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Deno-Web-Development | deno web development a href https www packtpub com in web development getting started with deno img src https www packtpub com media catalog product cache 4cdce5a811acc0d2926d7f857dceb83b 9 7 9781800205666 original 137 jpeg alt book name height 256px align right a write test maintain and deploy javascript and typescript web applications using deno what is this book about deno is a javascript and typescript runtime with secure defaults and a great developer experience with deno web development you ll learn all about deno s primitives its principles and how you can use them to build real world applications the book is divided into three main sections an introduction to deno building an api from scratch and testing and deploying a deno application this book covers the following exciting features understand why you should use deno get to grips with tooling and the deno ecosystem build deno web applications using existing node js knowledge and the newest ecma script 6 features explore the standard library and the benefit of deno s security model discover common practices and web frameworks to build a rest api in deno if you feel this book is for you get your copy https www amazon com dp 180020566x 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 deno deno is a javascript typescript runtime with secure defaults and a great developer experience getting started with deno will introduce deno s primitives its principles and how developers can use it to build real world applications the book is divided into three main sections introducing deno building an api from scratch and testing and deploying a deno application the first chapters present the runtime and the motivations behind its creation it explores some of the concepts introduced by node why many of them transitioned into deno and why new features were introduced after getting comfortable with deno and why it was created the reader will start to experiment with deno exploring the toolchain and writing simple scripts and cli applications as we transition in the second section of the book the reader will start with a very simple web application and will slowly add more features to it this application will evolve from a simple hello world api to a web application connected to the database with users authentication and a javascript client in the meantime topics like dependency management configuration testing and application structure among others will be addressed by the end of the read the reader is comfortable in using deno to create maintain and deploy secure and reliable web applications instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following const now new date console log now gethours now getminutes now getseconds following is what you need for this book this book is for developers who want to leverage their javascript and typescript skills in a secure simple and modern runtime using deno for web app development beginner level knowledge of node js is recommended but not required with the following software and hardware list you can run all code files present in the book chapter 1 10 software and hardware list chapter software required os required 2 10 deno 1 7 5 windows mac os x and linux any related products other books you may enjoy full stack react typescript and node packt https www packtpub com product full stack react typescript and node 9781839219931 amazon https www amazon com dp 1839219939 node cookbook fourth edition packt https www packtpub com product node cookbook fourth edition 9781838558758 utm source github utm medium repository utm campaign 9781838558758 amazon https www amazon com dp 1838558756 get to know the author alexandre portela dos santos is a software engineer passionate about products and startups for the last 8 years he s been working together with multiple companies using technology as an enabler for ideas and businesses with a big interest in education and getting people excited about technology he makes sure he s always involved with people that are learning about it being it via blog posts books open source contributions or meetups this is by itself a learning adventure that alexandre loves to be a part of being a true believer that great software only happens through collaboration ownership and teams of great people he strives to nurture those values in every project he works in index the book is composed of 10 chapters and 3 main sections the sections are introduction to deno building an application testing and deploying the chapters list and direct link to code is available below 1 what is deno 2 the toolchain https github com packtpublishing deno web development tree master chapter02 3 runtime and standard library https github com packtpublishing deno web development tree master chapter03 4 building a web application https github com packtpublishing deno web development tree master chapter04 5 adding users and migrating to oak https github com packtpublishing deno web development tree master chapter05 6 adding authentication and connecting to the database https github com packtpublishing deno web development tree master chapter06 7 https extracting configuration and deno on the browser https github com packtpublishing deno web development tree master chapter07 8 testing unit and integration https github com packtpublishing deno web development tree master chapter08 9 deploying a deno application https github com packtpublishing deno web development tree master chapter09 10 what s next https github com packtpublishing deno web development tree master chapter10 chapter that have multiple iterations of the same application have inside a sections folder with copies of the application at each state it should be possible to have access to the code 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 9781800205666 https packt link free ebook 9781800205666 a p | front_end |
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LunchQuest | lunchquest an overscoped and overengineered take home test and so it came to pass that as i was looking for my next source of income i approached a certain company that i actually like and asked for an opportunity to interview for an open position they had and they said sure but first you ll have to build us this sample app and i looked at the sample app and said are you kidding me and they probably would have let me get around it but it wasn t a good fit for where i stood in my career and had a better offer already so i politely declined but i wanted to try out building some reusable components and needed a not too complex app to try them out so i ended up building the app anyway the app the application will request access to the user s location during use on first launch and perform a search of restaurants around it the user can input search terms in the search field to fine tune what restaurants show up in the search i e pizza the results are shown in a list by default but can be displayed on a map as well in either type of display tapping on an entry list row or map pin will display a detail view with whatever i saw google api actually returning from place calls the extra bits a few reusable components saw their first prototyping with this app swiftux i had been playing with the idea of building reusable controller types inspired by my work at tome the old macos nscontroller kvo based classes and some fashionable libraries like redux and the action bits of tca for a while this was a good opportunity to fine tune the idea to its cleanest expression or close enough to it with the hope that i can rebuild the additional complexity on future work it needs a small adapter to work with swiftui which i left off the basic classes as having it around tended to confuse things swiftcache everyone wants an image cache everyone reinvents the image caching wheel so do i but i overkilled it in a multilayered data pipeline system that just incidentally is pretty good at caches the one used in this app is the mvp version but most of the enhancements can be built on top of this structure just within the layered components networkdependency filesystemdependency globaldependencies was already a thing based on some prior work where i needed to build a very simple global dependency injection framework for use in a new product swiftcache and the needs of this app made it clear that building up simple common dependencies as their own packages was an idea with potential both are kept exceedingly simple so far but it remains a good idea to build what you need and no more i m sure future projects will see new features in those as well imageloadingview contained within iutilitis it will probably see its own package at some point this is a good if still too simple replacement for sdwebimage facilities and other similar tools i included placeholder image support and i believe it would be generally usable with some additional configurability better testing coverage and better documentation but the added logic to uiimageview is solid and should also work well enough for a nsimageview enhancement | os |
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agds-next | ag ds next the agriculture design system agds is a new design system for the department of agriculture fisheries and forestry agds is a suite of tools and guidelines to help designers and developers build the steel threads of the export service with efficiency and consistency visit the agds website https design system agriculture gov au before you clone this repository if you are starting a new project you should clone the starter kit https github com steelthreads agds starter kit if you are looking to implement agds components you need to install packages from npm https design system agriculture gov au guides getting started if youre implementing components in an existing project you should only clone this repo if you are looking to contribute to it such as improving documentation updating existing components creating new components for consideration and feedback contributing system dependencies to run the dev and builds in this repository you will need a recent version of node js installed 18 and the yarn v1 package manager https classic yarnpkg com lang en docs install we recommend using nvm to manage node versions https github com nvm sh nvm getting started 1 clone the repo to your local machine 2 cd into the repo and run yarn to install and link dependencies for development run one or more of the following commands yarn docs dev start the docs site yarn storybook dev start storybook yarn playroom dev start playroom yarn example dev start the example site yarn example form dev start the example form site website deployment of the website is handled by github actions https github com steelthreads agds next actions workflows deploy docs yml the site is deployed automatically anytime changes are merged to the main branch you can run the builds locally for testing the order of commands here is important because storybook playroom the example site and example form site is being bundled into the docs site it must be built first sh yarn storybook build yarn playroom build yarn example build yarn example form build yarn docs build note if you see an error during the build like the following sh build error occurred error enoent no such file or directory open packages logo package json check if the package has been removed or renamed if so you may need to delete the folder | os |
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LLM_Beginner | llm beginner related resources llm reading list sebastian raschka understanding large language models a transformative reading list https sebastianraschka com blog 2023 llm reading list html andrew ng tweet ask abot llms paper list https twitter com andrewyng status 1622683606492778496 gpt related lambdalabs openai gpt 3 language model a technical overview https lambdalabs com blog demystifying gpt 3 lambdalabs gpt 3 a hitchhikers guide https lambdalabs com blog gpt 3 the gpt 3 architecture https dugas ch artificial curiosity gpt architecture html karpathy online course for nanogpt repo https github com karpathy nanogpt video https www youtube com watch v kcc8fmeb1ny uasge of openai gpt openai cookbook https github com openai openai cookbook openai tutotials website q amp a embeddings https platform openai com docs tutorials web qa embeddings twilio ultimate guide to openai gpt3 https www twilio com blog ultimate guide openai gpt 3 language model applications build a personal chatbot with openai gpt3 lenny chatbot https colab research google com drive 1p2aablavdksxly6h xnlosylmtoz7ndg usp sharing utm source substack utm medium email multiple input web audio text with openai gpt3 by jesse zhang https jessezhang org llmdemo videoques a tool that answers your queries on youtube videos linkedin post here https www linkedin com posts ravidesetty ai ml dl activity 7020599110953050112 eja utm source share utm medium member desktop docsques a tool that answers your questions on longer documents including pdfs linkedin post here https www linkedin com posts ravidesetty artificialintelligence machinelearning recruiters activity 7016972785293946880 rhkc utm source share utm medium member desktop github https github com ravi03071991 docques humanta https www humata ai chatgpt for your files tools projects gpt index https gpt index readthedocs io en latest utm source substack utm medium email openai to sqlite https github com simonw openai to sqlite save openai api results to a sqlite database how to implement q amp a against your documentation with gpt3 embeddings and datasette https simonwillison net 2023 jan 13 semantic search answers embetter https github com koaning embetter a bunch of useful embeddings opensource llm finetuning standford alpaca https github com tatsu lab stanford alpaca aims to build and share an instruction following llama model alpaca lora https github com tloen alpaca lora instruct tuning llama on consumer hardware cabrita https github com 22 hours cabrita finetuning instructllama with portuguese data alpacoom https huggingface co mrm8488 alpacoom this adapter was created with the peft https github com huggingface peft library and allowed the base model bigscience bloom 7b1 to be fine tuned on the stanford s alpaca dataset by using the method lora optimization llama cpp https github com ggerganov llama cpp port of facebook s llama model in c c textsynth server https bellard org ts server a web server proposing a rest api to large language models they can be used for example for text completion question answering classification chat translation image generation | ai |
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Top-Blockchain-paper | top blockchain paper all 2018 papers s p omniledger a secure scale out decentralized ledger via sharding br eleftherios kokoris kogias epfl philipp jovanovic epfl linus gasser epfl nicolas gailly epfl ewa syta trinity college and bryan ford epfl routing around congestion defeating ddos attacks and adverse network conditions via reactive bgp routing br jared m smith university of tennessee and max schuchard university of tennessee ccs checkmate practical security analysis of smart contracts br petar tsankov eth zurich andrei marian dan eth zurich dana drachsler cohen eth zurich arthur gervais imperial college london florian buenzli eth zurich martin vechev eth zurich bitml a calculus for bitcoin smart contracts massimo br bartoletti university of cagliari roberto zunino university of trento beat asynchronous bft made practical br sisi duan university of maryland baltimore county michael k reiter university of north carolina at chapel hill haibin zhang university of maryland baltimore county a better method to analyze blockchain consistency br lucianna kiffer northeastern university abhi shelat northeastern university rajmohan rajaraman northeastern university an end to end system for large scale p2p mpc as a service and low bandwidth mpc for weak participants mpc br assi barak bar ilan university martin hirt eth zurich lior koskas bar ilan university yehuda lindell bar ilan university ouroboros genesis composable proof of stake blockchains with dynamic availability br christian badertscher eth zurich peter gazi iohk aggelos kiayias university of edinburgh and iohk alexander russell university of connecticut vassilis zikas university of edinburgh and iohk nanopi extreme scale actively secure multi party computation mpc br ruiyu zhu indiana university darion cassel carnegie mellon university amr sabry indiana university yan huang indiana university secure computation with differentially private access patterns br sahar mazloom george mason university s dov gordon george mason university rapidchain fast blockchain consensus via full sharding br mahdi zamani visa research mahnush movahedi dfinity mariana raykova yale university fairswap how to fairly exchange digital goods br stefan dziembowski university of warsaw lisa eckey tu darmstadt sebastian faust tu darmstadt usenix security an empirical analysis of anonymity in zcash br george kappos haaroon yousaf mary maller and sarah meiklejohn university college london teether gnawing at ethereum to automatically exploit smart contracts br johannes krupp and christian rossow cispa enter the hydra towards principled bug bounties and exploit resistant smart contracts br lorenz breindenbach eth zurich phil daian cornell tech florian tramer stanford university ari juels cornell tech jacobs institute arbitrum scalable smart contracts br harry kalodner steven goldfeder xiaoqi chen matt weinberg and edward felten princeton university erays reverse engineering ethereum s opaque smart contracts br yi zhou deepak kumar surya bakshi joshua mason andrew miller and michael bailey university of illinois urbana champaign delegatee brokered delegation using trusted execution environments sgx br sinisa matetic and moritz schneider eth zurich andrew miller uiuc ari juels cornell tech srdjan capkun eth zurich ndss obliviate a data oblivious filesystem for intel sgx br adil ahmad purdue university kyungtae kim purdue university muhammad ihsanulhaq sarfaraz purdue university and byoungyoung lee purdue university zeus analyzing safety of smart contracts br sukrit kalra ibm research seep goel ibm research mohan dhawan ibm research and subodh sharma iit delhi chainspace a sharded smart contracts platform br mustafa al bassam university college london alberto sonnino university college london shehar bano university college london dave hrycyszyn constructiveproof com and george danezis university college london settling payments fast and private efficient decentralized routing for path based transactions br stefanie roos university of waterloo pedro moreno sanchez purdue university aniket kate purdue university and ian goldberg university of waterloo tls n non repudiation over tls enablign ubiquitous content signing br hubert ritzdorf eth zurich karl wust eth zurich arthur gervais imperial college london guillaume felley eth zurich and srdjan capkun eth zurich consensual and privacy preserving sharing of multi subject and interdependent data br alexandra mihaela olteanu epfl and unil hec lausanne kevin huguenin unil hec lausanne italo dacosta epfl and jean pierre hubaux epfl acsac finding the greedy prodigal and suicidal contracts at scale br ivica nikolic school of computing nus aashish kolluri school of computing nus ilya sergey university college london prateek saxena school of computing nus aquinas hobor yale nus college and school of computing nus osiris hunting for integer bugs in ethereum smart contracts br christof ferreira torres snt university of luxembourg julian sch tte fraunhofer aisec radu state snt university of luxembourg smartor smarter tor with smart contracts br andre greubel university of wuerzburg alexandra dmitrienko university of wuerzburg samuel kounev university of wuerzburg obscuro a bitcoin mixer using trusted execution environments br muoi tran national university of singapore loi luu kyber network min suk kang national university of singapore iddo bentov cornell university prateek saxena national university of singapore raid identifying key leakage of bitcoin users br sichael brengel christian rossow cispa saarland university asiaccs on the strategy and behavior of bitcoin mining with n attackers br hanqing liu na ruan rongtian du and weijia jia shanghai jiao tong university icics umine a blockchain based on human miners br henning kopp frank kargl christoph b sch ulm university germany and andreas peter university of twente netherlands lockchain based secure data provenance for cloud storage br yuan zhang uestc china xiaodong lin university of ontario institute of technology canada and chunxiang xu university of science and technology of china china micropaying to a distributed payee with instant confirmation short br peifang ni hongda li and dongxue pan iie cas china application of public ledgers to revocation in distributed access control short br thanh bui and tuomas aura aalto university finland nsdi zkledger privacy preserving auditing for distributed ledgers br neha narula willy vasquez madars virza osdi an analysis of network partitioning failures in cloud systems br ahmed alquraan hatem takruri mohammed alfatafta samer al kiswany eurosys hyperledger fabric a distributed operating system for permissioned blockchains br elli androulaki artem barger vita bortnikov christian cachin konstantinos christidis angelo de caro david enyeart christopher ferris gennady laventman yacov manevich srinivasan muralidharan chet murthy binh nguyen manish sethi gari singh keith smith alessandro sorniotti chrysoula stathakopoulou marko vukolic sharon weed cocco jason yellick sosp 2017 algorand scaling byzantine agreements for cryptocurrencies br yossi gilad rotem hemo silvio micali georgios vlachos nickolai zeldovich dsn a byzantine fault tolerant ordering service for the hyperledger fabric blockchain platform br jo o sousa alysson bessani marko vukolic ibbe sgx cryptographic group access control using trusted execution environments br stefan contiu rafael pires s bastien vaucher marcelo pasin pascal felber laurent r veill re dsn workshop protecting early stage proof of work based public blockchain br lin chen lei xu zhimin gao yang lu weidong shi challenges and pitfalls of partitioning blockchains br enrique fynn fernando pedone random mining group selection to prevent 51 attacks on bitcoin workshop br jaewon bae hyuk lim latency aware leader selection for geo replicated byzantine fault tolerant systems br michael eischer tobias distler towards model driven engineering of smart contracts for cyber physical systems br peter garamvolgyi imre kocsis benjamin gehl attila klenik towards low latency byzantine agreement protocols using rdma br signe r sch ines messadi r diger kapitza infocom understanding ethereum via graph analysis best paper br ting chen yuxiao zhu zihao li jiachi chen xiaoqi li xiapu luo xiaodong lin xiaosong zhang searching an encrypted cloud meets blockchain a decentralized reliable and fair realization br shengshan hu chengjun cai qian wang cong wang xiangyang luo kui ren certchain public and efficient certificate audit based on blockchain for tls connections br jing chen shixiong yao quan yuan kun he shouling ji ruiying du stochastic models and wide area network measurements for blockchain design and analysis br nikolaos papadis sem c borst anwar walid mohamed grissa leandros tassiulas randomized view reconciliation in permissionless distributed systems br ruomu hou irvan jahja loi luu prateek saxena haifeng yu sybil proof online incentive mechanisms for crowdsensing br jian lin ming li dejun yang guoliang xue socc untethered deployable blockchains for iot environments br kolbeinn karlsson danny adams gloire rubambiza zangyueyang xian robbert van renesse hakim weatherspoon stephen b wicker esorics a secure auction for blockchains br erik oliver blass florian kerschbaum strain channels horizontal scaling and confidentiality on permissioned blockchains br elli androulaki christian cachin angelo de caro eleftherios kokoris kogias dissemination of authenticated tree structured data with privacy protection and fine grained control in outsourced databases br jianghua liu jinhua ma wanlei zhou yang xiang xinyi huang fc c https fc19 ifca ai program html https fc19 ifca ai program html license cc0 http mirrors creativecommons org presskit buttons 88x31 svg cc zero svg https creativecommons org publicdomain zero 1 0 this list is released into the public domain | blockchain |
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AirHockeyEmbeddedSystems | airhockeyembeddedsystems ee319k final project embedded systems game design | os |
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Blockchain | project logo br div align center a href https github com ac12644 blockchain img src blockchain svg alt logo width 80 height 80 a h3 align center blockchain h3 p align center create blockchain with node js br a href https abhishek chauhan medium com e65dfc40479e strong explore the article on medium strong a p div table of contents details summary steps done summary ol li a creating a basic p2p network a li li a sending and receiving blocks a li li a registering miners and creating new blocks a li li a setting up a name value database leveldb a li li a creating a private public wallet a li li a creating an api a li li a creating a command line interface a li ol details getting started sh npm install sh node p2p js contributing contributing contributions are what make the open source community such an amazing place to learn inspire and create any contributions you make are greatly appreciated if you have a suggestion that would make this better please fork the repo and create a pull request you can also simply open an issue with the tag enhancement don t forget to give the project a star thanks again 1 fork the project 2 create your feature branch git checkout b feature amazingfeature 3 commit your changes git commit m add some amazingfeature 4 push to the branch git push origin feature amazingfeature 5 open a pull request p align right a href top back to top a p | blockchain leveldb peer2peer wallet cli crypto-js discovery-swarm nodejs | blockchain |
NLP | n atural l anguage p rocessing https blog csdn net yellow python article category 8270870 | ai |
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project-wobble | project wobble this is a program designed to incorporate several hardware components to produce a self balancing robot authors stuart alldrit skalldri kara vaillancourt kvaillancourt hardware nordic nrf52 microcontroller bluetooth radio http infocenter nordicsemi com pdf nrf52 dk user guide v1 2 pdf http infocenter nordicsemi com pdf nrf52832 ps v1 1 pdf mpu 9250 inertial measurement unit https www invensense com wp content uploads 2015 02 ps mpu 9250a 01 v1 1 pdf drv8825 stepper motor driver http www ti com lit ds symlink drv8825 pdf diymall oled screen based on ssd1306 driver https www olimex com products modules lcd mod oled 128x64 resources ssd1306 pdf dependancies clone git submodules with git submodule update init windows install chocolatey and then run env ps1 init in an admin prompt to install dependencies macos homebrew used to install software packages git code history ninja dependancy for zephyr python dependancy for ninja zephyros install all dependancies follow this guide http docs zephyrproject org getting started getting started html ninja menu config go into build folder ninja menuconfig device drivers console drivers use rtt console output all the debug statements device drivers spi hardware bus drivers spi port 2 port to talk to the mpu9250 device drivers spi hardware bus drivers port 2 interrupt priority 2 device drivers spi hardware bus drivers nrf spi nrfx drivers spi port 2 driver type sck pin number 15 device drivers spi hardware bus drivers nrf spi nrfx drivers spi port 2 driver type mosi pin number 14 device drivers spi hardware bus drivers nrf spi nrfx drivers spi port 2 driver type miso pin number 13 device drivers i2c drivers enable i2c port 0 device drivers i2c drivers enable i2c port 1 device drivers i2c drivers nrf twi nrfx drivers i2c port 0 driver type nerf twi 0 nrf twi 0 x device drivers i2c drivers nrf twi nrfx drivers i2c port 1 driver type nerf twi 1 nrf twi 1 x device drivers sensor drivers nrf5 temperature sensor temp 0 driver name device drivers sensor drivers nrf5 temperature sensor 1 temp interrupt priority o select prj conf file inside project wobble folder loads all files in the file setup set enviorment variables you must do this every new terminal session the command is export var name path to variable users kvaillancourt zephyr base project folder project wobble zephyr zephyr toolchain variant gccarmemb gccarmemb toolchain path gcc folder gcc arm none eabi 7 2018 q2 update configure project cmake gninja dboard nrf52 pca10040 run project ninja debug os print open telnet telnet open localhost 19021 download run firmware open vscode debug symbol play symbol debug breaks will ensue alternate debug method via terminal jlinkgdbserver if swd device nrf52832 xxaa | os |
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php-queue | php queue gitter https badges gitter im join 20chat svg https gitter im coderkungfu php queue utm source badge utm medium badge utm campaign pr badge utm content badge a unified front end for different queuing backends includes a rest server cli interface and daemon runners build status https secure travis ci org coderkungfu php queue png branch master https travis ci org coderkungfu php queue why php queue implementing a queueing system eg beanstalk amazon sqs rabbitmq for your application can be painful which one is most efficient performant learning curve to effectively implement the queue backend the libraries time taken to develop the application codes vendor locked in making it impossible to switch requires massive code change ie not flexible when use case for the queue changes php queue hopes to serve as an abstract layer between your application code and the implementation of the queue benefits job queue is backend agnostic just refer to the queue by name what it runs on is independent of the application code your code just asks for the next item in the phpqueue jobqueue and you ll get a phpqueue job object with the data and jobid flexible job queue implementation you can decide whether each phpqueue jobqueue only carries 1 type of work or multiple types of target workers you control the retrieval of the job data from the queue backend and how it is instantiated as a phpqueue job object each phpqueue job object carries information on which workers it is targeted for independent workers workers are independent of job queues all it needs to worry about is processing the input data and return the resulting data the queue despatcher will handle the rest workers will also be chainable powerful the framework is deliberately open ended and can be adapted to your implementation it doesn t get in the way of your queue system we ve build a simple rest server to let you post job data to your queue easily we also included a cli interface for adding and triggering workers all of which you can sub class and overwrite you can also include our core library files into your application and do some powerful heavy lifting several backend drivers are bundled memcache redis mongodb csv these can be used as the primary job queue server or for abstract fifo or key value data access installation installing via composer composer http getcomposer org is a dependency management tool for php that allows you to declare the dependencies your project needs and installs them into your project in order to use the php queue https packagist org packages coderkungfu php queue through composer you must do the following 1 add coderkungfu php queue as a dependency in your project s composer json file visit the packagist https packagist org packages coderkungfu php queue page for more details 2 download and install composer curl s http getcomposer org installer php 3 install your dependencies php composer phar install 4 all the dependencies should be downloaded into a vendor folder 5 require composer s autoloader php php require once path to vendor autoload php getting started you can have a look at the demo app inside vendor coderkungfu php queue src demo folder for a recommended folder structure htdocs folder htaccess index php queues folder queuenameincamelcase queue php workers folder workernameincamelcase worker php runners folder cli php file config php file i would also recommend putting the autoloader statement and your app configs inside a separate config php file recommended config php file content php php require once path to vendor autoload php phpqueue base queue path dir queues phpqueue base worker path dir workers altenative config php file you can also declare your application s namespace for loading the queues and workers php php require once path to vendor autoload php phpqueue base queue namespace myfabulousapp queues phpqueue base worker namespace myfabulousapp workers php queue will attempt to instantiate the phpqueue jobqueue and phpqueue worker classes using your namespace appended with the queue worker name ie myfabulousapp queues facebook it might be advisable to use composer s custom autoloader http getcomposer org doc 01 basic usage md autoloading for this note br if you declared phpqueue base queue path and or phpqueue base worker path together with the namespace the files will be loaded with require once from those folder path and instantiated with the namespaced class names rest server the default rest server can be used to interface directly with the queues and workers copy the htdocs folder in the demo app into your installation the index php calls the phpqueue rest defaultroutes method which prepares an instance of the respect rest rest server you might need to modify the path of config php within the index php file recomended installation use a new virtual host and map the htdocs as the webroot 1 add new job form post curl xpost http localhost queuename d var1 foo var2 bar json post curl xpost http localhost queuename h content type application json d var1 foo var2 bar 2 trigger next job curl xput http localhost queuename read the full documentation https github com respect rest on respect rest to further customize to your application needs eg basic auth command line interface cli copy the cli php file from the demo app into your installation this file implements the phpqueue cli class you might need to modify the path of config php within the cli php file 1 add new job php cli php queuename add data boo bar foo car 2 trigger next job php cli php queuename work you can extend the phpqueue cli class to customize your own cli batch jobs eg import data from a mysql db into a queue runners you can read more about the runners here https github com coderkungfu php queue blob master demo runners readme md interfaces the queue backends will support one or more of these interfaces atomicreadbuffer this is the recommended way to consume messages atomicreadbuffer provides the popatomic callback interface which rolls back the popped record if the callback returns by exception for example queue new phpqueue backend pdo options queue popatomic function message use processor processor churn message the message will only be popped if churn returns successfully fifoqueuestore a first in first out queue accessed by push and pop license this software is released under the mit license copyright c 2012 michael cheng chi mun permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | front_end |
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New_Project | new project information systems and technologies olha tiutiunyk https www hneu edu ua | server |
