demos image blurring html blurring a video with a median filter https alemart github io speedy vision demos median filter html normalize camera stream https alemart github io speedy vision demos normalize demo html image portals https alemart github io speedy vision demos image portal html linear algebra system of equations https alemart github io speedy vision demos system of equations html qr decomposition https alemart github io speedy vision demos qr decomposition html usage browser download the latest release of speedy vision https github com alemart speedy vision releases and include it in the head section of your html page html script src dist speedy vision min js script once you import the library the speedy object will be exposed check out the hello world demo https alemart github io speedy vision demos hello world html for a working example via cdn add the following to the head of your html page html script src https cdn jsdelivr net gh alemart speedy vision version dist speedy vision min js script replace version by github release latest by date https img shields io github v release alemart speedy vision color 23ffffff display name tag label 20 via npm simply run sh npm install speedy vision next import the speedy object as follows js import speedy from speedy vision motivation detecting features in an image is an important step of many computer vision algorithms traditionally the computationally expensive nature of this process made it difficult to bring interactive computer vision applications to the web browser the framerates were unsatisfactory for a compelling user experience speedy a short name for speedy vision is a javascript library created to address this issue speedy s real time performance in the web browser is possible thanks to its efficient webgl2 backend and to its gpu implementations of fast computer vision algorithms with an easy to use api speedy is an excellent choice for real time computer vision projects involving tasks such as object detection in videos pose estimation simultaneous location and mapping slam and others the pipeline the pipeline is a central concept in speedy it s a powerful structure that lets you organize the computations that take place in the gpu it s a very flexible yet conceptually simple way of working with computer vision and image processing let s define a few things a pipeline is a network of nodes in which data flows downstream from one or more sources to one or more sinks nodes have input and or output ports a node with no input ports is called a source a node with no output ports is called a sink a node with both input and output ports transforms the input data in some way and writes the results to its output port s a link connects an output port of a node to an input port of another node two nodes are said to be connected if there is a link connecting their ports data flows from one node to another by means of a link an input port may only be connected to a single output port but an output port may be connected to multiple input ports input ports expect data of a certain type e g an image output ports hold data of a certain type two ports may only be connected if their types match ports may impose additional constraints on the data passing through them for example an input port may expect an image and also impose the constraint that this image must be greyscale different nodes may have different parameters these parameters can be adjusted and are meant to modify the output of the nodes in some way nodes and their ports have names an input port is typically called in an output port is typically called out these names can vary e g if a node has more than one input output port speedy automatically assigns names to the nodes but you can assign your own names as well the picture below shows a visual representation of a pipeline that converts an image or video to greyscale data gets into the pipeline via the image source it is then passed to the convert to greyscale node finally a greyscale image goes into the image sink where it gets out of the pipeline convert to greyscale a simple pipeline assets network example png here s a little bit of code js load an image const img document queryselector img const media await speedy load img create the pipeline and the nodes const pipeline speedy pipeline const source speedy image source const sink speedy image sink const greyscale speedy filter greyscale set the media source source media media media is a speedymedia object connect the nodes source output connectto greyscale input greyscale output connectto sink input specify the nodes to initialize the pipeline pipeline init source sink greyscale run the pipeline const image await pipeline run image is a speedymedia create a canvas to display the result const canvas document createelement canvas canvas width image width canvas height image height document body appendchild canvas display the result const ctx canvas getcontext 2d ctx drawimage media source 0 0 speedy provides many types of nodes you can connect these nodes in a way that is suitable to your application and speedy will bring back the results you ask for api reference media routines a speedymedia object encapsulates a media object an image a video a canvas or a bitmap loading your media speedy load speedy load source htmlimageelement htmlvideoelement htmlcanvaselement imagebitmap options object speedypromise speedymedia tells speedy to load source the source parameter may be an image a video a canvas or a bitmap arguments source htmlimageelement htmlvideoelement htmlcanvaselement imagebitmap the media source options object optional additional options for advanced configuration see speedymedia options speedymediaoptions for details returns a speedypromise speedymedia that resolves as soon as the media source is loaded example js window onload async function let image document getelementbyid my image img id my image src let media await speedy load image speedy camera speedy camera width number height number speedypromise speedymedia speedy camera constraints mediastreamconstraints speedypromise speedymedia loads a camera stream into a new speedymedia object this is a wrapper around navigator mediadevices getusermedia provided for your convenience arguments width number optional the ideal width of the stream the browser will use this value or a close match defaults to 640 height number optional the ideal height of the stream the browser will use this value or a close match defaults to 360 constraints mediastreamconstraints a mediastreamconstraints https developer mozilla org en us docs web api mediastreamconstraints dictionary to be passed to getusermedia for complete customization returns a speedypromise speedymedia that resolves as soon as the media source is loaded with the camera stream example js display the contents of a webcam window onload async function const media await speedy camera const canvas createcanvas media width media height const ctx canvas getcontext 2d function render ctx drawimage media source 0 0 requestanimationframe render render function createcanvas width height const canvas document createelement canvas canvas width width canvas height height document body appendchild canvas return canvas speedymedia release speedymedia release null releases internal resources associated with this speedymedia returns returns null media properties speedymedia source speedymedia source htmlimageelement htmlvideoelement htmlcanvaselement imagebitmap read only the media source associated with the speedymedia object speedymedia type speedymedia type string read only the type of the media source one of the following image video canvas bitmap speedymedia width speedymedia width number read only the width of the media source in pixels speedymedia height speedymedia height number read only the height of the media source in pixels speedymedia size speedymedia size speedysize read only the size of the media in pixels speedymedia options speedymedia options object read only read only object defined when loading the media speedyload deprecated playing with your media speedymedia clone speedymedia clone speedypromise speedymedia clones the speedymedia object returns a speedypromise that resolves to a clone of the speedymedia object example js const clone await media clone speedymedia tobitmap speedymedia tobitmap speedypromise imagebitmap converts the media to an imagebitmap returns a speedypromise that resolves to an imagebitmap pipeline basic routines speedy pipeline pipeline speedy pipeline pipeline speedypipeline creates a new empty pipeline returns a new speedypipeline object speedypipeline init speedypipeline init nodes speedypipelinenode speedypipeline initializes a pipeline with the specified nodes arguments nodes speedypipelinenode the list of nodes that belong to the pipeline returns the pipeline itself example js const pipeline speedy pipeline create the pipeline and the nodes const source speedy image source const sink speedy image sink const greyscale speedy filter greyscale source media media set the media source source output connectto greyscale input connect the nodes greyscale output connectto sink input pipeline init source sink greyscale add the nodes to the pipeline speedypipeline release speedypipeline release null releases the resources associated with this pipeline returns returns null speedypipeline run speedypipeline run speedypromise object runs this pipeline returns returns a speedypromise that resolves to an object whose keys are the names of the sinks of the pipeline and whose values are the data exported by those sinks example js const sink1 sink2 await pipeline run speedypipeline node speedypipeline node name string speedypipelinenode null finds a node by its name arguments name string name of the target node returns returns a speedypipelinenode that has the specified name and that belongs to this pipeline or null if there is no such node speedypipelinenode input speedypipelinenode input portname string speedypipelinenodeport the input port of this node whose name is portname arguments portname string optional the name of the port you want to access defaults to in returns the requested input port speedypipelinenode output speedypipelinenode output portname string speedypipelinenodeport the output port of this node whose name is portname arguments portname string optional the name of the port you want to access defaults to out returns the requested output port speedypipelinenodeport connectto speedypipelinenodeport connectto port speedypipelinenodeport void creates a link connecting this port to another port basic properties speedypipelinenode name speedypipelinenode name string read only the name of the node speedypipelinenode fullname speedypipelinenode fullname string read only a string that exhibits the name and the type of the node speedypipelinenodeport name speedypipelinenodeport name string read only the name of the port speedypipelinenodeport node speedypipelinenodeport node speedypipelinenode read only the node to which this port belongs basic nodes speedy image source speedy image source name string speedypipelinenodeimageinput creates an image source with the specified name if the name is not specified speedy will automatically generate a name for you parameters media speedymedia the media to be imported into the pipeline ports port name data type description out image an image corresponding to the media of this node speedy image sink speedy image sink name string speedypipelinenodeimageoutput creates an image sink with the specified name if the name is not specified speedy will call this node image a speedymedia object will be exported from the pipeline ports port name data type description in image an image to be exported from the pipeline image processing image basics speedy image pyramid speedy image pyramid name string speedypipelinenodeimagepyramid generate a gaussian pyramid a pyramid is a texture with mipmaps port name data type description in image input image out image gaussian pyramid speedy image multiplexer speedy image multiplexer name string speedypipelinenodeimagemultiplexer an image multiplexer receives two images as input and outputs one of the them parameters port number which input image should be redirected to the output 0 or 1 defaults to 0 ports port name data type description in0 image first image in1 image second image out image either the first or the second image depending on the value of port speedy image buffer speedy image buffer name string speedypipelinenodeimagebuffer an image buffer outputs at time t the input image received at time t 1 it s useful for tracking note an image buffer cannot be used to store a pyramid at this time parameters frozen boolean a frozen buffer discards the input effectively increasing the buffering time defaults to false ports port name data type description in image input image at time t out image output image the input image at time t 1 speedy image mixer speedy image mixer name string speedypipelinenodeimagemixer an image mixer combines two images image0 and image1 as follows output alpha image0 beta image1 gamma the above expression will be computed for each pixel of the resulting image and then clamped to the 0 1 interval the dimensions of the resulting image will be the dimensions of the larger of the input images note both input images must have the same format if they re colored the above expression will be evaluated in each color channel independently tip if you pick an alpha between 0 and 1 set beta to 1 alpha and set gamma to 0 you ll get a nice alpha blending effect parameters alpha number a scalar value defaults to 0 5 beta number a scalar value defaults to 0 5 gamma number a scalar value defaults to 0 0 ports port name data type description in0 image input image the image0 above in1 image input image the image1 above out image output image image filters speedy filter greyscale speedy filter greyscale name string speedypipelinenodegreyscale convert an image to greyscale ports port name data type description in image input image out image the input image converted to greyscale speedy filter simpleblur speedy filter simpleblur name string speedypipelinenodesimpleblur blur an image using a box filter parameters kernelsize speedysize the size of the convolution kernel from 3x3 to 15x15 defaults to 5x5 ports port name data type description in image input image out image the input image blurred speedy filter gaussianblur speedy filter simpleblur name string speedypipelinenodegaussianblur blur an image using a gaussian filter parameters kernelsize speedysize the size of the convolution kernel from 3x3 to 15x15 defaults to 5x5 sigma speedyvector2 the sigma of the gaussian function in both x and y axes if set to the zero vector speedy will automatically pick a sigma according to the selected kernelsize defaults to 0 0 ports port name data type description in image input image out image the input image blurred speedy filter medianblur speedy filter medianblur name string speedypipelinenodemedianblur median filter parameters kernelsize speedysize one of the following 3x3 5x5 or 7x7 defaults to 5x5 ports port name data type description in image a greyscale image out image the result of the median blur example js const median speedy filter medianblur median kernelsize speedy size 7 7 speedy filter convolution speedy filter convolution name string speedypipelinenodeconvolution compute the convolution of an image using a 2d kernel parameters kernel speedymatrixexpr a 3x3 5x5 or 7x7 matrix ports port name data type description in image input image out image the result of the convolution example js sharpening an image const sharpen speedy filter convolution sharpen kernel speedy matrix 3 3 0 1 0 1 5 1 0 1 0 speedy filter normalize speedy filter normalize name string speedypipelinenodenormalize normalize the intensity values of the input image to the minvalue maxvalue interval parameters minvalue number a value in 0 255 maxvalue number a value in 0 255 greater than or equal to minvalue ports port name data type description in image greyscale image out image normalized image speedy filter nightvision speedy filter nightvision name string speedypipelinenodenightvision nightvision filter for local contrast stretching and brightness control parameters gain number a value in 0 1 the larger the number the higher the contrast defaults to 0 5 offset number a value in 0 1 that controls the brightness defaults to 0 5 decay number a value in 0 1 specifying a contrast decay from the center of the image defaults to zero no decay quality string quality level high medium or low defaults to medium ports port name data type description in image input image out image output image general transformations speedy transform resize speedy transform resize name string speedypipelinenoderesize resize an image parameters size speedysize the size of the output image in pixels if set to zero scale will be used to determine the size of the output defaults to zero scale speedyvector2 the size of the output image relative to the size of the input image this parameter is only applied if size is zero defaults to 1 1 meaning keep the original size method string resize method one of the following bilinear bilinear interpolation or nearest nearest neighbors defaults to bilinear ports port name data type description in image input image out image resized image speedy transform perspectivewarp speedy transform perspectivewarp name string speedypipelinenodeperspectivewarp warp an image using a homography matrix perspective transformation parameters transform speedymatrixexpr a 3x3 perspective transformation defaults to the identity matrix ports port name data type description in image input image out image warped image keypoints and descriptors a keypoint is a small patch in an image that is somehow distinctive for example a small patch with significant intensity changes in both x and y axes i e a corner is distinctive if we pick two similar images we should be able to locate a set of keypoints in each of them and then match those keypoints based on their similarity a descriptor is a mathematical object that somehow describes a keypoint two keypoints are considered to be similar if their descriptors are similar speedy works with binary descriptors meaning that keypoints are described using bit vectors of fixed length there are different ways to detect and describe keypoints for example in order to detect a keypoint you may take a look at the pixel intensities around a point or perhaps study the image derivatives you may describe a keypoint by comparing the pixel intensities of the image patch in a special way additionally it s possible to conceive a way to describe a keypoint in such a way that if you rotate the patch the descriptor stays roughly the same this is called rotational invariance and is usually a desirable property for a descriptor speedy offers different options for processing keypoints in multiple ways a novelty of this work is that speedy s implementations have been either adapted from the literature or conceived from scratch to work on the gpu therefore keypoint processing is done in parallel and is often very fast keypoint types speedykeypoint a speedykeypoint object represents a keypoint speedykeypoint position speedykeypoint position speedypoint2 the position of the keypoint in the image speedykeypoint x speedykeypoint x number the x position of the keypoint in the image a shortcut to position x speedykeypoint y speedykeypoint y number the y position of the keypoint in the image a shortcut to position y speedykeypoint lod speedykeypoint lod number read only the level of detail pyramid level from which the keypoint was extracted starting from zero defaults to 0 0 speedykeypoint scale speedykeypoint scale number read only the scale of the keypoint this is equivalent to 2 lod defaults to 1 0 speedykeypoint rotation speedykeypoint rotation number read only the rotation angle orientation of the keypoint in radians defaults to 0 0 speedykeypoint score speedykeypoint score number read only the score is a measure associated with the keypoint although different detection methods employ different measurement strategies the larger the score the better the keypoint is considered to be the score is always a positive value speedykeypoint descriptor speedykeypoint descriptor speedykeypointdescriptor null read only the descriptor associated with the keypoint if it exists speedykeypointdescriptor a speedykeypointdescriptor represents a keypoint descriptor speedykeypointdescriptor data speedykeypointdescriptor data uint8array read only the bytes of the keypoint descriptor speedykeypointdescriptor size speedykeypointdescriptor size number read only the size of the keypoint descriptor in bytes speedykeypointdescriptor tostring speedykeypointdescriptor tostring string returns a string representation of the keypoint descriptor speedytrackedkeypoint a speedytrackedkeypoint is a speedykeypoint with the following additional properties speedytrackerkeypoint flow speedytrackedkeypoint flow speedyvector2 read only a displacement vector associated with the tracked keypoint speedymatchedkeypoint a speedymatchedkeypoint is a speedykeypoint with the following additional properties speedymatchedkeypoint matches speedymatchedkeypoint matches speedykeypointmatch read only a list of keypoint matches associated with the keypoint they will be sorted by increasing distance better matches come first see also speedykeypointmatch speedykeypointmatch speedykeypointmatch a speedykeypointmatch represents a keypoint match speedykeypointmatch index speedykeypointmatch index number read only the non negative index of the matched keypoint in a database of keypoints or 1 if there is no match speedykeypointmatch distance speedykeypointmatch distance number read only a distance metric between the keypoint and the matched keypoint the lower the distance the better the match if there is no match then this field will be set to infinity keypoint basics speedy keypoint source speedy keypoint source name string speedypipelinenodekeypointsource creates a source of keypoints only the position score and scale of the provided keypoints will be imported to the pipeline descriptors if present will be lost parameters keypoints speedykeypoint the keypoints you want to import capacity number the maximum number of keypoints that can be imported to the gpu if you have an idea of how many keypoints you expect at most use a tight bound to make processing more efficient the default capacity is 2048 it can be no larger than 8192 ports port name data type description out keypoints the imported set of keypoints speedy keypoint sink speedy keypoint sink name string speedypipelinenodekeypointsink creates a sink of keypoints using the specified name if the name is not specified speedy will call this node keypoints an array of speedykeypoint objects will be exported from the pipeline parameters turbo boolean accelerate gpu cpu transfers you ll get the data from the previous frame defaults to false includediscarded boolean set discarded keypoints e g by a tracker to null in the exported set defaults to false meaning that discarded keypoints will simply be dropped from the exported set rather than being set to null ports port name data type description in keypoints a set of keypoints to be exported from the pipeline speedy keypoint clipper speedy keypoint clipper name string speedypipelinenodekeypointclipper clips a set of keypoints so that it outputs no more than a fixed quantity of them when generating the output it will choose the best keypoints according to their score metric the keypoint clipper is a very useful tool to reduce processing time since it can discard bad keypoints regardless of the sensitivity of their detector the clipping must be applied before computing any descriptors parameters size number a positive integer no more than this number of keypoints will be available in the output ports port name data type description in keypoints a set of keypoints out keypoints a set of at most size keypoints speedy keypoint borderclipper speedy keypoint borderclipper name string speedypipelinenodekeypointborderclipper removes all keypoints within a specified border of the edge of an image the border is specified in pixels as an ordered pair of integers the first is the size of the horizontal border and the second is the size of the vertical border parameters imagesize speedysize image size in pixels bordersize speedyvector2 border size in both x and y axes defaults to zero meaning that no clipping takes place ports port name data type description in keypoints a set of keypoints out keypoints the clipped set of keypoints speedy keypoint mixer speedy keypoint mixer name string speedypipelinenodekeypointmixer mixes merges two sets of keypoints ports port name data type description in0 keypoints a set of keypoints in1 keypoints another set of keypoints out keypoints the union of the two input sets speedy keypoint buffer speedy keypoint buffer name string speedypipelinenodekeypointbuffer a keypoint buffer outputs at time t the keypoints received at time t 1 parameters frozen boolean a frozen buffer discards the input effectively increasing the buffering time defaults to false ports port name data type description in keypoints a set of keypoints at time t out keypoints the set of keypoints received at time t 1 speedy keypoint multiplexer speedy keypoint multiplexer name string speedypipelinenodekeypointmultiplexer a keypoint multiplexer receives two sets of keypoints as input and outputs one of the them parameters port number which input set of keypoints should be redirected to the output 0 or 1 defaults to 0 ports port name data type description in0 image first set of keypoints in1 image second set of keypoints out image either the first or the second set of keypoints depending on the value of port speedy keypoint transformer speedy keypoint transformer name string speedypipelinenodekeypointtransformer applies a transformation matrix to a set of keypoints parameters transform speedymatrix a 3x3 homography matrix defaults to the identity matrix ports port name data type description in keypoints a set of keypoints out keypoints a transformed set of keypoints speedy keypoint subpixelrefiner speedy keypoint subpixelrefiner name string speedypipelinenodekeypointsubpixelrefiner refines the position of a set of keypoints down to the subpixel level note 1 filter the image to reduce the noise before working at the subpixel level note 2 if there are keypoints in multiple scales make sure to provide a pyramid as input note 3 the position of the keypoints is stored as fixed point this representation may introduce a loss of accuracy 0 1 pixel this is probably enough already but if you need higher accuracy ignore the output keypoints and work with the displacement vectors instead these are encoded as floating point in addition use the upsampling methods parameters method string the method to be used to compute the subpixel displacement see the table below maxiterations number the maximum number of iterations used by methods bicubic upsample and bilinear upsample defaults to 6 epsilon number the threshold used to determine when the subpixel displacement has reached convergence used with methods bicubic upsample and bilinear upsample defaults to 0 1 pixel table of methods method description quadratic1d maximize a 1d parabola fit to a corner strength function this is the default method taylor2d maximize a second order 2d taylor expansion of a corner strength function method quadratic1d seems to perform slightly better than this but your mileage may vary bicubic upsample iteratively upsample the image using bicubic interpolation in order to maximize a corner strength function repeat until convergence or until a maximum number of iterations is reached bilinear upsample analogous to bicubic upsample but this method uses bilinear interpolation instead ports port name data type description image image an image or pyramid from which you extracted the keypoints keypoints keypoints input set of keypoints out keypoints subpixel refined output set of keypoints displacements vector2 displacement vectors output speedy keypoint distancefilter speedy keypoint distancefilter name string speedypipelinenodekeypointdistancefilter given a set of pairs of keypoints discard all pairs whose distance is above a user defined threshold this is useful for implementing bidirectional optical flow the pairs of keypoints are provided as two separate sets in and reference keypoints that are kept will have their data extracted from the in set parameters threshold number distance threshold given in pixels ports port name data type description in keypoints a set of keypoints reference keypoints a reference set of keypoints out keypoints filtered set of keypoints speedy keypoint hammingdistancefilter speedy keypoint hammingdistancefilter name string speedypipelinenodekeypointhammingdistancefilter given a set of pairs of keypoints with descriptors discard all pairs whose hamming distance between their descriptors is above a user defined threshold the pairs of keypoints are provided as two separate sets in and reference keypoints that are kept will have their data extracted from the in set parameters threshold number distance threshold an integer ports port name data type description in keypoints a set of keypoints reference keypoints a reference set of keypoints out keypoints filtered set of keypoints speedy keypoint shuffler speedy keypoint shuffler name string speedypipelinenodekeypointshuffler shuffles the input keypoints optionally clipping the output set parameters maxkeypoints number maximum number of keypoints of the output set if unspecified the number of keypoints of the output set will be the number of keypoints of the input set ports port name data type description in keypoints a set of keypoints out keypoints the input set of keypoints shuffled and possibly clipped keypoint detection the following nodes expect greyscale images as input they output a set of keypoints speedy keypoint detector fast speedy keypoint detector fast name string speedypipelinenodefastkeypointdetector fast keypoint detector speedy implements the fast 9 16 variant of the algorithm to use the multi scale version of the algorithm pass a pyramid as input set the number of levels you want to scan and optionally set the scale factor after scanning all levels and performing non maximum suppression the scale of the keypoints will be set by means of interpolation using the scale that maximizes a response measure and its adjacent scales parameters threshold number an integer between 0 and 255 inclusive the larger the number the stronger your keypoints will be the smaller the number the more keypoint you will get numbers between 20 and 50 are usually meaningful levels number the number of pyramid levels you want to use defaults to 1 i e no pyramid is used when using a pyramid a value such as 7 is a reasonable choice scalefactor number the scale factor between two consecutive levels of the pyramid this is a value between 1 exclusive and 2 inclusive defaults to the square root of two this is applicable only when using a pyramid capacity number the maximum number of keypoints that can be detected by this node the default capacity is 2048 it can be no larger than 8192 ports port name data type description in image greyscale image or pyramid out keypoints detected keypoints speedy keypoint detector harris speedy keypoint detector harris name string speedypipelinenodeharriskeypointdetector harris corner detector speedy implements the shi tomasi corner response for best results to use the multi scale version of the algorithm pass a pyramid as input set the number of levels you want to scan and optionally set the scale factor after scanning all levels and performing non maximum suppression the scale of the keypoints will be set by means of interpolation using the scale that maximizes a response measure and its adjacent scales parameters quality number a value between 0 and 1 representing the minimum quality of the returned keypoints speedy will discard any keypoint whose score is lower than the specified percentage of the maximum keypoint score found in the image a typical value for this parameter is 0 10 10 levels number the number of pyramid levels you want to use defaults to 1 i e no pyramid is used when using a pyramid a value such as 7 is a reasonable choice scalefactor number the scale factor between two consecutive levels of the pyramid this is a value between 1 exclusive and 2 inclusive defaults to the square root of two this is applicable only when using a pyramid capacity number the maximum number of keypoints that can be detected by this node the default capacity is 2048 it can be no larger than 8192 ports port name data type description in image greyscale image or pyramid out keypoints detected keypoints keypoint description speedy keypoint descriptor orb speedy keypoint descriptor orb name string speedypipelinenodeorbkeypointdescriptor orb descriptors in order to improve robustness to noise apply a gaussian filter to the image before computing the descriptors ports port name data type description image image input image must be greyscale keypoints keypoints input keypoints out keypoints keypoints with descriptors example js this is our pipeline image convert to image fast corner keypoint orb keypoint source greyscale pyramid detector clipper descriptors sink gaussian blur const pipeline speedy pipeline const source speedy image source const greyscale speedy filter greyscale const pyramid speedy image pyramid const fast speedy keypoint detector fast const blur speedy filter gaussianblur const clipper speedy keypoint clipper const descriptor speedy keypoint descriptor orb const sink speedy keypoint sink source media media blur kernelsize speedy size 9 9 blur sigma speedy vector2 2 2 fast threshold 50 fast levels 8 pyramid levels fast scalefactor 1 19 approx 2 0 25 clipper size 800 up to how many features source output connectto greyscale input greyscale output connectto pyramid input pyramid output connectto fast input fast output connectto clipper input clipper output connectto descriptor input keypoints greyscale output connectto blur input blur output connectto descriptor input image descriptor output connectto sink input pipeline init source greyscale pyramid blur fast clipper descriptor sink keypoint tracking keypoint tracking is the process of tracking keypoints across a sequence of images it allows you to get a sense of how keypoints are moving in time i e how fast they are moving and where they are going speedy uses sparse optical flow algorithms to track keypoints in a video applications of optical flow are numerous you may get a sense of how objects are moving in a scene estimate how the camera itself is moving detect a transition in a film a cut between two shots and so on speedy keypoint sinkoftrackedkeypoints speedy keypoint sinkoftrackedkeypoints name string speedypipelinenodetrackedkeypointsink creates a sink of tracked keypoints using the specified name if the name is not specified speedy will call this node keypoints an array of speedytrackedkeypoint objects will be exported from the pipeline see also speedytrackedkeypoint speedytrackedkeypoint parameters the same as speedypipelinenodekeypointsink ports port name data type description in keypoints a set of keypoints to be exported from the pipeline flow vector2 a set of displacement vectors associated with each keypoint speedy keypoint tracker lk speedy keypoint tracker lk name string speedypipelinenodelkkeypointtracker pyramid based lk optical flow parameters windowsize speedysize the size of the window to be used by the feature tracker the algorithm will read neighbor pixels to determine the motion of a keypoint you must specify a square window typical sizes include 7x7 11x11 15x15 use positive odd integers defaults to 11x11 levels number specifies how many pyramid levels will be used in the computation the more levels you use the faster the motions you can capture defaults to 3 discardthreshold number a threshold used to discard keypoints that are not good candidates for tracking the higher the value the more keypoints will be discarded defaults to 0 0001 numberofiterations number maximum number of iterations for computing the local optical flow on each level of the pyramid defaults to 30 epsilon number an accuracy threshold used to stop the computation of the local optical flow of any level of the pyramid the local optical flow is computed iteratively and in small increments if the length of an increment is too small we discard it this property defaults to 0 01 ports port name data type description previousimage image input image at time t 1 must be greyscale nextimage image input image at time t must be greyscale previouskeypoints keypoints input keypoints at time t 1 out keypoints output keypoints at time t flow vector2 flow vectors output at time t note you need to provide pyramids as input if levels 1 keypoint matching keypoint matching is the process of matching keypoints based on their descriptors a distance metric is established in descriptor space two keypoints are said to be matched if the distance between their respective descriptors is minimized according to some criteria since speedy uses binary descriptors in practice we use the hamming distance i e the number of differing bits in two descriptors of same size keypoint matching is useful for object recognition object tracking rectification of images and more speedy keypoint sinkofmatchedkeypoints speedy keypoint sinkofmatchedkeypoints name string speedypipelinenodematchedkeypointsink create a sink of matched keypoints using the specified name if the name is not specified speedy will call this node keypoints an array of speedymatchedkeypoint objects will be exported from the pipeline see also speedymatchedkeypoint speedymatchedkeypoint parameters the same as speedypipelinenodekeypointsink ports port name data type description in keypoints a set of keypoints to be exported from the pipeline matches keypointmatches a set of keypoint matches associated with each keypoint speedy keypoint matcher bfknn speedy keypoint matcher bfknn name string speedypipelinenodebruteforceknnkeypointmatcher brute force k nearest neighbors keypoint matcher parameters k number the desired number of matches per keypoint defaults to 1 i e it will get you only the best match for each keypoint setting it to two gets you the first and the second best matches and so on ports port name data type description keypoints keypoints the input keypoints that you want to match database keypoints a collection of keypoints to be matched against out keypointmatches the k best matches for all elements of keypoints note i suggest using brute force to match two sets containing no more than a few hundreds of keypoints your mileage may vary if you need to match thousands of keypoints or more consider using an approximate matcher note 2 make sure that you use as input two sets of keypoints with the same type of descriptors speedy keypoint matcher lshknn speedy keypoint matcher lshknn name string speedypipelinenodelshknnkeypointmatcher fast approximate k nearest neighbors keypoint matcher based on my own gpu based variant of locality sensitive hashing lsh parameters k number the desired number of matches per keypoint defaults to 1 quality string the desired quality level for the search of the best matches one of the following default fastest or demanding changing this parameter impacts performance and possibly the quality of the results ports port name data type description keypoints keypoints the input keypoints that you want to match lsh lshtables lsh tables of the keypoints to be matched against the database out keypointmatches the k best matches approximately for all elements of keypoints tip the default quality is generally appropriate but if you set it to fastest consider increasing the number of lsh tables see below speedy keypoint matcher staticlshtables speedy keypoint matcher staticlshtables name string speedypipelinenodestaticlshtables generate lsh tables based on a known and potentially large collection of keypoints lsh tables can help you match up to hundreds of thousands of keypoints parameters keypoints speedykeypoint the known collection of keypoints to be used as a database for matching make sure that they have descriptors numberoftables number the number of lsh tables that you want to generate defaults to 8 this parameter can be as low as 4 and as high as 32 increasing it may increase the quality of the results at the expense of performance hashsize number the size of a descriptor hash in bits defaults to 15 this parameter can be as low as 10 and as high as 20 increasing it will substantially increase vram usage ports port name data type description out lshtables lsh tables associated with the known collection of keypoints tip speedy generates logs based on the numerical parameters that you define and on the number of keypoints in your database you may use these logs to help you tune the numerical parameters that being said the default parameters are generally good portals portals let you create loops within a pipeline they also let you transfer data between different pipelines a portal is defined by a set of nodes a portal sink and one or more portal sources the portal sink receives data from a node of a pipeline which is then read by the portal source s the portal source s feed s one or more pipelines the portal nodes may or may not belong to the same pipeline image portals speedy image portal source speedy image portal source name string speedypipelinenodeimageportalsource create a source of an image portal parameters source speedypipelinenodeimageportalsink a sink of an image portal ports port name data type description out image an image speedy image portal sink speedy image portal sink name string speedypipelinenodeimageportalsink create a sink of an image portal note pyramids can t travel through portals at this time ports port name data type description in image an image keypoint portals speedy keypoint portal source speedy keypoint portal source name string speedypipelinenodekeypointportalsource create a source of a keypoint portal parameters source speedypipelinenodekeypointportalsink a sink of a keypoint portal ports port name data type description out keypoints a set of keypoints speedy keypoint portal sink speedy keypoint portal sink name string speedypipelinenodekeypointportalsink create a sink of a keypoint portal ports port name data type description in keypoints a set of keypoints linear algebra matrix computations play a crucial role in computer vision applications speedy includes its own implementation of numerical linear algebra algorithms matrix operations are specified using a fluent interface that has been crafted to be easy to use and to mirror how we write matrix algebra using pen and paper since numerical algorithms may be computationally demanding speedy uses webassembly for extra performance most matrix related routines are written in c language matrices are stored in column major format https en wikipedia org wiki row and column major order typed arrays are used for storage there are two basic classes you need to be aware of speedymatrix and speedymatrixexpr the latter represents a symbolic expression whereas the former represents an actual matrix with data a speedymatrix is a speedymatrixexpr a speedymatrixexpr may be evaluated to a speedymatrix creating new matrices speedy matrix speedy matrix rows number columns number entries number speedymatrix speedy matrix expr speedymatrixexpr speedymatrix first form create a new matrix with the specified size and entries second form synchronously evaluate a matrix expression and store the result in a new matrix arguments rows number the number of rows of the matrix columns number optional the number of columns of the matrix if not specified it will be set to rows i e you ll get a square matrix entries number optional the elements of the matrix in column major format the length of this array must be rows columns expr speedymatrixexpr the matrix expression to be evaluated returns a new speedymatrix example js we use the column major format to specify the elements of the new matrix for example to create the 2x3 matrix 2 rows 3 columns below we first specify the elements of the first column then the elements of the second column and finally the elements of the third column m 1 3 5 2 4 6 const mat speedy matrix 2 3 1 2 3 4 5 6 alternatively we may write the data in column major format in a compact form const mat1 speedy matrix 2 3 1 2 first column 3 4 second column 5 6 third column print the matrices to the console console log mat tostring console log mat1 tostring speedy matrix zeros speedy matrix zeros rows number columns number speedymatrix create a new matrix filled with zeros arguments rows number the number of rows of the matrix columns number optional the number of columns of the matrix if not specified it will be set to rows square matrix returns a new rows x columns speedymatrix filled with zeros example js a 3x3 matrix filled with zeros const zeros speedy matrix zeros 3 speedy matrix ones speedy matrix ones rows number columns number speedymatrix create a new matrix filled with ones arguments rows number the number of rows of the matrix columns number optional the number of columns of the matrix if not specified it will be set to rows square matrix returns a new rows x columns speedymatrix filled with ones speedy matrix eye speedy matrix eye rows number columns number speedymatrix create a new matrix with ones on the main diagonal and zeros elsewhere arguments rows number the number of rows of the matrix columns number optional the number of columns of the matrix if not specified it will be set to rows identity matrix returns a new speedymatrix with the specified configuration example js a 3x3 identity matrix const eye speedy matrix eye 3 matrix properties speedymatrixexpr rows speedymatrixexpr rows number read only the number of rows of the matrix expression speedymatrixexpr columns speedymatrixexpr columns number read only the number of columns of the matrix expression speedymatrixexpr dtype speedymatrixexpr dtype string read only the constant float32 speedymatrix data speedymatrix data arraybufferview read only data storage speedymatrix step speedymatrix step0 number read only speedymatrix step1 number read only storage steps the i j entry of the matrix is stored at data i step0 j step1 reading from the matrices speedymatrix read speedymatrix read number read the entries of the matrix returns an array containing the entries of the matrix in column major format example js const mat speedy matrix 2 2 1 2 3 4 const entries mat read console log entries 1 2 3 4 speedymatrix at speedymatrix at row number column number number read a single entry of the matrix arguments row number index of the row of the desired element 0 based column number index of the column of the desired element 0 based returns the requested entry of the matrix or a nan if the entry is outside bounds example js const a speedy matrix 2 2 1 2 3 4 const a00 a at 0 0 first row first column const a10 a at 1 0 second row first column const a01 a at 0 1 first row second column const a11 a at 1 1 second row second column console log a00 a10 a01 a11 1 2 3 4 speedymatrixexpr tostring speedymatrixexpr tostring string convert a matrix expression to a string entries will only be included if this expression is a speedymatrix returns a string representation of the matrix expression writing to the matrices speedymatrix setto speedymatrix setto expr speedymatrixexpr speedypromise speedymatrix evaluate a matrix expression and store the result in this matrix arguments expr speedymatrixexpr a matrix expression returns a speedypromise that resolves to this matrix after evaluating expr example js let s add two matrices a 1 3 b 4 2 2 4 3 1 we ll set c to the sum a b const mata speedy matrix 2 2 1 2 3 4 const matb speedy matrix 2 2 4 3 2 1 set c a b const matc speedy matrix zeros 2 2 await matc setto mata plus matb print the result c 5 5 5 5 console log matc tostring speedymatrix fill speedymatrix fill value number speedypromise speedymatrix fill this matrix with a scalar arguments value number scalar value returns a speedypromise that resolves to this matrix example js create a 5x5 matrix filled with twos const twos speedy matrix zeros 5 await twos fill 2 synchronous writing speedy provides synchronous writing methods for convenience speedy matrix ready speedy matrix ready speedypromise void this method lets you know that the matrix routines are initialized and ready to be used the webassembly routines need to be loaded before usage you should only use the synchronous writing methods when the matrix routines are ready returns a speedypromise that resolves immediately if the matrix routines are already initialized or as soon as they are initialized speedymatrix settosync speedymatrix settosync expr speedymatrixexpr speedymatrix synchronously evaluate a matrix expression and store the result in this matrix arguments expr speedymatrixexpr a matrix expression returns returns this matrix after setting it to the result of expr example js speedy matrix ready then const mat speedy matrix eye 3 i identity matrix const pot 4 power of two for let i 0 i pot i mat settosync mat plus mat mat mat mat console log mat tostring mat will be 2 pot i speedymatrix fillsync speedymatrix fillsync value number speedymatrix synchronously fill this matrix with a scalar arguments value number scalar value returns returns this matrix after filling it with the provided value access by block speedy lets you work with blocks of matrices this is a very handy feature blocks share memory with the originating matrices if you modify the entries of a block of a matrix m you ll modify the corresponding entries of m columns and rows are examples of blocks speedymatrix block speedymatrix block firstrow number lastrow number firstcolumn number lastcolumn number speedymatrix extract a lastrow firstrow 1 x lastcolumn firstcolumn 1 block from the matrix all indices are 0 based they are all inclusive the memory of the matrix is shared with the block arguments firstrow number index of the first row 0 based lastrow number index of the last row 0 based use lastrow firstrow firstcolumn number index of the first column 0 based lastcolumn number index of the last column 0 based use lastcolumn firstcolumn returns a new speedymatrix representing the specified block example js we ll create the following 4x4 matrix a dot represents a zero 5 5 5 5 5 5 5 5 5 const mat speedy matrix zeros 4 await mat block 0 2 0 2 fill 5 console log mat tostring speedymatrix column speedymatrix column index number speedymatrix extract a column of the matrix arguments index number index of the column 0 based returns a new speedymatrix representing the specified column example js const mat speedy matrix 2 3 1 2 3 4 5 6 const firstcolumn mat column 0 1 2 t const secondcolumn mat column 1 3 4 t const thirdcolumn mat column 2 5 6 t console log firstcolumn tostring console log secondcolumn tostring console log thirdcolumn tostring speedymatrix row speedymatrix row index number speedymatrix extract a row of the matrix arguments index number index of the row 0 based returns a new speedymatrix representing the specified row example js we ll create the following matrix 0 0 0 0 1 1 1 1 2 2 2 2 0 0 0 0 const mat speedy matrix zeros 4 await mat row 1 fill 1 await mat row 2 fill 2 console log mat tostring speedymatrix diagonal speedymatrix diagonal speedymatrix extract the main diagonal of this matrix as a column vector returns a new speedymatrix representing the main diagonal of this matrix example js we ll create the following matrix a dot represents a zero 5 5 5 const mat speedy matrix zeros 5 create a 5x5 matrix filled with zeros const submat mat block 0 2 0 2 extract 3x3 submatrix at the top left const diag submat diagonal extract the diagonal of the submatrix await diag fill 5 fill the diagonal of the submatrix with a constant console log mat tostring print the entire matrix alternatively we may use this compact form await mat block 0 2 0 2 diagonal fill 5 elementary operations speedymatrixexpr transpose speedymatrixexpr transpose speedymatrixexpr transpose this matrix expression returns a speedymatrixexpr representing the tranpose of this matrix expression example js create a 2x3 matrix const mat speedy matrix 2 3 1 2 first column 3 4 second column 5 6 third column we ll store the transpose of mat in matt const matt speedy matrix zeros mat columns mat rows await matt setto mat transpose print the matrix and its transpose console log mat tostring console log matt tostring speedymatrixexpr plus speedymatrixexpr plus expr speedymatrixexpr speedymatrixexpr compute the sum between this matrix expression and expr both expressions must have the same shape arguments expr speedymatrixexpr another matrix expression returns a speedymatrixexpr representing the sum between this matrix expression and expr example js const mata speedy matrix 3 3 1 2 3 4 5 6 7 8 9 const ones speedy matrix ones 3 set b a 1 const matb speedy matrix zeros 3 await matb setto mata plus ones speedymatrixexpr minus speedymatrixexpr minus expr speedymatrixexpr speedymatrixexpr compute the difference between this matrix expression and expr both expressions must have the same shape arguments expr speedymatrixexpr another matrix expression returns a speedymatrixexpr representing the difference between this matrix expression and expr speedymatrixexpr times speedymatrixexpr times expr speedymatrixexpr speedymatrixexpr speedymatrixexpr times scalar number speedymatrixexpr matrix multiplication in the first form compute the matrix multiplication between this matrix expression and expr the shape of expr must be compatible with the shape of this matrix expression in the second form multiply this matrix expression by a scalar arguments expr speedymatrixexpr matrix expression scalar number a number returns a speedymatrixexpr representing the result of the multiplication example js const col speedy matrix 3 1 0 5 2 const row speedy matrix 1 3 1 2 3 const dot row times col 1x1 matrix expression inner product const out col times row 3x3 matrix expression outer product const len col transpose times col 1x1 matrix expression squared length of col const mat speedy matrix zeros 1 await mat setto len evaluate len console log mat read 29 0 0 5 5 2 2 speedymatrixexpr compmult speedymatrixexpr compmult expr speedymatrixexpr speedymatrixexpr compute the component wise multiplication between this matrix expression and expr both matrices must have the same shape arguments expr speedymatrixexpr matrix expression returns a speedymatrixexpr representing the component wise multiplication speedymatrixexpr inverse speedymatrixexpr inverse speedymatrixexpr compute the inverse of this matrix expression make sure it s square returns a speedymatrixexpr representing the inverse of this matrix expression speedymatrixexpr ldiv speedymatrixexpr ldiv expr speedymatrixexpr speedymatrixexpr left division this expr this is equivalent to solving a system of linear equations ax b where a is this and b is expr in a least squares sense if a is not square the number of rows of this must be greater or equal than its number of columns expr must be a column vector arguments expr speedymatrixexpr matrix expression returns a speedymatrixexpr representing the left division systems of equations speedy matrix solve speedy matrix solve solution speedymatrix a speedymatrix b speedymatrix options object speedypromise speedymatrix solve a system of linear equations ax b for x the solution where a is a n x n square matrix b is a n x 1 column vector and solution is a n x 1 column vector of unknowns n is the number of equations and the number of unknowns arguments solution speedymatrix the output column vector a speedymatrix a square matrix b speedymatrix a column vector options object optional options to be passed to the solver available keys method string one of the following qr defaults to qr returns a speedypromise that resolves to solution example js we ll solve the following system of equations y z 9 y z 6 let s write it in matrix form 1 1 y 9 1 1 z 6 the code below solves ax b for x where x y z is the column vector of unknowns const a speedy matrix 2 2 1 1 first column 1 1 second column const b speedy matrix 2 1 9 6 column vector solve ax b for x const solution speedy matrix zeros 2 1 await speedy matrix solve solution a b get the result console log solution read 7 5 1 5 speedy matrix ols speedy matrix ols solution speedymatrix a speedymatrix b speedymatrix speedypromise speedymatrix ordinary least squares given an overdetermined system of linear equations ax b where a is a m x n matrix b is a m x 1 column vector and solution x is a n x 1 column vector of unknowns find a solution x that minimizes the euclidean norm of the residual b ax m is the number of equations and n is the number of unknowns we require m n arguments solution speedymatrix the output column vector a speedymatrix a matrix b speedymatrix a column vector options object optional options to be passed to the solver available keys method string one of the following qr defaults to qr returns a speedypromise that resolves to solution matrix factorization speedy matrix qr speedy matrix qr q speedymatrix r speedymatrix a speedymatrix options object speedypromise void compute a qr decomposition of a m x n matrix a using householder reflectors q will be orthogonal and r will be upper triangular we require m n arguments q speedymatrix output matrix m x n if reduced m x m if full r speedymatrix output matrix n x n if reduced m x n if full a speedymatrix the matrix to be decomposed options object optional a configuration object that accepts the following keys mode string either full or reduced defaults to reduced returns returns a speedypromise that resolves as soon as the computation is complete example js we ll find a qr decomposition of this matrix const a speedy matrix 3 3 0 1 0 first column 1 1 0 second column 1 2 3 third column compute a qr decomposition of a const q speedy matrix zeros 3 3 const r speedy matrix zeros 3 3 await speedy matrix qr q r a print the result console log q tostring console log r tostring check the answer a qr const qr await speedy matrix zeros q rows r columns setto q times r console log qr tostring geometric transformations perspective transformation speedy matrix applyperspectivetransform speedy matrix applyperspectivetransform dest speedymatrix src speedymatrix transform speedymatrix speedypromise speedymatrix apply a perspective transform to a set of 2d points described by src and store the results in dest arguments dest speedymatrix a 2 x n output matrix src speedymatrix a 2 x n matrix encoding a set of n points one per column transform speedymatrix a 3x3 homography matrix returns a speedypromise that resolves to dest example js const transform speedy matrix 3 3 3 0 0 first column 0 2 0 second column 2 1 1 third column const src speedy matrix 2 4 0 0 1 0 1 1 0 1 const dest speedy matrix zeros src rows src columns await speedy matrix applyperspectivetransform dest src transform console log dest tostring result 2 5 5 2 1 1 3 3 speedy matrix perspective speedy matrix perspective homography speedymatrix src speedymatrix dest speedymatrix speedypromise speedymatrix compute a homography matrix using four correspondences of points arguments homography speedymatrix a 3x3 output matrix src speedymatrix a 2x4 matrix with the coordinates of four points one per column representing the corners of the source space dest speedymatrix a 2x4 matrix with the coordinates of four points one per column representing the corners of the destination space returns a speedypromise that resolves to homography example js const src speedy matrix 2 4 0 0 first point 1 0 second point 1 1 third point 0 1 fourth point const dest speedy matrix 2 4 0 0 3 0 3 2 0 2 const homography speedy matrix zeros 3 3 await speedy matrix perspective homography src dest console log homography tostring speedy matrix findhomography speedy matrix findhomography homography speedymatrix src speedymatrix dest speedymatrix options object speedypromise speedymatrix compute a homography matrix using a set of n 4 correspondences of points possibly with noise arguments homography speedymatrix a 3x3 output matrix src speedymatrix a 2 x n matrix with the coordinates of n points one per column representing the corners of the source space dest speedymatrix a 2 x n matrix with the coordinates of n points one per column representing the corners of the destination space options object optional a configuration object method string the method to be employed to compute the homography see the table of methods below table of methods method description default normalized direct linear transform dlt all points will be used to estimate the homography use this method