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wiki | tuni itc s crowdsourced wiki tuni itc github io wiki https tuni itc github io wiki this repo contains the source code for deployment of the crowdsourced wiki for faculty of information technology and communication sciences at tampere university how to contribute we have a dedicated wiki page https tuni itc github io wiki meta how to contribute for it check it out | wiki crowdsourcing website mkdocs mkdocs-material github-pages github-action ci-cd tampere-university unofficial | server |
rhythm-ui | rhythm ui build status https dev azure com dd managed services fed modules apis build status rhythm ui branchname master https dev azure com dd managed services fed modules build latest definitionid 45 branchname master quality gate status https sonarcloud io api project badges measure project deloittedigitalapac rhythm ui metric alert status https sonarcloud io dashboard id deloittedigitalapac rhythm ui security rating https sonarcloud io api project badges measure project deloittedigitalapac rhythm ui metric security rating https sonarcloud io dashboard id deloittedigitalapac rhythm ui vulnerabilities https sonarcloud io api project badges measure project deloittedigitalapac rhythm ui metric vulnerabilities https sonarcloud io dashboard id deloittedigitalapac rhythm ui bugs https sonarcloud io api project badges measure project deloittedigitalapac rhythm ui metric bugs https sonarcloud io dashboard id deloittedigitalapac rhythm ui dependabot status https api dependabot com badges status host github repo deloittedigitalapac rhythm ui https dependabot com please note this library is currently still in development and all api s are subject to change please use with caution rhythm ui is a white label product currently in development for building enterprise design systems it enables us to create consistent and delightful user experiences that are battle tested future proof and scalable that can be branded easily rhythm is designed to be integrated as easily as possible into any and all web frameworks browsers support img src https raw githubusercontent com alrra browser logos master src edge edge 48x48 png alt ie edge width 24px height 24px http godban github io browsers support badges br ie edge img src https raw githubusercontent com alrra browser logos master src firefox firefox 48x48 png alt firefox width 24px height 24px http godban github io browsers support badges br firefox img src https raw githubusercontent com alrra browser logos master src chrome chrome 48x48 png alt chrome width 24px height 24px http godban github io browsers support badges br chrome img src https raw githubusercontent com alrra browser logos master src safari safari 48x48 png alt safari width 24px height 24px http godban github io browsers support badges br safari img src https raw githubusercontent com alrra browser logos master src safari ios safari ios 48x48 png alt ios safari width 24px height 24px http godban github io browsers support badges br ios safari img src https raw githubusercontent com alrra browser logos master src opera opera 48x48 png alt opera width 24px height 24px http godban github io browsers support badges br opera ie11 edge last 2 versions last 2 versions last 2 versions last 2 versions last 2 versions ie11 and edge will require the web components polyfill https www webcomponents org polyfills contributing the main purpose of this repository is to continue to evolve rhythm ui making it better and easier to use development of rhythm happens on github and we welcome any contributions bug fixes and improvements please read below to learn how you can take part in improving rhythm code of conduct deloitte digital has adopted a code of conduct that we expect project participants to adhere to please read the code of conduct https github com deloittedigitalapac rhythm ui blob master code of conduct md so that you can understand what actions will and will not be tolerated contributing guide read our contributing guide https github com deloittedigitalapac rhythm ui blob master contributing md to learn about our development process how to propose bugfixes and improvements and how to build and test your changes to rhythm who is deloitte digital part business part creative part technology one hundred per cent digital pioneered in australia deloitte digital is committed to helping clients unlock the business value of emerging technologies we provide clients with a full suite of digital services covering digital strategy user experience content creative engineering and implementation across mobile web and social media channels http www deloittedigital com au http www deloittedigital com au thank you rhythm ui is made possible by the following organisations s and services img src https sonarcloud io images project badges sonarcloud white svg width 200 https sonarcloud io about img src https www browserstack com images mail browserstack logo footer png width 160 https www browserstack com img src https github githubassets com images modules logos page github logo png width 100 https github com img src https cdn shortpixel ai client to webp q glossy ret img https cdn productplan com wp content uploads 2017 06 azuredevops 2x png width 200 https azure microsoft com en au services devops license bsd 3 clause view license license back to top table of contents | os |
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android-class | android class mobile development | front_end |
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intro-to-ml-tidy | netlify status https api netlify com api v1 badges fc33b987 b580 4145 afc9 7d8427bf77f1 deploy status https app netlify com sites conf20 intro ml deploys markdown snippet deploy to netlify https www netlify com img deploy button svg https app netlify com start deploy repository https github com rstudio conf 2020 intro to ml tidy introduction to machine learning with the tidyverse rstudio conf 2020 by alison hill and garrett grolemund spiral calendar january 27 and 28 2020 alarm clock 09 00 17 00 hotel continental ballroom room 5 ballroom level writing hand https rstd io conf20 intro ml https rstd io conf20 intro ml bento lunch in grand ballroom a grand ballroom level overview this workshop provides a gentle introduction to machine learning and to the tidyverse packages that do machine learning you ll learn how to train and assess predictive models with several common machine learning algorithms as well as how to do feature engineering to improve the predictive accuracy of your models we will focus on learning the basic theory and best practices that support machine learning and we will do it with a modern suite of r packages known as tidymodels tidymodels packages like parsnip recipes and rsample provide a grammar for modeling and work seamlessly with r s tidyverse packages students should feel comfortable plotting with ggplot2 and using the dplyr and purrr packages you can brush up on these topics by working through the online tutorials at https rstudio cloud learn primers learning objectives students will learn to train assess and generate predictions from several common machine learning methods with the tidymodels suite of packages is this course for me this workshop is appropriate for attendees who answer yes to the questions below can you use mutate and purrr to transform a data frame that contains list columns can you use the ggplot2 package to make a large variety of graphs if you answered no to either question you can brush up on these topics by working through the online tutorials at https rstudio cloud learn primers if you already use machine learning methods like random forests neural networks cross validation or feature engineering this course is not for you register for max kuhn s applied machine learning workshop instead prework please bring a laptop to class that has the following installed a recent version of r 3 6 0 which is available for free at cran r project org a recent version of rstudio desktop 1 2 1500 available for free at www rstudio com download rstudio desktop open source license the r packages we will use which you can install by connecting to the internet opening rstudio and running at the command line install packages c tidyverse tidymodels remotes rpart plot rattle vip ameshousing kknn rpart ranger partykit and remotes install github c tidymodels workflows tidymodels tune tidymodels modeldata and don t forget your power cord on the day of the class we ll provide you with an rstudio server pro login that contains all of the course materials we will use the software listed above only as an important backup should there be problems with the classroom server connection schedule time activity 09 00 10 30 session 1 10 30 11 00 coffee break 11 00 12 30 session 2 12 30 13 30 lunch break 13 30 15 00 session 3 15 00 15 30 coffee break 15 30 17 00 session 4 materials overview https pedantic pike efd356 netlify com day 1 slides 00 overview 1 predicting https pedantic pike efd356 netlify com day 1 slides 01 predicting 1 https i creativecommons org l by 4 0 88x31 png this work is licensed under a creative commons attribution 4 0 international license https creativecommons org licenses by 4 0 | ai |
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skinny-framework | skinny framework ci build https github com skinny framework skinny framework workflows ci 20build badge svg maven central https img shields io maven central v org skinny framework skinny framework 2 13 svg label maven 20central http search maven org search 7cga 7c1 7cg 3a 22org skinny framework 22 20a 3a 22skinny framework 2 13 22 skinny is a full stack web app framework to build servlet applications to put it simply skinny framework s concept is scala on rails skinny is highly inspired by ruby on rails http rubyonrails org and it is optimized for sustainable productivity for ordinary servlet based app development logo https github com seratch skinny framework raw 1 1 x img logo png see the website in detail https skinny framework github io global skinny script via homebrew sh brew update brew install skinny try skinny framework now download latest skinny blank app zip and unzip it then just run skinny command on your terminal that s all if you re a windows user don t worry use skinny bat on cmd exe instead https github com skinny framework skinny framework releases under the mit license the mit license copyright c skinny framework org | scala web-framework orm http-client mail oauth2-client validation | front_end |
Repilot | mathbb r mathrm e mathbf pilot p align left a href https arxiv org abs 2309 00608 img src https img shields io badge arxiv 2309 00608 b31b1b svg style for the badge a href https doi org 10 5281 zenodo 8281250 img src https img shields io badge doi 10 5281 2fzenodo 8281250 blue style for the badge a href https hub docker com r universefly repilot tags img src https img shields io badge docker universefly 2frepilot 230db7ed svg style for the badge logo docker logocolor white a p welcome to the source code repo of repilot a patch generation tool introduced in our esec fse 23 paper copiloting the copilot fusing large language models with completion engines for automated program repair picture source media prefers color scheme light srcset assets repilot demo light svg source media prefers color scheme dark srcset assets repilot demo dark svg img alt repilot demo src assets repilot demo light svg picture repilot leverages the synergy between a semantics based code completion engine and an auto regressive large language model for more efficient valid patch generation important repilot is implemented for java patch generation as a complex hybrid system combining a modified eclipse jdt language server https github com universefly eclipse jdt ls and python s huggingface transformers https github com huggingface transformers interface for manipulating large language models correctly setting up the dependencies and configurations of repilot can be non trivial therefore we highly recommend directly using our out of the box docker image quick start with repilot s docker image bash pull the image and run a container this may take some time docker run it name repilot universefly repilot latest now you will get into a virtual environment provided by docker enter the repilot directory cd root repilot this is important because repilot relies on a meta config json file to work properly cat meta config json generate patches with the full repilot approach using codet5 active 1 python m repilot cli main repair b chart 9 method pruned mem d chart 9 repilot n 5 you will see logs about the patch generation and which tokens are accepted rejected validate the patch generation python m repilot cli main validate d chart 9 repilot print a table of the evaluation results python m repilot cli main evaluate d chart 9 repilot you ll see something like this repilot evaluation results tag average gen time compilable patches plausible patches plausible fixes correct fixes chart 9 repilot 1 33s 100 0 0 000 0 artifact for more comprehensive guidance on how to use repilot and how to reproduce the results in our paper we greatly encourage you to check out our artifact documentation readme artifact md how to build and use repilot from source warning building repilot from source is not recommended since there are many complex dependencies and configurations to handle it is only for advanced users who want to extend repilot if you want to build from source we also encourage you to check out our dockerfile https github com ise uiuc repilot blob main dockerfile for more details important environment requirements python 3 10 and git lfs https git lfs com are required all three versions of java 8 11 and 18 are required for convenient management of multiple java versions we recommend coursier https get coursier io docs cli java optional it s recommended to have an nvidia gpu with 6g memory for running repilot with codet5 and 30g memory for incoder 6 7b details summary download and build the modified eclipse jdt language server summary follow the instructions in the repo https github com universefly eclipse jdt ls to build the modified eclipse jdt language server note you will need java 11 bash git clone https github com universefly eclipse jdt ls cd eclipse jdt ls java home path to java 11 mvnw clean verify dskiptests true adjust the following command according to your build to dry run the language server bash java declipse application org eclipse jdt ls core id1 dosgi bundles defaultstartlevel 4 declipse product org eclipse jdt ls core product dlog level all noverify xmx1g add modules all system add opens java base java util all unnamed add opens java base java lang all unnamed jar plugins org eclipse equinox launcher 1 5 200 v20180922 1751 jar configuration config linux data path to data if everything goes well you can move on to the next step details details summary download and install repilot as a python package including its dependencies summary bash git clone https github com universefly repilot cd repilot do an editable install pip install e consider upgrading pip if you encounter any errors also make sure you are using python 3 10 this command should also install all the dependencies of repilot details details summary install the defects4j datasets summary repilot evaluates on the defects4j https github com rjust defects4j dataset please checkout to its v2 0 0 release https github com rjust defects4j releases tag v2 0 0 and follow its instructions to install the dataset warning if you directly download the release instead of doing a checkout you may encounter errors when running repilot as repilot will dump the metadata by collecting the meta information of these projects as git repos if they are not git repos repilot may fail you can check the installation by running path to defects4j info p chart details details summary prepare the runtime environment of repilot summary we need to prepare a meta config json file for repilot to work properly the file should be placed in the root directory of repilot please modify the following template according to your environment and save the file in the root directory of repilot json d4j home home yuxiang developer defects4j d4j checkout root home yuxiang developer d4j checkout jdt ls repo home yuxiang developer eclipse jdt ls java8 home home yuxiang cache coursier arc https github com adoptopenjdk openjdk8 binaries releases download jdk8u181 b13 openjdk8u jdk x64 linux hotspot 8u181b13 tar gz jdk8u181 b13 language server cmd home yuxiang cache coursier arc https github com adoptium temurin18 binaries releases download jdk 18 0 2 252b9 openjdk18u jdk x64 linux hotspot 18 0 2 9 tar gz jdk 18 0 2 9 bin java declipse application org eclipse jdt ls core id1 dosgi bundles defaultstartlevel 4 declipse product org eclipse jdt ls core product dlog level error noverify xmx1g add modules all system add opens java base java util all unnamed add opens java base java lang all unnamed jar home yuxiang developer eclipse jdt ls org eclipse jdt ls product target repository plugins org eclipse equinox launcher 1 6 400 v20210924 0641 jar configuration home yuxiang developer eclipse jdt ls org eclipse jdt ls product target repository config linux seed 0 now let s cd back to the root directory of repilot and run the following command to checkout all the defects4j bugs bash python m repilot cli init details details summary do an example run summary bash generate patches with the full repilot approach using codet5 active 1 python m repilot cli main repair b chart 9 method pruned mem d chart 9 repilot n 5 you will see logs about the patch generation and which tokens are accepted rejected validate the patch generation python m repilot cli main validate d chart 9 repilot print a table of the evaluation results python m repilot cli main evaluate d chart 9 repilot you will see a table of evaluation results if everything goes well details details summary optional unpack the pre generated patches summary the github repo also contains pre generated patches for the experiments in our paper you can unpack if you would like to check them first make sure you cd to the root directory of repilot then run the following command bash tar xvf data large tar xz then you will see the data large directory is populated with the pre generated patches details congratulations you have successfully built and used repilot from source | code-generation program-repair large-language-models code-completion program-synthesis | ai |
aws-machine-learning-university-accelerated-nlp | logo data mlu logo png machine learning university accelerated natural language processing class this repository contains slides notebooks and datasets for the machine learning university mlu accelerated natural language processing class our mission is to make machine learning accessible to everyone we have courses available across many topics of machine learning and believe knowledge of ml can be a key enabler for success this class is designed to help you get started with natural language processing nlp learn widely used techniques and apply them on real world problems youtube watch all nlp class video recordings in this youtube playlist https www youtube com playlist list pl8p z6c4gcuwfaq8pt6pbylck4oprhxsw from our youtube channel https www youtube com channel uc12lqyqtqybxatys9aa7nuw playlists playlist https img youtube com vi 0fxkbegz uu 0 jpg https www youtube com playlist list pl8p z6c4gcuwfaq8pt6pbylck4oprhxsw course overview there are three lectures and one final project in this class course overview is below lecture 1 title studio lab introduction to ml intro to nlp and text processing open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 text process ipynb bag of words bow open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 bow ipynb k nearest neighbors knn open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 knn ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 final project ipynb lecture 2 title studio lab tree based models open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 tree models ipynb regression models linear regression open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 linear regression ipynb br logistic regression open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 logistic regression ipynb optimization regularization hyperparameter tuning aws ai ml services open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 sagemaker ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 final project ipynb lecture 3 title studio lab neural networks open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 neural networks ipynb word embeddings open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 word vectors ipynb recurrent neural networks rnn open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 recurrent neural networks ipynb transformers open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 neural networks ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 final project ipynb final project practice working with a real world nlp dataset for the final project final project dataset is in the data final project folder https github com aws samples aws machine learning university accelerated nlp tree master data final project for more details on the final project check out this notebook https github com aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 final project ipynb interactives visuals interested in visual interactive explanations of core machine learning concepts check out our mlu explain articles https mlu explain github io to learn at your own pace contribute if you would like to contribute to the project see contributing contributing md for more information license the license for this repository depends on the section data set for the course is being provided to you by permission of amazon and is subject to the terms of the amazon license and access https www amazon com gp help customer display html nodeid 201909000 you are expressly prohibited from copying modifying selling exporting or using this data set in any way other than for the purpose of completing this course the lecture slides are released under the cc by sa 4 0 license the code examples are released under the mit 0 license see each section s license file for details | machine-learning natural-language-processing deep-learning python gluon mxnet sklearn | ai |
vision_kit | vision kit a computer vision kit for algorithm verification far from stable required opencv 3 1 0 https github com opencv opencv tree 3 1 0 eigen3 https github com rlovelett eigen tree 3 3 3 modules 1 base some universal functions and some definitions 2 optical flow algorithms for optical flow pyramidal lucas kanada algorithm 3 epipolar geometry function about fundamental matrix there are two methods to find the fundamental matrix 8 points algorithm normalized 8 point algorithm ransac self adaptive sample by the inliers number of current best model solve the fundamental matrix by 8 points algorithm 4 image patch alignment use inverse compositional and efficient second order minimization algorithm to align image patch in reference image to patch in current image the model contains pixel 2d drift iv i c mathbf x mathbf u i r mathbf x pixel 2d drift with bias illumination or exposure differences iv esm i c mathbf x mathbf u i r mathbf x beta usage first of all build the code mkdir build cd build cmake make j then run the demos base test base data desk1 png data desk2 png optical flow test opticalflow data floor1 png data floor2 png epipolar geometry test fundamental data desk1 png data desk2 png image alignment test align2d data floor1 png data floor2 png test align1d data floor1 png data floor2 png | ai |
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Face-Reading | man with gua pi mao face reading facial feature classification a physiognomy application which analysis people facial feature to evaluates a person s characteristic and then do the fortune telling demo docs demo png only tested in python3 depoly install dependcy bash pip3 install user r requirement txt gather data to build the dataset for training run build db py data are collect from google image search directly accordling to the facial description mentioned in data analysis json bash python build db py or you may download the builded dataset from google drive https drive google com file d 1glm 2v 7xdmjotmyx686g7zxlshksct5 view usp sharing and extract it into this folder train to train your own classifier run train py if you updated the face database just delete data train data pkl and let it generate a new one for you bash if need to regenerate the dataset run below line rm data train data pkl python train py test on image to run test on different source of image run test py bash python test py help usage test py h i image u url optional arguments h help show this help message and exit i image image image input image u url url url input image url | dlib facial-features | ai |
Altschool-cloud-exercise | altschool cloud exercise a submission of all the exercises on cloud engineering fo rsecond semester altschool exercises a compilation of all altschool exercises wlecome to my altschool exercises below are the outputs of my solutions exercise 1 1 this is the out put of ifconfig after configuring dhcp ifconfig ifconfig jpg 2 this is the out put of etc passwd users users jpg 3 this is the out put of etc group groups groups jpg exercise 2 10 most commonly used linux commands 1 concatenate or cat is one of the most frequently used linux commands it lists combines and writes file content to the standard output to run the cat command type cat followed by the file name and its extension for instance cat script sh cat ex2cat jpg 2 the cd command is used to change the current directory to run the cd command type cd followed by the directory name for instance home vagrant memory logs cd ex2cd jpg 3 the chmod command is used to change the permissions of a file or directory to run the chmod command type chmod followed by the permission code and the file name for instance chmod email sh x chmod ex2chmod jpg 4 the chown command is used to change the owner of a file or directory to run the chown command type chown followed by the new owner and the file name for instance chown vagrant email sh chown ex2chown jpg 5 the cp command is used to copy files and directories to run the cp command type cp followed by the file name and its extension and the destination directory for instance cp log file log logs cp ex2cp jpg 6 the date command is used to display the current date and time to run the date command type date for instance date date ex2date jpg 7 the df command is used to display the amount of disk space available on the file system to run the df command type df for instance df df ex2df jpg 8 the du command is used to display the amount of disk space used by a file or directory to run the du command type du followed by the file name and its extension for instance du log file log du ex2du jpg 9 the echo command is used to display a line of text to run the echo command type echo followed by the text for instance echo hostinger tutorials echo ex2echo jpg 10 the whoami command is used to display the current user to run the whoami command type whoami for instance whoami whoami ex2whoami jpg exercise 3 1 create 3 groups admin support engineering and add the admin group to sudoers groups created groups groups jpg 2 generate ssh keys for the user in the admin group ssh keys generated ssh ssh keygen jpg 3 contents of etc passwd passwd users jpg 4 contents of etc group group groups jpg 5 contents of etc sudoers sudoers ex3sudoers jpg exercise 4 install php 7 4 on your local linux machine using the ppa ondrej php package repo 1 content of php v command php ex4php v jpg 2 content of etc apt sources list sources ex4sourcelist jpg exercise 6 submit the output of 1 git config l git ex6gitconfig l jpg 2 git remote v git ex6gitremote v jpg exercise 8 submit the content of your script cronjob and a sample of the email sent all in the folder for this exercise 1 content of bashscript bash ex8bashscript jpg 2 content of cronjob cronjob ex8cronjob jpg 3 sample of email sent email ex8logfile jpg exercise 9 193 16 20 35 29 what is the network ip number of hosts range of ip addresses and broadcast ip from this subnet network ip 193 16 20 32 broadcast ip 193 16 20 32 number of hosts 6 subnetmask 255 255 248 class e | cloud |