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blaze-simtainer-game | simtainer a game to visualize the cloud stack of wehkamp real time with elements of resource management and chaos engineering open sourced so you can try to set it up for your own environment every building in the image below represents a container and every vehicle driving around network traffic the information is provider by a back end which is set up as generic as possible so you can visualize other things as well for the back end in the example marathon mesos and prometheus are used back end project can be found here https github com wehkamp blaze simtainer service alt text img simtainer gif example of the game 1 getting started before you proceed this game is still a work in progress if you find any bug please let us know and create an issue the open source version is without the assets you see in the picture above this version has own created prefabs which are created within an hour to strip the assets i m not a 3d artist unfortunately which are not quite nice you can of course download a free asset pack but it s not allowed to include these in this repo these assets are not complete so there is no scooter car van etc only a car plane tank and a bus the code base is the same as the one with the paid assets version if you download the compiled release the nice looking assets are included 1 1 installation to build compile this application you must install unity 2019 3 10f1 and to decorate the game like we did you can purchase the assets as listed below the application was mainly focused on webgl and therefore fully compatible with webgl the webgl performance is not that great with lots of data only when revealing the whole map windows mac builds perform way better if you only want to use the game download https github com wehkamp blaze simtainer game releases the game from the releases tab the game is available as windows mac or webgl download 1 2 used assets open source assets in game debug console for unity 3d https github com yasirkula unityingamedebugconsole by s leyman yasir kula paid assets that are not included city adventure https assetstore unity com packages 3d environments city adventure 65307 by beffio simple apocalypse cartoon assets https assetstore unity com packages 3d environments simple apocalypse cartoon assets 44678 free assets that are not included skybox add on https assetstore unity com packages 2d textures materials sky skybox add on 136594 you can replace all the assets you want in the config file that is included if you want to use these assets read chapter 5 1 1 3 used libraries signalr support for webgl included https github com evanlindsey unity webgl signalr simplejson included https wiki unity3d com index php simplejson unityruntimepreviewgenerator included https github com yasirkula unityruntimepreviewgenerator navmeshcomponents included https github com unity technologies navmeshcomponents 1 4 controls key action w up arrow move the camera forward a left arrow move the camera to the left s down arrow move the camera backwards d right arrow move the camera to the right escape go back to the main menu or escape different camera view f4 open the debug console left mouse button when clicked on a building or vehicle it displays information hold right mouse button rotate the camera around scroll kp or zoom in or out 2 configuration the game is fully configurable from a json file to change the configuration of the game open the config json src config json if you build the project yourself always include your config json in your game directory otherwise default settings will be used you can change the prefabs of buildings vehicles road etc this has been done to make it extremely easy to switch between asset packs the game makes use of so called asset bundles this is an unity feature where you can easily deploy asset bundles this will mean you only need to make a pack of assets edit the game config file and you can include them within the game for easy deployment no need to modify compile the game yourself assets to build the assets bundle read chapter 5 1 when you do not make use of webgl place the assetbundle in the data folder https docs unity3d com scriptreference application datapath html of the game if you re using the back end we provided rename the bundle ending with unityweb and edit the config json and include the extension in the name of the asset bundle assets can also be placed on a webserver if you change the name of the assetbundle starting with http the game will automatically detect it needs to get the assets from a webserver notes 1 if you use webgl and the base url is the same as the server your hosting on leave it blank 2 if you use webgl the config json must in the root of the webserver otherwise the game can not find it or you must edit the settingsmanager cs src assets scripts managers settingsmanager cs and change the location i have not found another solution for it yet 3 if you do not want to use real time updates signalr leave the eventhubendpoint blank example configuration jsonc api baseurl base url of the api leave it blank if you use webgl on the same server as the game is hosted on eventhubendpoint hubs cloudstack game endpoint to the signalr hub of the game gameendpoint v1 cloudstack game endpoint of the game information chaos enabled true enable or disable chaos engineering planeenabled true plane enabled to drop bombs on buildings destroy containers tankenabled true tank enabled to shoot at buildings destroy containers minimumbuildings 2 minimum amount of buildings that are required before a hit will happen in before production problems grid tilesperstreet 50 amount of tiles that are spawned per street layers enabled true enable or disable layers teams enabled true enable or disable teams assetbundle name free name of how you want the asset bundle to be called buildingdecayagethreshold 100 threshold of when buildings should be decayed age of a building stagingbuildingprefab buildingconstruction prefab when buildings are being built up buildings prefabs name extrasmallbuilding name of the prefab you want to use as building rotation 0 rotation of the prefab decayedprefabs name extrasmallbuildingdecayed name of the prefab you want to use as a decayed building rotation 0 rotation of the decayed prefab minsize 0 minimal size of a building to be used prefabs name smallbuilding rotation 0 decayedprefabs name smallbuildingdecayed rotation 0 label name buildings minsize 15 prefabs name mediumbuilding rotation 0 decayedprefabs name mediumbuildingdecayed rotation 0 label name buildingm minsize 20 prefabs name largebuilding rotation 0 decayedprefabs name largebuildingdecayed rotation 0 label name buildingl minsize 30 vehicles name car name of the category of a vehicle prefabnames car name of prefabs used a vehicle the first vehicle from this list is picked as a sprite for the ui minsize 0 minimal size of the vehicle speed 15 speed of the vehicle name truck prefabnames truck minsize 500 speed 10 grass grasstile destroyedtiles destroyed tiles are used when a building is destroyed and are randomly picked randomtiles tile grass destroyed1 tile grass destroyed2 tile grass destroyed3 fx destroy fx fx when a building is being destroyed layereffects prefabname fire fx fx when the threshold of a layer is 0 9 threshold 0 9 prefabname smoke fx fx when the threshold of a layer is 0 8 threshold 0 8 chaos tankprefab tank prefab for the tank planeprefab plane prefab for the plane bombprefab bomb prefab for the bomb dropping out of the plane explosionprefab explosion fx prefab effect when a building is being hit by a bomb or a tank roads prefabs for roads roadstraight road roadtsection road roadintersection road roadcorner road all of those settings are parsed in the settingsmanager cs class which you can find at settingsmanager cs src assets scripts managers settingsmanager cs 3 api set up 3 1 communicate with the api communication with the api happens in 2 forms initialization of the data and real time updating the data every api call for data is handled in the apimanager cs src assets scripts managers apimanager cs there is 1 more place where a call happens and that is in the layermanager cs src assets scripts managers layermanager cs it downloads an image from the back end to make the layers dynamic 3 2 initialization of the data data is being initialized by doing an api call to the endpoint v1 cloudstack game it receives a model the game model looks like the following jsonc neighbourhoods name authorization service name of a neighbourhood visualizedobjects type building type of the object can be staging building building or vehicle size 7 size of the object layervalues memorylayer 434 current layer value cpulayer 6 8 current layer value identifier 598af9be 7f40 46eb 8e3e 5059183396ac unique identifier of the building type building size 7 layervalues memorylayer 434 cpulayer 5 identifier 94f0fdb1 6dfe 4e92 afce 6a99fb774870 layervalues layertype cpulayer maxvalue 100 minvalue 0 layertype memorylayer maxvalue 500 minvalue 0 daysold 95 team authorization developers name search service visualizedobjects type building size 7 layervalues memorylayer 499 cpulayer 3 1 identifier 9f9180d9 5452 44cb be86 019798213cf4 type building size 7 layervalues memorylayer 499 cpulayer 3 1 identifier 011774df 0af3 4ff4 9672 f3bd16de5fc2 layervalues layertype cpulayer maxvalue 100 minvalue 0 layertype memorylayer maxvalue 500 minvalue 0 daysold 150 team search developers layers types of layers icon images cpu icon png location to image on the back end for example https simtainer yourorganization local images cpu icon png layertype cpulayer name of the layer icon images ram icon png layertype memorylayer teams authorization developers teams of your organization search developers 3 3 real time data updates data is real time updated by the signalr library the following plug in is used https github com evanlindsey unity webgl signalr if you want to use webgl you need to adjust your index html and add a reference to the signalr library in the header html script src https cdn jsdelivr net npm microsoft signalr 3 1 2 dist browser signalr min js script a json string must be sent to the client not a model the json is being parsed by the game you don t need to fill every key only the values you want to update the model for event updates is as followed jsonc neighbourhoodname authorization service addedneighbourhood name authorization service v2 visualizedobjects layervalues layertype cpulayer maxvalue 100 minvalue 0 layertype memorylayer maxvalue 500 minvalue 0 daysold 95 team authorization developers removedneighbourhood identifier of neighbourhood for example authorization service addedvisualizedobject type staging building size 7 layervalues null identifier 011774df 0af3 4ff4 9672 f3bd16de5fc2 removedvisualizedobject identifier of a building updatedlayervalues identifier of a building cpulayer 0 1 memorylayer 0 5 updatedneighbourhood name search service visualizedobjects layervalues daysold 150 team search developers updatedvisualizedobjects type staging building size 7 layervalues null identifier 011774df 0af3 4ff4 9672 f3bd16de5fc2 type staging building size 7 layervalues null identifier 12345678 0af3 4ff4 9672 f3bd16de5fc2 note it is important to set the neighbourhoodname this is because if a new visualized object is added the game doesn t know to which neighbourhood this belongs object types for now there are 4 types of visualized objects that can exists in the game of course you can expand these but the following are used in this project 1 building 2 staging building 3 vehicle 4 grass only used in the game not from the api for buildings vehicles size is relevant a simple formula i used for the back end is ram 10 cpu for example 1200 10 0 3 36 because a better cpu is more expensive but more ram is more expensive too so a building looks better bigger you can edit the configuration to change sizes for now there can only be 1 vehicle per building it is a todo for the future to expand this the vehicle should have the same identifier as the building and it automatically drive towards it 4 features 4 1 chaos engineering to use chaos engineering there has been decided to let a tank drive around together with an airplane flying over dropping bombs on buildings if a hit happens a building will collapse and a docker container will real time be destroyed if you click on the tank or the plane in the ui the camera will switch to the vehicle you can switch back to the normal view by pressing the button again there are settings to disable the airplane or tank there is also a setting where you can set the minimum required buildings it is more safe to set this to 2 if you use this in a production environment it could happen that a service with only 1 instance is getting killed when a team is selected the chaos feature only applies to the selected team the tank will change route if it does not meet the requirements it will go to a stand by mode to disable chaos engineering edit the config json 4 2 traffic traffic in the game represents in our case network traffic every building can have 1 vehicle at the moment if a vehicle has reached it s destination it will drive back to their spawn point to always keep traffic on the move you can set the sizes in the configuration depending on the size of the input the game will choose a vehicle to use you can edit this in the configuration 4 3 layers layers are fully dynamic depending on the api images should be placed at baseurl images example icon png these images are loaded in the game you can find the example json at paragraph 3 2 it is important to use a white icon since unity can only colorize white sprites we used the layers for metrics about cpu memory and 500 error s layers are managed in the layermanager class you can see the layers in action in the gif at the beginning of this readme to disable layers edit the config json 4 4 team selection teams can be selected in the dropdown in the top left corner when a team is selected transparency is enabled for every building that does not belong to the selected team to disable team selection edit the config json 5 adding your own prefabs 5 1 automatic if you purchased the assets packs that are listed in chapter 1 2 you can load the config config paid json from the preset config files folder by simply copying it to the root of the project and rename it to config json after that you only need to click on the assets menu and execute the following actions 1 make all meshes readable 2 fix asset properties and components 3 build assetsbundles read about assets in chapter 2 and when you look into the assets assetbundles folder you should see an asset bundle called paid of course you can also do this with your own prefab packs you only need to edit the config json to set the correct prefab names and then execute the actions as listed above note if you use the city adventure pack the creator forgot to set the right mesh in the mesh collider for all vehicles the only thing you need to do is go by all vehicles you want to use and set the right mesh on the mesh collider otherwise clicking on a vehicle will not working because of ray casting if you use the simple apocalypse pack please edit the sa veh tank prefab and tag the turret of the tank as a tankturret 5 2 manual adding prefabs you can add your own prefabs to this game like said in 1 2 the prefabs must be 10x10 if you want to use the default tile size steps to take when adding your own prefabs 1 import the prefab 2 place the prefab in the resources prefabs type folder 3 give the correct tag to the prefab if you re adding a tank don t forget to tag the tankturret as it rotates when it fires buildings roads 4 add a navmeshmodifier to the prefab if it s not a vehicle and set the right target check override area and for a road area with walkable selected for a building select not walkable vehicles 4 add a navmeshagent to the prefab for example for the scooter the following settings were used setting value agent type humanoid base offset 0 1 speed 20 angular speed 120 acceleration 8 stopping distance 0 auto braking enabled radius 0 5 height 1 quality none priority 50 auto traverse off mesh enabled auto re path enabled area mask walkable 5 if the prefab is a tank or a plane add a camera to the object align it correctly and give it the correct tag tankcamera or planecamera important buildings vehicles these steps are necessary only if you intend to use the team selection the material must not contain any white spaces 6 place the material you used in the resources materials folder 7 duplicate the material and rename the duplicated material to transparent material name i know this is not a really nice thing but it s because of webgl limitations 8 set the rendering mode to fade of the transparent material 9 edit the config json and set all the prefabs to the correct names for performance reasons it is advised to enable gpu instancing for the transparent material and the original prefab material 6 documentation most of the code is documented this means that if you have knowledge of unity and c it should be easy to make your own adjustments every manager in the game is a singleton all managers can reach to each others publics to retrieve information because the game is dynamic as possible most of the scripting in the game is event based the order of how the game is being loaded 1 settings manager fires settingsloaded event 2 assets manager fires assetsloaded event 3 api manager fires apiinitialized event and after that keeps firing apiupdate events 4 city manager fires cityupdated event as soon as the grid is changed 5 grid manager fires gridinitialized event as soon as the grid is generated any manager can hook into any of these events there are other managers that have events as well but the order does not matter for them 7 developer note this project is a graduation project if you have any questions feedback or suggestions feel free to contact us harm weites sebastiaan bekker or leroy van dijk 8 icons icons used in this project are from iconfinder com created by designerzbase | game | cloud |
teaching-nlpa | natural language processing and applications course http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa course ipynb intro http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa intro ipynb demo videos http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa intro demo videos ipynb unix command line tools database join http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa unix join ipynb making the brown corpus readable http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa unix cleanup ipynb downloading text from the internet http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa downloading tomsawyer ipynb word histogram http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa word histograms ipynb find and xargs http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip nlpa find xargs 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nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip hw2 improve tagging with wordnet ipynb homework classifier based tagging http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip hw2 classifier based tagging ipynb homework hmms and fsts http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip hw3 hmms and fsts ipynb homework regex generation http nbviewer ipython org urls bitbucket org tmbdev teaching nlpa raw tip hw3 regex generation ipynb | ai |
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webwork2 | webwork online homework delivery system version 2 branch github com openwebwork https webwork maa org wiki release notes for webwork 2 18 copyright 2000 2023 the webwork project https openwebwork org all rights reserved welcome to webwork webwork is an open source online homework system for math and sciences courses webwork is supported by the maa and the nsf and comes with an open problem library opl of over 30 000 homework problems problems in the opl target most lower division undergraduate math courses some advanced courses and some other stem subjects supported courses include college algebra discrete mathematics probability and statistics single and multivariable calculus differential equations linear algebra and complex analysis find out more at the main webwork webpage https openwebwork org information for users new users interested in getting started with their own webwork server or instructors looking to learn more about how to use webwork in their classes should take a look at one of the following resources the webwork project home page https openwebwork org general information and resources including announcements of events and important project news webwork wiki https webwork maa org wiki main page the main webwork wiki installing webwork https webwork maa org wiki manual installation guides installing webwork instructors https webwork maa org wiki instructors information for instructors problem authors https webwork maa org wiki authors information for problem authors forum http webwork maa org moodle mod forum index php id 3 the webwork forum for getting help from the community frequently asked questions https github com openwebwork webwork2 wiki frequently asked questions a list of frequently asked questions information for downloading the current version is webwork 2 18 and its companion pg 2 18 installation manuals can be found at https webwork maa org wiki category installation manuals information for developers people interested in developing new features for webwork can start at https webwork maa org wiki category developers or start a discussion on github https github com openwebwork webwork2 discussions to engage with the current developers people interested in developing new problems for webwork should visit problem authors http webwork maa org wiki authors | front_end |
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Advanced-Serverless-Architectures-with-Microsoft-Azure | github issues https img shields io github issues trainingbypackt advanced serverless architectures with microsoft azure svg https github com trainingbypackt command line fundamentals issues github forks https img shields io github forks trainingbypackt advanced serverless architectures with microsoft azure svg https github com trainingbypackt command line fundamentals network github stars https img shields io github stars trainingbypackt advanced serverless architectures with microsoft azure svg https github com trainingbypackt command line fundamentals stargazers prs welcome https img shields io badge prs welcome brightgreen svg https github com trainingbypackt advanced serverless architectures with microsoft azure pulls advanced serverless architectures with microsoft azure advanced serverless architectures with microsoft azure redefines your experience of designing serverless systems it shows you how to tackle challenges of varying levels not just the straightforward ones you ll be learning how to deliver features quickly by building systems which retain the scalability and benefits of serverless you ll begin your journey by learning how to build a simple completely serverless application then you ll build a highly scalable solution using a queue load messages onto the queue and read them asynchronously to boost your knowledge further the course also features durable functions and ways to use them to solve errors in a complex system you ll then learn about security by building a security solution from serverless components next you ll gain an understanding of observability and ways to leverage application insights to bring you performance benefits as you approach the concluding lessons you ll explore chaos engineering and the benefits of resilience by actively switching off a few of the functions within a complex system submitting a request and observing the resulting behavior by the end of this course you will have developed the skills you need to build and maintain increasingly complex systems that match evolving platform requirements what you will learn understand what true serverless architecture is study how to extend and scale architectures until they become complex implement durable functions in your design improve the observability of your serverless architecture implement security solutions using serverless services learn how to practise chaos engineering in production hardware requirements for an optimal student experience we recommend the following hardware configuration processor intel core i3 or equivalent memory 4 gb ram storage 1 gb available hard disk space software requirements you ll also need the following software installed in advance os any desktop linux version or macos or windows 10 browser use one of the latest browsers such as firefox chrome safari edge or ie11 for example | microsoft-azure azure azure-functions serverless chaos-monkey application-insights durable-functions microservices-architecture azure-storage azure-resource-manager azure-active-directory cosmosdb | os |
esg-nlp | analysing esg report using natural language processing summary environment social and corporate governance esg refers to the three central factors in measuring the sustainability and societal impact of an investment in a company or business these criteria help to better determine the future financial performance of companies return and risk this analysis extracts text from a esg report in pdf format from the internet performs nlp on these information summaries the key esg initiatives with word clouds tdidfs and discovers topics by building a latent dirichlet allocation lda model to keep this exercise as simple as possible only one esg report is being used specifically the citibank s 2019 esg report https www citigroup com citi about esg download 2019 global esg report 2019 pdf ienocache 967 given that esg is a broad topic different companies focus on different aspects of esg depending on their business operations and culture one can potentially ingest more esg reports from different companies across all sectors and industries to capture relevant esg topics this to be attempted in another analysis notebook 1 https github com edgetrader esg nlp blob master notebook esg report analysis ipynb reference 1 a data driven approach to environmental social and governance https databricks com blog 2020 07 10 a data driven approach to environmental social and governance html 2 higher esg ratings are generally positively correlated with valuation and profitability while negatively correlated with volatility https corpgov law harvard edu 2020 01 14 esg matters 3 topic modeling with gensim python https www machinelearningplus com nlp topic modeling gensim python 4 citibank s 2019 esg report https www citigroup com citi about esg download 2019 global esg report 2019 pdf ienocache 967 5 databricks esg reports https databricks com notebooks esg notebooks 01 esg report html 5 databricks data driven esg score https databricks com notebooks esg notebooks 02 esg scoring html 6 databricks esg market risk https databricks com notebooks esg notebooks 03 esg market html 7 topic modeling and latent dirichlet allocation lda in python https towardsdatascience com topic modeling and latent dirichlet allocation in python 9bf156893c24 8 evaluate topic models latent dirichlet allocation lda https towardsdatascience com evaluate topic model in python latent dirichlet allocation lda 7d57484bb5d0 9 topic modeling visualization how to present the results of lda models https www machinelearningplus com nlp topic modeling visualization how to present results lda models | ai |
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graph-node | graph node build status https github com graphprotocol graph node actions workflows ci yml badge svg https github com graphprotocol graph node actions workflows ci yml query branch 3amaster getting started docs https img shields io badge docs getting started brightgreen svg docs getting started md the graph https thegraph com is a protocol for building decentralized applications dapps quickly on ethereum and ipfs using graphql graph node is an open source rust implementation that event sources the ethereum blockchain to deterministically update a data store that can be queried via the graphql endpoint for detailed instructions and more context check out the getting started guide docs getting started md quick start prerequisites to build and run this project you need to have the following installed on your system rust latest stable how to install rust https www rust lang org en us install html note that rustfmt which is part of the default rust installation is a build time requirement postgresql postgresql downloads https www postgresql org download ipfs installing ipfs https docs ipfs io install profobuf compiler installing protobuf https grpc io docs protoc installation for ethereum network data you can either run your own ethereum node or use an ethereum node provider of your choice minimum hardware requirements to build graph node with cargo 8gb ram are required running a local graph node this is a quick example to show a working graph node it is a subgraph for gravatars https github com graphprotocol example subgraph 1 install ipfs and run ipfs init followed by ipfs daemon 2 install postgresql and run initdb d postgres followed by pg ctl d postgres l logfile start and createdb graph node 3 if using ubuntu you may need to install additional packages sudo apt get install y clang libpq dev libssl dev pkg config 4 in the terminal clone https github com graphprotocol example subgraph and install dependencies and generate types for contract abis yarn yarn codegen 5 in the terminal clone https github com graphprotocol graph node and run cargo build once you have all the dependencies set up you can run the following cargo run p graph node release postgres url postgresql username password localhost 5432 graph node ethereum rpc network name capabilities url ipfs 127 0 0 1 5001 try your os username as username and password for details on setting the connection string check the postgres documentation https www postgresql org docs current libpq connect html libpq connstring graph node uses a few postgres extensions if the postgres user with which you run graph node is a superuser graph node will enable these extensions when it initializes the database if the postgres user is not a superuser you will need to create the extensions manually since only superusers are allowed to do that to create them you need to connect as a superuser which in many installations is the postgres user bash psql q x u superuser graph node eof create extension pg trgm create extension pg stat statements create extension btree gist create extension postgres fdw grant usage on foreign data wrapper postgres fdw to username eof this will also spin up a graphiql interface at http 127 0 0 1 8000 6 with this gravatar example to get the subgraph working locally run yarn create local then you can deploy the subgraph yarn deploy local this will build and deploy the subgraph to the graph node it should start indexing the subgraph immediately command line interface usage graph node flags options ethereum ipc network name file ethereum rpc network name url ethereum ws network name url ipfs host port postgres url url flags debug enable debug logging h help prints help information v version prints version information options admin port port port for the json rpc admin server default 8020 elasticsearch password password password to use for elasticsearch logging env elasticsearch password elasticsearch url url elasticsearch service to write subgraph logs to env elasticsearch url elasticsearch user user user to use for elasticsearch logging env elasticsearch user ethereum ipc network name capabilities file ethereum network name e g mainnet optional comma seperated capabilities eg full archive and an ethereum ipc pipe separated by a ethereum polling interval milliseconds how often to poll the ethereum node for new blocks env ethereum polling interval default 500 ethereum rpc network name capabilities url ethereum network name e g mainnet optional comma seperated capabilities eg full archive and an ethereum rpc url separated by a ethereum ws network name capabilities url ethereum network name e g mainnet optional comma seperated capabilities eg full archive and an ethereum websocket url separated by a node id node id a unique identifier for this node instance should have the same value between consecutive node restarts default default http port port port for the graphql http server default 8000 ipfs host port http address of an ipfs node postgres url url location of the postgres database used for storing entities subgraph name ipfs hash name and ipfs hash of the subgraph manifest ws port port port for the graphql websocket server default 8001 advanced configuration the command line arguments generally are all that is needed to run a graph node instance for advanced uses various aspects of graph node can further be configured through environment variables https github com graphprotocol graph node blob master docs environment variables md very large graph node instances can also split the work of querying and indexing across multiple databases docs config md project layout node a local graph node graph a library providing traits for system components and types for common data core a library providing implementations for core components used by all nodes chain ethereum a library with components for obtaining data from ethereum graphql a graphql implementation with api schema generation introspection and more mock a library providing mock implementations for all system components runtime wasm a library for running wasm data extraction scripts server http a library providing a graphql server over http store postgres a postgres store with a graphql friendly interface and audit logs roadmap in progress feature complete additional testing required feature complete feature status ethereum indexing smart contract events handle chain reorganizations mappings wasm based mappings typescript to wasm toolchain autogenerated typescript types graphql query entities by id query entity collections pagination filtering block based filtering entity relationships subscriptions contributing please check contributing md contributing md for development flow and conventions we use here s a list of good first issues https github com graphprotocol graph node labels good 20first 20issue license copyright copy 2018 2019 graph protocol inc and contributors the graph is dual licensed under the mit license license mit and the apache license version 2 0 license apache unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either expressed or implied see the license for the specific language governing permissions and limitations under the license | graphql graphql-api blockchain protocol ethereum ipfs graphql-server developer-tools | blockchain |