if your data set is not polluted with outliers pransac pransac is a variant of ransac with bounded runtime that is designed for real time tasks it is able to reject outliers in the data set table of parameters parameter supported methods description reprojectionerror number pransac a threshold measured in pixels that lets speedy decide if a data point is an inlier or an outlier for a given model a data point is an inlier for a given model if the model maps its src coordinates near its dest coordinates i e if the euclidean distance is not greater than the threshold a data point is an outlier if it s not an inlier defaults to 3 pixels mask speedymatrix pransac an optional output matrix of shape 1 x n its i th entry will be set to 1 if the i th data point is an inlier for the best model found by the method or 0 if it s an outlier numberofhypotheses number pransac a positive integer specifying the number of models that will be generated and tested the best model found by the method will be refined and then returned if your inlier ratio is high this parameter can be set to a low number making the algorithm run even faster defaults to 500 bundlesize number pransac a positive integer specifying the number of data points to be tested against all viable models before the set of viable models gets cut in half over and over again defaults to 100 returns a speedypromise that resolves to homography example js map random points from 0 100 x 0 100 to 200 600 x 200 600 const numpoints 50 const noiselevel 2 const transform x 4 x 200 simulated model const randcoord 100 math random in 0 100 const randnoise math random 0 5 noiselevel const srccoords new array numpoints 2 fill 0 map randcoord const dstcoords srccoords map x transform x randnoise const src speedy matrix 2 numpoints srccoords const dst speedy matrix 2 numpoints dstcoords const mask speedy matrix zeros 1 numpoints const homography speedy matrix zeros 3 3 await speedy matrix findhomography homography src dst method pransac mask mask reprojectionerror 1 console log homography homography tostring console log mask mask tostring now let s test the homography using a few test points the points need to be mapped in line with our simulated model see above const tstcoords speedy matrix 2 5 0 0 100 0 100 100 0 100 50 50 const chkcoords speedy matrix zeros 2 5 await speedy matrix applyperspectivetransform chkcoords tstcoords homography console log chkcoords tostring affine transformation speedy matrix applyaffinetransform speedy matrix applyaffinetransform dest speedymatrix src speedymatrix transform speedymatrix speedypromise speedymatrix apply an affine transform to a set of 2d points described by src and store the results in dest arguments dest speedymatrix a 2 x n output matrix src speedymatrix a 2 x n matrix encoding a set of n points one per column transform speedymatrix a 2x3 affine transformation matrix returns a speedypromise that resolves to dest example js const transform speedy matrix 2 3 3 0 first column 0 2 second column 2 1 third column const src speedy matrix 2 4 0 0 1 0 1 1 0 1 const dest speedy matrix zeros src rows src columns await speedy matrix applyaffinetransform dest src transform console log dest tostring result 2 5 5 2 1 1 3 3 speedy matrix affine speedy matrix affine transform speedymatrix src speedymatrix dest speedymatrix speedypromise speedymatrix compute an affine transform using three correspondences of points arguments transform speedymatrix a 2x3 output matrix src speedymatrix a 2x3 matrix with the coordinates of three points one per column representing the corners of the source space dest speedymatrix a 2x3 matrix with the coordinates of three points one per column representing the corners of the destination space returns a speedypromise that resolves to transform example js const src speedy matrix 2 3 0 0 first point 1 0 second point 1 1 third point const dest speedy matrix 2 3 0 0 3 0 3 2 const transform speedy matrix zeros 2 3 await speedy matrix affine transform src dest console log transform tostring speedy matrix findaffinetransform speedy matrix findaffinetransform transform speedymatrix src speedymatrix dest speedymatrix options object speedypromise speedymatrix compute an affine transform using a set of n 3 correspondences of points possibly with noise arguments transform speedymatrix a 2x3 output matrix src speedymatrix a 2 x n matrix with the coordinates of n points one per column representing the corners of the source space dest speedymatrix a 2 x n matrix with the coordinates of n points one per column representing the corners of the destination space options object optional a configuration object method string the method to be employed to compute the affine transform see the table of methods below table of methods the same as speedy matrix findhomography speedymatrixfindhomography table of parameters the same as speedy matrix findhomography speedymatrixfindhomography returns a speedypromise that resolves to transform geometric utilities 2d vectors speedy vector2 speedy vector2 x number y number speedyvector2 creates a new 2d vector with the given coordinates arguments x number the x coordinate of the vector y number the y coordinate of the vector returns a new speedyvector2 instance example js const zero speedy vector2 0 0 speedy vector2 sink speedy vector2 sink name string speedypipelinenodevector2sink creates a sink of 2d vectors using the specified name if the name is not specified speedy will call this node vec2 an array of speedyvector2 objects will be exported from the pipeline parameters turbo boolean accelerate gpu cpu transfers you ll get the data from the previous frame defaults to false ports port name data type description in vector2 a set of 2d vectors to be exported from the pipeline speedyvector2 x speedyvector2 x number the x coordinate of the vector speedyvector2 y speedyvector2 y number the y coordinate of the vector speedyvector2 plus speedyvector2 plus offset speedyvector2 speedyvector2 vector addition returns a new vector corresponding to this offset speedyvector2 minus speedyvector2 minus offset speedyvector2 speedyvector2 vector subtraction returns a new vector corresponding to this offset speedyvector2 times speedyvector2 times scalar number speedyvector2 multiply a vector by a scalar returns a new vector corresponding to this scalar speedyvector2 length speedyvector2 length number computes the length of the vector euclidean norm returns the length of the vector example js const v speedy vector2 3 4 console log coordinates v x v y console log length v length 5 speedyvector2 normalized speedyvector2 normalized speedyvector2 returns a normalized version of this vector returns a new vector with the same direction as the original one and with length equal to one speedyvector2 dot speedyvector2 dot v speedyvector2 number dot product arguments v speedyvector2 a vector returns the dot product between the two vectors speedyvector2 distanceto speedyvector2 distanceto v speedyvector2 number computes the distance between two vectors arguments v speedyvector2 a vector returns the euclidean distance between the two vectors example js const u speedy vector2 1 0 const v speedy vector2 5 0 console log u distanceto v 4 speedyvector2 tostring speedyvector2 tostring string get a string representation of the vector returns a string representation of the vector speedyvector2 equals speedyvector2 equals v speedyvector2 boolean equality comparison returns returns true if the coordinates of this are equal to the coordinates of v or false otherwise 2d points speedy point2 speedy point2 x number y number speedypoint2 creates a new 2d point with the given coordinates arguments x number the x coordinate of the point y number the y coordinate of the point returns a new speedypoint2 instance example js const p speedy point2 5 10 speedypoint2 x speedypoint2 x number the x coordinate of the point speedypoint2 y speedypoint2 y number the y coordinate of the point speedypoint2 plus speedypoint2 plus v speedyvector2 speedypoint2 adds a vector to this point arguments v speedyvector2 a 2d vector returns a new speedypoint2 instance corresponding to this point translated by v speedypoint2 minus speedypoint2 minus p speedypoint2 speedyvector2 subtracts point p from this arguments p speedypoint2 a 2d point returns a new speedyvector2 instance such that p plus that vector equals this point speedypoint2 equals speedypoint2 equals p speedypoint2 boolean equality comparison returns returns true if the coordinates of this are equal to the coordinates of p or false otherwise 2d size speedy size speedy size width number height number speedysize creates a new object that represents the size of a rectangle arguments width number a non negative number height number a non negative number returns a new speedysize instance example js const size speedy size 640 360 speedysize width speedysize width number width property speedysize height speedysize height number height property speedysize equals speedysize equals anothersize speedysize boolean checks if two size objects have the same dimensions returns returns true if the dimensions of this and anothersize are equal speedysize tostring speedysize tostring string convert to string returns a string representation of the object extras promises speedy includes its own implementation of promises called speedypromises speedypromises can interoperate with standard es6 promises and are based on the promises a specification https promisesaplus com the main difference between speedypromises and standard es6 promises is that under certain circunstances speedypromises can be made to run faster than es6 promises speedypromises are specially beneficial when you have a chain of them when and if their turbocharged mode is invoked they will adopt a special non standard behavior and skip the microtask queue when settling promises in a chain this will save you a few milliseconds while a few milliseconds doesn t sound much in terms of standard web development for a real time library such as speedy it means a lot simply put we re squeezing out performance speedypromises are used internally by the library speedy promise speedy promise function used to create a new speedypromise object example js let promise new speedy promise resolve reject settimeout resolve 2000 promise then console log the speedypromise is now fulfilled catch console log the speedypromise is now rejected finally console log the speedypromise is now settled settings speedy settings powerpreference speedy settings powerpreference default low power high performance experimental the desired power preference for the webgl context this option should be set before creating any pipelines the browser uses this setting as a hint to balance rendering performance and battery life especially on mobile devices speedy settings gpupollingmode speedy settings gpupollingmode raf asap experimental gpu polling mode asap has slightly better performance than raf at the cost of higher cpu usage speedy settings logging speedy settings logging default none configures the logging mode default shows debug and warnings messages while none hides them all utilities speedy version speedy version string read only the version of the library speedy fps speedy fps number read only speedy includes a frames per second fps counter for testing purposes it will be created as soon as you access it example js console log speedy fps speedy issupported speedy issupported boolean checks if speedy is supported in this machine browser returns returns a boolean telling whether or not speedy is supported in the client environment example js if speedy issupported alert this application is not supported in this browser please use a different browser | computer-vision image-processing gpgpu opencv linear-algebra machine-vision gpu | ai |
cloud-engineering.co.uk | cloud engineering co uk cloud engineering website | cloud |
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ATTEST-Testsystem | attest automated and thorough testing of embedded software in teaching rtos testsystem is a python based test system for the real time operating systems course in the embedded automotive systems https iti tugraz at eas group at the institute of technical informatics https www tugraz at en institutes iti home at graz university of technology https www tugraz at home it was revised from scratch in the winter term of 2022 to improve its performance and functionality the system utilizes dedicated external hardware for testing to guarantee precise and reliable results it is firmly git oriented to embed its functionality in the best possible way for comprehensive documentation please visit the project website https eas attest github io attest testsystem index html getting started the test system is easiest to use with the provided docker file the current implementation uses msp430 microcontroller boards as target devices and 2205a picoscopes as measurement devices these devices form test units a test unit is the dedicated hardware where test cases are executed requirements and installation install docker by following the official setup instructions https docs docker com engine install ubuntu the test system is supposed to run as a docker container but there are still some requirements on the host system run the following command to install the required packages apt get update apt get install y git usbutils start the testsystem if you just want to start the test system run the following command bootstrap sh or run the hello world equivalent bootstrap sh hello testsystem if you already have an msp430 or a picoscope connected to your host you can check the availability by running the startup routine bootstrap sh run startup docker the test system runs in a docker container we suggest you first build the docker image because this may take a while docker build t attest latest when you have the docker image ready you can inspect the available commands of the test system docker run rm t attest latest python3 main py help the provided docker compos yml file is a ready to use setup for the test system it combines the test system with a mysql database for persistent storage adjust the environment variables and the volumes according to your system setup the docker compose template yml is used by the bootstrap script for automatically generating a compose file with the correct device configuration documentation for comprehensive documentation please visit the project website https eas attest github io attest testsystem index html or you build the documentation locally by running the following command after you have the docker image ready the documentation will be generated in the current working directory docker run rm t v pwd host attest latest bash c make html cp r build html host documentation contributing the recommended way for developing the test system is by using vscode and the development container feature the project contains a dev container configuration for vscode to make contributions as easy as possible to start the dev container press ctrl shift p type dev containers open folder in container and select the cloned test system directory the dev container configures the environment includes all the required packages and contains some useful vs code extensions for development if test units are available add the respective devices to the runargs section in the devcontainer devcontainer json file and restart the container by typing dev containers rebuild container into the vscode command palette authors meinhard kissich mailto meinhard kissich tugraz at klaus weinbauer mailto klaus weinbauer student tugraz at marcel baunach mailto baunach tugraz at cite inproceedings 10 1145 3593663 3593678 title attest automated and thorough testing of embedded software in teaching author kissich meinhard and weinbauer klaus and baunach marcel year 2023 doi 10 1145 3593663 3593678 booktitle proceedings of the 5th european conference on software engineering education pages 199 203 numpages 5 license this project is published under the mit license license txt | embedded-software embedded-systems real-time-operating-systems student-assessment testing | os |
Building-Systems-with-the-ChatGPT-API | building systems with the chatgpt api https www deeplearning ai short courses building systems with chatgpt in building systems with the chatgpt api you will learn how to automate complex workflows using chain calls to a large language model unlock new development capabilities and improve your efficiency in this brand new short course you ll build chains of prompts that interact with the completions of prior prompts systems where python code interacts with both completions and new prompts a customer service chatbot using all the techniques from this course you ll learn how to apply these skills to practical scenarios including classifying user queries to a chat agent s response evaluating user queries for safety and processing tasks for chain of thought multi step reasoning this one hour course taught by isa fulford openai and andrew ng deeplearning ai builds on the lessons taught in the popular chatgpt prompt engineering for developers https www deeplearning ai short courses chatgpt prompt engineering for developers though it is not a prerequisite hands on examples make each concept easy to understand built in jupyter notebooks allow you to seamlessly experiment with the code and prompts presented in the course | classification evaluation language-models moderation tokens chain-of-thought-reasoning chaining-prompts chat-format check-outputs chatgpt-api | ai |
whitepaper | bnb smart chain white paper revision version 0 1 2020 04 17 initial publish version 0 1 2020 05 25 add chinese version translated by community members version 0 1 2020 11 10 add filipino version translated by ricoz | blockchain bnb bnbchain | blockchain |
SystemDesign | systemdesign source educative system design basics system design basics key characteristics of distributed systems load balancing caching data partitioning indexes proxies redundancy and replication sql vs nosql long polling vs websockets vs server sent events system design problems system design interviews a step by step guide designing a url shortening service like tinyurl designing pastebin designing instagram designing dropbox designing facebook messenger designing twitter to be continued | system-design | os |
AirBnB_clone | airbnb clone the console discription airbnb is task provided byn alx school of software engineering it is a web app that is intergrated with frontend infrastructure with database and restful api in a clone of airbnb in this project i only implements the console this project aim how to create a python package how to create a command interpreter in python using the cmd module what is unit testing and how to implement it in a large project how to serialize and deserialize a class how to write and read a json file how to manage datetime what is an uuid what is args and how to use it what is kwargs and how to use it how to handle named arguments in a function classes airbnb utilizes the following classes basemodel filestorage user state city amenity place review public instance attributes id br created at br updated at inherits from basemodel inherits from basemodel inherits from basemodel inherits from basemodel inherits from basemodel inherits from basemodel public instance methods save br to dict all br new br save br reload public class attributes email br password br first name br last name name state id br name name city id br user id br name br description br number rooms br number bathrooms br max guest br price by night br latitude br longitude br amenity ids place id br user id br text private class attributes file path br objects storage baggage claim the above classes are handled by the abstracted storage engine defined in the filestorage models engine file storage py class every time the backend is initialized holbertonbnb instantiates an instance of filestorage called storage the storage object is loaded re loaded from any class instances stored in the json file file json as class instances are created updated or deleted the storage object is used to register corresponding changes in the file json console computer the console is a command line interpreter that permits management of the backend of holbertonbnb it can be used to handle and manipulate all classes utilized by the application achieved by calls on the storage object defined above using the console the holbertonbnb console can be run both interactively and non interactively to run the console in non interactive mode pipe any command s into an execution of the file console py at the command line echo help console py hbnb documented commands type help topic eof all count create destroy help quit show update hbnb alternatively to use the holbertonbnb console in interactive mode run the file console py by itself console py while running in interactive mode the console displays a prompt for input console py hbnb to quit the console enter the command quit or input an eof signal ctrl d console py hbnb quit console py hbnb eof console commands the holbertonbnb console supports the following commands create usage create class creates a new instance of a given class the class id is printed and the instance is saved to the file file json console py hbnb create basemodel 119be863 6fe5 437e a180 b9892e8746b8 hbnb quit cat file json echo basemodel 119be863 6fe5 437e a180 b9892e8746b8 updated at 2019 02 17t2 1 30 42 215277 created at 2019 02 17t21 30 42 215277 class base model id 119be863 6fe5 437e a180 b9892e8746b8 show usage show class id or class show id prints the string representation of a class instance based on a given id console py hbnb create user 1e32232d 5a63 4d92 8092 ac3240b29f46 hbnb hbnb show user 1e32232d 5a63 4d92 8092 ac3240b29f46 user 1e32232d 5a63 4d92 8092 ac3240b29f46 id 1e32232d 5a63 4d92 8092 a c3240b29f46 created at datetime datetime 2019 2 17 21 34 3 635828 updated at datetime datetime 2019 2 17 21 34 3 635828 hbnb hbnb user show 1e32232d 5a63 4d92 8092 ac3240b29f46 user 1e32232d 5a63 4d92 8092 ac3240b29f46 id 1e32232d 5a63 4d92 8092 a c3240b29f46 created at datetime datetime 2019 2 17 21 34 3 635828 updated at datetime datetime 2019 2 17 21 34 3 635828 hbnb destroy usage destroy class id or class destroy id deletes a class instance based on a given id the storage file file json is updated accordingly console py hbnb create state d2d789cd 7427 4920 aaae 88cbcf8bffe2 hbnb create place 3e 8329 4f47 9947 dca80c03d3ed hbnb hbnb destroy state d2d789cd 7427 4920 aaae 88cbcf8bffe2 hbnb place destroy 03486a3e 8329 4f47 9947 dca80c03d3ed hbnb quit cat file json echo all usage all or all class or class all prints the string representations of all instances of a given class if no class name is provided the command prints all instances of every class console py hbnb create basemodel fce2124c 8537 489b 956e 22da455cbee8 hbnb create basemodel 450490fd 344e 47cf 8342 126244c2ba99 hbnb create user b742dbc3 f4bf 425e b1d4 165f52c6ff81 hbnb create user 8f2d75c8 fb82 48e1 8ae5 2544c909a9fe hbnb hbnb all basemodel basemodel 450490fd 344e 47cf 8342 126244c2ba99 updated at datetime da tetime 2019 2 17 21 45 5 963516 created at datetime datetime 2019 2 17 21 45 5 963516 id 450490fd 344e 47cf 8342 126244c2ba99 bas emodel fce2124c 8537 489b 956e 22da455cbee8 updated at datetime datetime 2019 2 17 21 43 56 899348 created at datetime datetime 2019 2 17 21 43 56 899348 id fce2124c 8537 489b 956e 22da455cbee8 hbnb hbnb user all user 8f2d75c8 fb82 48e1 8ae5 2544c909a9fe updated at datetime datetim e 2019 2 17 21 44 44 428413 created at datetime datetime 2019 2 17 21 44 44 428413 id 8f2d75c8 fb82 48e1 8ae5 2544c909a9fe user b742dbc3 f4bf 425e b1d4 165f52c6ff81 updated at datetime datetime 2019 2 17 21 44 15 974608 created at datetime datetime 2019 2 17 21 44 15 974608 id b742dbc3 f4bf 425e b1d4 165f52c6ff81 hbnb hbnb all user 8f2d75c8 fb82 48e1 8ae5 2544c909a9fe updated at datetime datetim e 2019 2 17 21 44 44 428413 created at datetime datetime 2019 2 17 21 44 44 428413 id 8f2d75c8 fb82 48e1 8ae5 2544c909a9fe basemo del 450490fd 344e 47cf 8342 126244c2ba99 updated at datetime datetime 20 19 2 17 21 45 5 963516 created at datetime datetime 2019 2 17 21 45 5 963516 id 450490fd 344e 47cf 8342 126244c2ba99 user b742db c3 f4bf 425e b1d4 165f52c6ff81 updated at datetime datetime 2019 2 17 2 1 44 15 974608 created at datetime datetime 2019 2 17 21 44 15 97 4608 id b742dbc3 f4bf 425e b1d4 165f52c6ff81 basemodel fce2124c 8 537 489b 956e 22da455cbee8 updated at datetime datetime 2019 2 17 21 4 3 56 899348 created at datetime datetime 2019 2 17 21 43 56 899348 id fce2124c 8537 489b 956e 22da455cbee8 hbnb count usage count class or class count retrieves the number of instances of a given class console py hbnb create place 12c73223 f3d3 4dec 9629 bd19c8fadd8a hbnb create place aa229cbb 5b19 4c32 8562 f90a3437d301 hbnb create city 22a51611 17bd 4d8f ba1b 3bf07d327208 hbnb hbnb count place 2 hbnb city count 1 hbnb update usage update class id attribute name attribute value or class update id attribute name attribute value or class update id attribute dictionary updates a class instance based on a given id with a given key value attribute pair or dictionary of attribute pairs if update is called with a single key value attribute pair only simple attributes can be updated ie not id created at and updated at however any attribute can be updated by providing a dictionary console py hbnb create user 6f348019 0499 420f 8eec ef0fdc863c02 hbnb hbnb update user 6f348019 0499 420f 8eec ef0fdc863c02 first name holberton hbnb show user 6f348019 0499 420f 8eec ef0fdc863c02 user 6f348019 0499 420f 8eec ef0fdc863c02 created at datetime datetime 2019 2 17 21 54 39 234382 first name holberton updated at date time datetime 2019 2 17 21 54 39 234382 id 6f348019 0499 420f 8eec ef0fdc863c02 hbnb hbnb user update 6f348019 0499 420f 8eec ef0fdc863c02 address 98 mission s t hbnb user show 6f348019 0499 420f 8eec ef0fdc863c02 user 6f348019 0499 420f 8eec ef0fdc863c02 created at datetime datetime 2019 2 17 21 54 39 234382 address 98 mission st first name ho lberton updated at datetime datetime 2019 2 17 21 54 39 234382 id 6f348019 0499 420f 8eec ef0fdc863c02 hbnb hbnb user update 6f348019 0499 420f 8eec ef0fdc863c02 email holberton h olberton com last name school user 6f348019 0499 420f 8eec ef0fdc863c02 email holberton holberton co m first name holberton updated at datetime datetime 2019 2 17 21 54 39 234382 address 98 mission st last name school id 6f34 8019 0499 420f 8eec ef0fdc863c02 created at datetime datetime 2019 2 17 21 54 39 234382 hbnb testing straight ruler unittests for the holbertonbnb project are defined in the tests tests folder to run the entire test suite simultaneously execute the following command python3 unittest m discover tests alternatively you can specify a single test file to run at a time python3 unittest m tests test console py author kehinde omokungbe https www github com ok codeclinic requirements python scripts allowed editors vi vim emacs all your files will be interpreted compiled on ubuntu 20 04 lts using python3 version 3 8 5 all your files should end with a new line the first line of all your files should be exactly usr bin python3 a readme md file at the root of the folder of the project is mandatory your code should use the pycodestyle version 2 8 all your files must be executable he length of your files will be tested using wc all your modules should have a documentation python3 c print import my module doc all your classes should have a documentation python3 c print import my module myclass doc all your functions inside and outside a class should have a documentation python3 c print import my module my function doc and python3 c print import my module myclass my function doc a documentation is not a simple word it s a real sentence explaining what s the purpose of the module class or method the length of it will be verified python unit tests allowed editors vi vim emacs all your files should end with a new line all your test files should be inside a folder tests you have to use the unittest module all your test files should be python files extension py all your test files and folders should start by test your file organization in the tests folder should be the same as your project e g for models base model py unit tests must be in tests test models test base model py e g for models user py unit tests must be in tests test models test user py all your tests should be executed by using this command python3 m unittest discover tests you can also test file by file by using this command python3 m unittest tests test models test base model py all your modules should have a documentation python3 c print import my module doc all your classes should have a documentation python3 c print import my module myclass doc all your functions inside and outside a class should have a documentation python3 c print import my module my function doc and python3 c print import my module myclass my function doc we strongly encourage you to work together on test cases so that you don t miss any edge case aknowledgment all projects are being taught by alx all thanks to alx africa logo https i0 wp com aceworldpub com ng wp content uploads 2022 03 unnamed png resize 880 2c528 ssl 1 | server |
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go | div align center a href https stellar org img alt stellar src https github com stellar github raw master stellar logo png width 558 a br strong creating equitable access to the global financial system strong h1 stellar go monorepo h1 div p align center a href https github com stellar go actions workflows go yml query branch 3amaster event 3apush master github workflow https github com stellar go actions workflows go yml badge svg a a href https godoc org github com stellar go img alt godoc src https godoc org github com stellar go status svg a a href https goreportcard com report github com stellar go img alt go report card src https goreportcard com badge github com stellar go a p this repo is the home for all of the public go code produced by the stellar development foundation this repo contains various tools and services that you can use and deploy as well as the sdk you can use to develop applications that integrate with the stellar network package index horizon server services horizon full featured api server for stellar network go horizon sdk horizonclient clients horizonclient client for horizon server queries and transaction submission go horizon sdk txnbuild txnbuild construct stellar transactions and operations ticker services ticker an api server that provides statistics about assets and markets on the stellar network keystore services keystore an api server that is used to store and manage encrypted keys for stellar client applications servers for anchors financial institutions compliance server services compliance allows financial institutions to exchange kyc information federation server services federation allows organizations to provide addresses for users jane examplebank com dependencies this repository is officially supported on the last two releases of go it depends on a number of external dependencies go mod and uses go modules https github com golang go wiki modules to manage them running any go command will automatically download dependencies required for that operation you can choose to checkout this repository into a gopath https github com golang go wiki gopath or into any directory directory layout in addition to the other top level packages there are a few special directories that contain specific types of packages clients contains packages that provide client packages to the various stellar services exp contains experimental packages use at your own risk handlers contains packages that provide pluggable implementors of http handler that make it easier to incorporate portions of the stellar protocol into your own http server support contains packages that are not intended for consumption outside of stellar s other packages packages that provide common infrastructure for use in our services and tools should go here such as db or log support scripts contains single file go programs and bash scripts used to support the development of this repo services contains packages that compile to applications that are long running processes such as api servers tools contains packages that compile to command line applications each of these directories have their own readme file that explain further the nature of their contents other packages in addition to the packages described above this repository contains various packages related to working with the stellar network from a go program it s recommended that you use godoc https godoc org github com stellar go pkg subdirectories to browse the documentation for each package source layout while much of the code in individual packages is organized based upon different developers personal preferences many of the packages follow a simple convention for organizing the declarations inside of a package that aim to aid in your ability to find code in each package there may be one or more of a set of common files errors go this file should contains declarations both types and vars for errors that are used by the package example test go this file should contains example tests as described at https blog golang org examples main go internal go deprecated older packages may have a main go public symbols or internal go private symbols these files contain respectively the exported and unexported vars consts types and funcs for the package new packages do not follow this pattern and instead follow the standard go convention to co locate structs and their methods in the same files main go new convention if present this file contains a main function as part of an executable main package in addition to the above files a package often has files that contains code that is specific to one declared type this file uses the snake case form of the type name for example loggly hook go would correspond to the type logglyhook this file should contain method declarations interface implementation assertions and any other declarations that are tied solely to that type each non test file can have a test counterpart like normal whose name ends with test go the common files described above also have their own test counterparts for example internal test go should contains tests that test unexported behavior and more commonly test helpers that are unexported generally file contents are sorted by exported unexported then declaration type ordered as consts vars types then funcs then finally alphabetically test helpers often we provide test packages that aid in the creation of tests that interact with our other packages for example the support db package has the support db dbtest package underneath it that contains elements that make it easier to test code that accesses a sql database we ve found that this pattern of having a separate test package maximizes flexibility and simplifies package dependencies contributing contributions are welcome see contributing md contributing md for more details developing see guide for developers md services horizon internal docs guide for developers md for helpful instructions for getting started developing code in this repository stellar development foundation https stellar org | stellar blockchain cryptocurrency horizon | blockchain |
mac-setup | p align center img src https user images githubusercontent com 35331661 37242209 c5d740cc 2465 11e8 92b3 e6d786e98dda jpg width 100px p front end web development setup for macos this document describes how i set up front end web development environment on my macbook air with macos high sierra 10 13 3 system preferences system preferences terminal terminal bash bash homebrew homebrew git git node js nodejs node package manager node package manager web browsers web browsers visual studio code visual studio code system preferences after clean install of operating system there are a couple tweaks i like to make to the system preferences some of them are not strictly related to web development enviroment i use them because of my personal habits general user dark menu bar and dock general ask to keep changes when closing documents general close windows when quitting an app dock automatically hide and show the dock keyboard key repeat fast all the way to the right keyboard delay until repeat short all the way to the right set dock size shell defaults write com apple dock tilesize int 35 killall dock disable press and hold shell defaults write nsglobaldomain applepressandholdenabled bool false reset icons in launchpad i usually use this command after installing every application that i need it keeps apple applications on the first page and moves the rest to the next pages shell defaults write com apple dock resetlaunchpad bool true killall dock show hidden files this can also be done by pressing command shift shell defaults write com apple finder appleshowallfiles yes show path bar in finder shell defaults write com apple finder showpathbar bool true show status bar in finder shell defaults write com apple finder showstatusbar bool true terminal i use my custom terminal profile called flat you can download it by typing shell curl o https raw githubusercontent com appalaszynski mac setup master flat terminal to use it as default profile open downloaded flat terminal file and click shell use settings as default option bash shell alias brewup brew update brew upgrade brew prune brew cleanup brew doctor brew cask cleanup alias rmhis rm bash history history c logout export clicolor 1 export lscolors gxfxcxdxbxegedabagaced red 033 1 31m green 033 1 32m yellow 033 1 33m purple 033 1 35m gray 033 1 30m default 033 0m parse git branch git branch 2 dev null sed e d e s 1 export ps1 red u gray green h gray yellow w gray n git branch 2 dev null echo on purple parse git branch n gray default in my bash profile file i create a brewup alias to keep homebrew which we are going to install in a second up to date and rmhis to remove bash history i also set color scheme for ls command output and for custom prompt which contains username computer name working directory and current git branch to download bash profile and execute its content use shell cd curl o https raw githubusercontent com appalaszynski mac setup master bash profile source bash profile homebrew homebrew http brew sh package manager allows to install almost any app right from the command line installation shell usr bin ruby e curl fssl https raw githubusercontent com homebrew install master install brewfile installing each package separately may take some time that s why i use homebrew bundle https github com homebrew homebrew bundle which allows to automatically install all packages and applications listed in the brewfile file here are all the programs i install with a brief description cask https caskroom github io an extension to homebrew which allows to install almost any program that exists for a mac git https git scm com for version control mas cli https github com mas cli mas mac app store command line interface appcleaner https freemacsoft net appcleaner uninstall apps filezilla https filezilla project org ftp client firefox https www mozilla org firefox new web browser flux https justgetflux com better night shift google chrome https www google pl chrome browser desktop index html web browser keepingyouawake https github com newmarcel keepingyouawake prevent mac from entering sleep mode keka http www kekaosx com file archiver mamp https www mamp info en apache mysql and php package opera http www opera com web browser sequel pro http www sequelpro com gui for mysql databases skype https www skype com voice and video chat spectacle https www spectacleapp com easily move and resize windows transmission https transmissionbt com bittorrent client visual studio code https code visualstudio com code editor vlc https www videolan org vlc media player imovie https www apple com imovie video editor pages https www apple com pages text editor numbers https www apple com numbers spreadsheet below are the entire contents of my brewfile which will install all the above programs with a single command ruby tap caskroom cask brew git brew mas cask appcleaner cask filezilla cask firefox cask flux cask google chrome cask keepingyouawake cask keka cask mamp cask opera cask sequel pro cask skype cask spectacle cask transmission cask visual studio code cask vlc mas imovie id 408981434 mas numbers id 409203825 mas pages id 409201541 to check app store application s ids use shell mas search app name to download my brewfile file type shell curl o https raw githubusercontent com appalaszynski mac setup master brewfile to install listed applications use shell brew bundle in directory that contains brewfile file git you can set git global configuration two ways the first is to run bunch of commands which will update the git configuration file e g shell git config global user name first last git config global user email email email com the other and faster way is creating the git configuration file and input it all ourselves shell cd curl o https raw githubusercontent com appalaszynski mac setup master gitconfig open gitconfig properties user name first last email email email com github user username core editor editor credential helper osxkeychain here i set my name email github username core editor and connect git to the macos keychain so i don t have to type my username and password every time i want to push to github node js for installation of node js i like to use node version manager https github com creationix nvm nvm to download it type shell curl o https raw githubusercontent com creationix nvm v0 33 8 install sh bash you can check all available node js versions by shell nvm list remote to install specific version type shell nvm install version node package manager the only thing i use globally at the moment is gulp https gulpjs com gulp to install gulp globally use shell npm install global gulp cli web browsers currently i have installed all major web browsers chrome https www google com chrome safari https www apple com safari opera https www opera com firefox https www mozilla org en us firefox for main development i use google chrome chrome extensions ublock origin https chrome google com webstore detail ublock origin cjpalhdlnbpafiamejdnhcphjbkeiagm block ads jsonview https chrome google com webstore detail jsonview chklaanhfefbnpoihckbnefhakgolnmc validate and view json documents react developer tools https chrome google com webstore detail react developer tools fmkadmapgofadopljbjfkapdkoienihi inspect component hierarchies and states redux devtools https chrome google com webstore detail redux devtools lmhkpmbekcpmknklioeibfkpmmfibljd debug state changes visual studio code all settings changes in visual studio code are stored in settings json file json workbench startupeditor newuntitledfile workbench colortheme monokai workbench activitybar visible false workbench icontheme material icon theme editor fontsize 12 editor tabsize 2 editor multicursormodifier ctrlcmd editor minimap enabled false editor hidecursorinoverviewruler true editor formatonpaste true explorer openeditors visible 0 files insertfinalnewline true html autoclosingtags false files exclude node modules true vscode true material icon theme folders theme none todohighlight isenable true todohighlight keywords text todo color black backgroundcolor yellow overviewrulercolor yellow text fixme color white backgroundcolor red overviewrulercolor red todohighlight exclude public you can copy and paste them or just download whole file by shell cd users your username library application support code user curl o https raw githubusercontent com appalaszynski mac setup master settings json extensions auto rename tag https marketplace visualstudio com items itemname formulahendry auto rename tag automatically rename paired html tag debugger for chrome https marketplace visualstudio com items itemname msjsdiag debugger for chrome debug javascript code in the google chrome browser material icon theme https marketplace visualstudio com items itemname pkief material icon theme icons based on material design open in browser https marketplace visualstudio com items itemname techer open in browser open any file in specific browser right from vs code explorer path intellisense https marketplace visualstudio com items itemname christian kohler path intellisense autocomplete filenames project manager https marketplace visualstudio com items itemname alefragnani project manager manage projects right inside visual studio code scss intellisense https marketplace visualstudio com items itemname mrmlnc vscode scss autocomplete variables mixins functions and many other todo highlight https marketplace visualstudio com items itemname wayou vscode todo highlight highlight and list todo fixme or any annotations within code | front_end |
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vanilla-js-projects | vanilla javascript projects coffee p align center img src http i imgur com 14kw76s jpg p introduction during the first four weeks of the bootcamp we learned how to code with javascript using the nodejs platform but as you probably know javascript s beginnings were in the browser in 1995 https en wikipedia org wiki javascript beginnings at netscape at that time personal computers were starting to become more powerful and there was a realization that the web needed to become more dynamic before that time web browsers were quite dumber than they are now a browser would receive an html page from a web server either dynamically created or as a static file and render the page http www pathinteractive com blog design development rendering a webpage with google webmaster tools making additional requests for every img element therein after rendering a page the possible interactions were limited the user clicks on an a anchor element on the page making the browser issue a get request to the url specified in the href attribute of the anchor effectively loading another page the user fills out a form and submits it making the browser issue either a get or post request depending on the method attribute of the form to the url specified in the action attribute of the form this would also result in another page load this method of creating web sites and rendering them is exactly how you built your reddit clone project https github com decodemtl node express reddit clone using nodejs express and pug on the server side in this model the browser did very little this was fine for a while but with the advent of javascript in browsers in 1995 things would dramatically change browser javascript is the same language you learned while studying nodejs but it offers dynamic functionality that is meant for the browser while nodejs gives you modules to access the computer s file system https nodejs org api fs html create http servers https nodejs org api http html lower level socket servers https nodejs org api net html and a huge library of modules http npmjs com to connect to databases https www npmjs com package mysql read the keyboard https www npmjs com package prompt and even control robots https www npmjs com package johnny five browser javascript gives you the dom http htmldog com guides javascript intermediate thedom the dom document object model is accessible using the globally predefined document variable it is a representation of the html document that your browser has rendered as a tree of javascript objects with the dom you can change any part of an already rendered web page by writing code inside a script tag using the javascript language you already know events and callbacks http htmldog com guides javascript intermediate events through the dom you can make a page dynamic by listening to various events you can execute callback functions on page load page scroll mouse move mouse click keyboard input and many more ajax http htmldog com guides javascript intermediate ajax ajax asynchronous javascript and xml has become a buzzword over the years it refers to a functionality that allows you to dynamically make http requests using javascript code in a script tag without reloading the page throughout the years various apis were added to browser javascript allowing us to do things like drawing on a web page http htmldog com guides javascript advanced canvas store user information in the browser http htmldog com guides javascript advanced localstorage and manipulate the browser history https developer mozilla org en us docs web api history api to name a few together this set of functionalities allows us to build things like gmail netflix google maps and pretty much any application we can imagine all running inside a web browser it is this set of functionalities that allows facebook and twitter to show you notifications without having to refresh the page same thing for upvoting things on reddit while staying in place they are collectively referred to as vanilla js http vanilla js com in contrast with the many https angularjs org frameworks http emberjs com and libraries https jquery com that are built on top of them in this course we will be looking at two libraries built on top of browser javascript jquery https jquery com jquery was created as a response to a huge problem at the time namely the discrepancy between javascript implementations in browsers it provided a unified api to access the dom events ajax and more without having to worry about browser differences react https facebook github io react react is a user interface library that was created as a response to the difficulty of managing big browser based applications using the primitive vanilla js functions it turns out that manipulating the dom directly using the document object is error prone at scale and becomes quickly unmanageable react tries to solve this problem in a declarative manner similar to sql rather than calling dom functions to manipulate the content of a page react allows us to define user interface components in a declarative way and automatically takes care of calling the appropriate dom functions the similarity to sql lies in the fact that we never have to tell an sql database how we want things to be computed but simply what to compute think of it as the difference between order by createdat desc and data sort function a b projects before learning any of these libraries we will work on getting familiar with vanilla javascript since everything is built on top of it it will pay off to know how it works in order to do this we will be building three front end projects that run completely in the browser in these projects our web server s only role will be to produce a static index html file that contains a script tag where everything will happen one implication of this is that search engine crawlers like google bot will not be able to view the content of our applications since everything will happen after the page has loaded long after the http request response cycle has ended weather application p align center img src weather app demo gif p we will start by building a first project together it will be a simple weather application that asks the user for their city and dynamically displays a weather box with the current temperature basic weather conditions as well as an icon representing the current state of things flickr browser p align center img src flickr api project gif p even though flickr already has an excellent web app to browse photos we will use the flickr api to create an infinitely scrolling browser based on a search word blackjack game p align center img src blackjack gif p this one is a bit extreme but we re giving you quite a bit of code to help you make sure to personalize it so it doesn t look too generic baby steps before starting to work on the projects we ll have to learn about the building blocks of browser based javascript to follow along create a file called index html with the following structure html doctype html html head head body div id app div script src app js script body html then create an empty app js file in which you ll be able to test the functions you will learn about the browser will load app js as soon as it encounters the script tag and will execute the contained javascript in the context of the current page creating and manipulating elements the dom api can be accessed through the document global variable this variable is made available to you in javascript running in the context of the current page and it represents the currently displayed document let s start from the javascript console open your developer tools move to the console tab and write the following javascript document getelementbyid app you ll get something like this getelementbyid document getelementbyid png this is the dom in action we queried the document for an element by its id and since we have a div id app in our page we got that back as a return value now try this javascript document getelementsbytagname body this time notice you get something that looks like an array expand it and hover over the first element you ll see it light up on the page modern browsers have a way to query for elements using a css selector try this javascript document queryselector app you ll get back exactly the same element let s move to our app js and write the following javascript var app document queryselector app var thetitle document createelement h1 thetitle innertext hello world then refresh the page hmm it looks like something is missing when we create a new element using the dom we have to add it to an already existing element or else it won t appear on the page add the following line to complete this javascript app appendchild thetitle refresh the page one more time to see your dynamically created h1 appear on the page another way to accomplish the same thing is using the innerhtml property of an element javascript var app document queryselector app app innerhtml h1 hello world h1 in this case the browser will parse the html string and create the elements on the fly the flipside is that it will remove anything that was previously in app so it s not as useful as appendchild now modify your index html to add a link to a style css file in style css write the following css red color red this says that any element with the class red will have its text color in red then open your console and write the following javascript var h1 document queryselector h1 there s only one right now h1 classlist add red as you do this watch the text of the h1 change from its black default to red sometimes we need to manipulate the styling of an element without adding removing a class name we can do this using the inline style attribute of the element instead of taking a string it s an object with one property for each style try the following in the console after refreshing the page javascript document queryselector h1 style color red it will have the same effect it turns out that often manipulating the classes of an element is cleaner than changing its inline style because it s easier to remove the class by using classlist remove handling events handling events is done by calling the addeventlistener method of a dom element many types of events exist as an example there are mouse events form events keyboard events let s add a button to our page and do something when it is clicked change the code of app js to the following javascript var app document queryselector app add a title var thetitle document createelement h1 thetitle innertext hello world app appendchild thetitle add a button var thebutton document createelement button thebutton innertext click me app appendchild thebutton here s the new part thebutton addeventlistener click function thetitle classlist add red then reload the page and try clicking on the button notice the title text change to red change the classlist add to classlist toggle and try clicking the button many times move to your html file and put the following inside the app div html input type text id textbox button click me button then change the whole code of app js to the following javascript var textbox document queryselector textbox var thebutton document queryselector app button css for the element with tag name button inside the element with id app we could also have given the button an id textbox addeventlistener input function console log this value thebutton addeventlistener click function alert the value of the input box is textbox value then reload the page and go to the console tab of your developer tools they are open right type some text in the input box and see the console log s coming up at any point click on the button to get an alert notice the use of this inside the callback to the input event listener when an event handler is called this will always refer to the element on which addeventlistener was called this allows us to re use the same event handler for many elements stopping to listen to an event if we want to stop listening to an event on an element we have to call the removeeventlistener method on that element but to do this we need to have a reference to the function that was passed to addeventlistener if we used an inline function expression then we can t do that let s try with the following code javascript var textbox document queryselector textbox var thebutton document queryselector app button css for the element with tag name button inside the element with id app we could also have given the button an id function printvalue console log this value textbox addeventlistener input printvalue thebutton addeventlistener click function when the button is clicked stop listening to input events on the input box textbox removeeventlistener input printvalue try running this code in the browser and after pressing the button the event listening should stop default event behaviors some events have a default behavior attached to them for example when you click on an a element the browser will load its href property effectively leaving the current page this default behavior can be prevented by accessing the event object and calling its preventdefault method when an event handler gets called it receives the event object as its first argument let s see how that works clear the content of the app div in your html and change app js to the following code javascript var app document queryselector app var thelink document createelement a thelink innertext a link to decodemtl thelink setattribute href http www decodemtl com this is how we set html attributes app appendchild thelink thelink addeventlistener click function event event preventdefault console log prevented browsing to this href by using preventdefault try refreshing the page and clicking on the link and nothing should happen event bubbling the dom tree is a nested structure if you click on a nested element it seems logical that you also clicked on its parent and that parent s parent and so on let s visualize this behavior with an example change the content of the app div to the following html p id theparagraph this is a a id thelink href http www decodemtl com link a p then change app js to the following code javascript this gives some height to the app div so we can click it document queryselector app style height 400px this makes the app div visible note that the css background color is written backgroundcolor in the dom document queryselector app style backgroundcolor ccc document body addeventlistener click function console log the body was clicked document queryselector app addeventlistener click function console log app was clicked document queryselector theparagraph addeventlistener click function console log theparagraph was clicked document queryselector thelink addeventlistener click function event event preventdefault we need to do this otherwise we will leave the page if we click the link console log thelink was clicked then refresh the page in your browser and try clicking in different places click on the link then on the text inside the paragraph and then anywhere else on the page with one click up to four event handlers are getting called starting from the element that was clicked and bubbling up to the body at any point in time we can call the event object s stoppropagation method to stop this bubbling try adding event stoppropagation in the different event handlers and observe the result in your browser p align center img src event bubbling png p event delegation warning this topic is extremely important and you should understand it well before moving on let s look at an example by writing the following code inside the app div of our html html h1 event delegation h1 ul id thelist li first list item li li second list item li li third list item li li fourth list item li li fifth list item li ul then let s try to attach an event handler to each list item and print its inner text when it is clicked javascript var listitems document queryselectorall thelist li select