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DBRE | dbre database reliability engineering designing and operating resilient datebase systems | server |
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TensorFlow-Machine-Learning-Cookbook | tensorflow machine learning cookbook this is the code repository for tensorflow machine learning cookbook https www packtpub com big data and business intelligence tensorflow machine learning cookbook utm source github utm medium repository utm content 9781786462169 published by packt it contains all the supporting project files necessary to work through the book from start to finish about the book tensorflow is an open source software library for machine intelligence the independent recipes in this book will teach you how to use tensorflow for complex data computations and will let you dig deeper and gain more insights into your data than ever before you ll work through recipes on training models model evaluation sentiment analysis regression analysis clustering analysis artificial neural networks and deep learning each using google s machine learning library tensorflow this guide starts with the fundamentals of the tensorflow library which includes variables matrices and various data sources moving ahead you will get hands on experience with linear regression techniques with tensorflow the next chapters cover important high level concepts such as neural networks cnn rnn and nlp once you are familiar and comfortable with the tensorflow ecosystem the last chapter will show you how to take it to production instructions and navigations all of the code is organized into folders each folder starts with a number followed by the application name for example chapter 03 the code will look like the following import matplotlib pyplot as plt import numpy as np import tensorflow as tf from sklearn import datasets from tensorflow python framework import ops ops reset default graph software requirements python 3 with the following installed python libraries tensorflow numpy scikit learn requests and jupyter it is compatible in all three major operating systems mac windows and linux it requires no special hardware to run the scripts related products getting started with tensorflow https www packtpub com big data and business intelligence getting started tensorflow utm source github utm medium repository utm content 9781786468574 deep learning with tensorflow video https www packtpub com big data and business intelligence deep learning tensorflow video utm source github utm medium repository utm content 9781786464491 building machine learning systems with tensorflow video https www packtpub com big data and business intelligence building machine learning systems tensorflow video utm source github utm medium repository utm content 9781787281806 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781786462169 https packt link free ebook 9781786462169 a p | ai |
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finality-grandpa | finality grandpa crates io link crates badge crates build status https github com paritytech finality grandpa actions workflows rust yml badge svg branch master https github com paritytech finality grandpa actions workflows rust yml coverage status https coveralls io repos github paritytech finality grandpa badge svg branch master https coveralls io github paritytech finality grandpa branch master img align right width 150 height 150 src img grandpa png grandpa g host based r ecursive an cestor d eriving p refix a greement is a finality gadget for blockchains implemented in rust it allows a set of nodes to come to bft agreement on what is the canonical chain which is produced by some external block production mechanism it works under the assumption of a partially synchronous network model and with the presence of up to 1 3 byzantine nodes build test the only dependency required to build and run the tests is to have a stable version of rust installed git clone https github com paritytech finality grandpa cd finality grandpa cargo build cargo test usage add this to your cargo toml toml dependencies finality grandpa 0 16 features derive codec derive decode encode instances of parity scale codec parity scale codec for all the protocol messages test helpers expose some opaque types for testing purposes integration this crate only implements the state machine for the grandpa protocol in order to use this crate it is necessary to implement some traits to do the integration which are responsible for providing access to the underyling blockchain and setting up all the network communication chain chain docs the chain trait allows the grandpa voter to check ancestry of a given block and also to query the best block in a given chain which will be used for voting on environment environment docs the environment trait defines the types that will be used for the input and output stream to receive and broadcast messages it is also responsible for setting these up for a given round through round data as well as timers which are used for timeouts in the protocol the trait exposes callbacks for the full lifecycle of a round proposed prevoted precommitted completed as well as callbacks for notifying about block finality and voter misbehavior equivocations substrate the main user of this crate is substrate substrate and should be the main resource used to look into how the integration is done the substrate finality grandpa crate substrate finality grandpa should have most of the relevant integration code most importantly this crate does not deal with authority set changes it assumes that the set of authorities is always the same authority set handoffs are handled in substrate by listening to signals emitted on the underlying blockchain fuzzing to run the fuzzing test harness you need to install either afl or cargo fuzz you ll need a nightly rust toolchain for this sh cargo install cargo fuzz cargo install afl libfuzzer sh cargo fuzz run graph cargo fuzz run round afl sh cd fuzz cargo afl build features afl bin graph afl cargo afl build features afl bin round afl create some random input mkdir afl in dd if dev urandom of afl in seed bs 1024 count 4 cargo afl fuzz i afl in o afl out target debug graph afl cargo afl fuzz i afl in o afl out target debug round afl resources paper paper introductory blogpost blogpost polkadot wiki polkadot wiki sub0 presentation sub0 testnet testnet license usage is provided under the apache license version 2 0 see license license for the full details blogpost https medium com polkadot network grandpa block finality in polkadot an introduction part 1 d08a24a021b5 chain docs https docs rs finality grandpa latest finality grandpa trait chain html codecov badge https codecov io gh paritytech finality grandpa branch master graph badge svg codecov https codecov io gh paritytech finality grandpa crates badge https img shields io crates v finality grandpa svg crates https crates io crates finality grandpa environment docs https docs rs finality grandpa latest finality grandpa voter trait environment html paper https github com w3f consensus blob master pdf grandpa pdf parity scale codec https github com paritytech parity scale codec polkadot wiki https wiki polkadot network en latest polkadot learn consensus sub0 https www youtube com watch v qe8svrkvyou substrate https github com paritytech substrate substrate finality grandpa https github com paritytech substrate tree master client finality grandpa testnet https telemetry polkadot io alexander travis badge https travis ci org paritytech finality grandpa svg branch master travis https travis ci org paritytech finality grandpa | blockchain |
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Uber-Data-Engineering | uber data engineering introduction this is an end to end data engineering project using uber trip data from new york to create a dashboard using python google cloud storage compute engine vm mage opensource etl tool and finally looker studio for dashboard mage end to end data transformation etl pipeline p img src https github com kanchan20005 uber data engineering blob main mage etl 20 png align left alt mage ai width 100 height 100 p br dashboard br p img src https github com kanchan20005 uber data engineering blob main dashboard png align left alt dashboard width 100 height 100 p | cloud |
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oppurs | oppurs lab exercises for course software design for embedded systems to test the project run following commands in the root directory make make flash | os |
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libtuv | you can find project details in wiki https github com pando project libtuv wiki | server |
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FreeRTOS-rust | freertos rust this project is based on code from freertos rs https github com hashmismatch freertos rs and some additions to simplify the usage of freertos https github com freertos freertos kernel in embedded applications written in rust in contrast to freertos rs this crate differs in these points the application main entry point is written in rust the freertos scheduler can be started from rust the freertos heap memmang heap heap x c is used as global memory allocator for rust no need for a clang skeleton project how it works the freertos cargo build build dependency compiles the freertos code from its original c sources files into an archive to be linked against your rust app internally it uses the cc crate https docs rs crate cc and some meta info provided by your apps build rs 1 a path to the freertos https github com freertos freertos kernel sources 1 a path to the app specific freertosconfig h 1 a relative path to the freertos port to be used e g for arm cortex m3 cores 1 optional additional c code to be compiled the freertos rust dependency provides an interface to access all freertos functionality from your embedded rust app usage 1 checkout freertos https github com freertos freertos kernel 1 add dependencies to your rust apps cargo toml dependencies freertos rust build dependencies freertos cargo build 1 add this snippet to your apps build rs fn main let mut b freertos cargo build builder new path to freertos kernel or set env freertos src instead b freertos path to freertos kernel b freertos config src location of freertosconfig h b freertos port gcc arm cm3 port dir relativ to freertos kernel portable b heap heap 4 c set the heap c allocator to use from freertos kernel portable memmang default heap 4 c b get cc file more c optional additional c code to be compiled b compile unwrap or else e panic e to string used c compiler freertos cargo build depends on the cc crate https docs rs crate cc so the c compiler used can be set by using the cc enviroment variable or otherwise defined by internal defaults for the arm architecture this is the arm none eabi gcc which can be found here https developer arm com tools and software open source software developer tools gnu toolchain gnu rm downloads examples to get started there are examples in freertos rust examples freertos rust examples for cortex m33 nrf9160 cortex m3 stm32l151cbu6a cortex m4 stm32f411ceu6 windows more to come project crates to build a project using this create see freertos cargo build freertos cargo build the runtime dependency for you freertos rust application will be freertos rust freertos rust license this repository is using the mit license some parts might state different licenses that need to be respected when used the linux port https github com michaelbecker freertos addons is licensed under gplv2 | freertos embedded nrf9160 stm32 rtos rust | os |
ebookMLCB | m ngu n cu n ebook machine learning c b n v h u ti p ebook machine learning c b n pdf black white https github com tiepvupsu ebookmlcb blob master book ml pdf pdf color https github com tiepvupsu ebookmlcb blob master book ml color pdf m i h nh th c sao ch p in n u c n c s ng c a t c gi m i chia s u c n c d n ngu n t i https github com tiepvupsu ebookmlcb https github com tiepvupsu ebookmlcb ho c https machinelearningcoban com https machinelearningcoban com hi n s ch gi y kh ng c n c b n n a n u b n g p b t c l i n o ho c cho r ng n i dung c th c c i thi n b n c th t o m t issue t i y https github com tiepvupsu ebookmlcb issues click star n u b n th y n i dung cu n s ch c ch c m n b n | ai |
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decentr | decentr go version https img shields io github go mod go version decentr net decentr color blue testnet version https img shields io badge testnet 20version v1 6 2 blue svg https shields io mainnet version https img shields io badge mainnet 20version v1 6 2 brightgreen svg https shields io latest version https img shields io github v tag decentr net decentr label latest 20version color yellow decentr blockchain run local node quick start this assumes that you re running linux or macos and have installed go 1 19 https golang org dl this guide helps you build and install decentr allow you to name your node add seeds to your config file download genesis state start your node use decentrdcli to check the status of your node if you already have a previous version of decentr installed rm rf decentr mainnet build install and name your node bash clone decentr from the latest release git clone https github com decentr net decentr enter the folder decentr was cloned into cd decentr git checkout v1 6 2 compile and install decentr make install initialize decentrd in decentrd and name your node decentrd init yournodenamehere patch seeds bash sed e i s seeds seeds 7708addcfb9d4ff394b18fbc6c016b4aaa90a10a ares mainnet decentr xyz 26656 8a3485f940c3b2b9f0dd979a16ea28de154f14dd calliope mainnet decentr xyz 26656 87490fd832f3226ac5d090f6a438d402670881d0 euterpe mainnet decentr xyz 26656 3261bff0b7c16dcf6b5b8e62dd54faafbfd75415 hera mainnet decentr xyz 26656 5f3cfa2e3d5ed2c2ef699c8593a3d93c902406a9 hermes mainnet decentr xyz 26656 a529801b5390f56d5c280eaff4ae95b7163e385f melpomene mainnet decentr xyz 26656 385129dbe71bceff982204afa11ed7fa0ee39430 poseidon mainnet decentr xyz 26656 35a934228c32ad8329ac917613a25474cc79bc08 terpsichore mainnet decentr xyz 26656 0fd62bcd1de6f2e3cfc15852cdde9f3f8a7987e4 thalia mainnet decentr xyz 26656 bd99693d0dbc855b0367f781fb48bf1ca6a6a58b zeus mainnet decentr xyz 26656 home decentr config config toml download snapshot shell remove old data in decentr data rm rf decentr data mkdir p decentr data cd decentr data download snapshot snap name curl s https snapshots mainnet decentr xyz egrep o decentr tar gz tr d tail n 1 wget o https snapshots mainnet decentr xyz snap name tar xzf download genesis start your node check your node status bash download genesis json wget o home decentr config genesis json https raw githubusercontent com decentr net mainnets master 3 0 genesis json start decentrd decentrd start check your node s status decentrd status welcome to the decentr mainnet testnet build install and name your node bash clone decentr from the latest release git clone b v1 6 2 https github com decentr net decentr enter the folder decentr was cloned into cd decentr compile and install decentr make install initialize decentrd in decentrd and name your node decentrd init yournodenamehere patch seeds bash sed e i s seeds seeds 73fcfee94c476d185cb7a35863bf82fb444c500b ares testnet decentr xyz 26656 890fa479c89ba88facd964c30eb7d84fbfb0072b hera testnet decentr xyz 26656 600fc5298ac55e4af6c5c00f18714c6cd313bb5c hermes testnet decentr xyz 26656 2a13e93e8b27c09baacaf68fdd7db5401f4b9060 poseidon testnet decentr xyz 26656 345675d302faaf602d8e1eca791cc11766ff1832 zeus testnet decentr xyz 26656 home decentr config config toml download genesis start your node check your node status bash download genesis json wget o home decentr config genesis json https raw githubusercontent com decentr net testnets master 1 5 0 genesis json start decentrd decentrd start check your node s status decentrd status welcome to the decentr testnet dev tools requirements to build project you should have go 1 19 docker guide to fetch last proto 3rd party make proto update deps to generate go models from proto make proto gen to generate swagger from proto make proto swagger gen scripts scripts protocgen sh scripts protocgen sh generates goproto scripts protoc swagger gen sh scripts protoc swagger gen sh generates swagger dockerfile scripts dockerfile decentr docker image buildtools dockerfile scripts buildtools dockerfile docker image used in makefile contains proto compilers and swagger combine follow us your data is value decentr makes your data payable and tradeable online medium https medium com decentrnet twitter https twitter com decentrnet telegram https t me decentrnet discord https discord gg 9csxwkyejr | blockchain golang | blockchain |
BrAndroidSample | brandroidsample it s a simple project project to contribute and help with android developers in all levels my idea it s create a simple app without business layer using open source libraries to demonstrate important features in mobile development it s possible to you find some bugs in my code please give me a feedback about it i m not the best developer in the world i ll think that it s a good opportunity to improve my programming skills features at the moment we ve 1 binding with butterknife 2 material design with our toolbar 3 toolbar with window translucent status 4 circle image view like profile image in gmail app 5 floating action button and menu 6 openstreetmap android installation and usage clone this code open your android studio and play requirements at the moment android 15 thank you this section contains a link to some important features used in this application like a github of library webpage etc 1 butterknife http jakewharton github io butterknife 2 picasso http square github io picasso 3 circle image view https github com hdodenhof circleimageview 4 open street map osm android https github com osmdroid osmdroid 5 floating action button https github com clans floatingactionbutton license click here to see https github com brunogabriel brandroidsample blob master license | front_end |
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Sri_IITMadrasOct20ACSE | sri iitmadrasoct20acse programming projects for the course on advanced software engineering on cloud iot and blockchain from iit madras and greatlearning offered from oct 2020 | cloud |
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ow | this project includes a href http www normalesup org simonet soft ow eclipse less en html eclipse plugin for less a a href http www normalesup org simonet soft ow eclipse closure templates en html eclipse plugin for closure templates a a href http www normalesup org simonet soft ow eclipse closure javascript en html eclipse plugin for javascript with closure compiler closure library and closure linter a a href http www normalesup org simonet soft easyxtext index en html easyxtext a | front_end |
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smart-web-messaging | smart web messaging implementation guide the smart web messaging implementation guide content has moved from this readme to input pagecontent index md https github com hl7 smart web messaging blob master input pagecontent index md this document is used to feed the ig autopublisher which populates https build fhir org ig hl7 smart web messaging published content the ig autobuilder automatically publishes on pushes to the main branch of this repo via a github webhook it typically takes a few minutes for the build to detect changes and run autobuilder more details on the autobuilder are here https github com fhir auto ig builder fhir implementation guide auto builder latest you can view the latest published content here https build fhir org ig hl7 smart web messaging troubleshooting if you don t see published content when you view that link it means the autobuilder may be failing to build and publish the ig the ig autobuilder populates a few files as it works when the ig is failing to publish you can easily browse to those when visiting the link above however you can also view them when the publisher is working normally by visiting the links below build log the complete output of the autobuilder is saved here https build fhir org ig hl7 smart web messaging build log qa html the publisher produces an html page containing publication warnings and errors view it here https build fhir org ig hl7 smart web messaging qa html builds html you can view all the auto published builds here https fhir github io auto ig builder builds html making changes to update the published ig it is recommended that you test it locally before pushing changes back to the repo prerequisites you must have sushi to generate the ig https github com fhir sushi installation for sushi users to convert the ig into the pulishable html content you will run the latest version of the publisher which will automatically be downloaded by the build process below however to view the content locally you must have jekyll installed locally https jekyllrb com docs installation testing locally please check out this repo and run the build sh script see below this will run sushi locally on the repo files then apply the latest publisher to the sushi output to confirm that the publisher will work with the ig shell check out the repo mkdir p code hl7 cd git clone git github com hl7 smart web messaging git cd smart web messaging build and test the ig using a local sushi and publisher jar build sh the script will automatically open the local qa html and index html in your default browser building on windows prerequisites 1 install node https nodejs org en download 1 install fhir shorthand global npm install g fsh sushi local npm install fsh sushi 1 install java jdk 14 or later building 1 run updatepublisher bat answer y to all questions 1 run genonce bat | front_end |
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Paper_Reading_List | recommended papers maintenance https img shields io badge maintained yes brightgreen svg dub https img shields io badge mit license brightgreen svg license the goal of this document is to provide a reading list for deep learning in computer vision field recommended links deep learning paper reading roadmap from songrotek https github com songrotek if you are a newcomer to the deep learning area the first question you may have is which paper should i start reading from here is a reading roadmap of deep learning papers link https github com songrotek deep learning papers reading roadmap deep learning paper list from sbrugman https github com sbrugman deep learning papers by task link https github com sbrugman deep learning papers deep learning paper notes from dennybritz https github com dennybritz summaries and notes on deep learning research papers sorted by time link https github com dennybritz deeplearning papernotes paper collections cvpr 2017 papers related to attention model cvpr2017 attention model readme md paper list for instance aware tasks instance aware paper list readme md paper list related to deformable conv deformable conv readme md topics image salient object detection image 01 salient object detection md datasets evaluations https github com archerfmy sal eval toolbox object detection image 02 object detection md object localization image 03 object localization md semantic segmentation and scene parsing image 04 semantic segmentation and scene parsing md edge detection image 05 edge detection md pose estimation image 06 pose estimation md super resolution image 07 super resolution md image classification image 08 image classification md visual question answering image 09 visual question answering md video visual object tracking video 01 visual object tracking md video object segmentation video 02 video object segmentation md datasets davis https davischallenge org davis2017 soa compare html youtube vos https youtube vos org segtrack v2 https www cc gatech edu fli segtrack2 dataset html jumpcut http irc cs sdu edu cn jumpcut youtube objects source https data vision ee ethz ch cvl youtube objects masks http vision cs utexas edu projects videoseg others others others md notes for adding updating the list 1 fork the repository 2 update corresponding md file and add figures into data 3 send a pull request ai deadlines https aideadlin es countdowns to top cv nlp ml robotics ai conference deadlines | papers | ai |
TMDB-Movie-Revenue-Regression | tmdb movie revenue regression a example notebook of feature engineering and voting ensembles to predict the revenue of movies from the the movie database | server |