Python-Machine-Learning | python machine learning description a set of machine learing algorithms implemented in python 3 5 please also see my related repository for python data science https github com georgeseif data science python which contains various data science scripts for data analysis and visualisation programs can one of three implementations 1 algorithm is implemented from scratch in python 2 algorithm is implemented using scikit learn 3 algorithm is implemented both ways the included programs are regression linear regression neural network regression decision tree regression classification logistic regression for classification logistic regression for classification with pca naive bayes classification support vector machine classification neural network classification decision tree classification random forest classification clustering k means clustering k nearest neighbor mean shift clustering k mediods clustering dbscan clustering information here are the descriptions of the above machine learning algorithms linear and logistic regression regression is a technique used to model and analyze the relationships between variables and often times how they contribute and are related to producing a particular outcome together beginning with the simple case single variable linear regression is a technique used to model the relationship between a single input independant variable feature variable and an output dependant variable using a linear model i e a line the more general case is multi variable linear regression where a model is created for the relationship between multiple independant input variables feature variables and an output dependant variable the model remains linear in that the output is a linear combination of the input variables there is a third most general case called polynomial regression where the model now becomes a non linear combination of the feature variables this however requires knowledge of how the data relates to the output for all of these cases of the regression the output variable is a real number rather than a class category we can also do logistic regression where instead of predicting a real number we predict the class or group that the input variable represent this can be done by modifying the regression training such that the error is computed as the probability that the current example belongs to a particular class this can be done simply by taking the sigmoid of the regular linear regression result and using a one vs all scheme or simple applying the softmax function regression models can be trained using stochastic gradient descent sgd regression is fast to model and is particularly useful when the relationship to be modelled is not extremely complex if it is complex better off using something like a neural network neural networks a neural network consists of an interconnected group of nodes called neurons the input feature variables from the data are passed to these neurons as a multi variable linear combination where the values multiplied by each feature variable are known as weights a non linearity is then applied to this linear combination which gives the neural network the ability to model complex non linear relationships a neural network can have multiple layers where the output of one layer is passed to the next one in the same way at the output there is generally no non linearity applied neural networks are trained using stochastic gradient descent sgd and the backpropagation algorithm neural networks can be used for either classification or regression in the same way as the linear and logistic regressions description above since neural networks can have many layers with non linearities they are most suitable for modelling complex non linear relationships in which taking the time to train them properly is worthwhile naive bayes naive bayes is a classification technique based on bayes theorem with the assumption that all feature variables are independant thus a naive bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature naive bayes classifier combines 3 terms to compute the probability of a class the class probability in the dataset multiplied by the probability of the example feature variables occuring given the current class divided by the probability of those particular example feature variables occuring in general to compute the probability of particular feature variables occuring there are 3 main optional techniques one can assume that the value of a particular variable is gaussian distributed which can be a common case and thus this method is useful when the variables are real numbers multinomial division is good for feature variables that are categorical as it computes the probability based on histogram bins the final option is to use a bernouli probability model when the data is binary naive bayes is simplistic and easy to use yet can outperform other more complex classification algorithms is has fast computation and thus is well suited for application on large datasets support vector machines support vector machines svms are a learning algorithm mainly used for classification where the learned model is a set of hyperplanes that seperate the data into the respective classes the hyperplanes are selected by learning the parameters that maximize the distance from the support vectors to the hyperplane where the support vectors are the closest vectors to the plane if hyper plane having low margin is selected then there is high chance of miss classification if the data is not linearly seperable in the current dimensional space a kernel function can be used to transform the data into a higher dimensional space where it is seperable via a linear hyperplance svms have the strong advantage that they are highly effective in high dimensional feature space and at modelling complex relationships due to the fact that their kernal functions can be specified specifying a particular kernel function may require knowledge of the data however there are some common default ones that often work well in practice additionally the training of the svms only uses a very small subset of the data i e the support vectors and is thus very memory efficient the disadvantage to svms is there long training time as effective training involves complex quadratic programming algorithms decision trees and random forests beginning with the base case a decision tree is an intuitive model where by one traverses down the branches of the tree and selects the next branch to go down based on a decision at a node the model or tree building aspect of decision tree classification algorithms are composed of 2 main tasks tree induction and tree pruning tree induction is the task of taking a set of pre classified instances as input deciding which attributes are best to split on splitting the dataset and recursing on the resulting split datasets until all training instances are categorized while building the tree the goal is to split on the attributes which create the purest child nodes possible which would keep to a minimum the number of splits that would need to be made in order to classify all instances in our dataset this purity is generally measured by one of a number of different attribute selection measures purity is measured by the concept of information gain which relates to how much would need to be known about a previously unseen instance in order for it to be properly classified in practice this is measured by comparing entropy or the amount of information needed to classify a single instance of a current dataset partition to the amount of information to classify a single instance if the current dataset partition were to be further partitioned on a given attribute because of the nature of training decision trees they can be prone to major overfitting a completed decision tree model can be overly complex contain unnecessary structure and be difficult to interpret tree pruning is the process of removing the unnecessary structure from a decision tree in order to make it more efficient more easily readable for humans and more accurate as well tree pruning is generally done by compressing part of the tree into less strict and rigid decision boundaries into ones that are more smooth and generalize better scikit learn comes with a biult in tool to visualize the full decision tree including the specific decision boundaries set by each node random forests are simply an ensemble of decision trees the input vector is run through multiple decision trees for regression the output value of all the trees is averaged for classification a voting scheme is used to determine the final class clustering clustereing is an unsupervised learning technique which involves grouping the data points based on their intrinsic feature variable similarity a number of clustering algorithms are outlined below k means clustering first randomly select a number of classes groups and their respective center points the center points are vectors of the same length as each data point vector group the data points by computing the distance between the point and each group center then picking the closest one based on the grouped points recompute the group center by taking the mean of all the vectors in the group repeat these steps for a set number of iterations and optionally a set number of randomizations of starting points k mediods clustering this is the same as k means except instead of recomputing the center points using the mean we use the median vector of the group this method is less sensitive to outliers but is much slower for large datasets as sorting is required k nearest neighbor in knn k represents the number of voting points to group a data vector the distance from that vector to all of the vectors in a labelled dataset are computed commonly euclidean distance then the vectors from the labelled dataset with the closest distance to the data vector are chosen for voting the data vector is then classified into the group for which the majority of the k nearest vectors the voting group belong to the advantage of using knn is that it is generally fast and easy to interpret the drawback is that it is generally not as accurate as other clustering methods especially with outliers mean shift clustering consider a set of points in two dimensional space assume a circular window centered at c and having radius r as the kernel mean shift is a hill climbing algorithm which involves shifting this kernel iteratively to a higher density region until convergence every shift is defined by a mean shift vector the mean shift vector always points toward the direction of the maximum increase in the density first a starting point is randomly selected at every iteration the kernel is shifted to the centroid or the mean of the points within it the method of calculating this mean depends on the choice of the kernel in this case if a gaussian kernel is chosen instead of a flat kernel then every point will first be assigned a weight which will decay exponentially as the distance from the kernel s center increases at convergence there will be no direction at which a shift can accommodate more points inside the kernel these candidate points are then filtered in a post processing stage to eliminate near duplicates to form the final set of centroids in contrast to k means clustering there is no need to select the number of clusters as mean shift automatically discovers this then fact that the cluster centers converge towards the points of maximum density is also quite desirable as it is intuitivaley understandable the drawback is that the selection of the window size radius r is non trivial dbscan clustering dbscan is a density based clustered algorithm similar to mean shift it starts with an arbitrary starting point that has not been visited the neighborhood of this point is extracted using all points which are within the distance are neighborhood if there are sufficient number of points within this neighbourhood then the clustering process starts and that point is marked as visited else this point is labeled as noise later this point can become the part of the cluster if a point is found to be a part of the cluster then its neighborhood is also the part of the cluster and the above procedure is repeated for all neighborhood points this is repeated until all points in the cluster is determined a new unvisited point is retrieved and processed leading to the discovery of a further cluster or noise this process continues until all points are marked as visited the advantages of dbscan are that it does not require a pe set number of clusters it identifies outliers as noises and it is able to find arbitrarily size and arbitrarily shaped clusters however it does have the drawback that it has been found to perform worse than other clustering algorithms when using very high dimensional data it also can fail in cases of varying density clusters scikit learn machine learning cheat sheet alt text https github com georgeseif python machine learning blob master ml cheatsheet png helpers in addition the the main algorithm files we have the following set of helper functions in the ml helpers py file 1 train and test data splitting 2 random shuffling of data 3 compute euclidean distance 4 compute mean and variance of features 5 normalize data 6 divide dataset based on feature threshold 7 retrieve a random subset of the data with a random subset of the features 8 compute entropy 9 compute mean squared error 10 sigmoid function 11 derivative of the sigmoid function 12 compute the covariance matrix 13 perform pca dimensionality reduction 14 gaussian function 1d 15 gaussian function 2d requirements 1 python 3 5 2 numpy 3 scipy 4 scikit learn 5 matplotlib installation the above packages can be installed by running the commands listed in the install txt file | python-machine-learning classification python algorithm scikit-learn machine-learning regression neural-network support-vector-machines | ai |
tictacgo | tictacgo csci 352 mobile software development project 4 networking collaborators ryan fuller jonathon qualls taylor aishman audience the audience for this app is anyone want to play tic tac to with their friends from their phone and play and replay it infinetly app functionality the app is a simple tic tac to game to entertain you and your friends usefulness sometimes you just need something to occupy your time and quick and easy to use phone games are a perfect solution to that screenshots screenshot screenshot1 png screenshot screenshot2 png screenshot screenshot3 png screenshot screenshot4 png screenshot screenshot5 png screenshot screenshot6 png | front_end |
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unified2021 | a unified speech enhancement front end for online dereverberation acoustic echo cancellation and source separation yueyue na ziteng wang zhang liu yun li gang qiao biao tian qiang fu machine intelligence technology alibaba group yueyue nyy ziteng wzt yinan lz yl yy songjiang qg tianbiao tb fq153277 alibaba inc com abstract dereverberation dr acoustic echo cancellation aec and blind source separation bss are the three most important submodules in speech enhancement front end in traditional systems the three submodules work independently in a sequential manner each submodule has its own signal model objective function and optimization policy although this architecture has high flexibility the speech enhancement performance is restricted since each submodule s optimum cannot guarantee the entire system s global optimum in this paper a unified signal model is derived to combine dr aec and bss together and the online auxiliary function based independent component vector analysis aux ica iva technique is used to solve the problem the proposed approach has unified objective function and optimization policy the performance improvement is verified by simulated experiments links https mp weixin qq com s wjvdgwhplym3vzwl5ajvdg | dereverberation acoustic-echo-cancellation blind-source-separation independent-component-analysis independent-vector-analysis | front_end |
ICT502-Database-Engineering | ict502 database engineering this repository contains the database engineering end of semester project it uses php as the back end processing language with crud create read update delete functionality to meet the subject s requirements you may also view the erd diagram on the database structure folder development and deployment environment software description sublime text ide mysql relational database phpmyadmin administration ui | server |
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ECE | ece a database for the electrical and computer engineering s robotics club to get this to work you need to create a new database using the eceonequery file in sql after that you go to app config and modify the connection string to match the name and server of your database it should work after that if not i am available to help at ijk22701 gmail com | server |
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amkam-blog | amkam blog project screenshots image https user images githubusercontent com 50941074 161973727 d2c4d404 bf03 4513 b312 f6321bed3516 png image https user images githubusercontent com 50941074 161249966 dd04aa9c eeff 4498 b296 e94cdd124560 png image https user images githubusercontent com 50941074 161249804 51cd1395 58e2 4887 bc6b 67afe96324b0 png image https user images githubusercontent com 50941074 161250215 a66d3499 32a2 4c59 bbd5 dac06e4fe28d png additional description about the project and its features built with ruby on rails how to setup you can simply clone or download this repository https github com amadukamara amkam blog and use your favorite browser or code editor to run this program to open the project after download simply double click the index html file to open this project using vs code for this example or your favorite code editor you can follow the guide below in your cmd or command line navigate to where this project is located then cmd cd amkam blog thereafter run cmd code how to run the app through terminal to run the application through trminal make sure ruby and irb is installed in your computer then follow the guide below in your cmd or command line navigate to where this project is located then cmd cd amkam blog install gems cmd bundle install then initialize the database cmd rails db reset incase its the first time cmd rails db create then cmd rails db migrate cmd rails db test prepare thereafter run cmd rails s to run test cmd bundle exec rspec or mdx rspec spec authors amadu kamara amkam github https github com amadukamara linkedin https www linkedin com in amadu kamara 3b60a25b twitter https twitter com devamkam facebook https www facebook com amadus kamara 7 lilian francis github lilyfrancis https github com lilyfrancis twitter ifnotlily https twitter com ifnotlily linkdin lilianfrancis https www linkedin com in lilianfrancis contributing contributions issues and feature requests are welcome feel free to check the issues page https github com amadukamara amkam blog issues show your support give a if you like this project acknowledgments microverse team for facilitating project requirements and resources license this project is mit mit md licensed | server |
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cloud-mdk-samples | sap mobile development kit samples reuse status https api reuse software badge github com sap samples cloud mdk samples https api reuse software info github com sap samples cloud mdk samples welcome to the mobile development kit mdk samples repository the samples provided in this repository are targeted at intermediate to experienced mdk users and are intended to showcase some of the how to topics that come up in discussions with customers we may also provide more full featured sample applications that demonstrate the capabilities of the mdk some samples may be provided more as self contained demonstrations rather than full fledged applcations to showcase a particular topic some samples may also include a mobile odata service project or cap project that will provide the backend data for use in the samples if you are new to using the mdk we highly recommend visiting the mobile development kit page on the sap developers site https developers sap com topics mobile development kit html and going through the tutorials to get familiar with the basics of mdk before trying to work with the samples in this repository requirements the included resources depend on availability of an sap business technology platform btp account to use the resources you will need the following 1 an sap btp account users who don t have an account can register for a free trial account see get a free account on sap btp trial https www sap com developer tutorials hcp create trial account html 1 some of the resources connect to the sample odata service that is part of sap mobile services cockpit based on espm model enterprise sales procurement model and is available for developers to use during development and testing the sample odata service also lets you evaluate how delta tokens are handled in your test application this service is specific to an sap btp account 1 some of the resources connect to a mobile odata service backend use of these backends will require quota within your sap btp account before attempting to run these backends please ensure you have appropriate quota available 1 the mobile development kit sdk environment users can download the mobile development kit sdk from the sap software downloads center or the latest version is available on the sap developers site https developers sap com trials downloads html search mobile 20development 20kit please be sure to run the mdk dependency installer within the mdk sdk to ensure your environment is setup before trying to create an mdk client using the samples in this repository the resources can also be used as a general code reference and starting point for the mobile development kit please be advised that any downloads are subject to the following these resources are provided without any warranty or support obligations these resources are subject to the full terms of the license agreement see below download and installation clone the repository or download it to your file system please refer to the readme md file supplied with each sample for additional information about how to use it how to obtain support these resources are provided without any warranty or support obligations for any issues you can create a new issue in the issues https github com sap cloud mdk samples issues section of this repository but please remember that these resources are provided without any warranty or support obligations registered users can also log in and submit their question in the sap community by following this link https answers sap com questions ask html primarytagid 73555000100800001081 please select the primary tag mobile development kit client license copyright c 2020 sap se or an sap affiliate company all rights reserved this project is licensed under the apache software license version 2 0 except as noted otherwise in the license licenses apache 2 0 txt file | sample sample-application mobile-development-kit mobile mdk sap-mobile-services sap-btp btp-use-case-factory | front_end |
Embedded-projects | embedded projects this is my embedded systems projects designed by me during my journey of learning embedded systems software | os |
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details 80474443 style guide solidity https mp weixin qq com s dl9q7kga2qramqg05nsyrq contracts solidity https mp weixin qq com s m 4filuz3akcdvbswitcmw 20180520 https github com cdtakumi hiblock learning wiki e5 8c ba e5 9d 97 e9 93 be e8 b5 b0 e5 90 91 e4 bd 95 e6 96 b9 ef bc 8c e6 88 96 e8 ae b8 e4 bb 8e e7 be 8e e5 9b bd e8 af 81 e5 8a b5 e5 8f b2 e5 8f af e4 bb a5 e5 be 97 e5 88 b0 e7 ad 94 e6 a1 88 solidity remix https www jianshu com p 2110ed61d2cc bob smart contract traveller guide smart contract traveller guides md hyperledger fabric https www jianshu com p dbca08046432 cryptozombies solidity cryptozombies solidity md hyperledger fabric composer https www jianshu com p 9ff2cca70981 https blog csdn net lxfgzm article details 80399755 20180513 bob youtube https www youtube com playlist list plnp6du8kobc qzhcbhfwibmhwoxpqkq9p csdn https edu csdn net course detail 8078 hyperledger fabric https www jianshu com p 267ac1f2d67d remix solidity https www bihu com article 374536 linux hyperledger fabric 1 1 https www jianshu com p 749c16a32097 https github com hiblock blockchain learning blob master contracts rst 20180525 dapp web3 js https www jianshu com p 47174718960b https docs google com spreadsheets d 1192tcjgnvehpl470y5y2z3az9so0imzucxbkm5nxvn0 edit usp sharing hiblock hiblock github https github com hiblock hiblock eos http hiblock net topics node16 blockathon https github com hiblock hiblock tree master meetup | blockchain ethereum learning training teaching eos truffle embark solidity hyperledger-fabric hyperledger-fabric-composer solidity-remix | blockchain |
Machine-Learning-for-Cyber-Security | machine learning for cyber security awesom https 3 bp blogspot com ol6mgvgyn3a whvhkxyg6ri aaaaaaaab1s ozsvrkl7glc5i7tr4gluintxvkm2iugsgclcb s1600 machine 2blearning 2bfor 2bcyber 2bsecurity png http kalitut com a curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security table of contents datasets datasets papers papers books books talks talks tutorials tutorials courses courses miscellaneous miscellaneous table of contents datasets samples of security related dats http www secrepo com darpa intrusion detection data sets https www ll mit edu ideval data stratosphere ips data sets https stratosphereips org category dataset html open data sets http csr lanl gov data data capture from national security agency http www westpoint edu crc sitepages datasets aspx the adfa intrusion detection data sets https www unsw adfa edu au australian centre for cyber security cybersecurity adfa ids datasets nsl kdd data sets https github com defcom17 nsl kdd malicious urls data sets https sysnet ucsd edu projects url multi source cyber security events http csr lanl gov data cyber1 malware training sets a machine learning dataset for everyone http marcoramilli blogspot cz 2016 12 malware training sets machine learning html table of contents papers fast lean and accurate modeling password guessability using neural networks https www usenix org conference usenixsecurity16 technical sessions presentation melicher outside the closed world on using machine learning for network intrusion detection http ieeexplore ieee org document 5504793 reload true anomalous payload based network intrusion detection https link springer com chapter 10 1007 978 3 540 30143 1 11 malicious pdf detection using metadata and structural features http dl acm org citation cfm id 2420987 adversarial support vector machine learning https dl acm org citation cfm id 2339697 exploiting machine learning to subvert your spam filter https dl acm org citation cfm id 1387709 1387716 camp content agnostic malware protection http www covert io research papers security camp 20 20content 20agnostic 20malware 20protection pdf notos building a dynamic reputation system for dns http www covert io research papers security notos 20 20building 20a 20dynamic 20reputation 20system 20for 20dns pdf kopis detecting malware domains at the upper dns hierarchy http www covert io research papers security kopis 20 20detecting 20malware 20domains 20at 20the 20upper 20dns 20hierarchy pdf pleiades from throw away traffic to bots detecting the rise of dga based malware http www covert io research papers security from 20throw away 20traffic 20to 20bots 20 20detecting 20the 20rise 20of 20dga based 20malware pdf exposure finding malicious domains using passive dns analysis http www covert io research papers security exposure 20 20finding 20malicious 20domains 20using 20passive 20dns 20analysis pdf polonium tera scale graph mining for malware detection http www covert io research papers security polonium 20 20tera scale 20graph 20mining 20for 20malware 20detection pdf nazca detecting malware distribution in large scale networks http www covert io research papers security nazca 20 20 20detecting 20malware 20distribution 20in 20large scale 20networks pdf payl anomalous payload based network intrusion detection http www covert io research papers security payl 20 20anomalous 20payload based 20network 20intrusion 20detection pdf anagram a content anomaly detector resistant to mimicry attacks http www covert io research papers security anagram 20 20a 20content 20anomaly 20detector 20resistant 20to 20mimicry 20attack pdf applications of machine learning in cyber security https www researchgate net publication 283083699 applications of machine learning in cyber security an investigation of byte n gram features for malware classi cation http www readcube com articles 10 1007 s11416 016 0283 1 author access token y2ftvow3bbixrthyixocg e4rwlqnchnbyi7wbcmay4nw74db1mhzzqdqyj1tm7y kzqnwixrhzc64f6sux0bowkkoy4ro nfzsgos2sw2kg7i6cmzb9g3i0tfgplo rzlh mf7kz2i qxjmai shw 3d 3d table of contents books data mining and machine learning in cybersecurity http amzn to 2iuwdyx machine learning and data mining for computer security http amzn to 2jnchbs network anomaly detection a machine learning perspective http amzn to 2jlpsgm machine learning for hackers case studies and algorithms to get you started http amzn to 2jybzpo table of contents talks using machine learning to support information security https www youtube com watch v tukidi5vubs defending networks with incomplete information https www youtube com watch v 36it9vggr0g applying machine learning to network security monitoring https www youtube com watch v vy jpfpm1au measuring the iq of your threat intelligence feeds https www youtube com watch v yg6qlhoawie data driven threat intelligence metrics on indicator dissemination and sharing https www youtube com watch v 6jmeknes w0 applied machine learning for data exfil and other fun topics https www youtube com watch v dgwh7m4n8de secure because math a deep dive on ml based monitoring https www youtube com watch v tyvcvzejhhq machine duping 101 pwning deep learning systems https www youtube com watch v jagdpjffm2a delta zero kingphish3r weaponizing data science for social engineering https www youtube com watch v l7u0pdcsklg defeating machine learning what your security vendor is not telling you https www youtube com watch v oius1dyfnd8 crowdsource crowd trained machine learning model for malware capability det https www youtube com watch v u6a7afsd39a defeating machine learning systemic deficiencies for detecting malware https www youtube com watch v sptbdujjhbk packet capture village theodora titonis how machine learning finds malware https www youtube com watch v 2cqrspfsy s build an antivirus in 5 min fresh machine learning 7 a fun video to watch https www youtube com watch v ilnhvwsu9ea t 245s hunting for malware with machine learning https www youtube com watch v zt 4zdtvr30 machine learning for threat detection https www youtube com watch v qvwktoa f34 machine learning and the cloud disrupting threat detection and prevention https www youtube com watch v frklx97igiw fraud detection using machine learning deep learning https www youtube com watch v ghtn4ju69w0 the applications of deep learning on traffic identification https www youtube com watch v b7okgc3ajvm defending networks with incomplete information a machine learning approach https www youtube com watch v 0crsf6ypb4 machine learning data science https vimeo com 112702666 table of contents tutorials click security data hacking project http clicksecurity github io data hacking using neural networks to generate human readable passwords http fsecurify com using neural networks to generate human readable passwords machine learning based password strength classification http fsecurify com machine learning based password strength checking using machine learning to detect malicious urls http fsecurify com using machine learning detect malicious urls big data and data science for security and fraud detection http www kdnuggets com 2015 12 big data science security fraud detection html using deep learning to break a captcha system https deepmlblog wordpress com 2016 01 03 how to break a captcha system data mining for network security and intrusion detection https www r bloggers com data mining for network security and intrusion detection an introduction to machine learning for cybersecurity and threat hunting http blog sqrrl com an introduction to machine learning for cybersecurity and threat hunting table of contents courses data mining for cyber security by stanford https web stanford edu class cs259d table of contents miscellaneous system predicts 85 percent of cyber attacks using input from human experts https news mit edu 2016 ai system predicts 85 percent cyber attacks using input human experts 0418 a list of open source projects in cyber security using machine learning http www mlsecproject org open source projects please have a look at best hacking books http www kalitut com 2016 12 best ethical hacking books html best reverse engineering books http www kalitut com 2017 01 best reverse engineering books html best machine learning books http www kalitut com 2017 01 machine learning book html best 5 books programming books http www kalitut com 2017 01 top programming books html best java books http www kalitut com 2017 01 best java programming books html | ai |
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van-turbo-repo | turborepo starter this is an official starter turborepo using this example run the following command sh npx create turbo latest what s inside this turborepo includes the following packages apps apps and packages docs a next js https nextjs org app web another next js https nextjs org app ui a stub react component library shared by both web and docs applications eslint config custom eslint configurations includes eslint config next and eslint config prettier tsconfig tsconfig json s used throughout the monorepo each package app is 100 typescript https www typescriptlang org utilities this turborepo has some additional tools already setup for you typescript https www typescriptlang org for static type checking eslint https eslint org for code linting prettier https prettier io for code formatting build to build all apps and packages run the following command cd my turborepo pnpm build develop to develop all apps and packages run the following command cd my turborepo pnpm dev remote caching turborepo can use a technique known as remote caching https turbo build repo docs core concepts remote caching to share cache artifacts across machines enabling you to share build caches with your team and ci cd pipelines by default turborepo will cache locally to enable remote caching you will need an account with vercel if you don t have an account you can create one https vercel com signup then enter the following commands cd my turborepo npx turbo login this will authenticate the turborepo cli with your vercel account https vercel com docs concepts personal accounts overview next you can link your turborepo to your remote cache by running the following command from the root of your turborepo npx turbo link useful links learn more about the power of turborepo tasks https turbo build repo docs core concepts monorepos running tasks caching https turbo build repo docs core concepts caching remote caching https turbo build repo docs core concepts remote caching filtering https turbo build repo docs core concepts monorepos filtering configuration options https turbo build repo docs reference configuration cli usage https turbo build repo docs reference command line reference | os |
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DigitalAndEmbeddedSystems | digital and embedded systems vhdl projects done in xilinx ise design suite during digital and embedded systems course uk ady cyfrowe i systemy wbudowane 1 at the university authors wojciech ormaniec github themesoria bartosz rodziewicz i tried my best to describe the task description for each labs every solution is widely described poland in the report which is in reports labx there are missing reports for last two labs as we didn t have to do them the code was written for zl 9572 and the two last labs for spartan3e more info can be found here http www zsk ict pwr wroc pl zsk dyd intinz uc poland github themesoria https github com themesoria | university embedded-systems collection vhdl xilinx-ise | os |
deis-labs | deis labs raspberry default ip 192 168 1 11 labs in the course design of embedded intelligent systems | os |
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selfcheckgpt | selfcheckgpt project page for our paper selfcheckgpt zero resource black box hallucination detection for generative large language models https arxiv org abs 2303 08896 we investigated several variants of the selfcheck approach bertscore question answering n gram nli and llm prompting 18 07 2023 selfcheck nli is added additional experiments show that using an entailment classifier e g deberta v3 fine tuned on mnli performs well selfcheck nli method requires considerably less computation than selfcheck prompt 11 08 2023 slides from ml collective talk link to slides https drive google com file d 13lubpum4y1nlkigzxxhn7cl2lw5kugbc view 11 10 2023 the paper is accepted and to appear at emnlp 2023 demo selfcheck qa prompt png code package installation pip install selfcheckgpt selfcheckgpt usage bertscore qa n gram there are three variants of selfcheck scores in this package as described in the paper selfcheckbertscore selfcheckmqag selfcheckngram all of the variants have predict which will output the sentence level scores w r t sampled passages you can use packages such as spacy to split passage into sentences for reproducibility you can set torch manual seed before calling this function see more details in jupyter notebook demo selfcheck demo1 ipynb demo selfcheck demo1 ipynb python include necessary packages torch spacy from selfcheckgpt modeling selfcheck import selfcheckmqag selfcheckbertscore selfcheckngram device torch device cuda if torch cuda is available else cpu selfcheck mqag selfcheckmqag device device set device to cuda if gpu is available selfcheck bertscore selfcheckbertscore rescale with baseline true selfcheck ngram selfcheckngram n 1 n 1 means unigram n 2 means bigram etc llm s text e g gpt 3 response to be evaluated at the sentence level split it into sentences passage michael alan weiner born march 31 1942 is an american radio host he is the host of the savage nation sentences sent text strip for sent in nlp passage sents spacy sentence tokenization print sentences michael alan weiner born march 31 1942 is an american radio host he is the host of the savage nation other samples generated by the same llm to perform self check for consistency sample1 michael alan weiner born march 31 1942 is an american radio host he is the host of the savage country sample2 michael alan weiner born january 13 1960 is a canadian radio host he works at the new york times sample3 michael alan weiner born march 31 1942 is an american radio host he obtained his phd from mit selfcheck mqag score for each sentence where value is in 0 0 1 0 and high value means non factual additional params for each scoring method counting at answerability threshold i e questions with answerability score at are rejected bayes at beta1 beta2 bayes with alpha beta1 beta2 sent scores mqag selfcheck mqag predict sentences sentences list of sentences passage passage passage before sentence split sampled passages sample1 sample2 sample3 list of sampled passages num questions per sent 5 number of questions to be drawn scoring method bayes with alpha options counting bayes bayes with alpha beta1 0 8 beta2 0 8 additional params depending on scoring method print sent scores mqag 0 30990949 0 42376232 selfcheck bertscore score for each sentence where value is in 0 0 1 0 and high value means non factual sent scores bertscore selfcheck bertscore predict sentences sentences list of sentences sampled passages sample1 sample2 sample3 list of sampled passages print sent scores bertscore 0 0695562 0 45590915 selfcheck ngram score at sentence and document level where value is in 0 0 inf and high value means non factual as opposed to selfcheck mqag and selfcheck bertscore selfcheck ngram s score is not bounded sent scores ngram selfcheck ngram predict sentences sentences passage passage sampled passages sample1 sample2 sample3 print sent scores ngram sent level sentence level score similar to mqag and bertscore variant avg neg logprob 3 184312 3 279774 max neg logprob 3 476098 4 574710 doc level document level score such that avg neg logprob is computed over all tokens avg neg logprob 3 218678904916201 avg max neg logprob 4 025404834169327 selfcheckgpt usage nli entailment or contradiction score with input being the sentence and a sampled passage can be used as the selfcheck score we use deberta v3 large fine tuned to multi nli and we normalize the probability of entailment or contradiction classes and take prob contradiction as the score python from selfcheckgpt modeling selfcheck import selfchecknli device torch device cuda if torch cuda is available else cpu selfcheck nli selfchecknli device device set device to cuda if gpu is available sent scores nli selfcheck nli predict sentences sentences list of sentences sampled passages sample1 sample2 sample3 list of sampled passages print sent scores nli 0 334014 0 975106 based on the example above selfcheckgpt usage llm prompt in addition we ve tried using llms to assess information consistency in a zero shot setup we query a llm to assess whether the i th sentence is supported by the sample as the context using the following prompt context sentence is the sentence supported by the context above answer yes or no initial investigation showed that gpt 3 text davinci 003 will output either yes or no 98 of the time while any remaining outputs can be set to n a the output is converted to score yes 0 0 no 1 0 n a 0 5 the inconsistency score is then calculated by averaging dataset the wiki bio gpt3 hallucination dataset currently consists of 238 annotated passages v3 you can find more information in the paper or our data card on huggingface https huggingface co datasets potsawee wiki bio gpt3 hallucination to use this dataset you can either load it through huggingface dataset api or download it directly from below in the json format update we ve annotated gpt 3 wikibio passages further and now the dataset consists of 238 annotated passages here is the link https drive google com file d 1n3 zqmr9ybbsop2jcpgiea9oiniu78xw view usp sharing for the ids of the first 65 passages in the v1 option1 huggingface python from datasets import load dataset dataset load dataset potsawee wiki bio gpt3 hallucination option2 manual download download from our google drive https drive google com file d 1ayq7u9nylzguzlm5jbdx6cffwb esnv view usp share link then you can load it in python python import json with open dataset json r as f content f read dataset json loads content each instance consists of gpt3 text gpt 3 generated passage wiki bio text actual wikipedia passage first paragraph gpt3 sentences gpt3 text split into sentences using spacy annotation human annotation at the sentence level wiki bio test idx id of the concept individual from the original wikibio dataset testset gpt3 text samples list of sampled passages do sample true temperature 1 0 experiments probability based baselines e g gpt 3 s probabilities as described in our paper probabities and generation entropies of the generative llm can be used to measure its confidence check our example implementation of this approach in demo experiments probability based baselines ipynb demo experiments probability based baselines ipynb experimental results full details can be found in our paper note that our new results show that llms such as gpt 3 text davinci 003 or chatgpt gpt 3 5 turbo are good at text inconsistency assessment based on this finding we try selfcheckgpt prompt where each sentence to be evaluated is compared against each and every sampled passage by prompting chatgpt selfcheckgpt prompt is the best performing method results on the wiki bio gpt3 hallucination dataset method nonfact auc pr factual auc pr ranking pcc random guessing 72 96 27 04 gpt 3 avg logp 83 21 53 97 57 04 selfcheck bertscore 81 96 44 23 58 18 selfcheck qa 84 26 48 14 61 07 selfcheck unigram 85 63 58 47 64 71 selfcheck nli 92 50 66 08 74 14 selfcheck prompt 93 42 67 09 78 32 miscellaneous mqag multiple choice question answering and generation https arxiv org abs 2301 12307 was proposed in our previous work our mqag implementation is included in this package which can be used to 1 generate multiple choice questions 2 answer multiple choice questions 3 obtain mqag score mqag usage python from selfcheckgpt modeling mqag import mqag mqag model mqag it has three main functions generate answer score we show an example usage in demo mqag demo1 ipynb demo mqag demo1 ipynb acknowledgements this work is supported by cambridge university press assessment cup a a department of the chancellor masters and scholars of the university of cambridge and the cambridge commonwealth european international trust citation misc manakul2023selfcheckgpt title selfcheckgpt zero resource black box hallucination detection for generative large language models author potsawee manakul and adian liusie and mark j f gales year 2023 eprint 2303 08896 archiveprefix arxiv primaryclass cs cl | ai |