all the lis inside thelist it turns out that queryselectorall does not return an array but an array like object called a nodelist here s one way we can iterate over the nodelist items using the array prototype array prototype foreach call listitems function listitem add one event listener per list item listitem addeventlistener click function console log you clicked on this innertext nodelist actually has a foreach method but it s not the same as arrays it doesn t however have map or filter or any of the other useful array methods try running your code in the browser to show yourself that it works even though this code works there are mainly two things that are wrong with it 1 if we dynamically add new lis to the ul after setting up the event listeners clicking them will do nothing ask yourself why 2 setting up multiple event handlers can be extremely resource intensive and we can do much better imagine an image gallery with 100s of images one event handler per image will simply not cut it it s not by accident that we looked at event propagation in the last section using the propagation we can solve these two issues in one shot first off though write the following code in your developer tools console javascript var thelist document queryselector thelist var newitem document createelement li newitem innertext sixth list item thelist appendchild newitem then try clicking on the sixth li that was added dynamically and prove to yourself that nothing happens now let s fix it change the code of app js to the following javascript var thelist document queryselector thelist thelist addeventlistener click function event while this represents the thelist element event has a property called target which represents the actual originator of the event we can use this to our advantage first check if the target is an li that is a direct child of the list if event target parentnode thelist we definitely clicked on an li console log you clicked on event target innertext with this code in place we should get the same initial behavior as the previous code one advantage is that we only have a single event handler no matter how many lis now try running the following code in your console javascript var thelist document queryselector thelist var newitem document createelement li newitem innertext sixth list item thelist appendchild newitem then try clicking on the sixth li and notice the difference it gets console logged this is event delegation in a nutshell and you should use it wherever you can this is also a great transition for the next section where we will learn about ajax making http requests in the context of a web page will often result in adding new elements dynamically setting up event delegation will enable us to listen to clicks on those new elements without having to manually attach event handlers to each one of them making http requests browser javascript would be pretty boring if all you could do was manipulate the page what makes things really interesting is the ability to make http requests in the context of an already loaded web page this enables us to load external information then use what we already learned to make that information appear on the page originally this was accomplished with a contraption called xmlhttprequest and the api for it was less than stellar modern browsers offer a much better alternative using the globally available fetch function as a bonus fetch uses promises to give back its results allowing us to write much nicer code the process of fetching external information and updating the page as a result is often called ajax which stands for asynchronous javascript and xml after four weeks of bootcamping we now understand what asynchronous means we also know what javascript means we haven t really looked at xml it turns out to be quite a heavy data format both in terms of its size and its parsing many new apis prefer to use the json format instead since json is native to javascript this is great for us let s look at how we can use fetch at first only to make an http request and then eventually to build something with it let s start by changing the code of app js to the following javascript fetch http www rbcroyalbank com then function response return response text parsing the response as text returns another promise so we chain it then function textresponse console log textresponse try refreshing the browser and look at your console tab cors error cors error png what s that an error what the browser is telling us here is that we re not allowed to see the response from the royal bank site since this request is running in the context of our web browser this makes a lot of sense because such a request would have access to our cookies seeing the response could allow us to retrieve some sensitive information from anyone who loads up our web page in their browser imagine that we were allowed to see the content of this 3rd party site here s a hand waving example of what we could do javascript fetch https www onlinebank com accounts then function response return response text then function textresponse we now have an html page with the user s bank accounts let s send it to ourselves fetch http www evil domain com bank accounts method post body textresponse having setup such a page we can then send the link to unsuspecting people and harvest all their banking information browsers prevent this behavior by default and only allow us to look at the response of fetch requests when they are made to our own domain name called the origin a system called cors cross origin resource sharing allows web servers to cooperate with web application builders by setting appropriate headers in the http response a web server can tell the browser that the response can be allowed to be seen by the originator of the ajax request your online bank would never do such a thing but a lot of apis offering publicly available information will enable cors on some of their endpoints one example of such an api is the reddit json api this is probably the last time we will look at reddit in this bootcamp because quite frankly we re starting to be fed up with it let s look at an example of making an ajax call to the reddit json api parsing the result and doing something with it javascript fetch https www reddit com r montreal json then function response return response json parsing as json returns a promise let s chain it then function jsonresponse var posts jsonresponse data children posts foreach function post i console log post i 1 post data title here we are retrieving the front page of r montreal and prining each title on the console try it for yourself from then on the possibilities are endless rather than printing the result in the console we can construct some dom elements and display them on the page let s try to do that write the following code in app js javascript fetch https www reddit com r montreal json then function response return response json parsing as json returns a promise let s chain it then function jsonresponse jsonresponse data children map function post post post data reddit has a weird format create a box for each post var linkbox document createelement p create a link element for each post var link document createelement a link setattribute href post url link setattribute target blank make the link open in a new tab link innertext post title add the link to the paragraph linkbox appendchild link return the paragraph from the map callback return linkbox foreach function linkparagraph document body appendchild linkparagraph try it again by refreshing the browser as you can imagine this ajax code could be executed as a result of an event a timer or anything else that you can implement using javascript conclusion by marrying events ajax calls and dom manipulation we can build fully functioning web applications that run in the browser we ll do that in the next section by building three small web applications one of the things that we ll see while doing this is that directly using the dom can be quite cumbersome and managing the state of our application can quickly get out of hand next week we ll start looking at how to solve some of those problems in a declarative way using the react ui library built by facebook https facebook github io react project 1 weather app introduction for this first project we will be holding your hand throughout we will build a basic version of a weather application that shows the current temperature in a city here s what the final basic version of the project will look like p align center img src weather app demo gif p as discussed in class if you want to use this project as part of your portfolio it would be interesting for you to do more than the basic version some suggestions will be given to you at the end of the workshop instuctions preparing to prepare coding for the project we will get ourselves some keys for the two apis we will use dark sky weather api let s get an api key for the dark sky api 1 go to https darksky net dev and signup 2 confirm your email 3 login and find your api key here https darksky net dev account 4 copy your api key somewhere and we ll add it to the code later p align center img src dark sky api key png p google maps geocoding api let s get an api key for the google maps geocoding api 1 go to https developers google com maps documentation geocoding get api key 2 click on get a key 3 enter a name for your application 4 copy your api key somewhere and we ll add it to the code later p align center img src google maps api key png p let s start to code our app will be composed of a basic index html file that has the main user interface and an app js where all the interesting stuff will happen create an index html file with the following content html doctype html html head meta charset utf 8 title weather app title link rel stylesheet href style css head body div id app h1 weather app h1 div class main app form class city form input type text class city input button type button class get weather button get weather button form div class city weather the content diplayed here will be generated by dom operations div div div script src app js script body html this html file lays out the structure of our application as will always be the case everything will happen inside the app div this is better than throwing everything directly in the body think of it as a namespace then let s create the app js that our html is referring to the first thing we ll do is add our api keys to the javascript file before that though let s make sure our api keys are working go to your browser and open the index html file that you just created open the developer tools and move to the console tab then try running the following command making sure to replace the part that says your api key here with your api key javascript fetch https api darksky net forecast your api key here 37 8267 122 4233 then response response json then data console log data oops what happened dark sky is preventing us to make http requests to it from a browser in a real application this would make sense because your api key should be kept secret if we were building a real application we would have to create our own web server that acts as a proxy to the dark sky api this way we could keep our api key hidden on the server and even add our own logic to do rate limiting caching and so on since we are in development mode here we are going to use a shortcut a service called cors anywhere https cors anywhere herokuapp com will allow us to proxy our requests through it and that service will automatically add the appropriate access control allow origin header in its response but wait how can such a service exist isn t it unsafe to allow us to make ajax calls to a site that doesn t allow it in the first place well not really if our browser is making request to the cors anywhere domain then the cookies of the original website will not be passed along so we will get a pretty generic response in our case we re using this to bypass the dark sky protection of our api key we simply have to know that what we re doing would be bad in a real application where our secret api key would become exposed let s try that same request but make it go thru the cors anywhere proxy javascript fetch https cors anywhere herokuapp com https api darksky net forecast your api key here 37 8267 122 4233 then response response json then data console log data finally we are getting some data the url looks weird but basically everything after the herokuapp com will be passed as the path to the proxy and the proxy will make the request on our behalf from the server side safe of any cookies the google maps api does not have this limitation so we can geocode directly from the browser try it anyway just to be sure that your api key works javascript fetch https maps googleapis com maps api geocode json address montreal key your api key here then response response json then data console log data now that we ve tested our apis let s add the necessary information to our app js file create the file and add the following code in it javascript var darksky api url https api darksky net forecast var darksky api key your api key here var cors proxy https cors anywhere herokuapp com var google maps api key your api key here var google maps api url https maps googleapis com maps api geocode json next we ll create some utility functions that will allow us to access the two apis and return only the data that we need let s start by creating a function called getcoordinatesforcity the function will take a city string as parameter and return a promise for a coordinates object here is the code of the function add it to your app js javascript this function returns a promise that will resolve with an object of lat lng coordinates function getcoordinatesforcity cityname this is an es6 template string much better than verbose string concatenation var url google maps api url address cityname key google maps api key return fetch url returns a promise for a response then response response json returns a promise for the parsed json then data data results 0 geometry location transform the response to only take what we need test your function by going in the browser and refreshing your page since the app js code we are writing is in the global scope the function we just created is available call it like so javascript getcoordinatesforcity montreal then console log and make sure that you see an object with lat lng properties printed out then we ll add our second utility function called getcurrentweather it will take an object with lat lng and query the dark sky api for the current weather we only care about the current weather so we can optimize our api call let s add this to our app js javascript function getcurrentweather coords template string again i hope you can see how nicer this is var url cors proxy darksky api url darksky api key coords lat coords lng units si exclude minutely hourly daily alerts flags return fetch url then response response json then data data currently same idea different api the units si exclude minutely hourly daily alerts flags part in the query string of the url is explained in the dark sky api documentation https darksky net dev docs forecast basically units si means we ll get things back in celcius and kilometers and the exclude says to only send us the currently data it makes the api response smaller and therefore faster to transfer across the wire again let s test our function by refreshing the browser and trying the following code javascript getcurrentweather lat 45 5 lng 73 5 then console log this should print an object in the console that contains a bunch of weather properties for the basic version we will only be using the temperature the two functions we created will make up our basic flow of data we can test them together in the browser s console this way javascript getcoordinatesforcity montreal then getcurrentweather then data console log the current temperature is data temperature this should print out the current temperature for the city you asked for warning warning warning before moving on the the next section make sure that you understand everything that we did so far otherwise if you simply copied and pasted what we gave you without understanding it you will not have learned anything if you cannot explain exactly what is going on then ask one of your classmates or a ta to be sure wiring it up to the dom we are building this project following a logical progression if this was a console based project we would be done the last section gave us the data that we needed and we were able to print it on the console however this project also has a user interface component in the browser the user interface is created by using html and css along with javascript for dom manipulations and events this is exactly what we ll do in this section we already wrote the html for the user interface and the css will be left to you as an optional but strongly suggested exercise let s write the javascript code for the ui now first let s create one variable for each dom element we will need to target add the following code to app js after the two functions you wrote in the last section javascript var app document queryselector app var cityform app queryselector city form var cityinput cityform queryselector city input var getweatherbutton cityform queryselector get weather button var cityweather app queryselector city weather notice that we are introducing a new thing here the queryselector method does not only exist on the document object but also on all the elements inside the document if we call queryselector on an element we will only get back elements that are its descendants this is much more robust than querying the whole document now that we have a reference to all the needed dom elements let s wire up an event handler we ll do it a first time the wrong way then we ll see why it s wrong and we ll fix it after what we want to do is setup the app so that when the user clicks on the get weather button we start the process of fetching the data let s add the following code at the end of our app js javascript getweatherbutton addeventlistener click function var city cityinput value grab the current value of the input getcoordinatesforcity city get the coordinates for the input city then getcurrentweather get the weather for those coordinates then function weather cityweather innertext current temperature weather temperature refresh your browser enter a city name and click the get weather button wait a few seconds and you should see a message displayed in the page with the current temperature we just ajaxed now let s see why this is not the best way to setup our event refresh your browser type a city name in the input field and press enter on your keyboard what s happening well by default the browser tries to submit the form using the standard browser mechanism except our form is not meant to be submitted let s fix this first go back to your html and change the button type button to button type submit the button now becomes a submit button for the form this means that now whether you click the button or press enter in the input field the form will first fire off a submit event let s change our event code a little bit to this javascript cityform addeventlistener submit function event this line changes event preventdefault prevent the form from submitting this code doesn t change var city cityinput value getcoordinatesforcity city then getcurrentweather then function weather cityweather innertext current temperature weather temperature now refresh the browser and try again you should get a consistent behavior whether you press enter in the input field or click the get weather button we are done with the basics congrats cleaning things up a bit let s clean up our code a bit in two ways first off notice that we re not using the getweatherbutton variable anymore so let s remove it from the code then let s clean up all the global variables that we just polluted our scope with whenver javascript runs in the context of your web page it runs in the global scope as you already know one way to create a new scope in javascript is to write code inside a function it turns out that all the code that we wrote in app js doesn t need to expose any variables to the outside world we only used those variables to help run our logic in order to fix this we will wrap all the code that we wrote inside a contraption called iife or immediately invoked function expression http benalman com news 2010 11 immediately invoked function expression the code of app js will look like this javascript function var darksky api url https api darksky net forecast var darksky api key your api key here var cors proxy https cors anywhere herokuapp com var google maps api key your api key here var google maps api url https maps googleapis com maps api geocode json function getcurrentweather coords var url cors proxy darksky api url darksky api key coords lat coords lng return fetch url then response response json then data data currently function getcoordinatesforcity cityname var url google maps api url address cityname key google maps api key return fetch url then response response json then data data results 0 geometry location var app document queryselector app var cityform app queryselector city form var cityinput cityform queryselector city input var getweatherbutton cityform queryselector get weather button var cityweather app queryselector city weather cityform addeventlistener submit function event event preventdefault prevent the form from submitting var city cityinput value getcoordinatesforcity city then getcurrentweather then function weather cityweather innertext current temperature weather temperature this says here s an anonymous function now run it the advantage is that all the variables and functions we declared are now scoped to this function and will not pollute the global scope since we don t need those variables outside of that scope this is perfectly fine it is a good practice to wrap your code in an iife to prevent it from polluting the global scope do it whenever possible the reason for the extra parentheses is to prevent a syntaxerror as explained in the article linked above possible improvements as discussed in class if you want to use this project for your portfolio you can make a lot of enhancements to it here are some suggestions 1 when we click get weather right now there is no indication that the browser is doing anything useful if the two api calls take more than a few milliseconds to run we will not see what is going on this is bad for the user experience if you look at the gif at the beginning of this seciton you ll notice that after entering a city name the word loading appears below to be replaced by the result when it arrives implement this in your own app 2 add css to make it look nice here the sky is the limit bigger fonts custom fonts custom colors and backgrounds you can use the skycons http darkskyapp github io skycons icons since the api returns an icon property in the data you could also look for another set of icons that is more original or in line with the style of your app 3 add more weather information the currently section contains the wind speed wind direction and many other interesting bits of information about the weather find a nice way to display them and do it 4 add a five day forecast to your application with its own styling and icons 5 when geocoding we are taking the first result that google maps api gives us inside this result there is the full name of the location that we requested for example if you query for address montreal the response will contain the string montreal qc canada use this to display it along with your weather information 6 use google places autocomplete https developers google com maps documentation javascript places autocomplete to provide an input box that will suggest options to the user this is better than guessing what the user wanted to type and will make you learn about a new api project 2 flickr api photo browser it s your turn now based on what you learned while doing the previous project you will build a flickr photo browser here s an example of what a super basic version of the browser will look if you want to use this as part of your portfolio you should definitely follow some of the improvements that we suggest p align center img src flickr api project gif p 1 get an api key here https www flickr com services api misc api keys html you ll have to get a yahoo account yes we know for this app you only need the key and not the secret flickr api key flickr api key png 2 read the documentation for the flickr search api https www flickr com services api flickr photos search html even though it mentions xml results you can get json back by using this url format https api flickr com services rest method flickr photos search format json nojsoncallback 1 api key your api key text the search text 3 read the documentation on how to build urls for flickr images https www flickr com services api misc urls html 4 write a function called getphotosforsearch that takes a search term and returns an array of photo objects you ll have to transform the flickr response quite a bit ideally you will return an array of objects with each object having thumb large and title properties these properties should be urls built using the documentation in step 3 5 wire up a search form submit event to start the search using the word s in the form input when receiving the results clear a pre existing container div and put the results in there each result should have this shape html a href url of the large image target blank img src url of the thumbnail alt title of the image a in order to create such elements it would help to have a helper function called createflickrthumb that returns an a element like this javascript function createflickrthumb photodata var link document createelement a link setattribute href photodata large link setattribute target blank var image document createelement img image setattribute src photodata thumb image setattribute alt photodata title link appendchild image return link 6 once the basics are working it s time to add some improvements make the gallery look nice with css make the gallery responsive using a block grid instead of linking to each image prevent the click and display a popup image with an x infinite scroll using window addeventlistener scroll try to figure out when the scrolling has reached the bottom of the page and start loading the next page of results project 3 vanilla blackjack introduction in this workshop you will be using the elegant deckofcardsapi http deckofcardsapi com in order to build your own game of blackjack once complete your game will look something like the picture below at which point you will be able to customize and skin it to your own liking by adding onto index html and styles css p align center img src blackjack gif p you are provided with 3 files index html app js styles css while index html and styles css are sufficiently complete for the basic game of blackjack app js is fairly empty only the function names and state variables are provided for you you re job is to complete app js in order to create a functioning game of blackjack getting starting create an index html file with the following content html doctype html html head meta charset utf 8 title vanilla blackjack title link rel stylesheet type text css href styles css head body h1 vanilla blackjack h1 button id new game shuffle new deck button div class game container div id game area div id dealer area h2 dealer span id dealer number span h2 div id dealer cards cards will appear here div div div id player area h2 player span id player number span span id announcement span h2 div id player cards cards will appear here div div div div id action area button id next hand style display none next hand button button id hit me style display none hit me button button id stay style display none i ll stay button div div script type text javascript src app js script body html create a styles css with the following content css body height 100vh background darkslategray h1 margin 2rem text align center color white font family fantasy h2 font family fantasy font size 26px color navy game container display flex flex flow row nowrap align items center perspective 1000px game area z index 1 width 85 transform rotatex 35deg padding 0 2rem 2rem 2rem background forestgreen dealer area margin bottom 2rem dealer cards transform translatey 40px display flex justify content center height 168px player area announcement margin left 25 player cards display flex justify content center height 168px action area z index 10 width 15 display flex flex direction column next hand hit me margin bottom 3rem button background steelblue padding 1rem font size 1rem border radius 1rem outline none img width 130px height 190px media min width 1200px body padding 0 12 testing the deck of cards api visit the deckofcardsapi http deckofcardsapi com and familiarize yourself with the first two api calls shuffle the cards draw a card open a new tab in chrome and open your console in developer tools paste the following into the console in order to observe the parsed response from deckofcardsapi javascript fetch https deckofcardsapi com api deck new shuffle deck count 6 then response response json then data console log data inside the response find the deck id and replace the value in the following api call in order to draw 4 cards from the deck you just shuffled javascript fetch https deckofcardsapi com api deck deck id draw count 4 then response response json then data console log data writing the game logic create an app js with the following content instructions are given inside each of the hallowed out functions your job is to fill out these functions to get the game running smoothly javascript app state these variables represent the state of our application they tell us at any given moment the state of our blackjack game you might find it useful to use these to debug issues by console logging them in the functions below var deckid var dealercards var playercards var playerscore 0 var dealerscore 0 var roundlost false var roundwon false var roundtied false game play nodes these nodes will be used often to update the ui of the game assign this variable to the dom node which has id dealer number var dealerscorenode select the dom node which has id player number var playerscorenode select the dom node which has id dealer cards var dealercardsnode select the dom node which has id player cards var playercardsnode selec the dom node which has id announcement var announcementnode selec the dom node which has id new game var newdecknode selec the dom node which has id next hand var nexthandnode selec the dom node which has id hit me var hitmenode selec the dom node which has id stay var staynode on click events these events define the actions to occur when a button is clicked these are provided for you and serve as examples for creating further possible actions of your own choosing newdecknode onclick getnewdeck nexthandnode onclick newhand hitmenode onclick hitme player staynode onclick settimeout dealerplays 600 game mechanics functions function getnewdeck this function needs to 1 call the resetplayingarea function 2 make a call to deckofcardsapi in order to retrieve a new deck id 3 set the value of our state variable deckid to the retrieved deck id 4 change the display property of style on the nexthandnode element in order to provide the player with the next hand button 5 hide the hit me and stay buttons by changing their style display to none 6 catch any errors that may occur on the fetch and log them function computescore cards this function receives an array of cards and returns the total score function newhand this function needs to 1 call the resetplayingarea function 2 make a call to deckofcardsapi using the deckid state variale in order to retrieve draw 4 cards from the deck 3 once 4 cards have been drawn push 2 of them to our dealercards state array and 2 to our playercards state array 4 set our dealerscore state variable to and then set the textcontent value of the dealerscorenode to dealerscore 5 foreach card in playercards and dealercards create an img element and assign the src of these to their respective card images don t forget to append these newly created img elements to the respective dealer cards and player cards dom elements in order to have them show up in the html 6 finally compute the player s score by calling computescore and update the playerscorenode to reflect this 7 if player score is 21 announce immediate victory by setting roundwon true announcementnode textcontent blackjack you win 8 catch and log possible error from the fetch function resetplayingarea this function needs to 1 reset all state variables to their defaults 2 reset the gameplay ui by updating textcontent of all nodes which may be displaying data from a previous round in the game ex dealerscorenode 3 remove all img elements inside dealercardsnode and playercardsnode function hitme target this function needs to 1 if any of roundlost or roundwon or roundtied is true return immediately 2 using the same deckid fetch to draw 1 card 3 depending on wether target is player or dealer push the card to the appropriate state array playercards or dealercards 4 create an img and set it s src to the card image and append it to the appropriate dom element for it to appear on the game play ui 5 if target player compute score and immediately announce loss if score 21 by setting roundlost true and updating announcementnode to display a message delivering the bad news 6 if target dealer just call the dealerplays function immediately after having appended the img to the game play ui 7 catch error and log function dealerplays this function needs to 1 if any of roundlost or roundwon or roundtied is true return immediately 2 compute the dealer s score by calling the computescore function and update the ui to reflect this if dealerscore 17 a delay here makes for nicer game play because of suspence settimeout hitme dealer 900 else if dealerscore 21 roundwon true update the ui to reflect this else if dealerscore playerscore roundlost true update the ui to reflect this else if dealerscore playerscore roundtied true update the ui to reflect this else roundwon true update the ui to reflect this hiding the dealer s first card now that the game is running smoothly we need to hide the dealer s first card in order for this to be real blackjack use this image and modify your app js to hide the dealer s first card until it is his turn to play p align center img src card png p challenge now that your game is running smoothly here are your options for challenges on this project 1 add betting to the game 2 make the app look professional | front_end |
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webalchemy | pypi version https img shields io pypi v webalchemy svg https crate io packages webalchemy pypi downloads https img shields io pypi dm webalchemy svg https crate io packages webalchemy alt webalchemy https i imgur com su7tdad png webalchemy modern web development with python3 mit licensed license txt powered by pythonium https github com pythonium pythonium tornado https github com facebook tornado and sockjs https github com sockjs sockjs client some examples angular style todomvc demo http skariel org webalchemy todomvc html source https github com skariel webalchemy tree master examples todomvc meteor style realtime and live editing video https vimeo com 74150054 demo http weba colors herokuapp com source https github com skariel webalchemy blob master examples colors meteor colors meteor example py webgl earth demo http skariel org webalchemy webglearth html source https github com skariel webalchemy blob master examples three d earth three d earth py contributions are welcome open a pull request open an issue mail me code or post in the mailing list https groups google com forum forum webalchemy getting started installation make sure you are using python3 then pip install webalchemy note that this does not install the examples you can download the examples from the zip https github com skariel webalchemy archive master zip tutorial and documentation a webalchemy application is a regular python class no need to inherit anything that provides relevant methods used by the webalchemy server the only required method is initialize which is called when the client side is ready for automation here we create a header and append it to the document body python class hellowworldapp def initialize self kwargs kwargs remote document body element h1 hello world to serve through a websocket just feed it to the run function and set your browser to http 127 0 0 1 8080 python from webalchemy import server from myapp import hellowworldapp server run hellowworldapp try to change the header text content and save the file to see how the client changes accordingly note that the app has to be imported for the live editing to work correctly it is also possible to add some styles python class hellowworldapp def initialize self kwargs h1 kwargs remote document body element h1 hello world h1 style color ff0000 marginleft 75px margintop 75px background 00ff00 want to use css rules to style all h1 s no problem python class hellowworldapp def initialize self kwargs self rdoc kwargs remote document self rdoc body element h1 hello world self rdoc body element h1 self rdoc stylesheet rule h1 style color ff0000 marginleft 75px margintop 75px background 00ff00 adding event listerners is also possible let s add a click listener using the elements events property like this python self rdoc body element h1 hello world events add click self clicked translate true let s define a simple event in which each click deletes the 1st letter of the heading this is run on the client and it is translated to js behind the scenes python def clicked self self textcontent self textcontent 1 in addition if we want to notify the server that the button was clicked we could use an rpc call from the client to the server we just need to add one line python def clicked self self textcontent self textcontent 1 rpc self handle click on backend some message just so you see how to pass paramaters in the server we can do something like this python def handle click on backend self sender id m1 m2 self rdoc body element h1 m1 m2 the program looks like this python class hellowworldapp def initialize self kwargs self rdoc kwargs remote document self rdoc body element h1 hello world events add click self clicked translate true self rdoc body element h2 self rdoc stylesheet rule h1 style color ff0000 marginleft 75px margintop 75px background 00ff00 def clicked self self textcontent self textcontent 1 rpc self handle click on backend some message just so you see how to pass paramaters def handle click on backend self sender id m1 m2 self rdoc body element h1 m1 m2 this how you push and pull data to and from the client see this program live on heroku http weba hello herokuapp com now you may wonder if it s dangerous for the client to be able to call any function on the server actually the client can only call registered functions in our case handle click on backend got registered when we assigned it to the rpc call on the client including external scripts use include to import external scripts into your project python class threedearth include https rawgithub com mrdoob three js master build three min js def initialize self kwargs do something cool here including external stylesheets use stylesheets to import external stylesheets python class jquerymobileexample include the jquery mobile stylesheet and the jquery jquery mobile scripts stylesheets http code jquery com mobile 1 4 0 jquery mobile 1 4 0 min css include http code jquery com jquery 1 10 2 min js http code jquery com mobile 1 4 0 jquery mobile 1 4 0 min js def initialize self kwargs do something cool here this will produce the following html in your client html doctype html html head base href http 127 0 0 1 8080 base link href http code jquery com mobile 1 4 0 jquery mobile 1 4 0 min css rel stylesheet link script src http code jquery com jquery 1 10 2 min js type text javascript script script src http code jquery com mobile 1 4 0 jquery mobile 1 4 0 min js type text javascript script hosting webalchemy uses sockjs so no need for a host supporting websockets howvever real websockets can still provide a benefit in terms of performance if you are interested to support websockets try hosting with heroku https www heroku com or openshift https www openshift com these are two good options further help join the mailing list https groups google com forum forum webalchemy webalchemy supports the mvc pattern usage of existing html several configuration options and much more all this missing documentation is wip we also suggest you play a bit try making a few containers you can spread them across multiple python files it s not too complicated the examples below demonstrate a few more features example 1 realtime meteor colors we translated meteor colors app to webalchemy the app can be seen in action here https vimeo com 74150054 and the meteor original here http www meteor com screencast the source is in the examples directory here https github com skariel webalchemy blob master examples colors meteor colors meteor example py it can be executed like this python from webalchemy import server from examples colors meteor colors meteor example import colorsmeteorapp server run colorsmeteorapp a live version is here http weba colors herokuapp com example 2 todomvc this is a client only app it can be served from a websocket like this python from webalchemy import server from examples todomvc todomvc import apptodomvc as app server run app or it can be frozen to be served from a static folder see live demo http skariel org webalchemy todomvc html like this python from webalchemy import server from examples todomvc todomvc import apptodomvc as app server generate static app this will generate todomvc html as defined in the configuration in app note the references to static files in the html you have to place the file where these are accessible or just change the paths more on this app here https github com skariel webalchemy tree master examples todomvc example 3 webgl earth python from webalchemy import server from examples three d earth three d earth import threedearth server run threedearth see the frozen app here http skariel org webalchemy webglearth html and the source here https github com skariel webalchemy blob master examples three d earth three d earth py philosophy the main idea is to write all server side code and automate the client using proxy objects this works well for all kinds of apps for e g if you want 100 client side just tell the server to generate or serve the client code if you want 99 server then don t use any client code except for passing events to the server which will decide what to do this is like the opposite of what meteor does but seems to have several advantages the best advantage is that you get to enjoy all the python ecosystem on the server want to do some number crunching machine learning natural language analysis or cartography no problem other advantages are server side code and html generation python on client side scaling with zmq etc what to expect documentation tutorials major cleanup pyzmq https github com zeromq pyzmq for some real scalability like with ipython data binding for general usage not just the dom use it with pixi js https github com goodboydigital pixi js sprites use it to bind server side model with client side model etc a few api changes the project is still very young a community | front_end |
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bitbot | bitbot gem version https img shields io gem v bitbot svg http badge fury io rb bitbot build status https img shields io travis jejacks0n bitbot svg https travis ci org jejacks0n bitbot maintainability https api codeclimate com v1 badges 7e22d47bd547a055c63e maintainability https codeclimate com github jejacks0n bitbot maintainability test coverage https api codeclimate com v1 badges 7e22d47bd547a055c63e test coverage https codeclimate com github jejacks0n bitbot test coverage license https img shields io badge license mit brightgreen svg https opensource org licenses mit bitbot is a lightweight rack endpoint specifically intended for slack webhooks it can be mounted within a rails app or can be run as a standalone rack app with the config ru provided as an example you can write custom responders that take advantage of the logic within your larger application responders have support for custom routing and can utilize wit ai http wit ai natural language processing for more complex responder examples check out the bitbot responders https github com modeset bitbot responders project installation ruby gem bitbot github jejacks0n bitbot rails bitbot can run fine without rails but if you re using rails you can run the install generator the generator will provide an initializer and mount the rack app within your routes be sure to update both the initializer and route if you change where it s mounted shell rails generate bitbot install configuration bitbot requires being configured but to simplify the readme it s not included here please check the config ru https github com modeset bitbot blob master config ru for an example and configuration documentation the config ru file is provided as a convenience and only serves as an example it is not included with the gem you can grab the config ru and run the listener with rackup or if you ve installed the generator you can test your setup by starting your rails server and running based on your configuration and port shell curl data text help me user name tester channel none token token http localhost 9292 rack bitbot webhook you should get a json response back if you don t bitbot is intentionally vague about what could ve gone wrong but the likely causes are that the token isn t correct the request isn t a post or that the username was the same as the bots she doesn t respond to herself setting up slack to get all of the configuration tokens and urls you ll need to go to slack and add the incoming webhooks and outgoing webhooks integrations you can get your incoming url and outgoing token by doing this which you can then set as environment variables and load them into your configuration when setting up the outgoing webhook integration you will need to know where you have configured the rack endpoint so you can provide that as the url that will be used adding responders there s a basic dsl for creating responders which allows you to register help for the various commands and define responder routes bitbot considers commands to be routable and so you can define them using route here s an example responder that specifies category help and a single route the category indicates grouping within the default help responder but is somewhat arbitrary in it s meaning should you do something else with it ruby class myresponder bitbot responder category greetings help hi bot description i ll respond with a greeting route say hi back hi bot i do respond with awesome hi message user name end end a route must be named and provide a regexp matcher here s another example but here we capture a value from the message in general a responder route will return a hash that s then sent back to the slack request but additional messages can be announced from within the responder as a general rule you should always use the respond with method in your responder routes because it can determine if it should return the hash or make the announcement itself you can also use the private message or public message helper methods which always announce and don t return a hash ruby route echo echo i do string respond with heard message user name say string in message channel end confirmations confirmations are included as a base feature but need redis to work provide your own redis connection in the configuration and you can add confirmations and more to your responders by default the configuration assumes redis is running locally and is available at redis current otherwise it will try to connect to redis at the standard port ruby route say hi back hi bot i do confirm were you saying hi to me yes do respond with awesome hi message user name end end wit ai we think wit ai http wit ai is pretty rad for a bot setup but it does take some work to get it trained and working the way you want this is part of the fun and part of the challenge to use wit ai in your responders you need to require wit ruby and include the wit module in your responder then you can define intents and which route they go to as well as any entities that are within them in the most complex form this would look something like the following note wit ruby expects env wit ai token to be defined read more https github com gching wit ruby ruby class myresponder bitbot responder include bitbot responder wit category greetings help hi bot my name is name description i ll respond with a greeting intent greeting say hi back entities contact e e value route say hi back hi bot my name is i do specified name respond with awesome hi specified name i m bot end end now if you train wit to understand hello i m jeremy jackson including the name portion as a wit contact entity it will make it through to the responder as the specified name argument to the block again this is a complex thing to setup and train so have fun with it you may also note that the route has a fallback regexp that allows using directly even if wit ai wasn t able to determine what the intent was worth mentioning the proc that you see in the entities above doesn t need to be specified if all you want is the value but if it s a proc it will call the proc with the entity hash some entities have complex structures like duration where you may want to pull out the seconds instead of the number of minutes or hours that may have been provided in those cases use duration e e normalized value but in our above example we could ve just used contact nil and the value would be pulled automatically for us announcing you can announce any message into any channel on slack using the bot for instance in a background job to have something happen on an action or predefined schedule you must configure bitbot s webhook url by setting up an incoming webhook integration on slack before this will work however ruby bitbot announce text hello all channel general you can send private messages if you like as well ruby bitbot announce text hello you channel username you can also reuse any of the existing responder routes by having the responder handle the route directly obviously in these cases you must provide anything that that the might expect from the message which always includes text and may include common things like channel or user name since responder routes can be pretty vague and implement any number of things you may have to provide additional information as well since responders can make their own announcements or return a hash you can use the bitbot announce method based on configuration ruby bitbot announce myresponder new respond to text hi bot channel general user name system or myresponder new respond to text hi bot channel general user name system regex can get hairy so the respond to method also accepts a block to return any additional context you may want to use inside your route ruby myresponder new respond to text archive user channel admin user name system id 2 route archive archive user i do access info from blockk more info context block call do something with the info user find more info id archive respond with you got it user with id more info id has been archived end license licensed under the mit license http creativecommons org licenses mit copyright 2019 jejacks0n https github com jejacks0n make code not war | ruby wit slack rack rails | ai |
msecs-macc-server | keeptime keeptime is an enterprise oriented collaborative time tracker it is composed by two parts an android application https github com aserpi msecs macc app and a rails server server the server acts both as an api backed for the app and as a control panel for system administrators admins can create and modify employees workspaces clients projects and activities and check the current cost for each element app the application is for exclusive use of workers they can change their bill rate and add new schedule items project masters can also create and modify new tasks and assign workers to them | cloud |
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BlockchainSwift | blockchainswift a simple blockchain with swift license http img shields io badge license mit lightgrey svg style flat http mit license org twitter https img shields io badge twitter shu223 blue svg style flat http twitter com shu223 what is this this is an implementation of blockchain with swift based on this great article learn blockchains by building one hacker noon https hackernoon com learn blockchains by building one 117428612f46 how to build open blockchainswift xcodeproj with xcode build and run blockchainsample gif what is interesting just seeing the sample app is not fun at all trying to implement by yourself according to the reference articles would be fun the implementation is quite simple less than 200 lines for example here is the block swift block swift struct block codable let index int let timestamp double let transactions transaction let proof int let previoushash data hashes a block func hash data let encoder jsonencoder let data try encoder encode self return data sha256 here are the articles learn blockchains by building one hacker noon https hackernoon com learn blockchains by building one 117428612f46 python https qiita com hidehiro98 items 841ece65d896aeaa8a2a swift swift https qiita com shu223 items ebe59325f36fbf25e3d6 the consensus part is available in the feature consensus https github com shu223 blockchainswift tree feature consensus branch | blockchain |
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rylc-html5 | readme zu rylc html5 beispielcode zum kapitel 9 modularisierung und build im buch mobile web apps mit javascript opitz consulting com go javascriptbuch voraussetzungen java development kit 1 6 oder neuer apache maven 3 0 4 oder neuer bauen der backend komponente rylc backend https github com mjswa rylc backend klonen in das verzeichnis rylc backend wechseln das backend mittels mvn clean install pproduction bauen bauen des projekts inkl integrationstests mvn clean verify pintegration dazu muss chrome http www google com chrome ber den kommandozeilen befehl chrome gestartet werden k nnen alternativ kann die property browser in pom xml angepasst und dort der gew nschte befehl zum starten von chrome eingetragen werden manuelles ausf hren der tests via jstestdriver f r un x systeme 1 mvn jetty run war pintegration ausf hren 1 jstd server sh ausf hren 1 einen browser ber die url http localhost 9876 http localhost 9876 mit jstestdriver verbinden 1 zum ausf hren der tests jstd unit sh bzw jstd ui sh aufrufen manuelles starten und ausf hren der tests 1 jetty starten mittels mvn jetty run pdevelopment 1 zum ausf hren von unit tests in chrome den unit spec runner http localhost 8585 rylc html5 unitspecrunner html aufrufen 1 zum ausf hren von ui tests in chrome den ui spec runner http localhost 8585 rylc html5 uispecrunner html aufrufen starten und aufrufen der web app 1 jetty starten mittels mvn jetty run 1 startseite http localhost 8585 rylc html5 der app aufrufen 1 mit benutzername fred und password pass anmelden | front_end |
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label-studio-ml-backend | what is the label studio ml backend the label studio ml backend is an sdk that lets you wrap your machine learning code and turn it into a web server the web server can be then connected to label studio to automate labeling tasks and dynamically retrieve pre annotations from your model there are several use cases for the ml backend pre annotate data with a model use active learning to select the most relevant data for labeling interactive ai assisted labeling model fine tuning based on recently annotated data if you just need to load static pre annotated data into label studio running an ml backend might be overkill for you instead you can import preannotated data https labelstud io guide predictions html quickstart follow this example tutorial to create a ml backend service 1 install the latest label studio ml sdk bash git clone https github com humansignal label studio ml backend git cd label studio ml backend pip install e 2 create a new ml backend directory bash label studio ml create my ml backend you can go to the my ml backend directory and modify the code to implement your own inference logic the directory structure should look like this my ml backend dockerfile docker compose yml model py wsgi py readme md requirements txt dockefile and docker compose yml are used to run the ml backend with docker model py is the main file where you can implement your own training and inference logic wsgi py is a helper file that is used to run the ml backend with docker you don t need to modify it readme md is a readme file with instructions on how to run the ml backend requirements txt is a file with python dependencies 3 run the ml backend server bash docker compose up the ml backend server will be available at http localhost 9090 you can use this url to connect it to label studio go to the project settings machine learning and add a new ml backend this ml backend is an example provided by label studio it actually doesn t do anything if you want to implement the actual inference logic go to the next section implement prediction logic in your model directory locate the model py file for example my ml backend model py the model py file contains a class declaration inherited from labelstudiomlbase this class provides wrappers for the api methods that are used by label studio to communicate with the ml backend you can override the methods to implement your own logic python def predict self tasks context kwargs make predictions for the tasks return predictions the predict method is used to make predictions for the tasks it uses the following tasks label studio tasks in json format https labelstud io guide task format html context label studio context in json format https labelstud io guide ml html passing data to ml backend for interactive labeling scenario predictions predictions array in json format https labelstud io guide export html raw json format of completed tasks once you implement the predict method you can see predictions from the connected ml backend in label studio implement training logic you can also implement the fit method to train your model the fit method is typically used to train the model on the labeled data although it can be used for any arbitrary operations that require data persistence for example storing labeled data in database saving model weights keeping llm prompts history etc by default the fit method is called at any data action in label studio like creating a new task or updating annotations you can modify this behavior in label studio settings webhooks to implement the fit method you need to override the fit method in your model py file python def fit self event data kwargs train the model on the labeled data old model self get old model write your logic to update the model self set new model new model with event event type can be annotation created annotation updated etc data the payload received from the event check more on webhook event reference https labelstud io guide webhook reference html additionally there are two helper methods that you can use to store and retrieve data from the ml backend self set key value store data in the ml backend self get key retrieve data from the ml backend both methods can be used elsewhere in the ml backend code for example in the predict method to get the new model weights other methods and parameters other methods and parameters are available within the labelstudiomlbase class self label config returns the label studio labeling config https labelstud io guide setup html as xml string self parsed label config returns the label studio labeling config https labelstud io guide setup html as json self model version returns the current model version run without docker to run without docker for example for debugging purposes you can use the following command bash pip install r my ml backend label studio ml start my ml backend modify the port to modify the port use the p parameter bash label studio ml start my ml backend p 9091 deploy your ml backend to gcp before you start 1 install gcloud https cloud google com sdk docs install 2 init billing for account if it s not activated https console cloud google com project billing enable 3 init gcloud type the following commands and login in browser bash gcloud auth login 4 activate your cloud build api 5 find your gcp project id 6 optional add gcp region with your default region to your env variables to start deployment 1 create your own ml backend 2 start deployment to gcp bash label studio ml deploy gcp ml backend local dir from model python script gcp project id gcp project id label studio host https app heartex com label studio api key your label studio api key 3 after label studio deploys the model you will get model endpoint in console | ai |