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Chandra-HAL | chandra hal a c 14 header only library designed for use in microcontroller projects targeting robotics signal processing control systems and other related deeply embedded systems chandra in the wider sense is viewed as being an all inclusive set of libraries which will provide support for a wide range of projects with hardware rich real time requirements chandra hal specifically is the component which will act as the core functionality and the hardware abstraction layer hal other chandra components will then layer on top of the hal as required currently chandra hal contains some basic hardware interface functionality adc gpio spi accelerometer imu etc drivers have been written for a few specific devices however there is currently a need to add many more drivers to take advantage of the available interface design in addition to the direct hardware connection chandra hal provides several support options which are especially useful first there is a units quantities system which does efficient conversion between units and error checking assignment and arithmetic of quantities is checked at compile time the design of sensor drivers is such that they return quantities with attached units rather than bare numbers of course it is possible to extract a unitless number with no run time overhead and reduced safety but the library supports the programmer s ability to write intrensically correct code whenever possible a second non hardware based feature that chandra hal provides is a small set of linear algebra operations matricies for storage and arithmetic are supported the matricies may be based on any arithmetic datatype including quantities quaternions also are provided chandra hal is very much a work in progress at some point a road map may end up in this spot as it stands however the library is serving its purpose quite well in a couple of projects which i will get up onto github when they have reached beyond the point of being fragile test devices much to do | os |
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Autonomous-Ai-drone-scripts | fully autonomous ai powered drone p align center img src https github com sieuwe1 autonomous ai drone scripts raw main demo media logo png alt drawing width 600 p align center this repository pushes to create an state of the art fully autonomous navigation and obstacle avoidance system for multi rotor vehicles our approach is based on the novel idea of an fully end 2 end ai model which takes the sensor inputs and directly output the desired control commands for the drone currently we are working on creating the necessary code for training and running this approach this project also contains an fully autonomous system for tracking a moving target using an camera and an lidar this sytem uses an ai based object detection model for detecting the target this system has already been fully tested in real flights and is fully functional autonomous point to point flight demo p align center img src https github com sieuwe1 autonomous ai drone scripts raw main demo media dronevis gif alt drawing width 800 p align center this video shows the performance of our end 2 end ai based pilot compared to a human pilot on a real flight the blue dots represent the joystick commands from the human pilot and the red dots represent the joystick commands from our ai pilot just take a look at how the ai pilot avoids threes just like the human pilot all tooling for gathering training and validating ai based pilots for pixhawk based multi rotors are avalible in this repo the autonomous point to point navigation system based on end 2 end technology is currently become property of mrr drones multirotorresearch com as mrr we are still dedicated to release awsome open source software however we do want to protect our software from being used by others to sell this is why we will be working on creating a open source version of our ai based pilot which anyone can use for free however all tooling and method for creating these models will stay secret but we will publish fully trained models for free autonomous person following flight demo p align center img src https github com sieuwe1 autonomous ai drone scripts raw main demo media flight gif alt drawing width 800 p align center this video shows the performance of our person following system an ai model detects the person and a pid controller then follows the person hardware our quadcopter is fitted with a jetson nano https www nvidia com en us autonomous machines embedded systems jetson nano running advanced ai models and algorithms real time on the edge on the quadcopter itself this makes our quadcopter able to work fully autonomsly without the need of a data connection to offload heavy workloads because of optimizations our algorithms can work in the computational limited environment of the jetson nano https www nvidia com en us autonomous machines embedded systems jetson nano drone image https github com sieuwe1 autonomous ai drone scripts raw main demo media img1 jpg drone image https github com sieuwe1 autonomous ai drone scripts raw main demo media img2 jpg setup to setup and build the project on a nvidia jetson nano https developer nvidia com embedded jetson nano developer kit please follow the instruction located at build md build md these instructions should guide you towards a fully operational jetson nano including all the required software to start flying autonomously scripts follow person the follow person script can autonomously follow a person using a live rgb camera feed and a mobilenet ai model this model can detect a person and use the centerpoint to calculate the yaw commands for the drone roll commands are calculated by using a tf luna solid state lidar this lidar can measure the distance between the person and the drone both axis commands are generated using pid controllers usage ensure the arducopter is in guided mode then execute the following command sh sudo python3 follow person main py mode flight debug path debug flight1 for ardupilot sitl simulator testing you can have to set the mode to test this can be done by changing the mode parameter from flight to test press q to exit the script the drone will automatically land note this is currently not working in real flight switch ardupilot to loiter mode and then manually land the drone data plotter a script was also made to plot the debug data from the pid controllers usage execute the following command to debug the data from the pid controllers sh python3 data plotter py ensure the file name in the script is set to the correct debug file this can be done by editing the data plotter py script keras to tensorrt model for faster execution tansform keras model to tensorrt models in repo have been converted already use tf2onnx tool for this python m tf2onnx convert saved model tensorflow model path output model onnx note this project is still under heavy development all code is experimental so please use the code with caution we are not responsible for damage to people or property from the usage of these scripts | ardupilot autonomous-quadcoptor computer-vision ai lidar gps | ai |
ghostbuster | ghostbuster detecting text ghostwritten by large language models https arxiv org abs 2305 15047v1 https arxiv org abs 2305 15047v1 we introduce ghostbuster a state of the art system for detecting ai generated text our method works by passing documents through a series of weaker language models and running a structured search over possible combinations of their features then training a classifier on the selected features to determine if the target document was ai generated crucially ghostbuster does not require access to token probabilities from the target model making it useful for detecting text generated by black box models or unknown model versions in conjunction with our model we release three new datasets of human and ai generated text as detection benchmarks that cover multiple domains student essays creative fiction and news and task setups document level detection author identification and a challenge task of paragraph level detection ghostbuster averages 99 1 f1 across all three datasets on document level detection outperforming previous approaches such as gptzero and detectgpt by up to 32 7 f1 main figure docs figure jpg usage installation to use ghostbuster install the requirements and the package as follows pip install r requirements txt pip install e we provide the classify py script to run a given text document through ghostbuster usage python3 classify py file input file here openai key openai key to run the experiment files create a file called openai config in the main directory with the following template javascript organization organization api key api key disclaimer we evaluate ghostbuster on three datasets that represent a range of domains but note that these datasets are not representative of all writing styles or topics and contain predominantly british and american english text thus users wishing to apply ghostbuster to real world cases of potential off limits usage of text generation e g identifying chatgpt written student essays should be wary that no model is infallible and incorrect predictions by ghostbuster are particularly likely when the text involved is shorter or in domains that are further from those examined in this paper to avoid perpetuation of algorithmic harms due to these limitations we discourage incorporation of ghostbuster into any systems that automatically penalize students or other writers for alleged usage of text generation without human supervision note since pre printing we have altered the essay data to filter out de anonymized examples the filtered dataset contains 1444 human examples and 1444 gpt examples | ai |
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gradio | do not edit this file directly instead edit the readme template md or guides 1 getting started 1 quickstart md templates and then run render readme py script div align center img src readme files gradio svg alt gradio width 300 https gradio app br em build share delightful machine learning apps easily em gradio backend https github com gradio app gradio actions workflows backend yml badge svg https github com gradio app gradio actions workflows backend yml gradio ui https github com gradio app gradio actions workflows ui yml badge svg https github com gradio app gradio actions workflows ui yml pypi https img shields io pypi v gradio https pypi org project gradio pypi downloads https img shields io pypi dm gradio https pypi org project gradio python version https img shields io badge python 3 8 important twitter follow https img shields io twitter follow gradio style social label follow https twitter com gradio website https gradio app documentation https gradio app docs guides https gradio app guides getting started https gradio app getting started examples demo readme files zh cn readme div gradio build machine learning web apps in python gradio is an open source python library that is used to build machine learning and data science demos and web applications with gradio you can quickly create a beautiful user interface around your machine learning models or data science workflow and let people try it out by dragging and dropping in their own images pasting text recording their own voice and interacting with your demo all through the browser interface montage readme files header image jpg gradio is useful for demoing your machine learning models for clients collaborators users students deploying your models quickly with automatic shareable links and getting feedback on model performance debugging your model interactively during development using built in manipulation and interpretation tools quickstart prerequisite gradio requires python 3 8 or higher that s all what does gradio do one of the best ways to share your machine learning model api or data science workflow with others is to create an interactive app that allows your users or colleagues to try out the demo in their browsers gradio allows you to build demos and share them all in python and usually in just a few lines of code so let s get started hello world to get gradio running with a simple hello world example follow these three steps 1 install gradio using pip bash pip install gradio 2 run the code below as a python script or in a jupyter notebook or google colab https colab research google com drive 18odkjvyxhuttn0p5apwygfo xwncghdz usp sharing python import gradio as gr def greet name return hello name demo gr interface fn greet inputs text outputs text demo launch we shorten the imported name to gr for better readability of code using gradio this is a widely adopted convention that you should follow so that anyone working with your code can easily understand it 3 the demo below will appear automatically within the jupyter notebook or pop in a browser on http localhost 7860 http localhost 7860 if running from a script hello world demo demo hello world screenshot gif when developing locally if you want to run the code as a python script you can use the gradio cli to launch the application in reload mode which will provide seamless and fast development learn more about reloading in the auto reloading guide https gradio app developing faster with reload mode bash gradio app py note you can also do python app py but it won t provide the automatic reload mechanism the interface class you ll notice that in order to make the demo we created a gr interface this interface class can wrap any python function with a user interface in the example above we saw a simple text based function but the function could be anything from music generator to a tax calculator to the prediction function of a pretrained machine learning model the core interface class is initialized with three required parameters fn the function to wrap a ui around inputs which component s to use for the input e g text image or audio outputs which component s to use for the output e g text image or label let s take a closer look at these components used to provide input and output components attributes we saw some simple textbox components in the previous examples but what if you want to change how the ui components look or behave let s say you want to customize the input text field for example you wanted it to be larger and have a text placeholder if we use the actual class for textbox instead of using the string shortcut you have access to much more customizability through component attributes python import gradio as gr def greet name return hello name demo gr interface fn greet inputs gr textbox lines 2 placeholder name here outputs text demo launch hello world 2 demo demo hello world 2 screenshot gif multiple input and output components suppose you had a more complex function with multiple inputs and outputs in the example below we define a function that takes a string boolean and number and returns a string and number take a look how you pass a list of input and output components python import gradio as gr def greet name is morning temperature salutation good morning if is morning else good evening greeting f salutation name it is temperature degrees today celsius temperature 32 5 9 return greeting round celsius 2 demo gr interface fn greet inputs text checkbox gr slider 0 100 outputs text number demo launch hello world 3 demo demo hello world 3 screenshot gif you simply wrap the components in a list each component in the inputs list corresponds to one of the parameters of the function in order each component in the outputs list corresponds to one of the values returned by the function again in order an image example gradio supports many types of components such as image dataframe video or label let s try an image to image function to get a feel for these python import numpy as np import gradio as gr def sepia input img sepia filter np array 0 393 0 769 0 189 0 349 0 686 0 168 0 272 0 534 0 131 sepia img input img dot sepia filter t sepia img sepia img max return sepia img demo gr interface sepia gr image shape 200 200 image demo launch sepia filter demo demo sepia filter screenshot gif when using the image component as input your function will receive a numpy array with the shape height width 3 where the last dimension represents the rgb values we ll return an image as well in the form of a numpy array you can also set the datatype used by the component with the type keyword argument for example if you wanted your function to take a file path to an image instead of a numpy array the input image component could be written as python gr image type filepath shape also note that our input image component comes with an edit button which allows for cropping and zooming into images manipulating images in this way can help reveal biases or hidden flaws in a machine learning model you can read more about the many components and how to use them in the gradio docs https gradio app docs chatbots gradio includes a high level class gr chatinterface which is similar to gr interface but is specifically designed for chatbot uis the gr chatinterface class also wraps a function but this function must have a specific signature the function should take two arguments message and then history the arguments can be named anything but must be in this order message a str representing the user s input history a list of list representing the conversations up until that point each inner list consists of two str representing a pair user input bot response your function should return a single string response which is the bot s response to the particular user input message other than that gr chatinterface has no required parameters though several are available for customization of the ui here s a toy example python import random import gradio as gr def random response message history return random choice yes no demo gr chatinterface random response demo launch chatinterface random response demo demo chatinterface random response screenshot gif you can read more about gr chatinterface here https gradio app guides creating a chatbot fast blocks more flexibility and control gradio offers two approaches to build apps 1 interface and chatinterface which provide a high level abstraction for creating demos that we ve been discussing so far 2 blocks a low level api for designing web apps with more flexible layouts and data flows blocks allows you to do things like feature multiple data flows and demos control where components appear on the page handle complex data flows e g outputs can serve as inputs to other functions and update properties visibility of components based on user interaction still all in python if this customizability is what you need try blocks instead hello blocks let s take a look at a simple example note how the api here differs from interface python import gradio as gr def greet name return hello name with gr blocks as demo name gr textbox label name output gr textbox label output box greet btn gr button greet greet btn click fn greet inputs name outputs output api name greet demo launch hello blocks demo demo hello blocks screenshot gif things to note blocks are made with a with clause and any component created inside this clause is automatically added to the app components appear vertically in the app in the order they are created later we will cover customizing layouts a button was created and then a click event listener was added to this button the api for this should look familiar like an interface the click method takes a python function input components and output components more complexity here s an app to give you a taste of what s possible with blocks python import numpy as np import gradio as gr def flip text x return x 1 def flip image x return np fliplr x with gr blocks as demo gr markdown flip text or image files using this demo with gr tab flip text text input gr textbox text output gr textbox text button gr button flip with gr tab flip image with gr row image input gr image image output gr image image button gr button flip with gr accordion open for more gr markdown look at me text button click flip text inputs text input outputs text output image button click flip image inputs image input outputs image output demo launch blocks flipper demo demo blocks flipper screenshot gif a lot more going on here we ll cover how to create complex blocks apps like this in the building with blocks https gradio app blocks and event listeners section for you congrats you re now familiar with the basics of gradio go to our next guide https gradio app key features to learn more about the key features of gradio open source stack gradio is built with many wonderful open source libraries please support them as well img src readme files huggingface mini svg alt huggingface height 40 https huggingface co img src readme files python svg alt python height 40 https www python org img src readme files fastapi svg alt fastapi height 40 https fastapi tiangolo com img src readme files encode svg alt encode height 40 https www encode io img src readme files svelte svg alt svelte height 40 https svelte dev img src readme files vite svg alt vite height 40 https vitejs dev img src readme files pnpm svg alt pnpm height 40 https pnpm io img src readme files tailwind svg alt tailwind height 40 https tailwindcss com img src readme files storybook svg alt storybook height 40 https storybook js org img src readme files chromatic svg alt chromatic height 40 https www chromatic com license gradio is licensed under the apache license 2 0 found in the license license file in the root directory of this repository citation also check out the paper gradio hassle free sharing and testing of ml models in the wild https arxiv org abs 1906 02569 icml hill 2019 and please cite it if you use gradio in your work article abid2019gradio title gradio hassle free sharing and testing of ml models in the wild author abid abubakar and abdalla ali and abid ali and khan dawood and alfozan abdulrahman and zou james journal arxiv preprint arxiv 1906 02569 year 2019 | machine-learning models ui ui-components interface python data-science data-visualization deep-learning data-analysis gradio gradio-interface python-notebook deploy hacktoberfest | ai |
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project or client hope ui free open source bootstrap design system can help build a robust and understandable admin dashboard or website without spending much time designing the whole thing from scratch what can you do with the hope ui hope ui is packed with finely crafted ui elements multiple styles of menu a set of graphs charts and animated icons built ideally for developers designers and startups or creators hope ui brings design consistency and acts as a strong foundation to manage a successful web or app project 5 irresistible reasons to have hope ui unlike any other design system hope ui is fully responsive which facilitates business owners to view the admin panel right from their mobile screens without losing the resolution with the purpose of data presentation this best free bootstrap 5 design system comes with extensive elements and widgets to add texts and images besides being super easy and rapidly adaptable features hope ui is the cost effective way to control and overview web or app project performance with a pre coded design system and examples hope ui is a strongly built system a layout that focuses on both the scalability and performance of the project hope ui packs 100 fully codes elements and widgets backed with scss and gulp to make the development easy and fast features support with bootstrap 5 scss component based design hbs handlebar based html fully responsive clean code demo pages a href https www youtube com watch v 3omj6nqduaa title hope ui target blank img src https assets iqonic design hope ui github hope ui youtube png alt hope ui video a table of contents quick start quick start method 1 direct download method 1 direct download method 2 using cdn method 2 using cdn method 3 using npm method 3 using npm documentation documentation version version public roadmap public roadmap file structure file structure browser support browser support don t buy a coffee for us instead support us dont buy a coffee for us instead support us more from 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inference | roboflow inference banner https github com roboflow inference blob main banner png raw true pip install inference roboflow https roboflow com inference is the easiest way to use and deploy computer vision models inference supports running object detection classification instance segmentation and even foundation models like clip and sam you can train and deploy your own custom model https github com roboflow notebooks or use one of the 50 000 fine tuned models shared by the community https universe roboflow com there are three primary inference interfaces a python native package pip install inference a self hosted inference server inference server start a fully managed auto scaling api https docs roboflow com getting started get up and running with inference on your local machine in 3 minutes sh pip install inference or inference gpu if you have cuda setup your roboflow private api key https app roboflow com settings api by exporting a roboflow api key environment variable or adding it to a env file sh export roboflow api key your key here run an open source rock paper scissors model https universe roboflow com roboflow 58fyf rock paper scissors sxsw on your webcam stream python import inference inference stream source webcam or rtsp stream or camera id model rock paper scissors sxsw 11 from universe on prediction lambda predictions image print predictions now hold up your hand now let s extend the example to use supervision https roboflow com supervision to visualize the predictions and display them on screen with opencv python import cv2 import inference import supervision as sv annotator sv boxannotator inference stream source webcam or rtsp stream or camera id model rock paper scissors sxsw 11 from universe output channel order bgr use main thread true for opencv display on prediction lambda predictions image print predictions now hold up your hand cv2 imshow prediction annotator annotate scene image detections sv detections from roboflow predictions cv2 waitkey 1 more examples the examples https github com roboflow inference tree main examples directory contains code samples for working with and extending inference including using foundation models like clip http and udp clients and an insights dashboard along with community examples prs welcome inference in action check out inference running on a video of a football game https github com roboflow inference assets 37276661 121ab5f4 5970 4e78 8052 4b40f2eec173 why inference inference provides a scalable method through which you can manage inferences for your vision projects inference is composed of thousands of pre trained community models https universe roboflow com that you can use as a starting point foundation models like clip sam and ocr a tight integration with supervision https roboflow com supervision an http server so you don t have to reimplement things like image processing and prediction visualization on every project and you can scale your gpu infrastructure independently of your application code and access your model from whatever language your app is written in standardized apis for computer vision tasks so switching out the model weights and architecture can be done independently of your application code a model registry so your code can be independent from your model weights you don t have to re build and re deploy every time you want to iterate on your model weights active learning integrations so you can collect more images of edge cases to improve your dataset model the more it sees in the wild seamless interoperability with roboflow https roboflow com for creating datasets training deploying