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Data_Engineering_Pipeline_on_the_Cloud | data engineering pipeline on the cloud this is a link to my article on medium on data engineering automation of a data pipeline in the clouds content web scraping api using data storing in the clouds automating the data pipeline https medium com natalia sudarchikova automation of a data pipeline in the clouds dd662c5fa0d8 | cloud |
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recursive-bf | recursive bilateral filtering developed by qingxiong yang is pretty fast compared with most edge preserving filtering methods computational complexity is linear in both input size and dimensionality takes about 43 ms to process a one megapixel color image i7 1 8ghz 4gb mem about 18x faster than fast high dimensional filtering using the permutohedral lattice about 86x faster than gaussian kd trees for fast high dimensional filtering results table tr td img src https cloud githubusercontent com assets 2270240 26041579 7d7c034e 3960 11e7 9549 912685043e39 jpg width 300px br p align center original image p td td img src https cloud githubusercontent com assets 2270240 26041586 8b4afb42 3960 11e7 9bd8 62bbb924f1e9 jpg width 300px br p align center opencv s bf 896ms p td td img src https cloud githubusercontent com assets 2270240 26041590 8d08c16c 3960 11e7 8a0c 95a77d6d9085 jpg width 300px br p align center recursivebf 18ms p td tr tr td td td img src https cloud githubusercontent com assets 2270240 26041583 86ea7b22 3960 11e7 8ded 5109b76966ca jpg width 300px br p align center gaussian blur p td td img src https cloud githubusercontent com assets 2270240 26041584 88dfc9b4 3960 11e7 8c9d 2634eac098d0 jpg width 300px br p align center median blur p td tr table for more details of the algorithm please refer to the original paper inproceedings yang2012recursive title recursive bilateral filtering author yang qingxiong booktitle european conference on computer vision pages 399 413 year 2012 organization springer optionally you can cite this repo misc ming2017recursive author ming yang title a lightweight c library for recursive bilateral filtering year 2017 publisher github journal github repository howpublished url https github com ufoym recursivebf | computer-vision image-processing bilateral-filter | ai |
eps | eps machine learning for ruby build predictive models quickly and easily serve models built in ruby python r and more check out this post https ankane org rails meet data science for more info on machine learning with rails build status https github com ankane eps workflows build badge svg branch master https github com ankane eps actions installation add this line to your application s gemfile ruby gem eps on mac also install openmp sh brew install libomp getting started create a model ruby data bedrooms 1 bathrooms 1 price 100000 bedrooms 2 bathrooms 1 price 125000 bedrooms 2 bathrooms 2 price 135000 bedrooms 3 bathrooms 2 price 162000 model eps model new data target price puts model summary make a prediction ruby model predict bedrooms 2 bathrooms 1 store the model ruby file write model pmml model to pmml load the model ruby pmml file read model pmml model eps model load pmml pmml a few notes the target can be numeric regression or categorical classification pass an array of hashes to predict to make multiple predictions at once models are stored in pmml https en wikipedia org wiki predictive model markup language a standard for model storage building models goal often the goal of building a model is to make good predictions on future data to help achieve this eps splits the data into training and validation sets if you have 30 data points it uses the training set to build the model and the validation set to evaluate the performance if your data has a time associated with it it s highly recommended to use that field for the split ruby eps model new data target price split listed at otherwise the split is random there are a number of other options validation options as well performance is reported in the summary for regression it reports validation rmse root mean squared error lower is better for classification it reports validation accuracy higher is better typically the best way to improve performance is feature engineering feature engineering features are extremely important for model performance features can be 1 numeric 2 categorical 3 text numeric for numeric features use any numeric type ruby bedrooms 4 bathrooms 2 5 categorical for categorical features use strings or booleans ruby state ca basement true convert any ids to strings so they re treated as categorical features ruby city id city id to s for dates create features like day of week and month ruby weekday sold on strftime a month sold on strftime b for times create features like day of week and hour of day ruby weekday listed at strftime a hour listed at hour to s text for text features use strings with multiple words ruby description a beautiful house on top of a hill this creates features based on word count https en wikipedia org wiki bag of words model you can specify text features explicitly with ruby eps model new data target price text features description you can set advanced options with ruby text features description min occurences 5 min times a word must appear to be included in the model max features 1000 max number of words to include in the model min length 1 min length of words to be included case sensitive true how to treat words with different case tokenizer s how to tokenize the text defaults to whitespace stop words and the words to exclude from the model full example we recommend putting all the model code in a single file this makes it easy to rebuild the model as needed in rails we recommend creating a app ml models directory be sure to restart spring after creating the directory so files are autoloaded sh bin spring stop here s what a complete model in app ml models price model rb may look like ruby class pricemodel eps base def build houses house all train data houses map v features v model eps model new data target price split listed at puts model summary save to file file write model file model to pmml ensure reloads from file model nil end def predict house model predict features house end private def features house bedrooms house bedrooms city id house city id to s month house listed at strftime b listed at house listed at price house price end def model model eps model load pmml file read model file end def model file file join dir price model pmml end end build the model with ruby pricemodel build this saves the model to price model pmml check this into source control or use a tool like trove https github com ankane trove to store it predict with ruby pricemodel predict house monitoring we recommend monitoring how well your models perform over time to do this save your predictions to the database then compare them with ruby actual houses map price predicted houses map predicted price eps metrics actual predicted for rmse and mae alert if they rise above a certain threshold for me alert if it moves too far away from 0 for accuracy alert if it drops below a certain threshold other languages eps makes it easy to serve models from other languages you can build models in python r and others and serve them in ruby without having to worry about how to deploy or run another language eps can serve lightgbm linear regression and naive bayes models check out onnx runtime https github com ankane onnxruntime and scoruby https github com asafschers scoruby to serve other models python to create a model in python install the sklearn2pmml https github com jpmml sklearn2pmml package sh pip install sklearn2pmml and check out the examples lightgbm regression test support python lightgbm regression py lightgbm classification test support python lightgbm classification py linear regression test support python linear regression py naive bayes test support python naive bayes py r to create a model in r install the pmml https cran r project org package pmml package r install packages pmml and check out the examples linear regression test support r linear regression r naive bayes test support r naive bayes r verifying it s important for features to be implemented consistently when serving models created in other languages we highly recommend verifying this programmatically create a csv file with ids and predictions from the original model house id prediction 1 145000 2 123000 3 250000 once the model is implemented in ruby confirm the predictions match ruby model eps model load pmml model pmml preload houses to prevent n 1 houses house all index by id csv foreach predictions csv headers true converters numeric do row house houses row house id expected row prediction actual model predict bedrooms house bedrooms bathrooms house bathrooms success actual is a string actual expected actual expected abs 0 001 raise bad prediction for house house id exp expected act actual unless success putc end data a number of data formats are supported you can pass the target variable separately ruby x x 1 x 2 x 3 y 1 2 3 eps model new x y data can be an array of arrays ruby x 1 2 2 0 3 1 y 1 2 3 eps model new x y or numo arrays ruby x numo narray cast 1 2 2 0 3 1 y numo narray cast 1 2 3 eps model new x y or a rover data frame ruby df rover read csv houses csv eps model new df target price or a daru data frame ruby df daru dataframe from csv houses csv eps model new df target price when reading csv files directly be sure to convert numeric fields the table method does this automatically ruby csv table data csv map row row to h algorithms pass an algorithm with ruby eps model new data algorithm linear regression eps supports lightgbm default linear regression naive bayes lightgbm pass the learning rate with ruby eps model new data learning rate 0 01 linear regression by default an intercept is included disable this with ruby eps model new data intercept false to speed up training on large datasets with linear regression install gsl https github com ankane gslr gsl installation with homebrew you can use sh brew install gsl then add this line to your application s gemfile ruby gem gslr group development it only needs to be available in environments used to build the model probability to get the probability of each category for predictions with classification use ruby model predict probability data naive bayes is known to produce poor probability estimates so stick with lightgbm if you need this validation options pass your own validation set with ruby eps model new data validation set validation set split on a specific value ruby eps model new data split column listed at value date parse 2019 01 01 specify the validation set size the default is 0 25 which is 25 ruby eps model new data split validation size 0 2 disable the validation set completely with ruby eps model new data split false database storage the database is another place you can store models it s good if you retrain models automatically we recommend adding monitoring and guardrails as well if you retrain automatically create an active record model to store the predictive model sh rails generate model model key string uniq data text store the model with ruby store model where key price first or initialize store update data model to pmml load the model with ruby data model find by key price data model eps model load pmml data jupyter iruby you can use iruby https github com sciruby iruby to run eps in jupyter https jupyter org notebooks here s how to get iruby working with rails https ankane org jupyter rails weights specify a weight for each data point ruby eps model new data weight weight you can also pass an array ruby eps model new data weight 1 2 3 weights are supported for metrics as well ruby eps metrics actual predicted weight weight reweighing is one method to mitigate bias http aif360 mybluemix net in training data upgrading 0 3 0 eps 0 3 0 brings a number of improvements including support for lightgbm and cross validation there are a number of breaking changes to be aware of lightgbm is now the default for new models on mac run sh brew install libomp pass the algorithm option to use linear regression or naive bayes ruby eps model new data algorithm linear regression or naive bayes cross validation happens automatically by default you no longer need to create training and test sets manually if you were splitting on a time use ruby eps model new data split column listed at value date parse 2019 01 01 or randomly use ruby eps model new data split validation size 0 3 to continue splitting manually use ruby eps model new data validation set test set it s no longer possible to load models in json or pfa formats retrain models and save them as pmml 0 2 0 eps 0 2 0 brings a number of improvements including support for classification we recommend 1 changing eps regressor to eps model 2 converting models from json to pmml ruby model eps model load json model json file write model pmml model to pmml 3 renaming app stats models to app ml models history view the changelog https github com ankane eps blob master changelog md contributing everyone is encouraged to help improve this project here are a few ways you can help report bugs https github com ankane eps issues fix bugs and submit pull requests https github com ankane eps pulls write clarify or fix documentation suggest or add new features to get started with development sh git clone https github com ankane eps git cd eps bundle install bundle exec rake test | machine-learning rubyml automl | ai |
mlbook | machine learning from scratch welcome to the repo for my free online book machine learning from scratch the book itself can be found here https dafriedman97 github io mlbook content introduction html a somewhat ugly version of the pdf can be found in the book pdf file above in the master branch note that jupyterbook is currently experimenting https jupyterbook org advanced pdf html with the pdf creation for suggested changes to the book please create pull requests to the gh pages branch any comments or questions are much appreciated either here or via email at dafrdman gmail com thanks so much | ai |
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ICS_IoT_Shodan_Dorks | ics and iot shodan dork collection who austrian energy cert aec is the computer emergency response team cert for the austrian energy industry for more information please see https www energy cert at en what is a dork usually the term is used in the context of web search engine queries using advanced search operators to find information in our case it refers to search terms and operators for searching shodan io simply said you can paste the dork into the search field of the shodan web interface and should be able to get results motivation why did we publish this dork list first of all this information is already public services connected to the internet are already sending out their banner to anybody how asks for them there are also already publicly available presentations out there which contains lists of dorks to search for and find ics and or iot devices what was missing so far at least to our knowledge is a central place to find quality assured dorks in an easy to use format we are trying to fill the gap with this project a community can move faster together we hope to find a few people who are willing to help in improving this list by improving we mean correct errors for example in vendor attribution make it more complete by adding dorks and additional information or find and improve inefficient error prone dorks disclaimer the list is not perfect there is no guarantee that a dork only returns the searched for ics or iot devices use the search results with care and make your own validation if you find errors or inconsistencies in the list please remember that we are happy to accept help and input to make this list better format it is a standard csv file delimiter following this structure column description vendor if the vendor is known and the attribution is somewhat stable the vendor name is listed in this column product if the product is known and the attribution is somewhat stable the product is listed in this column comment any generic comment which might be helpful dorks shodan search term also called dork you can paste the content of this column into the search field of the shodan web interface in case you want to script the searches or use them with the command line interface of shodan you are on your own when it comes to escaping quotation and so on satisfying all possible scripts interpreters and tools is beyond the scope of this project explanation of generic vendor or product if an attribution to a specific vendor or product is not reasonable generic is set an example for a not reasonable attribution to a vendor is the dork tag ics this tag is assigned by shodan if there are enough indicators to shodan that the responding service has to be categorized as in industrial control system this happens fully unrelated to a specific vendor producing the gear software behind the service | server |
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IOS-Project-IG4 | ios project ig4 projet ig4 ios licette aur lien et maachi reda | os |
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gcp_data_engineer | gcp data engineer all resources used in the data engineering with google cloud professional certificate | cloud |
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edgeEngine-MC-TI-Linux-TI-RTOS | edgeengine mc ti linux ti rtos edgeengine for ti linux platform installation guide 1 download latest release here https github com edgeengine edgeengine mc ti linux ti rtos releases 2 create new directory 3 move package to newly created directory 4 open terminal and navigate to the newly created directory that now has the downloaded tar file 5 untar package ex tar xvf edgeengine ti linux v3 0 0 tar 6 run start script to start edgeengine start sh 7 please visit https developer mimik com and create your account and get more information note a directory may be made after untaring navigate into that directory to find start sh script do not close terminal window where edgeengine is running closing this window will terminate edgeengine process to stop edgeengine simply close or use keyboard shortcut ctrl c in terminal window where edgeengine is running edgechild ti rtos edgechild for ti rtos platform currently it is based on vision apps prepare environment compile and running it please read the ti document https software dl ti com jacinto7 esd processor sdk rtos jacinto7 latest exports docs vision apps docs user guide build and run html building instructions edgechild ti rtos is provided as a static library it should be linked into the tda4vm mcu2 0 rtos build and call the mimik main c function to start the edgechild 1 download latest release here https github com edgeengine edgeengine mc ti linux ti rtos releases 2 untar package ex tar xvf edgechild ti rtos v1 0 0 tar 3 replace the folder vision apps platform j721e rtos mcu2 0 with mcu2 0 in this repository set up environment variables for ti psdk rtos path and psdk linux path as the following shell export psdkr path home user1 jacinto ti processor sdk rtos j721e evm 08 02 00 05 export psdkl path home user1 jacinto ti processor sdk linux j7 evm 08 02 00 03 4 at vision apps directory build vision apps including the edge runtime for rtos mcu2 0 as the following shell make vision apps j3 install the vision apps with the edge runtime for rtos insert the prepared vision apps sdcard on the linux machine at vision apps directory install the vision apps with the edge runtime for rtos as the following shell make linux fs install sd run the vision apps with the edge runtime for rtos plugin the sdcard to the ti jacinto 7 j721e board and power up the board the bootloader will load the mcu image and run it the edge runtime is also running to see the running log login the the j721e linux as the following shell ssh root 10 0 0 96 e g the j721e ip is 10 0 0 96 to see the all the logs as the following shell cd opt vision apps vision apps init sh it shows all the cpu mcu and dsp processors running logs | os |
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Employee-Database- | employee database background it s a beautiful spring day and it s been two weeks since you were hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remains of the database of employees from that period are six csv files you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform data modeling data engineering and data analysis instructions this is divided into three parts data modeling data engineering and data analysis data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the provided information to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys verify that the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes two primary keys to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table hint to avoid errors be sure to import the data in the same order that the tables were created also remember to account for the headers when importing references mockaroo llc 2021 realistic data generator https www mockaroo com https www mockaroo com 2022 trilogy education services a 2u inc brand all rights reserved | server |
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lx | lx license extension originally a new command for npm now a standalone module using npm technology for determining license 5 tuples of license related information per node module package version license type e g mit required notice e g copyright c 2014 ibm repository optionally a sixth field license text if lx cannot find a license text under node modules it attempts to find a license using scrapers for github bitbucket or google code making requests in parallel to do so duplicate name versions are ignored a simple command line client lx cli is provided to scan node module directories and store the results as json lxwebgui is a webapp provided to scan store scan results as json edit 5 tuples where necessary and save the edited results examples command line bin lx cli js prefix usr local lib tmp lx usrlocallib json lxwebgui cd node modules lxwebgui node index js open localhost 8888 in firefox or google chrome alternatively node modules lxwebgui bin boot sh or boot bat may be used to start the lxwebguiapp on http localhost 8888 programmatic access lx also exports its scanning functionality for use in other projects lx scan type path options callback arguments scan type is a string representing the project type you want to scan ex node path is a string indicating the path to either a root level project directory or a folder containing a node modules folder to be scanned the callback is called as callback error license information license information is an array of javascript objects representing identified packages and the licensing information gathered about them the attributes for those objects are name the package s name version the installed version of the package label the name and version joined by name version repository if a git url corresponding to the package s git repository is found during the scan that url is stored here otherwise the url is the same as the package s home page homepage a url pointing to the package s home page for example its github page licensefile an array of objects containing information about the licenses the package is distributed under each of these license objects potentially has a licensepath attribute which identifies either the local file path or remote url to the license s text notice which is an extracted legal notice corresponding to the license and text which is the full text of the license license this is an object with information about any permissive license that the package is distributed under it has two attributes type which is a quick identifier ex mit and url which is a web url to the license text options is an object which can contain a variety of parameters as attributes noremote if boolean noremote is true then the scanner will not attempt to obtain licensing information from the internet diff objects if you want to perform a diff scan against a group of other license objects those objects can be passed as this attribute js var options diff objects name abbrev label abbrev 1 0 5 version 1 0 5 licensefile licensepath home user node modules lx node modules abbrev license notice copyright 2009 2010 2011 isaac z schlueter text snip license type mit url https github com isaacs abbrev js raw master license repository http github com isaacs abbrev js homepage https github com isaacs abbrev js license objects will not include abbrev 1 0 5 lx node home user node modules lx options function error license objects example js var lx require lx scan scans lx gathers licensing information and then prints out the array of package information lx node home user documents node modules lx function error license objects console log license objects python scanning experimental lx experimentally supports scanning python 2 6 packages and install bases lx python path to python binary options callback the python scanner searches packages installed using the python executable indicated at path to python binary for a package matching the name options package name if a binary isn t indicated your global install base will be used if a package name isn t indicated information will be collected about the entire install base callback is called as callback error license information where license information is an array of objects that contain licensing information about the scanned packages js scans the entirety of the global install base and prints out licensing information lx python function error license objects console log license objects python scanning requires both python and setuptools to be installed it obtains licensing information about the package and then does the same for all packages that would be listed as required by the pip show package name command this is done recursively until no more required packages are found and then the result is outputted for the package eazytext pip show returns pip show eazytext name eazytext version 0 94 location home pedwards newpip lib python2 6 site packages requires ply pygments paste zope interface zope component lx s python scanner will collect licensing information about eazytext and then perform the same process for all of ply pygments etc performance lenovo w520 laptop with 8gb memory uname a linux 2 6 32 431 30 1 el6 x86 64 1 smp wed jul 30 14 44 26 edt 2014 x86 64 x86 64 x86 64 gnu linux npm install g phonegap 534 node modules counting duplicates installed in usr local lib node modules time bin lx cli js prefix local lib dev null real 0m6 829s user 0m4 027s sys 0m0 590s contributors see authors license and copyright copyright c 2014 international business machines corporation | server |
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lecture-security_engineering | security engineering lecture slides and some program sources of a lecture security engineering at university of hyogo licence sample codes are published under mit licence mit slides their tex source codes images are published under cc by nc sa 4 0 https creativecommons org licenses by nc sa 4 0 legalcode cc by nc sa 4 0 https creativecommons org licenses by nc sa 4 0 legalcode contents standardized cryptographic techniques fido2 webauthn hybrid public key encryption in ietf past years content are contained in 202x branches as archives 202x | lecture-slides security lecture university-course | server |
KaggleBengaliAIHandwrittenGraphemeClassification | kaggle bengali ai handwritten grapheme classification in this repository you can find some of the code i use for the kaggle bengali ai competition kernel in the folder kagglekernelefficientnetb3 you can find the part of the code i used to train the models used in my inference kernel https www kaggle com rsmits keras efficientnet b3 training inference as posted on kaggle the model scored 0 9703 on the public board and 0 9182 on the private board to be able to train the model you need to first download the dataset as available for the kaggle competition https www kaggle com c bengaliai cv19 the model consists of an efficientnet b3 pre trained on imagenet model with a generalized mean pool and custom head layer for image preprocessing i just invert normalize and scale the image nothing else no form of augmentation is used the code should be able to run on any python 3 6 3 7 environment major packages used were tensorflow 2 1 0 keras 2 3 1 efficientnet 1 0 0 opencv python iterative stratification 0 1 6 i ve trained the model for 80 epochs and picked some model weight files todo an ensemble in the inference kernel i first tested the training part by using 5 6th of the training data for training and 1 6th for validation based on the validation and some leaderboard submissions i found that the highest scoring epochs were between epoch 60 70 in the final training as is used in this code i use a different distribution of the training data for every epoch the downside of this is that validation doesn t tell you everything anymore the major benefit is however that it increases the score about 0 005 to 0 008 compared to the use of the fixed training set this way it get close to what a cross validation ensemble would do just without the training needed for that the model weight files as used in the inference kernel are available in folder kagglekernelefficientnetb3 model weights it contains the following 6 files for each file mentioned the lb score when used as a single file to generate the submission train1 model 57 h5 public lb 0 9668 private lb 0 9132 train1 model 59 h5 public lb 0 9681 private lb 0 9151 train1 model 64 h5 public lb 0 9679 private lb 0 9167 train1 model 66 h5 public lb 0 9685 private lb 0 9157 train1 model 68 h5 public lb 0 9691 private lb 0 9167 train1 model 70 h5 public lb 0 9700 private lb 0 9174 to start training the model you need to use the train py file and at least verify modify the following values data dir this should be the directory with the bengali ai dataset train dir this should be the directory where you want to store the generated training images generate images whether or not the training images must be pre generated should be done initially to run the inference on the models you can use the jupyter notebook keras efficientnet b3 training inference ipynb note that you do need to modify some paths to the model weight files competition final submission in the folder kagglefinalsubmission you can find the part of the code i used together with my kaggle team to train the models used our final submission s in the final submissions we also used some se resnext models in an ensemble the same guidelines apply to this model as mentioned above just the efficientnet b3 model however already gave the same score as the multi model ensemble the model scored 0 9739 on the public board and 0 9393 on the private board with our submission we achieved a 47th place out of 2000 participants use the final submission inference kernel and the provided model weights to try it out to be able to train the model you need to first download the dataset as available for the kaggle competition https www kaggle com c bengaliai cv19 | computer-vision efficientnet classification | ai |
StockForecast | chinese stock market forecast a br our project is aimed at predicting the price trend of individual stocks using deep learning and natural language processing the system architecture is shown below br img src https github com ddddwy stockforecast raw master img system 20architecture png 1 data collection code https github com ddddwy stockforecast tree master crawler br java webcollector br use java web crawler framework webcollector to write multithreaded news crawlers which can fetch the individual stock news and captial flow data from sina finance website the process of the web crawler is shown below br img src https github com ddddwy stockforecast raw master img webcollector 20core 20framework png width 60 2 data preprocessing code news text data preprocessing python data cleaning write python script to delete abnormal characters and empty values text segmentation introduce custom financial terms dictionary and use jieba text segmentation framework to segment the chinese texts br table style border 1px solid e8e8e8 thead tr th th th br before introducing custom dictionary th th br after introducing custom dictionary th tr thead tbody tr th recall th td 87 5 td td 95 2 td tr tr th precision th td 77 9 td td 90 8 td tr tr th f measure th td 82 4 td td 92 9 td tr tbody table br company financial data preprocessing missing value processing for the suspened stock set the stock price to the closing price of the day before the stop trading day z score data normalization use the z score method to scale down different financial indicators to the same magnitude 3 model building https github com ddddwy stockforecast tree master cnn rnn 20000 80 cnn rnn news classification model based on news headlines code https github com ddddwy stockforecast tree master cnn rnn classify the important financial issues based on the news headlines build cnn rnn multi classification model to classify 80 financial issues based on 20000 news headlines and the model structure is shown below br img src https github com ddddwy stockforecast raw master img cnn rnn png br https github com ddddwy stockforecast tree master forecast t 14 t 1 lstm t stock trend prediction model code https github com ddddwy stockforecast tree master forecast take the text data and the company financial data from the t 14 trading day to the t 1 trading day as one unit sample data feed into the 1 layer lstm network to do time series analysis and finally output the forecast result of stock price change on the t trading day br img src https github com ddddwy stockforecast raw master img predict model png br 4 backtesting result analysis 2015 11 1 2017 11 1 a training set from 2015 11 01 to 2017 11 01 a shares stocks news data captial flow data pe and pb data from sina finance website the stock includes all a shares of the mining pharmaceutical industries which are subject to the regulation of the china securities regulatory commission 2017 11 1 2018 1 12 a testing set from 2017 11 01 to 2018 01 12 a shares stocks news data captial flow data pe and pb data from sina finance website the stock includes all a shares of the mining pharmaceutical industries which are subject to the regulation of the china securities regulatory commission br img src https github com ddddwy stockforecast raw master img mine results png width 45 img src https github com ddddwy stockforecast raw master img pharmaceutical results png width 45 br table style border 1px solid e8e8e8 thead tr th br stock price changeratio th th br mining industry th th br pharmaceutical industry th tr thead tbody tr td 1 td td 73 2 td td 71 82 td tr tr td 2 td td 71 74 td td 77 36 td tr tr td 3 td td 76 22 td td 76 94 td tr tr td 4 td td 64 29 td td 64 td tr tr td 5 td td 77 td td 71 td tr tr td 6 td td 74 td td 76 td tr tr td 7 td td 65 td td 60 td tr tr td 8 td td 70 td td 60 td tr tr td 9 td td 70 td td 50 td tr tbody table reference webcollector http datahref com archives category webcollector e6 95 99 e7 a8 8b second international chinese word segmentation bakeoff http sighan cs uchicago edu bakeoff2005 multi class text classification cnn rnn https github com jiegzhan multi class text classification cnn rnn deep learning with python https www manning com books deep learning with python | time-series-analysis stock-price-prediction deep-learning natural-language-processing web-crawler | ai |