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cs224n | cs224n new stanford cs224n natural language processing with deep learning winter 2017 note that the new cs224n is the merger of previous cs224n and cs224d cs224n cs224n cs224d notes lecture exercises assignments issue links syllabus http web stanford edu class cs224n syllabus html youtube videos https www youtube com playlist list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 | ai |
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xuperchain | xuperchain build status https travis ci org xuperchain xuperchain svg branch master https travis ci org xuperchain xuperchain go report card https goreportcard com badge github com xuperchain xuperchain https goreportcard com report github com xuperchain xuperchain golangci https golangci com badges github com golangci golangci lint svg https golangci com license https img shields io github license xuperchain xuperchain style flat square license release https img shields io github v release xuperchain xuperchain style flat square latestrelease readme cn md english what is xuperchain xuperchain the first open source project of xuperchain lab introduces an underlying solution to build the super alliance network based on the dynamic kernel of xupercore you can use xuperchain as a blockchain infrastructure to build a compliant blockchain network xuperchain is the underlying solution for union networks with following highlight features dynamic kernel based on the dynamic kernel technology the free extension kernel components without kernel code intrusion and lightweight extension customized kernel engine are implemented to meet the needs of blockchain implementation for various scenarios it provides a comprehensive and high performance implementation of standard kernel components comprehensively reduce the cost of blockchain research and development and open a new era of one click chain development high performance creative xupermodel technology makes contract execution and verification run parallel tdpos ensures quick consensus in a large scale network wasm vm using aot technology solid security contract account protected by multiple private keys ensures assets safety flexible authorization system supports weight threshold ak sets and could be easily extended high scalability robust p2p network supports a large scale network with thousands of nodes branch management on ledger makes automatic convergence consistency and supports global deployment multi language support support pluggable multi language contract vm using xuperbridge technology flexibility modular and pluggable design provides high flexibility for users to build their blockchain solutions for various business scenarios quick start requirements os support linux and mac os go 1 14 x or later gcc 4 8 x or later git build clone the repository git clone https github com xuperchain xuperchain note master branch contains the latest features but might be unstable for production use please check out the latest release latestrelease enter the xuperchain folder and build the code cd xuperchain make note that if you are using go 1 11 or later go modules are used to download 3rd party dependencies by default you can also disable go modules and use the prepared dependencies under vendor folder run test make test run run single node blockchain there is an output folder if build successfully enter the output folder create a default chain start blockchains cd output sh control sh start by doing this a blockchain named xuper is created you can find the data of this blockchain at data blockchain xuper by default the xuper chain will produce a block every 3 seconds try the following command to see the trunkheight of chain and make sure it s growing bin xchain cli status run multi nodes blockchain generate multi nodes before running the following command make sure you have run make to make the code make testnet enter the testnet directory and then start three nodes separately make sure the port is not used cd testnet node1 sh control sh start cd node2 sh control sh start cd node3 sh control sh start observe the status of each node bin xchain cli status h 37101 bin xchain cli status h 37102 bin xchain cli status h 37103 documentation we have new documentation of chinese version at xuperchain chinese docs docs the english version is coming soon how to contribute we encourage you to contribute to xuperchain please review the contribution guidelines contribution for information on how to get started contributing to the project license xuperchain is under the apache license version 2 0 https github com xuperchain xuperchain blob master license contact for business cooperation please email xchain help baidu com note with source github if you are interested in the open source technology and application of xuperchain welcome to add in wechat join the baidu super chain developer community by replying and have in depth exchanges with baidu senior engineers wechat qr code is as follows img width 291 alt 496bd829f51cda8f4c8027daf0e6b543 src https user images githubusercontent com 51440377 210493618 b0b5ee12 4809 4702 accf 4954aa74d86d png contribution docs en us contribute contribute guideline md latestrelease https github com xuperchain xuperchain releases latest docs https xuper baidu com n xuperdoc index html | blockchain |
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dynamips-gdb-mod | dynamips gdb mod dynamips gdb mod is a patch to the dynamips cisco ios emulator which facilitates debugging and reverse engineering process of cisco ios by allowing the integration with most used existing debugging and disassembler tools such as gdb and ida pro | os |
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feature-extraction | computer vision feature extraction toolbox for image classification the goal of this toolbox is to simplify the process of feature extraction of commonly used computer vision features such as hog sift gist and color for tasks related to image classification the details of the included features are available in a href features md features md a in addition to providing some of the popular features the toolbox has been designed for use with the ever increasing size of modern datasets the processing is done in batches and is fully parallelized on a single machine using parfor and can be easily distributed across multiple machines with a common file system the standard cluster setup in many universities the features extracted in a bag of words manner color hog2x2 hog3x3 sift ssim are encoded using locality constrained linear coding to allow the use of a linear classifier for fast training testing in my experients i have found hog2x2 or hog3x3 to be most effective as global image features and tend to perform even better when combined with color features which contain complementary information the toolbox works on matlab and octave octave may still have some compatibility issues though and doesn t support paralell processing installation before you can use the code you need to download this repository and compile the mex code git clone http github com adikhosla feature extraction cd feature extraction matlab compile to the best of my knowledge there should be no issues compiling on linux mac or windows octave currently isn t able to compile but most features should be working basic usage the basic usage is relatively simple addpath genpath datasets pascal sun specify name of datasets train lists pascal1 jpg sun1 jpg sun2 jpg specify lists of train images test lists pascal2 jpg pascal3 jpg sun3 jpg specify lists of test images feature hog2x2 specify feature to use c conf load the config structure datasets feature datasets train lists test lists feature c perform feature extraction train features load feature datasets 1 feature train c load train features of pascal test features load feature datasets 2 feature test c load test features of sun the list of available features is pre color gist hog2x2 hog3x3 lbp sift ssim pre details are given a href features md here a the i datasets feature i function can be run on multiple machines in parallel to speed up feature extraction this function handles the complete pipeline of building a dictionary for bag of words features coding features to the dictionary and pooling them together in a spatial pyramid you can use a single or multiple datasets as shown above a seperate folder will be created for each dataset and a different dictionary will be learned unless specified otherwise in the a href config structure configuration structure a demo there is a demo script provided in this code to extract color features on a provided set of train and test images then the features are used in a nearest neighbor classifier to predict the class of the test images demo the demo above will show the train and test images and the nearest neighbors of the test images from the training set config structure there are various options available through the config structure created using the i conf i function b cache b main folder where all the files will be stored b feature config feature name b contains the configuration of feature name such as dictionary size b batch size b batch size for feature processing reduce for less ram usage b cores b specify number of cores to use for parfor 0 use all b verbosity b used to change how much is output to screen during feature computation 0 low 1 high b common dictionary b used to share a common dictionary across datasets dictionary is learned using equal number of samples from each dataset useful for eccv 2012 paper additional options are described in i a href util conf m conf m a i bundled code there are functions or snippets of code included with or without modification from the following packages feature coding a href http www ifp illinois edu jyang29 codes cvpr10 llc rar locality constrained linear coding a pixelwise hog gist a href http labelme csail mit edu labelmetoolbox labelmetoolbox zip labelme toolbox a for color features a href http lear inrialpes fr people vandeweijer code colornaming tar color naming a sift features a href http www cs illinois edu homes slazebni research spatialpyramid zip spatial pyramid matching code a lbp features a href http www cse oulu fi cmv downloads lbpmatlab lbp matlab code a ssim a href http www robots ox ac uk vgg software selfsimilarity vgg ssim package a a href http cseweb ucsd edu elkan fastkmeans tar fast k means a disclaimer most of the features included in this toolbox have not been designed by me i have either fine tuned them or used them as is from existing work this toolbox simply unifies existing code bases which are credited in the a href bundled code bundled code a section in an easy to use architecture i have done my best to highlight where different snippets of code originate from but please do not hesitate to contact me if you find that i have missed anything b most importantly please cite the a href features md original inventors a of the different features in addition to a subset of the a href reference references a below when you use this toolbox in your work b reference the provided code was used for feature extraction in the following papers aditya khosla tinghui zhou tomasz malisiewicz alexei a efros antonio torralba br a href http undoingbias csail mit edu undoing the damage of dataset bias a eccv 2012 aditya khosla jianxiong xiao antonio torralba aude oliva br a href http people csail mit edu khosla projects regionmem memorability of image regions a nips 2012 aditya khosla wilma a bainbridge antonio torralba aude oliva br a href http people csail mit edu khosla modifying the memorability of face photographs a iccv 2013 please cite a subset of the above papers if you use this code acknowledgements i am extremely grateful to oscar beijbom hamed pirsiavash and tinghui zhou for being the initial users of this toolbox and providing useful comments and various bug fixes questions and comments if you have any feedback please email a href http people csail mit edu khosla aditya khosla a at a href mailto khosla csail mit edu khosla csail mit edu a | ai |
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GANs-in-Computer-Vision | div align center img src cover ebook cover png width 884 height 512 div gans in computer vision abstract in this article series we are reviewing the most fundamental works of generative adversarial networks in computer vision we start from the very beginning from concepts such as generative learning adversarial learning we provide some code and illustrations for educational purposes the goal is to focus on the intuition of the models by tackling the multiple problems that arise when training a gan we have thoroughly analyzed more than 20 papers in 6 different articles in a chronological order we will continue to update the gan series based on the newer publications or older ones that we skipped we do hope that this series will provide you a big overview of the field so that you will not need to read all the literature by yourself independent of your background on gans update 07 2020 free ebook is realesed in the ai summer website https theaisummer com gans computer vision ebook introduction to generative learning part 1 link to the article introduction to generative learning part 1 https theaisummer com gan computer vision first author published title code conference journal ian j goodfellow 10 jun 2014 generative adversarial networks http papers nips cc paper 5423 generative adversarial nets pytorch https github com black0017 3d gan pytorch nips mehdi mirza 6 nov 2014 conditional generative adversarial nets https arxiv org abs 1411 1784 tensorflow https github com znxlwm tensorflow mnist cgan cdcgan arxiv alec radford 19 nov 2015 unsupervised representation learning with deep convolutional generative adversarial networks https arxiv org abs 1511 06434 tensorflow https github com carpedm20 dcgan tensorflow arxiv xi chen 12 jun 2016 infogan interpretable representation learning by information maximizing generative adversarial nets https arxiv org abs 1606 03657 tensorflow https github com openai infogan nips tim salimans 10 jun 2016 improved techniques for training gans https arxiv org abs 1606 03498 tensorflow https github com openai improved gan nips conditional image synthesis and 3d object generation part2 link to the article conditional image synthesis and 3d object generation part 2 https theaisummer com gan computer vision object generation first author published title code conference journal augustus odena 30 oct 2016 conditional image synthesis with auxiliary classifier gans https arxiv org abs 1610 09585 keras https github com lukedeo keras acgan tf https github com buriburisuri ac gan pytorch https github com eriklindernoren pytorch gan arxiv jiajun wu 24 oct 2016 learning a probabilistic latent space of object shapes via 3d generative adversarial modeling https arxiv org abs 1610 07584 pytorch https github com black0017 3d gan pytorch nips zinan lin 12 dec 2017 pacgan the power of two samples in generative adversarial networks http papers nips cc paper 7423 pacgan the power of two samples in generative adversarial networks tf https github com fjxmlzn pacgan nips phillip isola 21 nov 2016 image to image translation with conditional adversarial networks https arxiv org abs 1611 07004 tf https github com affinelayer pix2pix tensorflow pytorch https github com junyanz pytorch cyclegan and pix2pix arxiv jun yan zhu 30 mar 2017 unpaired image to image translation using cycle consistent adversarial networks https arxiv org abs 1703 10593 tf https github com junyanz cyclegan pytorch https github com junyanz pytorch cyclegan and pix2pix arxiv improved training with wasserstein distance game theory control and progressively growing schemes part3 link to the article improved training with wasserstein distance game theory control and progressively growing schemes part3 https theaisummer com gan computer vision incremental training first author published title code conference journal martin arjovsky 26 jan 2017 wasserstein gan http proceedings mlr press v70 arjovsky17a html tf https github com lynnho dcgan lsgan wgan gp dragan tensorflow 2 pytorch https github com martinarjovsky wassersteingan pmlr david berthelot 31 mar 2017 began boundary equilibrium generative adversarial networks https arxiv org abs 1703 10717 tf https github com junyanz cyclegan pytorch https github com junyanz pytorch cyclegan and pix2pix nips tero karras 27 oct 2017 progressive growing of gans for improved quality stability and variation https arxiv org abs 1710 10196 tf https github com tkarras progressive growing of gans pytorch https github com facebookresearch pytorch gan zoo iclr 2k image and video synthesis and large scale class conditional image generation part 4 link to the article 2k image and video synthesis and large scale class conditional image generation part4 https theaisummer com gan computer vision video synthesis first author published title code conference journal ting chun wang 30 nov 2017 high resolution image synthesis and semantic manipulation with conditional gans https arxiv org abs 1711 11585 pytorch https github com nvidia pix2pixhd cvpr ting chun wang 20 aug 2018 video to video synthesis https arxiv org abs 1711 11585 pytorch https github com nvidia vid2vid nips andrew brock 28 sep 2018 large scale gan training for high fidelity natural image synthesis https arxiv org abs 1809 11096 tf https github com taki0112 biggan tensorflow pytorch https github com ajbrock biggan pytorch iclr self supervised adversarial training and high resolution image synthesis with style incorporation part 5 link to the article self supervised adversarial training and high resolution image synthesis with style incorporation part 5 https theaisummer com gan computer vision style gan first author published title code conference journal ting chen 27 nov 2018 self supervised gans via auxiliary rotation loss https arxiv org abs 1811 11212 pytorch https github com vandit15 self supervised gans pytorch cvpr tero karras 12 dec 2018 a style based generator architecture for generative adversarial networks https arxiv org abs 1812 04948 tf https github com nvlabs stylegan pytorch https github com facebookresearch pytorch gan zoo cvpr semantic image synthesis and learning a generative model from a single image part 6 link to the article semantic image synthesis and learning a generative model from a single image part 6 https theaisummer com gan computer vision semantic synthesis first author published title code conference journal taesung park 18 mar 2019 semantic image synthesis with spatially adaptive normalization https arxiv org abs 1903 07291 pytorch https github com nvlabs spade cvpr tamar rott shaham 2 may 2019 singan learning a generative model from a single natural image https arxiv org abs 1903 07291 pytorch https github com tamarott singan iccv other awesome gan repositories and resources play with generative adversarial networks gans in your browser https poloclub github io ganlab the gan zoo https github com hindupuravinash the gan zoo a list of all named gans facebook ai gan repo https github com facebookresearch pytorch gan zoo a mix of gan implementations including progressive growing pytorch multiple implementations https github com eriklindernoren pytorch gan of generative adversarial networks another pytorch gan library https github com kwotsin mimicry that reproduces research results for popular gans cvpr 2020 workshop keras implementations https github com eriklindernoren keras gan of generative adversarial networks open questions about generative adversarial networks augustus odena distill https distill pub 2019 gan open problems 2019 generative adversarial networks in computer vision a survey and taxonomy https arxiv org abs 1906 01529 from gan to wgan https lilianweng github io lil log 2017 08 20 from gan to wgan html lil log blog 2017 the article series can be cited as python article adaloglou2020gans title gans in computer vision author adaloglou nikolas and karagiannakos sergios journal https theaisummer com year 2020 url https theaisummer com gan computer vision | gan gans computer-vision deep-learning adverserial learning | ai |
Lab-Project-FreeRTOS-POSIX | freertos posix portable operating system interface posix threading wrapper for freertos the portable operating system interface posix is a family of standards specified by the ieee computer society for maintaining compatibility between operating systems freertos posix implements a small subset of the posix threading https pubs opengroup org onlinepubs 7908799 xsh threads html api this subset allows application developers familiar with posix api to develop a freertos application using posix like threading primitives freertos posix does not implement more than 80 of the posix api therefore an existing posix compliant application or a posix compliant library cannot be ported to run on freertos kernel using only this wrapper this repository only contains source code for demo applications please visit https github com freertos freertos labs to consume freertos posix it is recommended to use this repository as a submodule please refer to git tools submodules https git scm com book en v2 git tools submodules notes this project is undergoing optimizations or refactorization to improve memory usage modularity documentation demo usability or test coverage | os |
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indic_nlp_library | indic nlp library the goal of the indic nlp library is to build python based libraries for common text processing and natural language processing in indian languages indian languages share a lot of similarity in terms of script phonology language syntax etc and this library is an attempt to provide a general solution to very commonly required toolsets for indian language text the library provides the following functionalities text normalization script information word tokenization and detokenization sentence splitting word segmentation syllabification script conversion romanization indicization note shatanuvadak translation and brahminet transliteration apis are no longer supported you can use newer indictrans https github com ai4bharat indictrans translation and indicxlit https github com ai4bharat indicxlit transliteration models we developed at ai4bharat https ai4bharat iitm ac in in fact you can find many state of the art datasets and models on the ai4bharat homepage the data resources required by the indic nlp library are hosted in a different repository these resources are required for some modules you can download from the indic nlp resources https github com anoopkunchukuttan indic nlp resources project if you are interested in indian language nlp resources you should check the indic nlp catalog https github com indicnlpweb indicnlp catalog for pointers pre requisites python 3 x for python 2 x version check the tag python 2 7 final jan 2019 not actively supporting python 2 x anymore but will try to maintain as much compatibility as possible indic nlp resources https github com anoopkunchukuttan indic nlp resources urduhack https github com urduhack urduhack needed only if urdu normalization is required it has other dependencies like tensorflow other dependencies are listed in setup py configuration installation from pip pip install indic nlp library if you want to use the project from the github repo add the project to the python path clone this repository install dependencies pip install r requirements txt run export pythonpath pythonpath project base directory in either case export the path to the indic nlp resources directory run export indic resources path path to indic nlp resources usage you can use the python api to access all the features of the library many of the most common operations are also accessible via a unified commandline api getting started check this ipython notebook http nbviewer jupyter org url anoopkunchukuttan github io indic nlp library doc indic nlp examples ipynb for examples to use the python api you can find the python 2 x notebook here http nbviewer jupyter org url anoopkunchukuttan github io indic nlp library doc indic nlp examples 2 7 ipynb documentation you can find detailed documentation here https indic nlp library readthedocs io en latest this documents the python api as well as the commandline reference citing if you use this library please include the following citation misc kunchukuttan2020indicnlp author anoop kunchukuttan title the indicnlp library year 2020 howpublished url https github com anoopkunchukuttan indic nlp library blob master docs indicnlp pdf you can find the document here docs indicnlp pdf website http anoopkunchukuttan github io indic nlp library author anoop kunchukuttan anoop kunchukuttan gmail com anoop kunchukuttan gmail com companies organizations projects using indicnlp library ai4bharat indicnlpsuite https indicnlp ai4bharat org the classical language toolkit http cltk org microsoft nlp recipes https github com microsoft nlp recipes facebook m2m 100 https github com pytorch fairseq tree master examples m2m 100 revision log 0 81 26 may 2021 bug fix in version number extraction 0 80 24 may 2021 improved sentence splitting bug fixes support for urdu normalizer 0 71 03 sep 2020 improved documentation bug fixes 0 7 02 apr 2020 unified commandline improved documentation added setup py 0 6 16 dec 2019 new romanizer and indicizer script unifiers improved script normalizers added contrib directory for sample uses changed to mit license 0 5 03 jun 2019 improved word tokenizer to handle dates and numbers added sentence splitter that can handle common prefixes honorofics and uses some heuristics added detokenizer added acronym transliterator that can convert english acronyms to brahmi derived scripts 0 4 28 jan 2019 ported to python 3 and lots of feature additions since last release primarily around script information script similarity and syllabification 0 3 21 oct 2014 supports morph analysis between indian languages 0 2 13 jun 2014 supports transliteration between indian languages and tokenization of indian languages 0 1 12 mar 2014 initial version supports text normalization license indic nlp library is released under the mit license | python indian-languages natural-language-processing | ai |
BlockchainSpider | blockchainspider blockchain spiders aim to collect data of public chains including transaction subgraph the subgraph with a center of specific address label data the labels of address or transaction block data the blocks on chains for more info in detail see our documentation https 870167019 gitbook io blockchainspider getting started install let s start with the following command shell git clone https github com wuzhy1ng blockchainspider git and then install the dependencies shell pip install r requirements txt crawl a transaction subgraph we will demonstrate how to crawl a transaction subgraph of kucoin hacker https etherscan io address 0xeb31973e0febf3e3d7058234a5ebbae1ab4b8c23 on ethereum and trace the illegal fund of the hacker run on this command as follow shell scrapy crawl txs eth ttr a source 0xeb31973e0febf3e3d7058234a5ebbae1ab4b8c23 you can find the transaction data on data 0xeb3 c23 csv on finished try to import the transaction data and the importance of the addresses in the subgraph data importance 0xeb3 c23 csv to gephi https gephi org http 120 78 210 226 8000 readme kucoin png the hacker is related to tornado cash a mixing server it shows that the hacker took part in money laundering collect label data in this section we will demonstrate how to collect labeled addresses in ofac sanctions list https home treasury gov policy issues financial sanctions specially designated nationals and blocked persons list sdn human readable lists run this command as follow shell scrapy crawl labels ofac you can find the label data on data labels ofac each row of this file is a json object just like this json net eth label entity info uid 30518 address 0x72a5843cc08275c8171e582972aa4fda8c397b2a first name null last name secondeye solution identities id type email address id number support secondeyesolution com id type email address id number info forwarderz com note please indicate the source when using crawling labels collect block data in this section we will demonstrate how to collect block data in ethereum https ethereum org run this command as follow shell scrapy crawl trans blocks web3 you can find the label data on data in which blockitem csv saves the metadata for blocks such as minter timestamp and so on transactionitem csv saves the external transactions of blocks important tips if you want to get the best performance of blockchainspider please read the settings of apikeys https 870167019 gitbook io blockchainspider settings apikeys and cache https 870167019 gitbook io blockchainspider settings cache about tracer please cite our paper https arxiv org abs 2201 05757 and the respective papers of the methods used if you use this code in your own work latex misc wu2022tracer title tracer scalable graph based transaction tracing for account based blockchain trading systems author zhiying wu and jieli liu and jiajing wu and zibin zheng year 2022 eprint 2201 05757 archiveprefix arxiv primaryclass cs cr please execute the code in test to reproduce the experimental results in the paper parameters py parameter sensitivity experiment compare py comparative experiment metrics py export evaluation metrics for more information please refer to test readme md | blockchain spider python scrapy graph-sampling labeling-tool dataset | blockchain |
Sparkify-Data-Model-ETL | img src weston mackinnon 3pcrw jrkm8 unsplash jpg width 1250 height 250 photo by a href https unsplash com betteratf8 utm source unsplash utm medium referral utm content creditcopytext weston mackinnon a on a href https unsplash com s photos sheet music utm source unsplash utm medium referral utm content creditcopytext unsplash a sparkify songplay data extract transform load etl introduction this project is intended to create a postgres database for sparkify platform song and artist data and combine that information with sparkify songplay data to create a descriptive set of records of sparkify platform song plays table of contents installation usage database design etl process files data folder log data song data create tables py etl py sql queries py etl ipynb test ipynb installation python 3 pip install pyscopg2 usage from the command line run these commands python create tables py python etl py database design the sparkify database consists of only 5 separate tables linked by primary and foreign keys below are descriptions of the tables and schema table songplay contains data to show the songs listened to by sparkify platform users also shows the time and location of when and where the user listened to the song type column type pk songplay id int fk start time timestamp fk user id int null level varchar fk song id varchar fk artist id varchar null session id varchar null location varchar null user agent varchar table user contains data on the sparkify platform user type column type pk user id int null first name varchar null last name varchar null gender varchar null level varchar table song contains song data type column type pk song id varchar null title varchar fk artist id varchar null year int null duration decimal table artist contains artist data type column type pk artist id varchar null name varchar null location varchar null latitude varchar null longitude varchar table time contains timestamp data from song listens as well as other time related type column type pk start time timestamp null hour int null day int null week int null month int null year int null weekday int etl process song data insert song data into song table song data insert artist data into artist data log data extract timestamp and time information from songplay insert into time table find artist id and song id from artist and song tables insert artist song and songplay data into songplay table files data folder log data json records of sparkify song plays song data json records of sparkify song and artist information create tables py python module that drops tables if they exist and creates them in the database using queries from sql queries etl py python module that moves songplay song and artist data from the song data and log data folders and inserts data into the songplay table etl ipynb notebook for developing etl py module sql queries py python module that contains all sql queries for table creation and inserts test ipynb notebook for testing etl py module | sql database data-engineering data-engineering-nanodegree | server |
nycda-js-algorithm-exercises | prerequisites you will need to install nodejs https nodejs org if you don t have it yet osx install homebrew https brew sh brew install nodejs ubuntu sudo apt get install nodejs setup in terminal npm install testing tests will be run using mocha https mochajs org to test one file npm test leveln file js to test multiple files npm test leveln file1 js leveln file2 js to run all tests npm run test all the test implementations currently all use the assert library provided by chai http chaijs com suggested order of exercises level 1 1 equalswithepsilon 2 camelize 3 countwords 4 counter 5 issorted 6 isprime 7 reversestring 8 ispalindrome 9 countletters level 2 1 city 2 isdiagonal 3 map 4 summarize 5 iscorrectlyparanthesised 6 genprimes 7 greet level 3 level 4 1 iscorrectlybracketed 2 studentcourse performing the exercises after cloning and setting up the repository you are strongly encouraged to create your own git branch to work on whenever you solve an exercise create a commit with each exercise first make sure to read the comments on top of the file these will generally explain to you in human terms what are you trying to achieve and inform you of any constraints afterwards look through the tests and try to understand what they mean for example if you see a line such as let foo new foo 42 you can immediately note two things 1 you will need to define the type foo i e you will definitely need a class foo 2 the constructor of foo is supposed to accept a single numeric parameter note that this parameter may be optional and there might be other optional parameters as well similarly if you later see something like foo bar then you will know that the foo object is supposed to have an attribute bar the tests are run by performing a series of checks that aim to verify that the code you wrote functions as intended these checks are encoded as assertions in the testing code the meaning of these assertions should be fairly evident assert equals for example checks that the provided two values are equal the check will pass if they are but fail otherwise assertions are grouped into it statements which are meant to test one aspect of the functionality that your code is meant to provide as you work through an exercise start simple and aim to produce a working or at least partially working solution before making the solution clean and elegant a solution that satisfies all of the tests is a great start but remember to pay a lot of attention to having a clean coding style neatly and consistently formatted code good and describe variable attribute function class names comments pointing out any caveats or explaining your approach and idea it should be self explanatory that you are not allowed to modify the testing code updating your local copy occassionally i will make updates to this repository which will mainly involve creating new exercises or reorganising existing ones when this happens you can update your local repository like so 1 commit any uncommited changes you may have you need to have a clean working directory to continue 2 switch to the master branch git checkout master 3 pull the changes git pull 4 switch back to your own branch 5 merge in the updates from the master branch git merge master you might have merge conflicts after the last step if you do you ll have to sort them out yourself make sure to incorporate all of my changes i e the changes coming from the remote into your local copy once you ve fixed the conflicts in the files use git add to mark those conflicts as resolved and finally create a commit to indicate that all conflicts have been resolved solutions the reference solutions can be found on the solutions branch https github com shdnx nycda js algorithm exercises tree solutions please do not abuse this fact contributions if you have spotted a bug or have an idea for an improvement please open an issue for it in the issue tracker https github com shdnx nycda js algorithm exercises issues however you re strongly encouraged to fix it yourself and submit a pull request https github com shdnx nycda js algorithm exercises pulls license mit | front_end |
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3Dpose_ssl | 3d human pose machines with self supervised learning keze wang liang lin chenhan jiang chen qian and pengxu wei 3d human pose machines with self supervised learning https arxiv org abs 1901 03798 to appear in ieee transactions on pattern analysis and machine intelligence t pami 2019 this repository implements a 3d human pose machine to resolve 3d pose sequence generation for monocular frames and includes a concise self supervised correction mechanism to enhance our model by retaining the 3d geometric consistency the main part is written in c and powered by caffe https github com bvlc caffe deep learning toolbox another is written in python and powered by tensorflow https github com tensorflow tensorflow results we proposed results on the human3 6m kth football ii and mpii dataset p align center img src http www sysu hcp net wp content uploads 2019 01 wechat screenshot 20190111210435 png p p align center img src http www sysu hcp net wp content uploads 2019 01 wechat screenshot 20190111210519 png p p align center img src http www sysu hcp net wp content uploads 2019 01 wechat screenshot 20190111210546 png p license this project is only released for academic research use get started clone the repo git clone https github com chanyn 3dpose ssl git or directly download from https www dropbox com s qycpjinof2ishw9 3dpose ssl tar gz dl 0 including datasets and well compiled caffe under cuda 8 0 our code is organized as follows caffe 3dssl support caffe models pretrained models and results prototxt network architecture definitions tensorflow code for online refine test script that run results split by action tools python and matlab code requirements 1 nvidia gpu and cudnn are required to have fast speeds for now cuda 8 0 with cudnn 5 1 has been tested the other versions should be working 2 caffe python wrapper is required 3 tensorflow 1 1 0 4 python 2 7 13 5 matlab 6 opencv python installation 1 build 3dssl caffe cd root caffe 3dssl follow the caffe installation instructions here http caffe berkeleyvision org installation html if you re experienced with caffe and have all of the requirements installed and your makefile config in place then simply do make all j 8 make pycaffe 2 install tensorflow datasets human3 6m we change annotation of human3 6m to hold 16 points rfoot rknee rhip lhip lknee lfoot hip spine thorax head rwrist relbow rshoulder lshoulder lelbow lwrist in keeping with mpii we have provided count mean file and protocol i protocol iii split list of human3 6m https drive google com open id 1uxy bdfs8nt6dghsi6gdqwmcb84 jl3o follow human3 6m website http vision imar ro human3 6m description php to download videos and api we split each video per 5 frames you can directly download processed square data in this link https pan baidu com s 1ielhh9w8tnkpcb836jxtcw and list format of 16skel train test is img path p1 sub 2dx sub p1 sub 2dy sub p2 sub 2dx sub p2 sub 2dy sub p1 sub 3dx sub p1 sub 3dy sub p1 sub 3dz sub p2 sub 3dx sub p2 sub 3dy sub p2 sub 3dz sub clip clip 0 denote reset lstm shell files construction h36m gt 2d and 3d annotations splited by actions hg2dh36m 2d estimation predicted by hourglass square denotes prediction of square image ours 2d 2d prediction from our model ours 3d 3d coarse prediction of model extension mask3d 16skel train 2d3d clip txt train list of protocol i 16skel test 2d3d clip txt 16skel train 2d3d p3 clip txt train list of protocol iii 16skel test 2d3d p3 clip txt 16point mean limb scaled max min csv 16 points normalize by x min max min after setting up human3 6m dataset following its illustration and download the above training testing list you should update root folder paths in caffe root examples prototxt for images and annotation director mpii we crop and square single person from all images and update 2d annotation in train h36m txt resort points according to order of human3 6m points mkdir data mpii cd data mpii wget v https drive google com open id 16gqjvf4whleconstloh5y7ezcnbuhom tar xzvf mpii square tar gz rm f mpii square tar gz training offline phase our model consists of two cascade modules so the training phase can be divided into the following steps cd caffe root 1 pre train the 2d pose sub network with mpii you can follow cpm https arxiv org abs 1602 00134 or hourglass https arxiv org abs 1603 06937 or other 2d pose estimation method we provide pretrained cpm caffemodel https drive google com open id 1fufc7nwfbwopmdpfbgu3ipfk8no6pfim please put it into caffe root models 2 train 2d to 3d pose transformer module with human3 6m and we fix the parameters of the 2d pose sub network the corresponding prototxt file is in examples 2d to 3d bilstm prototxt sh examples 2d to 3d train sh 3 to train 3d to 2d pose projector module we fix the above module weights and we need in the wild 2d pose dataset to help training we choose mpii sh sh examples 3d to 2d train sh 4 fine tune the whole model jointly we provide trained model and coarse prediction of protocol i and protocol iii https drive google com open id 1ds50na6fbtag mhvzfbinx9ae5wgpqa6 sh sh examples finetune whole train sh 5 model extension add rand mask to relieve model bias we provide corresponding model files in examples mask3d sh sh examples mask3d train sh model inference 3d to 2d project module is initialized from the well trained model and they will be updated by minimizing the difference between the predicted 2d pose and projected 2d pose inference with provided models https drive google com open id 1dmupud jdhumimapwe2dwgj2igk04xhq shell step1 download the trained model cd project root mkdir models cd models wget v https drive google com open id 1dmupud jdhumimapwe2dwgj2igk04xhq unzip model extension mask3d zip rm r model extension mask3d zip cd step2 save coarse 3d prediction cd test change data root in test human16 sh change root folder in template 16 merge prototxt test human16 sh 1 deploy prototxt 2 trained model 3 save dir 4 batchsize sh test human16 sh models model extension mask3d mask3d iter 400000 caffemodel mask3d 5 step3 online refine 3d pose prediction protocal 1 3 default is 1 pose2d ours hourglass gt default is ours coarse 3d saved results in sept2 python pred v2 py trained model models model extension mask3d mask3d 400000 pkl protocol 1 data dir data h36m coarse 3d test mask3d save srr results pose2d hourglass inference with our2d model https drive google com open id 19ktyttzunm 1 7hewonkcxpp2qvo zck shell maybe you want to predict 2d the model we use to predict 2d pose is similar to our 3dpredict model without ssl module or you can use hourglass https github com princeton vl pose hg demo to predict 2d pose step1 1 download the trained merge model cd project root mkdir models cd models wget v https drive google com open id 19ktyttzunm 1 7hewonkcxpp2qvo zck unzip our2d zip rm r our2d zip move 2d prototxt to project root test mv our2d 2d test cd step1 2 save 2d prediction cd test change data root in test human16 sh change root folder in 2d template 16 merge prototxt test human16 sh 1 deploy prototxt 2 trained model 3 save dir 4 batchsize sh test human16 sh 2d models our2d 2d iter 800000 caffemodel our2d 5 replace predict 2d pose in data dir or change data dir in tensorflow pred v2 py mv our2d data h36m ours 2d bilstm2d p1 800000 step2 is same as above step3 online refine 3d pose prediction protocal 1 3 default is 1 pose2d ours hourglass gt default is ours coarse 3d saved results in sept2 python pred v2 py trained model models model extension mask3d mask3d 400000 pkl protocol 1 data dir data h36m coarse 3d test mask3d save srr results pose2d ours inference with yourself the only difference is that you should transfer caffemodel of 3d to 2d project module to pkl file we provide gen refinepkl py in tools sh follow above step1 2 to produce coarse 3d prediction and 2d pose transfer caffemodel of srr module to python pkl file python tools gen refinepkl py caffe root caffemodel dir pkl dir model pkl online refine 3d pose prediction python pred v2 py trained model model pkl evaluation shell print mpjp run tools eval h36m m visualization of 2dpose 3d gt pose 3d coarse pose 3d refine pose please change data root in visualization m before running run visualization m citation article wang20193d title 3d human pose machines with self supervised learning author wang keze and lin liang and jiang chenhan and qian chen and wei pengxu journal ieee transactions on pattern analysis and machine intelligence year 2019 publisher ieee | ai |
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cloud-native-engineering-gitops | cloud native engineering gitops cloud native engineering with gitops devops kathmandu | cloud |
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Graduation-Project | graduation project towards the automation of semantically enriching point cloud datasets an interactive method incorporating machine learning for fast semantic classification of building indoor scenes by guido porteners msc construction management engineering eindhoven university of technology byg72 http duraark eu wp content uploads 2016 01 cita byg72 august jpg image from duraark data repository introduction point clouds are a valuable datatype for the aec industry and construction management sector as they can be used for accurate scan to bim processes and allow construction managers to compare the as built and as designed situation of construction projects this provides practical implications such as progress monitoring analysis of structural deviations and quality control however before being pragmatic raw point cloud data needs to be enriched with semantic information for which segmentation and classification are key activities manual processes require a significant experience and time investment and automation is therefore desired this is a contemporary studied problem in the field of construction management and several methods are proposed that incorporate contextual reasoning and a hierarchical workflow however these methods break down as buildings are vastly different and construction elements are often ambiguous therefore researchers turned to methods incorporating machine learning for object recognition specifically deep learning and neural networks as they appear to closely resemble human reasoning and learning contextual and semantic relationships unfortunately current state of the art methods incorporating deep learning are not tailored to the aec industry and because of their complexity are poorly understood and impractical for construction management practitioners to work with this research proposes a hybrid method for segmenting and classifying point cloud datasets for the aec industry which combines both machine learning and user interaction to exploit their advantages and to balance out their disadvantages this is achieved by training a deep neural network as a base model which outputs a valuable set of parameters which can be used for a region growing algorithm to operate as a smart and quick selection tool for selecting specific element class categories prerequisites software python 3 5 https www python org downloads release python 354 pcl 1 8 or higher http pointclouds org blender v2 79 https www blender org download cloudcompare for transforming and subsampling almost any point cloud data to ply http www cloudcompare org python packages numpy 1 13 1 matplotlib 2 0 2 h5py 2 7 1 pandas 0 20 3 pyflann3 1 8 4 1 scikit learn 0 19 0 prerequisites for training artificial neural networks keras 2 1 1 tensorflow 1 4 0 as keras back end required when training on gpu requires tensorflow gpu version nvidia gpu with compute capability 3 0 cuda toolkit 8 0 cudnn 5 1 blender packages numpy 1 13 1 ply import export workflow to be updated | cloud |
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Neneco-FreeRTOS-On-RPI4B | neneco version neneco 1 tomoe sharingan v0 1 neneco project is an unofficial port of freertos for raspberry pi 4 model b neneco provide an abstraction layer in order to make the development of programs on rpi4b with freertos as easy as possible this project began when the main developer needed to create a program with real time requirements and as constrains the rpi4b board why sharingan names as said before neneco emerge from the need to develop a real time application this project it is called susanoo tribute to susanoo from naruto and not the japanese god susanoo project is a mixed criticality application on automotive sector the goal of susanoo is reducing the number of ecu s inside a vehicle to achieve that strong virtualization techniques are being used like a hypervisor to provide spacial and temporal isolation between the critical and non critical subsystems thus neneco is the critical subsystem of susanoo the main goal is developing neneco until mangeky sharingan the only sharingan able to invoke susanoo available drivers on currently version the supported drivers are x gpio x gci 400 interrupt controller uart the driver only implements uart2 in further versions will be added suport to all uart s i2c the driver only implements i c device 1 in further versions will be added suport to all i c devices spi the driver only implements spi device 0 in further versions will be added suport to all spi devices pwm the driver only implements pwm device 0 channel 1 in further versions will be added suport to all pwm s and channels gpio clock manager the driver only implements pwm clock in further versions will be added suport to all clocks extra neneco implements some drivers for board comonly used in projects for now neneco give support to x ads1115 a four adc channel to i c x l298n a dual h bridge motor driver which allows speed and direction control of two dc motors at the same time x implement a elm327 driver a interface to read car can bus network to do list the to do list is in order by priority implement a buil in interpreter in progress implement a shared memory module to give support to communication between guest in multi guest implementations scale the uart driver to all uart devices scale the i2c driver to all i2c devices scale the spi driver to all spi devices scale the gpio clock manager driver to all gpio clock devices scale the pwm driver to all pwm devices organization neneco is divided into two folders freertos port and rpi4 drivers the freertos port contains all the necessary files for the raw freertos port thank you eggman https github com eggman freertos raspi3 for the amazing job the rpi drivers contains the drivers to give support to rpi4b contains the main application as well but the location of main application can be changed main application the available main file always provide a complete test over the implemented drivers thus the version 1 tomoe sharingan provide a complete test over the drivers specified above to test a specific driver the only thing to do is uncomment the driver function on main function but this is covered later pre requisites to be able to compile and use neneco project some tools are needed the approach explained here is not unique but it is probably the easier one building a default image so this project need u boot to run in order to obtain this and the image for the rpi4b buildroot https buildroot org will be used 1 install buildroot zsh git clone https github com buildroot buildroot git 2 default configuration list zsh make list defconfigs 3 select a default configuration for rpi4b 64 bit zsh make raspberrypi4 64 defconfig 4 open the menu for further modifications zsh make menuconfig 5 select u boot as bootloader bootloaders u boot in board defconfig write rpi 4 buildroot condiguration to have u boot assets u boot png 6 compile the image zsh make j nproc 7 flash the sd card with the compiled image 8 change the name of u boot bin to kernel8 img 9 uart1 configuration this uart is used to interact with the u boot to enable this uart in the config txt write enable uart 1 building the cross compiler in order to compile neneco a cross compiler must be used you can use the toolchain created by buildroot when you compile the linux image or you can use a different compiler i will use the aarch64 elf bare metal target because i need buildroot for other projects do the download in https developer arm com tools and software open source software developer tools gnu toolchain gnu a downloads how to use neneco 1 clone the repository zsh git clone https github com d3boker1 neneco freertos over rpi4 git 2 go to the makefile and change the cross compiler for your compiler 3 go to the folder neneco compile neneco zsh make 4 copy the neneco elf file to the boot partition of your sd card 5 put the card on rpi4b and power up now wait until u boot start printing the boot process when it appears click in some key quickly the u boot prompt should appear write the following commands to start running neneco zsh dcache off fatload mmc 0 0x28000000 neneco elf dcache flush bootelf 0x28000000 6 depending on what you uncomment in main file probably you have messages on uart2 demo you can see the neneco demo in demo brach https github com d3boker1 neneco freertos on rpi4b tree demo documentation this project is being documented at the same time as it is being developed however some drivers are not documented yet and it is not the most priority task at the moment support use the github issues to report any type of issues or problems feel free to help me improving neneco francisco marques university of minho fmarques 00 protonmail com | freertos c assembly critical real-time | os |