custom models and more use the inference server you can learn more about roboflow inference docker image build pull and run in our documentation https inference roboflow com quickstart docker run on x86 cpu bash docker run net host roboflow roboflow inference server cpu latest run on nvidia gpu bash docker run network host gpus all roboflow roboflow inference server gpu latest details close summary more docker run options summary run on arm64 cpu bash docker run p 9001 9001 roboflow roboflow inference server arm cpu latest run on nvidia gpu with tensorrt runtime bash docker run network host gpus all roboflow roboflow inference server trt latest run on nvidia jetson with jetpack 4 x bash docker run privileged net host runtime nvidia roboflow roboflow inference server jetson latest run on nvidia jetson with jetpack 5 x bash docker run privileged net host runtime nvidia roboflow roboflow inference server jetson 5 1 1 latest details extras some functionality requires extra dependencies these can be installed by specifying the desired extras during installation of roboflow inference extra description clip ability to use the core clip model by openai gaze ability to use the core gaze model http ability to run the http interface sam ability to run the core segment anything model by meta ai note both clip and segment anything require pytorch to run these are included in their respective dependencies however pytorch installs can be highly environment dependent see the official pytorch install page https pytorch org get started locally for instructions specific to your enviornment example install with clip dependencies bash pip install inference clip inference client to consume predictions from inference server in python you can use the inference sdk package bash pip install inference sdk python from inference sdk import inferencehttpclient image url https media roboflow com inference soccer jpg replace roboflow api key with your roboflow api key client inferencehttpclient api url http localhost 9001 or https detect roboflow com for hosted api api key roboflow api key with client use model soccer players 5fuqs 1 predictions client infer image url print predictions visit our documentation https inference roboflow com to discover capabilities of inference clients library single image inference after installing inference via pip you can run a simple inference on a single image vs the video stream example above by instantiating a model and using the infer method don t forget to setup your roboflow api key environment variable or env file python from inference models utils import get roboflow model model get roboflow model model id soccer players 5fuqs 1 you can also infer on local images by passing a file path a pil image or a numpy array results model infer image https media roboflow com inference soccer jpg confidence 0 5 iou threshold 0 5 print results getting clip embeddings you can run inference with openai s clip model https blog roboflow com openai clip using python from inference models import clip image url https media roboflow com inference soccer jpg model clip embeddings model embed image image url print embeddings using sam you can run inference with meta s segment anything model https blog roboflow com segment anything breakdown using python from inference models import segmentanything image url https media roboflow com inference soccer jpg model segmentanything embeddings model embed image image url print embeddings inference process to standardize the inference process throughout all our models roboflow inference has a structure for processing inference requests the specifics can be found on each model s respective page but overall it works like this for most models img width 900 alt inference structure src https github com stellasphere inference assets 29011058 abf69717 f852 4655 9e6e dae19fc263dc supported models load from roboflow you can use models hosted on roboflow with the following architectures through inference yolov5 object detection yolov5 instance segmentation yolov8 object detection yolov8 classification yolov8 segmentation yolact segmentation vit classification core models core models are foundation models and models that have not been fine tuned on a specific dataset the following core models are supported 1 clip 2 l2cs gaze detection 3 segment anything sam license the roboflow inference code is distributed under an apache 2 0 license https github com roboflow inference blob master license md the models supported by roboflow inference have their own licenses view the licenses for supported models below model license inference models clip mit https github com openai clip blob main license inference models gaze mit https github com ahmednull l2cs net blob main license apache 2 0 https github com google mediapipe blob master license inference models sam apache 2 0 https github com facebookresearch segment anything blob main license inference models vit apache 2 0 https github com roboflow inference main inference models vit license inference models yolact mit https github com dbolya yolact blob master readme md inference models yolov5 agpl 3 0 https github com ultralytics yolov5 blob master license inference models yolov7 gpl 3 0 https github com wongkinyiu yolov7 blob main readme md inference models yolov8 agpl 3 0 https github com ultralytics ultralytics blob master license inference cli we ve created a cli tool with useful commands to make the inference usage easier check out docs inference cli readme md enterprise with a roboflow inference enterprise license you can access additional inference features including server cluster deployment device management active learning yolov5 and yolov8 commercial license to learn more contact the roboflow team https roboflow com sales documentation visit our documentation https inference roboflow com for usage examples and reference for roboflow inference contribution we would love your input to improve roboflow inference please see our contributing guide https github com roboflow inference blob master contributing md to get started thank you to all of our contributors explore more roboflow open source projects project description supervision https roboflow com supervision general purpose utilities for use in computer vision projects from predictions filtering and display to object tracking to model evaluation autodistill https github com autodistill autodistill automatically label images for use in training computer vision models inference https github com roboflow inference this project an easy to use production ready inference server for computer vision supporting deployment of many popular model architectures and fine tuned models notebooks https roboflow com notebooks tutorials for computer vision tasks from training state of the art models to tracking objects to counting objects in a zone collect https github com roboflow roboflow collect automated intelligent data collection powered by clip br div align center div align center a href https youtube com roboflow img src https media roboflow com notebooks template icons purple youtube png ik sdk version javascript 1 4 3 updatedat 1672949634652 width 3 a img src https raw githubusercontent com ultralytics assets main social logo transparent png width 3 a href https roboflow com img src https media roboflow com notebooks template icons purple roboflow app png ik sdk version javascript 1 4 3 updatedat 1672949746649 width 3 a img src https raw githubusercontent com ultralytics assets main social logo transparent png width 3 a href https www linkedin com company roboflow ai img src https media roboflow com notebooks template icons purple linkedin png ik sdk version javascript 1 4 3 updatedat 1672949633691 width 3 a img src https raw githubusercontent com ultralytics assets main social logo transparent png width 3 a href https docs roboflow com img src https media roboflow com notebooks template icons purple knowledge png ik sdk version javascript 1 4 3 updatedat 1672949634511 width 3 a img src https raw githubusercontent com ultralytics assets main social logo transparent png width 3 a href https disuss roboflow com img src https media roboflow com notebooks template icons purple forum png ik sdk version javascript 1 4 3 updatedat 1672949633584 width 3 img src https raw githubusercontent com ultralytics assets main social logo transparent png width 3 a href https blog roboflow com img src https media roboflow com notebooks template icons purple blog png ik sdk version javascript 1 4 3 updatedat 1672949633605 width 3 a a div div div | computer-vision inference-api inference-server vit yolact yolov5 yolov7 yolov8 jetson tensorrt classification instance-segmentation object-detection onnx deployment docker inference machine-learning python server | ai |
Sl-Rtos | rtos build build cmake dboard family nucleo stm32f746zg make cmake makefile dboard family nucleo stm32f746zg make build bin cmakelists txt demo add subdirectory demo proc manager download linux st link st link linux https www bilibili com video av76683533 https sourcelink top 2019 11 22 stlink driver install 1 bin st flash write bin xxx bin 0x8000000 2 cmake cmake 1 cmd make flash rtos 01 https www bilibili com video av74074508 02 https www bilibili com video av74081139 03 https www bilibili com video av75258795 04 stlink https www bilibili com video av76683533 05 https www bilibili com video av77364549 06 https www bilibili com video av78234770 07 https www bilibili com video av78389762 08 vscode stm32 https www bilibili com video av78709442 09 https www bilibili com video av79545686 rtos 01 https sourcelink top 02 https sourcelink top 2019 10 31 rtos need condition 03 https sourcelink top 2019 11 10 os principle 04 stlink https sourcelink top 2019 11 22 stlink driver install 05 https sourcelink top 2019 11 27 rtos assembly 06 https sourcelink top 2019 12 05 rtos kernel managmer 07 vscode stm32 https sourcelink top 2019 12 08 vscode rtos 08 https sourcelink top 2019 12 16 kernel list | rtos c | os |
aradict | aradict open source arabic word dictionary for information technology distribution all tranlations are for now organized in a simple format allowing easy addition for beginners and nearly no need for conflects managent content fornat just add a new file with the english word you want to tanslate with a json extension all in lowercase example to translate the word sample add a file sample json under translation folder the file will have a simple json data composed of list of an object with fields context a phrase or explanatory text for the word s usage translation the translation in arabic usage example of usage in arabic or a translation of the context dual the dual form if the word is countable arabic has singular dual and plural forms plural the dual form if the word is countable for example the content of the sample json file should be json context this file includes a sample of the test data translation usage dual plural access translation there are multiple options for accessing the data the most obvious way is cloning the repository and pull regulary to check for new translations an other option is to check if the tranlation exist using curl or other cli tool and as a simple api bash curl l fail https github com ezzarghili aradict raw master translation sample json this will fail if the translation file does not exist or return the tranlation data if it exists we will be adding new dict release formats that are automatically generated from the tranlation folder once there are enough data license all code would is licensed under mit all translations a k a content under translation folder will be licensed under cc0 1 0 https creativecommons org publicdomain zero 1 0 | server |
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gowut | welcome godoc https godoc org github com icza gowut gwu status svg https godoc org github com icza gowut gwu build status https travis ci org icza gowut svg branch master https travis ci org icza gowut go report card https goreportcard com badge github com icza gowut https goreportcard com report github com icza gowut gowut go web ui toolkit is a full featured easy to use platform independent web ui toolkit written in pure go no platform dependent native code is linked or called for documentation please visit the gowut wiki https github com icza gowut wiki development takes place in the dev branch https github com icza gowut tree dev quick install to quickly install or update to the latest version type go get u github com icza gowut quick test to quickly test it and see it in action run the following example applications let s assume you re in the root of the gowut project cd gopath src github com icza gowut 1 showcase of features this one auto opens itself in your default browser go run examples showcase showcase go the showcase of features is also available live https gowut demo herokuapp com show 2 a single window example this one auto opens itself in your default browser go run examples simple simple demo go and this is how it looks full app screenshot https github com icza gowut raw dev images full app example png https github com icza gowut wiki full app example 3 login window example with session management change directory so that the demo can read the test cert and key files cd examples login go run login demo go open the page https localhost 3434 guitest in your browser to see it godoc of gowut you can read the godoc of gowut online here http godoc org github com icza gowut gwu 1 star gowut | web-framework web | front_end |
Front-end-developer | front end developer information technology | server |
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front-end-challenge | amaro front end challenge this is our challenge for the front end developer position at amarofashion https github com amarofashion you re probably already participating in our hiring process but if you stumble here by accident read the document to the end and if you are interested you can start the process from here if you are not a developer you can have a look at our other opportunities at amaro com jobs https amaro com jobs about the challenge this is a challenge not a college test so there are multiple correct answers we ll give you some requirements that must be done and for which you ll be evaluated but you re free to choose a solution method what we expect to learn from you with this challenge your work style how you think and solve problems how you communicate what we expect that you learn from us how we work as a team have a close look at some of the problems we face daily next steps 1 fork this repository to your personal account 2 follow the instructions in the challenge description challenge description md file 3 solve the challenge in the best way you can 4 send us a pr with your solution considerations we won t limit your choice of tools or libraries but make choices that suits your needs there s no need to use a bazooka to kill an ant but the point of the challenge is to evaluate your skills despite not having a time limit we recommend that you don t spend more than 10 to 12 hours working on this challenge try to write the best code you possibly can it will make our life easier when evaluating your solution and remember you ll have to explain it in person for us doubts do you have in doubt related to the process open an issue https github com amaroteam front end challenge issues and we ll be happy to help break a leg | front-end challenge react | front_end |
easyRTOS | easy rtos this open source framework aim at easy to port embeded rtos to the appliaction easy to port any mcu platform to the application architeture image https github com coversb easyrtos blob master doc resume png folders common base type defination hardware board mcu bsp hal hardware abstract layer middleware the encapsulation of rtos s api modules drivers device drivers modules crypto encryption and decryption utility project ide project files thirdparty third party open source library configurations board config h pin define and feature control os config h os feature control | os |
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Awesome-Natural-Language-Processing | various natural language processing api s this repository contain various natural language processing api s like goole natural language processing ibm natural language processing spacy natural language processing nltk corenlp from stanford textblob | ai |
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SIOP2017-NLPTutorial | siop2017 nlptutorial this repository contains materials shared in the reproducible research conference track at siop 2017 the materials are part of the master tutorial titled natural language processing text mining for i o presented saturday april 29th 2017 video demonstration in addition to the raw data and scripts provided on github there are 5 vidoes on youtube which show how the code is applied to the raw data the run time for all 5 videos is 25 minutes the narration provides additional information about what each step in the code is doing beyond the comments in the scripts the playlist of 5 videos is located here https www youtube com playlist list ploya960weszqiqk6 kjd41vsxky2ss w9 permissions we grant you a attribution noncommercial sharealike 4 0 international creative commons license to this information https creativecommons org licenses by nc sa 4 0 raw data source the data provided in the csv file was collected on amazon mechanical turk in late 2015 early 2016 participants completed 5 tasks in this order for which they were paid if they completed the tasks correctly 1 demographics questionnaire file contains a portion of data from this section 2 picture description task file contains complete data from this section 3 open ended interview style questions file contains no data from this section 4 deductive reasoning assessment file contains a portion of data from this section 5 personality assessment file contains no data from this section images the two images used for the picture description task were originally taken by ceb corporate photographers the license above also applies to the images scripts the r scripts included in the folder of attendee materials were developed by the original investigators named below and may not be suitable for all use cases please review them carefully and make your own determinations about how and where to use them word lists the word lists included in the folder of attendee materials were developed by the original investigators named below and may not be suitable for all use cases please review the contents of the lists and make your own determinations about how and where to use them contact persons if you have questions about the data provided or the permissions please contact any of the following original investigators cory kind ckind cebglobal com andrea kropp kroppa cebglobal com allison yost ayost cebglobal com | ai |
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DOMQL | about an sql like language for querying the dom more info here http amasad github com domql development domql is an interpereted language powered by the sizzle http sizzlejs com selector engine most select queries can be directly compiled to a sizzle query see src test coffee domql is written using the jison http zaach github com jison parser generator source files overview dql coffee is the main file responsible for initiating the parser and contains some utils domready templates etc grammer coffee is the language grammer file written in coffeescript s jison dsl lexer coffee is the language lexer nodes coffee contains all the nodes for the syntax tree also responsible for compiling to sizzle evaluating the code test coffee the test suite ran by index html getting the code git clone git github com amasad domql git dependencies nodejs for the development environment browserify for node requires to work in the browser express for development server uglify js for minifying build file jison for creating the parser you only need to install nodejs all the modules are checked in note that browserify has been altered to work around a bug running using coffeescript s cakefile you could do the following cake buildparser builds the parser cake dev starts the development server at localhost 8080 and watches the grammer file for changes to rebuild the parser cake build builds and minifies to domql min js tests run cake dev and navigate your browser to http localhost 8080 then open your javascript console to see the test results license the mit license copyright 2012 amjad masad amjad masad gmail com | front_end |
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be-the-hero | h1 align center style border bottom 1px solid eee margin 20px 0 padding bottom 10px img src img logo svg width 400px alt logo br p seja um her i p h1 div align center the mit license https img shields io badge license mit green svg style flat square https github com jvictorfarias be the hero blob master license md codacy badge https api codacy com project badge grade e03bf9946db44b37bc31318b5e9de000 https www codacy com manual jvictorfarias be the hero utm source github com utm medium referral utm content jvictorfarias be the hero utm campaign badge grade github last commit https img shields io github last commit jvictorfarias be the hero netlify status https api netlify com api v1 badges 749225ed 51f3 4f2b 85be 5a8eb32897f0 deploy status https app netlify com sites bethehero jvictorfarias deploys p align center a href fire pr via da aplica o fire pr via da aplica o a a href rocket tecnologias usadas rocket tecnologias usadas a a href hammer deploy da aplica o hammer deploy da aplica o a a href thinking como contribuir thinking como contribuir a a href zap executando o projeto zap executando o projeto a p div bookmark tabs sobre o projeto be the hero um projeto que tem como foco ajudar as ongs organiza es n o governamentais a angariar fundos para os seus projetos e o nome se d pela possibilidade de voc contribuir com os objetivos e projetos das ongs e se tornar um her i com isso fire pr via da aplica o div align center demo img preview gif div rocket tecnologias usadas o projeto foi desenvolvido usando as seguintes tecnologias nodejs https nodejs org en reactjs https pt br reactjs org reactnative https reactnative dev expressjs https expressjs com pt br jwt https jwt io knex http knexjs org axios https github com axios axios yup https github com jquense yup sucrase https sucrase io styled components https styled components com eslint https eslint org prettier https prettier io thinking como contribuir fa a um fork deste reposit rio bash clone o seu fork git clone url do seu fork cd be the hero crie uma branch com sua feature ou corre o de bugs git checkout b minha branch fa a o commit das suas altera es git commit m feature bugfix minhas altera es fa a o push para a sua branch git push origin minha branch depois que o merge da sua pull request for feito voc pode deletar a sua branch hammer deploy da aplica o frontend https bethehero jvictorfarias netlify app backend https be the hero jvictorfarias herokuapp com zap executando o projeto clonando o projeto sh git clone https github com jvictorfarias be the hero git cd be the hero yarn iniciando a api sh cd api yarn yarn knex migrate latest yarn dev a href https insomnia rest run label be the hero uri https 3a 2f 2fgithub com 2fjvictorfarias 2fbe the hero 2fblob 2fmaster 2fapi 2finsomnia 2020 04 05 json target blank img src https insomnia rest images run svg alt run in insomnia a iniciando o frontend sh cd frontend yarn yarn start iniciando o mobile android sh cd mobile yarn yarn android yarn start memo licen a este projeto foi desenvolvido sob a licen a mit veja o arquivo license license md para saber mais detalhes p align center style margin top 20px img src img heroes png width 400px alt heroes p p align center style margin top 20px made with purple heart by strong joao victor farias p | nodejs reactjs jwt-authentication knex expressjs styled-components dark-theme | front_end |
medicalapp-backend | medicalapp backend repository for the medical app project back end and documentation seamlessly manage patient data appointments and medical records this repository contains the code for both the backend and frontend components along with comprehensive documentation to guide development and usage | server |
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Microsoft-Cloud-Services-for-Android | microsoft cloud services for android plugin for easy and fast development of android apps connected to azure mobile services notification hub and office 365 services the plugin offers integrated development environment with these microsoft cloud services within intellij idea and android studio | front_end |