Data-Engineering-Projects | data engineering projects data engineering snowflake data bricks apache nifi apache airflow aws s3 kafka spark dvc amazon redshift google cloud collection of data engineering projects showcasing data pipeline development real time data streaming etl process automation data lake architecture build a data pipeline with apache nifi tools apache nifi apache kafka postgresql concepts data ingestion from various sources real time data streaming using apache kafka data transformation and cleansing with apache nifi loading data into a postgresql database implement an etl process with apache airflow tools apache airflow python postgresql concepts workflow automation using apache airflow etl extract transform load processes scheduling and monitoring data pipelines data cleansing and transformation data lake architecture with aws s3 tools aws s3 aws glue python concepts designing and setting up a data lake on aws s3 data cataloging and metadata management with aws glue data ingestion and storage best practices querying data from a data lake real time data streaming with kafka and spark tools apache kafka apache spark hadoop hdfs concepts real time data streaming architecture setting up and configuring kafka clusters stream processing with apache spark streaming storing processed data in hadoop hdfs building data integration connectors tools python rest apis oauth concepts developing connectors to third party apis e g twitter facebook data synchronization and data consistency handling data format conversions e g json to csv error handling and retry mechanisms implementing data version control with dvc tools data version control dvc git python concepts version controlling data and machine learning models managing large datasets efficiently with dvc collaborative data science workflows reproducible data pipelines data warehouse optimization with amazon redshift tools amazon redshift sql concepts performance tuning of amazon redshift clusters database optimization techniques e g indexing partitioning query optimization for large datasets etl processes for data warehouse loading data ingestion and processing in google cloud tools google cloud pub sub google dataflow google bigquery concepts streaming data ingestion with google cloud pub sub data processing using google dataflow apache beam storing and querying data in google bigquery real time analytics pipelines in google cloud | apache-airflow apache-kafka aws-s3 awsglue bigquery-ml data-science dataengineering googlecloudplatform hadoop-hdfs hadoop-mapreduce outh postgresql redshift rest-api spark | cloud |
mobile-first-sass-tool | mobile first sass tool a boilerplate for real mobile first development without the cross browser headaches overview the mobile first sass tool outputs two source files app css and app basic css rendering the alternate style sheets like this if gte ie 9 ie link rel stylesheet href css app css endif if lte ie 8 link rel stylesheet href css app basic css endif mobile first development means develop for the most basic experience and then progressively enhance using media queries towards a full progressively enhanced experience this is the problem with mobile first container container column width 100 basic experience column is full width display block media screen and min width 600px include clearfix container column width 50 progressively enhance 2 columns per container display inline block float left with real mobile first twitter might look like this might look like this in ie8 mobile first twitter https github com elisechant mobile first sass tool blob master images mobile first twitter jpg raw true to get the stoopid browsers to get a two column layout in desktop viewports you need the mobile first sass tool app basic css is a stripped down version of app css it keeps only the relevant content for the full desktop experience stripping away the media queries and locks the viewport so these browser can not resize enabling developers to practice real mobile first without all of the cross browser headaches currently ie8 browsers how to use 1 define your media queries at scss app config scss tool mq group2 em 600px mq group3 em 768px mq group4 em 1024px 2 define your minimum media query for app basic at scss app app basic scss anything less than this value will not be included in the export fix mqs mq group3 change this to your minimum media query 3 only use the media query mixins outlined at scss app tool scss example usage at scss app tests media query parsing scss mygeneralexample include respond min mq group2 content general example render something greater than mq group2 myadvancedexample include respond min mq group2 include respond max mq group4 content advanced example render something between mq group2 and mq group4 sample output mobile first sass tool sample output https github com elisechant mobile first sass tool blob master images screen shot 2014 05 04 at 10 39 03 am png raw true | front_end |
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Coldairarrow.Fx.Core.Easyui.GitHub | coldairarrow fx core easyui github web netcore2 1 https github com coldairarrow coldairarrow fx core easyui github wiki | front_end |
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cacti | open in visual studio code https img shields io static v1 logo visualstudiocode label message open 20in 20visual 20studio 20code labelcolor 2c2c32 color 007acc logocolor 007acc https open vscode dev hyperledger cactus license https img shields io github license hyperledger cactus cii best practices https bestpractices coreinfrastructure org projects 4089 badge https bestpractices coreinfrastructure org projects 4089 github issues https img shields io github issues hyperledger cactus cacti logo color images hl cacti logo color png gh light mode only cacti logo color images hl cacti logo colorreverse svg gh dark mode only hyperledger cacti hyperledger cacti is a multi faceted pluggable interoperability framework to link networks built on heterogeneous distributed ledger and blockchain technologies and to run transactions spanning multiple networks this project is the result of a merger of the weaver lab https github com hyperledger labs weaver dlt interoperability project with hyperledger cactus which was subsequently renamed to cacti it draws on the cutting edge technological features of both constituent projects to provide a common general purpose platform and toolkit for dlt interoperability this was the first of a kind merger of two systems architecture and code bases to create a new project under the hyperledger foundation see this hyperledger foundation blog article https www hyperledger org blog 2022 11 07 introducing hyperledger cacti a multi faceted pluggable interoperability framework for more information about the merger cacti is a graduated hyperledger project https www hyperledger org blog hyperledger cacti a general purpose modular interoperability framework moves to graduated status information on the different stages of a hyperledger project and graduation criteria can be found in the hyperledger project incubation exit criteria document https wiki hyperledger org display tsc project incubation exit criteria scope of project the existence of several blockchain and distributed ledger technologies of different flavors in the market as well as networks of varying scopes and sizes built on them necessitates the need for interoperability and integration lest we end up with a fragmented ecosystem where digital assets and the workfows often contracts governing them remain isolated in silos the solution to this is not to force all chains to coalesce i e a single chain to rule them all but rather enable the networks to orchestrate transactions spanning their boundaries without sacrificing security privacy or governance autonomy i e self sovereignty hyperledger cacti offers a family of protocols modules libraries and sdks that can enable one network to be interoperable with and carry out transactions directly with another while eschewing the need for a central or common settlement chain cacti will allow networks to share ledger data and exchange and transfer assets atomically and manage identities across their boundaries as illustrated in the figure below img src images cacti vision png as a fusion of two earlier systems cactus and weaver that have similar philosophies and goals yet offer distinct mechanisms backed by differemt design and trust assumptions cacti offers a spectrum of selectable and configurable features for cross network transaction orchestrations an example illustrated below shows how distributed applications running on fabric and besu ledgers respectively can carry out the same set of cross network transactions using the node server cactus legacy or through relays weaver legacy img src images tx orchestration modes png the current cacti code base contains the legacy cactus and weaver source code in aggregated form with their original folder structures intact but the packages built from the two sections of code are unified and released under a common cacti namespace and the ci cd pipelines for testing and releases are also integrated under a common set of github actions a deeper merge and integration of source code is part of our roadmap and will be carried out over a longer time period but the current setup of code and release packages makes it easy for new users to navigate cacti and for legacy users to carry out seamless upgrades reference for legacy users cactus source code lies here i e the root folder excluding the weaver folder weaver source code lies within the weaver weaver folder documentation see the official hyperledger cacti documentation https hyperledger github io cacti to get all your questions answered about the project to get started with setup testing and evaluation and to get hands on with code and configurations here you can find separate and specific instructions for getting started with running and experimenting with cactus modules https hyperledger github io cacti cactus introduction and weaver modules https hyperledger github io cacti weaver introduction respectively roadmap see roadmap md roadmap md for details on the envisioned integration inclusive language statement these guiding principles are very important to the maintainers and therefore we respectfully ask all contributors to abide by them as well consider that users who will read the docs are from different backgrounds and cultures and that they have different preferences avoid potential offensive terms and for instance prefer allow list and deny list to white list and black list we believe that we all have a role to play to improve our world and even if writing inclusive documentation might not look like a huge improvement it s a first step in the right direction we suggest to refer to microsoft bias free writing guidelines https docs microsoft com en us style guide bias free communication and google inclusive doc writing guide https developers google com style inclusive documentation as starting points contact mailing list cacti lists hyperledger org mailto cacti lists hyperledger org discord channel https discord com invite hyperledger https discord com invite hyperledger contributing we welcome contributions to hyperledger cacti in many forms and there s always plenty to do please review contributing contributing md guidelines to get started license this distribution is published under the apache license version 2 0 found in the license license file | hacktoberfest | blockchain |
tensor2robot | tensor2robot this repository contains distributed machine learning and reinforcement learning infrastructure it is used internally at alphabet and open sourced with the intention of making research at robotics google more reproducible for the broader robotics and computer vision communities projects and publications using tensor2robot qt opt research qtopt grasp2vec research grasp2vec watch try learn research vrgripper bc z research bcz features tensor2robot t2r is a library for training evaluation and inference of large scale deep neural networks tailored specifically for neural networks relating to robotic perception and control it is based on the tensorflow tensorflow org deep learning framework a common task in robotics research involves adding a new sensor modality or new label tensor to a neural network graph this involves 1 changing what data is saved 2 changing data pipeline code to read in new modalities at training time 3 adding a new tf placeholder to handle the new input modality at test time the main feature of tensor2robot is the automatic generation of tensorflow code for steps 2 and 3 tensor2robot can automatically generate placeholders for a model to match its inputs or alternatively exports a savedmodel that can be used with a tfexportedsavedmodelpolicy so that the original graph does not have to be re constructed another common task encountered in ml involves cropping transforming input modalities such as jpeg decoding and applying random image distortions at training time the preprocessor class declares its own input features and labels and is expected to output shapes compatible with the input features and labels of the model example preprocessors can be found in preprocessors preprocessors design decisions scalability this codebase is designed for training large scale real world robotic perception models with algorithms that do not require a tight perception action learning loop supervised learning off policy reinforcement learning of large computer vision models an example setup might involve multiple gpus pulling data asynchronously from a replay buffer and training with an off policy rl algorithm while data collection agents periodically update their checkpoints and push experiences to the same replay buffer this can also be run on a single workstation for smaller scale experiments t2r is not a general purpose reinforcement learning library due to the sizes of models we work with e g grasping from vision inference is assumed to be within 1 10hz and without real time guarantees and training is assumed to be distributed by default if you are doing reinforcement learning with small networks e g two layer perceptron with on policy rl e g ppo or require hard real time guarantees this is probably not the right codebase to use we recommend using tf agents https github com tensorflow agents or dopamine https github com google dopamine for those use cases minimize boilerplate tensor2robot models auto generate their own data input pipelines and provide sensible defaults for optimizers common architectures actors critics and train eval scaffolding models automatically work with both gpus and tpus via tpuestimator parsing bmp gif jpeg png encoded images gin configurable gin config https github com google gin config is used to configure models policies and other experiment hyperparameters quickstart requirements python 3 git clone https github com google tensor2robot optional create a virtualenv python3 m venv venv source venv bin activate pip install r tensor2robot requirements txt python m tensor2robot research pose env pose env test install protoc and compile the protobufs pip install protobuf cd tensor2robot proto protoc i python out pwd tensor2robot t2r proto python m tensor2robot research pose env pose env models test t2rmodel to use tensor2robot a user defines a t2rmodel object that define their input requirements by specifications one for their features feature spec and one for their labels label spec these specifications define all required and optional tensors in order to call the model fn an input pipeline parameterized with the model s input pipeline will ensure that all required specifications are fulfilled note we always omit the batch dimension and only specify the shape of a single element at training time the t2rmodel provides model train fn or model eval fn as the model fn argument tf estimator estimator class both model train fn and model eval fn are defined with respect to the features labels and outputs of inference network fn which presumably implements the shared portions of the train eval graphs bash class mymodel t2rmodel def get feature specification self mode spec tensorspec utils tensorspecstruct spec state extendedtensorspec shape 8 128 dtype tf float32 name s def get label specification self mode spec tensorspec utils tensorspecstruct spec action extendedtensorspec shape 8 dtype tf float32 name a def inference network fn self features tensorspec utils tensorspecstruct labels optional tensorspec utils tensorspecstruct mode tf estimator modekeys config runconfigtype none params paramstype none dictorspec inference outputs inference outputs predictions layers fully connected features state 8 return inference outputs def model train fn self features tensorspec utils tensorspecstruct labels tensorspec utils tensorspecstruct inference outputs dictorspec mode tf estimator modekeys config runconfigtype none params paramstype none modeltrainoutputtype see base class del features config loss tf losses mean squared error labels action inference outputs predictions return loss note how the key on the left hand side has a value for name that is different from the one on the right hand side within the extendedtensorspec the key on the left is used within the model fn to access the loaded tensor whereas the name is used when creating the parse tf example fn or numpy feed dict we ensure that the name is unique within the whole spec unless the specs match otherwise we cannot guarantee the mapping functionality benefits of inheriting a t2rmodel self contained input specifications for features and labels auto generated tf data dataset pipelines for tf train examples and tf train sequenceexamples for policy inference t2rmodels can generate placeholders or export savedmodel s that are hermetic and can be used with exportsavedmodelpolicy automatic construction of model fn for estimator for training and evaluation graphs that share a single inference network fn it is possible to compose multiple models inference network fn and model train fn together under a single model this abstraction allows us to implement generic meta learning models e g maml that call their sub model s model train fn automatic support for distributed training on gpus and tpus policies and placeholders for performance reasons policy inference is done by a vanilla session run or a predict fn call on the output of a model instead of estimator predict tensorspec utils make placeholders automatically creates placeholders from a spec structure which can be used in combination with a matching hierarchy of numpy inputs to create a feed dict bash batch size 1 no batch size will be prepended to the spec batch size none we will prepend none have a variable batch size batch size 0 we will have a fixed batch size placeholders tensorspec utils make placeholders hierarchical spec batch size none feed dict inference model makefeeddict placeholders numpy inputs this can be passed to a sess run function to evaluate the model if you use tfexportedsavedmodelpolicy note that your t2rmodel should not query the static batch shape x shape 0 in the graph this is because placeholder generation creates inputs with unknown batch shape none causing static shape retrieval to fail instead use tf shape x 0 to access batch shapes dynamically working with tensor specifications specifications can be a hierarchical data structure of either dictionaries dict tuples tuple lists list or tensorspecstruct preferred the leaf elements have to be of type tensorspec or extendedtensorspec preferred in the following we will present some examples using extendedtensorsec and tensorspecstruct to illustrate the different usecases we use tensorspec utils tensorspecstruct for specifying specs since this data structure is mutuable provides attribute dot access and item iteration further we can use pytype to ensure compile time type checking this data structure is both hierarchical and flat the dictionary interface using items is flat representing hierarchical data with paths as shown later on however we maintain a hierarchical interface using attribute access also shown later on therefore we can use this data structure in order to create and alter hierarchical specifications which work with both tpuestimator and estimator since both apis operate on the dictionary view hierarchical example creating a hierarchical spec from spec using tensorspec utils copy tensorspec bash simple spec tensorspec utils tensorspecstruct simple spec state extendedtensorspec shape 8 128 dtype tf float32 name s simple spec action extendedtensorspec shape 8 dtype tf float32 name a hierarchical spec tensorspec utils tensorspecstruct hierarchical spec train tensorspec utils copy tensorspec simple spec prefix train note we use attribute access to define the train hierarchy this will copy all our specs from simple spec internally to bash train train state simple spec state and train action simple spec action we forbid the following pattern bash hierarchical spec train tensorspec utils tensorspecstruct and encourage the user to use this pattern instead bash train tensorspec utils tensorspecstruct train stuff extendedtensorspec hierarchical spec train train or hierarchical spec train stuff extendedtensorspec bash all of the following statements are true hierarchical spec train state simple spec state hierarchical spec train action simple spec action hierarchical spec keys train state train action hierarchical spec train keys state action now we want to use the same spec another time for our input bash hierarchical spec val tensorspec utils copy tensorspec simple spec prefix val all of the following statements are true hierarchical spec keys train state train action val state val action hierarchical spec train keys state action hierarchical spec train state name train s hierarchical spec val keys state action hierarchical spec val state name val s manually extending creating a hierarchical spec from an existing simple spec is also possible tensorspec is an immutable data structure therefore the recommend way to alter a spec is bash hierarchical train state extendedtensorspec from spec hierarchical train state parameters to overwrite a different way of changing a hierarchical spec would be bash for key value in simple spec items hierarchical test key extendedtensorspec from spec value name something random value name hierarchical spec keys train state train action val state val action test state test action hierarchical spec train keys state action hierarchical spec val keys state action hierarchical spec test keys state action hierarchical spec test state name something random s sequential inputs tensor2robot can parse both tf train example and tf train sequenceexample protos useful for training recurrent models like lstms to declare a model whose data is parsed from sequenceexamples set is sequence true bash spec state extendedtensorspec shape 8 128 dtype tf float32 name s is sequence true this will result in a parsed tensor of shape b 8 128 where b is the batch size and the second dimension is the unknown sequence length only known at run time note that if is sequence true for any extendedtensorspec in the tensorspecstruct the proto will be assumed to be a sequenceexample and non sequential tensors will be assumed to reside in example context flattening hierarchical specification structures any valid spec structure can be flattened into a tensorspec utils tensorspecstruct in the following we show different hierarchical data structures and the effect of flatten spec structure bash flat hierarchy tensorspec utils flatten spec structure hierarchical note tensorspec utils tensorspecstruct will have flat dictionary access we can print access change all elements of our spec by iterating over the items bash for key value in flat hierarchy items print path spec format key value this will print path train state spec extendedtensorspec shape 8 128 dtype tf float32 name train s path train action spec extendedtensorspec shape 8 dtype tf float32 name train a path val state spec extendedtensorspec shape 8 128 dtype tf float32 name val s path val action spec extendedtensorspec shape 8 dtype tf float32 name val a path test state spec extendedtensorspec shape 8 128 dtype tf float32 name something random s path test action spec extendedtensorspec shape 8 dtype tf float32 name something random a this data structure still maintains the hierarchical user interface train flat hierarchy train for key value in flat hierarchy items print path spec format key value this will print path state spec extendedtensorspec shape 8 128 dtype tf float32 name train s path action spec extendedtensorspec shape 8 dtype tf float32 name train a note the path has changed but the name is still from the hierarchy this is an important distinction the model could access the data in a different manner but the same name is used to access tf examples of feed dicts in order to feed the tensors an alternative hierarchical spec using namedtuples bash hierarchy namedtuple hierarchy train val sample namedtuple sample state action hierarchy hierarchy train sample state extendedtensorspec shape 8 128 dtype tf float32 name train s action extendedtensorspec shape 8 dtype tf float32 name train a eval sample state extendedtensorspec shape 8 128 dtype tf float32 name val s action extendedtensorspec shape 8 dtype tf float32 name val a flat hierarchy tensorspec utils flatten spec structure hierarchy for key value in flat hierarchy items print path spec format key value this will print path train state spec extendedtensorspec shape 8 128 dtype tf float32 name train s path train action spec extendedtensorspec shape 8 dtype tf float32 name train a path val state spec extendedtensorspec shape 8 128 dtype tf float32 name val s path val action spec extendedtensorspec shape 8 dtype tf float32 name val a note hierarchy namedtuple is immutable whereas flat hierarchy is a mutable instance of tensorspecstruct validate and flatten or pack tensorspec utils validate and flatten and tensorspec utils validate and pack allow to verify that an existing e g loaded spec data structure filled with tensors fulfills our expected spec structure and is flattened or packed into a hierarchical structure disclaimer this is not an official google product external support not guaranteed the api may change subject to alphabet needs file a github issue if you have questions | ai |
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Advanced-JavaScript | github issues https img shields io github issues trainingbypackt advanced javascript svg https github com trainingbypackt advanced javascript issues github forks https img shields io github forks trainingbypackt advanced javascript svg https github com trainingbypackt advanced javascript network github stars https img shields io github stars trainingbypackt advanced javascript svg https github com trainingbypackt advanced javascript stargazers prs welcome https img shields io badge prs welcome brightgreen svg https github com trainingbypackt advanced javascript pulls advanced javascript in this course you will gain a deep understanding of javascript you will learn how to write javascript in a professional environment using the new javascript syntax in es6 leverage on javascript s asynchronous nature using callbacks and promises and understand how to set up test suites and test your code you will be introduced to javascript s functional programming style and you will apply everything learned to build a simple application in various javascript frameworks and libraries for backend and frontend development advanced javascript by zachary shute what you will learn examine major features in es6 and implement those features to build applications create promise and callback handlers to work with asynchronous processes develop asynchronous flows using promise chaining and async await syntax manipulate the dom with javascript handle javascript browser events explore test driven development and build code tests with javascript code testing frameworks list the benefits and drawbacks of functional programming compared to other styles construct applications with the node js backend framework and the react frontend framework hardware requirements for an optimal student experience we recommend the following hardware configuration processor intel core i5 or equivalent memory 4gb ram storage 35gb available space software requirements you ll also need the following software installed in advance os windows 7 sp1 64 bit windows 8 1 64 bit or windows 10 64 bit browser google chrome latest version atom ide babel node js and npm | javascript html | front_end |
drone | crates io https img shields io crates v drone svg https crates io crates drone maintenance https img shields io badge maintenance actively developed brightgreen svg drone cli cargo rdme start cli utility for drone an embedded operating system documentation refer to the drone book https book drone os com for documentation usage the program requires nightly channel of rust make sure you have it installed shell rustup toolchain install nightly install the latest version from crates io shell cargo nightly install drone check the built in help shell drone help cargo rdme end license licensed under either of apache license version 2 0 license apache license apache or http www apache org licenses license 2 0 mit license license mit license mit or http opensource org licenses mit at your option contribution unless you explicitly state otherwise any contribution intentionally submitted for inclusion in the work by you as defined in the apache 2 0 license shall be dual licensed as above without any additional terms or conditions | embedded asynchronous concurrency no-std os async real-time bare-metal rtos framework rust | os |
Full-Stack-Web-Dev-with-React-Specialization | full stack web dev with react specialization course material full stack web development with react specialization coursera | front_end |
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vision-for-action | does computer vision matter for action brady zhou http www bradyzhou com philipp kr henb hl http www philkr net and vladlen koltun http www vladlen info science robotics 4 30 2019 paper https robotics sciencemag org content 4 30 eaaw6661 ijkey z3zmgrf4sfjn6 keytype ref siteid robotics project http www bradyzhou com visionforaction code to accompany the paper img src logo jpg style width 300px installation git clone recursive https github com intel isl vision for action git vizdoom we require building from source using the included submodule which includes hacks for getting labels for the walls and floors bash cd vizdoom cmake dcmake build type release dbuild python3 on make link vizdoom and helpers to pythonpath bash export pythonpath pythonpath pwd vizdoom bin python3 6 pip package export pythonpath pythonpath pwd gta v this is supported on windows machines only and will take a little bit of effort you will need to install the following a modified gamehook https github com bradyz gamehook a modified gamehook gta v plugin https github com bradyz gamehook gtav a modified pyhookv https github com bradyz pyhookv after successfully building all of these projects you should have dxgi dll renamed from gamehook dll python hk server hk gta5 hk pyhookv pyd take all of these files and put them in the directory where gtav exe lives additionally move the following files from the gta v directory to the same directory agents privileged py constants py controller py controls py message packer py pyhookv utils py presets py scenarios py citation article zhou2019doescv title does computer vision matter for action author brady zhou and philipp kr a henb u hl and vladlen koltun journal science robotics volume 4 number 30 year 2019 | ai |
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CodehelpYTWebDev | codehelpytwebdev playlist link https bit ly 3nvveyl welcome to our concise web development course on the mern stack learn how to build modern web applications using mongodb express js react and node js from setup to deployment master front end and back end development apis and more join us and unlock the power of the mern stack by babbar | front_end |
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Lego-Mindstorms-ATM | lego mindstorms atm this repository features the code behind a fully functional atm including an integrated security system alongside three of my mechatronics engineering colleagues we designed built developed and presented our very own atm controlled by a lego mindstorms nxt 2 0 microcontroller the source code is written in robotc which is just a variation of c | os |
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lxmls-toolkit | travis ci build status travis image travis url requirements status requires image requires url travis image https travis ci org lxmls lxmls toolkit svg branch master travis url https travis ci org lxmls lxmls toolkit requires image https requires io github lxmls lxmls toolkit requirements svg branch master requires url https requires io github lxmls lxmls toolkit requirements branch master lxmls 2023 machine learning toolkit for natural language processing written for lisbon machine learning summer school lxmls it pt this covers scientific python and mathematical background linear classifiers sequence models structured prediction syntax and parsing feed forward models in deep learning sequence models in deep learning reinforcement learning machine learning toolkit for natural language processing written for lxmls lisbon machine learning summer school http lxmls it pt instructions for students use the student branch https github com lxmls lxmls toolkit tree student not this one install with anaconda or pip if you are new to python the simplest method is to use anaconda to handle your packages just go to https www anaconda com download and follow the instructions we strongly recommend using at least python 3 if you prefer pip to anaconda you can install the toolkit in a way that does not interfere with your existing installation for this you can use a virtual environment as follows virtualenv venv source venv bin activate on windows venv scripts activate pip install pip setuptools upgrade pip install editable this will install the toolkit in a way that is modifiable if you want to also virtualize you python version e g you are stuck with python2 on your system have a look at pyenv bear in mind that the main purpose of the toolkit is educative you may resort to other toolboxes if you are looking for efficient implementations of the algorithms described running run from the project root directory if an importing error occurs try first adding the current path to the pythonpath environment variable e g export pythonpath development to run the all tests install tox and pytest pip install tox pytest and run tox note to combine the coverage data from all the tox environments run windows set pytest addopts cov append tox other pytest addopts cov append tox | ai |
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FIFO | fifo design and testbench of a fifo first in first out in the discipline of embedded systems | os |
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SoftwareEngineering | softwareengineering this repository contains some of the labs related to the software engineering course database integration testing among others | server |