PyWaves | pywaves pywaves is an object oriented python interface to the waves blockchain platform getting started you can install pywaves using pip install pywaves documentation the library utilizes classes to represent various waves data structures pywaves address pywaves asset pywaves assetpair pywaves order code example python import pywaves as pw myaddress pw address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s otheraddress pw address 3pntcniuzppqxdl9rzrk3bcftbujifqrafm myaddress sendwaves otheraddress 10000000 mytoken myaddress issueasset token1 my token 1000 0 while not mytoken status pass myaddress sendasset otheraddress mytoken 50 address class pywaves address address publickey privatekey seed creates a new address object attributes address publickey privatekey seed methods balance assetid confirmations 0 returns balance of waves or other assets assets returns a list of assets owned by the address issueasset name description quantity decimals 0 reissuable false txfee default asset fee timestamp 0 issue a new asset reissueasset asset quantity reissuable false txfee default asset fee timestamp 0 reissue an asset burnasset asset quantity txfee default asset fee timestamp 0 burn the specified quantity of an asset sendwaves recipient amount attachment txfee default tx fee timestamp 0 send specified amount of waves to recipient masstransferwaves transfers attachment timestamp 0 sending waves tokens via a mass transfer sendasset recipient asset amount attachment txfee default tx fee timestamp 0 send specified amount of an asset to recipient masstransferwaves self transfers attachment timestamp 0 sending an asset via mass transfer cancelorder assetpair order cancel an order buy assetpair amount price maxlifetime 30 86400 matcherfee default matcher fee timestamp 0 post a buy order tradablebalance assetpair get tradable balance for the specified asset pair sell assetpair amount price maxlifetime 30 86400 matcherfee default matcher fee timestamp 0 post a sell order lease recipient amount txfee default lease fee timestamp 0 post a lease transaction leasecancel leaseid txfee default lease fee timestamp 0 cancel a lease getorderhistory assetpair get order history for the specified asset pair cancelopenorders assetpair cancel all open orders for the specified asset pair deleteorderhistory assetpair delete order history for the specified asset pair createalias alias txfee default alias fee timestamp 0 create alias sponsorasset assetid minimalfeeinassets txfee pywaves default sponsor fee timestamp 0 sponsoring assets setscript script txfee pywaves default script fee timestamp 0 sets a script for this address datatransaction data timestamp 0 sets data for the account data should be a json array with entries including type bool binary int string key and value deletedataentry key deletes a given data entry identified by key from the data storage of the account setscript scriptsource txfee pywaves default script fee timestamp 0 issue a smart asset setassetscript asset scriptsource txfee pywaves default asset script fee timestamp 0 set a new script for a smart asset invokescript dappaddress functionname params payments feeasset none txfee pywaves default invoke script fee invoke a script on a given dapp address asset class pywaves asset assetid creates a new asset object attributes status assetid issuer name description quantity decimals 0 reissuable false methods status returns issued if the asset exists assetpair class pywaves assetpair asset1 asset2 creates a new assetpair object with 2 asset objects attributes asset1 asset2 methods orderbook get order book ticker get ticker with 24h ohlcv data last get traded price open get 24h open price high get 24h high price low get 24h low price close get 24h close price same as last vwap get 24h vwap price volume get 24h volume pricevolume get 24h price volume trades n get the last n trades trades from to get the trades in from to interval candles timeframe n get the last n candles in the specified timeframe candles timeframe from to get the candles in from to interval in the specified timeframe order class pywaves order orderid assetpair address creates a new order object attributes status orderid assetpair address matcher matcherpublickey methods status returns current order status cancel cancel the order wxfeecalculator class this class is meant to provide the necessary functionality to calculate fees according to the new wx fee structure all values here for price and amounts of tokens are always in the smallest unit of the token satoshis methods calculatedynamicfee calculates the dynamic fee for a trade calculatedynamicdiscountfee calculates the dynamic discounted fee for a trade calculatepercentsellingfee priceassetid amountassetid amounttosell calculates the percentage selling fee for a trade calculatepercentdiscountedsellingfee priceassetid amountassetid amounttosell calculates the discounted percentage selling fee for a trade calculatepercentbuyingfee priceassetid price amounttobuy calculates the percentage buying fee for a trade calculatepercentdiscountedbuyingfee priceassetid price amounttobuy calculates the discounted percentage buying fee for a trade example import pywaves as pw config amountasset waves priceasset 25feqejrkqk6yckit7lz6sayz7gufctxfcchnrvfd5at privatekey xxx pw setnode https nodes testnet wavesnodes com chain testnet pw setmatcher http matcher testnet waves exchange wxfeecalculator pw wxfeecalculator address pw address privatekey config privatekey tradingpair pw assetpair pw asset config amountasset pw asset config priceasset price 5 amounttobuy 1000000000 matcherfee wxfeecalculator calculatepercentdiscountedbuyingfee config priceasset price amounttobuy tx address buy tradingpair amounttobuy price matcherfee matcherfee matcherfeeassetid emamlxdnv3xiz8rxg8btj33jcew3wlczl3jkyymuubpc print tx other functions pywaves setnode node chain chain id set node url http ip address port and chain either mainnet or testnet or any other chain if you also define the chain id pywaves setchain chain chain id set chain either mainnet or testnet or any other chain if you also supply the chain id pywaves setoffline switch to offline mode sign tx locally without broadcasting to network pywaves setonline switch to online mode sign tx locally a broadcast to network pywaves validateaddress address checks if the provided address is a valid waves address pywaves setmatcher node set matcher url http ip address port pywaves setdatafeed node set datafeed url http ip address port pywaves height get blockchain height pywaves lastblock get last block pywaves block n get block at specified height pywaves tx id get transaction details pywaves statechangefortx id get the state changes for the given tx by id pywaves statechangesforaddress address limit 1000 get the last limit with a default of 1000 state changes for the given address pywaves symbols get list of symbol asset mapping pywaves markets get all traded markets with tickers pywaves symbol name get predefined asset for the specified symbol pywaves waves pywaves btc pywaves usd default fees the fees for waves asset transfers asset issue reissue burn and matcher transactions are set by default as follows default tx fee 100000 default asset fee 100000000 default matcher fee 1000000 default lease fee 100000 default alias fee 100000 default sponsor fee 100000000 default script fee 100000 more examples playing with addresses python import pywaves as pw generate a new address myaddress pw address some address myaddress generate set an address with an address myaddress pw address 3p6wfa4qytkgwvaswiib6yaea2x8zyxncjh get an existing address from seed myaddress pw address seed seven wrist bargain hope pattern banner plastic maple student chaos grit next space visa answer get an existing address from privatekey myaddress pw address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s get an existing address from a publickey address pw address publickey eynusmw4adtcc6amczyxkihmpmf2bz2xxvjpbip3ufzl get an address from a seed with a different nonce this is especially useful for accessing addresses generated by nodes myaddress pw address seed seven wrist bargain hope pattern banner plastic maple student chaos grit next space visa answer nonce 1 balances python import pywaves as pw myaddress pw address 3p6wfa4qytkgwvaswiib6yaea2x8zyxncjh get waves balance print your balance is 18d myaddress balance get waves balance after 20 confirmations print your balance is 18d myaddress balance confirmations 20 get an asset balance print your asset balance is 18d myaddress balance dhgwrrvvyqjsepd32ybbquedh4gj1n984x8qoekjgh8j waves and asset transfers python import pywaves as pw myaddress pw address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s send waves to another address myaddress sendwaves recipient pw address 3pntcniuzppqxdl9rzrk3bcftbujifqrafm amount 100000000 send asset to another address mytoken pw asset 4zzed8wjxsvuo2mem2bmz87azw8sx7tvc6ufsua5lytv myaddress sendasset recipient pw address 3pntcniuzppqxdl9rzrk3bcftbujifqrafm asset mytoken amount 1000 issuing an asset python import pywaves as pw mytoken myaddress issueasset name mytoken description this is my first token quantity 1000000 decimals 2 create an alias python import pywaves as pw pw setnode node http 127 0 0 1 6869 chain testnet myaddress pw address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s myaddress createalias myalias1 mass payment python import pywaves as pw recipients 3pbbp6bg2yenhfdjtym7jzzxyqeb7sx5ofg 3p4a27acd3sknja46pcgrlyenk36tkszgup 3p81u3ujotnuwzmwaldcjqlzbvbrauuqmfs 3pgckemwqcebmel8jhe9nzqrbncndchcozp 3pkjtzz4fhkrjuikbq1hrk5xbwvkdytyvkn myaddress pw address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s for address in recipients myaddress sendwaves pw address address 1000000 mass transfer of waves feature 11 python import pywaves as pw transfers recipient 3n1xca2dy8aewqrdajpzugy99eq8j9h4rb3 amount 1 recipient 3n3ywbq27nnk7tek6asfh38bj93gulxxsi1 amount 2 recipient 3mwib5ukwxt4x1qj8dqpp2lpm3m48v1z5rc amount 3 address pw address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s address masstransferwaves transfers mass transfer of assets feature 11 python import pywaves as pw transfers recipient 3n1xca2dy8aewqrdajpzugy99eq8j9h4rb3 amount 1 recipient 3n3ywbq27nnk7tek6asfh38bj93gulxxsi1 amount 2 recipient 3mwib5ukwxt4x1qj8dqpp2lpm3m48v1z5rc amount 3 address pw address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s address masstransferassets transfers pw asset 9dtbndybcyvilzhptyf1hbqk73f6s7nq5dxhnhubtbhd data transaction python import pywaves as py myaddress py address privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s data type string key test value testval myaddress datatransaction data token airdrop python import pywaves as pw myaddress pw address privatekey mytoken pw asset 4zzed8wjxsvuo2mem2bmz87azw8sx7tvc6ufsua5lytv amount 1000 with open recipients txt as f lines f readlines for address in lines myaddress sendasset pw address address strip mytoken amount add a script to an account python import pywaves as pw import base64 pw setnode node node chain testnet script match tx n case true n address pw address privatekey private key tx address setscript script txfee 1000000 issue a smart asset python imort pywaves as pw import base64 pw setnode node node chain testnet script match tx n case true n address pw address privatekey private key tx address issuesmartasset smarttestasset an asset for testingsmart assets 1000 script 2 set a new script for a smart asset python import pywaves as pw import base64 pw setnode node node chain testnet script match tx n case true n address pw address privatekey private key tx address setassetscript pw asset asset id script invoking a script on a dapp address python import pywaves as pw pw setnode node node chain testnet address pw address privatekey private key tx address invokescript 3n5wq22blsf3gt5vwhtcrbrneteswput8kk fundrecipient type integer value 100 type string value test type boolean value true amount 100 assetid bgnvlgpklwibiz7vwlcy3r92mzppcu2duub4tv9w6gmi working with contracts python import pywaves as pw pw setnode node node t contract pw contract 3n7xfieej8dhymjfs7amukzxkb1pfmxzhzi seed contract faucet working with oracles querrying oracles python import pywaves as pw oracle pw oracle oracleaddress 3p4pcxsjqmzqbalo8zanhtbdzrrquobhqp7 getting all data entries for an oracle print oracle getdata getting data for a specific key of an oracle print oracle getdata order total eeh5drjdmnoydhnbtklsrnzq95etjuqwtvmdbcxojboy getting all data entries of an oracle filtered by a regular expression print oracle getdata regex order total storing data in an oracle python import pywaves as pw pw setnode https testnode1 wavesnodes com t oracle pw oracle seed your seed here print oracle storedata oracle test string test entry from oracle class working with more than one network python import pywaves as pw config pw parallelpywaves config setnode https testnode1 wavesnodes com testnet taddress pw address seed test test test pywaves config address pw address seed test test test print taddress address print address address print taddress address playing with waves matcher node dex python import pywaves as pw set matcher node to use pw setmatcher node http 127 0 0 1 6886 post a buy order btc pw asset 4zzed8wjxsvuo2mem2bmz87azw8sx7tvc6ufsua5lytv usd pw asset 6wuo2htadypqvceetj1fc5p4womvcgmynasn8ym4bgil btc usd pw assetpair btc usd myorder myaddress buy assetpair btc usd amount 15e8 price 95075 post a sell order wct pw asset 6wuo2htadypqvceetj1fc5p4womvcgmynasn8ym4bgil incent pw asset flbgxzrpqkvuczqshdcnxeptkh2chmei4gdbfdrrjvof wct incent pw assetpair wct incent myorder myaddress sell assetpair wct incent amount 100e8 price 25e8 post a buy order using waves as price asset btc pw asset 4zzed8wjxsvuo2mem2bmz87azw8sx7tvc6ufsua5lytv btc waves pw assetpair btc pw waves myorder myaddress buy assetpair btc waves amount 1e8 price 50e8 cancel an order myorder cancel or myaddress cancelorder assetpair myorder getting market data from waves data feed wdf python import pywaves as pw set the asset pair waves btc pw assetpair pw waves pw btc get last price and volume print s s waves btc last waves btc volume get ticker ticker waves btc ticker print ticker 24h open print ticker 24h vwap get last 10 trades trades waves btc trades 10 for t in trades print s s s s t buyer t seller t price t amount get last 10 daily ohlcv candles ohlcv waves btc candles 1440 10 for t in ohlcv print s s s s s t open t high t low t close t volume lpos python import pywaves as pw connect to a local testnet node pw setnode node http 127 0 0 1 6869 chain testnet myaddress pw address privatekey csbpqpne3z1thnms9vjpaxqywn9hgmhd9asaprm3tiuj mineraddress pw address 3nbthmvjmcexzj9itp9kiic2k6qngqwpqmq lease 1000 waves to mineraddress leaseid myaddress lease mineraddress 100000000000 revoke the lease myaddress leasecancel leaseid doing simple multisig thanks to the new functionality of the txsigner and txgenerator classes new functionality like multisig is possible python pw setnode https nodes testnet wavesnodes com t firstaddress address address seed this is just a simple test seed one secondaddress address address seed this is just a simple test seed two generator txgenerator txgenerator signer txsigner txsigner tx generator generatesendwaves secondaddress 1 firstaddress publickey txfee 500000 signer signtx tx firstaddress privatekey signer signtx tx secondaddress privatekey res firstaddress broadcasttx tx using pywaves in a python shell check an address balance import pywaves as pw pw address 3p31zvgdh6ai6jk6zz18tjyzjsa1b83ypoj address 3p31zvgdh6ai6jk6zz18tjyzjsa1b83ypoj publickey privatekey seed balances waves 1186077288304570 bdmryzsmdzpgkhdm7futknkcubvvkdpmcqej31puzjmy tokes 43570656915 rrbqh2xxcwadlyedsickm589vb4rcembcph5mjawhu9 ripto bux 4938300000000 4rmhfoscycjz1imndvtz45doouvrqqdpbx7xdflb4guf incentcoffee 7 ftim86cxm6hanxarjxzs2fq7xls3njvgbzzewqwwqn6n waves 2117290600000000 e4ip4jztc4pcvebyn1818t4lnoybvl3y4y4dmpatgwa9 bitcoin 500000000000 flbgxzrpqkvuczqshdcnxeptkh2chmei4gdbfdrrjvof incent 12302659925430 gqr2fpkfmwjmazcbqmxefbiwgvpcngydev7xpux6xqce kiss 1000 dxg3plganynzajhgzvwljc4p3t2cpkbgxy4j9ejaaupw ultracoin 200000000000000 4ewbpyy4xnpsfloqk3iuvufamqkldu5o6zqcyyp9d8ae like 1000 generate a new address import pywaves as pw newaddress pw address some address newaddress generate address 3p6wfa4qytkgwvaswiib6yaea2x8zyxncjh publickey eynusmw4adtcc6amczyxkihmpmf2bz2xxvjpbip3ufzl privatekey ctmqwjzqfc7przswimkagmwfm4q2vn5fmcyykdbpdx6s seed seven wrist bargain hope pattern banner plastic maple student chaos grit next space visa answer balances waves 0 check an asset import pywaves as pw pw asset dhgwrrvvyqjsepd32ybbquedh4gj1n984x8qoekjgh8j status issued assetid dhgwrrvvyqjsepd32ybbquedh4gj1n984x8qoekjgh8j issuer 3ppkf2ph4kmygsdixjrhnwrpycvhr1ye37v name wavescommunity description waves community token quantity 1000000000 decimals 2 reissuable false post an order and check its status myorder myaddress buy pw assetpair token1 token2 1 25 myorder status accepted id arzdygfxz3ksrmvhngelljnn3cqnz7rca7u6dvw3zert asset1 afzl992fqbhcgszgkdkairwcjtthm55yvce99hwbhf88 asset2 49aha2rr2eunr3kzfwedfdi7k9v5mlqblycmvdp2qkzt sender address 3p6wfa4qytkgwvaswiib6yaea2x8zyxncjh sender publickey eynusmw4adtcc6amczyxkihmpmf2bz2xxvjpbip3ufzl matcher http 127 0 0 1 6886 cancel the order myorder cancel myorder status cancelled id arzdygfxz3ksrmvhngelljnn3cqnz7rca7u6dvw3zert asset1 afzl992fqbhcgszgkdkairwcjtthm55yvce99hwbhf88 asset2 49aha2rr2eunr3kzfwedfdi7k9v5mlqblycmvdp2qkzt sender address 3p6wfa4qytkgwvaswiib6yaea2x8zyxncjh sender publickey eynusmw4adtcc6amczyxkihmpmf2bz2xxvjpbip3ufzl matcher http 127 0 0 1 6886 offline signing and custom timestamps offline signing a future transaction import pywaves as pw pw setoffline myaddress pw address privatekey f2jvbjrkzjusz1aqrdnd8mmxfc85nqz5jwvzx4bxswxv recipient pw address 3p8ya6ary5gzwnzbbxdp3xjeng97jeipcda sign a future tx to transfer 100 waves to recipient the tx is valid on jan 1st 2020 12 00pm myaddress sendwaves recipient amount 100e8 timestamp 1577880000000 api endpoint assets broadcast transfer api type post api data fee 100000 timestamp 1577880000000 senderpublickey 27zdzba1q46rcmamz8gw2xrtgypznbzxs5j1y2hbumev amount 10000000000 attachment recipient 3p8ya6ary5gzwnzbbxdp3xjeng97jeipcda signature yetpoptjwc4wbpxbnewv9g6yep6j9g9rquzwjewjdqnfbmaxtxjrrsuu69nzzhebvzuglrhqiffoguxjwdun8bh offline signing time lock unlock transactions import pywaves as pw pw setoffline myaddress pw address privatekey f2jvbjrkzjusz1aqrdnd8mmxfc85nqz5jwvzx4bxswxv generate a lockbox address lockaddress pw address some address lockaddress generate sign the lock tx to send 100e8 to the lockbox valid on nov 1st 2017 myaddress sendwaves lockaddress 100e8 timestamp 1509537600000 api endpoint assets broadcast transfer api type post api data fee 100000 timestamp 1509537600000 senderpublickey 27zdzba1q46rcmamz8gw2xrtgypznbzxs5j1y2hbumev amount 10000000000 attachment recipient 3p3ubyqm9w7wztgjyklubrpzzewsiutccpv signature 5vgt6qwxjwxeyrxfnfsi67qqbyuigq9ka7hvzgovrttdt8nlryuqv2wbajqhrixdkttv6zsqmhnbkh8kecafpont sign the unlock tx to send funds back to myaddress valid on jan 1st 2020 lockaddress sendwaves myaddress 100e8 200000 txfee 200000 timestamp 1577880000000 api endpoint assets broadcast transfer api type post api data fee 200000 timestamp 1577880000000 senderpublickey 52xnbgnavzmw1cho9ajpimsvmitwengsnn9ayj7cdtx4 amount 9999800000 attachment recipient 3p7tfdcatyycfg5ojxnahejdss4mz7ybxby signature 3beyz1sqkefp96laxwt3cxdprw86daxcj6wgwpyykq3sgdotvqnkywxdyehnbzcq1nc7ja9cchtmo1c1ivav6c4t delete lockbox address and private key del lockaddress connecting to a different node or chain pywaves supports both mainnet and testnet chains by default pywaves connects to the mainnet rpc server at https nodes wavesnodes com it s possible to specify a different server and chain with the setnode function python import pywaves as pw connects to a local testnet node pw setnode node http 127 0 0 1 6869 chain testnet connects to a local mainnet node pw setnode node http 127 0 0 1 6869 chain mainnet license code released under the mit license https github com pywaves pywaves blob master license | blockchain |
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interlock | introduction interlock https github com usbarmory interlock copyright c withsecure corporation the interlock application is a file encryption front end developed for but not limited to usage with the usb armory https github com usbarmory usbarmory the primary interface consists of a web based file manager for an encrypted partition running on the device hosting the json application server e g usb armory the file manager allows uploading downloading of files to from the encrypted partition as well as symmetric asymmetric cryptographic operations on the individual files interlock screenshot https github com usbarmory interlock wiki images interlock png a command line mode is available to execute selected operations locally without the web interface authors andrea barisani andrea barisani withsecure com daniele bianco daniele bianco withsecure com documentation the main documentation is included in the present file https github com usbarmory interlock blob master readme md additional information can be found on the project wiki https github com usbarmory interlock wiki binary releases pre compiled binary releases for arm targets are available here https github com usbarmory interlock releases architecture the package provides a web application client side and its counterpart json application server implementing the protocol specified in the api document a command line mode is available to execute selected operations locally without the web interface this is primarily intended to aid encryption decryption operation with key derivation using hsm support on embedded firmwares the json application server is written in golang the client html javascript application is statically served by the application server implementing the presentation layer the interaction between the static html javascript and the json application server is entirely documented in the api document the web application authentication is directly tied to linux unified key setup luks disk encryption setup on the server side a successful login unlocks the specified encrypted volume while logging out locks it back design goals clear separation between presentation and server layer to ease auditability and integration minimum amount of external dependencies currently no code outside of go standard and supplementary libraries is required for the basic server binary authentication process directly tied to luks partition locking unlocking support for additional symmetric asymmetric encryption on individual files directories minimize exposure of sensitive data to the client with support for disposable authentication passwords server side operations key generation encryption decryption archive creation extraction and locking of private keys minimal footprint single statically linked binary to ease integration execution on the usb armory platform ciphers encrypted volumes luks encrypted partitions asymmetric ciphers openpgp using golang org x crypto openpgp symmetric ciphers aes 256 ofb w pbkdf2 password derivation sha256 4096 rounds and hmac sha256 security tokens time based one time password algorithm totp rfc6238 https datatracker ietf org doc html rfc6238 implementation google authenticator hardware security modules the hsm support allows symmetric ciphering using device specific secret keys allowing to uniquely tie derived keys to the specific hardware unit being used an hsm specific aes ofb based symmetric cipher is exposed with keys derived from the user password as well as device specific secret additionally the luks password for accessing encrypted volumes can filtered through the hsm to make it device specific finally the tls certificates can also be stored encrypted for a specific device supported drivers nxp security controller sccv2 nxp cryptographic acceleration and assurance module caam nxp data co processor dcp key storage a pre defined directory stored on the encrypted filesystem is assigned to public and private key storage see the configuration section for related settings the keys can be uploaded using the file manager imported as free text or generated server side the key storage directory structure is the following key path cipher name private public key identifier key format once uploaded in their respective directory private keys can only be deleted or overwritten they cannot be downloaded moved or copied the keys for otp ciphers e g totp implementing google authenticator generate a valid otp code for the current time when the key information is queried key info action on the right click menu requirements operation the use of interlock is coupled with the presence of at least one luks encrypted partition its initial creation is left to the user an example setup using cryptsetup and lvm2 follows the example uses a microsd partition to illustrate typical usb armory setup the partition mmcblk0p2 is assumed to have been previously created with fdisk using the desired size and the linux lvm type 8e pvcreate dev mmcblk0p2 initialize physical volume vgcreate lvmvolume dev mmcblk0p2 create volume group lvcreate l 20g n encryptedfs lvmvolume create logical volume of 20 gb cryptsetup y cipher aes xts plain64 set up encrypted partition key size 256 hash sha1 luksformat with default cryptsetup dev lvmvolume encryptedfs settings cryptsetup luksopen dev lvmvolume encryptedfs interlockfs mkfs ext4 dev mapper interlockfs create ext4 filesystem cryptsetup luksclose interlockfs the login procedure of interlock prompts for an encrypted volume name e g encryptedfs in the previous example and one valid password for luksopen a successful login unlocks the encrypted partition a successful logout locks it back once logged in users can change add remove luks passwords within interlock any login password can be disposed of using a dedicated flag during login this deletes the password from its luks key slot right after encrypted partition unlocking warning removing the last remaining password makes the luks encrypted container permanently inaccessible this is a feature not a bug the following sudo configuration meant to be included in etc sudoers illustrates the permission requirements for the user running the interlock server the example assumes username interlock with home directory home interlock and volume group set to its default lvmvolume interlock all root nopasswd bin date s sbin poweroff bin mount dev mapper interlockfs home interlock interlock mnt bin umount home interlock interlock mnt bin chown interlock home interlock interlock mnt sbin cryptsetup luksopen dev lvmvolume interlockfs sbin cryptsetup luksopen dev lvmvolume sbin cryptsetup luksclose dev mapper interlockfs sbin cryptsetup luksclose dev mapper sbin cryptsetup lukschangekey dev lvmvolume sbin cryptsetup lukschangekey dev lvmvolume sbin cryptsetup luksremovekey dev lvmvolume sbin cryptsetup luksremovekey dev lvmvolume sbin cryptsetup luksaddkey dev lvmvolume sbin cryptsetup luksaddkey dev lvmvolume compiling the interlock app requires a working go 1 4 2 environment to be compiled or cross compiled under linux it is not supported by or designed for other oses at this time git clone https github com usbarmory interlock cd interlock make this compiles the interlock binary that can be executed with options illustrated in the next section alternatively you can automatically download compile and install the package under your gopath as follows go install github com usbarmory interlock latest options h options help b 0 0 0 0 4430 binding address port pair c interlock conf configuration file path o operation unlock volume lock derive data d false debug mode t false test mode warning disables authentication the operation flag allows selected actions to be performed locally without a web interface the following operations are supported unlock volume unlock luks volume to mapping interlockfs prompts password once uses hsm key derivation when configured lock lock the luks volume mapped to interlockfs derive data hsm key derivation from data e g diversifier specified in hex format e g derive 12ef derive hsm key derivation from password prompted twice interactively configuration debug enable debugging logs set time use the client browser time to set server time at login useful on non routed usb armory devices unable to set the clock on their own bind address ip address port pair tls on use tls cert and tls key paths as https tls keypair gen generate a new tls keypair and save it to tls cert and tls key paths when pointing to non existent files otherwise behaves like on useful for testing and tofu trust on first use schemes off disable https tls cert https server tls certificate tls key https server tls key tls client ca optional ca for https client authentication client certificate requires tls web client authentication x509v3 extended key usage extension to be correctly validated hsm model options enable model hsm support with options multiple options can be combined in a comma separated list e g mxc scc2 luks tls cipher off disable hsm support available modules mxc scc2 nxp security controller sccv2 requires kernel driver mxc scc2 https github com usbarmory mxc scc2 caam keyblob nxp cryptographic acceleration and assurance module caam note stores encrypted derived keys in luks kb which must be accompanied to the luks partition itself when creating data backups requires kernel driver caam keyblob https github com usbarmory caam keyblob mxs dcp nxp data co processor dcp requires kernel driver mxs dcp https github com usbarmory mxs dcp available options luks use hsm secret key to aes encrypt luks passwords and make them device specific before use luks login and password operations add change remove fallback in case of failure to plain ones in order to allow change of credentials on pre hsm deployments tls use hsm secret key to aes 256 ofb encrypt the https server tls key tls key automatically convert existing plaintext keys cipher expose aes 256 ofb derived symmetric cipher with password key derivation through hsm encryption to make it device specific key path path for public private key storage on the encrypted filesystem volume group volume group name ciphers array of cipher names to enable supported values are openpgp aes 256 ofb totp the following example illustrates the configuration file format plain json and its default values debug false set time false bind address 0 0 0 0 4430 tls on tls cert certs cert pem tls key certs key pem tls client ca hsm off key path keys volume group lvmvolume ciphers openpgp aes 256 ofb totp at startup the interlock server dumps the applied configuration in its file format logging the application generates debug audit notification and error logs debugging logs are only generated when debug is set to true in the configuration file or command line switch in debug mode all logs are printed on standard output and never saved audit and error logs are shown live in a dedicated area on the web client application logs and saved on the root directory of the encrypted partition in the interlock log file notifications are shown live in a dedicated area on the web client current activity they are only kept in memory in a circular buffer and never stored on disk any non debug log generated outside an unauthenticated session is issued through standard syslog facility license interlock https github com usbarmory interlock copyright c withsecure corporation this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation under version 3 of the license this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details see accompanying license file for full details | front_end |
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CS179J-Sp2019-Group-10 | cs179j spring 2019 madlads embedded systems senior design table of contents general info general info technologies technologies setup setup sources sources team team general info the mobile automated drone liftoff airborne delivery service madlads is a drone delivery system designed for professional use the drone can be piloted manually with a custom controller or can operate autonomously it features a camera lens for packagae identification and a claw mechanism for acquisition and dropoff madlads is inspired by amazon prime air amazon s ceo jeff bezos first proposed a drone delivery service in 2013 although initially met with heavy criticism amazon continued developing the service and has patented a beehive like structure to store and charge delivery drones in cities in 2016 amazon prime air completed its first delivery to cambridge england drones may very well become the new standard for fast secure and reliable deliveries sup 2 sup technologies this project is created with arduino raspberry pi opencv i2c rf gps setup to run this project perform the following arm the drone turn on the controller deprecated these modules have been deprecated replaced or deemed non essential to function web app sources 1 amazon prime air https www amazon com b node 8037720011 ref aa art btn pf rd r pbchphz7294p1hn8jwa7 pf rd p c507fba8 5301 4f03 8293 6be2bb6cc10b 2 amazon prime air wiki https en wikipedia org wiki amazon prime air team adriel bustamante colton vosburg jonathan woolf joshua riley | os |
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MU-LLaMA | div h1 mu llama br music understanding large language model img src assets logo png height 100px align right h1 div pwc https img shields io badge f0 9f 93 8e 20arxiv paper red https arxiv org abs 2308 11276 pwc https img shields io badge f0 9f a4 97 20hugging 20face musicqa 20dataset green https arxiv org abs 2308 11276 https huggingface co datasets mu llama musicqa pwc https img shields io endpoint svg url https paperswithcode com badge music understanding llama advancing text to music question answering on musicqa dataset https paperswithcode com sota music question answering on musicqa dataset p music understanding llama advancing text to this is the official repository for music understanding llama advancing text to music generation with question answering and captioning https arxiv org abs 2308 11276 the demo page with more information regarding the mu llama model is avilable here https crypto code github io mu llama demo introduction the mu llama model is music understanding language model designed with the purpose of answering questions based on music our model is also designed with the purpose of captioning music files to generate text to music generation datasets the model uses mert llama as the backbone and employs an adapter to encoperate music context information to guide llama s output mert was chosen as the music encoder for our model after comparison of different music representation models which can be viewed here https github com crypto code music representation comparison we also provide the code for generating our musicqa dataset from musiccaps https www kaggle com datasets googleai musiccaps and the magnatagatune https mirg city ac uk codeapps the magnatagatune dataset datasets p align center img src assets mu llama png p mu llama demo for the working of our model facebook s llama 2 model weights are required details on obtaining these weights are given on huggingface https huggingface co docs transformers main model doc llama our pretrained weights for the mu llama model finetuned from llama 7b 2 can be downloaded here https huggingface co mu llama mu llama tree main once downloaded store the files in the ckpts folder within the mu llama directory once downloaded the directory structure will be as shown below mu llama ckpts llama 7b checklist chk consolidated 00 pth params json llama sh tokenizer model tokenizer checklist chk 7b pth checkpoint pth we use python 3 9 17 for this project and the library requirements are given in requirements txt requirements txt the demo can be run using gradio app py mu llama gradio app py python gradio app py model ckpts checkpoint pth llama dir ckpts llama training mu llama to train the mu llama model follow the steps as below musicqa dataset we use the musiccaps https www kaggle com datasets googleai musiccaps and the magnatagatune https mirg city ac uk codeapps the magnatagatune dataset dataset to generate our training musicqa dataset and the mtg jamendo https github com mtg mtg jamendo dataset for evaluation you can download the generated musicqa dataset here https huggingface co datasets mu llama musicqa to generate the dataset yourself first download the musiccaps mtt and mtg datasets once downloaded the directory structure would be as shown musicqa mtt audios annotations final csv musiccaps audios musiccaps public csv mtg audios 00 01 raw 30s cleantags 50artists tsv mtt process py musiccaps process py mtg process py generate dataset py the musicqa dataset generation is a very computationally intensive process which takes around 8 days per dataset on a tesla v100 sxm2 32gb gpu so it is recommended to download our generated dataset 128221 notes run the following command to flatten the mtt audio file structure once downloaaded and extracted find mtt audios mindepth 2 type f exec mv t mtt audios i we only use the folders 00 to 09 from the mtg dataset by running musiccaps process py musicqa musiccaps process py mtt process py musicqa mtt process py and mtg process py musicqa mtg process py you can generate the question answer pairs from each of the datasets and by running generate dataset py musicqa generate dataset py the final datasets for pretraining finetuning and evaluation will be generated usage generate dataset py h mtt mtt mtg mtg musiccaps musiccaps musicqa musicqa optional arguments h help show this help message and exit mtt mtt directory of the mtt dataset mtg mtg directory of the mtg dataset musiccaps musiccaps directory of the musiccaps dataset musicqa musicqa directory of the musicqa dataset to be generated mu llama pretraining to pretrain the mu llama model we use the musiccaps part of the musicqa dataset and the alpaca instruction dataset with the pretrain sh mu llama pretrain sh script pretrain sh ckpts llama 2 configs pretrain yaml ckpts mu llama pretrain this will pretrain the mu llama model for 150 epochs the hyperparameters can be modified in the pretrain sh mu llama pretrain sh file mu llama finetuning to finetune the mu llama model we use the mtt part of the musicqa dataset with the finetune sh mu llama finetune sh script finetune sh ckpts llama 2 ckpts mu llama pretrain checkpoint pth configs finetune yaml ckpts mu llama finetune this will finetune the mu llama model for 20 epochs the hyperparameters can be modified in the finetune sh mu llama finetune sh file the mu llama model with 7b parameters takes approximately 2 days to train on a tesla v100 sxm2 32gb gpu once trained the model can be tested using the gradio demo mu llama inference to test the model without gradio the inference py mu llama inference py script can be used usage inference py h model model llama type llama type llama dir llama dir mert path mert path audio path audio path question question optional arguments h help show this help message and exit model model name of or path to the trained checkpoint llama type llama type type of llama original weight llama dir llama dir path to llama pretrained checkpoint mert path mert path path to mert pretrained checkpoint audio path audio path path to the input music file question question question to ask the model mu llama evaluation our model was compared against audio enabled models such as the listen think and understand ltu model and the llama adapter model trained on our musicqa dataset we evaluate the models using bleu b u meteor m r rouge sub l sub r l and bert score bert s which are common evaluation metrics for text generation for the bleu score a weighted average of bleu sub 1 sub bleu sub 2 sub bleu sub 3 sub and bleu sub 4 sub weight 0 25 for each is used the evaluation scripts are given in the modelevaluations folder the generate scripts are used to generate the answers for all the questions in the dataset usage generate mullama py h model model knn knn llama type llama type llama dir llama dir mert path mert path optional arguments h help show this help message and exit model model name of or path to the trained checkpoint knn knn name of or path to the directory with knn checkpoint llama type llama type type of llama original weight llama dir llama dir path to llama pretrained checkpoint mert path mert path path to mert pretrained checkpoint usage generate ltu py h demo demo optional arguments h help show this help message and exit demo demo link to the ltu demo page usage generate llama adapter py h model model llama dir llama dir optional arguments h help show this help message and exit model model name of or path to the trained checkpoint llama dir llama dir path to llama pretrained checkpoint once generated evaluate py modelevaluations evaluate py can be used to evaluated the generated answers for the three models the results are shown below model b u 8593 m r 8593 r l 8593 bert s 8593 ltu 0 242 0 274 0 326 0 887 llama adapter 0 273 0 334 0 413 0 895 mu llama 0 306 0 385 0 466 0 901 acknowledgements this code contains elements from the following repos opengvlab llama adapter https github com opengvlab llama adapter yizhilll mert https github com yizhilll mert cite our work if you find this repo useful please consider citing bibtex article liu2023music title music understanding llama advancing text to music generation with question answering and captioning author liu shansong and hussain atin sakkeer and sun chenshuo and shan ying journal arxiv preprint arxiv 2308 11276 year 2023 | ai |
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comsat | comsat br scalable concurrent web apps build status http img shields io travis puniverse comsat svg style flat https travis ci org puniverse comsat dependency status https www versioneye com user projects 52dfc913ec1375318800039f badge png style flat https www versioneye com user projects 52dfc913ec1375318800039f version http img shields io badge version 0 7 0 blue svg style flat https github com puniverse comsat releases license http img shields io badge license epl blue svg style flat https www eclipse org legal epl v10 html license http img shields io badge license lgpl blue svg style flat https www gnu org licenses lgpl html getting started add the following maven gradle dependencies feature artifact servlet integration for defining fiber per request servlets co paralleluniverse comsat servlet 0 7 0 a fiber blocking clojure ring https github com ring clojure ring adapter based on jetty 9 3 co paralleluniverse comsat ring jetty9 0 7 0 http kit http www http kit org client html based fiber blocking http client co paralleluniverse comsat httpkit 0 7 0 jersey server https jersey java net integration for defining rest services co paralleluniverse comsat jersey server 0 7 0 dropwizard http dropwizard io integration including jersey apachehttpclient and jdbi co paralleluniverse comsat dropwizard 0 7 0 spring framework http projects spring io spring framework web mvc fiber blocking controller methods integration co paralleluniverse comsat spring webmvc 0 7 0 spring boot http projects spring io spring boot auto configuration support for web mvc controllers co paralleluniverse comsat spring boot 0 7 0 spring security http projects spring io spring security configuration support for fibers co paralleluniverse comsat spring security 0 7 0 jax rs client https jersey java net documentation latest client html integration for http calls with fibers co paralleluniverse comsat jax rs client 0 7 0 apachehttpclient http hc apache org httpcomponents client ga integration for http calls with fibers co paralleluniverse comsat httpclient 0 7 0 retrofit http square github io retrofit integration with fibers co paralleluniverse comsat retrofit 0 7 0 jdbi http jdbi org integration with fibers co paralleluniverse comsat jdbi 0 7 0 jdbc integration with fibers co paralleluniverse comsat jdbc 0 7 0 jooq http www jooq org integration with fibers co paralleluniverse comsat jooq 0 7 0 mongodb fiber blocking integration for the allanbank api http www allanbank com mongodb async driver index html co paralleluniverse comsat mongodb allanbank 0 7 0 okhttp https github com square okhttp http spdy client integration co paralleluniverse comsat okhttp 0 7 0 the web actors api co paralleluniverse comsat actors api 0 7 0 deploy http sse and websocket web actors as undertow http undertow io handlers co paralleluniverse comsat actors undertow 0 7 0 deploy http sse and websocket web actors as netty http netty io handlers co paralleluniverse comsat actors netty 0 7 0 deploy http sse and websocket web actors in j2ee 7 servlet and websocket jsr 356 embedded and standalone containers co paralleluniverse comsat actors servlet 0 7 0 use comsat in the tomcat servlet container without the java agent co paralleluniverse comsat tomcat loader 0 7 0 jdk8 for jdk 8 optionally add the jdk8 classifier use comsat in the jetty servlet container without the java agent co paralleluniverse comsat jetty loader 0 7 0 jdk8 for jdk 8 optionally add the jdk8 classifier spring framework http projects spring io spring framework web integration allows using fiber blocking controllers co paralleluniverse comsat spring web 0 7 0 apache kafka http kafka apache org producer integration module co paralleluniverse comsat kafka 0 7 0 apache shiro http shiro apache org realms integration module co paralleluniverse comsat shiro 0 7 0 or build and install from sources after installing gradle http www gradle org locally in your local maven repository with gradle install the full testsuite can be run with gradle build usage documentation http docs paralleluniverse co comsat javadoc http docs paralleluniverse co comsat javadoc a gradle template https github com puniverse comsat gradle template project and a maven archetype https github com puniverse comsat mvn archetype using various integration modules and featuring setup with both dropwizard and standalone tomcat are also available for jumpstart and study both have a without comsat branch which is useful to clearly see the minimal if any porting effort required branches comparison works very well for this purporse there s a comsat ring clojure leiningen template https github com puniverse comsat ring template as well which includes an auto instrument branch that doesn t need any explicit suspendable marking code suspendable defsfn sfn etc thanks to pulsar s new auto instrumentation feature http docs paralleluniverse co pulsar automatic instrumentation you can also have a look at additional examples https github com puniverse comsat examples finally there are several regularly updated third party bootstrap projects comsat dropwizard jooq https github com circlespainter comsat jooq gradle template comsat web actors stock quotes ported from akka https github com circlespainter quasar stocks spring mvc tomcat standalone servlet container https github com circlespainter spring4 mvc gradle annotation hello world getting help questions and suggestions are welcome at this forum mailing list https groups google com forum forum comsat user contributions including pull requests please have a look at some brief information for contributors https github com puniverse comsat blob master contributing md license comsat is free software published under the following license copyright c 2013 2016 parallel universe software co all rights reserved this program and the accompanying materials are dual licensed under either the terms of the eclipse public license v1 0 as published by the eclipse foundation or per the licensee s choosing under the terms of the gnu lesser general public license version 3 0 as published by the free software foundation | front_end |
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JITP-opinions-voting-technology | jitp opinions voting technology replication materials for alvarez levin and li s fraud convenience and e voting how voting experience shapes opinions about voting technology journal of information technology amp politics 2018 | server |
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insolar | img src https github com insolar doc pics raw master st github readme banner png http insolar io utm source github insolar platform is the most secure scalable and comprehensive business ready blockchain toolkit in the world insolar s goal is to give businesses access to features and services that enable them to launch new decentralized applications quickly and easily whether a minimum viable product or full scale production software insolar builds and integrates applications for your enterprise s existing systems cii best practices https bestpractices coreinfrastructure org projects 2150 badge https bestpractices coreinfrastructure org projects 2150 golangci https golangci com badges github com insolar insolar svg https golangci com r github com insolar insolar go report card https goreportcard com badge github com insolar insolar https goreportcard com report github com insolar insolar godoc https godoc org github com insolar insolar status svg https godoc org github com insolar insolar codecov https codecov io gh insolar insolar branch master graph badge svg https codecov io gh insolar insolar insolar platform 1 0 insolar platform 1 0 resides in this repository insolar mainnet application and its benchmark resides in the insolar mainnet repository https github com insolar mainnet and runs on top of platform 1 0 assured ledger insolar s platform as a service solution is being actively developed in the insolar assured ledger repository https github com insolar assured ledger quick start to learn what distinguishes insolar from other blockchain projects go through the list of our features https insolar io platform utm source github to get a grip on how insolar works take a look at the big picture https docs insolar io en latest basics html and explore the architecture https docs insolar io en latest architecture html to run the insolar platform 1 0 locally install it and deploy as described below install 1 install the latest 1 12 version of the golang programming tools https golang org doc install install make sure the gopath environment variable is set 2 download the insolar package go get github com insolar insolar 3 go to the package directory cd gopath src github com insolar insolar 4 install dependencies and build binaries make deploy locally to deploy the insolar network locally run the launcher insolar scripts insolard launchnet sh g the launcher generates bootstrap data starts a pulse watcher and launches a number of nodes in local setup the nodes are simply services listening on different ports the default number of nodes is 5 you can uncomment more in scripts insolard bootstrap template yaml when the pulse watcher says insolar state ready the network is up and running contribute feel free to submit issues fork the repository and send pull requests to make the process smooth for both reviewers and contributors familiarize yourself with the list of guidelines 1 open source contributor guide https github com freecodecamp how to contribute to open source 2 style guide effective go https golang org doc effective go html 3 list of shorthands for go code review comments https github com golang go wiki codereviewcomments when submitting an issue include a complete test function that demonstrates it thank you for your intention to contribute to the insolar project as a company developing open source code we highly appreciate external contributions to our project faq for more information check out our faq https github com insolar insolar wiki faq contacts if you have any additional questions join our developers chat https t me insolartech our social media img src https github com insolar doc pics raw master st ico social facebook png width 36 height 36 https facebook com insolario img src https github com insolar doc pics raw master st ico social twitter png width 36 height 36 https twitter com insolario img src https github com insolar doc pics raw master st ico social medium png width 36 height 36 https medium com insolar img src https github com insolar doc pics raw master st ico social youtube png width 36 height 36 https youtube com insolar img src https github com insolar doc pics raw master st ico social reddit png width 36 height 36 https www reddit com r insolar img src https github com insolar doc pics raw master st ico social linkedin png width 36 height 36 https www linkedin com company insolario img src https github com insolar doc pics raw master st ico social instagram png width 36 height 36 https instagram com insolario img src https github com insolar doc pics raw master st ico social telegram png width 36 height 36 https t me insolarannouncements license this project is licensed under the terms of the mit license license md | blockchain golang networking ledger distributed-systems smart-contracts blockchain-platform trust | blockchain |
nepali-nlp-resources | nepali nlp resources a curated list of nlp resources for the nepali language speech resources high quality tts data for nepali http www openslr org 43 multi speaker tts data for nepali ne np 2 000 sentences 48khz 16 bit mono wave audio large nepali asr training data set http www openslr org 54 nepali asr training data set containing 157k utterances 16khz 16 bit mono flac audio devanagiri numbers spoken audio https drive google com drive folders 15g57qa1tqa4ix6 mic6v1wieouqp0xal text resources news source 16nepalinews corpus https github com sndsabin nepali news classifier 65k nepali sentences https github com sanjaalcorps nepalidatasets blob master raw sentences np 65k csv 1000 sport news https github com aryal007 nepali text generation blob master data sports news nepali 1000 txt nagarik news corpus https github com ashmitbhattarai nepali language modeling using lstm tree master nepali corpus nagarik setopati news corpus https github com ashmitbhattarai nepali language modeling using lstm tree master nepali corpus setopati nepali news in 10 different categories https github com kamalacharya2044 nepalinewsdataset nepali news in english corpus https github com sharad461 english corpus nepal title article pairs from news https drive google com file d 1l56k0zonmk6xpelkaxpm45wcmt 9ps3x view nepali news classification dataset https drive google com drive folders 1vm0uj3ffwp 3gusan3fzsov4q7ryujig wikipedia 39k nepali wikipedia articles https www kaggle com disisbig nepali wikipedia articles translational resources nepali translation parallel corpus https drive google com file d 1uthfjkjfvdgtu263dnbz wpnlqoarz 0 view facebook low resource mt benchmark flores test sets https github com facebookresearch flores embeddings pretrained fasttext word vectors https fasttext cc docs en crawl vectors html bin https dl fbaipublicfiles com fasttext vectors crawl cc ne 300 bin gz txt https dl fbaipublicfiles com fasttext vectors crawl cc ne 300 vec gz trained on common crawl and wikipedia 300 d word embeddings word2vec for nepali language https github com rabindralamsal word2vec embeddings for nepali language the text corpus contains more than 90 million running words ner nepali ner https github com oya163 nepali ner other resources nepali text extraction https github com tnagorra nepali text extraction laxmi prasad devkota poems https github com devkotasawal1 poem generator blob master lspd txt nepali names https github com datafiction oya nepali nlp blob master data names nepali txt nepali ngram https github com virtualanup nepalingram nepali stopwords https github com sanjaalcorps nepalistopwords blob master nepalistopwords txt nepali word list https github com tesseract ocr langdata blob master nep nep wordlist nepali transliteration https github com achilleskarki nepalilipi nepali textbooks by cornell anthropology https ecommons cornell edu handle 1813 24179 nepali textbooks from grade 1 to 12 http lib moecdc gov np catalog opac css index php lvl cmspage pageid 6 id rubrique 105 nepali spelling correction dataset https github com tnagorra nspell tree master data nepali contemporary dictionary http ltk org np nepalisabdakos dict np dictionary db sql gz nepali names list https github com prabinzz nepali wordlist english to nepali dictionary https github com nirooj56 nepdict blob master database data csv important githubs and references indic nlp library https github com anoopkunchukuttan indic nlp library nlp processes for nepali language https github com sushil79g nepali nlp nlp for nepali https github com goru001 nlp for nepali nepali bhasa https github com nepali bhasa ml datasets https github com amitness ml datasets openslr http openslr org contribute contributions are always welcome | natural-language-processing nepali-nlp-resources nepali-nlp nepali-language | ai |
GroceryStore | a href https github com quintuslabs grocerystore img src screen logo png height 150px width 150px title grocery alt grocery store a grocerystore grocery app is online shopping ui kit geniune mobile application to which you can rely on it guides you to formulate incredible mobile application it comprise of 10 screens with different features to navigate screens easily various different screens are assembled to construct systematic structure moreover design and xml code file compose of various explicit comments that can help your client to construct application more effortlessly code used as well as layers are well organized and are named precisely for the user to interpret properly code which is being used to model the application is composed of today s mobile trend moreover one can get free icons fonts and updates for user s assistance changing text colours and graphics or placing photos will never be a big issue you can do it with ease pixel perfect resolution gives perfect design while customised elements assist in easy editing grocery store template is created as a wonderful solution for any agricultural or organic food shop android app ui template it is a template for an android developer that want to create grocery application with a clean design the template is only lay outing without data flow and communication with the backend system this ui template can reduce your development time and will loved by developer that hate lay outing design you can use this app as one big super market app to sale product of your store this app make easy for user to buy product from store with easy steps and store can get easy order screen content the screen contains more than 60 icons and most of all required elements required to design an application like this i news ui kit in 20 screen available in this kit the xml and java files contains comments at each and every point for easy understanding everything was made with a detail oriented style and followed by today s web trends clean coded layers are well organized carefully named and grouped description grocery app ui is supported in all devices change text colours an graphics add or place photos customize every elements as much or as little as you want customise elements easy to edit 100 free fonts perfect pixel high quality design very clean and cool ui free updates license screen img src screen screen1 png img src screen screen2 png img src screen screen3 png img src screen screen4 png img src screen screen5 png img src screen screen6 png | server |
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mslearn-dp100 | note this repository is archived as there now is a new version of these labs the new labs that cover azure machine learning v2 instead of v1 can be found at https github com microsoftlearning mslearn azure ml https github com microsoftlearning mslearn azure ml azure machine learning lab exercises this repository contains the hands on lab exercises for microsoft course dp 100 designing and implementing a data science solution on azure https docs microsoft com learn certifications courses dp 100t01 and the equivalent self paced modules on microsoft learn https docs microsoft com learn paths build ai solutions with azure ml service the labs are designed to accompany the learning materials and enable you to practice using the technologies described them when using these labs in an instructoe led course students should use the azure subscription provided for the course what are we doing to support this course we will need to make frequent updates to the course content to keep it current with the azure services used in the course we are publishing the lab instructions and lab files on github to keep the content current with changes in the azure platform we hope that this brings a sense of collaboration to the labs like we ve never had before when azure changes and you find it first during a live delivery go ahead and submit a pull request to update the lab content help your fellow mcts how should i use these files relative to the released moc files the instructor guide and powerpoints are still going to be your primary source for teaching the course content these files on github are designed to be used in the course labs it will be recommended that for every delivery trainers check github for any changes that may have been made to support the latest azure services what about changes to the student handbook we will review the student handbook on a quarterly basis and update through the normal moc release channels as needed how do i contribute any mct can submit a pull request to the code or content in the github repo microsoft and the course author will triage and include content and lab code changes as needed if you have suggestions or spot any errors please report them as issues https github com microsoftlearning mslearn dp100 issues notes classroom materials the labs are provided in this github repo rather than in the student materials in order to a share them with other learning modalities and b ensure that the latest version of the lab files is always used in classroom deliveries this approach reflects the nature of an always changing cloud based interface and platform anyone can access the files in this repo but microsoft learning support is limited to mcts teaching this course only | ai |