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llm_finetuning | test workflow https github com taprosoft llm finetuning actions workflows tests yml badge svg code style black https img shields io badge code 20style black 000000 svg https github com psf black pre commit https img shields io badge pre commit enabled brightgreen logo pre commit logocolor white https github com pre commit pre commit memory efficient fine tuning of large language models lora quantization this repository contains a convenient wrapper for fine tuning and inference of large language models llms in memory constrained environment two major components that democratize the training of llms are parameter efficient fine tuning peft https github com huggingface peft e g lora adapter and quantization techniques 8 bit 4 bit however there exists many quantization techniques and corresponding implementations which make it hard to compare and test different training configurations effectively this repo aims to provide a common fine tuning pipeline for llms to help researchers quickly try most common quantization methods and create compute optimized training pipeline this repo is built upon these materials alpaca lora https github com tloen alpaca lora for the original training script gptq for llama https github com qwopqwop200 gptq for llama for the efficient gptq quantization method exllama https github com turboderp exllama for the high performance inference engine key features memory efficient fine tuning of llms on consumer gpus 16gib by utilizing lora low rank adapter and quantization techniques support most popular quantization techniques 8 bit 4 bit quantization from bitsandbytes https github com timdettmers bitsandbytes and gptq https github com qwopqwop200 gptq for llama correct peft checkpoint saving at regular interval to minimize risk of progress loss during long training correct checkpoint resume for all quantization methods support distributed training on multiple gpus with examples support gradient checkpointing for both gptq and bitsandbytes switchable prompt templates to fit different pretrained llms support evaluation loop to ensure lora is correctly loaded after training inference and deployment examples fast inference with exllama https github com turboderp exllama for gptq model usage demo notebook see notebook llm finetuning ipynb or on colab open in colab https colab research google com assets colab badge svg https colab research google com github taprosoft llm finetuning blob main llm finetuning ipynb setup 1 install default dependencies bash pip install r requirements txt 2 if bitsandbytes doesn t work install it from source https github com timdettmers bitsandbytes blob main compile from source md windows users can follow these instructions https github com tloen alpaca lora issues 17 3 to use 4 bit efficient cuda kernel from exllama and gptq for training and inference bash pip install r cuda quant requirements txt note that the installation of above packages requires the installation of cuda to compile custom kernels if you have issue looks for help in the original repos gptq https github com qwopqwop200 gptq for llama exllama https github com turboderp exllama for advices data preparation prepare the instruction data to fine tune the model in the following json format json instruction do something with the input input input string output output string you can supply a single json file as training data and perform auto split for validation or prepare two separate train json and test json in the same directory to supply as train and validation data you should also take a look at templates templates readme md to see different prompt template to combine the instruction input output pair into a single text during the training process the model is trained using causallm objective text completion on the combined text the prompt template must be compatible with the base llm to maximize performance read the detail of the model card on hf example https huggingface co wizardlm wizardlm 30b v1 0 to get this information prompt template can be switched as command line parameters at training and inference step we also support for raw text file input and sharegpt conversation style input see templates templates readme md training finetune py this file contains a straightforward application of peft to the llama model as well as some code related to prompt construction and tokenization we use common hf trainer to ensure the compatibility with other library such as accelerate https github com huggingface accelerate simple usage bash bash scripts train sh or python finetune py base model decapoda research llama 7b hf data path yahma alpaca cleaned output dir lora output where data path is the path to a json file or a directory contains train json and test json base model is the model name on hf model hub or path to a local model on disk we can also tweak other hyperparameters see example in train sh scripts train sh bash python finetune py base model decapoda research llama 7b hf data path yahma alpaca cleaned output dir lora output mode 4 batch size 128 micro batch size 4 num epochs 3 learning rate 1e 4 cutoff len 512 val set size 0 2 lora r 8 lora alpha 16 lora dropout 0 05 lora target modules q proj v proj resume from checkpoint checkpoint 29 adapter model some notables parameters micro batch size size of the batch on each gpu greatly affect vram usage batch size actual batch size after gradient accumulation cutoff len maximum length of the input sequence greatly affect vram usage gradient checkpointing use gradient checkpointing to save memory however training speed will be lower mode quantization mode to use acceptable values 4 8 16 or gptq resume from checkpoint resume training from existings lora checkpoint download model from hf hub download py you can use the helper script python download model py model name to download a model from hf model hub and store it locally by default it will save the model to models of the current path make sure to create this folder or change the output location output quantization mode selection on the quantization mode effects on training time and memory usage see note benchmark readme md generally 16 and gptq mode has the best performance and should be selected to reduce training time however most of the time you will hit the memory limitation of the gpu with larger models which mode 4 and gptq provides the best memory saving effect overall gptq mode has the best balance between memory saving and training speed note to use gptq mode you must install the required package in cuda quant requirements also since gptq is a post hoc quantization technique only gtpq quantized model can be used for training look for model name which contains gptq on hf model hub such as thebloke orca mini v2 7b gptq https huggingface co thebloke orca mini v2 7b gptq to correctly load the checkpoint gptq model requires offline checkpoint download as described in previous section if you don t use wandb and want to disable the prompt at start of every training run wandb disabled training on multiple gpus by default on multi gpus environment the training script will load the model weight and split its layers accross different gpus this is done to reduce vram usage which allows loading larger model than a single gpu can handle however this essentially wastes the power of mutiple gpus since the computation only run on 1 gpu at a time thus training time is mostly similar to single gpu run to correctly run the training on multiple gpus in parallel you can use torchrun or accelerate to launch distributed training check the example in train torchrun sh scripts train torchrun sh and train accelerate sh scripts train accelerate sh training time will be drastically lower however you should modify batch size to be divisible by the number of gpus bash bash scripts train torchrun sh evaluation simply add eval and resume from checkpoint to perform evaluation on validation data bash python finetune py base model decapoda research llama 7b hf data path yahma alpaca cleaned resume from checkpoint output checkpoint 29 adapter model eval inference inference py this file loads the fine tuned lora checkpoint with the base model and performs inference on the selected dataset output is printed to terminal output and stored in sample output txt example usage bash python inference py base models thebloke vicuna 13b v1 3 0 gptq delta lora output mode exllama type local data data test json important parameters base model id or path to base model delta path to fine tuned lora checkpoint optional data path to evaluation dataset mode quantization mode to load the model acceptable values 4 8 16 gptq exllama type inference type to use acceptable values local api guidance note that gptq and exllama mode are only compatible with gptq models exllama is currently provide the best inference speed thus is recommended inference type local is the default option use local model loading to use inference type api we need an instance of text generation inferece server described in deployment deployment readme md inference type guidance is an advanced method to ensure the structure of the text output such as json check the command line inference py help and guidance https github com microsoft guidance for more information checkpoint export merge lora checkpoint py this file contain scripts that merge the lora weights back into the base model for export to hugging face format they should help users who want to run inference in projects like llama cpp https github com ggerganov llama cpp or text generation inference https github com huggingface text generation inference currently we do not support the merge of lora to gptq base model due to incompatibility issue of quantized weight deployment see deployment deployment readme md quantization with gptq to convert normal hf checkpoint go gptq checkpoint we need a conversion script see gptq for llama https github com qwopqwop200 gptq for llama and autogptq https github com panqiwei autogptq for more information benchmarking this document benchmark readme md provides a comprehensive summary of different quantization methods and some suggestions for efficient training inference recommended models recommended models to start 7b thebloke vicuna 7b v1 3 gptq https huggingface co thebloke vicuna 7b v1 3 gptq lmsys vicuna 7b v1 3 https huggingface co lmsys vicuna 7b v1 3 13b thebloke vicuna 13b v1 3 0 gptq https huggingface co thebloke vicuna 13b v1 3 0 gptq lmsys vicuna 13b v1 3 https huggingface co lmsys vicuna 13b v1 3 33b thebloke airoboros 33b gpt4 1 4 gptq https huggingface co thebloke airoboros 33b gpt4 1 4 gptq resources https github com ggerganov llama cpp highly portable llama inference based on c https github com huggingface text generation inference production level llm serving https github com microsoft guidance enforce structure to llm output https github com turboderp exllama high perfomance gptq inference https github com qwopqwop200 gptq for llama gptq quantization https github com oobabooga text generation webui a flexible web ui with support for multiple llms back end https github com vllm project vllm high throughput llm serving acknowledgements disarmyouwitha exllama fastapi https github com turboderp exllama issues 37 issuecomment 1579593517 turboderp exllama https github com turboderp exllama johnsmith0031 alpaca lora 4bit https github com johnsmith0031 alpaca lora 4bit timdettmers bitsandbytes https github com timdettmers bitsandbytes tloen alpaca lora https github com tloen alpaca lora oobabooga text generation webui https github com oobabooga text generation webui | ai |
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ce-toolkit | cloud engineering toolkit this is a collection of tools to perform the typical operations involved in cloud engineering work how to build 1 install docker on your machine https docs docker com get docker 2 open your favorite shell command prompt powershell bash etc and cd to the directory you cloned this repository into the one containing this readme md file 3 create a docker volume in which you ll store all your data e g code repos so that they persist if you have to rebuild the toolkit docker volume create ce toolkit data 4 set your desired username in an environment variable export user username for bash and bash compatible shells env user username for powershell 5 optional if you want to pin specific versions of the tools currently supported for ansible and terraform only set the corresponding environment variables export ansible version x y z export terraform version x y z for bash and bash compatible shells env ansible version x y z env terraform version x y z for powershell 6 launch the build process docker compose build 7 once the build is finished run your brand new ce toolkit container docker run it name ce toolkit hostname ce toolkit network host env git committer name yourfullname env git committer email youremail env git author name yourfullname env git author email youremail v ce toolkit data home user data add host ce toolkit 127 0 0 1 ce toolkit for bash and bash compatible shells docker run it name ce toolkit hostname ce toolkit network host env git committer name yourfullname env git committer email youremail env git author name yourfullname env git author email youremail v ce toolkit data home env user data add host ce toolkit 127 0 0 1 ce toolkit for powershell note replace yourfullname and youremail with your own data e g luther blissett and luther blissett internet com respectively these are shown in your commits should you make one directly from inside the toolkit instead of using your local ide 8 when you exit the shell the container will also exit if you don t want to always run a new one you can also restart it manually and connect back to it with the following command docker exec it ce toolkit bash this is particularly useful if you use some form of integration such as remote containers for vs code 1 avoids you to reinstall vscode server extensions etc 1 https marketplace visualstudio com items itemname ms vscode remote remote containers | cloud |
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nlp-paradigm-shift | paradigm shift in nlp welcome to the webpage for paradigm shift in natural language processing https link springer com content pdf 10 1007 s11633 022 1331 6 pdf some resources of the paper are constantly maintained here such as a full list of papers of paradigm shift an interactive sankey diagram https txsun1997 github io nlp paradigm shift sankey html to depict the trend of paradigm shift etc what is paradigm shift first of all what is paradigm and what is paradigm shift paradigm is the general framework to model a class of tasks for example sequence labeling seqlab is a popular paradigm to solve named entity recognition ner we summarize the mainstream paradigms that are widely used for common nlp tasks as class matching seqlab mrc seq2seq seq2aseq m lm paradigm shift is a phenomena of solving a task that is usually solved with some paradigm with another paradigm for example li et al 2020 https www aclweb org anthology 2020 acl main 519 uses the mrc paradigm to solve ner which is previously solved with seqlab then we can say that the paradigm of ner shifted from seqlab to mrc the figure below shows the observed shift or transfer of the seven paradigms in recent years img src https txsun1997 github io nlp paradigm shift paradigm shift png style zoom 48 paradigm shift in nlp tasks we collect the papers of paradigm shift in the table below which is an extension of the table 1 in our original paper this table will be constantly updated task class matching seqlab mrc seq2seq seq2aseq m lm tc kim 2014 https aclanthology org d14 1181 br liu et al 2016 https www ijcai org abstract 16 408 br devlin et al 2019 https aclanthology org n19 1423 chai et al 2020 http proceedings mlr press v119 chai20a html br yin et al 2020 https www aclweb org anthology 2020 emnlp main 660 br wang et al 2021 https arxiv org abs 2104 14690 yang et al 2018 https aclanthology org c18 1330 brown et al 2020 https proceedings neurips cc paper 2020 hash 1457c0d6bfcb4967418bfb8ac142f64a abstract html br schick schutze 2021 https aclanthology org 2021 eacl main 20 br schick schutze 2021 https www aclweb org anthology 2021 naacl main 185 br gao et al 2021 https aclanthology org 2021 acl long 295 nli devlin et al 2019 https aclanthology org n19 1423 chen et al 2017 http aclweb org anthology p17 1152 mccann et al 2018 http arxiv org abs 1806 08730 schick schutze 2021 https aclanthology org 2021 eacl main 20 br schick schutze 2021 https www aclweb org anthology 2021 naacl main 185 br gao et al 2021 https aclanthology org 2021 acl long 295 ner xia et al 2019 https www aclweb org anthology p19 1138 br fisher vlachos 2019 https www aclweb org anthology p19 1585 br yu et al 2020 https www aclweb org anthology 2020 acl main 577 br fu et al 2021 https aclanthology org 2021 acl long 558 ma hovy 2016 https aclanthology org p16 1101 br lample 2016 http aclweb org anthology n16 1030 li et al 2020 https www aclweb org anthology 2020 acl main 519 yan et al 2021 https aclanthology org 2021 acl long 451 lample et al 2016 http aclweb org anthology n16 1030 br dai et al 2020 https www aclweb org anthology 2020 acl main 520 ma et al 2021 https arxiv org abs 2109 13532 absa wang et al 2016 https aclanthology org d16 1058 sun et al 2019 https aclanthology org n19 1035 mao et al 2021 https ojs aaai org index php aaai article view 17597 br chen et al 2021 https ojs aaai org index php aaai article view 17500 yan et al 2021 https aclanthology org 2021 acl long 188 br zhang et al 2021 https aclanthology org 2021 acl short 64 pdf li et al 2021 https arxiv org abs 2109 08306 re zeng et al 2014 https aclanthology org c14 1220 levy et al 2017 https aclanthology org k17 1034 br li et al 2019 https aclanthology org p19 1129 br zhao et al 2020 https www ijcai org proceedings 2020 546 zeng et al 2018 https aclanthology org p18 1047 han et al 2021 https arxiv org abs 2105 11259 summ zhong et al 2020 https aclanthology org 2020 acl main 552 cheng lapata 2016 https aclanthology org p16 1046 nallapati et al 2016 https aclanthology org k16 1028 aghajanyan et al 2021 https arxiv org abs 2107 06955 parsing rodr guez vilares 2018 https aclanthology org d18 1162 br strzyz et al 2019 https aclanthology org n19 1077 br vilares rodr guez 2020 https aclanthology org 2020 emnlp main 221 br vacareanu et al 2020 https aclanthology org 2020 lrec 1 643 gan et al 2021 https arxiv org abs 2105 07654 vinyals et al 2015 https proceedings neurips cc paper 2015 hash 277281aada22045c03945dcb2ca6f2ec abstract html br li et al 2018 https aclanthology org c18 1271 br rongali et al 2020 https dl acm org doi 10 1145 3366423 3380064 chen et al 2014 https aclanthology org d14 1082 br dyer et al 2015 https aclanthology org p15 1033 choe charniak 2016 https aclanthology org d16 1257 trends to intuitively depict the trend of paradigm shift in nlp we also draw an interactive sankey diagram https txsun1997 github io nlp paradigm shift sankey html which is an extension of the figure 2 in our original paper also this diagram is constantly updated as the table above changed contributing this line of research is difficult to be comprehensively surveyed so welcome any additions modifications and suggestions please feel free to submit pull request or directly contact me https txsun1997 github io citation if you find this webpage or the paper helpful to your research please cite our paper bibtex article sun2022paradigm author tianxiang sun and xiangyang liu and xipeng qiu and xuanjing huang title paradigm shift in natural language processing journal machine intelligence research year 2022 volume 19 pages 169 183 url https doi org 10 1007 s11633 022 1331 6 | machine-learning deep-learning natural-language-processing nlp-tutorial | ai |
bluebase | bluebase mainscreen https lizard company images bluebase1 png adminscreen https lizard company images bluebase3 png loginscreen https lizard company images bluebase2 png bluebase is an authentication program written in php and python for the purpose of providing an easy and lightweight username password authentication system for openvpn specifically for my circumvention project known as bluebust bluebust https lizard company boards lcrad 2 bluebust setup is a combination of openvpn stunnel and iptables with an optional bind server to create a robust filter circumvention system that was specifically targeted at bluecoat bluebase is an extension of this body of work as the original bluebust system relied on a public key infrastructure in order to authenticate users which is slightly more secure than username password combinations but has a higher administrative burden this new system allows temporary disabling and automatic expiration of users which allows for monetization or at least more control to make sure users do not abuse the service passwords are salted and hashed using bcrypt and the expiration date is nullable but requires iso 8601 format if used the dashboard also comes with all the necessary javascript and css files within the directory so there is no need to make any external requests and possibly require unencrypted content if you run it over ssl the dashboard allows you to create edit delete and examine all the users and gives a nice little pie chart of the percentage of users enabled disabled and expired the dashboard is now protected using standard php login system with passwords that are also secured by bcrypt published 2015 08 19 what this means much more scalability and ease of administration to run any openvpn server when implemented allows people to make money off of their openvpn service by adding the capability to automatically expire users but it is a completely optional field gives more privacy to users because deleting the record removes every trace of that user but using easy rsa a revoked user must be permanently accounted for within the system setting up these instructions are designed for debian 8 systems debian 7 systems could work with this system however the php version does not support password hashing which means an extra file needs to be included wherever the password hash method is used run the following command sudo apt get install python python bcrypt ntp php5 php5 mysql apache2 openvpn easy rsa python mysql connector openvpn and other configurations are not detailed here look to the bluebust setup for specific instructions on how to setup a bluebust instance 1 create a database and use the install php file to then import the schema then configure the parameters located within the auth py file and the config json file to reflect the database and respective user 2 place the auth py file in the etc openvpn directory and place the contents of the frontend directory in your webroot or in a directory within the webroot webroot on debian is var www 3 give all ownership to the group user www data sudo chown r www data www data var www 4 add the following lines to your openvpn config to switch from using certificate based authentication to username password auth user pass verify auth py via file client cert not required username as common name 5 go to the folder where you installed it and login using the following credentials username admin password password 6 change your password and username for security todo stress testing adding support for multiple database systems license this program is released under gplv3 https www gnu org licenses gpl html | openvpn dashboard bcrypt administration php | os |
autonomous_driving | autonomous driving videos training animation smaller gif autonomous driving is one of the great technical challenges of our time there are tons of interesting applications of computer vision to autononmous driving we ll explore a few of them in this module lectures order notebook slides required viewing reading notes 1 how to drive a car with a camera part 1 https github com unccv autonomous driving blob master notebooks how 20to 20drive 20a 20car 20with 20a 20camera 20 5bpart 201 5d ipynb sdcs part 1 https www youtube com watch v cexjbbwofcw t 26s vits paper https sites cs ucsb edu mturk papers alv pdf 2 how to drive a car with a camera part 2 https github com unccv autonomous driving blob master notebooks how 20to 20drive 20a 20car 20with 20a 20camera 20 5bpart 202 5d ipynb sdcs part 2 https www youtube com watch v h0igip6hg1k alvinn paper http citeseerx ist psu edu viewdoc download doi 10 1 1 830 2188 rep rep1 type pdf the alvinn implementation in this notebook should be helpful on your programming challenge note about tensorflow we ll be using tensorflow to build a neural network in one lecture but tensorflow or other deep learning libraries are not permitted in your programming challenge setup the python 3 anaconda distribution https www anaconda com download is the easiest way to get going with the notebooks and code presented here optional you may want to create a virtual environment for this repository conda create n autonomous driving python 3 source activate autonomous driving you ll need to install the jupyter notebook to run the notebooks conda install jupyter you may also want to install nb conda enables some nice things like change virtual environments within the notebook conda install nb conda this repository requires the installation of a few extra packages you can install them all at once with pip install r requirements txt optional jupyterthemes https github com dunovank jupyter themes can be nice when presenting notebooks as it offers some cleaner visual themes than the stock notebook and makes it easy to adjust the default font size for code markdown etc you can install with pip pip install jupyterthemes recommend jupyter them for presenting these notebook type into terminal before launching notebook jt t grade3 cellw 90 fs 20 tfs 20 ofs 20 dfs 20 recommend jupyter them for viewing these notebook type into terminal before launching notebook jt t grade3 cellw 90 fs 14 tfs 14 ofs 14 dfs 14 | ai |
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CIT-261-Portfolio | cit 261 portfolio git portfolio for cit 261 javascript mobile software development purpose this portfolio contains study topic information obtained from research study and application the folder structure aligns to the topics which include 00 miscellaneous sandbox for coding trials and testing 01 loops conditional statements functions variables parameters arrays associative arrays 02 object creation functions inheritance properties methods instantiation 03 json parse stringify 04 using xmlhttprequest to consume a json web service 05 local storage api storing and retrieving simple data arrays associative arrays and objects 06 dom manipulation using createelement appendchild insertbefore removechild etc 07 manipulating css class properties using javascript 08 creating css3 transitions and animations in css and triggering them with javascript 09 standard javascript events including those for mobile devices ex ontouchbegin onload etc and animation and transition events 10 html5 tags video audio and canvas 11 designing defining and triggering css3 transitions without custom libraries thought library 12 designing defining and triggering css3 transforms without custom libraries thought library 13 designing defining and triggering css3 animations without custom libraries thought library | front_end |
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sql-challenge | sql challenge data engineering and data analysis of an employee database | server |