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RTOS_LPC2148 | e yantra summer internship http www e yantra org img eyantralogolarge png logo https github com eysip 2016 autonomous drone blob master datasheets readme images iitbblack jpg rtos lpc2148 freertos is an opensource software that enables us to use various concepts of rtos in our embedded application codes folder consist of all the sample programs that have been executed on lpc2148 which illustrates basic concepts of rtos like multitasking binary semaphore mutex counting semaphore via dining philosopher mailbox queue context switching collision avoidance using rtos collision avoidance collision avoidance using c sensor test spi state collector python script for saving data obtained from state collector non tested code of fi pi for lpc2148 spi1 and spi adc codes to obtain data from slaves documentation folder consist of a starters guide for freertos on lpc2148 which contain brief introduction to basic concepts of rtos sample codes illustrating basic concepts explanantion of codes implemented using freertos experiments illustrating advantages of rtos datasheets consists of all the datasheets releated to lpc2148 research folder consists of all the reffered links for the project freertos report folder is the final project report links to used softwares freertos http www freertos org a00104 html https sourceforge net projects freertos files latest download source files keil uvision version 4 download link https www keil com demo eval arm htm installation steps https github com akshar100 eyantra firebird resources tree master fire 20bird 20v 20lpc2148 202010 12 29 flash magic version 5 7 download link http www flashmagictool com installation steps https github com akshar100 eyantra firebird resources tree master fire 20bird 20v 20lpc2148 202010 12 29 python idle version 3 5 https www python org downloads x ctu version 6 3 1 http www digi com products xbee rf solutions xctu software xctu productsupport utilities | os |
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Action-Systems | action systems action systems consists of some basic development environments of how i design action based systems there are some action based systems for some embedded system platforms as well arduino esp32 etc | os |
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Shipwars-IoT-ESE-516 | shipwars devpost link https devpost com software ship wars youtube link https www youtube com watch v ryzes1qmrgo feature emb imp woyt compilation a recreation of battleship game using embedded system the game can be played remotely by 2 players using identical hardware pcbs we designed for altium pcb design files refer to altium design shipwars | os |
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aims2021 | 3d computer vision this series of lectures will focus on 3d computer vision we will start with an introduction on 3d representations and explore state of the art models for 3d deep learning each lecture will consist of a video lecture links to online video lectures along with reading material covering the topics for each lecture a q a session a live q a session a lab a practical lab session followed by a short 1 page report after the end of the week a quiz a set of multiple choice questions covering the material taught throughout the week lecture 1 3d representations 3d shape from images lecturer georgia gkioxari in the first lecture we will introduce some of the most common 3d representations and dive into state of the art models for 3d shape inference from a single image lecture1 md lecture1 md reading material and video lectures lab1 md lab1 md lab session and short report assignment deadline sunday april 18 lecture 2 differentiable rendering lecturer nikhila ravi in the second lecture we will dive into an in depth analysis of differentiable rendering lecture2 md lecture2 md reading material and video lectures lab2 md lab2 md lab session and short report assignment deadline sunday april 18 lecture 3 more topics in 3d deep learning lecturer georgia gkioxari in the third lecture we will cover more topics and applications on 3d deep learning lecture3 md lecture3 md reading material and video lectures quiz quiz md quiz md a test with multiple choice questions on the material deadline sunday april 18 schedule the schedule for the class can be found in schedule md schedule md | ai |
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docksal | docksal latest release https img shields io github release docksal docksal svg logo github style for the badge https github com docksal docksal releases latest build status https img shields io github workflow status docksal docksal default label actions logo github style for the badge https github com docksal docksal actions workflows default yaml documentation https img shields io badge documentation blue svg style for the badge https docs docksal io blog https img shields io badge tips 20and 20updates orange svg style for the badge https blog docksal io github discussions https img shields io github discussions docksal docksal style for the badge https github com docksal docksal discussions docker powered web development environments for macos windows and linux unified environment for your team regardless of the os why docksal full power of docker compose to suit any project without artificial syntax limitations unprecedented ability to automate routine tasks slashing the time to on board new team members create your custom automation https docs docksal io fin custom commands in bash php or node to spin up your project anywhere where docksal is with a single command or automate routine tasks best in class filesystem performance running drupal wordpress magento laravel symfony backdrop grav hugo gatsby and others is one step away with ready to use boilerplates a name setup a a name updates a a name getting started a welcome onboard install docksal https docs docksal io getting started setup cd to the desired directory and run bash fin project create the wizard will guide you throughout the creation of a new project using any of the instant boilerplate projects listed below the wizard will create the proper docker containers stack depending on the project choice and install a working empty project preconfigured for running in docksal that you can build upon instant boilerplate projects in case you want to be in full control you can clone one the boilerplate projects below and run fin init drupal 9 https github com docksal boilerplate drupal9 drupal 9 composer https github com docksal boilerplate drupal9 composer drupal 9 blt https github com docksal boilerplate drupal9 blt drupal 8 https github com docksal boilerplate drupal8 drupal 8 composer https github com docksal boilerplate drupal8 composer drupal 8 blt https github com docksal boilerplate drupal8 blt drupal 7 https github com docksal boilerplate drupal7 wordpress https github com docksal boilerplate wordpress magento https github com docksal boilerplate magento magento with sample content https github com docksal boilerplate magento demo laravel https github com docksal boilerplate laravel symfony skeleton https github com docksal boilerplate symfony skeleton symfony web app https github com docksal boilerplate symfony webapp grav cms https github com docksal boilerplate grav backdrop cms https github com docksal boilerplate backdrop hugo https github com docksal boilerplate hugo gatsbyjs https github com docksal boilerplate gatsby angular https github com docksal boilerplate angular more advanced use cases and tutorials are available in the documentation https docs docksal io and blog http blog docksal io contributing to docksal ready to give back check the contributing contributing md docs on how to get involved we have github discussions and chat rooms on slack where questions can be asked and answered github discussions https github com docksal docksal discussions drupal slack https app slack com client t06gx3jts c6gpeeev8 if you have experience with docksal and docker please stick around in the rooms to help others roadmap for the list of current and past releases check the projects https github com orgs docksal projects section change records are published there and in the changelog changelog md docs | docker devops drupal wordpress lamp magento macos linux laravel windows web-development | front_end |
HTML-Learn | html learn my files from udemy web development course saving these just for reference | front_end |
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Multidimensional-Databases-Operators | multidimensional databases operators this project implements the multidimensional databases operators for rakesh agrawal s paper rakesh agrawal ashish gupta and sunita sarawagi 1997 modeling multidimensional databases in proceedings of the thirteenth international conference on data engineering icde 97 ieee computer society usa 232 243 plan to support operators pull push distory dimension restriction join associate merge usage before start 1 edit config json to connect to mysql backend 2 start cube py in interactive mode example date supplier product sales jan 1 s1 p1 5 sept 1 s2 p2 9 import a cube from backend c1 cube t1 save to backend c1 save t1 visualize a cube c1 visualize use operators c c1 push product push product dimension into elements c c1 join c2 f1 k f 1 k f elem dimensions software architecture design cube store backend connection visulization controller | server |
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Stock-Prediction-Models | p align center a href readme img alt logo width 50 src output evolution strategy png a p p align center a href https github com huseinzol05 stock prediction models blob master license img alt mit license src https img shields io badge license apache license 2 0 yellow svg a a href img src https img shields io badge deeplearning 30 models success svg a a href img src https img shields io badge agent 23 models success svg a p stock prediction models gathers machine learning and deep learning models for stock forecasting included trading bots and simulations table of contents models models agents agents realtime agent realtime agent data explorations data explorations simulations simulations tensorflow js tensorflow js misc misc results results results agent results agent results signal prediction results signal prediction results analysis results analysis results simulation results simulation contents models deep learning models deep learning 1 lstm 2 lstm bidirectional 3 lstm 2 path 4 gru 5 gru bidirectional 6 gru 2 path 7 vanilla 8 vanilla bidirectional 9 vanilla 2 path 10 lstm seq2seq 11 lstm bidirectional seq2seq 12 lstm seq2seq vae 13 gru seq2seq 14 gru bidirectional seq2seq 15 gru seq2seq vae 16 attention is all you need 17 cnn seq2seq 18 dilated cnn seq2seq bonus 1 how to use one of the model to forecast t n how to forecast ipynb deep learning how to forecast ipynb 2 consensus how to use sentiment data to forecast t n sentiment consensus ipynb deep learning sentiment consensus ipynb stacking models stacking 1 deep feed forward auto encoder neural network to reduce dimension deep recurrent neural network arima extreme boosting gradient regressor 2 adaboost bagging extra trees gradient boosting random forest xgb agents agent 1 turtle trading agent 2 moving average agent 3 signal rolling agent 4 policy gradient agent 5 q learning agent 6 evolution strategy agent 7 double q learning agent 8 recurrent q learning agent 9 double recurrent q learning agent 10 duel q learning agent 11 double duel q learning agent 12 duel recurrent q learning agent 13 double duel recurrent q learning agent 14 actor critic agent 15 actor critic duel agent 16 actor critic recurrent agent 17 actor critic duel recurrent agent 18 curiosity q learning agent 19 recurrent curiosity q learning agent 20 duel curiosity q learning agent 21 neuro evolution agent 22 neuro evolution with novelty search agent 23 abcd strategy agent data explorations misc 1 stock market study on tesla stock tesla study ipynb misc tesla study ipynb 2 outliers study using k means svm and gaussian on tesla stock outliers ipynb misc outliers ipynb 3 overbought oversold study on tesla stock overbought oversold ipynb misc overbought oversold ipynb 4 which stock you need to buy which stock ipynb misc which stock ipynb simulations simulation 1 simple monte carlo monte carlo drift ipynb simulation monte carlo drift ipynb 2 dynamic volatility monte carlo monte carlo dynamic volatility ipynb simulation monte carlo dynamic volatility ipynb 3 drift monte carlo monte carlo drift ipynb simulation monte carlo drift ipynb 4 multivariate drift monte carlo btc usdt with bitcurate sentiment multivariate drift monte carlo ipynb simulation multivariate drift monte carlo ipynb 5 portfolio optimization portfolio optimization ipynb simulation portfolio optimization ipynb inspired from https pythonforfinance net 2017 01 21 investment portfolio optimisation with python tensorflow js stock forecasting js i code lstm recurrent neural network deep learning 1 lstm ipynb and simple signal rolling agent agent simple agent ipynb inside tensorflow js you can try it here huseinhouse com stock forecasting js https huseinhouse com stock forecasting js you can download any historical csv and upload dynamically misc misc 1 fashion trending prediction with cross validation fashion forecasting ipynb misc fashion forecasting ipynb 2 bitcoin analysis with lstm prediction bitcoin analysis lstm ipynb misc bitcoin analysis lstm ipynb 3 kijang emas bank negara kijang emas bank negara ipynb misc kijang emas bank negara ipynb results results agent this agent only able to buy or sell 1 unit per transaction 1 turtle trading agent turtle agent ipynb agent 1 turtle agent ipynb img src output agent turtle agent png width 70 align 2 moving average agent moving average agent ipynb agent 2 moving average agent ipynb img src output agent moving average agent png width 70 align 3 signal rolling agent signal rolling agent ipynb agent 3 signal rolling agent ipynb img src output agent signal rolling agent png width 70 align 4 policy gradient agent policy gradient agent ipynb agent 4 policy gradient agent ipynb img src output agent policy gradient agent png width 70 align 5 q learning agent q learning agent ipynb agent 5 q learning agent ipynb img src output agent q learning agent png width 70 align 6 evolution strategy agent evolution strategy agent ipynb agent 6 evolution strategy agent ipynb img src output agent evolution strategy agent png width 70 align 7 double q learning agent double q learning agent ipynb agent 7 double q learning agent ipynb img src output agent double q learning png width 70 align 8 recurrent q learning agent recurrent q learning agent ipynb agent 8 recurrent q learning agent ipynb img src output agent recurrent q learning png width 70 align 9 double recurrent q learning agent double recurrent q learning agent ipynb agent 9 double recurrent q learning agent ipynb img src output agent double recurrent q learning png width 70 align 10 duel q learning agent duel q learning agent ipynb agent 10 duel q learning agent ipynb img src output agent double q learning png width 70 align 11 double duel q learning agent double duel q learning agent ipynb agent 11 double duel q learning agent ipynb img src output agent double duel q learning png width 70 align 12 duel recurrent q learning agent duel recurrent q learning agent ipynb agent 12 duel recurrent q learning agent ipynb img src output agent duel recurrent q learning png width 70 align 13 double duel recurrent q learning agent double duel recurrent q learning agent ipynb agent 13 double duel recurrent q learning agent ipynb img src output agent double duel recurrent q learning png width 70 align 14 actor critic agent actor critic agent ipynb agent 14 actor critic agent ipynb img src output agent actor critic png width 70 align 15 actor critic duel agent actor critic duel agent ipynb agent 14 actor critic duel agent ipynb img src output agent actor critic duel png width 70 align 16 actor critic recurrent agent actor critic recurrent agent ipynb agent 16 actor critic recurrent agent ipynb img src output agent actor critic recurrent png width 70 align 17 actor critic duel recurrent agent actor critic duel recurrent agent ipynb agent 17 actor critic duel recurrent agent ipynb img src output agent actor critic duel recurrent png width 70 align 18 curiosity q learning agent curiosity q learning agent ipynb agent 18 curiosity q learning agent ipynb img src output agent curiosity q learning png width 70 align 19 recurrent curiosity q learning agent recurrent curiosity q learning ipynb agent 19 recurrent curiosity q learning agent ipynb img src output agent recurrent curiosity q learning png width 70 align 20 duel curiosity q learning agent duel curiosity q learning agent ipynb agent 20 duel curiosity q learning agent ipynb img src output agent duel curiosity q learning png width 70 align 21 neuro evolution agent neuro evolution ipynb agent 21 neuro evolution agent ipynb img src output agent neuro evolution png width 70 align 22 neuro evolution with novelty search agent neuro evolution novelty search ipynb agent 22 neuro evolution novelty search agent ipynb img src output agent neuro evolution novelty search png width 70 align 23 abcd strategy agent abcd strategy ipynb agent 23 abcd strategy agent ipynb img src output agent abcd strategy png width 70 align results signal prediction i will cut the dataset to train and test datasets 1 train dataset derived from starting timestamp until last 30 days 2 test dataset derived from last 30 days until end of the dataset so we will let the model do forecasting based on last 30 days and we will going to repeat the experiment for 10 times you can increase it locally if you want and tuning parameters will help you by a lot 1 lstm accuracy 95 693 time taken for 1 epoch 01 09 img src output lstm png width 70 align 2 lstm bidirectional accuracy 93 8 time taken for 1 epoch 01 40 img src output bidirectional lstm png width 70 align 3 lstm 2 path accuracy 94 63 time taken for 1 epoch 01 39 img src output lstm 2path png width 70 align 4 gru accuracy 94 63 time taken for 1 epoch 02 10 img src output gru png width 70 align 5 gru bidirectional accuracy 92 5673 time taken for 1 epoch 01 40 img src output bidirectional gru png width 70 align 6 gru 2 path accuracy 93 2117 time taken for 1 epoch 01 39 img src output gru 2path png width 70 align 7 vanilla accuracy 91 4686 time taken for 1 epoch 00 52 img src output vanilla png width 70 align 8 vanilla bidirectional accuracy 88 9927 time taken for 1 epoch 01 06 img src output bidirectional vanilla png width 70 align 9 vanilla 2 path accuracy 91 5406 time taken for 1 epoch 01 08 img src output vanilla 2path png width 70 align 10 lstm seq2seq accuracy 94 9817 time taken for 1 epoch 01 36 img src output lstm seq2seq png width 70 align 11 lstm bidirectional seq2seq accuracy 94 517 time taken for 1 epoch 02 30 img src output bidirectional lstm seq2seq png width 70 align 12 lstm seq2seq vae accuracy 95 4190 time taken for 1 epoch 01 48 img src output lstm seq2seq vae png width 70 align 13 gru seq2seq accuracy 90 8854 time taken for 1 epoch 01 34 img src output gru seq2seq png width 70 align 14 gru bidirectional seq2seq accuracy 67 9915 time taken for 1 epoch 02 30 img src output bidirectional gru seq2seq png width 70 align 15 gru seq2seq vae accuracy 89 1321 time taken for 1 epoch 01 48 img src output gru seq2seq vae png width 70 align 16 attention is all you need accuracy 94 2482 time taken for 1 epoch 01 41 img src output attention is all you need png width 70 align 17 cnn seq2seq accuracy 90 74 time taken for 1 epoch 00 43 img src output cnn seq2seq png width 70 align 18 dilated cnn seq2seq accuracy 95 86 time taken for 1 epoch 00 14 img src output dilated cnn seq2seq png width 70 align bonus 1 how to forecast img src output how to forecast png width 70 align 2 sentiment consensus img src output sentiment consensus png width 70 align results analysis 1 outliers study using k means svm and gaussian on tesla stock img src misc outliers png width 70 align 2 overbought oversold study on tesla stock img src misc overbought oversold png width 70 align 3 which stock you need to buy img src misc which stock png width 40 align results simulation 1 simple monte carlo img src simulation monte carlo simple png width 70 align 2 dynamic volatity monte carlo img src simulation monte carlo dynamic volatility png width 70 align 3 drift monte carlo img src simulation monte carlo drift png width 70 align 4 multivariate drift monte carlo btc usdt with bitcurate sentiment img src simulation multivariate drift monte carlo png width 70 align 5 portfolio optimization img src simulation portfolio optimization png width 40 align | lstm lstm-sequence evolution-strategies stock-prediction-models seq2seq trading-bot stock-market stock-price-prediction stock-price-forecasting deep-learning-stock deep-learning monte-carlo strategy-agent learning-agents monte-carlo-markov-chain | ai |
cs231a-spr1617 | cs231a spring 2016 2017 my solutions for assignments of computer vision from 3d reconstruction to recognition at stanford university use it for learning purposes do not steal it for classes | ai |
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explorer | explorer the deso block explorer development server run ng serve for a dev server navigate to http localhost 4200 the app will automatically reload if you change any of the source files code scaffolding run ng generate component component name to generate a new component you can also use ng generate directive pipe service class guard interface enum module build run ng build to build the project the build artifacts will be stored in the dist directory use the prod flag for a production build | deso | blockchain |
cubesats | these matherials relate to my master thesis design of cooperative communication system based on cubesat satellites http opac lbs ilmenau gbv de db 1 ppn ppn 898368146 that were completed in august 2017 and developed more deeply during 2017 2019 as a hobby table of contents 1 wireless radio link budget calculation https nbviewer jupyter org github kirlf cubesats blob master linkbudget lb ipynb 2 statistical channel model survey https github com kirlf cubesats blob master statistical model md 3 list of cubesat transceivers https github com kirlf cubesats blob master fec md additional wireless optical link budget calculation https nbviewer jupyter org github kirlf cubesats blob master optical isl lb ipynb does not belong to the master thesis scope however cubesats are also considered introduction so i guess it s also necessary to say couple of words about research object cubesat satellites briefly it is the type of miniaturized satellites that used primiraly for education and science in the begining of their history the standard was developed in 1999 nowadays cubesats are used in navigation and other commercial applications more and more we believe that communication is also branch for cubesats and therefore we ve tried to increase their radiolink capacity during our research in short we can mention that theoretically it is possible and prospective http www cubesat org news feed 2016 1 14 ula announces cubesat launch program gitbook assets cube jpg table 1 1 cubesat s basic configurations cubesat format dimensions cm mass kg 1u 10x10x10 1 33 1 5u 15x10x10 2 2u 20x10x10 2 66 3u 30x10x10 4 6u 30x20x10 12 actually cubesats are very interesting topic and if you want to learn more you can visit the official site of the aiaa usu conference on small satellites https digitalcommons usu edu smallsat thank you for your attention and you are welcome m sc vladimir fadeev december 2017 january 2022 kazan list of our papers related to the topic 1 gaysin artur vladimir fadeev and marko hennh fer survey of modulation and coding schemes for application in cubesat systems https www researchgate net publication 318801748 survey of modulation and coding schemes for application in cubesat systems comments 2017 systems of signal synchronization generating and processing in telecommunications sinkhroinfo ieee 2017 2 gibalina zlata and vladimir fadeev optical inter satellite link in comparison with rf case in cubesat system http jre cplire ru jre oct17 6 text pdf zhurnal radioelektroniki journal of radio electronics 10 2017 with additional materials https nbviewer jupyter org github kirlf cubesats blob master optical isl lb ipynb 3 gibalina z s fadeev v a korsukova k a hennh fer m haardt m 2018 july estimation of capabilities of cooperative cubesat systems based on alamouti transmission scheme http www5 tu ilmenau de nt generic paper pdfs 08456940 pdf in 2018 systems of signal synchronization generating and processing in telecommunications synchroinfo pp 1 6 ieee | satellite cubesat fec ipynb link-budget communication cubesat-satellites channels channel-coding digital-modulation | os |
PROFILEGPT | profilegpt profilegpt is a tool for analyzing profiles and hashtags on twitter the application exploits various technologies and apis to collect data and generate information for users features profile analysis users can enter a twitter username to retrieve information about the user s profile including description creation date display name location followers count friends count and favorites count the app uses the twitter api and scraping techniques to collect the data hashtag analysis users can input a hashtag to scrape and analyze tweets related to that hashtag the app retrieves a specified number of recent tweets performs sentiment analysis and provides insights into the content context language used sentiment possible hidden meanings and potential network of interactions based on replies likes and retweets openai s gpt api is utilized for analyzing the tweets technologies used python flask html css bootstrap snscrape openai gpt api getting started 1 clone the repository git clone 2 install the required dependencies pip install r requirements txt 3 set up your openai gpt api credentials by signing up for an api key on the openai website https openai com 5 replace your openai api key in sncraper py like openai api key sk your api key 6 run the application python3 app py 7 access the application in your web browser at http localhost 5000 usage 1 enter a twitter username or hashtag in the provided input fields 2 click the search button to retrieve and analyze the data 3 view the generated insights and reports on the app s user interface acknowledgements openai https openai com for providing the gpt api bootstrap https getbootstrap com for the responsive design and ui components snscrape https github com justanotherarchivist snscrape for the twitter scraping library | server |
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azure-iot-remote-monitoring | this is the deprecated version of azure iot remote monitoring to view the updated remote monitoring solution please see the following links remote monitoring dotnet https github com azure azure iot pcs remote monitoring dotnet remote monitoring java https github com azure azure iot pcs remote monitoring java | server |
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mtg_card_detector | magic the gathering card detector mtg card detector is a real time application that can identify magic the gathering playing cards from either an image or a video it utilizes various computer vision techniques to process the input image and uses perceptual hashing https jenssegers com 61 perceptual image hashes to identify the detected image of the cards with the matching cards from the database of mtg cards refer to opencv dnn py https github com hj3yoo mtg card detector blob master opencv dnn py for more detailed implementation demo demo 1 https img youtube com vi bzkrzdyhmre 0 jpg https www youtube com watch v bzkrzdyhmre demo 1 you can run the demo using the following python3 opencv dnn py i path to input file o path to output directory hs one of 16 32 dsp dbg gph initially the project used a powerful neural network named you only look once yolo https arxiv org pdf 1506 02640v5 pdf to detect individual cards but it has been removed as of oct 12th 2018 note https github com hj3yoo mtg card detector oct 12th 2018 in favour of classical cv techniques demo demo 2 https img youtube com vi kfe k mwo2a 0 jpg https www youtube com watch v kfe k mwo2a demo 2 you can still find the files used to train them tiny yolo cfg https github com hj3yoo mtg card detector blob master cfg tiny yolo cfg tiny yolo final weights https github com hj3yoo mtg card detector blob master weights second general tiny yolo final weights obj data https github com hj3yoo mtg card detector blob master data obj data and obj names https github com hj3yoo mtg card detector blob master data obj names fetch data py https github com hj3yoo mtg card detector blob master fetch data py aggregates card images and database from scryfall com https scryfall com transform data py https github com hj3yoo mtg card detector blob master transform data py generate training images using the aggregated card images and database setup train py https github com hj3yoo mtg card detector blob master setup train py create train txt and test txt required to train yolo from the training dataset day 0 sep 6th 2018 uploading all the progresses on the model training for the last few days first batch of model training is completed where i used 40 000 generated images of mtg cards laid out in one of the pre defined pattern img src https github com hj3yoo darknet blob master figures 0 training set example 1 jpg width 360 img src https github com hj3yoo darknet blob master figures 0 training set example 2 jpg width 360 img src https github com hj3yoo darknet blob master figures 0 training set example 3 jpg width 360 after 5000 training epochs the model got 88 validation accuracy on the generated test set img src https github com hj3yoo darknet blob master figures 0 detection result 1 jpg width 360 img src https github com hj3yoo darknet blob master figures 0 detection result 2 jpg width 360 img src https github com hj3yoo darknet blob master figures 0 detection result 3 jpg width 360 img src https github com hj3yoo darknet blob master figures 0 detection result 4 jpg width 360 however there are some blind spots on the model notably fails to spot some of the obscured cards where only a fraction of them are shown fairly fragile against any glaring or light variations cannot detect any skewed cards example of bad detections img src https github com hj3yoo darknet blob master figures 0 detection result 5 jpg width 360 img src https github com hj3yoo darknet blob master figures 0 detection result 6 jpg width 360 img src https github com hj3yoo darknet blob master figures 0 detection result 7 jpg width 360 the second and third problems should easily be solved by further augmenting the dataset with random lighting and image skew i ll have to think more about the first problem though sept 7th 2018 added several image augmentation techniques to apply to the training set noise dropout light variation and glaring img src https github com hj3yoo darknet blob master figures 1 augmented set example 1 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 augmented set example 2 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 augmented set example 3 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 augmented set example 4 jpg width 360 currently trying to generate enough images to start model training hopefully this helps recompiled darknet with opencv and cudnn installed and recalculated anchors i ve ran a quick training with tiny yolo configuration with new training data and voila the model performs significantly better than the last iteration even against some hard images with glaring skew the first prediction model can t detect anything from these new test images so this is a huge improvement to the model img src https github com hj3yoo darknet blob master figures 1 detection result 1 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 decision result 2 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 decision result 3 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 decision result 4 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 decision result 5 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 decision result 6 jpg width 360 img src https github com hj3yoo darknet blob master figures 1 learning curve jpg width 640 the video demo can be found here https www youtube com watch v kfe k mwo2a feature youtu be sept 10th 2018 i ve been training a new model with a full yolov3 configuration previous one used tiny yolov3 and it s been taking a lot more resources img src https github com hj3yoo darknet blob master figures 2 learning curve jpg width 640 the author of darknet did mention that full network will take significantly more training effort so i ll just have to wait at this rate it should reach 50k epoch in about a week sept 13th 2018 the training for full yolov3 model has turned sour the loss saturated around 0 45 and didn t seem like it would improve in any reasonable amount of time img src https github com hj3yoo darknet blob master figures 3 learning curve jpg width 640 as expected the performance of the model with 0 45 loss was fairly bad not to mention that it s quite slower too i ve decided to continue with tiny yolov3 weights i tried to train it further but it was already saturated and was the best it could get bad news i couldn t find any repo that has python wrapper for darknet to pursue this project further there is a python example https github com alexeyab darknet blob master darknet py in the original repo of this fork but it doesn t support video input https github com alexeyab darknet issues 955 other darknet repos are in the same situation i suppose there is a poor man s alternative feed individual frames from the video into the detection script for image i ll have to give it a shot sept 14th 2018 thankfully opencv had an implementation for dnn which supports yolo as well they have done quite an amazing job and the speed isn t too bad either i can score about 20 25fps on my tiny yolo without using gpu sept 15th 2018 i tried to do an alternate approach instead of making model identify cards as annonymous train the model for every single card as you may imagine this isn t sustainable for 10000 different cards that exists in mtg but i thought it would be reasonable for classifying 10 different cards result suprisingly effective img src https github com hj3yoo darknet blob master figures 4 detection result 1 jpg width 360 img src https github com hj3yoo darknet blob master figures 4 detection result 2 jpg width 360 img src https github com hj3yoo darknet blob master figures 4 detection result 3 jpg width 360 img src https github com hj3yoo darknet blob master figures 4 detection result 4 png width 360 they re of course slightly worse than annonymous detection and impractical for any large number of cardbase but it was an interesting approach i ve made a quick opencv algorithm to extract cards from the image and it works decently well img src https github com hj3yoo darknet blob master figures 4 detection result 5 jpg width 360 at the moment it s fairly limited the entire card must be shown without obstruction nor cropping otherwise it won t detect at all unfortunately there is very little use case for my trained network in this algorithm it s just using contour detection and perceptual hashing to match the card sept 16th 2018 i ve tweaked the opencv algorithm from yesterday and ran for a demo https www youtube com watch v bzkrzdyhmre feature youtu be oct 4th 2018 with the current model i have there seems to be little hope i simply don t have enough knowledge in classical cv technique to separate overlaying cards even if i could perceptual hash will be harder to use if i were to use only a fraction of a card image to classify it there is an alternative to venture into instance segmentation with mask r cnn https arxiv org pdf 1703 06870 pdf at the cost of losing real time processing speed and considerably more development time maybe worth a shot although i d have to nearly start from scratch other than training data generation oct 10th 2018 i ve been trying to fiddle with the mask r cnn using this repo https github com matterport mask rcnn s implementation and got to train them with 60 manually labelled image set the result is not too bad considering such a small dataset was used however there was a high fp rate overall again probably because of small dataset and the simplistic features of cards img src https github com hj3yoo mtg card detector blob master figures 5 rcnn result 1 png width 360 img src https github com hj3yoo mtg card detector blob master figures 5 rcnn result 2 png width 360 img src https github com hj3yoo mtg card detector blob master figures 5 rcnn result 3 png width 360 img src https github com hj3yoo mtg card detector blob master figures 5 rcnn result 4 png width 360 img src https github com hj3yoo mtg card detector blob master figures 5 rcnn result 5 png width 360 although it may be worth to generate large training dataset and train the model more thoroughly i m being short on time as there are other priorities to do i may revisit this later i will be cleaning this repo in the next few days wrapping it up for now oct 12th 2018 i ve been able to significantly cut down the processing time of the current implementation for n cards detected in the video the latency has decreased from 65 50n ms to 7 16n ms there were two major bottlenecks that was slowing the program down in order to identify the card from the snippet of the card image i m using perceptual hashing when the card is detected in yolo i compute its phash value from its image and compare it with the phash of every cards in the database to find the match this process has a speed of o n m where n is the number of cards detected in the image and m is the number of cards in the database with more than 10000 different cards printed in mtg history this computation was the first bottleneck for the 50ms increment per detected card mentioned above majority of that time was spent trying to subtract two 1024 bit hashes 10000 times that s more than 10 10 comparisons right there first there were some overhead that was coming from the implementation of the library the following is the elapsed time for subtracting phash for all 10000 elements in pandas database hash size elapsed time ms 8 23 01 16 25 72 32 33 38 64 65 98 if you plot them using hash size 2 and elapsed time you get almost a linear graph with a huge constant y intercept img src https github com hj3yoo mtg card detector blob master figures 6 time plot 1 png where is this fat constant of 22 4ms coming from well we d better look at how the imagehash library https github com johannesbuchner imagehash blob master imagehash init py l67 l72 deals with subtraction def sub self other if other is none raise typeerror other hash must not be none if self hash size other hash size raise typeerror imagehashes must be of the same shape self hash shape other hash shape return numpy count nonzero self hash flatten other hash flatten the code flattens both hashes before comparison you might think how slow is that going to be apparently fair amount a np ones 1 1024 dtype np bool start time time for in range 10000 if a is none raise typeerror other hash must not be none if a size a size raise typeerror imagehashes must be of the same shape self hash shape other hash shape a flatten a flatten end time time elapsed end start 1000 print f elapsed the execution time of that code snippet on average is 11 65ms which is slightly over half of 22 4ms of constant delay that s a lot of time that can be cut out by pre emptively flattening the hashes and using the hash subtraction s code yes i know it s not a good oop design but this is too much of a tradeoff that constant time can be cut out significantly hash size elapsed time ms 8 9 9 16 11 54 32 18 55 64 45 79 furthermore turns out that hash size of 16 is sufficient enough to distinguish each cards in most of the case halving the hash size further knocked down 7 9ms as you only need to compare about quarter of the bits compared to hash size of 32 the other bottleneck is a something unfortunate turns out feeding the image through yolo network consumes a constant 50 60ms per frame remember the processing time of 65 50 ms above yeah that s where the 65ms is coming from as hilarious and ironic it is i would have to remove the network entirely to speed up the program facepalm into another dimension the program still works by replacing neural net with contour detection oct 13th 2018 cleaning up everything to wrap up this project for now if i can figure out how to move from bounding boxes of overlapping cards notes https github com hj3yoo mtg card detector oct 4th 2018 i may come back to upgrade the project in the future if you have any suggestion regarding this issue please don t hesitate to let me know thank you for reading all the way up to here hope this project has helped you in some way | python computer-vision machine-learning yolov3-tiny phash | ai |