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blog | about here is a git repository for the backup and index of my blog you can find the blog in english here https blog huli tw en https blog huli tw en 2014 3 17 logdown huli s blog http huli logdown com 2017 6 logdown github pages hexo po medium https medium com hulitw po techbridge blog https blog techbridge cc techbridge repo issues issue follow 2022 05 22 2022 01 06 idea upvote https github com aszx87410 blog discussions 92 huli https www facebook com huli blog cymetrics tech blog https tech blog cymetrics io 2019 09 25 github issues seo hexo https blog huli tw 2023 09 26 hitcon ctf 2023 seccon ctf 2023 https github com aszx87410 blog issues 144 2023 09 26 img src mp4 safari https github com aszx87410 blog issues 143 2023 09 02 corctf 2023 sekai ctf 2023 https github com aszx87410 blog issues 142 2023 09 02 intigriti 0823 math jail https github com aszx87410 blog issues 141 2023 07 28 googlectf zer0ptsctf imaginaryctf 2023 https github com aszx87410 blog issues 140 2023 07 28 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blog issues 125 2022 08 03 uiuctf 2022 https github com aszx87410 blog issues 124 2022 07 09 googlectf 2022 https github com aszx87410 blog issues 123 2022 07 09 justctf 2022 https github com aszx87410 blog issues 122 2022 07 08 def con ctf 2022 qualifier https github com aszx87410 blog issues 121 2022 07 08 atomic css tailwind css https github com aszx87410 blog issues 120 2022 05 22 m0lecon ctf 2022 https github com aszx87410 blog issues 119 2022 05 22 ngstromctf 2022 https github com aszx87410 blog issues 118 2022 05 22 script type https github com aszx87410 blog issues 117 2022 05 22 javascript regexp https github com aszx87410 blog issues 116 2022 04 19 picoctf 2022 https github com aszx87410 blog issues 115 2022 04 11 iframe window open https github com aszx87410 blog issues 114 2022 04 07 line ctf 2022 https github com aszx87410 blog issues 113 2022 03 21 javascript https github com aszx87410 blog issues 112 2022 03 02 tsj ctf 2022 web nim notes https github com aszx87410 blog issues 111 2022 03 01 susctf 2022 writeup https github com aszx87410 blog issues 110 2022 03 01 javascript https github com aszx87410 blog issues 109 2022 02 17 runtime https github com aszx87410 blog issues 108 2022 02 17 dicectf 2022 js https github com aszx87410 blog issues 107 2022 02 02 chrome origin trials https github com aszx87410 blog issues 106 2022 01 30 javascript https github com aszx87410 blog issues 105 2022 01 16 same site same origin https github com aszx87410 blog issues 96 2022 01 15 javascript https github com aszx87410 blog issues 95 2022 01 07 matters https github com aszx87410 blog issues 94 2022 01 07 log4j log4shell https github com aszx87410 blog issues 93 2022 01 07 cpsa crest practitioner security analyst https github com aszx87410 blog issues 92 2021 11 14 intigriti xss html https github com aszx87410 blog issues 91 2021 11 13 https github com aszx87410 blog issues 90 2021 11 13 xss https github com aszx87410 blog issues 89 2021 11 13 js prototype pollution https github com aszx87410 blog issues 88 2021 09 26 open redirect https github com aszx87410 blog issues 87 2021 09 26 clickjacking https github com aszx87410 blog issues 86 2021 08 22 hexo eleventy https github com aszx87410 blog issues 85 2021 08 22 cdnjs https github com aszx87410 blog issues 84 2021 08 06 intigriti xss https github com aszx87410 blog issues 83 2021 07 10 cookie dos cookie https github com aszx87410 blog issues 82 2021 06 19 xss https github com aszx87410 blog issues 81 2021 06 12 paged js pdf html https github com aszx87410 blog issues 80 2021 06 07 intigriti s 0521 xss https github com aszx87410 blog issues 79 2021 05 25 intigriti s 0421 xss challenge https github com aszx87410 blog issues 78 2021 05 25 xss https github com aszx87410 blog issues 77 2021 04 17 eslint plugin https github com aszx87410 blog issues 76 2021 03 20 css https github com aszx87410 blog issues 75 2021 02 20 ctf web https github com aszx87410 blog issues 74 2021 02 19 cors https github com aszx87410 blog issues 73 2021 02 19 cors https github com aszx87410 blog issues 72 2021 02 19 cors https github com aszx87410 blog issues 71 2021 02 19 cors cors https github com aszx87410 blog issues 70 2021 02 19 cors cors https github com aszx87410 blog issues 69 2021 02 19 cors cors https github com aszx87410 blog issues 68 2021 01 23 dom clobbering https github com aszx87410 blog issues 67 2020 12 29 javascript https github com aszx87410 blog issues 66 2020 12 01 console log 1 https github com aszx87410 blog issues 65 2020 10 31 i don t know react https github com aszx87410 blog issues 64 2020 09 09 react state useeffect https github com aszx87410 blog issues 63 2020 09 05 sessionstorage spec https github com aszx87410 blog issues 62 2020 08 07 vite es modules https github com aszx87410 blog issues 61 2020 07 11 styled components bug https github com aszx87410 blog issues 60 2020 06 15 class function component https github com aszx87410 blog issues 59 2020 05 16 regular expression https github com aszx87410 blog issues 58 2020 04 18 javascript function https github com aszx87410 blog issues 57 2020 03 24 console log https github com aszx87410 blog issues 56 2020 03 23 online judge https github com aszx87410 blog issues 55 2020 02 21 v8 bytecode let var https github com aszx87410 blog issues 54 2020 01 22 webpack snowpack https github com aszx87410 blog issues 53 2019 12 27 from nand to tetris https github com aszx87410 blog issues 52 2019 12 01 don t break the web smooshgate keygen https github com aszx87410 blog issues 51 2019 11 01 https github com aszx87410 blog issues 50 2019 10 04 javascript callback https github com aszx87410 blog issues 49 2019 09 18 spa router https github com aszx87410 blog issues 48 2019 08 21 https github com aszx87410 blog issues 47 2019 08 09 session cookie express php rails https github com aszx87410 blog issues 46 2019 08 09 session cookie rfc https github com aszx87410 blog issues 45 2019 07 12 medium https github com aszx87410 blog issues 44 2019 06 14 418 i am a teapot https github com aszx87410 blog issues 43 2019 05 18 lidemy http challenge https github com aszx87410 blog issues 42 2019 04 19 spectrum bug https github com aszx87410 blog issues 41 2019 03 24 react keypress keydown https github com aszx87410 blog issues 40 2019 02 23 javascript this https github com aszx87410 blog issues 39 2019 01 27 functional css https github com aszx87410 blog issues 38 2019 01 03 redux dan abramov https github com aszx87410 blog issues 37 2018 12 29 https github com aszx87410 blog issues 36 2018 12 08 js closure https github com aszx87410 blog issues 35 2018 11 10 hoisting https github com aszx87410 blog issues 34 2018 10 13 pwa https github com aszx87410 blog issues 33 2018 09 14 aws lambda github api google sheet https github com aszx87410 blog issues 32 2018 08 18 cors https github com aszx87410 blog issues 31 2018 06 23 javascript call by value reference https github com aszx87410 blog issues 30 2018 03 31 react fiber lifecycles https github com aszx87410 blog issues 29 2018 03 12 css keylogger https github com aszx87410 blog issues 28 2018 02 03 github classroom travis ci https github com aszx87410 blog issues 27 2018 01 05 react immutable data shouldcomponentupdate https github com aszx87410 blog issues 26 2017 12 08 rxjs https github com aszx87410 blog issues 25 2017 10 04 payment request api https github com aszx87410 blog issues 24 2017 09 06 spa https github com aszx87410 blog issues 23 2017 08 19 javascript https github com aszx87410 blog issues 22 2017 07 15 dom https github com aszx87410 blog issues 21 2017 06 17 http cache https github com aszx87410 blog issues 20 2017 05 20 ajax https github com aszx87410 blog issues 19 2017 04 22 javascript https github com aszx87410 blog issues 18 2017 03 24 cookie https github com aszx87410 blog issues 17 2017 02 25 csrf https github com aszx87410 blog issues 16 2017 01 27 https github com aszx87410 blog issues 15 2016 12 31 https github com aszx87410 blog issues 14 2016 12 03 hls https github com aszx87410 blog issues 13 2016 11 10 react native in 24 hours https github com aszx87410 blog issues 12 2016 09 24 https github com aszx87410 blog issues 11 2016 08 12 ddos nginx iptables fail2ban https github com aszx87410 blog issues 10 2016 07 16 javasript jsfuck aaencode https github com aszx87410 blog issues 9 2016 06 18 redis https github com aszx87410 blog issues 8 2016 05 20 apk https github com aszx87410 blog issues 7 2016 04 23 node js restful api https github com aszx87410 blog issues 6 2016 03 20 apk https github com aszx87410 blog issues 5 2015 09 03 redux middleware https github com aszx87410 blog issues 4 2015 09 01 redux https github com aszx87410 blog issues 3 2015 08 26 promise generator async es6 https github com aszx87410 blog issues 2 2015 08 24 es6 generator https github com aszx87410 blog issues 1 security 2023 09 26 hitcon ctf 2023 seccon ctf 2023 https github com aszx87410 blog issues 144 2023 09 02 corctf 2023 sekai ctf 2023 https github com aszx87410 blog issues 142 2023 09 02 intigriti 0823 math jail https github com aszx87410 blog issues 141 2023 07 28 googlectf zer0ptsctf imaginaryctf 2023 https github com aszx87410 blog issues 140 2023 07 28 ctf ejs https github com aszx87410 blog issues 139 2023 06 13 redos regexp https github com aszx87410 blog issues 137 2023 03 27 line ctf 2023 https github com aszx87410 blog issues 135 2023 03 27 dicectf 2023 https github com aszx87410 blog issues 134 2023 01 28 api https github com aszx87410 blog issues 133 2022 12 26 2022 ctf web js https github com aszx87410 blog issues 132 2022 12 26 rctf 2022 https github com aszx87410 blog issues 131 2022 12 08 web js ctf https github com aszx87410 blog issues 130 2022 12 08 hack lu ctf 2022 https github com aszx87410 blog issues 129 2022 12 08 sekaictf 2022 concurrent limit https github com aszx87410 blog issues 128 2022 10 05 css css injection https github com aszx87410 blog issues 127 2022 10 05 css css injection https github com aszx87410 blog issues 126 2022 10 05 angularjs csp bypass prototype js https github com aszx87410 blog issues 125 2022 08 03 uiuctf 2022 https github com aszx87410 blog issues 124 2022 07 09 googlectf 2022 https github com aszx87410 blog issues 123 2022 07 09 justctf 2022 https github com aszx87410 blog issues 122 2022 07 08 def con ctf 2022 qualifier https github com aszx87410 blog issues 121 2022 05 22 m0lecon ctf 2022 https github com aszx87410 blog issues 119 2022 05 22 ngstromctf 2022 https github com aszx87410 blog issues 118 2022 05 22 script type https github com aszx87410 blog issues 117 2022 05 22 javascript regexp https github com aszx87410 blog issues 116 2022 04 11 picoctf 2022 https github com aszx87410 blog issues 115 2022 04 11 iframe window open https github com aszx87410 blog issues 114 2022 04 07 line ctf 2022 https github com aszx87410 blog issues 113 2022 03 01 susctf 2022 writeup https github com aszx87410 blog issues 110 2022 02 17 dicectf 2022 js https github com aszx87410 blog issues 107 2021 11 14 intigriti xss html https github com aszx87410 blog issues 91 2021 11 13 https github com aszx87410 blog issues 90 2021 11 13 xss https github com aszx87410 blog issues 89 2021 11 13 js prototype pollution https github com aszx87410 blog issues 88 2021 09 26 open redirect https github com aszx87410 blog issues 87 2021 09 26 clickjacking https github com aszx87410 blog issues 86 2021 08 22 cdnjs https github com aszx87410 blog issues 84 2021 08 06 intigriti xss https github com aszx87410 blog issues 83 2021 07 10 cookie dos cookie https github com aszx87410 blog issues 82 2021 06 19 xss https github com aszx87410 blog issues 81 2021 06 07 intigriti s 0521 xss https github com aszx87410 blog issues 79 2021 05 25 intigriti s 0421 xss challenge https github com aszx87410 blog issues 78 2021 05 25 xss https github com aszx87410 blog issues 77 2021 02 20 ctf web https github com aszx87410 blog issues 74 2020 01 23 dom clobbering https github com aszx87410 blog issues 67 javascript 2022 03 01 javascript https github com aszx87410 blog issues 109 2022 01 30 javascript https github com aszx87410 blog issues 105 2020 12 29 javascript https github com aszx87410 blog issues 66 2020 12 01 console log 1 https github com aszx87410 blog issues 65 2020 04 18 javascript function https github com aszx87410 blog issues 57 2020 02 21 v8 bytecode let var https github com aszx87410 blog issues 54 2019 10 04 javascript callback https github com aszx87410 blog issues 49 2019 02 23 javascript this https github com aszx87410 blog issues 39 2018 12 08 js closure https github com aszx87410 blog issues 35 2018 11 10 hoisting https github com aszx87410 blog issues 34 2018 06 23 javascript call by value reference https github com aszx87410 blog issues 30 2017 04 22 javascript https github com aszx87410 blog issues 18 2016 07 16 javasript jsfuck aaencode https github com aszx87410 blog issues 9 2015 08 26 promise generator async es6 https github com aszx87410 blog issues 2 2015 08 24 es6 generator https github com aszx87410 blog issues 1 web 2021 02 19 cors https github com aszx87410 blog issues 73 2021 02 19 cors https github com aszx87410 blog issues 72 2021 02 19 cors https github com aszx87410 blog issues 71 2021 02 19 cors cors https github com aszx87410 blog issues 70 2021 02 19 cors cors https github com aszx87410 blog issues 69 2021 02 19 cors cors https github com aszx87410 blog issues 68 2020 09 05 sessionstorage spec https github com aszx87410 blog issues 62 2019 12 01 don t break the web smooshgate keygen https github com aszx87410 blog issues 51 2019 08 09 session cookie express php rails https github com aszx87410 blog issues 46 2019 08 09 session cookie rfc https github com aszx87410 blog issues 45 2019 06 14 418 i am a teapot https github com aszx87410 blog issues 43 2023 09 26 img src mp4 safari https github com aszx87410 blog issues 143 2022 07 08 atomic css tailwind css https github com aszx87410 blog issues 120 2022 02 02 chrome origin trials https github com aszx87410 blog issues 106 2021 06 12 paged js pdf html https github com aszx87410 blog issues 80 2021 04 17 eslint plugin https github com aszx87410 blog issues 76 2021 03 20 css https github com aszx87410 blog issues 75 2020 08 07 vite es modules https github com aszx87410 blog issues 61 2020 07 11 styled components bug https github com aszx87410 blog issues 60 2020 03 24 console log https github com aszx87410 blog issues 56 2020 01 22 webpack snowpack https github com aszx87410 blog issues 53 2019 09 18 spa router https github com aszx87410 blog issues 48 2019 08 21 https github com aszx87410 blog issues 47 2019 01 27 functional css https github com aszx87410 blog issues 38 2018 08 18 cors https github com aszx87410 blog issues 31 2018 03 12 css keylogger https github com aszx87410 blog issues 28 2017 12 08 rxjs https github com aszx87410 blog issues 25 2017 10 04 payment request api https github com aszx87410 blog issues 24 2017 09 06 spa https github com aszx87410 blog issues 23 2017 07 15 dom https github com aszx87410 blog issues 21 2017 06 17 http cache https github com aszx87410 blog issues 20 2017 05 20 ajax https github com aszx87410 blog issues 19 2017 03 24 cookie https github com aszx87410 blog issues 17 2017 02 25 csrf https github com aszx87410 blog issues 16 react 2020 10 31 i don t know react https github com aszx87410 blog issues 64 2020 09 09 react state useeffect https github com aszx87410 blog issues 63 2020 06 15 class function component https github com aszx87410 blog issues 59 2019 03 24 react keypress keydown https github com aszx87410 blog issues 40 2018 03 31 react fiber lifecycles https github com aszx87410 blog issues 29 2018 01 05 react immutable data shouldcomponentupdate https github com aszx87410 blog issues 26 2015 09 03 redux middleware https github com aszx87410 blog issues 4 2015 09 01 redux https github com aszx87410 blog issues 3 mobile 2018 10 13 pwa https github com aszx87410 blog issues 33 2016 11 10 react native in 24 hours https github com aszx87410 blog issues 12 2016 05 20 apk https github com aszx87410 blog issues 7 2016 03 20 apk https github com aszx87410 blog issues 5 2016 08 12 ddos nginx iptables fail2ban https github com aszx87410 blog issues 10 2016 06 18 redis https github com aszx87410 blog issues 8 2016 04 23 node js restful api https github com aszx87410 blog issues 6 2019 11 01 https github com aszx87410 blog issues 50 2017 08 19 javascript https github com aszx87410 blog issues 22 2016 09 24 https github com aszx87410 blog issues 11 2023 07 28 chatgpt https github com aszx87410 blog issues 138 2023 04 21 https github com aszx87410 blog issues 136 2021 08 22 hexo eleventy https github com aszx87410 blog issues 85 2020 05 16 regular expression https github com aszx87410 blog issues 58 2020 03 23 online judge https github com aszx87410 blog issues 55 2019 12 27 from nand to tetris https github com aszx87410 blog issues 52 2019 07 12 medium https github com aszx87410 blog issues 44 2019 05 18 lidemy http challenge https github com aszx87410 blog issues 42 2019 01 03 redux dan abramov https github com aszx87410 blog issues 37 2018 12 29 https github com aszx87410 blog issues 36 2018 09 14 aws lambda github api google sheet https github com aszx87410 blog issues 32 2018 02 03 github classroom travis ci https github com aszx87410 blog issues 27 2017 01 27 https github com aszx87410 blog issues 15 2016 12 31 https github com aszx87410 blog issues 14 2016 12 03 hls https github com aszx87410 blog issues 13 | front_end |
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ESP8266-RTOS-DHT | esp8266 rtos dht esp8266 rtos sdk library for dht11 dht22 or si7021 this is a port from esp open rtos for espressif official sdk esp8266 rtos sdk compatibility espressif esp8266 rtos sdk v3 2 usage clone this project in your components folder c include stdio h include freertos freertos h include freertos task h include esp err h include dht dht h define dht gpio 5 d1 pin void temperature task void arg esp error check dht init dht gpio false vtaskdelay 2000 porttick period ms while 1 int humidity 0 int temperature 0 if dht read data dht type dht22 dht gpio humidity temperature esp ok e g in dht22 604 60 4 252 25 2 c if you want to print float data you should run make menuconfig to enable full newlib and call dht read float data here instead printf humidity d temperature d n humidity temperature else printf fail to get dht temperature data n vtaskdelay 5000 porttick period ms vtaskdelete null void app main xtaskcreate temperature task temperature task 2048 null tskidle priority null | esp8266 esp8266-rtos dht dht22 dht11 si7021 temperature-sensor | os |
Databases | p align center img src images databases png p databases p align justify this repository holds the implementation of a database system which was completed during my third year of studies in the computer engineering and informatics department at the university of patras the aim of the project was to get acquainted with the design implementation and input of data in a relational database which was connected with interfaces guis graphical user interfaces that were implemented in java using jdbc technology the database concerns an electronic support system for the operations of a daily press publishing house this system connects journalists who write articles editors in chief administrative employees and the publisher of each newspaper p structure p align justify ul li u part a u li ul li em codes em inside this folder exist the code for the creation of the database in the sql programming language the operations are the creation of the tables the insertion of data inside the tables the stored procedures and the triggers that were necessary for the smooth deployment of the database li li em er em this pdf represents the entity relational diagramm of the database li li em relational model em this pdf represents the relational model of the database li ul li u part b u li ul li em project db em inside this folder exist the code for the creation of the guis of the database and the necessary connections in the java programming language and using jdbc technology li li em report em a written report explaining everything that concerns our system in greek li ul ul p | server |
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teatro-ucsal-v2 | teatro ucsal v2 | server |
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libbitcoin-database | this branch is not usable in its current state please see version3 https github com libbitcoin libbitcoin database tree version3 for the latest functional branch | blockchain |
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CC2511 | cc2511 embedded system design | os |
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Mobile-Application-Development | mobile application development lab program vtu 6th sem logo https svcengg edu in wp content uploads 2020 12 svce1 1024x279 jpg mobile application development share this link https shubhaam13 github io mobile application development website https shubhaam13 github io mobile application development created by shubham kumar in collaboration with assistant professor mrs rakshitha k s contact if any queries email imsky004 gmail com or http shubhaam13 github io effective from the academic year 2018 2019 semester vi course code 18csmp68 laboratory objectives this laboratory 18csmp68 will enable students to learn and acquire the art of android programming configureandroid studio to run the applications understand and implement android s user interface functions create modify and query on sqlite database inspect different methods of sharing data using services | vtu vtulab vtulabprogrammes mad 18csmp68 6thsemcse 6thsemvtu students project android laboratory phone | front_end |
ISIT | isit welcome to information systems and technologies | server |
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spider | spider class project for cs221 1 and cs229 2 contributors muntasir mashuq lei sun ziyan zhou 1 http www stanford edu class cs221 2 http cs229 stanford edu | ai |
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Food-Delivery-Web-App | food delivery web app i develop food delivery web app in mern stack development with rest full apis this web app authenticate the user with jwt user can buy different meal items i have use cluster in mongoo db for database add to cart functionality is implemented data sending on front end also stored in backend | server |
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addons | home assistant add ons the official repository add ons for home assistant allow you to extend the functionality around your home assistant setup these add ons can consist of an application that home assistant can integrate with e g a mqtt broker mosquitto readme md or database server mariadb readme md or allow access to your home assistant configuration e g via samba samba readme md or using the file editor configurator readme md add ons can be installed and configured via the home assistant frontend on systems that have installed home assistant add ons provided by this repository cec scanner cec scan readme md scan discover hdmi cec devices and their addresses deconz deconz readme md control a zigbee network using conbee or raspbee hardware by dresden elektronik dhcp server dhcp server readme md a simple dhcp server dnsmasq dnsmasq readme md a simple dns server duck dns duckdns readme md automatically update your duck dns ip address with integrated https support via let s encrypt file editor configurator readme md simple browser based file editor for home assistant genie almond readme md the open source privacy preserving voice assistant git pull git pull readme md load and update configuration files for home assistant from a git repository google assistant sdk google assistant readme md a virtual personal assistant developed by google let s encrypt letsencrypt readme md manage an create certificates from let s encrypt mariadb mariadb readme md mariadb database for home assistant mosquitto broker mosquitto readme md an open source mqtt broker nginx home assistant ssl proxy nginx proxy readme md sets up an ssl proxy with nginx and redirects traffic from port 80 to 443 rpc shutdown rpc shutdown readme md shutdown windows machines remotely samba share samba readme md share your configuration over the network using windows file sharing ssh server ssh readme md allow logging in remotely to home assistant using ssh or just the web terminal with ingress tellstick tellstick readme md tellstick and tellstick duo service z wave js zwave js readme md allow home assistant to talk to a z wave network via a usb controller deprecated add ons hey ada ada readme md voice assistant powered by home assistant homematic homematic readme md homematic central based on occu deprecated in favor of the much more advanced third party raspberrymatic ccu https github com jens maus raspberrymatic tree master home assistant addon openzwave zwave readme md allow home assistant to talk to a z wave network via a usb controller deprecated in favor of z wave js zwave js readme md support got questions you have several options to get them answered the home assistant discord chat server discord the home assistant community forum forum join the reddit subreddit reddit in r homeassistant reddit in case you ve found a bug please open an issue on our github issue developing your own add ons in case you are interested in developing your own add on this repository can be a great source of inspiration for more information about developing an add on please see our documentation for developers dev docs discord https discord gg c5dvz4e forum https community home assistant io i386 shield https img shields io badge i386 no red svg issue https github com home assistant addons issues reddit https reddit com r homeassistant dev docs https developers home assistant io docs add ons | docker home automation iot hacktoberfest | server |
planray | industrial electric trace heating data scripts for the self made data pipeline including data fetch from rest api raw data preprocess feature engineering online database mariadb implementation over ssh connection after data process and feature engineering the target feature is predicted with two different types of neural networks fully connected fc regression neural network and 2 dimensional convolution nn 2d cnn all scripts have a docstring documentation with a brief description of their purpose and parameters pipeline https user images githubusercontent com 91312571 185053006 bf1b71bf c4f7 474b a932 270abf1d851f jpg there is also documentation in finnish for this which is not publicly available the document contains for example data analysis and images further development ideas architecture description and analysis of the results of machine learning methods model s accuracy testdata image https user images githubusercontent com 91312571 186219073 47ffc0bd 6891 4644 a0e2 aad54ce436f5 png the model s accuracy for data it hasn t seen before image https user images githubusercontent com 91312571 185051492 8eb29ac3 5ad3 49f7 9784 606fb7b5802d png the model predicts the power kwh of the heating circuit y axis number of samples x axis mean absolute error mae 0 0672 | data-analysis data-engineering python tensorflow | server |
explorer | div align center ping wallet public logo svg h1 ping dashboard h1 ping dashboard is not only an explorer but also a wallet and more version https img shields io github tag ping pub explorer svg https github com ping pub explorer releases latest github https img shields io github license ping pub explorer svg https github com ping pub explorer blob master license twitter url https img shields io twitter url https twitter com bukotsunikki svg style social label follow 20 40ping pub https twitter com ping pub https discord gg cmjyvsr6gw https img shields io badge discord join 7289da svg logo discord longcache true style flat https discord gg cmjyvsr6gw div ping dashboard is a light explorer for cosmos based blockchains https ping pub what sets ping dashboard apart from other explorers ping dashboard stands out by providing a real time exploration of blockchain data without relying on caching or pre processing it exclusively fetches data from the cosmos full node via lcd rpc endpoints ensuring a truly authentic experience this approach is referred to as the light explorer are you interested in listing your blockchain on https ping pub to make this repository clean please submit your request to https github com ping pub ping pub git why does ping dashboard rely on official trusted third party public lcd rpc servers there are two primary reasons for this choice trust in a decentralized system it is crucial to avoid relying solely on a single entity by utilizing official trusted third party public lcd rpc servers ping dashboard ensures that the data is sourced from a network of trusted participants limited resources as ping dashboard plans to list hundreds of cosmos based blockchains in the future it is impractical for the ping team to operate validators or full nodes for all of them leveraging trusted third party servers allows for more efficient resource allocation donation your donation will help us make better products thanks in advance address for erc20 usdc usdt eth 0x88bfec573dd3e4b7d2e6bfd4d0d6b11f843f8aa1 donations from project point network 1000usdc and 1000 worth of point bitsong 50k btsg irisnet 100k iris hire us you can hire us by submiting an issue and fund the issue on issuehunter https issuehunt io r ping pub explorer contributors developers liangping dingyiming | blockchain | blockchain |
iotagent-json | fiware iot agent for a json based protocol fiware iot agents https nexus lab fiware org static badges chapters iot agents svg https www fiware org developers catalogue license apgl https img shields io github license telefonicaid iotagent json svg https opensource org licenses agpl 3 0 support badge https img shields io badge tag fiware iot orange svg logo stackoverflow https stackoverflow com questions tagged fiware iot br quay badge https img shields io badge quay io fiware 2fiotagent json grey logo red 20hat labelcolor ee0000 https quay io repository fiware iotagent json docker badge https img shields io badge docker telefonicaiot 2fiotagent json blue logo docker https hub docker com r telefonicaiot iotagent json br documentation badge https img shields io readthedocs fiware iotagent json svg https fiware iotagent json readthedocs io en latest badge latest ci https github com telefonicaid iotagent json workflows ci badge svg https github com telefonicaid iotagent json actions query workflow 3aci coverage status https coveralls io repos github telefonicaid iotagent json badge svg branch master https coveralls io github telefonicaid iotagent json branch master status https nexus lab fiware org static badges statuses iot json svg cii best practices https bestpractices coreinfrastructure org projects 4695 badge https bestpractices coreinfrastructure org projects 4695 an internet of things agent for a json based protocol with amqp https www amqp org http https www w3 org protocols and mqtt https mqtt org transports this iot agent is designed to be a bridge between json https json org and the ngsi https swagger lab fiware org url https raw githubusercontent com fiware specifications master openapi ngsiv2 ngsiv2 openapi json interface of a context broker it is based on the iot agent node js library https github com telefonicaid iotagent node lib further general information about the fiware iot agents framework its architecture and the common interaction model can be found in the library s github repository this project is part of fiware https www fiware org for more information check the fiware catalogue entry for the iot agents https github com fiware catalogue tree master iot agents books documentation https fiware iotagent json readthedocs io mortar board academy https fiware academy readthedocs io en latest iot agents idas img style height 1em src https quay io static img quay favicon png quay io https quay io repository fiware iotagent json whale docker hub https hub docker com r telefonicaiot iotagent json dart roadmap https github com telefonicaid iotagent json blob master docs roadmap md contents background background install build install usage usage api api command line client command line client contributing contributing testing testing license license background this iot agent is designed to be a bridge between an mqtt http json based protocol and the fiware ngsi standard used in fiware this project is based in the node js iot agent library more information about the iot agents can be found within the library s github repository https github com telefonicaid iotagent node lib a quick way to get started is to read the step by step manual docs stepbystep md as is the case in any iot agent this one follows the interaction model defined in the node js iot agent library https github com telefonicaid iotagent node lib that is used for the implementation of the northbound apis information about the iotagent s architecture can be found on that global repository this documentation will only address those features and characteristics that are particular to the json iotagent if you want to contribute to the project check out the development section development and the contribution guidelines docs contribution md additional information about operating the component can be found in the operations logs and alarms docs operations md document install information about how to install the json iotagent can be found at the corresponding section of the installation administration guide docs installationguide md a dockerfile is also available for your use further information can be found here docker readme md usage information about how to use the iot agent can be found in the user programmers manual docs usermanual md the following features are listed as deprecated docs deprecated md api apiary reference for the configuration api can be found here https telefonicaiotiotagents docs apiary io reference configuration api more information about iot agents and their apis can be found in the iot agent library documentation https iotagent node lib readthedocs io the latest iot agent for json documentation is also available on readthedocs https fiware iotagent json readthedocs io en latest command line client the json iot agent comes with a client that can be used to test its features simulating a device the client can be executed with the following command console bin iotajsontester js this will show a prompt where commands can be issued to the mqtt broker for a list of the currently available commands type help the client loads a global configuration used for all the commands containing the host and port of the mqtt broker and the api key and device id of the device to simulate this information can be changed with the config command in order to use any of the mqtt commands you have to connect to the mqtt broker first if no connection is available mqtt commands will show an error message reminding you to connect the command line client gets its default values from a config file in the root of the project client config js this config file can be used to permanently tune the mqtt broker parameters or the default device id and apikey contributing if you d like to contribute start by searching through the issues and pull requests to see whether someone else has raised a similar idea or question before contributing please check out contribution guidelines docs contribution md testing mocha https mochajs org test runner should js https shouldjs github io assertion library the test environment is preconfigured to run bdd testing style module mocking during testing can be done with proxyquire https github com thlorenz proxyquire to run tests type console npm test requirements all the tests are designed to test end to end scenarios and there are some requirements for its current execution mqtt v5 broker like mosquitto v1 6 7 server running mongodb v4 x server running rabbitmq to run requirements you can type docker run d p 27017 27017 hostname mongo name mongo mongo 4 4 19 docker run d p 1883 1883 l mosquitto eclipse mosquitto 1 6 15 docker run d p 5672 5672 hostname my rabbit name some rabbit rabbitmq 3 11 13 license the iot agent for json is licensed under affero general public license gpl version 3 license 2023 telefonica investigaci n y desarrollo s a u details summary strong further information on the use of the agpl open source license strong summary are there any legal issues with agpl 3 0 is it safe for me to use there is absolutely no problem in using a product licensed under agpl 3 0 issues with gpl or agpl licenses are mostly related with the fact that different people assign different interpretations on the meaning of the term derivate work used in these licenses due to this some people believe that there is a risk in just using software under gpl or agpl licenses even without modifying it for the avoidance of doubt the owners of this software licensed under an agpl 3 0 license wish to make a clarifying public statement as follows please note that software derived as a result of modifying the source code of this software in order to fix a bug or incorporate enhancements is considered a derivative work of the product software that merely uses or aggregates i e links to an otherwise unmodified version of existing software is not considered a derivative work and therefore it does not need to be released as under the same license or even released as open source details | fiware iot iot-agent | server |
IIT | iit introduction to information technology | server |
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MassSurveillanceDB | masssurveillancedb this sql database has been created for the databases course electrical and computer engineering department of auth includes the following files db mass mwb database model in mysql workbench format db png database model diagram as a png file extracted from mysql workbench db mass sql sql script that contains the dump for creating the database tables constraints inserted data views users sql sql script for creating the users of the database and their rights query1 sql query2 sql sql scripts that include queries to the database | server |
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DataEngineeringGCP | dataengineeringgcp all my code for data engineering on google cloud platform goes here | cloud |
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Phoenix | phoenix a multi system emulator and library manager designed to be both powerful and easy to use powered by qt 5 and libretro it is available on windows os x and linux free now and forever under gpl v2 we ve designed phoenix with the hope that it ll be perfect for everyone whether you re someone who hardly plays videogames a hardcore gamer a speedrunner or even a homebrew programmer build status https secure travis ci org team phoenix phoenix png http travis ci org team phoenix phoenix chat https img shields io badge chat on 20discord 7289da svg https discord gg umkbq6a b phoenix is still in early development expect crashes b screenshot https github com team phoenix designs raw gh pages screenshots screen png a message for developers we need you we re looking for c c qml programmers to help us build phoenix check out our wiki https github com team phoenix phoenix wiki for build instructions ways to contact the lead developers and more | os |
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Paddle | p align center img align center src doc imgs logo png width 1600 p english readme cn md readme ja md documentation status https img shields io badge docs latest brightgreen svg style flat https paddlepaddle org cn documentation docs en guides index en html documentation status https img shields io badge brightgreen svg https paddlepaddle org cn documentation docs zh guides index cn html release https img shields io github release paddlepaddle paddle svg https github com paddlepaddle paddle releases license https img shields io badge license apache 202 blue svg license twitter https img shields io badge twitter 1ca0f1 svg logo twitter logocolor white https twitter com paddlepaddle welcome to the paddlepaddle github paddlepaddle as the first independent r d deep learning platform in china has been officially open sourced to professional communities since 2016 it is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks basic model libraries end to end development kits tools components as well as service platforms paddlepaddle is originated from industrial practices with dedication and commitments to industrialization it has been widely adopted by a wide range of sectors including manufacturing agriculture enterprise service and so on while serving more than 8 million developers 220 000 companies and generating 800 000 models with such advantages paddlepaddle has helped an increasing number of partners commercialize ai installation latest paddlepaddle release v2 5 https github com paddlepaddle paddle tree release 2 5 our vision is to enable deep learning for everyone via paddlepaddle please refer to our release announcement https github com paddlepaddle paddle releases to track the latest features of paddlepaddle install latest stable release cpu pip install paddlepaddle gpu pip install paddlepaddle gpu for more information about installation please view quick install https www paddlepaddle org cn install quick now our developers can acquire tesla v100 online computing resources for free if you create a program by ai studio you will obtain 8 hours to train models online per day click here to start https aistudio baidu com aistudio index four leading technologies agile framework for industrial development of deep neural networks the paddlepaddle deep learning framework facilitates the development while lowering the technical burden through leveraging a programmable scheme to architect the neural networks it supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved the neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts support ultra large scale training of deep neural networks paddlepaddle has made breakthroughs in ultra large scale deep neural networks training it launched the world s first large scale open source training platform that supports the training of deep networks with 100 billion features and trillions of parameters using data sources distributed over hundreds of nodes paddlepaddle overcomes the online deep learning challenges for ultra large scale deep learning models and further achieved real time model updating with more than 1 trillion parameters click here to learn more https github com paddlepaddle fleet high performance inference engines for comprehensive deployment environments paddlepaddle is not only compatible with models trained in 3rd party open source frameworks but also offers complete inference products for various production scenarios our inference product line includes paddle inference https paddle inference readthedocs io en master guides introduction index intro html native inference library for high performance server and cloud inference paddle serving https github com paddlepaddle serving a service oriented framework suitable for distributed and pipeline productions paddle lite https github com paddlepaddle paddle lite ultra lightweight inference engine for mobile and iot environments paddle js https www paddlepaddle org cn paddle paddlejs a frontend inference engine for browser and mini apps furthermore by great amounts of optimization with leading hardware in each scenario paddle inference engines outperform most of the other mainstream frameworks industry oriented models and libraries with open source repositories paddlepaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the industry some of these models have won major prizes from key international competitions in the meanwhile paddlepaddle has further more than 200 pre training models some of them with source codes to facilitate the rapid development of industrial applications click here to learn more https github com paddlepaddle models documentation we provide english https www paddlepaddle org cn documentation docs en guides index en html and chinese https www paddlepaddle org cn documentation docs zh guide index cn html documentation guides https www paddlepaddle org cn documentation docs en guides index en html you might want to start from how to implement deep learning basics with paddlepaddle practice https www paddlepaddle org cn documentation docs zh tutorial index cn html so far you have already been familiar with fluid and the next step should be building a more efficient model or inventing your original operator api reference https www paddlepaddle org cn documentation docs en api index en html our new api enables much shorter programs how to contribute https www paddlepaddle org cn documentation docs en guides 08 contribution index en html we appreciate your contributions communication github issues https github com paddlepaddle paddle issues bug reports feature requests install issues usage issues etc qq discussion group 441226485 paddlepaddle forums https aistudio baidu com paddle forum discuss implementations research etc courses server deployments https aistudio baidu com aistudio course introduce 19084 courses introducing high performance server deployments via local and remote services edge deployments https aistudio baidu com aistudio course introduce 22690 courses introducing edge deployments from mobile iot to web and applets copyright and license paddlepaddle is provided under the apache 2 0 license license | paddlepaddle deep-learning scalability machine-learning neural-network python efficiency distributed-training | ai |
Department_of_CSE_by_PHP-JavaScript-HTML5-CSS3 | department of cse by php javascript html5 css3 a full stack web engineering project front end development html5 css3 javascript amp bootstrap 4 back end development php database mysql coding ide visual studio code database ide sqlyog operating system microsoft windows linux android ios | server |
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rust-blockchain | rust blockchain a very simple blockchain in rust in progress multiple nodes synchronize chain from other nodes chains table of content dependencies dependencies block structure block structure start nodes start nodes create many nodes create many nodes start a node start a node check node ip address check node ip address find peers find peers manually add a peer manually add a peer list peers list peers blocks blocks add block add block list blocks list blocks dependencies time time routines used to easily get the current utc timestamp for block creation sha1 sha1 hashing routines in order to hash the previous block of the current created one bincode serialization routines used to serialize a block into raw binary used for hashing serde serde derive serialization deserialization routines in order to make a block serializable termion provides terminal graphical routines block structure a block of the ledger contains the following fields the timestamp of the block creation the data of the block signed integer in order to be similar to cryptocurrencies the hash digest of the previous ledger block empty string if the current block is the genesis one the hash digest of the current ledger block stored as a string hexadecimal digest the previous and current hash digests are directly fields of the block the timestamp and the data fields are part of a hashable content structure itself a field of the block structure in fact in order to keep things as simple as possible only the timestamp and the data are hashed into previous and current the block structure implements the serialize trait in order to keep everything simple and in order to prevent custom serialization functions the block structure only contains primitive types i64 i32 and string as they all already implement the trait every block of the chain contains the hash of the previous block this is exactly how blockchain maintains integrity for instance changing a block data from the middle of the chain would require to change all the blocks that come after start nodes this section explains how to use the blockchain create many nodes blockchain usually works with multiple nodes in order to create multiple nodes you can simply git clone or copy the project at different location rename the project environment variable from the vagrantfile for instance rust blockchain 0 rust blockchain 1 rust blockchain 2 and execute vagrant up for each of these program beware do not copy the hidden directory vagrant start a node to start a node simply build the container sh vagrant up connect using ssh to the container sh vagrant ssh start the service sh target release rust blockchain check node ip address ip address is necessary when synchronizing the local blockchain of a node in order to check the private ip address of a node within the docker lan simply execute sh docker inspect rust blockchain dev this is also possible to set the ip address of a node into the vagrantfile ruby config vm network private network ip 10 10 10 10 find peers this section is about communication between nodes manually add a peer the easiest way to link nodes together is to manually register a peer locally simply add a peer with add peer sh add peer 172 0 0 10 list peers sh list peers blocks this section is about chain blocks add block add a block and broadcast it to every peer for example to add a block with the data 20 sh add block 20 list blocks list all the blocks of the local chain sh list blocks | blockchain |
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iot-center-v2 | iot center v2 this repository contains the iot center application that provides a web ui that shows how to use influxdb v2 in various use cases it also contains independent clients that write into influxdb features simple device management automatic registration of devices in influxdb arduino pyton javasript clients various data visualizations dashboards quick start prerequisites node 14 or newer yarn 1 9 4 or newer run iot center application from source cd app yarn install yarn build yarn start open http localhost 5000 or docker compose up open http localhost 5000 docker docker images are available on github packages https github com bonitoo io iot center v2 packages with nightly tag docker pull ghcr io bonitoo io iot center v2 iot center nightly docker run name iot center detach env influx url http 10 100 10 100 9999 env influx token my token env influx org my org publish 5000 5000 ghcr io bonitoo io iot center v2 iot center nightly develop and play with iot center application hot swap enabled cd app yarn install yarn dev realtime demo only mqtt locally each line in separate terminal no environment variables has to be set cd app server mqtt docker mosquitto run mqtt server sh cd app server yarn mqtt dev cd app ui yarn start application is running at http localhost 3000 realtime mqtt can be checked with cd app server yarn mqtt subscriber websocket broker ui side connection can be checked with cd app server yarn mqtt subscriber broker mqtt with influx setup register account in influxdb cloud 2 if not exists or install local influxdb 2 instance edit app dev sh and set environment values influx token influx url influx org and uncomment mqtt topic mqtt url mqtt url can t be localhost or 127 0 0 1 to run with real devices install telegraf and copy example configuration cd app server mqtt cp n telegraf example conf telegraf conf edit telegraf conf and set urls token organization under outputs influxdb v2 and servers iot center under inputs mqtt consumer to be same as in app dev sh nano telegraf conf run everything have mqtt running public or run locally with command cd app server mqtt docker mosquitto run mqtt server sh start telgraf cd app server mqtt telegraf config telegraf conf start app cd app dev sh mqtt can be checked with cd app server yarn mqtt subscriber open browser at http localhost 3000 realtime xdg open http localhost 3000 realtime environment installation install latest node js install latest yarn download this git repository install local influxdb 2 instance or register account in influxdb cloud 2 if not exists create admin token set environment variable influx token with the admin token from the previous point set environment variable influx url do not use localhost or 127 0 0 1 iot devices need external address set environment variable influx org run iot center license the project is under the mit license https opensource org licenses mit | server |
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AddressBook | addressbook database and software engineering lab project | server |
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Intellij-IDEA-F2E | intellij idea f2e intellij idea http www jetbrains com idea phpstorm http www jetbrains com phpstorm webstorm http www jetbrains com webstorm ide markdown html markdown html webstorm node js less phpstorm php webstorm intellij idea java ide webstorm intellij idea qq t xx xxx xxxxxx http www beiww com doc oss smart questions html 149725975 css html javascript php 244908708 java tomcat jsp android intellij idea intellij idea http www oschina net shop jetbrains settings appearance look and feel intellij idea 12 darcula intellij idea icls jar xml icls icls for mac os library preferences intellijidea13 colors for linux intellijidea13 config colors for windows c documents and settings user intellijidea13 config colors jar file import settings material theme ui https github com chrisrm material theme jetbrains installation intellij colors solarized https github com jkaving intellij colors solarized twilight https github com eed3si9n color themes tree master intellij idea twilight obsidian https github com mekwall obsidian color scheme a dark color scheme for code editors and highlighters tomorrow https github com chriskempson tomorrow theme tree master jetbrains idea color schemes http ideacolorschemes com phpstorm themes http www phpstorm themes com sublime text2 obsidian color scheme https github com mekwall obsidian color scheme sublime text2 idea https github com jetbrains colorschemetool ash settings editor colors fonts font 17px idea12 source code pro adobe intellij idea http bigc at intellij idea font famliy orz 2 project i o excluded svn git ssd faq windows 7 sometimes breaks ftp connections on java 7 if firewall is enabled command netsh advfirewall set global statefulftp disable usually fixes the problem http youtrack jetbrains com issue wi 17206 idea 30 http blogs jetbrains com idea tag 30 days guide help default keymap reference settings editor use soft wraps in editor use soft wraps st2 http www jetbrains com idea whatsnew img 13 ij131 multiple selections gif settings appearance show right margin settings editor allow placement of caret after end of line setting appearance override default fonts by tag split vertically split horizontally view enter full screen code style java comment code line comment at first column project module http stackoverflow com questions 1147336 how to get intellij idea to display directories intellij idea http pan baidu com s 1nt9gmdn http note youdao com share id 973d61880d78c34797a978afc5bc8846 type note java development kit jdk http willerce com post jdk php http bigc at intellij idea php orz project http bigc at intellij idea project orz appearance http bigc at intellij idea appearance orz intellij idea http bigc at intellij idea font famliy orz remote host http www cssha com webstorm phpstorm remote host webstorm ide http www cssha com webstorm idea http willerce com post intellij idea config sync idea http willerce com post intellij ide fontconfig less http www screenr com yn47 webstorm 6 less css https github com damao intellij idea f2e wiki compile less to css with intellij idea idea 12 intellij idea http www youmeek com category software system my intellij idea intellij idea phonegap 3 3 http bigc at phonegap with intellij idea orz http www cnblogs com sky100 archive 2009 01 22 1379949 html quickjump http bigc at intellij idea quickjump orz local history http bigc at intellij idea local history orz live template http bigc at intellij idea live template orz task http bigc at intellij idea task orz file path http bigc at intellij idea file path orz surround unwrap http bigc at intellij idea surround unwrap orz refactor http bigc at intellij idea refactor orz liveedit plugin http bigc at intellij idea liveedit plugin orz webstorm http willerce com post intellij external tools yabo css http bigc at yabo orz intellij idea gbk http nornor net intellij idea gbk fix htm http blog jetbrains com webide 2013 02 comparing files and folders within your ide emmet zencoding http docs emmet io cheat sheet webstorm ide http www cssha com webstorm tab http bigc at tabs vs spaces orz bug svn 1 8 13 idea 94942 http youtrack jetbrains com issue idea 94942 ftp sftp intellij idea http bigc at intellij idea font famliy orz win8 qq 4 5 http pan baidu com s 1vl2sn a4k3 qq 4 6 jdk oracle jrockit http www oracle com technology products jrockit idea12 server log idea bin idea exe vmoptions dfile encoding utf 8 idea12 server log http www kafeitu me tools 2013 03 26 intellij deal chinese disorderly code html idea tomcat https github com damao intellij idea f2e wiki idea e4 b8 8b e5 90 af e5 8a a8tomcat e6 8a a5 e9 94 99 intellij idea markdown https github com nicoulaj idea markdown github | front_end |
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nuttx-testing | incubator nuttx testing the contents of this repository have been moved to the ci section of the main apache nuttx os repo https github com apache incubator nuttx tree master tools ci please file any issues and prs there | nuttx rtos embedded real-time mcu microcontroller ci | os |
mobile-development | mobile development mobile development | efrei efrei-paris java android | front_end |
4iz110 | 4iz110 https isis vse cz katalog syllabus pl predmet 100604 typ 1 jazyk 3 vystup 1 lang en 2013 screenshot screenshot png | server |
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its | its information technology service team | server |
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BlockChainSimpleDemo | python3 demo python3 pow 1 pow 1 1 block py python import hashlib class block def init self timestamp data previous hash param timestamp param data param previous hash hash param hash hash self previous hash previous hash self timestamp timestamp self data data self hash self calculate hash def calculate hash self return sha256 hash raw str self previous hash str self timestamp json dumps self data ensure ascii false sha256 hashlib sha256 sha256 update raw str encode utf 8 hash sha256 hexdigest return hash hashlib hash block init hash data data calculate hash hash table tr td bgcolor 0096ff font color white tip hash sha256 sha hash hash hash font td tr table 1 2 blockchain py python from block import block import time class blockchain def init self self chain self create genesis block staticmethod def create genesis block return timestamp time mktime time strptime 2018 06 11 00 00 00 y m d h m s block block timestamp return block def get latest block self return return self chain 1 def add block self block param block return block previous hash self get latest block hash block hash block calculate hash self chain append block def verify blockchain self return for i in range 1 len self chain current block self chain i previous block self chain i 1 if current block hash current block calculate hash hash hash return false if current block previous hash previous block calculate hash hash hash hash return false return true if name main blockchain blockchain blockchain add block block time time amount 100 blockchain add block block time time amount 200 print if blockchain verify blockchain else blockchain chain 1 data amount 10 print if blockchain verify blockchain else 2 pow hash pow 2 1 pow pow hash hash pow hash 0 pow hash hash nonce nonce hash hash hash hash 10 2 2 pow block nonce 0 python def init self timestamp data previous hash param timestamp param data param previous hash hash param hash hash self previous hash previous hash self timestamp timestamp self data data self nonce 0 self hash self calculate hash calculate hash hash nonce python def calculate hash self return sha256 hash raw str self previous hash str self timestamp json dumps self data ensure ascii false str self nonce sha256 hashlib sha256 sha256 update raw str encode utf 8 hash sha256 hexdigest return hash python def mine block self difficulty param difficulty return time start time clock hash difficulty 0 while self hash 0 difficulty join 0 difficulty self nonce 1 self hash self calculate hash print s f self hash time clock time start 2 3 blockchain difficulty 5 hash 5 0 python def init self self chain self create genesis block self difficulty 5 add block python def add block self block param block return block previous hash self get latest block hash block mine block self difficulty self chain append block python blockchain blockchain blockchain add block block time time amount 100 blockchain add block block time time amount 200 https sww wordpress oss cn beijing aliyuncs com 2018 06 mine block time png p2p 3 3 1 transaction py python class transaction def init self from address to address amount param from address param to address param amount self from address from address self from address to address self amount amount json transactionencoder json jsonencoder python import json class transaction def init self from address to address amount param from address param to address param amount self from address from address self to address to address self amount amount class transactionencoder json jsonencoder def default self o if isinstance o transaction return o dict return json jsonencoder default self o if name main tran transaction aaa bbb 100 print tran print json dumps tran ensure ascii false cls transactionencoder 3 2 python class blockchain def init self self chain self create genesis block self difficulty 5 self pending transactions self mining reward 100 blockchain pending transactions mining reward add block add transaction python def add block self block param block return block previous hash self get latest block hash block mine block self difficulty self chain append block def add transaction self transaction param transaction return self pending transactions append transaction block data transactions block python import hashlib import json import time from transaction import transactionencoder class block def init self timestamp transactions previous hash param timestamp param transactions param previous hash hash param hash hash self previous hash previous hash self timestamp timestamp self transactions transactions self nonce 0 self hash self calculate hash def calculate hash self return sha256 hash raw str self previous hash str self timestamp json dumps self transactions ensure ascii false cls transactionencoder str self nonce sha256 hashlib sha256 sha256 update raw str encode utf 8 hash sha256 hexdigest return hash def mine block self difficulty param difficulty return time start time clock hash difficulty 0 while self hash 0 difficulty join 0 difficulty self nonce 1 self hash self calculate hash print s f self hash time clock time start json cls transactionencoder transaction json dumps blockchain python def mine pending transaction self mining reward address param mining reward address return block block time time self pending transactions self chain 1 hash block mine block self difficulty self chain append block self pending transactions transaction none mining reward address self mining reward def get balance of address self address param address return balance 0 for block in self chain for trans in block transactions if trans from address address balance trans amount if trans to address address balance trans amount return balance 3 3 python if name main blockchain blockchain blockchain add transaction transaction address1 address2 100 blockchain add transaction transaction address2 address1 50 address3 blockchain mine pending transaction address3 print address1 blockchain get balance of address address1 print address2 blockchain get balance of address address2 print address3 blockchain get balance of address address3 address2 blockchain mine pending transaction address2 print address1 blockchain get balance of address address1 print address2 blockchain get balance of address address2 print address3 blockchain get balance of address address3 https sww wordpress oss cn beijing aliyuncs com 2018 06 blockchain mine balance png | blockchain |
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protocol-assets | protocol assets npm version https img shields io npm v feather icons svg style flat square https www npmjs com package mozilla protocol assets this repository contains a set of reusable assets for mozilla s websites these assets are available as both svg and png files assets include logos and icons protocol icons are a derivative of feather icons https feathericons com used under the mit license https github com colebemis feather blob master license what s included icons for the most part icons are black svgs there are a few white variations for historical reasons these have the sufix white in the file name there is also a set of colorful brand icons in svg format in four different color schemes drawn from the firefox brand palette logos svg logo and logo wordmark files are included for a number of mozilla and firefox products sizing varies you should declare the height and width you want when you use them variations logo the logo is part of the file word the wordmark is in the file ver the logo is above the wordmark hor the logo is to the left of the wordmark stack each word in the wordmark is on a new line flat the image is a single solid color with no shading or gradients white the image is intended for use on a dark background if the image does not have white in the file name it s meant for use on a light background og this is an open graph image for the product favicons are not included at this time we encourage you to generate them yourself from the files included here code of conduct this repository is governed by mozilla s code of conduct and etiquette guidelines for more details please see the mozilla community participation guidelines participation and developer etiquette guidelines etiquette participation https www mozilla org about governance policies participation etiquette https bugzilla mozilla org page cgi id etiquette html license this software is licensed under the mpl version 2 0 mpl for more information read the file license mpl https www mozilla org mpl | os |