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Handson-Blockchain-Development-with-Hyperledger | hands on blockchain with hyperledger a href https www packtpub com big data and business intelligence hands blockchain hyperledger utm source github utm medium repository utm campaign 9781788994521 img src https d255esdrn735hr cloudfront net sites default files imagecache ppv4 main book cover b10152 newcover png alt hands on blockchain with hyperledger height 256px align right a this is the code repository for hands on blockchain with hyperledger https www packtpub com big data and business intelligence hands blockchain hyperledger utm source github utm medium repository utm campaign 9781788994521 published by packt what is this book about blockchain and hyperledger technologiesare hot topics today hyperledger fabric and hyperledger composer are open source projects that help organizations create private permissioned blockchain networks these find application in finance banking supply chain and iot among several other sectors this book will be an easy reference to explore and build blockchain networks using hyperledger technologies this book covers the following exciting features discover why blockchain is a game changer in the technology landscape set up blockchain networks using basic hyperledger fabric deployment understand the considerations for creating decentralized applications learn to integrate business networks with existing systems write smart contracts quickly with hyperledger composer if you feel this book is for you get your copy https www amazon com dp 1788994523 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders the code will look like the following func checkinit t testing t stub shim mockstub args byte res stub mockinit 1 args if res status shim ok fmt println init failed string res message t failnow following is what you need for this book the book benefits business leaders as it provides a comprehensive view on blockchain business models governance structure and business design considerations of blockchain solutions technology leaders stand to gain a lot from the detailed discussion around the technology landscape technology design and architecture considerations in the book with model driven application development this guide will speed up understanding and concept development for blockchain application developers the simple and well organized content will put novices at ease with blockchain concepts and constructs with the following software and hardware list you can run all code files present in the book chapter 1 12 software and hardware list chapter software required os required 1 git windows mac os x and linux any 2 gpg windows mac os x and linux any 3 travis ci windows mac os x and linux any 4 hyperledger fabric composer windows mac os x and linux any related products paste books from the other books you may enjoy section building blockchain projects packt https www packtpub com big data and business intelligence building blockchain projects utm source github utm medium repository utm campaign 9781787122147 amazon https www amazon com dp 178712214x mastering blockchain second edition packt https www packtpub com big data and business intelligence mastering blockchain second edition utm source github utm medium repository utm campaign 9781788839044 amazon https www amazon com dp 1788839048 get to know the authors nitin gaur nitin gaur as the director of ibm s blockchain labs is responsible for instituting a body of knowledge and organizational understanding around blockchain technology and industry specific applications tenacious and customer focused he is known for his ability to analyze opportunities and create technologies that align with operational needs catapult profitability and dramatically improve customer experience he is also an ibm distinguished engineer luc desrosiers luc desrosiers is an ibm certified it architect with 20 years of experience throughout his career he has taken on different roles developer consultant and pre sales architect he recently moved from canada to the uk to work in a great lab ibm hursley this is where he had the opportunity to join the ibm blockchain team he is now working with clients across multiple industries to help them explore how blockchain technologies can enable transformative uses and solutions venkatraman ramakrishnar venkatraman ramakrishna is an ibm researcher with 10 years of experience following a btech from iit kharagpur and phd from ucla he worked in the bing infrastructure team in microsoft building reliable application deployment software at ibm research he worked in mobile computing and security before joining the blockchain team he has developed applications for trade and regulation and is now working on improving the performance and privacy preserving characteristics of the hyperledger platform petr novotny petr novotny is a research scientist at ibm research with 15 years of experience in engineering and research of software systems he received an msc from university college london and phd from imperial college london where he was also a post doctoral research associate he was a visiting scientist at the u s army research lab at ibm he works on innovations of blockchain technologies and leads the development of blockchain solutions and analytical tools dr salman a baset dr salman a baset is the cto of security in ibm blockchain solutions he oversees the security and compliance of blockchain solutions being built by ibm in collaboration with partners such as walmart and maersk and interfaces with clients on blockchain solutions and their security he drives the implementation of the general data protection regulation for blockchain based solutions he has also built the identity management system used by fortune 500 companies involved in global trade digitization and ibm food trust blockchain solutions anthony o dowd anthony o dowd works in ibm s blockchain team he is based in europe as part of a worldwide team that helps users build solutions that benefit from blockchain tech anthony has a background in middle and back office systems and has led the development of key ibm middleware in enterprise messaging and integration he likes to work in different industries to understand how they can exploit middleware to build more efficient integrated business systems suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions trade application this is a use case on trade finance and logistics designed to demonstrate the capabilities of hyperledger mainly fabric blockchain tools use case scenario overview the trade scenario consists of 6 participants exporter an entity that is selling goods importer an entity that is buying goods from an exporter in exchange for money importer s bank maintains a bank account for an importer and issues a letter of credit on its behalf exporter s bank maintains a bank account for an exporter and handles l cs and payments on its behalf carrier entity that ships goods from exporter s to importer s location regulatory auhority public or governmental body that approves the export of goods and audits the process the flow diagam below illustrates the steps from a trade agreement to the final payment settlement for more details see the full description docs use case description docx alt text docs flow diagram png application folder structure chaincode middleware application composer network docs readme md the chaincode chaincode folder contains the source code for all versions of the chaincode the middleware middleware folder contains wrapper functions that use the fabric sdk library to implement channel and chaincode operations the application application folder contains code to start a web server and offer a rest api for users to register login set up the channel and application and run trade operations the network network folder contains configurations and code to launch a single machine docker network setup corresponding to a minimal version of the trade application scenario the docs docs folder contains more documentation on the use case testing and running the application to test and run the complete application you will need to perform the following steps in sequence test the chaincode smart contract navigate to the chaincode chaincode folder for instructions launch a trade network navigate to the network network folder for instructions prepare the middleware navigate to the middleware middleware folder for instructions start and exercise the application server navigate to the application application folder for instructions augmenting the application two kinds of augmentation are currently supported add a new organization and peers see instructions in network network to create configuration files for the new organization and peers see instructions in middleware middleware to update the running application to incorporate the new organization and peers alternatively see instructions in application application to upgrade chaincode through the application server upgrade chaincode note the upgrade version of the chaincode in this repository assumes that the new organization has already been added above to test chaincode upgrade without new organizations modify and test the upgrade version of the chaincode suitably see instructions in chaincode chaincode to validate the upgrade version or apply custom modifications followed by unit testing see instructions in middleware middleware to upgrade chaincode in a running trade application alternatively see instructions in application application to upgrade chaincode through the application server errata in the diagram on page 82 pdf in chapter 3 the directionality of arrows marked 1 and 2 should be reversed here is the correct image for the same alt text https github com packtpublishing handson blockchain development with hyperledger blob master b10152 03 1 png raw true | blockchain |
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SotA-CV | sota cv a repository of state of the art deep learning results in computer vision it aims to collect and maintain up to date information on the latest developments in in computer vision facilitating the research effort in deep learning unlike other attempts in collaborative tracking of research progress this repository provides aggregate results of quantitative evaluation such practice allows to greatly simplify both the initial literature search and preparing a comparative study of your own results tasks contents semantic segmentation content semantic segmentation md image classification content image classification md object detection content object detection md monocular depth estimation content depth estimation md pose estimation content pose estimation md image quality assessment content image quality assessment md todo semi supervised classification weakly supervised semantic segmentation scene recognition action recognition shape recognition face recognition face alignment keypoint and landmark detection instance segmentation human parsing saliency detection structure from motion image captioning surface reconstruction inverse graphics object localization optical character recognition image representations and feature learning medical imaging image co segmentation visual tracking visual question answering optical flow estimation image retrieval stereo matching image synthesis structure learning image inpainting trajectory prediction image warping domain adaptation adversarial attacks and defences datasets contents pascal voc 2012 semantic segmentation content datasets pascal voc 2012 segmentation md ade20k content datasets ade20k md cityscapes semantic segmentation content datasets cityscapes semantic md pascal context content datasets pascal context md coco detection 2016 content datasets coco detection md contributions pull requests are most welcome to make the material more coherent please follow the examples in dataset dataset template md and problem problem template md templates for your convenience use incoming papers list incoming papers md in supplementary docs supplementary md there s a tutorial on metrics and datasets | computer-vision deep-learning research compendium neural-network neural-networks | ai |
DevClub-Assignment1 | devclub assignment1 you have learnt about html css and github in our first session now use them to create a webpage by yourself task covid guide create a webpage for users to get information and guidelines about covid things to include a navigation bar with internal links to sections in your page the home banner with a short description of what the webpage is about and attractive background graphics some tips with images showing how to follow them a table of common symptoms and recommended advice for them a list of common myths or faqs regarding covid especially the new variants embed a youtube video which you think would be helpful for people finish with a footer giving yourself credits and also your contact details a figma prototype https www figma com proto 12acpyspyyku0yrhjdos7e covidguide has been provided for reference along with the figma source file https www figma com file 12acpyspyyku0yrhjdos7e covidguide for the design use the drive link https csciitd my sharepoint com f g personal tt1201099 iitd ac in etuo6iwxaxnlkx 5pqqiszybyjcejp6saed dzi2evf3nq e m8xtko to download the image files bonus task create a new branch bonus in your fork of this repo this bonus task has to be done on the bonus branch only while the main task stays on the main branch do not merge them put your iit delhi email for contact in the footer and use mailto https en wikipedia org wiki mailto to give pre filled subject and body for the query for example the subject may be initialized with query regarding covidguide and the body with hey i visited your website and had a query add a favicon https en wikipedia org wiki favicon to your website embed a google map of hospitals near iit delhi make the website responsive so it looks good on mobile screens feel free to show your creativity in the design you can use the css frameworks materialize https materializecss com or tailwind https tailwindui com but not bootstrap you can use animate on scroll https michalsnik github io aos or vanta https www vantajs com too deploy this bonus branch using firebase static hosting https firebase google com docs hosting github integration set up the github action such that the site should be rebuilt each time you push a commit to the repo finally put the url in the description about website for your fork of this repo the url should look something like https web app index html where can be your choice use cdn option for including any libraries frameworks please take only free subscriptions wherever applicable using any paid features for the assignments is not allowed submission instructions first create your github account you can use your personal email id for it fork this repository by clicking the fork button on top right in your fork of this repo write the code in the index html file you can choose to either edit it directly on github website or maybe download it edit on your machine and then upload or maybe clone the repo make changes commit and then push feel free to upload helping files css and images to your repo make sure you include them properly with relative paths in the index html file host the webpage using github pages you do not need to create a pull request to this main repository feel free to add more comments in the readme md file finally fill this google form https forms gle zglhpp4ptmas77yq6 with your details and url to the hosted github pages site ask any doubts on our discord server https discord gg hedc9gw3ar in html css git 14jan channel resources mozilla developer docs https developer mozilla org en us docs learn w3schools https www w3schools com programmingwithmosh s html https www youtube com watch v qz0agyrrlhu hussein nasser s web https www youtube com watch v dh406o2v 1c cs50 html css js https www youtube com watch v 2vaufs071pg web dev css https web dev learn css kevin powell s html and css basics https www youtube com playlist list pl4 ik0avhvjm0xe0k2uzrvsm7lkihspt and advanced https www youtube com playlist list pl4 ik0avhvjp27yzlww gkpggrps0ccnp playlist github pages documentation https docs github com en pages youtube player embed api documentation https developers google com youtube iframe api reference | html css figma covid beginner iitd github-pages github-actions firebase-hosting tailwind materialize | front_end |
learning_tinyOS | rtos 3 1 1 1 count 2 2 count count count count 2 1 2 pendsv 3 1 2 3 2 1 1 1 1 2 2 3 1 0 0 0 fcarm output name not specified please check options for target utilities 3 3 3 5 3 6 task delayticks delayticks 0 tasktable task delayticks systick 3 7 ttask tasktable list tasktable 4 1 4 2 1 2 4 3 task info 5 1 0 5 2 5 3 6 1 6 2 7 1 7 2 7 3 7 4 8 1 c malloc free rtos rtos ram malloc free ram | os |
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jsRealB | jsrealb a javascript bilingual text realizer for web development version 4 6 5 october 2023 natural language generation nlg is a field of artificial intelligence that focuses on the development of systems that produce text for different applications for example the textual description of massive datasets or the automation of routine text creation the web is constantly growing and its content getting progressively more dynamic is well suited to automation by a realizer however existing realizers are not designed with the web in mind and their integration in a web environment requires much knowledge complicating their use jsrealb is a text realizer designed specifically for the web easy to learn and to use this realizer allows its user to build a variety of french and english expressions and sentences to add html tags to them and to easily integrate them into web pages jsrealb can also be used in javascript application by means of a node js module available also as npm package it also accepts an input specification in json the documentation can be accessed here http rali iro umontreal ca jsrealb current documentation user html you can switch between english and french in the upper right corner of the page the specification of the json input format is described here http rali iro umontreal ca jsrealb current data jsrealb jsoninput html the companion project pyrealb https github com lapalme pyrealb implements in python a text realizer using the same notation for syntactic elements as jsrealb jsrealb can be used out of the box the github in fact in a web page by using jsrealb js in the dist dist directory caution node js https nodejs org is necessary for the javascript application examples the current build process relies on the availability of webpack https webpack js org directories architecture architecture readme md description of the architecture of the system the second section goes into details of the organization of the source files and describes the main methods data data lexicographic information that is bundled with the dist jsrealb js lexicon en json a comprehensive english lexicon 33926 entries in json format lexicon fr json a comprehensive french lexicon 52512 entries in json format rule en js english conjugation and declension tables rule fr js french conjugation and declension tables demos see next section dist dist pre built javascript files ready for production use they already include the english and french lexicons and the english and french rule tables jsrealb js packages all js files of the build directory as a module and exports only main functions and constants for use in a web page script src path to dist jsrealb js script for use as a node js module import jsrealb from path to dist jsrealb js jsrealb filter mjs example of use of the node js module to create a unix filter for jsrealb jsrealb server mjs example of use of the node js module to start a web server that realizes sentences testserver py python script using the jsrealb server package json necessary for publishing the jsrealb npm package readme md short presentation and example of use of the npm package displayed at https www npmjs com package jsrealb information for the maintainer when a new version is to be put on npm in principle it should be enough to issue the two following commands from within the dist directory after a npm login 1 npm version major minor patch ideally the resulting version number should the same as jsrealb version in jsrealb js 2 npm publish because of the npmignore hidden file in this directory only jsrealb js is published documentation documentation in both english and french the examples are generated on the fly by embedding jsrealb in the page consult the documentation http rali iro umontreal ca jsrealb current documentation user html jsrealbfrompython html documentation for creating the json input format in python user html html of the core of the page div id correspond to variables in user infos js style css style sheet user infos js definitions of variables containing the examples user js javascript helper script examples examples examples of integration of jsrealb into web pages or node js applications see index html examples index html for use cases ide ide an integrated development environment built upon the node js read eval print loop that includes jsrealb to easily get the realization of an expression to consult the lexicon the conjugation and declination tables it is also possible to get a lemmatization i e the jsrealb expression corresponding to a form see the readme html ide readme html file to see how to use it node modules node modules used for transpiling with webpack src src sources to create the javascript library more details in the document on the architecture of the system architecture readme md jsdoc documentation directory of the source files of jsrealb js consult the documentation src jsdoc index html build this directory by running jsdoc d jsdoc js in the src directory for the moment ignore warning about unable to parse lexicon js constituent js constituent is the top class for methods shared between phrase s and terminal s dependent js subclass of constituent for creating complex phrases using the dependency notation json tools js functions for dealing with the json input format jsrealb js main module that gathers all exported symbols from other classes and exports them in a single list it also defines other utility functions and constants lexicon js english and french lexicons with their associated functions nonterminal js functions and constants that are shared between dependent js and phrase js number js utility functions for number formatting phrase js subclass of constituent for creating complex phrases using the constituent notation terminal js subclass of constituent for creating a single unit most often a single word warnings js list of functions to generate warnings in case of erroneous specifications using jsrealb itself tests tests unit tests using qunit https qunitjs com qunit of jsrealb in both french and english testall html load this file in a browser to run all tests in visual studio code the launch configuration takes for granted that a local web server has been launched in the jsrealb directory e g with http server c 1 jsrealb tutorial tutorial read the tutorial http rali iro umontreal ca jsrealb current tutorial tutorial html jsrealb is also available an an npm package use npm js is a simple example of its use after it is install ed on the system files in the current directory readme md this file package json file with parameters for building jsrealb using npm using npm run build dev or npm run build prod test demos sh launch all web demos in safari and the jsrealb server with the weather python demo test node js import the jsrealb package installed with npm and realize a simple english sentence tests dev js node js application that loads jsrealb js from the dist directory it also has functions with many examples that were useful during the development web dev html load the current dist jsrealb js webpack module in a web page thus allowing interactive testing webpack config cjs configuration file for building the jsrealb js package in the dist directory vscode hidden directory containing configuration for visual studio code demos simple examples on a single sentence evaluate a jsrealb expression and display its realization in a web page in either english or french execute http rali iro umontreal ca jsrealb current demos evaluation index html show the use of loops in javascript to create repetitive texts english 99 bottles of beer demos 99bottlesofbeer execute http rali iro umontreal ca jsrealb current demos 99bottlesofbeer index html french 1 km pied demos kilometresapied execute http rali iro umontreal ca jsrealb current demos kilometresapied index html tests of specific features sentences modified with time number and conjugation date generation demos date execute http rali iro umontreal ca jsrealb current demos date index html sentence with sentence modifiers sentence variants demos variantesdephrases execute http rali iro umontreal ca jsrealb current demos variantesdephrases index html french or english conjugation and declension of a word conjugation and declension demos inflection execute http rali iro umontreal ca jsrealb current demos inflection index html pronouns generate a table both in english and french showing the different forms of pronouns using the original specification using the tonic and clitic options this table is now part of the documentation user interface to create a sentence with options the system shows the jsrealb expression and its realization it is also possible to ask for a random sentence using words of the lexicon randomgeneration demos randomgeneration execute in english http rali iro umontreal ca jsrealb current demos randomgeneration english html execute in french http rali iro umontreal ca jsrealb current demos randomgeneration french html linguistic games generate spelling and grammar exercises from a simple sentence structure in both english and french exercicesorthographe demos exercicesorthographe execute in english http rali iro umontreal ca jsrealb current demos exercicesorthographe index en html execute in french http rali iro umontreal ca jsrealb current demos exercicesorthographe index fr html translation game from english to french and french to english simple sentences are randomly generated in the source language and generated in the target using the same options of jsrealb the used must build the target sentence by selecting words the system checks if the translation is correct if not is displays the differences with the expected sentence bilinguo demos bilinguo execute http rali iro umontreal ca jsrealb current demos bilinguo index html text realization exercise in style https en wikipedia org wiki exercises in style which creates the structure of the original story of raymond queneau in both french and english using menus some elements of the text can be modified and the modifications are highlighted in the web page exercises in style demos exercicesdestyle execute http rali iro umontreal ca jsrealb current demos exercicesdestyle index html l augmentation generate a text in french for asking a pay raise following a flowchart as originally described by george perec using menus some elements of the text can be modified the path in the flowchart is displayed in the web page and it is possible to highlight a step in the flowchart with the corresponding text l augmentation demos augmentation execute http rali iro umontreal ca jsrealb current demos augmentation augmentation html universal dependencies structure used for generating the original sentence from its annotation in english execute http rali iro umontreal ca jsrealb current demos udregenerator udregenerator en html in french execute http rali iro umontreal ca jsrealb current demos udregenerator udregenerator fr html paper describing the approach http rali iro umontreal ca jsrealb current demos udregenerator udregenerator pdf syntaxfest 2021 paper https aclanthology org 2021 udw 1 9 classical fairy tale reproduction in which hovering over a sentence shows the underlying jsrealb expression in french le petit chaperon rouge execute http rali iro umontreal ca jsrealb current demos petitchaperonrouge petitchaperonrouge html in english little red riding hood execute http rali iro umontreal ca jsrealb current demos petitchaperonrouge littleredridinghood html data to text applications jsrealb for the e2e challenge https doi org 10 1016 j csl 2019 06 009 browser for the datasets training development and test used in the end to end generation challenge 2017 2018 the page also shows the english and french output