DataCamp-Data-Engineer-with-Python | datacamp data engineer with python career track through hands on exercises you ll add cloud and big data tools such as aws boto pyspark spark sql and mongodb to your data engineering toolkit | data-engineering databases | cloud |
WebEngProjects | webengprojects iversity web engineering ii projects database | server |
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Resources | resources my compiled list of web development resources html css css frameworks bootstrap https getbootstrap com docs 4 4 getting started introduction bulma https bulma io documentation overview start materialize https materializecss com getting started html pure https purecss io start semantic https semantic ui com introduction getting started html tailwind https tailwindcss com flowbite tailwind components https flowbite com daisyui tailwind components https daisyui com material design lite https getmdl io foundation https get foundation css precompilers sass https sass lang com guide postcss https postcss org css libraries animate https daneden github io animate css css generators css gradient generator https cssgradient io gradient magic https www gradientmagic com accordion slider https accordionslider com best css button generator https www bestcssbuttongenerator com css button creator https cssbuttoncreator com web code tools https webcode tools clippy http bennettfeely com clippy cubic bezier http cubic bezier com separator generator https wweb dev resources css separator generator css grid generator https cssgrid generator netlify com shadow generator https brumm af shadows css effects https emilkowalski github io css effects snippets animista https animista net tailwind css blocks https blocks wickedtemplates com whatamesh https whatamesh vercel app glassmorphism https markodenic com tools glassmorphism css generator shape divider https www shapedivider app 9elements fancy border radius generator https 9elements github io fancy border radius smooth shadow https shadows brumm af haikei background generator https app haikei app pattern svg generators blobmaker https www blobmaker app getwaves https getwaves io patterns https bansal io pattern css triangles paaatterns https products ls graphics paaatterns scribbbles https www scribbbles design svgator https www svgator com hola svg loader generator https loaders holasvg com patternpad https patternpad com svg backgrounds and svg tocss https www svgbackgrounds com tools svg to css color generators coolors https coolors co app paletton https paletton com flat ui colors https flatuicolors com lol colors https www webdesignrankings com resources lolcolors color hunt https colorhunt co palette ninja https palette ninja colors lol https colors lol happy hues https www happyhues co colormixer https colormixer web app 02007115ff623007ff9bc91b64440301ffff7c5f55610300 sunset colorspace https mycolor space json generators objgen http www objgen com json demo true json validator jsonlint https jsonlint com learning flexbox froggy https flexboxfroggy com grid garden https cssgridgarden com css tricks https css tricks com ui design https learnui design shift nudge https shiftnudge com reference bem cheatsheet https 9elements com bem cheat sheet html5 canvas cheatsheet https simon html5 org dump html5 canvas cheat sheet html css3 media queries cheatsheet https mac blog org ua css 3 media queries cheat sheet bootstrap 5 cheatsheet https bootstrap cheatsheet themeselection com seo cheatsheet https moz com learn seo seo cheat sheet assets images videos undraw https undraw co unsplash free stock photos https unsplash com hero patterns http www heropatterns com coverr free stock videos https coverr co looka brand designer https looka com autodraw https www autodraw com allthefreestock https allthefreestock com mixkit https mixkit co pexels https www pexels com photo creator https photos icons8 com creator removebg https www remove bg ai generated photos https generated photos faces duotone https duotone shapefactory co openpeeps https www openpeeps com humaaans https www humaaans com ouch https icons8 com ouch avataaars https getavataaars com lookup design https lookup design 404 illustrations https error404 fun icons icomoon https icomoon io iconfinder https www iconfinder com allthefreestock https allthefreestock com linea icons https themeui net linea free outline iconset css icons https css gg tilda https tilda cc free icons ikonate https ikonate com eva icons https akveo github io eva icons icons8 animated icons https icons8 com animated icons icon fonts fontawesome https fontawesome com from io fontawesome cheatsheet https fontawesome com cheatsheet from io material icons https material io resources icons icon person style baseline mdi icons https materialdesignicons com fontello http fontello com heroicons https heroicons com fonts fontsquirrel https www fontsquirrel com google fonts https fonts google com fonts arena https fontsarena com befonts https befonts com fontjoy https fontjoy com design tools responsively https responsively app javascript front end libraries frameworks angular https angular io aurelia https aurelia io ember https emberjs com react https reactjs org svelte https svelte dev vue https vuejs org jquery https jquery com jquery ui https jqueryui com jquery mobile https jquerymobile com back end node js https nodejs org en express https expressjs com meteor https www meteor com static site generators gatsby https www gatsbyjs com hugo https gohugo io other tooling prisma https www prisma io libraries chart js https www chartjs org moment js https momentjs com docs displaying sweet alert https sweetalert js org docs tippy js https atomiks github io tippyjs popper js https popper js org lodash https lodash com vue component frameworks buefy bulma vue https buefy org mdbootstrap vue https mdbootstrap com docs vue vuetify https vuetifyjs com en getting started quick start vue frameworks nuxt https nuxtjs org guide vue design system https vueds com inertia https inertiajs com vue plugins vue toastification https github com maronato vue toastification react frameworks reactstrap https reactstrap github io material ui https material ui com next js https nextjs org learning wes bos courses https wesbos com courses vue mastery https www vuemastery com vue developers https vuejsdevelopers com design systems and cms s storybook https storybook js org docs basics introduction storyblok https www storyblok com strapi https strapi io documentation developer docs latest getting started introduction html vuepress https vuepress vuejs org guide reference npm tips https corgibytes com blog 2017 04 18 npm tips php reference php net https www php net php fig https www php fig org php the right way https phptherightway com onramp from tighten https onramp dev back end frameworks cakephp https cakephp org codeigniter https codeigniter com laravel https laravel com laravel frontend https laravel com docs 7 x frontend lighthouse graphql framework forlaravel https lighthouse php com jetstream https jetstream laravel com livewire https laravel livewire com inertiajs https inertiajs com alpinejs https alpinejs dev slim https www slimframework com symfony https symfony com content management systems cms wordpress https wordpress com magento https magento com drupal https www drupal org joomla https www joomla org libraries carbon https carbon nesbot com docs api introduction faker https github com fzaninotto faker fakerproviderlorem learning laracasts https laracasts com regex regexr learn build and test regex https regexr com api graphql graphql https graphql org learn graphiql https github com graphql graphiql graphql playground https github com prismagraphql graphql playground vue apollo https apollo vuejs org guide mobile utilities frameworks adobe phonegap https phonegap com apache cordova https cordova apache org flutter https flutter dev docs framework7 https framework7 io templates ionic https ionicframework com vue native https vue native io nativescript vue https nativescript vue org general web development learning freecodecamp https www freecodecamp org udemy https udemy com pluralsight paid https pluralsight com the odin project https www theodinproject com mozilla developer network https developer mozilla org en us docs learn codecademy https www codecademy com learn paths web development web developer roadmap https github com kamranahmedse developer roadmap execute program https executeprogram com state of js https 2020 stateofjs com udemy courses advanced css and sass by jonas schmedtmann https www udemy com course advanced css and sass learn lecture 8274498 start 390 overview the complete javascript course by jonas schmedtmann https www udemy com course the complete javascript course learn web design for web developers by jonas schmedtmann https www udemy com course web design secrets learn lecture 2744010 overview modern javascript bootcamp by andrew mead https www udemy com course modern javascript learn the complete react developer course by andrew mead https www udemy com course react 2nd edition learn the complete node js developer course by andrew mead https www udemy com course the complete nodejs developer course 2 learn web developer bootcamp by colt steele https www udemy com course the web developer bootcamp learn lecture 3861190 overview the ultimate mysql bootcamp by colt steele https www udemy com course the ultimate mysql bootcamp go from sql beginner to expert learn vue js 2 the complete guide by maximilian schwarzmuller https www udemy com course vuejs 2 the complete guide learn react the complete guide by maximilian schwarzmuller https www udemy com course react the complete guide incl redux learn angular the complete guide by maximilian schwarzmuller https www udemy com course dashboard redirect course id 756150 ultimate web developer by brad hussey https www udemy com course web developer course learn the complete web developer course 2 0 by rob percival https www udemy com course the complete web developer course 2 learn the complete web development bootcamp by dr angela yu https www udemy com course the complete web development bootcamp learn lecture 12638830 start 0 overview complete python bootcamp by jose portilla https www udemy com course complete python bootcamp learn svelte js the complete guide incl sapper js by maximilian schwarzmuller https www udemy com course sveltejs the complete guide learn learn how to code google s go golang by todd mcleod https www udemy com course sveltejs the complete guide learn data structures and algorithms by andrei neagoie https www udemy com course master the coding interview data structures algorithms learn the complete junior to senior web developer roadmap by andrei neagoie https www udemy com course the complete junior to senior web developer roadmap learn the complete web developer zero to mastery by andrei neagoie https www udemy com course the complete web developer zero to mastery learn advanced javascript concepts by andrei neagoie https www udemy com course advanced javascript concepts learn javascript the complete guide 2020 beginner to advanced by maximilian schwarzmuller https www udemy com course javascript the complete guide 2020 beginner advanced learn laracasts courses by jeffrey way the php practitioner https laracasts com series php for beginners laravel 6 from scratch https laracasts com series laravel 6 from scratch object oriented principles in php https laracasts com series object oriented principles in php reference learn web development https developer mozilla org en us docs learn emmet cheatsheet https docs emmet io cheat sheet markdown cheatsheet https en support wordpress com markdown quick reference html mdn https developer mozilla org en us docs web html element css mdn https developer mozilla org en us docs web css reference javascript mdn https developer mozilla org en us docs web javascript reference css easing http easings net css for people who hate css https paulcpederson com articles css for people who hate css concepts atomic design by brad frost https atomicdesign bradfrost com bem block element modifier http getbem com introduction blogs and subreddits dev to https dev to devhints io https devhints io hacker news https news ycombinator com r css https www reddit com r css r frontend https www reddit com r frontend r javascript https www reddit com r javascript r jquery https www reddit com r jquery r laravel https www reddit com r laravel r php https www reddit com r php r phphelp https www reddit com r phphelp r reactjs https www reddit com r reactjs r vuejs https www reddit com r vuejs r web design https www reddit com r web design r webdev https www reddit com r webdev design and prototyping tools figma https www figma com invision https www invisionapp com design resources laws of ux https lawsofux com placeholder text lipsum https lipsum lipsum com feed html hipsum https hipsum co zombie ipsum http www zombieipsum com cupcake ipsum http www cupcakeipsum com samuel l ipsum http slipsum com heisenberg ipsum http heisenbergipsum com mail utilities mailtrap smtp testing https mailtrap io public api s list of public api s https github com public apis public apis jikan https jikan docs apiary io spotify https developer spotify com documentation web api yelp https www yelp com developers thetvdb https thetvdb com zip code api https www zipcodeapi com 15 fun apis for your next project https dev to biplov 15 fun apis for your next project 5053 10 fun apis to use for your next project https dev to hb 10 fun apis to use for your next project 2lco apilist fun https apilist fun game development phaser https phaser io tutorials getting started phaser3 index local development environments installing amp stack on mac https jasonmccreary me articles install apache php mysql mac os x sierra mac web development environment https mallinson ca posts 5 the perfect web development environment for your new mac devilbox http devilbox org laragon https laragon org mamp https www mamp info en windows wamp windows only http www wampserver com en laravel valet mac only https laravel com docs 6 x valet setup wsl on windows for web dev https github com cdterry87 setupwsl digital ocean setup link a domain https www digitalocean com community tutorials how to point to digitalocean nameservers from common domain registrars initial server setup https www digitalocean com community tutorials initial server setup with ubuntu 18 04 setup lamp stack https www digitalocean com community tutorials how to install linux apache mysql php lamp stack ubuntu 18 04 reset mysql password https www digitalocean com community tutorials how to reset your mysql or mariadb root password on ubuntu 18 04 setup mod rewrite in apache for cleaner urls https www digitalocean com community tutorials how to rewrite urls with mod rewrite for apache on ubuntu 16 04 install git https www digitalocean com community tutorials how to install git on ubuntu 18 04 install composer https www digitalocean com community tutorials how to install and use composer on ubuntu 18 04 install node js https www digitalocean com community tutorials how to install node js on ubuntu 18 04 deploy laravel to digitalocean https medium com franciscojbarrera how to deploy laravel on digital ocean lamp e69046906fe digital ocean setup advanced setup virtual hosts https www digitalocean com community tutorials how to set up apache virtual hosts on ubuntu 16 04 setup ssl certificates with letsencrypt https www digitalocean com community tutorials how to secure apache with let s encrypt on ubuntu 18 04 letsencrypt wildcard certificates with cerbot https www digitalocean com community tutorials how to create let s encrypt wildcard certificates with certbot setting up ssl cert with a wildcard with certbot replace your tld com below with your top level domain name sudo certbot certonly manual preferred challenges dns server https acme v02 api letsencrypt org directory agree tos d your tld com your tld com or if you have the dns digitalocean certbot plugin https certbot dns digitalocean readthedocs io en stable installed sudo certbot certonly dns digitalocean dns digitalocean credentials certbot creds ini d your tld com d your tld com web design inspirations web design inspirations https www webdesign inspiration com codemyui https codemyui com landingfolio https www landingfolio com webdevresources https webdevresources info undesign https undesign learn uno muzli https muz li browser compatibility can i use http caniuse com tools vscode https code visualstudio com eslint by dirk baeumer laravel blade snippets by winnie lin material icon theme by philipp kief php intelephense by ben mewburn pocstcss language support by csstools prettier code formatter by esben petersen rainglow by dayle rees scss intellisense by mrmlnc vetur by pine wu vue vscode snippets by sarah drasner vscode cheatsheet https code visualstudio com shortcuts keyboard shortcuts windows pdf phpstorm https www jetbrains com phpstorm transmit https panic com transmit tableplus https tableplus com sequelpro has issues on mac catalina https www sequelpro com hyper alternative and themeable terminal https hyper is affinitydesigner alternative to photoshop https affinity serif com en us designer insomnia https insomnia rest postman https www postman com bootstrap 5 cheatsheet https bootstrap cheatsheet themeselection com cloud hosting options digital ocean https www digitalocean com linode https www linode com netlify https www netlify com heroku https www heroku com render https render com debugging logging and content management utilities sentry https sentry io welcome sanity https www sanity io cloudinary https cloudinary com logrocket https logrocket com | hacktoberfest | front_end |
iotex-core | iotex core official golang implementation of the iotex protocol join the forum https img shields io badge discuss iotex 20community blue https community iotex io c research development protocol go version https img shields io badge go 1 18 5 blue svg https github com moovweb gvm go report card https goreportcard com badge github com iotexproject iotex core https goreportcard com report github com iotexproject iotex core coverage https codecov io gh iotexproject iotex core branch master graph badge svg https codecov io gh iotexproject iotex core godoc http img shields io badge go documentation blue svg style flat square https godoc org github com iotexproject iotex core releases https img shields io github release iotexproject iotex core all svg style flat square https github com iotexproject iotex core releases license https img shields io badge license apache 202 0 blue svg license a href https iotex io img src logo iotex png height 200px a welcome to the official go implementation of iotex protocol iotex is building the next generation of the decentralized blockchain protocol for powering real world information marketplace in a decentralized yet scalable way refer to iotex whitepaper https iotex io research for details a href https iotex io devdiscord target blank img src https github com iotexproject halogrants blob 880eea4af074b082a75608c7376bd7a8eaa1ac21 img btn discord svg height 36px a new to iotex please visit https iotex io official website or iotex onboard pack https onboard iotex io to learn more about iotex network run a delegate please visit iotex delegate manual https github com iotexproject iotex bootstrap for detailed setup process building the source code minimum requirements components version description golang https golang org ge 1 18 5 go programming language protoc https developers google com protocol buffers ge 3 6 0 protocol buffers required only when you rebuild protobuf messages compile download the code to your desired local location doesn t have to be under gopath src git clone git github com iotexproject iotex core git cd iotex core if you put the project code under your gopath src you will need to set up an environment variable export go111module on set go111module on for windows build the project for general purpose server ioctl by make build the project for broader purpose server ioctl injector by make all if the dependency needs to be updated run go get u go mod tidy if you want to learn more advanced usage about go mod you can find out here https github com golang go wiki modules run unit tests only by make test build the docker image by make docker run iotex core start or resume a standalone server to operate on a blockchain by make run restart the server from a clean state by make reboot if make run fails due to corrupted or missing state database while block database is in normal condition e g failing to get factory s height from underlying db please try to recover state database by make recover then make run again use cli users could interact with iotex blockchain by ioctl command refer to cli document https docs iotex io developer ioctl install html for more details contact mailing list iotex dev iotex dev iotex io dev forum forum https community iotex io c research development protocol bugs issues https github com iotexproject iotex core issues contribution we are glad to have contributors out of the core team contributions including but not limited to style bug fixes implementation of features proposals of schemes algorithms and thorough documentation are welcomed please refer to our contribution guideline contributing md for more information development guide documentation is here https github com iotexproject iotex core wiki developers 27 guide for any major protocol level changes we use iip https github com iotexproject iips to track the proposal decision and etc contributors thank you for considering contributing to the iotex framework a href https github com iotexproject iotex core graphs contributors img src https contrib rocks image repo iotexproject iotex core a license this project is licensed under the apache license 2 0 license | blockchain cryptography distributed-systems crypto internet-of-things internet-of-everything machinefi depin | blockchain |
alist-web | usage those templates dependencies are maintained via pnpm https pnpm io via pnpm up lri this is the reason you see a pnpm lock yaml that being said any package manager will work this file can be safely be removed once you clone a template bash npm install or pnpm install or yarn install learn more on the solid website https solidjs com and come chat with us on our discord https discord com invite solidjs available scripts in the project directory you can run npm dev or npm start runs the app in the development mode br open http localhost 5173 http localhost 5173 to view it in the browser the page will reload if you make edits br npm run build builds the app for production to the dist folder br it correctly bundles solid in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes br your app is ready to be deployed deployment you can deploy the dist folder to any static host provider netlify surge now etc | front_end |
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Ionic-Infotrafic | ionic infotrafic mobile development | front_end |
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lime | lime build status https travis ci org marcotcr lime svg branch master https travis ci org marcotcr lime binder https mybinder org badge logo svg https mybinder org v2 gh marcotcr lime master this project is about explaining what machine learning classifiers or models are doing at the moment we support explaining individual predictions for text classifiers or classifiers that act on tables numpy arrays of numerical or categorical data or images with a package called lime short for local interpretable model agnostic explanations lime is based on the work presented in this paper https arxiv org abs 1602 04938 bibtex here for citation https github com marcotcr lime blob master citation bib here is a link to the promo video a href https www youtube com watch v hunrcxnydcc target blank img src https raw githubusercontent com marcotcr lime master doc images video screenshot png width 450 alt kdd promo video a our plan is to add more packages that help users understand and interact meaningfully with machine learning lime is able to explain any black box classifier with two or more classes all we require is that the classifier implements a function that takes in raw text or a numpy array and outputs a probability for each class support for scikit learn classifiers is built in installation the lime package is on pypi https pypi python org pypi lime simply run sh pip install lime or clone the repository and run sh pip install we dropped python2 support in 0 2 0 0 1 1 37 was the last version before that screenshots below are some screenshots of lime explanations these are generated in html and can be easily produced and embedded in ipython notebooks we also support visualizations using matplotlib although they don t look as nice as these ones two class case text negative blue words indicate atheism while positive orange words indicate christian the way to interpret the weights by applying them to the prediction probabilities for example if we remove the words host and nntp from the document we expect the classifier to predict atheism with probability 0 58 0 14 0 11 0 31 twoclass doc images twoclass png multiclass case multiclass doc images multiclass png tabular data tabular doc images tabular png images explaining prediction of cat in pros and cons img src https raw githubusercontent com marcotcr lime master doc images images png width 200 tutorials and api for example usage for text classifiers take a look at the following two tutorials generated from ipython notebooks basic usage two class we explain random forest classifiers https marcotcr github io lime tutorials lime 20 20basic 20usage 2c 20two 20class 20case html multiclass case https marcotcr github io lime tutorials lime 20 20multiclass html for classifiers that use numerical or categorical data take a look at the following tutorial this is newer so please let me know if you find something wrong tabular data https marcotcr github io lime tutorials tutorial 20 20continuous 20and 20categorical 20features html tabular data with h2o models https marcotcr github io lime tutorials tutorial h2o continuous and cat html latin hypercube sampling doc notebooks latin 20hypercube 20sampling ipynb for image classifiers images basic https marcotcr github io lime tutorials tutorial 20 20images html images faces https github com marcotcr lime blob master doc notebooks tutorial 20 20faces 20and 20gradboost ipynb images with keras https github com marcotcr lime blob master doc notebooks tutorial 20 20image 20classification 20keras ipynb mnist with random forests https github com marcotcr lime blob master doc notebooks tutorial 20 20mnist 20and 20rf ipynb images with pytorch https github com marcotcr lime blob master doc notebooks tutorial 20 20images 20 20pytorch ipynb for regression simple regression https marcotcr github io lime tutorials using 2blime 2bfor 2bregression html submodular pick submodular pick https github com marcotcr lime tree master doc notebooks submodular 20pick 20examples ipynb the raw non html notebooks for these tutorials are available here https github com marcotcr lime tree master doc notebooks the api reference is available here https lime ml readthedocs io en latest what are explanations intuitively an explanation is a local linear approximation of the model s behaviour while the model may be very complex globally it is easier to approximate it around the vicinity of a particular instance while treating the model as a black box we perturb the instance we want to explain and learn a sparse linear model around it as an explanation the figure below illustrates the intuition for this procedure the model s decision function is represented by the blue pink background and is clearly nonlinear the bright red cross is the instance being explained let s call it x we sample instances around x and weight them according to their proximity to x weight here is indicated by size we then learn a linear model dashed line that approximates the model well in the vicinity of x but not necessarily globally for more information read our paper https arxiv org abs 1602 04938 or take a look at this blog post https www oreilly com learning introduction to local interpretable model agnostic explanations lime img src https raw githubusercontent com marcotcr lime master doc images lime png width 300px contributing please read this contributing md | ai |
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wildwebdeveloper | eclipse wild web developer web dev in eclipse ide edit of html css javascript typescript json schema xml schema yaml schema kubernetes and debug node js and html js web apps simply and efficiently in the eclipse ide to see it in action open the desired file with the generic editor that s included by default in the eclipse ide rich edition for html css scss sass less javascript ecmascript typescript json including schema support yaml including schema support kubernetes schema built in xml including schema support xsl xsd dtd relaxng and kubernetes angular components templates in typescript and html files react jsx tsx embedded html eslint for javascript and typescript vue js supported features for edition are validation diagnostics markers code completion hover outline rename refactoring jump to declaration find references color preview and other features part of the language server protocol supported debugging targets node js firefox chromium chrome screenshot documentation files wildwebdeveloper screenshot png wild web developer screenshot get it now as full ide package get an eclipse ide for javascript and web developers download from https download eclipse org wildwebdeveloper releases latest ides https download eclipse org wildwebdeveloper releases latest ides unpack and run as extension into an existing eclipse ide installation with eclipse marketplace client and https marketplace eclipse org content wild web developer html css javascript typescript nodejs angular json yaml kubernetes xml choose one of click here to install https mickaelistria github io redircttoeclipseideclonecommand redirecttomarketplace html entryid 5578890 help eclipse marketplace search wild web developer then click install or drag a href http marketplace eclipse org marketplace client intro mpc install 5578890 class drag title drag to your running eclipse workspace requires eclipse marketplace client img class img responsive src https marketplace eclipse org sites all themes solstice public images marketplace btn install png alt drag to your running eclipse workspace requires eclipse marketplace client a into eclipse ide or with help install new software https help eclipse org latest topic org eclipse platform doc user tasks tasks 124 htm with p2 repo https download eclipse org wildwebdeveloper snapshots media library see release notes release notes md for details about recent major improvement in the project january 2020 quick and complete demo of wild web developer and related strategy about language server and debug adapters at fosdem https fosdem org 2020 schedule event lspdebug january 2020 demo of eslint in wild web developer https youtu be o wi niez3e october 2019 demo debugging javascript in html with chrome https youtu be 6l071ztnuok october 2019 demo angular template edition assistance https www screencast com t 6gti4jf6svr september 2019 demo debugging javascript in html with firefox https www youtube com watch v 4q ctvsejy june 2019 the eclipse ide for web and javascript developers strikes back https www eclipse org community eclipse newsletter 2019 june eclipsetldr php on eclipse foundation newsletter jun 2019 demo of terminal and build integration for npm https www screencast com t bbwikutjyi95 apr 2019 eclipse wild web developer adds a powerful yaml editor with built in kubernetes support https developers redhat com blog 2019 04 10 eclipse wild web developer adds a powerful yaml editor with built in kubernetes support on red hat developers blog mar 2019 demo of yaml editor support https youtu be p9ettuhiuco feb 2019 demo of xml editor support https youtu be fikuduzfdzg jan 2019 demo of node js launch and debug https www screencast com t 0qrpxsa3m7qy apr 2017 xml json css javascript typescript demo http www screencast com t bac9dxhiqd design and architecture wild web developer is based on the eclipse generic editor framework from eclipse platform https projects eclipse org projects eclipse platform eclipse lsp4e https projects eclipse org projects technology lsp4e and eclipse tm4e https github com eclipse tm4e in order to provide editors based on textmate grammars visual studio code html css json language servers theia s typescript and javascript language servers https github com theia ide typescript language server red hat s xml languageserver https github com angelozerr lsp4xml and yaml language server https github com redhat developer yaml language server and on eclipse debug stack lsp4e debug adapter protocol support and visual studio code node debug adapter to provide debugging get involved see contribution guide contributing md | hacktoberfest | front_end |
LS-Neural-Networks-NLP | tss 2023 neural networks and large language models misc banner png ls neural networks nlp this repository will contain all the resources and assignments of the learners space 2023 neural networks amp large language models course week 1 neural networks week1 week 2 natural language processing week2 week 3 recurrent neural networks week3 week 4 transformers and llms week4 final project final 20project | ai |
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deploying-machine-learning-models | deployment of machine learning models accompanying repo for the online course deployment of machine learning models for the documentation visit the course on udemy https www udemy com deployment of machine learning models couponcode tidrepo | ai |