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computer_vision_literature_review | computer vision literature review jinwoo s literature review on computer vision and machine learning papers contents action recognition action recognition object recognition object recognition pose estimation pose estimation action recognition spatio temporal action detection human action localization with sparse spatial supervision https arxiv org pdf 1605 05197 pdf p weinzaepfel et al arxiv2017 action detection using sparse spatial supervision only use 1 5 bounding box annotation s per tube for training use human detector and tracking by detection method to obtain human tubes human detector is faster r cnn trained on mpii human pose dataset classify the human tubes afterwards use idt convnet features introduce a new untrimmed weakly supervised action detection dataset daly using all bounding box annotations and 1 5 bounding box annotation s show similar video map however even with the all bounding box annotations the performance is inferior than the state of the art methods unsupervised action discovery and localization in videos http openaccess thecvf com content iccv 2017 papers soomro unsupervised action discovery iccv 2017 paper pdf k soomro and m shah iccv2017 unsupervised spatio temporal action detection first paper on unsupervised action detection problem discriminative clustering to discovery which labels are presented in a dataset use sepctral clustering to get initial clusters iteratively selects videos from the non dominant set obtain spatio temporal annotations by oversegmenting the video using supervoxel constructing dag solving knapsack optimization with temporal constraints determine wether to include a supervoxel in the current action or not shows competitive performance in terms of auc compared to supervised methods might be applied for weakly supervised action detection problem solving spatial aware object embeddings for zero shot localization and classification of actions https arxiv org pdf 1707 09145 pdf p mettes and c g m snoek iccv2017 zero shot action detection classification method using actor object actor object relationship and global context zero shot learning method no training videos of action required proposed spatial aware object embedding during test time on top of object and action detectors actions objects and their interactions are used to detect classify actions in the given frame use word2vec representation to narrow down the possible objects given an action class global objects objects far away from the actors are also incorporated to boost the performance action tubelet detector for spatio temporal action localization https arxiv org abs 1705 01861 v kalogeiton et al iccv2017 code https github com vkalogeiton caffe tree act detector project web http thoth inrialpes fr src actdetector tube convolutional neural network t cnn for action detection in videos https 128 84 21 199 pdf 1703 10664 pdf r hou http www cs ucf edu rhou et al iccv2017 project web http crcv ucf edu projects tcnn chained multi stream networks exploiting pose motion and appearance for action classification and detection https arxiv org abs 1704 00616 m zolfaghari et al iccv2017 project web https lmb informatik uni freiburg de projects action chain tornado a spatio temporal convolutional regression network for video action proposal http openaccess thecvf com content iccv 2017 papers zhu tornado a spatio temporal iccv 2017 paper pdf h zhu et al iccv2017 spatio temporal action proposal using spatial and temporal networks in this paper two networks are introduced to capture spatial and temporal contexts for spatio temporal action proposal generation temporal context is captured by convlstm and spatial context is captured by a plain convnet each network predicts frame level bounding box proposals with confidence and actionness backgroundness scores they link the frame level proposals temporally to generate tube proposals by dynamic programming with confidence scores and overlaps then the tube proposals are temporally trimmed by the peak actionness detection algorithm they use both rgb and flow as input modalities evaluation metrics are abo mabo and recall ucf 101 and ucf sports are their testbeds online real time multiple spatiotemporal action localisation and prediction https arxiv org pdf 1611 08563v3 pdf g singh http gurkirt github io et al iccv2017 code https github com gurkirt realtime action detection amtnet action micro tube regression by end to end trainable deep architecture https arxiv org pdf 1704 04952 pdf s saha et al iccv2017 propose 3d rpn using two frames with an arbitrary interval only using rgb frames no flow frames incorporating temporal dependencies by 3d rpn using two frames 3d rpn is a straghtforward extension of 2d rpn input of the 3d rpn is an element wise summation of conv5 features from two vggnets for two frames with an arbitrary interval using 1 or 2 frames interval in practice output of the 3d rpn is two sets of bounding boxes corresponding to two input frames one set of bounding boxes for the first frame and the other set of bounding boxes for the second frame instead of roipooling bilinear interpolation is used to get a fixed size feature vector only using rgb frames no flow frames not showing very strong performance video map on ucf 101 or j hmdb am i done predicting action progress in videos https arxiv org abs 1705 01781 f becattini et al bmvc2017 generic tubelet proposals for action localization https arxiv org abs 1705 10861 j he et al arxiv2017 incremental tube construction for human action detection https arxiv org pdf 1704 01358 pdf h s behl et al arxiv2017 multi region two stream r cnn for action detection https www robots ox ac uk vgg rg papers peng16eccv pdf x peng http xjpeng weebly com and c schmid eccv2016 code https github com pengxj action faster rcnn spot on action localization from pointly supervised proposals http jvgemert github io pub spotoneccv16 pdf p mettes et al eccv2016 action localization using pointly supervised proposals use apt trajectory clustering based method to obtain tube proposals incoroporate an overlap measure between annotated points and proposals into the mining process of mil deep learning for detecting multiple space time action tubes in videos https arxiv org abs 1608 01529 s saha et al bmvc2016 code https bitbucket org sahasuman bmvc2016 code project web http sahasuman bitbucket org bmvc2016 learning to track for spatio temporal action localization http www cv foundation org openaccess content iccv 2015 papers weinzaepfel learning to track iccv 2015 paper pdf p weinzaepfel et al iccv2015 action detection by implicit intentional motion clustering http www cv foundation org openaccess content iccv 2015 papers chen action detection by iccv 2015 paper pdf w chen and j corso iccv2015 finding action tubes https people eecs berkeley edu gkioxari actiontubes action tubes pdf g gkioxari and j malik cvpr2015 code https github com gkioxari actiontubes project web https people eecs berkeley edu gkioxari actiontubes apt action localization proposals from dense trajectories http jvgemert github io pub gemertbmvc15aptactionproposals pdf j gemert et al bmvc2015 code https github com jvgemert apt cluster trajectories and use the resulting tubes for action detection spatio temporal object detection proposals https hal inria fr hal 01021902 pdf proof pdf d oneata et al eccv2014 code https bitbucket org doneata proposals project web http lear inrialpes fr oneata 3dproposals action localization with tubelets from motion http isis data science uva nl cgmsnoek pub jain tubelets cvpr2014 pdf m jain et al cvpr2014 action localizationn by hierarchical merging supervoxels and use dense trajectory features for tube classification spatiotemporal deformable part models for action detection http crcv ucf edu papers cvpr2013 cvpr2013 sdpm pdf y tian http www cs ucf edu ytian index html et al cvpr2013 code http www cs ucf edu ytian sdpm html action localization in videos through context walk http www cv foundation org openaccess content iccv 2015 papers soomro action localization in iccv 2015 paper pdf k soomro et al iccv2015 fast action proposals for human action detection and search http www cv foundation org openaccess content cvpr 2015 papers yu fast action proposals 2015 cvpr paper pdf g yu and j yuan cvpr2015 note code for fap is not available online note aka fap temporal action detection temporal action detection with structured segment networks http cn arxiv org pdf 1704 06228v2 y zhao et al iccv2017 code https github com yjxiong action detection project web http yjxiong me others ssn temporal action detection using temporal pyramid feature and completeness classifier works on top of temporal proposal method temporal proposal method is also proposed temporal actionness grouping tag on top of temporal actionness scores use water shed algorithm to group the temporal actioneess scores to obtain temporal proposals given an input proposal generate an augmented proposal augmented proposal has a longer temporal extent to both directions before and after notion of start end and course stages course means the initial proposal start means the frames before the initial proposal starts end means the frames after the initial proposal ends incorporate the temporal context before and after the actions divide the augmented proposal into 9 snippets construct a temporal pyramid feature for the course stage two classifiers action classifier and completeness classifier action classifier a normal multi class classifer completeness classifier a class specific binary classifier to determine each actions is complete or not state of the art performance on temporal action detecction temporal context network for activity localization in videos https arxiv org pdf 1708 02349 pdf x dai et al iccv2017 temporal activity detection method incorporating temporal context temporal context means a temporal proposal with a temporal extent larger than the actual action extent propose temporal anchors with various scales for each temporal position when encoding a feature concat features from two scales to incorporate temporal context apply temporal convolution to further incorporate temporal context temporal context does help it is shown by ablation study state of the art performance on activitynet and thumos14 detecting the moment of completion temporal models for localising action completion https arxiv org abs 1710 02310 f heidarivincheh et al arxiv2017 a trial for detecting action completions using convnet hmm lstm in this paper we try to detect a moment of an action completion we want to separate pre completion and post completion of an action frame by frame we define the completion as the goal of an action is achieved we use hmm and lstm on top of convnet feature to detect a completion of an action for hmm we have 2 hidden states pre and post the parameters of hmm initial and transition probs covariance matrices and mean vectors are learnt from training data for lstm we feed fc7 feature and per frame labels pre or post to lstm as an input experimental results are quite trivial both models can detect the completion of an action with a reasonable accuracy 75 within 10 frames under strong assumptions temporally trimmed sequences no multiple actions per sequence momentary completion completion should be detected using only one frame even for human and uniform prior for completion 50 50 chance of complete vs incomplete cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos https arxiv org abs 1703 01515 z shou et al cvpr2017 code https bitbucket org columbiadvmm cdc sst single stream temporal action proposals http vision stanford edu pdf buch2017cvpr pdf s buch et al cvpr2017 code https github com shyamal b sst r c3d region convolutional 3d network for temporal activity detection https arxiv org abs 1703 07814 h xu et al arxiv2017 code https github com visionlearninggroup r c3d project web http ai bu edu r c3d daps deep action proposals for action understanding https ivul kaust edu sa documents publications 2016 daps 20deep 20action 20proposals 20for 20action 20understanding pdf v escorcia et al eccv2016 code https github com escorciav daps raw data https github com escorciav daps online action detection using joint classification regression recurrent neural networks https arxiv org abs 1604 05633 y li et al eccv2016 noe rgb d action detection temporal action localization in untrimmed videos via multi stage cnns http dvmmweb cs columbia edu files dvmm scnn paper pdf z shou et al cvpr2016 code https github com zhengshou scnn note aka s cnn fast temporal activity proposals for efficient detection of human actions in untrimmed videos http www cv foundation org openaccess content cvpr 2016 papers heilbron fast temporal activity cvpr 2016 paper pdf f heilbron et al cvpr2016 code https github com cabaf sparseprop note depends on c3d http vlg cs dartmouth edu c3d aka sparseprop actionness estimation using hybrid fully convolutional networks https arxiv org abs 1604 07279 l wang et al cvpr2016 code https github com wanglimin actionness estimation note the code is not a complete verision it only contains a demo not training project web http wanglimin github io actionness hfcn index html learning activity progression in lstms for activity detection and early detection http cs brown edu ls publications cvpr2016ma pdf s ma et al cvpr2016 end to end learning of action detection from frame glimpses in videos http vision stanford edu pdf yeung2016cvpr pdf s yeung et al cvpr2016 code https github com syyeung frameglimpses project web http ai stanford edu syyeung frameglimpses html note this method uses reinforcement learning fast action proposals for human action detection and search http www cv foundation org openaccess content cvpr 2015 papers yu fast action proposals 2015 cvpr paper pdf g yu and j yuan cvpr2015 note code for fap is not available online note aka fap bag of fragments selecting and encoding video fragments for event detection and recounting https staff fnwi uva nl t e j mensink publications mettes15icmr pdf p mettes et al icmr2015 action localization in videos through context walk http www cv foundation org openaccess content iccv 2015 papers soomro action localization in iccv 2015 paper pdf k soomro et al iccv2015 spatio temporal convnets deep temporal linear encoding networks https arxiv org abs 1611 06678 a diba et al cvpr2017 project web https rohitgirdhar github io attentionalpoolingaction code https github com rohitgirdhar attentionalpoolingaction temporal convolutional networks a unified approach to action segmentation and detection https arxiv org pdf 1611 05267 pdf c lea et al cvpr 2017 code https github com colincsl temporalconvolutionalnetworks long term temporal convolutions https arxiv org pdf 1604 04494v1 pdf g varol et al tpami2017 project web http www di ens fr willow research ltc code https github com gulvarol ltc temporal segment networks towards good practices for deep action recognition https arxiv org pdf 1608 00859 pdf l wang et al arxiv 2016 code https github com yjxiong temporal segment networks action classification attentional pooling for action recognition https arxiv org abs 1711 01467 r girdhar and d ramanan nips2017 new pooling method with attention for action recognition in this paper an attention weighted pooling method is proposed with a rank 1 approximation of second order pooling and manipulating the order of matrix multiplications attention pooling can be veiwed as a combination of class agnostic bottom up saliency and class specific top down attention we can replace the average pooling operations in the resnet architecture by the proposed attention pooling with the attention pooling we can get state of the art performance on hmdb51 video hico and mpii image dataset fully context aware video prediction https arxiv org pdf 1710 08518v1 pdf byeon et al arxiv2017 dynamic image networks for action recognition https www robots ox ac uk vgg publications 2016 bilen16a bilen16a pdf h bilen et al cvpr2016 code https github com hbilen dynamic image nets project web http www robots ox ac uk vgg publications 2016 bilen16a long term recurrent convolutional networks for visual recognition and description http www cv foundation org openaccess content cvpr 2015 papers donahue long term recurrent convolutional 2015 cvpr paper pdf j donahue et al cvpr2015 code https github com lisaanne lisa caffe public tree lstm video deploy project web http jeffdonahue com lrcn describing videos by exploiting temporal structure http arxiv org pdf 1502 08029v4 pdf l yao et al iccv2015 code https github com yaoli arctic capgen vid note from the same group of rcn paper delving deeper into convolutional networks for learning video representations two stream sr cnns for action recognition in videos http wanglimin github io papers zhangwwqw cvpr16 pdf l wang et al bmvc2016 real time action recognition with enhanced motion vector cnns http arxiv org abs 1604 07669 b zhang et al cvpr2016 code https github com zbwglory mv release action recognition with trajectory pooled deep convolutional descriptors http www cv foundation org openaccess content cvpr 2015 papers wang action recognition with 2015 cvpr paper pdf l wang et al cvpr2015 code https github com wanglimin tdd convolutional two stream network fusion for video action recognition https arxiv org pdf 1604 06573 pdf c feichtenhofer et al cvpr2016 code https github com feichtenhofer twostreamfusion video representation a closer look at spatiotemporal convolutions for action recognition https arxiv org pdf 1711 11248 pdf d tran et al cvpr2018 code https github com facebookresearch r2plus1d 2d 1d separate convolution is better than 3d convolution 3d convnet architecture search on action classification task baselines implemented using vanilla resnet like architecture has a skip connection fr2d 2d convolutions over frames independently r2d 2d convolutions over the entire clip reshape 4d input tensor x of shape lxhxwx3 to 3lxhxw r3d use 3d convolutions mcx use 3d convolutions in the first x layers use 2d convolutions in the remaining layers rmcx use 2d convolutions in the first x layers use 3d convolutions in the remaining layers r 2 1 d use 2d convolutions 1d convolutions throughout the entire network note that r 2 1 d and r3d have roughly same number of parameters and same computation complexity for all the baselines they sample bunch of clips to do a video classification avg pooling is conducted to aggreate clip level predictions in contrast i3d just use a single clip with l 64 frames by random sampling for both training and testing datasets used training from scratch sports 1m kinetics transfer learning ucf101 hmdb51 observations 2d 1d convolution is better than 3d convolution 2d convolution and 3d and 2d convolutions mixed mixed 3d and 2d models mcx 3d conv early is beter than rmcx 3d conv in deeper layers motion pattern is important in earlier layers this is an opposite observation from xie et al https arxiv org pdf 1712 04851 pdf performance for rgb only and flow only models r 2 1 d is better than i3d r 2 1 d two stream model shows slightly worse performance than i3d two stream model on kinetics note that i3d is pretrained on imagenet while r 2 1 d is trained from scratch why r 2 1 d is better than r3d single rgb flow models double number of non linearity layers better for optimization note r 2 1 d shows lower training error than r3d rethinking spatiotemporal feature learning for video understanding https arxiv org pdf 1712 04851 pdf s xie et al arxiv2017 improving i3d called s3d g in this paper i3d which inflates all the 2d filters of the inceptionnet to 3d is enhanced first we replace 3d convolutions in a bottom layers to 2d and get higher accuracy and computation efficiency and more compact model second we separate temporal convolution from spatial convolution in every 3d convolution layer this also makes higher accuracy more compact model and faster speed finally spatiotemporal gating is introduced to further boost the accuracy we show their model performance on the large scale kinetics dataset for an ablation study also we show the proposed model s3d g is generalizable to other tasks such as action classification and detection action classification performance 96 8 on ucf 101 75 9 on hmdb 51 pretrained on kinetics action detection performance 80 1 on ucf 101 72 1 on jhmdb pretrained on kinetics maybe most gains come from the kinetics dataset pretraining convnet architecture search for spatiotemporal feature learning https arxiv org abs 1708 05038 d tran et al arxiv2017 note aka res3d code https github com facebook c3d in the repository c3d v1 1 is the res3d implementation 3d version of resnet in this paper a 3d version of residual network is introduced to better encode spatio temporal information in a video by extensive experimental search we fix the number of parameters to 33m and conduct extensive experiments to find an optimal architecture the res3d contains 1 skip connections 2 using frame sampling rate of 2 or 4 optimal on ucf 101 3 spatial resolution 112x112 4 layer depth 18 we also find that using 3d conv is better than using 2d conv or 2 5d conv spatial and temporal conv separated shows higher accuracy than c3d on ucf101 and hmdb51 85 8 vs 82 3 and 54 9 vs 51 6 respectively 2 times faster speed and 2 times smaller model size learning spatio temporal representation with pseudo 3d residual networks http openaccess thecvf com content iccv 2017 papers qiu learning spatio temporal representation iccv 2017 paper pdf z qui et al iccv2017 code https github com zhaofanqiu pseudo 3d residual networks quo vadis action recognition a new model and the kinetics dataset https arxiv org pdf 1705 07750 pdf j carreira et al cvpr2017 note aka i3d code https github com deepmind kinetics i3d training code is not provied unofficial code https github com chuckcho kinetics i3d training code is provided but not offical spatiotemporal residual networks for video action recognition https arxiv org pdf 1611 02155 pdf c feichtenhofer et al nips2016 code https github com feichtenhofer st resnet learning spatiotemporal features with 3d convolutional networks http vlg cs dartmouth edu c3d c3d video pdf d tran et al iccv2015 the official caffe code https github com facebook c3d project web http vlg cs dartmouth edu c3d note aka c3d python wrapper https github com chuckcho c3d tree python wrapper note that the official caffe does not support python wrapper tensorflow https github com hx173149 c3d tensorflow tensorflow keras https github com axon research c3d keras another tensorflow implemetation https github com frankgu c3d tensorflow git keras c3d project web https imatge upc edu web resources c3d model keras trained over sports 1m keras code https gist github com albertomontesg d8b21a179c1e6cca0480ebdf292c34d2 pretrained weights https www dropbox com s ypiwalgtlrtnw8b c3d sports1m weights h5 dl 0 miscellaneous pathtrack fast trajectory annotation with path supervision http openaccess thecvf com content iccv 2017 papers manen pathtrack fast trajectory iccv 2017 paper pdf s manen et al iccv2017 fast bounding box annotation generation method using path supervision we may apply this kind of technique to solve weakly supervised detection tasks in this paper the goal is generate a large scale multiple object tracking mot dataset using a path level supervision with amazon mechanical turk they get inputs from users to annotation bounding boxes of objects in various videos the input annotations are point wise paths using an off the shelf object detector and the path annotations they can automatically generate the full bounding box trajectory annotations they link and label the detections by optimizing an energy function consists of a unary term and a pairwise term the unary term penalizes the label outside the bounding box and the pairwise term penalizes the affine detections being assigned to diffrent clusters by using the proposed method they can generate a large scale dataset for mot a with minimum supervision envolved cortexnet a generic network family for robust visual temporal representations https arxiv org pdf 1706 02735 pdf a canziani and e culurciello arxiv2017 code https github com atcold pytorch cortexnet project web https engineering purdue edu elab cortexnet slicing convolutional neural network for crowd video understanding http www ee cuhk edu hk jshao papers jshao jshao cvpr16 scnn pdf j shao et al cvpr2016 code https github com amandajshao slicing cnn two stream rgb and flow pretrained model weights https github com craftgbd caffe gbd tree master models action recognition action recognition datasets moments in time http moments csail mit edu paper http moments csail mit edu data moments paper pdf ava https research google com ava paper https arxiv org abs 1705 08421 inria web http thoth inrialpes fr ava getava php for missing videos kinetics https deepmind com research open source open source datasets kinetics paper https arxiv org pdf 1705 07750 pdf daly http thoth inrialpes fr daly daily action localization in youtube videos note weakly supervised action detection dataset annotations consist of start and end time of each action one bounding box per each action per video 20bn jester https www twentybn com datasets jester 20bn something something https www twentybn com datasets something something activitynet http activity net org note they provide a download script and evaluation code here https github com activitynet charades http allenai org plato charades sports 1m http cs stanford edu people karpathy deepvideo classes html large scale action recognition dataset thumos14 http crcv ucf edu thumos14 note it overlaps with ucf 101 http crcv ucf edu data ucf101 php dataset thumos15 http www thumos info home html note it overlaps with ucf 101 http crcv ucf edu data ucf101 php dataset hollywood2 http www di ens fr laptev actions hollywood2 spatio temporal annotations https staff fnwi uva nl p s m mettes index html data ucf 101 http crcv ucf edu data ucf101 php annotation provided by thumos 14 http crcv ucf edu iccv13 action workshop index files ucf101 24action detection annotations zip and corrupted annotation list https github com jinwchoi jinwoo computer vision and machine learing papers to read blob master ucf101 spatial annotation corrupted file list ucf 101 corrected annotations https github com gurkirt corrected ucf101 annots and different version annotaions https github com jvgemert apt and there are also some pre computed spatiotemporal action detection results https drive google com drive folders 0b lzm05qedk0ag5pte94vfi1suk ucf 50 http crcv ucf edu data ucf50 php ucf sports http crcv ucf edu data ucf sports action php note the train test split link in the official website is broken instead you can download it from here http pascal inrialpes fr data2 oneata data ucf sports videos txt hmdb http serre lab clps brown edu resource hmdb a large human motion database j hmdb http jhmdb is tue mpg de liris harl http liris cnrs fr voir activities dataset kth http www nada kth se cvap actions msr action https www microsoft com en us download details aspx id 52315 note it overlaps with kth http www nada kth se cvap actions datset video annotation efficiently scaling up crowdsourced video annotation http cvrr ucsd edu ece285 spring2014 papers vondrick ijcv2013 pdf c vondrick et al ijcv2013 code https github com cvondrick vatic the design and implementation of viper https www cs umd edu grad scholarlypapers papers davidm viper pdf d mihalcik and d doermann technical report object recognition object detection image detectron https github com facebookresearch detectron open source object detection framework from facebook ai research includes mask r cnn fpn and etc caffe2 implementation faster r cnn https arxiv org abs 1506 01497 s ren et al nips2015 official matcaffe code https github com shaoqingren faster rcnn pycaffe https github com rbgirshick py faster rcnn tensorflow https github com smallcorgi faster rcnn tf another tf implementation https github com charlesshang tffrcnn keras https github com yhenon keras frcnn state of the art object detector yolo https pjreddie com media files papers yolo pdf j redmon et al cvpr2016 official code https github com pjreddie darknet git tensorflow https github com gliese581gg yolo tensorflow fast object detector yolo9000 https arxiv org abs 1612 08242 j redmon and a farhadi cvpr2017 official code https pjreddie com darknet yolo state of the art object detector which can detect 9000 objects in realtime ssd https arxiv org abs 1512 02325 w liu et al eccv2016 official pycaffe code https github com weiliu89 caffe tree ssd tensorflow https github com balancap ssd tensorflow keras https github com rykov8 ssd keras state of the art object detector with realtime processing speed mask r cnn https arxiv org abs 1703 06870 k he et al tensorflow keras https github com matterport mask rcnn mxnet https github com tusimple mx maskrcnn tensorflow https github com charlesshang fastmaskrcnn pytorch https github com felixgwu mask rcnn pytorch state of the art object detection instance segmentation algorithm video object detection detect to track and track to detect c feichtenhofer et al iccv2017 code https github com feichtenhofer detect track project web http www robots ox ac uk vgg research detect track video object detection and tracking using r fcn on top of two frame level convnets one is for frame t and the other is for frame t tau propose a multi task objective consists of 1 classification loss 2 bbox regression loss 3 tracking loss the tracking loss is smooth l1 loss between ground truth and a tracking regression value for frame t tau correlation feature map between the detection at frame t and search candidates at frame t tau is computed roi pooling operation is applied to the correlation feature map evaluation on imagenet vid dataset flow guided feature aggregation for video object detection x zhu et al iccv2017 code https github com msracver flow guided feature aggregation aka fgfa using optical flow to guide the temporal feature aggregation for frame level detection temporally aggregating the frame level features use flownet to estimate the motion between reference frame and nearby frames warp the nearby frames feature map by a bilinear warping function to the reference frame temporally aggregate the feature map of the reference frame and feature maps of the warped nearby frame use element wise summation with adaptive weights for the aggregation adaptive weights are computed by cosine similarity measure between the reference frame feature and the nearyby frame feature apply temporal dropout during training dropping out the random nearby frames e g dropping out 3 frames when testing frame range is 5 and training frame range is 2 this means we could incorporate long term temporal context by using long testing frame range but at the same time we could use only training frame range 2 to reduce the computation memory requirements when training video object detection datasets imagenet vid http image net org challenges lsvrc 2017 download images 1p39 php youtube 8m https research google com youtube8m technical report https arxiv org abs 1609 08675 youtube bb https research google com youtube bb technical report https arxiv org pdf 1702 00824 pdf pose estimation pose estimation detect and track efficient pose estimation in videos https arxiv org abs 1712 09184 r girdhar et al arxiv2017 pose tracking by 3d mask r cnn two stage approach 1 dense prediction 2 link track afterwards use 3d mask r cnn to detect body keypoints every frame convert the 2d convolutions of resnet to 3d convolutions first show that using the 2d mask r cnn achieves the state of the art performance then show that using the proposed inflated 3d mask r cnn shows better performance than 2d counter part when using the same backbone architecture propose tube proposal network which regresses tube anchors tube anchors are nothing but spatial anchors duplicated in time use bipartite matching to link the predictions over time evaluate on posetrack dataset openpose library https github com cmu perceptual computing lab openpose caffe based realtime pose estimation library from cmu realtime multi person 2d pose estimation using part affinity fields https arxiv org abs 1611 08050 z cao et al cvpr2017 code https github com zhec realtime multi person pose estimation depends on the caffe rt pose https github com cmu perceptual computing lab caffe rtpose git earlier version of openpose from cmu licenses license cc0 http i creativecommons org p zero 1 0 88x31 png http creativecommons org publicdomain zero 1 0 to the extent possible under law jinwoo choi https sites google com site jchoivision has waived all copyright and related or neighboring rights to this work contributing please read the contribution guidelines contributing md then please feel free to send me pull requests https github com jinwchoi action recognition pulls or email jinchoi vt edu to add links | ai |
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SQL_Python_European-Soccer-Database | european soccer database dataset a href https www kaggle com datasets hugomathien soccer strong link strong a about dataset the ultimate soccer database for data analysis and machine learning what you get 25 000 matches 10 000 players 11 european countries with their lead championship seasons 2008 to 2016 players and teams attributes sourced from ea sports fifa video game series including the weekly updates team line up with squad formation x y coordinates betting odds from up to 10 providers detailed match events goal types possession corner cross fouls cards etc for 10 000 matches description pandas and sql are used to query data of interest for analysis project on kaggle https www kaggle com code nhanbaoho european soccer database with sql | server |
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robovision | alt text images logo png raw true logo robovision ai and machine leaning based computer vision for a robot motivation surveillance cameras are passive they need humans to watch the footage caputered by them and to make decisions or take actions robovision is a smart trained machine learning ai based system which can see make decisions listen and speak features face detection from an image using haar cascade classifier eyes detection from an image using cascade classifiers command line tool under development for various image processing features auto mark attendance of an employee with ai camera software neo a well trained speaking robot a quick video demo robovision demo screenshots video demo png raw true video demo https www youtube com watch v ccotpa1 its t 15s dependencies pyqt5 opencv 3 4 x setup installation use python 3 x pip install r requirements py use run sh to run the application dont hesitate to report the bugs and issues you face coming soon robot neo that will be monitoring all acitvties in app the neo will talk or chat with you neo will accept your audio commands iris identification office code of conduct compliance checks eg check if an employee is talking on phone when it is not allowed in office talk to users for several actions eg greeting good morning when an employee enters an office on the fly train itself to identify new comers in the office some screenshots alt text screenshots s9 png raw true screenshot alt text screenshots s2 png raw true screenshot alt text screenshots s1 png raw true screenshot alt text screenshots s3 png raw true screenshot | opencv computer-vision face-detection face-recognition pyqt5 surveillance artificial-intelligence machine-learning haar-cascade-classifier classification | ai |
Coursera-Stanford-ML-Python | coursera stanford ml python coursera stanford machine learning course assignments in python join the chat at https gitter im sgang007 coursera stanford ml python https badges gitter im join 20chat svg https gitter im sgang007 coursera stanford ml python utm source badge utm medium badge utm campaign pr badge utm content badge assignments for andrew ng s machine learning course implemented in python without solutions in line with the coursera code of honor https www coursera org about terms honorcode coursera honor code the code is structurally equivalent to the matlab implementation from coursera and the results are numerically equivalent with the correct python implementation of the incomplete scripts after completing each assignment students can submit for grading to the coursera website by executing the submit py script e g below osx or linux on windows change export pythonpath to set pythonpath cd coursera stanford ml python ex1 export pythonpath python submit py login email address token part name score feedback warm up exercise 10 10 nice work computing cost for one variable 0 40 gradient descent for one variable 0 50 feature normalization 0 0 computing cost for multiple variables 0 0 gradient descent for multiple variables 0 0 normal equations 0 0 10 100 the login credentials will be saved to a file for subsequent submissions please see the wiki https github com mstampfer coursera stanford ml python blob master coursera 20stanford 20ml 20python 20wiki ipynb for a short tutorial on using python | ai |
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esp-va-sdk | table of contents 0 important note 0 important note 1 introduction 1 introduction 1 1 solution architecture 11 solution architecture 1 2 the software 12 the software 1 3 voice assistants 13 voice assistants 1 3 1 alexa voice service avs 131 alexa voice service avs 1 3 2 avs for iot afi 132 avs for iot afi 1 3 3 google voice assistant gva 133 google voice assistant gva 1 3 4 google dialogflow 134 google dialogflow 1 4 the esp32 vaquita dspg development board 14 the esp32 vaquita dspg development board 1 4 1 buttons 141 buttons 2 development setup 2 development setup 2 1 host setup 21 host setup 2 2 getting the repositories 22 getting the repositories 2 3 building the firmware 23 building the firmware 2 4 flashing the firmware 24 flashing the firmware 3 additional setup 3 additional setup 4 device provisioning 4 device provisioning 4 1 configuration steps 41 configuration steps 4 2 additional device settings 42 additional device settings 5 customising for your board 5 customising for your board 6 integrating other components 6 integrating other components 6 1 esp rainmaker 61 esp rainmaker 6 1 1 environment setup 611 environment setup 6 1 2 device provisioning 612 device provisioning 6 1 3 customisation 613 customisation 6 2 smart home 62 smart home 6 2 1 usage 621 usage 6 2 2 customisation 622 customisation 6 3 audio player 63 audio player 6 3 1 enabling custom player 631 enabling custom player 6 3 2 customisation 632 customisation 6 4 equalizer 64 equalizer 6 4 1 enabling equalizer 641 enabling equalizer 7 production considerations 7 production considerations 7 1 over the air updates ota 71 over the air updates ota 7 2 manufacturing 72 manufacturing 7 2 1 mass manufacturing utility 721 mass manufacturing utility 7 2 2 pre provisioned modules 722 pre provisioned modules 7 3 security 73 security 7 3 1 secure boot 731 secure boot 7 3 2 flash encryption 732 flash encryption 7 3 3 nvs encryption 733 nvs encryption a1 appendix faqs a1 appendix faqs a1 1 compilation errors a11 compilation errors a1 2 device setup using the mobile app a12 device setup using the mobile app a1 3 device crashing a13 device crashing a1 4 device not crashed but not responding a14 device not crashed but not responding 0 important note the wake word alexa recognition software that is part of the github repository https github com espressif esp va sdk is for evaluation only please contact sales espressif com for production ready wake word recognition dsp firmware that is available from our dsp partners please refer to changelog changelog md to track release changes and known issues 1 introduction espressif s voice assistant sdk allows customers to build alexa and google built in smart devices this allows customers to leverage espressif s rich iot device development capability while simultaneously incorporating voice enablement in these devices 1 1 solution architecture the typical solution architecture of the product is shown as below center img src https github com espressif esp va sdk wiki va images esp va sdk solution architecture png alt solution architecture block diagram title solution architecture block diagram width 800 center the following are the relevant blocks for the solution 1 esp32 this is the primary microcontroller that controls the operations of the product 1 voice assistant client it runs the voice assistant client that manages the audio communication with the voice assistant s cloud esp32 is also responsible for any state management audio encode decode operations 2 iot device it also runs the software that interfaces with your peripherals providing the smart device functionality that you wish to expose 2 dsp the dsp typically performs the noise reduction nr acoustic echo cancellation aec and run the wake word engine wwe the dsp is interfaced with the mic array for the audio input and it subsequently interacts with the esp32 for relaying the audio input 3 codec the playback data is received by the codec which it subsequently sends to the speaker 1 2 the software the software that is part of this sdk is sulf sufficient to provide a full voice assistant capability for your device typically as a device manufacturer you may want to customise certain configurations of this software if you also wish to expose some additional functionality beyond voice assistant switch fan water purifier etc you will also have to write the device drivers for controlling this center img src https github com espressif esp va sdk wiki va images esp va sdk software components png alt software components block diagram title software components block diagram width 900 center the above block diagram indicates the various components of the voice assistant sdk 1 3 voice assistants 1 3 1 alexa voice service avs alexa is amazon s personal virtual assistant which listens to user s voice commands and responds with appropriate answers apart from conversing with the user alexa lets you play music from a variety of music streaming services alexa also helps you manage to do lists and allows for voice assisted shopping from amazon this particular flavour of alexa is helpful when you are building a speaker class of device with esp va sdk avs also supports playing music through bluetooth working in conjunction with music from alexa 1 3 2 avs for iot afi avs for iot afi is also known as avs integrated with aws iot aia the amazon cloud does the audio decoding from various sources and sends them to the device this reduces the processing and memory usage on the device this flavour of alexa would be particularly helpful if you are building a voice assistant end device that is not just a speaker but additionally something else switch fan water purifier etc aia also supports avs smart home integration let s consider you are a alexa enabled light bulb smart home integration implies that if you say a query like alexa switch off the light where the light is your own device then the aia cloud service will decode this into actionable data that comes back to your device and you can parse it and execute the action in this case switching off the light 1 3 3 google voice assistant gva gva is google s version of a personal voice assistant it is multilingual and allows users to converse in their preferred language apart from general queries it allows users to check on the traffic conditions emails weather conditions and much more note that this sdk only includes a proof of concept poc implementation for gva this is not recommended for production 1 3 4 google dialogflow dialogflow previously known as api ai is a voice enabled conversational interface from google it enables iot users to include natural language user interface in their applications services and devices the advantages of dialogflow wrt voice assistants are less complexity pay as you go pricing custom wakeword allowed and no certification hassles unlike other voice assistants dialogflow let s you configure every step of the conversation and it won t answer other trivia questions like voice assistants typically do for e g a dialogflow agent for laundry project will provide information only about the configurable parameters of the laundry like state temperature wash cycle etc the implementation here facilitates the audio communication of esp32 with a google dialogflow agent using its v2beta1 grpc apis 1 4 the esp32 vaquita dspg development board the esp32 vaquita dspg development board is amazon certified for alexa functionality the solution consists of the esp32 micro controller paired with dsp g s dbmd5p soc the esp32 provides the wi fi connectivity and implements the voice assistant client the dbmd5p runs the acoustic front end and the wake word engine the following is a picture of the esp32 dbmd5p dev kit center img src https github com espressif esp va sdk wiki va images esp32 vaquita dspg base board png alt esp32 vaquita dspg board title esp32 vaquita dspg board width 500 img src https github com espressif esp va sdk wiki va images esp32 vaquita dspg mic board png alt esp32 vaquita dspg board title esp32 vaquita dspg board width 500 center the kit has the following contents dev kit esp32 as the host micro controller dbmd5p running the acoustic frontend and wake word engine 2 push buttons 5 rgb leds linear 2 mic mic array board 1 4 1 buttons push to talk button1 press this button to initiate conversation with the assistant without saying the wake word microphone mute button2 press this button to disable enable microphone on the device reset to factory this wipes all the settings network configuration alexa google login credentials from the device and the device goes back to default factory settings press and hold mute push to talk buttons together for 10 seconds until you see orange leds reset wi fi configuration use this to switch the device into wi fi change mode the device will stay in this mode for 3 minutes after which it will go back to the normal mode of operation you can use the phone apps within this time frame to re configure the wi fi credentials of the dev kit the new wi fi credentials will overwrite the previous wi fi configuration press and hold push to talk button for 5 seconds until you see orange leds note all the supported boards are mentioned here audio board https github com espressif esp va sdk tree master components audio hal audio board 2 development setup this sections talks about setting up your development host fetching the git repositories and instructions for build and flash 2 1 host setup you should install drivers and support packages for your development host windows linux and mac os x are supported development hosts please see get started https docs espressif com projects esp idf en release v4 2 get started index html for the host setup instructions 2 2 getting the repositories git clone recursive https github com espressif esp idf git cd esp idf git checkout release v4 2 git submodule init git submodule update init recursive install sh cd git clone https github com espressif esp va sdk git 2 3 building the firmware cd esp va sdk examples amazon aia for aia or amazon alexa for avs or google voice assistant or google dialogflow export espport dev cu slab usbtouart or dev ttyusb0 or dev ttyusb1 on linux or comxx on mingw export idf path path to esp idf idf path export sh set audio board path e g for esp32 vaquita dspg export audio board path path to esp va sdk components audio hal audio board audio board vaquita dspg menuconfig changes do this change only if you are using esp32 wrover e module idf py menuconfig component config esp32 specific minimum supported esp32 revision change rev 0 to rev 3 do these changes only if your board uses spiffs partition for storing the dsp firmware refer to the audio board path audio board cmake file idf py menuconfig partition table custom partition csv file change to partitions spiffs csv do these changes only if you are using esp32 module with 4mb flash size refer to the audio board path audio board cmake file idf py menuconfig serial flasher config flash size change to 4mb partition table custom partition csv file change to partitions 4mb flash csv 2 4 flashing the firmware when flashing the sdk for the first time it is recommended to do idf py erase flash to wipe out entire flash and start out fresh idf py flash monitor 3 additional setup the device would need additional configuration on the cloud as well as the device firmware for it to work check the readme in the example directory for the voice assistant specific project setup 4 device provisioning for google voice assistant and google dialogflow please refer to the readmes in the respective examples instead of the description that follows below the configuration step consists of a configuring the wi fi network and b signing into your alexa account and linking the device espressif has released the following phone applications that facilitate the same ios ios app https apps apple com in app esp alexa id1464127534 br android android app https play google com store apps details id com espressif provbleavs please install the relevant application on your phone before your proceed 4 1 configuration steps here are the steps to configure the dev kit on first boot up the dev kit is in configuration mode this is indicated by orange led pattern please ensure that the led pattern is seen as described above before you proceed launch the phone app select the option add new device center img src https github com espressif esp va sdk wiki va images esp alexa app home png alt app home title app home width 300 center a list of devices that are in configuration mode is displayed note that the devices are discoverable over ble bluetooth low energy please ensure that the phone app has the appropriate permissions to access bluetooth on android the location permission is also required for enabling bluetooth center img src https github com espressif esp va sdk wiki va images esp alexa app discover devices png alt app discover devices title app discover devices width 300 center now you can sign in to your amazon alexa account if you have amazon shopping app installed on the same phone app will automatically sign in with the account the shopping app is signed in to otherwise it will open a login page on the phone s default browser it is recommended to install the amazon shopping app on your phone to avoid any other browser related errors center img src https github com espressif esp va sdk wiki va images esp alexa app sign in png alt app sign in title app sign in width 300 center you can now select the wi fi network that the dev kit should connect with and enter the credentials for this wi fi network center img src https github com espressif esp va sdk wiki va images esp alexa app wifi scan list png alt app scna list title app scan list width 300 img src https github com espressif esp va sdk wiki va images esp alexa app wifi password png alt app wi fi password title app wi fi password width 300 center on successful wi fi connection you will see a list of few of the voice queries that you can try with the dev kit center img src https github com espressif esp va sdk wiki va images esp alexa app things to try png alt app things to try title app things to try width 300 center you are now fully setup you can now say alexa followed by the query you wish to ask 4 2 additional device settings some device settings like volume control locale change etc can also be controlled through the phone app launch the phone app select the option manage devices center img src https github com espressif esp va sdk wiki va images esp alexa app home png alt app home title app home width 300 center make sure you are connected to the same network as the device and also that ssdp packets can be sent on your network now select your device from the list of devices for the device settings 5 customising for your board for integrating customising your own board refer to components audio hal readme md 6 integrating other components 6 1 esp rainmaker 6 1 1 environment setup additional setup that needs to be done for integrating esp rainmaker https rainmaker espressif com get the repository git clone recursive https github com espressif esp rainmaker git setting cloud agent export cloud agent path path to esp rainmaker menuconfig changes idf py menuconfig voice assistant configuration enable cloud support enable this 6 1 2 device provisioning the combined app for esp rainmaker esp alexa is still under development till then both the apps can be used separately for provisioning open the esp rainmaker app and sign in click on add device scan the qr code and complete the wi fi setup the app will verify the setup for gvs and dialogflow refer to their respective readmes for provisioning make sure you are connected to the same network as the device open the esp alexa app manage devices find your device and sign in into alexa 6 1 3 customisation to customise your own device you can edit the file examples additional components app cloud app cloud rainmaker c you can check the examples in esp rainmaker for some more device examples 6 2 smart home this is only for aia avs support will be added soon note there is a bug where the device name is being set as demo light instead of what is being set by the device default is light one way to add the smart home functionality is to use esp rainmaker 91 esp rainmaker and the other way is to use examples additional components app smart home this can be initialized in the appilication uncomment app smart home init in app main c 6 2 1 usage once provisioning is done and the device has booted up the smart home feature of the device can be used via voice commands or through the alexa app example by default the device configured is a light with power and brightness functionalities voice commands like turn on the light or change light brightness to 50 can be used in the alexa app this device will show up as light and the power and brightness can be controlled 6 2 2 customisation to customise your own device you can edit the file examples additional components app smart home app smart home c you can refer the files components voice assistant include smart home h and components voice assistant include alexa smart home h for additional apis a device can have the following types of capabilities features parameters power a device can only have a single power param toggle this can be used for params which can be toggled example turning on off the swinging of the blades in an air conditioner range this can be used for params which can have a range of values example changing the brightness of a light mode this can be used for params which need to be selected from a pre defined set of strings example selecting the modes of a washing machine 6 3 audio player the audio player components voice assistant include audio player h can be used to play custom audio files from any source http url local spiffs etc the focus management what is currently being played is already implemented internally by the sdk for aia speech alert music from alexa has higher priority than what is played via the audio player so for example if custom music is being played via the audio player and a query is asked then the music will be paused and the response from alexa will be played once the response is over the music will be resumed unless already stopped basically all alexa audio gets priority over custom audio for avs speech alert from alexa has higher priority than what is played via the audio player so for example if custom music is being played via the audio player and a query is asked then the music will be paused and the response from alexa will be played once the response is over the music will be resumed unless already stopped another example if custom music is being played via audio player and a query is asked for playing music via the cloud then the custom music will be stopped and the music from alexa will take over if alexa music was playing and custom music is played then alexa music will stop and the custom music will take over basically music has the same priority from whichever source it is being played from all other alexa audio gets priority over music for gva and dialogflow speech alerts and music is not yet supported from google has higher priority than what is played via the audio player so for example if custom music is being played via the audio player and a query is asked then the music will be paused and the response from alexa will be played once the response is over the music will be resumed unless already stopped basically all google audio gets priority over custom audio 6 3 1 enabling custom player the examples additional components custom player is an example using the audio player the default example of the custom player can play from http url and or local spiffs and or local sdcard but can be easily extended to play from any other source easiest way to try custom player is using http url include custom player h in the application and call custom player init after the voice assistant early initialisation has been done when the application is now built and flashed on the device the custom player will play the 3 files showing the usage of the audio player 6 3 2 customisation the default custom player just has a demo code which can be used as a reference to build your own player the audio player for now just supports mp3 and aac audio formats for http urls and only mp3 audio format for local files 6 4 equalizer this is only for avs equalizer lets you control the bass mid range and treble of the audio you can use the following commands to get the values for the equalizer set treble to 3 set bass to 3 reset equalizer set movie mode the sdk will give a callback to the application in equalizer c with the respective values for the equalizer the application can then use these values and adjust the audio output 6 4 1 enabling equalizer to enable the equalizer along with alexa include alexa equalizer h in the application and call alexa equalizer init before the voice assistant initialisation has been done 7 production considerations 7 1 over the air updates ota esp idf has a component for ota from any url more information and details about implementing can be found here esp https ota https docs espressif com projects esp idf en latest esp32 api reference system esp https ota html esp https ota 7 2 manufacturing 7 2 1 mass manufacturing utility the devices generally require unique ids and certificates to connect to the cloud server for example in aia aws iot operations require that all devices have a unique certificate and key pair programmed on each device which is used for authentication with the aws iot cloud service these are generally programmed in factory nvs partitions that are unique per device esp idf provides a utility to create instances of factory nvs partition images on a per device basis for mass manufacturing purposes the nvs partition images are created from csv files containing user provided configurations and values details about using the mass manufacturing utility can be found here mass manufacturing https docs espressif com projects esp idf en latest api reference storage mass mfg html 7 2 2 pre provisioned modules esp32 modules can be pre flashed with the factory nvs partition during manufacturing itself and then be shipped to you for example in aia the device certificates are signed by your certificate authority ca and when you register this ca in your cloud all the devices can connect to the cloud out of the box this saves you the overhead of securely generating encrypting and then programming the nvs partition into the device at your end pre provisioning is an optional service which espressif provides please contact your espressif contact person for more information 7 3 security 7 3 1 secure boot secure boot ensures that only trusted code runs on the device esp32 supports rsa based secure boot scheme whereby the bootrom verifies the software boot loader for authenticity using the rsa algorithm the verified software boot loader then checks the partition table and verifies the active application firmware and then boots it details about implementing the secure boot can be found here secure boot https docs espressif com projects esp idf en latest security secure boot html 7 3 2 flash encryption flash encryption prevents the plain text reading of the flash contents esp32 supports aes 256 based flash encryption scheme the esp32 flash controller has an ability to access the flash contents encrypted with a key and place them in the cache after decryption it also has ability to allow to write the data to the flash by encrypting it both the read write encryption operations happen transparently details about implementing the flash encryption can be found here flash encryption https docs espressif com projects esp idf en latest security flash encryption html 7 3 3 nvs encryption for the manufacturing data that needs to be stored on the device in the nvs format esp idf provides the nvs image creation utility which allows the encryption of nvs partition on the host using a randomly generated per device unique or pre generated common for a batch nvs encryption key a separate flash partition is used for storing the nvs encryption keys this flash partition is then encrypted using flash encryption so flash encryption becomes a mandatory feature to secure the nvs encryption keys details about implementing the nvs encryption can be found here nvs encryption https docs espressif com projects esp idf en latest api reference storage nvs flash html nvs encryption a1 appendix faqs a1 1 compilation errors i cannot build the application make sure you are on the correct esp idf branch run git submodule update init recursive to make sure the submodules are at the correct heads make sure you have the correct audio board path selected for your board delete the build directory and also sdkconfig and sdkconfig old and then build again if you are still facing issues reproduce the issue on the default example and then contact espressif for help please make sure to share these the esp va sdk and esp idf branch you are using and the audio board path that you have set the complete build logs a1 2 device setup using the mobile app i cannot add a new device through the phone app if the device is not being shown while adding a new device make sure the required permissions are given to the app also make sure that your bluetooth is turned on android typically requires the location permission also for enabling bluetooth if you are still facing issues update the app to the latest version and try again force closing the app and rebooting the device works in most cases if either of them have gone into an unknown state if you are still facing issues reproduce the issue on the default example for the device and then contact espressif for help make sure to share these screenshots of the mobile app where it is not working mobile app version mobile phone model and the android version or any skin it is running complete device logs taken over uart the esp va sdk and esp idf branch you are using and the audio board path that you have set i cannot manage device through the phone app if the device is not being shown while managing devices make sure you are connected to the same network as the device if you are still facing issues update the app to the latest version and try again force closing the app and rebooting the device works in most cases if either of them have gone into an unknown state if you are still facing issues reproduce the issue on the default example for the device and then contact espressif for help make sure to share these screenshots of the mobile app where it is not working mobile app version mobile phone model and the android version or any skin it is running complete device logs taken over uart the esp va sdk and esp idf branch you are using and the audio board path that you have set a1 3 device crashing my device is crashing given the tight footprint requirements of the device please make sure any issues in your code have been ruled out if you believe the issue is with the alexa sdk itself please recreate the issue on the default example application without any changes and go through the following steps make sure you are on the correct esp idf branch run git submodule update init recursive to make sure the submodules are at the correct heads make sure you have the correct audio board path selected for your board delete the build directory and also sdkconfig and sdkconfig old and then build and flash again if you are still facing issues reproduce the issue on the default example for the device and then contact espressif for help make sure to share these the steps you followed to reproduce the issue complete device logs from device boot up taken over uart voice assistant elf file from the build directory if you have gdb enabled run the command backtrace and share the output of gdb too the esp va sdk and esp idf branch you are using and the audio board path that you have set a1 4 device not crashed but not responding my device is not responding to audio queries make sure your device is connected to the wi fi internet if the device is not taking the wake word make sure the mic is turned on try using the tap to talk button and then ask the query if you are still facing issues reproduce the issue on the default example for the device and then contact espressif for help make sure to share these the steps you followed to reproduce the issue complete device logs taken over uart voice assistant elf file from the build directory the esp va sdk and esp idf branch you are using and the audio board path that you have set also check the appendix sections in the respective voice assistant s directories | alexa voice-assistant iot | server |