produced by a rule based generator using jsrealb for a selection of feature values there is also a short description of the implementation of the realizer execute http rali iro umontreal ca jsrealb current demos e2echallenge index html personalized descriptions of restaurants how jsrealb can be used for varying the linguistic style of the generated text according to a user profile defined as one of the big five https en wikipedia org wiki big five personality traits model of personality execute http rali iro umontreal ca jsrealb current demos personage index html description of phones jsrealb version of an example used in the rosaenlg tutorials in english and french phones demos phones run with node js demos phones show phones js description in french of a list of events and associated informations given as a json file v nements demos evenements execute http rali iro umontreal ca jsrealb current demos evenements index html description of list of steps for the building of a house given information about tasks the duration and the precedence relations between them screen copy of the application demos data2text building small jpg construction of a building img src demos data2text building small jpg width 800 the system first computes the critical path to find the start and end times of each task it then creates a graphic for displaying the pert diagram and an accompanying text to explain the steps to follow it is possible to interactively change the start date and to explore the graphic with the mouse which also uses jsrealb to generate the text of the tooltips english demos data2text building html execute http rali iro umontreal ca jsrealb current demos data2text building html french demos data2text batiment html execute http rali iro umontreal ca jsrealb current demos data2text batiment html itinerary description in an optimistic montr al m tro network the system shows an interactive map of the montr al m tro station with a new line when a user clicks two stations the systems realizes a text describing the itinerary to go from the first station to the second screen copy of the application tutorial metro jpg finding a path in the metro img src demos itinerarydescription metro jpg width 800 the language of the web page and of the realization can be changed interactively by clicking in the top right of the page metro demos itinerarydescription metro html execute http rali iro umontreal ca jsrealb current demos itinerarydescription metro html weather bulletin generation in english and french an example of use of the python api for jsrealb taking weather information in json it generates bulletin in both english and french this tutorial describes the organization of the system http rali iro umontreal ca jsrealb current demos weather bulletin generation html which shows how jsrealb can be used in a real life situation in conjunction with python for data manipulation interactive use with observable two observable https observablehq com notebooks are available for trying jsrealb expressions and seeing their realizations english https observablehq com lapalme exprimenting with jsrealb experimenting with jsrealb guy lapalme observable fran ais https observablehq com lapalme nouvelles experiences avec jsrealb nouvelles exp xe9 riences avec jsrealb guy lapalme observable design of the system version 4 5 was a redesign and reimplementation of the previous version while keeping intact the external interface i e same name of functions for building constituents for option names and for global functions this means that applications using only the external interface of jsrealb can be run unchanged version 4 0 added the dependency notation this document architecture readme md describes the transformation steps within the realizer using a few examples it also gives an overview of the implementation explaining the role of the main classes and methods authors jsrealb was updated developed and brought to its current version by guy lapalme http www iro umontreal ca lapalme building on the work of 1 francis gauthier http www etud iro umontreal ca gauthif as part of his summer internship at rali in 2016 2 paul molins http paul molins fr as part of an internship from insa lyon spent at rali university of montreal in 2015 3 nicolas daoust mailto n daou st developed the original concept in the jsreal realizer for french only in 2013 for more information contact guy lapalme http rali iro umontreal ca lapalme | front_end |
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Gooo | gooo go lang web app framework showcasing straightforward no magic web development with the go language http www golang org includes batch template processing and interaction with postgresql databases and model view architecture quick start make sure your gopath and goroot are set for help run go help gopath install run this performs the following commands go build gooo philosophy anti magic so simple that it s complex so complex that it works if it doesn t work publish it modular architecture model struct type no special tags or fields models are just go structs business logic straightforward db methods to be used in the view module view parses templates in tmpl folder and defines how they are rendered uses html template http golang org pkg html template to parse and render fetches rows from database as type model interfaces router handles dynamic and static routes request methods main handler matches the request url against the routes middleware filters for restful routes introspection models implement interface and interface types go s dynamic feature is interface type conversion generally checked at runtime interface json interface byte interface struct interface map string interface getstructvalues interface interface interfacename interface string util generic error handler handlererr err error martin odersky http i imgur com jb8aa jpg 1 tested and approved by typeunsafe copy corporation tell me more why go gophers http golang org doc gopher frontpage png hip new unproven is it good are you good let s gooo test the example blog app resolve dependencies and install install don t want to use postgresql sign up for a free heroku postgres account here https postgres heroku com create a database and save the connection params for the next step or skip this step configure the database connection variable dbparams in the model package model model go gooo http localhost 8080 hello world http localhost 8080 hello world gooo celebrate gooo outside let s gooo write your own gooo app resolve dependencies and install install don t want to use postgresql sign up for a free heroku postgres account here https postgres heroku com create a database and save the connection params for the next step define your model interfaces and configure the database connection in the model package model model go implement your model interface types with anonymous basemodel field use json for type safety and go lang future proofing implement functions and variables available to all models with anonymous basemodel field in the model interface type define your views as request handler functions in the view package view view go write your templates in tmpl go text template syntax http golang org pkg text template define routes in main package gooo go gooo http localhost 8080 hello world http localhost 8080 hello world gooo celebrate gooo outside gooo read these text template http golang org pkg text template html template http golang org pkg html template net http http golang org pkg net http database sql http golang org pkg database sql encoding json http golang org pkg encoding json p align center img src http i imgur com nsscm jpg alt gopher p enjoy aaron lifton | front_end |
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MEAN-Stack | what is mean each letter in the word mean has some specific meaning here m stands for mongodb e stands for express a stands for angular and n stands for node js it is one of the most popular stacks used for developing the full stack application let s understand the basic idea behind the mean stack as we can observe in the above figure that there is a front end app back end app and database the front end app can be developed using either angular js or react js and back end can be developed using node js which is further connected to the mongodb database the front end app and back end app communicate with each other through the restapi the back end app exposes the restapi endpoints whereas the front end app consumes the restapi endpoints let s look at each component of mean js one by one node js node js is an open source platform and provides a runtime environment for executing the javascript code it is mainly used for building the back end application since there are two types of apps such as web apps and mobile apps where web apps run on the browser and mobile apps run on mobile devices both web app and mobile app are the client apps to which the user interacts these apps require to communicate with the backend services to store the data send emails push notifications node js is an ideal platform to build highly scalable data intensive and real time applications it can be used for agile development and highly scalable services for example paypal is a java and spring based application using node js advantages of node js the node js applications are faster than the other framework based applications and require fewer people to build the app it requires fewer lines of code the node app has a 35 faster response time than the other apps the major advantage of using node js is that node js uses javascript if you are a front end developer then you can easily transit from the front end to the full stack developer angular js angular js is a javascript framework that is used to develop web applications this framework is developed by the google now the question arises that there are many javascript frameworks available in the market why do we prefer angular js over the other frameworks for developing the web application advantages of angular js it is a two way data binding which means that it keeps the model and view in sync if any changes are made in the model then automatically view will also be updated accordingly the angular js is designed with testing in mind the components of angular js application can be tested with both the testing such as unit testing and end to end testing with the help of angular js it is easy to develop the application in an mvc architecture mongodb mongodb is the database used in web development it is a nosql database and a nosql database can be defined as a non relational and document oriented database management system as it is a document oriented database management system so it stores the data in the form of documents the sql databases use sql query language to query the database whereas the mongodb is a nosql database that uses bson language to query the database json is a text based format but it is inefficient in terms of speed and space in order to make mongodb efficient in terms of space and speed bson was invented bson basically stores the json format in the binary form that optimizes the space speed and complexity since the mongodb is an unstructured schema and the data is not related to each other then the question arises why do we need to use the mongodb mongodb is mainly useful for projects which are huge express js express js is a free and open source software used as a back end web application framework it is commonly used in the popular development stacks like mean with a mongodb database the express js was developed by tj holowaychuk | server |
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awesome-financial-nlp | awesome financial nlp researches for natural language processing for financial domain financial applications of nlp text data is one of the important resources for the financial domain for example we read the news when buying stock and we evaluate the plan when providing finance in the past the volume and velocity of textual data were manageable enough to be manually analyzed by teams of human experts but recent growth is intractable recent progress of nlp techniques helps this situation and it will reveal how the experts determine various judgements application market analysis apply classification clustering method to analyze market micro stock price prediction etc macro unravel market movement risk management apply classification method etc to detect fraud or money laundering compliance apply various nlp methods to verify compatibility to internal investment loan rule asset management apply various nlp methods to organize unstructured documents etc internal utilize internal documents external utilize internal documents like sns etc customer engagement apply question answering dialog etc to communicate with customer classification risk management jingguang han utsab barman jeremiah hayes jinhua du edward burgin and dadong wan nextgen aml distributed deep learning based language technologies to augment anti money laundering investigation https www aclweb org anthology p18 4007 in acl 2018 weikang wang jiajun zhang qian li chengqing zong and zhifei li are you for real detecting identity fraud via dialogue interactions https www aclweb org anthology d19 1185 in emnlp 2019 christoph kilian theil sanja tajner and heiner stuckenschmidt word embeddings based uncertainty detection in financial disclosures https www aclweb org anthology w18 3104 in acl econlp 2018 john s zdanowicz detecting money laundering and terrorist financing via data mining https www researchgate net publication 27297312 detecting money laundering and terrorist financing via data mining in kdd adf 2019 armineh nourbakhsh and grace bang a framework for anomaly detection using language modeling and its applications to finance https arxiv org abs 1908 09156 in kdd adf 2019 feng mai shaonan tian chihoon lee and ling ma deep learning models for bankruptcy prediction using textual disclosures https www researchgate net publication 328406629 deep learning models for bankruptcy prediction using textual disclosures elsevier vol 274 2 pages 743 758 compliance youness mansar and sira ferradans sentence classification for investment rules detection https www aclweb org anthology w18 3106 in acl econlp 2018 asset management sian gooding and ted briscoe active learning for financial investment reports https www aclweb org anthology w19 6404 in nodalida fnlp 2019 jo o filgueiras lu s barbosa gil rocha henrique lopes cardoso lu s paulo reis jo o pedro machado and ana maria oliveira complaint analysis and classification for economic and food safety https www aclweb org anthology d19 5107 in emnlp econlp 2019 frederick blumenthal and ferdinand graf utilizing pre trained word embeddings to learn classification lexicons with little supervision https www aclweb org anthology w19 6402 in nodalida fnlp 2019 sentiment analysis market analysis tobias daudert paul buitelaar and sapna negi leveraging news sentiment to improve microblog sentiment classification in the financial domain https www aclweb org anthology w18 3107 in acl econlp 2018 narges tabari piyusha biswas bhanu praneeth armin seyeditabari mirsad hadzikadic and wlodek zadrozny causality analysis of twitter sentiments and stock market returns https www aclweb org anthology w18 3102 in acl econlp 2018 thomas gaillat bernardo stearns gopal sridhar ross mcdermott manel zarrouk and brian davis implicit and explicit aspect extraction in financial microblogs https www aclweb org anthology w18 3108 in acl econlp 2018 sven buechel simon junker thore schlaak claus michelsen and udo hahn a time series analysis of emotional loading in central bank statements https www aclweb org anthology d19 5103 in emnlp econlp 2019 linyi yang ruihai dong tin lok james ng and yang xu leveraging bert to improve the fears index for stock forecasting https www aclweb org anthology w19 5509 in finnlp 2019 asset management antonio moreno sandoval pablo alfonso haya ana gisbert marta guerrero and helena montoro tone analysis in spanish financial reporting narratives https www aclweb org anthology w19 6406 in fnp 2019 clustering unsupervised classification haodong bai frank xing erik cambria and win bin huang business taxonomy construction using concept level hierarchical clustering https www aclweb org anthology w19 5501 in finnlp 2019 question answering dialog customer engagement tuan lai trung bui sheng li and nedim lipka a simple end to end question answering model for product information https www aclweb org anthology w18 3105 in acl econlp 2018 jared rivera jan caleb oliver pensica jolene valenzuela alfonso secuya and charibeth cheng annotation process for the dialog act classification of a taglish e commerce q a corpus https www aclweb org anthology d19 5108 in emnlp econlp 2019 knowledge extraction event extraction market analysis liat ein dor ariel gera orith toledo ronen alon halfon benjamin sznajder lena dankin yonatan bilu yoav katz and noam slonim financial event extraction using wikipedia based weak supervision https www aclweb org anthology d19 5102 in emnlp econlp 2019 shuang sophie zhai and zhu drew zhang forecasting firm material events from 8 k reports https www aclweb org anthology d19 5104 in emnlp econlp 2019 deli chen yanyan zou keiko harimoto ruihan bao xuancheng ren and xu sun incorporating fine grained events in stock movement prediction https www aclweb org anthology d19 5105 in emnlp econlp 2019 deli chen shuming ma keiko harimoto ruihan bao qi su and xu sun group extract and aggregate summarizing a large amount of finance news for forex movement prediction https www aclweb org anthology d19 5106 in emnlp econlp 2019 kiyoshi izumi and hiroki sakaji economic causal chain search using text mining technology https www aclweb org anthology w19 5510 in finnlp 2019 relation extraction asset management berke oral erdem emekligil se il arslan and g l en eryi it extracting complex relations from banking documents https www aclweb org anthology d19 5101 in emnlp econlp 2019 ananth balashankar sunandan chakraborty and lakshminarayanan subramanian unsupervised word influencer networks from news streams https www aclweb org anthology w18 3109 in acl econlp 2018 workshops econlp economics and natural language 2018 https julielab de econlp 2018 2019 https sites google com view econlp 2019 fnlp financial narrative processing 2018 http wp lancs ac uk cfie fnp2018 2019 https www aclweb org anthology volumes w19 64 finnlp finsdb 2019 https sites google com nlg csie ntu edu tw finnlp home kdf knowledge discovery from unstructured data in financial services 2020 https aaai kdf2020 github io robust ai in fs 2019 https sites google com view robust ai in fs 2019 home adf anomaly detection in finance 2017 https sites google com view kdd adf 2017 2019 https sites google com view kdd adf 2019 dataset corpus sebastian g m h ndschke sven buechel jan goldenstein philipp poschmann tinghui duan peter walgenbach and udo hahn a corpus of corporate annual and social responsibility reports 280 million tokens of balanced organizational writing https www aclweb org anthology w18 3103 in acl econlp 2018 tobias daudert and sina ahmadi cofif a corpus of financial reports in french language https www aclweb org anthology w19 5504 in in emnlp econlp 2019 reference machine learning in uk financial services https www bankofengland co uk media boe files report 2019 machine learning in uk financial services pdf for the first time nasdaq is using artificial intelligence to surveil u s stock market https www nasdaq com articles for the first time nasdaq is using artificial intelligence to surveil u s stock market | machine-learning natural-language-processing financial-analysis finance | ai |
Conjecture | conjecture build status https travis ci org etsy conjecture svg branch master https travis ci org etsy conjecture conjecture is a framework for building machine learning models in hadoop using the scalding dsl the goal of this project is to enable the development of statistical models as viable components in a wide range of product settings applications include classification and categorization recommender systems ranking filtering and regression predicting real valued numbers conjecture has been designed with a primary emphasis on flexibility and can handle a wide variety of inputs integration with hadoop and scalding enable seamless handling of extremely large data volumes and integration with established etl processes predicted labels can either be consumed directly by the web stack using the dataset loader or models can be deployed and consumed by live web code currently binary classification assigning one of two possible labels to input data points is the most mature component of the conjecture package tutorial there are a few stages involved in training a machine learning model using conjecture create training data we represent the training data as feature vectors which are just mappings of feature names to real values in this case we represent them as a java map of strings to doubles although we have a class stringkeyedvector which provides convenience methods for feature vector construction we also need the true label of each instance which we represent as 0 and 1 the mapping of these binary labels to e g male and female is up to the user we construct binarylabeledinstances which are just wrappers for a feature vector and a label val bl new binarylabeledinstance 0 0 bl addterm bias 1 0 bl addterm some feature 0 5 training a classifier classifiers are essentially trained by presenting the labeled instances to them there are several kinds of linear classifiers we implement among them logistic regression perceptron mira a large margin perceptron model passive aggressive these models all have several options such as learning rate regularization parameters and so on we supply reasonable defaults for these parameters although they can be changed readily to train a linear model simply call the update function with the labeled instance val p new logisticregression p update bl in order to make this procedure tractable for large datasets we provided scalding wrappers for the training these operate by training several small models on mappers then aggregating them into a final complete model on the reducers this wrapper is called like so new binarymodeltrainer args train instances instance model write sequencefile model map model model x updateablebinarymodel new com google gson gson tojson x write tsv model json this code segment will train a model using a pipe called instances which has a field called instance which contains the binarylabeledinstance objects it produces a pipe with a single field containing the completed model which can then be written to disk this class uses the command line args object from scalding in order to let you set some options on the command line some useful options are argument possible values default meaning model mira logistic regression passive aggressive passive aggressive the type of model to use iters 1 2 3 1 the number of iterations of training to perform zero class prob one class prob 0 1 1 to see all the command line options see the binarymodeltrainer class evaluating a classifier it is important to get a sense of the performance you can expect out of your classifier on unseen data in order to do this we recommend to use cross validation in essence your input set of instances is split up into testing and training portions multiple different ways then a classifier is trained on each training portion and evaluated against the true labels which are present using the testing portion this is all wrapped up in a class called binarycrossvalidator it is used like so new binarycrossvalidator args 5 crossvalidate instances instance write tsv model xval this class also takes the command line arguments which it passes to a model trainer for each fold this allows the specification of options to the cross validated models on the command line the output contains statistics about the performance of the model as well as the confusion matrices for each fold a script is included which cross validates a logistic regression model on the iris dataset | non-sox | ai |
Database-Systems-Course | yazd university database systems fall 2018 material from yazd university computer engineering department database systems course fall 2018 offering http mdashti com database fall2018 adapted from stanford cs 145 fall 2017 https github com stanford futuredata cs145 2017 materials | server |
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ITP20NYPL | nypl add alt text information technology project | server |
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NoobChain-Tutorial-Part-2 | noobchain tutorial part 2 a simple java blockchain for educational purposes this is for https medium com programmers blockchain create simple blockchain java tutorial from scratch 6eeed3cb03fa tutorial if you have any other questions you can message me on the blockchain developers club https discord gg zsyqqyk discord server a simple java blockchain with transactions still missing networking dependencies you will need to import both bouncy castle and gson bouncy castle bcprov jdk15on 159 jar https www bouncycastle org download bcprov jdk15on 159 jar gson gson 2 8 2 jar http central maven org maven2 com google code gson gson 2 8 2 gson 2 8 2 jar java version jdk1 8 0 77 contact kasscrypto gmail com | blockchain |