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deuxdrop | warning this project is not actively maintained warning proceed at own risk no good deuxdrop secure messaging with the distributed federation and deliver model of email but with the contact list model of chat systems like jabber raindrop 2 raindrop deux deuxdrop checking it out note the recursive option git clone recursive git github com mozilla deuxdrop git directory structure clients https github com mozilla deuxdrop tree develop clients clients and client specific js code common https github com mozilla deuxdrop tree develop common js code shared between client server deploy https github com mozilla deuxdrop tree develop deploy cobbler puppet automation for setting up servers dev machines servers https github com mozilla deuxdrop tree develop servers servers and server specific js code system deps see the install instructsions https github com mozilla deuxdrop wiki install instructions for help redis http redis io currently for the prototype node js https github com joyent node npm https github com isaacs npm building note make sure you checked us out with git clone recursive if not do git submodule init and then git submodule update cd servers note make sure you have npm https github com isaacs npm and node js https github com joyent node installed see instructsions https github com mozilla deuxdrop wiki install instructions npm install problems installing checkout our build faq https github com mozilla deuxdrop wiki install instructions running the cmdline tool will help you get things running use cmdline help for more cmdline run server docs see the deuxdrop wiki https github com mozilla deuxdrop wiki for more documentation debugging try the built in loggest displays the client daemon s logs are available at about loggest the server s logs are available at about loggest server if the server is run with loggest web debug have firefox display websocket connection details by enabling prlogging for it export nspr log modules nswebsocket 5 then run to get on stdout export nspr log file tmp firefox log if you don t want it on stdout on windows use set nspr log modules nswebsocket 5 and set nspr log file temp log txt | os |
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giisi_database_design | giisi database design projects and practical exercises of the subject database design in computer engineering of information systems degree | server |
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ng-lightning | ng lightning build status https travis ci org ng lightning ng lightning svg branch master https travis ci org ng lightning ng lightning sauce test status https saucelabs com buildstatus ng lightning https saucelabs com u ng lightning npm version https badge fury io js ng lightning svg https www npmjs com package ng lightning npm https img shields io npm dm ng lightning svg maxage 2592000 https www npmjs com package ng lightning this library contains native angular https angular io components and directives written from scratch in typescript using the lightning design system https www lightningdesignsystem com css framework we are looking for community help to find and fix bugs improve demo site and create new components installation install through npm bash npm install save ng lightning dependencies this library depends on salesforce s lds markup and css tested with 2 9 we don t ship any css file but you have to take care of including lds css rules in your page there are various ways to achieve this for example compiling through their source files salesforce ux design system https github com salesforce ux design system or by adding this into your head html link rel stylesheet href https unpkg com salesforce ux design system assets styles salesforce lightning design system min css svg icons because of various cross domain issues if you want to use slds icons you must provide a copy of the various sprite files ie salesforce ux design system assets icons action sprite svg symbols svg served locally through your server ie11 support unfortunately ie11 does not support two important features svg external content https css tricks com svg use with external reference take 2 used to load svg icons from a spritemap in order to support this you will need to use a small script called svg4everybody https github com jonathantneal svg4everybody available on npm cdn here https unpkg com svg4everybody element classlist on svg elements used by angular s renderer setelementclass see here https github com angular angular issues 6327 for more information use classlist js https github com eligrey classlist js shim available on npm cdn here https unpkg com classlist js typically these shims should be placed within the head element usage demo http ng lightning github io ng lightning contributing we are always looking for high quality contributions please check the contributing md contributing md doc for guidelines need help for questions on how to use ng lightning or how to contribute please post questions to img alt stack overflow src https cdn sstatic net sites stackoverflow company img logos so so logo svg v 2bb144720a66 width 140 https stackoverflow com questions tagged ng lightning using the ng lightning tag if you find a bug in the source code or a mistake in the documentation you can help us by submitting an issue to our github repository companies using ng lightning viabill https viabill dk zulutrade http zulutrade com browsers we support the same browsers and versions supported by both angular and salesforce s lightning design system cross browser environment testing is performed through saucelabs https saucelabs com | ng-lightning angular lightning salesforce-lightning | os |
vision-tutorial | vision tutorial p align center img width 100 src https media thumbs golden com olqzmrmwazy1p7sl29k2t9wjjdm 200x200 smart golden storage production s3 amazonaws com topic images e08914afa10a4179893eeb07cb5e4713 png img width 100 src https keras io img keras logo small wb png p vision tutorial is a tutorial for who is studying computer vision basic architectures using pytorch and keras most of the models about vision were implemented with less than 100 lines of code except comments or blank lines the list of these papers is a list that professor sung kim https github com hunkim recommended data was used as overfitting to show simple model learning one image about cat or dog https github com graykode vision tutorial tree master data the accuracy of the model is not important in this project because it is affected by data i recommend that you focus on the structure of the model the number of parameters the learning process and paper detailed implementation sota basic vision models introduction how to handle image in pytorch and keras image resizing cropping introduction cnn convolutional neural networks in pytorch and keras how does number of channels filter size kernel grid and padding affect convolution paper object recognition with gradient based learning http yann lecun com exdb publis pdf lecun 99 pdf alexnet 2012 09 paper imagenet classification with deep convolutional neural networks https papers nips cc paper 4824 imagenet classification with deep convolutional neural networks pdf model 2 alexnet model jpg zfnet 2013 11 paper visualizing and understanding convolutional networks https arxiv org abs 1311 2901 vgg16 2014 09 paper very deep convolutional networks for large scale image recognition https arxiv org abs 1409 1556 inception v1 a k a googlenet 2014 09 paper going deeper with convolutions https arxiv org abs 1409 4842 inception v2 v3 2015 12 paper rethinking the inception architecture for computer vision https arxiv org abs 1512 00567 resnet 2015 12 paper deep residual learning for image recognition https arxiv org abs 1512 03385 model 7 resnet model jpeg inception v4 2016 02 paper inception v4 inception resnet and the impact of residual connections on learning https arxiv org abs 1602 07261 densenet 2016 08 paper densely connected convolutional networks https arxiv org abs 1608 06993 model 9 densenet model jpg xception 2016 10 paper xception deep learning with depthwise separable convolutions https arxiv org abs 1610 02357 mobilenet 2017 04 paper mobilenets efficient convolutional neural networks for mobile vision applications https arxiv org abs 1704 04861 senet 2017 09 paper squeeze and excitation networks https arxiv org abs 1709 01507 to be continue implementation in other repository v semantic segmentation fcn 2014 11 fully convolutional networks for semantic segmentation https arxiv org abs 1411 4038 u net 2015 05 u net convolutional networks for biomedical image segmentation https arxiv org abs 1505 04597 https arxiv org abs 1606 00915 segnet 2015 11 segnet a deep convolutional encoder decoder architecture for image segmentation https arxiv org abs 1511 00561 deeplab 2016 06 deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs https arxiv org abs 1606 00915 enet 2016 07 enet a deep neural network architecture for real time semantic segmentation https arxiv org abs 1606 02147 pspnet 2016 12 pyramid scene parsing network https arxiv org abs 1612 01105 icnet 2017 04 icnet for real time semantic segmentation on high resolution images https arxiv org abs 1704 08545 v generative adversarial networks gan 2014 06 generative adversarial networks https arxiv org abs 1406 2661 dcgan 2015 11 unsupervised representation learning with deep convolutional generative adversarial networks https arxiv org abs 1511 06434 pix2pix 2016 11 image to image translation with conditional adversarial networks https arxiv org abs 1611 07004 wgan 2017 01 wasserstein gan https arxiv org abs 1701 07875 cyclegan 2017 05 unpaired image to image translation using cycle consistent adversarial networks https arxiv org abs 1703 10593 v object detection rcnn 2013 11 rich feature hierarchies for accurate object detection and semantic segmentation https arxiv org abs 1311 2524 fast rcnn 2015 04 fast r cnn https arxiv org abs 1504 08083 faster rcnn 2015 06 faster r cnn towards real time object detection with region proposal networks https arxiv org abs 1506 01497 yolo 2015 06 you only look once unified real time object detection https arxiv org abs 1506 02640 ssd 2015 12 ssd single shot multibox detector https arxiv org abs 1512 02325 yolo9000 2016 12 yolo9000 better faster stronger https arxiv org abs 1612 08242 mask r cnn 2017 05 mask r cnn https arxiv org abs 1703 06870 retinanet 2017 08 focal loss for dense object detection https arxiv org abs 1708 02002 author tae hwan jung jeff jung graykode author email nlkey2022 gmail com mailto nlkey2022 gmail com | ai |
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microcli | microcli microcli is a light weight command line interpreter for embedded platforms and resource constrained environments features 1 light weight compile options to disable un wanted features 1 portable easy to integrate into systems with non standard i o 1 can run without dynamic memory if command history is disabled 1 configurable at runtime print verbosity levels can be modified programmatically at runtime usage see some of the examples for reference | os |
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BeliefSat | beliefsat beliefsat is a 2p pocketqube standard student nano satellite being developed by the undergraduate students of k j somaiya institute of engineering and information technology sion mumbai https kjsieit somaiya edu en the satellite itself is a sub part of team s proposal under ps4 orbital platform program https www isro gov in update 15 jun 2019 announcement of opportunity ao orbital platform of isro wherein team aims to demonstrate indegenously developed technologies for pocketqube standard nano satellites as a part of this demonstration beliefsat will be launched out of somaiyapod which is a pocketqube standard deployer being indegenously developed at the institute the unique construction technique combination of cots components for communication on board computer and power sub systems together constitute of somaiyapqbus around which the satellite is being made is also one of the technologies that the team wants to demonstrate and open source to enable use by future missions so what will beliefsat do in space and what results it will give 1 if it is successfully deployed out of the somaiyapod and completes startup procedure it will be dead inside the pod and will only get power after it is out of pod and then sends a beacon reliability of both deployer and the design of the satellite will be confirmed 2 every 1 minute it will send a telemetry packet which will have values from different cots sensors the team aims to project the activity of telemetry reception as a way of involving more students in to software defined radio and rf in general 3 it will have a functionality as of a amateur radio digipeater which means if you hold a proper amateur radio license then you can send a text message to the satellite and the satellite will repeat it after delay of upto 10 seconds depending on other important processes running on board making it a utility for amateur radio community 4 not only it can digipeat a message it also has the functionality to repeat voice for using this functionality you need a uhf transmitter and you can send a voice message to the satellite and the satellite will repeate it after some delay depending on other important processes running on board it also will be a big utility for amateur radio community 5 if the satellite is able to operate for more than 3 months while responding to the commands and sending the health data at proper intervals it will prove the reliablity of the unique design and set of cots hardware used thus future mission could use flight proven hardwares for communication and power generation what is it made of what does it look like what are its dimenssions and mass beliefsat s structure is made by joining plates pcbs made out of fr4 material this is a very novel approach in designing the structure of a satellite generally satellites are made from aluminium or its alloys but using our novel approach we were able to design a very sturdy but cost effective sateliite although this technique is generally limited to nano satellites the pcbs that make the structure of our satellite have the solar panels and other components embedded in itself so the complexity of assembly is minimized this is a concept render of our beliefsat satfolded https user images githubusercontent com 67508161 85923794 c3ac7880 b8ab 11ea 8a19 435cf76c867a jpg the dimension of the beliefsat is in accordance with the pocketqube standards this standard includes a base plate on which the satellite is mounted and then it slides out of the satellite deployer via rails as per the standards our beliefsat is 2p ie base plate is 58mm x 128mm x 1 6mm wxlxh whereas the satellite is 58mm x 128mm x 50mm wxlxh the estimated mass of beliefsat is around 450 grams the dimensions of our deployer somaiyapod are 145mm x 200mm x 95mm wxlxh how is it powered the satellite is powered by altadevices single juntion cells which unfortunately are out of buisness as of date of writing https pv magazine usa com 2019 12 31 shutdown continues at hanergy owned alta devices high efficiency pv pioneer each of these cell produce 0 25w power when illumniated at intensity of 1000w m 2 there are 4 such cells on long sides and 2 on shorter sides so when illuminated satellite may produce 0 5w watt 1watt 1w depending on the orientation the power remaining after being used is then stored in 2 parallely connected 2600mah samsung 18650 batteries https robokits co in batteries chargers samsung premium li ion battery 3 7v samsung li ion batteries samsung icr 18650 26j 2600mah li ion cell original gclid cjwkcajwlth3brb6eiwahj0iuhalyab d4shw phnjm4cjyqy68a7cgykcscms i6jljsgfchlp92boc6 4qavd bwe spv1040 https www st com en power management spv1040 html is used on every panel for maximum power point tracking and giving regulated voltage from the solar cells the choice of spv1040 was made based on flight history in fossasat 1 https github com fossasystems fossasat 1 now that altadevices cells won t be available in market team will also be making one prototype with sm141k06l by ixys https www digikey com product detail en ixys sm141k06l sm141k06l nd 9990462 and will be publishing test results for it too how does it communicate transciever specs vhf name dra818v http www dorji com products php keyword dra818v frequency range 134 174 mhz channel space 12 5 25 khz tx current 450 750 ma temperature 20 c 70 c output power 27 30 dbm reciever sensitivity 122 dbm as per datasheet observed sensititvity yet to be calculated uhf name dra818u http www dorji com products php keyword dra818u frequency range 400 470 mhz channel space 25 khz tx current 450 750 ma temperature 20 c 70 c output power 27 30 dbm reciever sensitivity 122 dbm as per datasheet observed sensititvity yet to be calculated uhf will only be used for voice repeater uplink antenna dipole made from measuring tape which is in folded state during launch once in orbit fishing cord which holds the antenna in folded position is burnt by heating a resistor around which it is tied link budget orbit height 600 km proposed may vary as per allocation by launch provider slant range at 25 degree elevation 1213 4 km approximately free space path loss at this distance for 145 825 mhz frequency 137 4 dbm free space path loss at this distance for 435 6 mhz frequency 146 9 dbm received power neglecting mismatch connector atmospheric pointing and polarization losses and receiving and transmitting antenna gains 118 2 121 2 op 30 27dbm note a good directional antenna at receving groundstation e g a 6 element yagi plans for which will be released soon can ensure the above mentioned losses are made up for and the signals can be received at even lower angles however to ensure best performance circular polarized antennas copuled with a good lna e g lna4all http lna4all blogspot com mounted over a azimuth elevation antenna rotator e g satnogs rotator https wiki satnogs org satnogs rotator v3 sarcnet rotator https www sarcnet org rotator mk2 html rotatormk2a yaesu g5500 https www yaesu com indexvs cfm cmd displayproducts prodcatid 104 encprodid 79a89cec477aa3b819ee02831f3fd5b8 wraps portable antenna rotator https ukamsat files wordpress com 2013 12 wraps mark spencer wa8sme qst jan 2014 copyright arrl pdf is recommended data rates in bps telemetry downlink 1200 digipeater downlink 1200 command uplink 1200 digipeater uplink 1200 how the satellite complies with the requirment of turning off the transmitters when required all the uplinks and downlinks occur on same frequency reciever is disabled while transmitting to avoid interrupts to controller during the transmission however these transmissions last no more than 2 sec this will ensure that one can relay a command during any pass also the transmitter is implemented to be active only when microcontroller is active so if the main computer fails the transmitter won t turn on if the spacecraft computer is hung up for more than 8 sec it resets along with the transciever the automatic image capture mode is automatically turned off if battery voltage falls thus adding extra layer of governance on the mode where long and continous transmission occurs the satellite is designed to automatically turn off the microcontroller and thus the transciever in case of any thermal or short circuit event it is also protected against latchup situations caused by brown outs or radiation flipping a bit by the means of internal watchdog which resets non responsive or improperly finctioning controller after 8 sec whenever required satellites transmitters can be turned off until next command to turn those on by uplinking a command to do so if the shut down request comes when there is no pass over parent groundstation the uplink packe format can be shared with a station which will have nearest pass what are different packet types and packet formats telemetry packet aprs telemetry format the on air packet telemetry format is as follows t sss 111 222 333 444 555 666 777 888 999 aaa xxxxxxxx where sss lsb of total number of system resets 111 msb of total number of system resets 222 lsb of total number of resets induced by watch dog 333 msb of total number of resets induced by watch dog 444 temperature sensor 1 reading 555 temperature sensor 2 reading 666 gyro sensor 1 reading 777 gyro sensor 1 reading 888 gyro sensor 1 reading 999 current reading aaa voltage reading xxxxxxxx if the last two bits are 10 then digipeater is on and if they are 01 then voice repeater is on references for converting sensor data into readable format 1 temperature sensor ds18b20 datasheet https pdf1 alldatasheet com datasheet pdf view 58557 dallas ds18b20 html 2 gyro sensor mpu 6050 datasheet https invensense tdk com wp content uploads 2015 02 mpu 6000 datasheet1 pdf 3 current power sensor ina219 datasheet https www ti com lit ds symlink ina219 pdf ts 1593455980944 ref url https 253a 252f 252fwww google com 252f reference for transreceivers 1 dra818v datasheet http www dorji com docs data dra818v pdf 2 dra818u datasheet http www dorji com docs data dra818u pdf | server |
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Engineering-Challenge-iOS | ios developer challenge version 1 2 challenge status open welcome we ve been expecting you holmusk is a big data based high tech company specializing in healthcare in singapore if you re someone who bleeds code and aches to make a difference in the world then you are at the right place you will be part of a world class team working on the most exciting ground breaking technology in an inspiring and collaborative environment basics this is the holmusk ios developer challenge the rules of the challenge are very simple and are as follows you are required to code in swift you will be able to submit the challenge anytime you are ready provided the challenge is still open your code should be commented you should implement autolayout and size classes to support all iphone sizes only one orientation is enough we re not too fiesty on that you are required to fork this repo and submit a pull request if you wish to not make public your submission please complete the code in your local repository and email a patch file to careers holmusk com please note that you will also be judged on the elegance of your code level of abstraction and technical skills presented in the implementation for more details refer to the judging criteria section below the challenge what you ll need to build you ll need to build an app that is able to retrieve nutrition information for different food types persist it locally and display it to the user in a very interesting manner bits and pieces to take note of use kimono s api builer https www kimonolabs com this is so that you can save time to extract food related data from myfitnesspal https www myfitnesspal com fatsecrets http www fatsecret com sg or other food websites such as calorieking http www calorieking com the data that you retrieve from each of these 3 sources might not be interoperable thus we expect you to distinguish in your view food from these sources obtain data from at least 3 or more websites and present them in your app how you present will be a direct reflection of your creativity and motivation so we encourage you to spend as much time on this part as possible you are not limited to tableviews or collectionviews feel free to create your own representations one of our favorites is the parallax based scrollview you will need to implement autocomplete with a search view with a maximum of 10 results so that the interface does not look cluttered so that users are able to easily enter food items they have had you will need to store all of your results into coredata realm http realm io fmdb sqlite in the most efficient way possible please do log the time taken for data storage and retrieval in the console you will also need to allow users to enter new food items which should then be synced with your local datastore coredata realm with that said we wish you good luck and look forward to receiving your submission judging criteria what you have produced will determine your final outcome 60 of your product from our point of view depends on your user experience and user interfaces for this challenge thus we would encourage you to make the best use of the coreanimation quartzcore and coregraphics libraries because we love people who have a passion for expanding their horizons your background with these libraries do not matter so much provided you are able to demonstrate your learning ability bonus points at holmusk we do our best to go the extra mile and as such you would recieve brownie points if your app provides simple analytics such as keeping track of a users diet for the day and providing relevant graphs animations etc | os |
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Lock_App_Embedded | ece1160 embedded systems design | os |
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RailFence | railfence ios application that encrypt and decrypt plain text using rail fence cypher it was swe314 software security engineering course assignment img width 400 src img 6941 jpg | os |
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Hotel_Management_System | online hotel management system description a database project for managing the daily services on hotel in online services that can be managed from this website is room booking ordering food ordering for services see bills payment in online demo language php html css javascript sql software required xampp | server |
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nostyle | nostyle design system by adam silver this contains all the stlyes components and patterns from my book form design patterns published by smashing magazine in 2018 enjoy | a11y component-library design-systems nodejs pattern-library css javascript forms | os |
Fabric-defect-detection | fabric defect detection fabric defect detection based on computer vision 1 2 3 4 5 image https github com johncheng1 fabric defect detection blob master image 1 1 jpg image https github com johncheng1 fabric defect detection blob master image 1 2 jpg image https github com johncheng1 fabric defect detection blob master image 1 3 jpg image https github com johncheng1 fabric defect detection blob master image 1 4 jpg image https github com johncheng1 fabric defect detection blob master image 1 5 jpg image https github com johncheng1 fabric defect detection blob master image 1 6 jpg image https github com johncheng1 fabric defect detection blob master image 1 7 jpg | ai |
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code-buddies | camping blackboard course tile https user images githubusercontent com 52185677 121786099 cd49f180 cb8b 11eb 9ea5 d8be90d9b843 png learning resources swift programming https github com jocelyn boyd code buddies blob main swift programming md data structures and algorithms https github com jocelyn boyd code buddies blob main dsa md ios development https github com jocelyn boyd code buddies blob main ios development md something to listen to developer tea podcast https podcasts apple com us podcast developer tea id955596067 professional self help for developers code newbie podcast https podcasts apple com us podcast codenewbie id919219256 stories and interviews from people on their coding journey rwdevcon 2017 inspiration talk i m an idiot by richard turton https www raywenderlich com 498 rwdevcon 2017 inspiration talk i m an idiot by richard turton | os |
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patient-folder | mixaniki logismikou | server |
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python-for-database | python for database this is a repo created for learning python for database engineering | server |
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IIT | iit introduction to information technology | server |
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Healfood | img src https i loli net 2019 11 08 oqvlshyamxbwz6r png width 400 https healfood herokuapp com info30005 web information technologies project fall 2019 https healfood herokuapp com introduction a web application designed for restaurant rating core functionalities restaurant related add restaurant edit restaurant delete restaurant view restaurant list view restaurant detail review related add review edit review delete review view review list map view all restaurant locations view single restaurant location authentication sign up log in detail restaurants this functionality is for editing posting displaying and deleting restaurants relevant urls https healfood herokuapp com restaurants https healfood herokuapp com edit restaurant routes method url get restaurants get restaurants id id get edit restaurant get edit restaurant id post edit restaurant post edit restaurant id reviews this functionality is for editing posting displaying and deleting reviews relevant urls https healfood herokuapp com reviews routes method url get reviews get reviews id id get edit review rstrnt id get edit review rstrnt id review id post edit review rstrnt id post edit review rstrnt id review id map this functionality is for showing the locations of restaurants on the map relevant urls https healfood herokuapp com map routes routes routes js method url get map authentication this functionality gives users ability to register a account and log in our website authentication is required if a user wants to edit any information on the website you can use the following login details if wanted email xiandew student unimelb edu au password 12345678 relevant urls https healfood herokuapp com login https healfood herokuapp com signup routes method url get user get login get signup get logout get confirm email token get resend post login post signup post resend todo folding long paragraph in lists reset password search add roles for users eg customer owner admin | server |
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cptserver | cptserver a student web development environment using vagrant virtualbox and puppet virtualbox is software that lets you create virtual machines allowing you to run another operating system in a sandbox vagrant is used to customize load and access that virtual machine once it s running puppet is used to manage the software on the virtual machine puppet makes sure things like apache and mysql are installed running as a service configured properly and restarted when necessary all together this creates an ubuntu lamp webserver that can be run on your current computer whether it be windows apple osx or linux itself many developers prefer this type of environment instead of installing something like wamp xampp or mamp one major benefit is that it doesn t interfere with your current computer another is that when on a team or in a class everybody could be developing on the exact same environment with the same versions of all software features it uses a single config file for everything no need to dig into puppet code to customize it adds a repository that will give you the latest php 5 6 and apache 2 4 it installs phpmyadmin for you so that you can manage databases it also configures mysql so that you can use local db software on your computer like mysql workbench it installs composer globally in your path for you to easily manage php dependencies it installs and configures xdebug so that you may debug with local ides like phpstorm and netbeans it allows you to configure php error reporting levels it sets up ssl with a self signed certificate so you can develop for https installation these steps should get you going but i ve also created a video too https www youtube com watch v teaznpm8rua 1 first install virtualbox then vagrant you don t need puppet on your host computer it will exist on the linux virtual machine after installing vagrant you ll need to reboot virtualbox https www virtualbox org wiki downloads vagrant http www vagrantup com 2 next download this repository you could just download the zip file and extract it where you keep your work but cloning with git is preferred if you haven t learned git yet you really should git http git scm com downloads go to your project folder on windows it s probably something like c users scott documents open a terminal command prompt there and type git clone https github com pigeontech cptserver git this creates a copy on your computer like c users scott documents cptserver go into that folder 3 open the config config yaml file in a text editor and change anything that you feel needs changed right now the only thing that probably matters is the default mysql password for root and email smtp server settings so your websites can send email you can add virtual hosts later also if your computer is already using port 80 like if you run a media server to stream movies to your tv you might need to change port 80 to something else like 8080 4 reopen the terminal if you closed it and go into the cptserver folder that git made for you if you left it open good idea just type cd cptserver 5 now type vagrant up it will streamline the entire process for you from creating the virtual machine to installing php and its modules the first time you run it it will be slow because it must download and install a linux box which is a few hundred mb 6 it s ready inside the www default folder is a sample website open your browser and go to http localhost and see if it works check out http localhost phpmyadmin as well and try logging with the username root and the password you set in the config yaml file if you changed the port these urls would look like http localhost 8080 and http localhost 8080 phpmyadmin virtual hosts you will probably build more than one website using http dev foldername is too ugly so we use virtual hosts a few have already been created in the config yaml file there is another step you must do though on your actual computer you have to edit the hosts file on windows it s located at c windows system32 drivers etc you may need to open notepad with admin privilages for it to allow you to save changes make the file look something like this 127 0 0 1 localhost localhost dev www localhost dev 127 0 0 1 default default dev www default dev 127 0 0 1 dev dev dev www dev dev by looking at config yaml you ll see that the localhost and default urls all route to a folder named default and the dev urls route to the entire www directory listing if you didn t add a custom localhost for a site you re building like wordpress that routes to a blog folder you could still access the blog folder via http dev blog more vagrant commands these are more commands you ll need as before make sure the terminal is open in the root of the cptserver folder vagrant provision any time you make changes to the config yaml file like adding a vhost you need to run this command vagrant reload provision if you make changes in top vagrant portion of the config yaml you need to run this vagrant halt this is how you turn it off when you re done your computer won t go into sleep mode when a virtual machine is running vagrant up ready to start working on your website again run this vagrant destroy this will completely wipe out the linux installation and delete the virtual machine use with great caution as you ll lose your databases only run this command if things are broken you re lost and you want to start over vagrant ssh once you re up and running this is a command you ll use most often it lets you into ubuntu as if you were really sitting at the computer terminal from here you can run commands like sudo apt get install something often you ll want to immediately go into the web root with cd var www any changes made here are also mirrored on your host computer s www folder try it if you re not familiar with the linux file structure it s worth doing a google search when you re readty to go back to your normal computer s terminal type exit composer composer is a dependency manager for common libraries such as the popular laravel framework or the phpunit testing library it will automatically download the latest specified version of them as well as any dependencies that they rely on it will then create an autoload php file which you include into your actual project to load the library and dependencies composer like git is one of those technologies that separates the new school from the old school don t get left behind it s more important than ever for web developers to become familiar with these command line tools 1 log in via vagrant ssh and navigate to your website folder such as cd var www mywebsite 2 create a composer json file in the same place containing the following require dev phpunit phpunit 3 7 14 don t make things difficult you don t have to use vi or nano to create and edit this file you can do this step on your normal computer with sublime text notepad etc remember that the www folder is shared between your computer and the vm changes to one happen to both that s the whole point of using vagrant 3 back to the terminal type composer install this will create a vendor folder download all of the software packages you specified in the json file and also download their dependencies for example if you specify phpunit as above your project and vendor folder will look like this var www mywebsite vendor composer json composer lock composer phar index php var www mywebsite vendor bin composer phpunit symfony autoload php 4 now in your index php file you d put something like require vendor autoload php 5 keep in mind that you need to do all of these steps for each website you build composer is dependency managment on a per project basis so if you create another website like var www lolcatpics it will need its own composor json and you ll need to run composer install in that directory and a vendor folder will be created 6 visit the following websites to learn more about fitting these tools into your workflow http daylerees com composer primer https www digitalocean com community articles how to install and use composer on your vps running ubuntu https jtreminio com 2013 03 unit testing tutorial introduction to phpunit | front_end |
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