Deep-Learning-For-Computer-Vision-With-Python | deep learning for computer vision with python the code for the book of deep learning for computer vision with python | deep-learning computer-vision python3 | ai |
baseui.framerfx | base web uber s base web design system https baseweb design as a framer x package also known as baseui base web https github com framer baseui framerfx raw master metadata background png base web sections all components have been split into sections with the intention to replicate base web s documentation page sections https github com framer baseui framerfx raw master metadata sections png sections known issues select s dropdowns are sometimes rendered below other components there seems to be a z index problem here file uploader currently does nothing theming support is to be considered a work in progress faq what is the code generated folder baseui framerx was created using the framerjs component importer https www npmjs com package framerjs component importer this folder should be cleaned up at some point since many of the generated files are not used anymore the importer config json is also a product of the component importer and should also be cleaned up at some point | os |
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terravaloper1qqu376azltyc5wnsje5qgwru5mtj2yqdhar97v readme md station page https station terra money validator terravaloper1qqu376azltyc5wnsje5qgwru5mtj2yqdhar97v ubik capital profile validators terravaloper14lggrv6qm9zu7r8r936zn3x6dawgurdmw3ksug readme md station page https station terra money validator terravaloper14lggrv6qm9zu7r8r936zn3x6dawgurdmw3ksug athens project profile validators terravaloper1dmkdw7q23acc72knqv9yu46pz2m363705cnt27 readme md station page https station terra money validator terravaloper1dmkdw7q23acc72knqv9yu46pz2m363705cnt27 y profile validators terravaloper18pmvpy25zc7x0u8ppefuyyl2zaafe2wut0nm5e readme md station page https station terra money validator terravaloper18pmvpy25zc7x0u8ppefuyyl2zaafe2wut0nm5e 0base vc profile validators terravaloper1plxp55happ379eevsnk2xeuwzsrldsmqduyu36 readme md station page https station terra money validator terravaloper1plxp55happ379eevsnk2xeuwzsrldsmqduyu36 jesusislord profile validators terravaloper16e0s5t7q69elnlchrupryw3h7vu8zk23pe5wh8 readme md station page https station terra money validator terravaloper16e0s5t7q69elnlchrupryw3h7vu8zk23pe5wh8 what is a validator profile as a validator it s not enough to maintain rock solid reliable infrastructure you also need to market yourself effectively and court delegations from prospecting delegators the validator profiles hosted on this github repository give you a platform to give potential delegators and clients a brief introduction on your team philosophy architecture and infrastructure and to present your ecosystem contributions badges may be applied by the maintainers of this repo only for claims that can be validated how to change your validator profile 1 fork this repository to your own github account 2 clone the repo from your own account make sure you clone the repository from your account your fork not the original repo sh git clone git github com xxxxxx validator profiles git cd validator profiles 3 create and switch to a new branch named after your validator for example sh git checkout b wraith profile 4 copy the template readme md template readme md and json profile template profile json into your folder inside validators your valoper address 5 change the contents and add your information as necessary you can modify anything within your own designated validator folder including adding image files new folders etc 6 commit and push the information to your repo sh git add a git commit m edit wraith profile git push origin wraith profile 7 under your repo page click the new pull request button make a pull request to our repository with a summary of the changes name your pr with your validator moniker and identify whether it is a new profile or an edit to existing profile 8 your pr will be reviewed as soon as possible if there are no problems with your pr it will be merged into the master branch which will update your validator profile if it is your first time creating a profile you will be added to the validator directory validator directory profile json registered validators validators who have submitted a profile can opt in to receiving support from terra by putting their contact information in a profile json file terra provides various validator support such as automatic notifications when your validator is missing blocks or votes javascript contact contact info for delegators outreach email validator example com telegram validator notifications notification settings email technicalemail example com email to receive notifications terra station checkmark inclusion in asset repo a complete profile including a terravaloper and a contact email in the profile json file must be submitted in order to receive a checkmark in terra station new profiles are manually reviewed by the repo owner and a second reviewer approves the addition to the station assets repo this is the final step in completing a profile | blockchain |
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easyROI | stargazers https img shields io github stars saharshleo easyroi issues https img shields io github issues saharshleo easyroi forks https img shields io github forks saharshleo easyroi license https img shields io github license saharshleo easyroi easyroi https github com saharshleo easyroi downloads https pepy tech badge easyroi https pepy tech project easyroi downloads https pepy tech badge easyroi month https pepy tech project easyroi pypi version https badge fury io py easyroi svg https badge fury io py easyroi helper library for drawing roi in computer vision applications now you can crop the drawn roi s demo https github com saharshleo easyroi blob main assets run gif table of contents table of contents easyroi easyroi table of contents table of contents about the project about the project tech stack tech stack file structure file structure getting started getting started prerequisites prerequisites installation installation usage usage using easyroi in your project using easyroi in your project rectangular roi rectangular roi line roi line roi circle roi circle roi polygon roi polygon roi cropping drawn roi cropping drawn roi formats of roi formats of roi rectangle rectangle line line circle circle polygon polygon future work future work contributors contributors acknowledgements and resources acknowledgements and resources license license about the project about the project tech stack python https www python org opencv https opencv org numpy https numpy org file structure easyroi init py easyroi py utils py input overpass mp4 output dev main py code for testing during developing phase test library py code for testing during testing phase dev readme md readme for developing phase license readme md getting started getting started prerequisites python 3 6 pip installation 1 create virtual environment sh python3 m venv venv easy roi source venv easy roi bin activate 2 install easyroi sh pip install easyroi if you already have opencv installed sh pip install no dependencies easyroi usage examples usage read the instruction in terminal while drawing roi using easyroi in your project initializing python from easyroi import easyroi roi helper easyroi verbose true rectangular roi rectangle demo https github com saharshleo easyroi blob main assets rectangle gif python rect roi roi helper draw rectangle frame 3 quantity 3 specifies number of rectangles to draw frame temp roi helper visualize roi frame rect roi see roi format in rectangle rectangle line roi line demo https github com saharshleo easyroi blob main assets line gif python line roi roi helper draw line frame 3 quantity 3 specifies number of lines to draw frame temp roi helper visualize roi frame line roi see roi format in line line circle roi circle demo https github com saharshleo easyroi blob main assets circle gif python circle roi roi helper draw circle frame 3 quantity 3 specifies number of circles to draw frame temp roi helper visualize roi frame circle roi see roi format in circle circle polygon roi polygon demo https github com saharshleo easyroi blob main assets polygon gif python polygon roi roi helper draw polygon frame 3 quantity 3 specifies number of polygons to draw frame temp roi helper visualize roi frame polygon roi see roi format in polygon polygon cropping drawn roi python polygon roi roi helper draw polygon frame 3 quantity 3 specifies number of polygons to draw cropped polys roi helper crop roi frame polygon roi can do similarly for rectangular circle roi returns dictionary with key value same as roi label and value as image of cropped version format of roi formats of roi rectangle quantity 1 roi 0 br x 573 br y 443 h 105 tl x 322 tl y 338 w 251 type rectangle line quantity 2 roi 0 point1 374 395 point2 554 438 1 point1 555 438 point2 830 361 type line circle quantity 2 roi 0 center 330 355 point2 552 375 radius 222 1 center 702 374 point2 700 475 radius 101 type circle polygon quantity 2 roi 0 vertices 586 435 534 582 200 504 356 403 1 vertices 1108 507 738 662 709 497 711 494 927 414 type polygon future work future work see todo md todo md for seeing developments of this project contributors contributors saharshleo https github com saharshleo acknowledgements and references acknowledgements and resources packaging https www codementor io ajayagrawal295 how to publish your own python package 12tbhi20tf license license license | computer-vision region-of-interest custom-region-of-interest line-roi polygon multiple-roi multiple-region-of-interest opencv | ai |
mx-chain-go | div style text align center img src https raw githubusercontent com multiversx mx chain go master multiversx logo svg alt multiversx div br https img shields io badge made 20by multiversx blue svg http multiversx com https img shields io badge project multiversx 20mainnet blue svg https explorer multiversx com go report card https goreportcard com badge github com multiversx mx chain go https goreportcard com report github com multiversx mx chain go codecov https codecov io gh multiversx mx chain go branch master graph badge svg token mys5edasoj https codecov io gh multiversx mx chain go contributors https img shields io github contributors multiversx mx chain go https github com multiversx mx chain go graphs contributors mx chain go the go implementation for the multiversx protocol installation and running in order to join the network as an observer or as a validator the required steps to build from source and setup explicitly are explained below step 1 install configure go the installation of go should proceed as shown in official golang installation guide https golang org doc install in order to run the node minimum golang version should be 1 17 6 step 2 clone the repository and build the binaries the main branch that will be used is the master branch alternatively an older release tag can be used set gopath if not set and export to profile along with go binary path if gopath then gopath home go fi mkdir p gopath src github com multiversx cd gopath src github com multiversx git clone https github com multiversx mx chain go cd mx chain go git checkout master cd cmd node go build the node depends on the wasm virtual machine which is automatically managed by the node step 3 creating the node s identity in order to be registered in the multiversx network a node must possess 2 types of secret key public key pairs one is used to identify the node s credential used to generate transactions having the sender field its account address and the other is used in the process of the block signing please note that this is a preliminary mechanism in the next releases the first private public key pair will be dropped when the staking mechanism will be fully implemented to build and run the keygenerator the following commands will need to be run cd gopath src github com multiversx mx chain go cmd keygenerator go build keygenerator start the node step 4a join multiversx testnet follow the steps outlined here https docs multiversx com validators nodes scripts config scripts this is because in order to join the testnet you need a specific node configuration or step 4b copying credentials and starting a node in a separate network the previous generated pem file needs to be copied in the same directory where the node binary resides in order to start the node cp validatorkey pem node config cd node node the node binary has some flags defined for a brief description the user can use help flag those flags can be used to directly alter the configuration values defined in toml json files and can be used when launching more than one instance of the binary running the tests go test compiling new fields in proto files should be updated when required pr will be merged in gogo protobuf master branch 1 download protoc compiler https github com protocolbuffers protobuf releases if you are running under linux on a x64 you might want to download protoc 3 11 4 linux x86 64 zip 2 expand archive copy the include google folder in usr include using br sudo cp r google usr include 3 copy bin protoc using br sudo cp protoc usr bin 4 fetch the repo github com multiversx protobuf 5 compile gogo slick copy binary using cd protoc gen gogoslick go build sudo cp protoc gen gogoslick usr bin done progress done x cryptography x schnorr signature x belare neven signature x bls signature x modified bls multi signature x datastructures x transaction x block x account x trie x execution x transaction x block x state update x synchronization x shard fork choice x peer2peer libp2p x consensus spos x sharding fixed number x transaction dispatcher x transaction x state x network message dispatching x metachain x data structures x block processor x interceptors resolvers x consensus x block k finality scheme x vm k framework x k framework go backend x iele core x iele core tests x iele adapter x smart contracts on a sharded architecture x concept reviewed x vm integration x sc deployment x governance x concept reviewed x economics x concept reviewed x optimizations x randomness x consensus x bootstrap from storage x testing x unit tests x integration tests x teamcity continuous integration x manual testing x epochs x nodes dispatcher shuffling x network sharding x optimized wiring protocol x vm x evm core x evm core tests x evm adapter x fee structure x smart contracts on a sharded architecture x async callbacks x testing x automate tests with aws x nodes monitoring x dex integration in progress smart contracts on a sharded architecture dependency checker sc migration storage rent sc backup restore adaptive state sharding splitting merging redundancy privacy interoperability optimizations smart contract governance sc for erd ip enforced upgrade mechanism for voted erd ip bugfixing contribution thank you for considering to help out with the source code we welcome contributions from anyone on the internet and are grateful for even the smallest of fixes to multiversx if you d like to contribute to multiversx please fork fix commit and send a pull request for the maintainers to review and merge into the main code base if you wish to submit more complex changes though please check up with the core developers first here on github to ensure those changes are in line with the general philosophy of the project and or get some early feedback which can make both your efforts much lighter as well as our review and merge procedures quick and simple please make sure your contributions adhere to our coding guidelines code must adhere to the official go formatting https golang org doc effective go html formatting guidelines code must be documented adhering to the official go commentary https golang org doc effective go html commentary guidelines pull requests need to be based on and opened against the master branch commit messages should be prefixed with the package s they modify e g outport process fixed a typo please see the documentation https docs multiversx com for more details on the multiversx protocol | blockchain golang multiversx cryptocurrency decentralized go p2p | blockchain |
FOXopen | foxopen foxopen http www foxopen net is an open source gpl v3 java based web framework enabling rapid development of secure workflow based web systems learn more building foxopen https github com fivium foxopen wiki building foxopen running foxopen https github com fivium foxopen wiki running foxopen developing foxopen applications https github com fivium foxopen wiki developing foxopen applications contributing to foxopen https github com fivium foxopen wiki contributing to foxopen license fox5 is released as open source software under the gpl v3 https opensource org licenses gpl 3 0 html license see the license license file in the project root for the full license text foxopen has been developed by fivium http fivium co uk in partnership with the uk department of energy and climate change https www gov uk government organisations department of energy climate change | front_end |
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BusinessCardReader | businesscardreader computer vision final project to find and extract information from business cards proposal batbouta gunderson cv final project proposal aaron gunderson andrew batbouta april 2015 overview the application of computer vision that we will be exploring is extracting contact info from business cards our software will ideally take in camera images for business cards find the card and transform it so that it is aligned parallel to the viewing plane this will be done by taking hough line transforms to outline the card processing will then be done to increase contrast to find the text we will apply an algorithm like the canny edge detector to find the text edges and then group text by finding rectangular regions of interest once a region has been isolated it will be run through ocr and the returned text will be matched to business card fields checks will be done to find if the quality of a card is sufficient enough to extract text one check that could be done is seeing if an image is sharp enough to extract text from by checking the gradient magnitude sources one great set of images we found was from stanford and available here http web cs wpi edu claypool mmsys dataset 2011 stanford mvs images business cards html this data is originally part of a collection of data sets for image search with smartphone photos it provides 100 business cards of varying styles and languages with 5 versions of each business card one version is a scanned high contrast and then the other 4 are of various orientations and blur levels taken with a droid e63 palm and a canon business cards can also be sourced for extra test cases and examples with relative ease and minimal effort helpful software tesseract is open source software maintained by google that will be helpful for reading the text once we have the text chunks extracted from the card this will make the difficult job of reading text much easier instead we can focus on making a robust system that can handle variations of cards and varying camera conditions and make them readable to the ocr then after running through ocr we should be able to associate fields with standard business card fields | ai |
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aero | p align center img src misc aero logo png p aero aero is a new modern experimental unix like operating system written in rust aero follows the monolithic kernel design and it is inspired by the linux kernel aero supports modern pc features such as long mode 5 level paging and smp multicore to name a few workflow https github com andy python programmer aero actions workflows build yml badge svg lines of code https img shields io endpoint url https andypythonappdevelop npkn net f0d517 https github com andy python programmer aero discord https img shields io discord 828564770063122432 https discord gg 8gwhttzwt8 is this a linux distribution no aero runs its own kernel that does not originate from linux and does not share any source code or binaries with the linux kernel official discord server https discord gg 8gwhttzwt8 screenshots img src misc dwm alacritty glxgears png p align center running a href https dwm suckless org dwm a a href https github com freedesktop mesa demos mesa demos a and a href https github com alacritty alacritty alacritty a in aero p features 64 bit higher half kernel 4 5 level paging preemptive per cpu scheduler modern uefi bootloader acpi support ioapic lapic symmetric multiprocessing smp on demand paging goals creating a modern safe beautiful and fast operating system targeting modern 64 bit architectures and cpu features good source level compatibility with linux so we can port programs over easily making a usable os which can run on real hardware not just on emulators or virtual machines how to build and run aero please make sure you have a unix like host system before building aero if you are using windows its highly recommended to use wsl 2 dependencies before building aero you need the following things installed rust should be the latest nightly nasm qemu optional required if you want to run it in the qemu emulator if you are building aero with sysroot run the following helper script to install additional dependencies sh make sure to run the script with root privileges tools deps sh you can optionally set the environment variable verbose to true which will pass through the output of your package manager for troubleshooting sh verbose true tools deps sh note if your host operating system is not in the list below you will need to determine the dependency packages names for your package manager contributions to this tool are welcome arch linux based pacman debian linux based apt macos homebrew hardware the following are not requirements but are recommendations 15gb of free disk space 8gb ram 2 cores internet access beefier machines will lead to much faster builds getting the source code the very first step to work on aero is to clone the repository shell git clone https github com andy python programmer aero cd aero building aero aero uses a custom build system that wraps cargo and takes care of building the kernel and userland for you it also builds the initramfs and disk image for you the main command we will focus on is aero py the source code can be found in the root of the repository and as the file name states it is written in python by default if you run aero py without any arguments it will build the kernel and userland in release mode with debug symbols and run it in qemu you can configure the behavior of the build system though if you want to you can use the help option to read a brief description of what it can do the build system acknowledges few different build modes which cannot be used together and they are clean check test and document clean option will clean all the build outputs check will build the kernel and userland using cargo s check command this build mode will not produce a disk image if you want one without actually running aero in the emulator read ahead test will run the built in aero test suite document will generate web based docs using cargo s doc command sysroot will build the full userland sysroot if not passed then the sysroot will only contain the aero shell and the init binaries note this command will require a relatively large amount of storage space you may want to have upwards of 10 or 15 gigabytes available if building with full sysroot each of these modes can be used with additional flags that will alter the behavior in different ways some of them will not work for some of these modes for example the la57 option will not have any effect when you are simply checking or documenting the build debug toggles off the release build flag when calling cargo summary if the debug flag is not passed then it will build aero in release mode and debug symbols will be available on the other hand if the debug flag is passed then it will be built in debug mode and debug symbols will be still available by default aero is built in release mode with debug symbols since it generates faster and smaller binaries which are easier to test no run prevents from running the built disk image in the emulator bios lets you choose the firmware the emulator will use when booting aero currently supported values are legacy and uefi features accepts a single comma separated list of kernel crate features please keep in mind that there cannot be spaces in between the values target lets you override the target architecture for which the kernel is built currently the default value is x86 64 aero os la57 tells the emulator to use 5 level paging if it supports it the built disk image is stored in the build directory under the name aero iso both the disk root and initramfs root are preserved in case you want to inspect them manually running aero in an emulator if you haven t used the no run option and you aren t using the check or document build mode the build system will run aero in the emulator for you nightly images want to give aero a shot without building it you can go to the latest job https github com andy python programmer aero actions workflows build yml query is 3asuccess branch 3amaster and download the latest nightly image aero iso under artifacts contributing contributions are absolutely positively welcome and encouraged check out contributing md contributing md for the contributing guidelines for aero license img src misc gpl png align right width 200x aero is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version see the license license file for license rights and limitations | operating-system rust aero unix uefi hacktoberfest | os |
FreeRTOS-template-for-STM32F4 | freertos for the stm32f4 in coocox ide i was surprised to not find a coocox project that had freertos running out the box for the stm32f4 so i have uploaded this so that one can quickly test out freertos a neat button debouncing technique is used to test freertos pressing a button toggles led4 | os |
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btcpayserver-design | btcpay server design system https design btcpayserver org developers on brink of new ui for btcpay server node ci https github com btcpayserver btcpayserver design workflows node 20ci badge svg development dependencies are managed via npm once you have cloned this repo you can setup the packages bash npm install create a build and rebuild on file change bash npm start this will bring up the dev server and pattern library on localhost 3000 http localhost 3000 bootstrap customizations to generate a custom version of bootstrap v5 that supports our light dark themeing we take a three step approach 1 where possible we customize the variables src bootstrap variables scss offered by bootstrap the general variable definitions can be looked up in the component files in node modules bootstrap scss after locally installing bootstrap as a dependency 2 we then generate the bootstrap css tasks generate bootstrap js and replace some of the generated hex values with our color variables this is done for the cases in which we cannot use our variables directly in step 1 because bootstrap passes them through modifier functions lighten darken which require actual color values 3 hard css overrides src bootstrap customizations css are used for extensions and customizations of the final bootstrap css these are appended to the generated file so that we can override things and maintain the ability to easily upgrade | btcpayserver design design-system pattern-library uiengine | os |
pi | readme pi is a new generation blockchain technology pi adopts a new consensus mechanism called ipos improved proof of stake which helps it reach a balance between fairness and efficiency pi introduces a new incentive algorithm to attract users to use and to construct the pi network pi derives from bitshares https github com bitshares bitshares core https github com bitshares bitshares core repository contents libraries this directory include all kinds of libraries used by different apps programs this directory include main files for different apps pi node is the most important app this is the core server node which produce blockchains and process all kinds of rpcs cli wallet is a reference wallet implement based on pi rpc protocol test unit tests and benchmarks docs documentation of pi development how to build pi uses cmake toolchain to build bash git clone https github com pidiscovery pi git cd pi git submodule update init recursive mkdir build cd build cmake make you ll find a built binary program named pi node at path pi build pi node license pi is open source and permissively licensed under the mit license see the license md file for more details | blockchain |
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Mastering-Blockchain-Programming-with-Solidity | mastering blockchain programming with solidity a href https www packtpub com uncategorized mastering blockchain programming with solidity utm source github utm medium repository utm campaign img src https www packtpub com media catalog product cache e4d64343b1bc593f1c5348fe05efa4a6 9 7 9781839218262 original jpeg alt mastering blockchain programming with solidity height 256px align right a this is the code repository for mastering blockchain programming with solidity https www packtpub com uncategorized mastering blockchain programming with solidity utm source github utm medium repository utm campaign published by packt write production ready smart contracts for ethereum blockchain with solidity what is this book about solidity is among the most popular and contract oriented programming languages used for writing decentralized applications dapps on ethereum blockchain if you re looking to perfect your skills in writing professional grade smart contracts using solidity this book can help you will get started with a detailed introduction to blockchain smart contracts and ethereum while also gaining useful insights into the solidity programming language a dedicated section will then take you through the different ethereum request for comments erc standards including erc 20 erc 223 and erc 721 and demonstrate how you can choose among these standards while writing smart contracts as you approach later chapters you will cover the different smart contracts available for use in libraries such as openzeppelin you ll also learn to use different open source tools to test review and improve the quality of your code and make it production ready toward the end of this book you ll get to grips with techniques such as adding security to smart contracts and gain insights into various security considerations by the end of this book you will have the skills you need to write secure production ready smart contracts in solidity from scratch for decentralized applications on ethereum blockchain this book covers the following exciting features test and debug smart contracts with truffle ganache remix and metamask gain insights into maintaining code quality with different tools get up to speed with erc standards such as erc 20 and erc 721 become adept at using design patterns while writing smart contracts use multisignature multisig wallets and improve the security of contracts use oracle services to fetch information from outside the blockchain if you feel this book is for you get your copy https www amazon com dp 1839218266 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following contract variablestorage uint storeuint uint256 storage variable following is what you need for this book this book is for developers and data scientists who want to learn ethereum blockchain and solidity to write smart contracts and develop production ready code basic knowledge of solidity is assumed with the following software and hardware list you can run all code files present in the book chapter 1 14 software and hardware list chapter software required os required 4 nodejs v8 3 0 metamask v6 0 1 internet browser plugin remix ide v0 7 remixd latest version ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 5 ganache gui v2 0 0 ganache cli v6 4 4 truffle v5 0 ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 6 surya v0 2 8 solhint v2 0 0 solium v1 2 3 solidity coverage v0 5 11 ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 9 openzeppelin openzeppelin solidity v2 1 1 truffle v5 0 0 nodejs v8 9 4 ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 10 truffle v3 4 11 metamask v6 3 1 ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 11 truffle v5 0 4 nodejs v8 9 4 ganache cli v6 4 4 zos v2 3 1 zos lib v2 3 1 openzeppelin eth v2 3 1 ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 12 nodejs v8 11 3 openzeppelin openzeppelin solidity v2 2 0 truffle v5 0 4 metamask v6 3 1 truffle hd wallet provider truffle hdwallet provider v1 0 6 openzeppelin test hepler openzeppelin test helpers v0 3 1 chai test framework v4 2 0 big number big number v2 0 0 ganache cli v6 4 4 ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 14 truffle v5 0 10 nodejs v8 11 3 ubuntu 14 04 centos 6 rhel 7 windows 7 macos 10 we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781839218262 colorimages pdf related products solidity programming essentials packt https www packtpub com in application development solidity programming essentials utm source github utm medium repository utm campaign amazon https www amazon com dp 1788831381 blockchain by example packt https www packtpub com in big data and business intelligence blockchain example utm source github utm medium repository utm campaign amazon https www amazon com dp 1788475682 get to know the author jitendra chittoda is a blockchain security engineer at chainsecurity his day job is to perform security audit on smart contracts and expose security vulnerabilities in solidity and scilla contracts he has also developed a non custodial decentralized p2p lending contracts for ethlend the solidity contracts that he has developed or audited handle over 100 million worth of cryptoassets he also served as a tech and security advisor in various ico projects before finding his passion for blockchain he coded in java for over 11 years he is the founder and leader of delhi ncr jug a non profit meetup group for java he holds a master s degree in computer applications and is regularly invited as a speaker at various conferences and meetups suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781839218262 https packt link free ebook 9781839218262 a p | blockchain |
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sandle | sandle docker build status https github com hltcoe sandle actions workflows docker build yml badge svg https github com hltcoe sandle actions workflows docker build yml python test status https github com hltcoe sandle actions workflows python test yml badge svg https github com hltcoe sandle actions workflows python test yml license https img shields io badge license bsd blue https github com hltcoe sandle blob main license run a large language modeling sandbox in your local environment sandle this repository provides a docker compose system for hosting and interacting with large language models on your own hardware it includes a web sandbox screen shot 2022 08 09 at 1 29 33 pm https user images githubusercontent com 457238 183720063 9c87ce24 e4d4 4a9d b883 b085a12f48a8 png and an openai like rest api screen shot 2022 08 09 at 1 14 44 pm https user images githubusercontent com 457238 183715419 56c1467f e5fe 4ebe 9c3f b1feb7c4e9b9 png setup to build and run sandle with the huggingface backend using docker compose do bash cp docker compose backend hf yml docker compose override yml docker compose up build by default the demo web interface and api endpoint will be bound to port 80 on the host go to http localhost in your browser to use the web interface you must have an api key to use the web interface or api endpoint by default one will be generated and logged on startup if you wish to specify the accepted api key explicitly instead of using a randomly generated key set the sandle auth token environment variable with the desired api key when running docker compose sandle auth token exampleapikey docker compose up build if you wish to limit the models that can be used perhaps you want to support a particularly large model and don t want to incur the overhead of loading it into memory more than once then set the sandle single model environment variable with the desired model name when running docker compose sandle single model bigscience bloom docker compose up build brtx the docker compose version installed on brtx is older and does not work with our configuration file which requires docker compose v1 28 0 or later to use docker compose on brtx install a new standalone version of docker compose https docs docker com compose install compose plugin install the plugin manually to your home directory and run that version instead of the system installed version for example to download docker compose standalone version 2 7 0 bash curl sl https github com docker compose releases download v2 7 0 docker compose linux x86 64 o docker compose chmod 755 docker compose docker compose version additionally on brtx the server will be bound to the local host using ipv4 but localhost will resolve to the local host using ipv6 when connecting to the api specify 127 0 0 1 or localhost4 instead of localhost usage authentication api keys the application should accept can be specified in a file as command line arguments or in an environment variable if no api keys are specified the default one will be generated and logged on startup as in the openai api https beta openai com docs api reference authentication an api key can be used either as a bearer authentication token or as a basic authentication password with the user being the empty string for more information about specifying api keys run the following docker compose run no deps openai wrapper help example api calls calling openai s service is similar to calling a sandle service an example call to openai bash curl https api openai com v1 completions h content type application json h authorization bearer your openai api key d model text davinci 002 prompt say this is a test and an equivalent call to a sandle service bash curl http your sandle server v1 completions h content type application json h authorization bearer your sandle api key d model facebook opt 2 7b prompt say this is a test note that sandle only comes with support for http not https if you need https but don t have a certificate you can set up a reverse proxy in front of sandle using certbot https certbot eff org api documentation see our api documentation https hltcoe github io sandle for a description of the subset of the openai api implemented by sandle this documentation is generated using the swagger ui on our api definition file at docs swagger yaml advanced usage this repository provides the following docker services backend services that implement a subset of the openai v1 completions api https beta openai com docs without authentication these services use single threaded web servers and are suitable for one user at a time serving the api for a single user without docker backend hf a backend on top of huggingface supporting models like opt and bloom from the huggingface hub backend llama a backend on top of llama backend stub a stub backend for development and testing openai wrapper a service that implements a subset of the openai v1 models v1 models model and v1 completions apis https beta openai com docs delegating to backend services accordingly this service uses a multi threaded web server and is suitable for multiple users demo a web server that provides as a reverse proxy in front of the openai wrapper service as well as a web interface that uses the proxied api these services can be run together on your local machine using docker compose https docs docker com compose by default docker compose will load configuration from docker compose yml and if it is present docker compose override yml alternatively configuration files may be explicitly specified on the command line for example the following command starts sandle with the huggingface backend by specifying the configuration files explicitly instead of implicitly as demonstrated at the beginning of this document bash docker compose f docker compose yml f docker compose backend hf yml up build any number of configuration files can be specified at once as long as their contents can be merged together for example to start sandle with both the huggingface and the llama backend bash docker compose f docker compose yml f docker compose backend hf yml f docker compose backend llama yml up build serving the api for a single user without docker if you only need the api for a single user you can run a backend service by itself outside of docker ensure the appropriate dependencies are installed then run for example using the huggingface backend bash python backend hf serve backend hf py port 12349 to serve the partial v1 completions api on port 12349 on your local host the equivalent docker usage would be approximately bash docker build t user backend hf backend hf docker run it p 12349 8000 user backend hf port 8000 development to set up a development environment for the demo web interface install a recent version of npm go to the demo subdirectory and do npm ci then configure your development app by copying env development to env development local and changing the values set in the file accordingly in particular make sure you set vite sandle url to the url of the api implementation you are using for development the demo service acts as a simple reverse proxy for the api implementation provided by the openai wrapper service so if you wish to run an api implementation yourself you can run docker compose up as usual then use http localhost as the url note by default the demo service port is bound to port 80 on the host system if this port is in use or if you don t have access to it you may need to override it to do so add the sandle demo port variable to your environment with the desired port as its value adjust vite sandle url in env development local accordingly and then run docker compose up as usual once you ve done that you can start a development web server with npm run dev this server will run on port 3000 by default and hot reload the ui when any source files change stubbing out the backend if you cannot or do not wish to run a full language model backend during testing and development you may use the stub backend instead to do so just use the stub backend configuration file in lieu of other backend configuration docker compose f docker compose yml f docker compose backend stub yml up build testing static analysis we use flake8 to automatically check the style and syntax of the code and mypy to check type correctness to perform the checks go into a component subdirectory for example backend hf or openai wrapper and do pip install r dev requirements txt flake8 mypy these checks are run automatically for each commit by github ci property testing we use hypothesis https hypothesis readthedocs io en latest to randomly generate test cases for the backend and assert properties of interest for the output for example for any valid input a basic property that we would like to test is that sandle doesn t crash on that input a slightly more advanced property might be that the output does not exceed the user specified length limit property tests are defined in backend hf tests test service py and automatically discovered and run by pytest to run the tests first go to the backend hf subdirectory the rest of this section assumes you are in that directory then install the basic test requirements pip install r dev requirements txt the tests assume a backend service exists at http localhost 8000 you must start this service yourself you can start the service in docker or directly on the host machine depending on your needs the following two examples illustrate how to use these methods to start the backend service listening to port 8000 and using the first gpu on your host system to start the service in docker publishing container port 8000 to host port 8000 docker build t backend hf docker run rm it gpus device 0 p 8000 8000 backend hf alternatively to start the service directly on your host install the requirements cuda pytorch and the requirements specified in requirements txt then run cuda visible devices 0 python serve backend hf py then you can test that the service is up curl http 127 0 0 1 8000 v1 completions h content type application json d model facebook opt 125m prompt say this is a test finally to run the explicit property test cases pytest hypothesis profile explicit alternatively to run explicit test cases and automatically generate and test new cases may take a while pytest fuzz testing to perform fuzz testing using the microsoft restler tool in docker do the following first bring up the sandle system with the huggingface backend and a fixed authentication token sandle auth token dgvzda docker compose f docker compose yml f docker compose backend hf yml up build then run fuzz test run bash with that same authentication token to build the restler fuzzer docker image if it does not exist and run restler on the api specification in docs swagger yaml bash fuzz test run bash dgvzda this script will create the directory fuzz test output bind it to the restler docker container and write the output for each step of the testing procedure to the appropriately named subdirectory of fuzz test output additionally at the end of each step the contents of fuzz test output step responsebuckets runsummary json with step replaced with the step name will be printed to the console if after any step the number of failures reported in that file is greater than zero the test procedure will terminate benchmarking example runtime test using the apache bench tool installed by default on os x ab n 10 c 1 s 60 p qa txt t application json a your api key m post http your sandle server v1 completions where qa txt is a text file in the current directory that contains the prompt json example file contents json model facebook opt 2 7b prompt say this is a test | api api-server docker docker-compose language-modeling large-language-models nlp | ai |
medication-blockchain | medication tracker built on hyperledger fabric image references image1 images image1 png query all of ledger image2 images image2 png single query image3 images image3 png create item image4 images image4 png change holder image5 images image5 png updated ledger this sample application serves as an example of how we might employ blockchain technology to address the significant problem of prescription medication abuse in the u s the problem deaths in the u s from prescription drug overdoses rose from 3 785 in 2000 to 17 741 in 2016 according to the centers for disease control and prevention opioid based painkillers including oxycodone and vicodin may be over prescribed by doctors or sold on the black market an investigation https www washingtonpost com graphics 2017 investigations dea drug industry congress utm term 4c490838c6a4 by the washington post details the curtailment of drug enforcement agency powers to freeze suspicious drug shipments the article refers to unrealistically large orders sent by distributors to pharmacies in one example distributor miami luken shipped 258 000 hydrocodone pills to one pharmacy in williamson west virginia that amount would mean 88 pills for each of williamson s 2 924 residents though the epidemic requires attention beyond technology blockchain could be useful in ensuring transparency and validity of transfer of drugs from manufacturer to pharmacy or doctor s office the resulting immutable ledger would provide a record of transfer of drugs ensure legitimacy of the supply chain and could alert authorities to potentially harmful or illegal distribution patterns drugs recovered from the black market could also be traced from source to find weak links how blockchain could help this is by no means a production ready app however serves as a micro demonstration of how we can create and transfer medication between actors on the supply chain the project is built on hyperledger fabric https www hyperledger org projects fabric an open source blockchain framework run by the linux foundation the application is a fork and adaption of the excellent sample app material found here https github com hyperledger fabric samples blockchain s distributed ledger approach differs from traditional databases as its records are decentralized there is no single point of failure for the data storage and the ledger is synchronized across the network which in this case could comprise manufacturers distributors pharmacies hospitals and authorities the process of keeping the ledger up to date with all parties in agreement is through hyperledger fabric s process of consensus this involves ordering of transactions or transfers of medication and participating peers on the network to agree on and commit changes to the ledger this application in contrast to open blockchain systems including bitcoin and ethereum hyperledger fabric is permissioned and private this means that all members have to enroll through its membership service provider module fabric also offers the creation of channels which allow a group of participants to create partitioned ledgers for transactions this may be appealing to commercial entities who may pay or charge different prices to different clients walkthrough the application s database is populated with some example drug packets alt text image1 the manufacturer id number of a particular drug packet timestamp of when it was added to the ledger the current holder and location are noted alt text image2 a single query for a packet id reveals its current location and holder alt text image3 here we add a packet to the registry and hyperledger generates a unique transaction id for this event alt text image4 we can change the holder in this case from pharmacy chain kvs to a doctor s office and again a transaction id logs this event alt text image5 the ledger now reflects the change for packet 1 this will be broadcast across the network installation instructions make sure you have node js https nodejs org en go https golang org doc install and docker https docs docker com install ce edition is fine installed install hyperledger https hyperledger fabric readthedocs io en latest getting started html clone this repository from the drup app folder to remove any pre existing docker containers run docker rm f docker ps aq start up hyperledger startfabric sh now we install the required node packages and register the admin and user components of the network before starting the application npm install node registeradmin js node registeruser js node server js the client should launch on localhost 8000 in any web browser | blockchain |
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carbon-for-ibm-dotcom | p align center a href https www carbondesignsystem com img alt carbon design system src https user images githubusercontent com 3901764 57545698 ce5f2380 7320 11e9 8682 903df232d7b0 png width 100 a p h1 align center carbon for ibm com h1 this library is an extension of the carbon ibm design system it contains unique components shared between the ibm com user journeys to unify its look and feel p align center a href https github com carbon design system carbon blob main license img src https img shields io badge license apache 2 0 blue svg alt carbon is released under the apache 2 0 license a a href https github com carbon design system carbon for ibm dotcom blob main github contributing md img src https img shields io badge prs welcome brightgreen svg alt prs welcome a a href https percy io 538fc19a carbon for ibm com web components img src https percy io static images percy badge svg alt this project is using percy io for visual regression testing a a href https discord gg j7jeuektrx img src https img shields io discord 689212587170201628 color 5865f2 alt chat with us on discord a p getting started if you re just getting started check out react https github com carbon design system carbon for ibm dotcom blob main packages react or web components https github com carbon design system carbon for ibm dotcom blob main packages web components if you re trying to find something specific here s a full list of packages that we support package name description carbon ibmdotcom react https github com carbon design system carbon for ibm dotcom blob main packages react ibm com react components and patterns carbon ibmdotcom web components https github com carbon design system carbon for ibm dotcom blob main packages web components ibm com web components carbon ibmdotcom services https github com carbon design system carbon for ibm dotcom blob main packages services ibm com es6 service classes carbon ibmdotcom styles https github com carbon design system carbon for ibm dotcom blob main packages styles framework agnostic styles package for ibm com components carbon ibmdotcom utilities https github com carbon design system carbon for ibm dotcom blob main packages utilities ibm com es6 utility classes carbon web components https github com carbon design system carbon for ibm dotcom blob main packages carbon web components carbon web components if you are building pages for ibm com see what is needed on the page https github com carbon design system carbon for ibm dotcom blob main docs building for ibm dotcom md storybook demos carbon ibmdotcom react https www ibm com standards carbon react carbon ibmdotcom web components https www ibm com standards carbon web components carbon web components https web components carbondesignsystem com documentation see our documentation site here http www ibm com standards carbon for full how to docs and guidelines contributing https github com carbon design system carbon for ibm dotcom blob main github contributing md guidelines for making contributions to this repo contributing we re always looking for contributors to help us fix bugs build new features or help us improve the project documentation if you re interested definitely check out our guides contributing guide https github com carbon design system carbon for ibm dotcom blob main github contributing md developer guide https github com carbon design system carbon for ibm dotcom blob main docs developing md contributing to the web components package https www ibm com standards carbon web components path story overview contributing to the web components package page contributing to the react package https www ibm com standards carbon react path story overview contributing to the react package page license licensed under the apache 2 0 license https github com carbon design system carbon for ibm dotcom blob main license | html scss javascript css carbon-design-system web-components lit-element lit-html react hacktoberfest | os |
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