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nlp-course | an introduciton to natural language processing nlp course img src https pbs twimg com media brpvg7wcmaak6qh png alt go find image align middle style width 350px the mystery lies in the use of language to express human life eudora welty logistics instructor brian spiering website github com brianspiering nlp course https github com brianspiering nlp course course description this course covers the fundamental concepts and algorithms in natural language processing nlp the goal of the course is to understand text using computational statistics this course will start with basic text processing techniques such as regular expressions and then cover advanced techniques text classification and topic modeling the emphasis will be on contemporary best practices in industry including deep learning and text embeddings along the way we will touch upon text mining information retrieval and computational linguistics this is course is a buffet format a sample of many things but you will not get full on any one topic people get a phd in each of these individual topics remember a little bit of knowledge and a lot of how to goes a long way in data science prerequisites working knowledge of probability e g calculate conditional probability and apply bayes theorem basic statistics e g the difference between pmf and pdf one course in machine learning intermediate python e g the ability to create classes based on previous classes the more python a student knows the more nlp he she learns during the course learning outcomes by the end of the course you should be able to 1 apply fundamental nlp concepts and algorithms to solve real world problems 1 write efficient code to process and model text data 1 classify and cluster text data 1 create and use vector representations of words and documents 1 build an end to end system to model meaning in text course topics 1 welcome 2 nlp overview 3 regular expressions 4 segmenting tokenizing stemming 5 language modeling 6 text embeddings words 7 text embeddings documents et al 8 word tagging pos part of speech and ner named entity recognition 9 text classification sentiment analysis with naive bayes 10 text classification with deep learning 11 information retrieval search engineering 12 topic modeling with latent dirichlet allocation lda topics not covered theory we are only going to cover applied parts of nlp aka tips n tricks for getting stuff done grammar grammar kinda sucks but it is a very powerful method for understanding language non english languages i other languages and they are very important to understanding nlp there is just enough not time machine translation again very important and incredible breakthroughs have been made there is not enough time to adequately cover it natural language understanding nlu finding meaning in text we ll spend most of our time focused on lower levels of processing natural language generation nlg we ll only going to briefly touch on how to programmatically create text during the language modeling section speech recognition for this class we ll assume audio waves have been digitized into text in the last couple of years speech based language processing has be revolutionized and it is well worth looking into grading item weight participation 30 labs 30 final project 40 course grades range from a to f the msds program considers a grade of a to represent exceptional work with respect to both the instructor s expectations and peer student achievements a grade of b represents the expected outcome what is called competence in a business setting a c grade represents achievements lower than the instructor s expectations for competence in the subject a grade of f represents little or no work in the course participation you must show up to each session prepared each person is important to the dynamic of the class and therefore students are required to participate in class activities expect to be cold called i call on students at random not to put you on the spot but to keep you engaged in the material at all times attendance is mandatory it is the responsibility of the student to attend all classes if you have to miss class due to sickness or other circumstances please notify your instructor by slack in advance supporting documents e g doctor s notes should accompany absences due to sickness labs the labs will be hands on activities they will require a combination of coding and writing the coding sections will be implementing algorithms from scratch or applying common libraries e g scikit learn nltk and keras the writing sections will focus on communication to technical and nontechnical audiences final project details in final project folder useful stuff to know about course structure this course will be partly flipped https en wikipedia org wiki flipped classroom basic lectures will be videos watched before class in class lectures will cover complex topics in an active learning https en wikipedia org wiki active learning style you ll be writing a lot of code and completing many projects during class time textbooks there are no required textbooks for this course preparation materials e g videos articles and blog posts will be assigned for each session | nlp natural-language-processing machine-learning python | ai |
metrocity | the metrocity beamer template metrocity pronounced as by megamind is a lightweight clean and modern beamer theme suitable for use in academic especially in the database and software engineering group of the university of magdeburg not yet convinced have a look at these demo slides p float left img src assets metrocity 1 png width 49 img src assets metrocity 2 png width 49 p p float left img src assets metrocity 3 png width 49 img src assets metrocity 4 png width 49 p p float left img src assets metrocity 5 png width 49 img src assets metrocity 6 png width 49 p installation for installation instructions see install md install md usage the following latex code is a minimal example of a beamer presentation using metrocity documentclass beamer use the metrocity theme usetheme metrocity title a minimal presentation date today author marcus pinnecke institute metrocity theme headquarter begin document maketitle section first section begin frame my first slide hello world end frame end document have a look at the templates templates directory where you will find some nice frame templates license the metrocity theme is licensed under mit license https opensource org licenses mit which means you must keep the copyright notice of metrocity intact when you use copy modify merge publish distribute sublicense and or sell copies of the metrocity theme source code this restriction does not affect presentations using the metrocity theme | server |
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Mobile-iOS-API | awi ios api what is the project the mobile dev project is a project that we had to do during our 4th year of engineering school the goal of this project is to create an rest api that will be used by an ios app this is the backend part of the project techlogies used a target blank href https nodejs org en img alt github link src https img shields io badge nodejs 16 13 2 orange style for the badge logo node js a a target blank href https expressjs com img alt github link src https img shields io badge express 4 18 2 orange style for the badge logo express a a target blank href https www mongodb com img alt github link src https img shields io badge mongodb 5 3 2 orange style for the badge logo mongodb a a target blank href https mongoosejs com img alt github link src https img shields io badge mongoose 6 9 0 orange style for the badge logo mongoose a a target blank href https firebase google com docs admin setup img alt github link src https img shields io badge firebase admin 11 5 0 orange style for the badge logo firebase a a target blank href https firebase google com docs functions img alt github link src https img shields io badge firebase functions 11 5 0 orange style for the badge logo firebase a a target blank href https www npmjs com img alt github link src https img shields io badge npm 8 1 2 orange style for the badge logo npm a links a target blank href https github com romainfrezier awi ios img alt github link src https img shields io badge github ios git black style for the badge logo github a romain frezier robin hermet polytech montpellier | express-js mongodb mongoose rest-api school-project firebase-auth firebase-functions | os |
xenia | xenia columbia engineering outreach programs database project this app was built to interact with data pertaining to columbia engineering outreach programs in the app account holding users are capable of adding editing deleting events faculty student groups and admins furthermore it s possible to create hashtag labels keywords akin to their use in twitter for better organization these keywords are also searchable via a search field presented at the home page projecting all labeled events in the results visualization tools were also built to summarize past event data and better plan ahead for future events built using flask http flask pocoo org sqlalchemy https www sqlalchemy org wtforms https wtforms readthedocs io en stable bootstrap https getbootstrap com coffee https en wikipedia org wiki coffee the beverage authors nan you as columbia seas data science grad student jaime campos salas as columbia gs computer science undergrad student acknowledgments yigang jack wang for providing guidance in requirement analysis engineering and data visualization emily ford and kayla quinnies for providing guidance in data requirements and user interface eugune wu for providing tutelage on database design and implementation via database coursework | server |
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awesome-few-shot-object-detection | awesome few shot object detection collect some papers about few shot object detection for computer vision additionally we briefly introduce the commonly used datasets for few shot object detection papers survey title venue pdf a survey of deep learning for low shot object detection arxiv 2022 pdf https arxiv org pdf 2112 02814 pdf a unified framework for attention based few shot object detection arxiv 2022 pdf https arxiv org pdf 2201 02052 pdf 2023 title venue dataset pdf code fs detr few shot detection transformer with prompting and without re training iccv 2023 pascal voc ms coco pdf https openaccess thecvf com content iccv2023 papers bulat fs detr few shot detection transformer with prompting and without re training iccv 2023 paper pdf adaptive decoupled prototype for few shot object detection iccv 2023 pascal voc ms coco fsod pdf https openaccess thecvf com content iccv2023 papers du s adaptive decoupled prototype for few shot object detection iccv 2023 paper pdf generating features with increased crop related diversity for few shot object detection cvpr 2023 pascal voc ms coco pdf https openaccess thecvf com content cvpr2023 papers xu generating features with increased crop related diversity for few shot object detection cvpr 2023 paper pdf meta tuning loss functions and data augmentation for few shot object detection cvpr 2023 pascal voc ms coco pdf https openaccess thecvf com content cvpr2023 papers demirel meta tuning loss functions and data augmentation for few shot object detection cvpr 2023 paper pdf niff alleviating forgetting in generalized few shot object detection via neural instance feature forging cvpr 2023 pascal voc ms coco pdf https openaccess thecvf com content cvpr2023 papers guirguis niff alleviating forgetting in generalized few shot object detection via neural cvpr 2023 paper pdf digeo discriminative geometry aware learning for generalized few shot object detection cvpr 2023 pascal voc ms coco lvis pdf https openaccess thecvf com content cvpr2023 papers ma digeo discriminative geometry aware learning for generalized few shot object detection cvpr 2023 paper pdf breaking immutable information coupled prototype elaboration for few shot object detection aaai 2023 pascal voc ms coco pdf https arxiv org pdf 2211 14782 pdf code https github com lxn96 icpe few shot object detection via variational feature aggregation aaai 2023 pascal voc ms coco pdf https arxiv org pdf 2301 13411 pdf code https github com csuhan vfa disentangle and remerge interventional knowledge distillation for few shot object detection from a conditional causal perspective aaai 2023 pascal voc ms coco pdf https arxiv org pdf 2208 12681 pdf code https github com zyn 1101 dandr git 2022 title venue dataset pdf code rethinking few shot object detection on a multi domain benchmark eccv 2022 lvis ms coco pdf https arxiv org pdf 2207 11169 pdf airdet few shot detection without fine tuning for autonomous exploration eccv 2022 pascal voc ms coco pdf https arxiv org pdf 2112 01740 pdf code https github com jaraxxus me airdet less than few self shot video instance segmentation eccv 2022 ms coco pdf https arxiv org pdf 2204 08874 pdf time reversed diffusion tensor transformer a new tenet of few shot object detection eccv 2022 pascal voc ms coco fsod pdf https arxiv org pdf 2210 16897 pdf code https github com zs123 lang tenet vizwiz fewshot locating objects in images taken by people with visual impairments eccv 2022 pascal voc ms coco pdf https arxiv org pdf 2207 11810 pdf code https vizwiz org multi faceted distillation of base novel commonality for few shot object detection eccv 2022 pascal voc ms coco pdf https arxiv org pdf 2207 11184 pdf code https github com wushuang1998 mfdc few shot object detection by knowledge distillation using bag of visual words representations eccv 2022 pascal voc ms coco pdf https arxiv org pdf 2207 12049 pdf few shot object detection with model calibration eccv 2022 pascal voc ms coco pdf https www ecva net papers eccv 2022 papers eccv papers 136790707 pdf code https github com fanq15 fewx few shot video object detectio eccv 2022 fsvod 500 pdf https arxiv org pdf 2104 14805 pdf code https github com fanq15 fewx few shot object counting and detection eccv 2022 fscd 147 fscd lvis pdf https arxiv org pdf 2207 10988 pdf code https github com vinairesearch counting detr mutually reinforcing structure with proposal contrastive consistency for few shot object detection eccv 2022 pascal voc ms coco pdf https www ecva net papers eccv 2022 papers eccv papers 136800388 pdf code https github com mmatx mrsn few shot end to end object detection via constantly concentrated encoding across heads eccv 2022 pascal voc ms coco pdf https www ecva net papers eccv 2022 papers eccv papers 136860056 pdf acrofod an adaptive method for cross domain few shot object detection eccv 2022 cityscapes sim10k pdf https arxiv org pdf 2207 11169 pdf code https github com hlings acrofod kernelized few shot object detection with efficient integral aggregation cvpr 2022 pascal voc ms coco pdf https openaccess thecvf com content cvpr2022 papers zhang kernelized few shot object detection with efficient integral aggregation cvpr 2022 paper pdf code https github com zs123 lang kfsod label verify correct a simple few shot object detection method cvpr 2022 pascal voc ms coco pdf https openaccess thecvf com content cvpr2022 papers kaul label verify correct a simple few shot object detection method cvpr 2022 paper pdf code https github com prannaykaul lvc few shot object detection with fully cross transformer cvpr 2022 pascal voc ms coco pdf https openaccess thecvf com content cvpr2022 papers han few shot object detection with fully cross transformer cvpr 2022 paper pdf meta faster r cnn towards accurate few shot object detection with attentive feature alignment aaai 2022 pascal voc ms coco pdf https arxiv org pdf 2104 07719 pdf code https github com guangxinghan meta faster r cnn few shot object detection by attending to per sample prototype wacv 2022 pascal voc ms coco pdf https openaccess thecvf com content wacv2022 papers lee few shot object detection by attending to per sample prototype wacv 2022 paper pdf 2021 title venue dataset pdf code generalized and discriminative few shot object detection via svd dictionary enhancement neurips 2021 pascal voc ms coco pdf https proceedings neurips cc paper 2021 hash 325995af77a0e8b06d1204a171010b3a abstract html code https github com amingwu svd dictionary enhancement few shot object detection via association and discrimination neurips 2021 pascal voc ms coco pdf https proceedings neurips cc paper 2021 hash 8a1e808b55fde9455cb3d8857ed88389 abstract html code https github com yhcao6 fadi adaptive image transformer for one shot object detection cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers chen adaptive image transformer for one shot object detection cvpr 2021 paper pdf code https github com wommow ait dense relation distillation with context aware aggregation for few shot object detection cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers hu dense relation distillation with context aware aggregation for few shot object detection cvpr 2021 paper pdf code https github com hzhupku dcnet generalized few shot object detection without forgetting cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers fan generalized few shot object detection without forgetting cvpr 2021 paper pdf code https github com megvii basedetection gfsd beyond max margin class margin equilibrium for few shot object detection cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers li beyond max margin class margin equilibrium for few shot object detection cvpr 2021 paper pdf code https github com bohao lee cme few shot object detection via classification refinement and distractor retreatment cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers li few shot object detection via classification refinement and distractor retreatment cvpr 2021 paper pdf transformation invariant few shot object detection cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers li transformation invariant few shot object detection cvpr 2021 paper pdf unit unified knowledge transfer for any shot object detection and segmentation cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers khandelwal unit unified knowledge transfer for any shot object detection and segmentation cvpr 2021 paper pdf code https github com ubc vision unit fapis a few shot anchor free part based instance segmenter cvpr 2021 ms coco pdf https openaccess thecvf com content cvpr2021 papers nguyen fapis a few shot anchor free part based instance segmenter cvpr 2021 paper pdf code https github com ducminhkhoi fapis semantic relation reasoning for shot stable few shot object detection cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers zhu semantic relation reasoning for shot stable few shot object detection cvpr 2021 paper pdf fsce few shot object detection via contrastive proposal encoding cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers sun fsce few shot object detection via contrastive proposal encoding cvpr 2021 paper pdf code https github com megviidetection fsce accurate few shot object detection with support query mutual guidance and hybrid loss cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers zhang accurate few shot object detection with support query mutual guidance and hybrid cvpr 2021 paper pdf hallucination improves few shot object detection cvpr 2021 pascal voc ms coco pdf https openaccess thecvf com content cvpr2021 papers zhang hallucination improves few shot object detection cvpr 2021 paper pdf code https github com pppplin hallucfsdet query adaptive few shot object detection with heterogeneous graph convolutional networks iccv 2021 pascal voc ms coco pdf https openaccess thecvf com content iccv2021 papers han query adaptive few shot object detection with heterogeneous graph convolutional networks iccv 2021 paper pdf code https github com guangxinghan qa fewdet universal prototype enhancing for few shot object detection iccv 2021 pascal voc ms coco pdf https openaccess thecvf com content iccv2021 papers wu universal prototype enhancing for few shot object detection iccv 2021 paper pdf code https github com amingwu up fsod defrcn decoupled faster r cnn for few shot object detection iccv 2021 pascal voc ms coco pdf https openaccess thecvf com content iccv2021 papers qiao defrcn decoupled faster r cnn for few shot object detection iccv 2021 paper pdf code https github com er muyue defrcn meta detr few shot object detection via unified image level meta learning arxiv 2021 pascal voc ms coco pdf https arxiv org pdf 2103 11731v2 pdf code https github com zhanggongjie meta detr 2020 title venue dataset pdf code restoring negative information in few shot object detection neurips 2020 pascal voc imagenet loc pdf https proceedings neurips cc paper 2020 hash 240ac9371ec2671ae99847c3ae2e6384 abstract html code https github com yang yk np repmet context transformer tackling object confusion for few shot detection aaai 2020 pascal voc ms coco pdf https ojs aaai org index php aaai article download 6957 6811 code https github com ze yang context transformer few shot object detection with attention rpn and multi relation detector cvpr 2020 ms coco imagenet loc pdf https openaccess thecvf com content cvpr 2020 papers fan few shot object detection with attention rpn and multi relation detector cvpr 2020 paper pdf code https github com fanq15 fsod code few shot object detection and viewpoint estimation for objects in the wild eccv 2020 pascal voc ms coco pdf https arxiv org pdf 2007 12107 pdf code http imagine enpc fr xiaoy fsdetview multi scale positive sample refinement for few shot object detection eccv 2020 pascal voc ms coco pdf https arxiv org pdf 2007 09384 pdf ref https githubhelp com code https github com jiaxi wu mpsr meta rcnn meta learning for few shot object detection iclr 2020 pascal voc imagenet loc pdf https openreview net pdf id b1xmogrfps frustratingly simple few shot object detection icml 2020 pascal voc ms coco lvis pdf https arxiv org pdf 2003 06957 pdf ref https githubhelp com code https github com ucbdrive few shot object detection 2019 title venue dataset pdf code few shot object detection via feature reweighting iccv 2019 pascal voc ms coco pdf https openaccess thecvf com content iccv 2019 papers kang few shot object detection via feature reweighting iccv 2019 paper pdf code https github com bingykang fewshot detection repmet representative based metric learning for classification and few shot object detection cvpr 2019 pascal voc imagenet loc pdf https openaccess thecvf com content cvpr 2019 papers karlinsky repmet representative based metric learning for classification and few shot object detection cvpr 2019 paper pdf code https github com jshtok repmet meta r cnn towards general solver for instance level low shot learning iccv 2019 pascal voc ms coco pdf https openaccess thecvf com content iccv 2019 papers yan meta r cnn towards general solver for instance level low shot learning iccv 2019 paper pdf code https github com yanxp metar cnn metadet meta learning to detect rare objects iccv 2019 pascal voc ms coco pdf https openaccess thecvf com content iccv 2019 papers wang meta learning to detect rare objects iccv 2019 paper pdf one shot object detection with co attention and co excitation neurips 2019 pascal voc ms coco pdf https proceedings neurips cc paper 2019 file 92af93f73faf3cefc129b6bc55a748a9 paper pdf code https github com timy90022 one shot object detection 2018 title venue dataset pdf code few example object detection with model communication tpami 2018 pascal voc ms coco ilsvrc pdf https arxiv org pdf 1706 08249 pdf lstd a low shot transfer detector for object detection aaai 2018 pascal voc ms coco imagenet2015 pdf https ojs aaai org index php aaai article view 11716 11575 datasets most few shot object detection papers usually follow the experimental setup in meta r cnn towards general solver for instance level low shot learning https openaccess thecvf com content iccv 2019 papers yan meta r cnn towards general solver for instance level low shot learning iccv 2019 paper pdf and few shot object detection via feature reweighting https openaccess thecvf com content iccv 2019 papers kang few shot object detection via feature reweighting iccv 2019 paper pdf and conduct experiments on pascal voc and ms coco datasets pascal voc for pascal voc the model is trained on the trainval sets of voc 2007 https link springer com article 10 1007 s11263 014 0733 5 and 2012 https link springer com article 10 1007 s11263 009 0275 4 and tested on voc 2007 test set the dataset is partitioned into three base novel splits where 5 categories are selected as novel classes and others are base classes the novel classes in each split are as follows split novel class 1 bird bus cow mbike sofa 2 aero bottle cow horse sofa 3 boat cat mbike sheep sofa ms coco ms coco https link springer com chapter 10 1007 978 3 319 10602 1 48 with 80 object categories including the 20 categories in pascal voc the 20 classes included in pascal voc are set as novel classes then the rest 60 classes in coco are as base classes the union of 80k train images and a 35k subset of validation images trainval35k are used for training and the evaluation is based on the remaining 5k val images minival acknowledgements thanks to yongqiang mao https wingkeungm github io for the ideas and templates | ai |
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udacity-c2-restapi | udagram rest api udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images getting setup installing project dependencies this project uses npm to manage software dependencies npm relies on the package json file located in the root of this repository after cloning open your terminal and run bash npm install tip npm i is shorthand for npm install installing useful tools 1 postbird https github com paxa postbird postbird is a useful client gui graphical user interface to interact with our provisioned postgres database we can establish a remote connection and complete actions like viewing data and changing schema tables columns ect 2 postman https www getpostman com downloads postman is a useful tool to issue and save requests postman can create get put post etc requests complete with bodies it can also be used to test endpoints automatically we ve included a collection udacity c2 restapi postman collection json which contains example requsts running the server locally to run the server locally in developer mode open terminal and run bash npm run dev developer mode runs off the typescript source any saves will reset the server and run the latest version of the codebase issue with npm run dev if you have some issue with npm run dev make this 2 following command for fix this bash npm install sequelize 4 44 4 npm install pg 8 2 1 | express nodejs sequelize | cloud |
Altschool-Capstone-Project | altschool capstone project group 23 img src assets architecture diagram png alt architectural diagram this repo contains a full setup for managing an application from the application code to infrastructure managed by terraform and ansible it also showcases how cicd can be implemented for fast delivery of code changes in the application and in terraform site reliability is monitored using grafana prometheus loki and alertmanager node agents such as node exporter blackbox cadvisor and docker metrics were used to expose server metrics container metrics and application health metrics security was taken seriously and ssl certs were generated for all http traffic also the database is housed in a private subnet safe from the general internet relevant links app https app de marauder me prometheus https prom monitoring de marauder me grafana https grafana monitoring de marauder me user admin password admin alertmanager https alert monitoring de marauder me configuration management all configuration is handled using ansible from setting up the database and application to implementing monitoring config files can be found here ansible procedure setup the database bash ansible playbook playbook deploy monitoring yml i inventories production servers yml tags db agent limit db server setup the application on all application servers bash ansible playbook playbook deploy monitoring yml i inventories production servers yml tags app agent limit application setup monitoring server bash ansible playbook playbook deploy monitoring yml i inventories production servers yml tags monitor limit monitoring server build yml file this file contains information for building the the docker image and pushes it to the docker repository on every push to the main branch the yml file runs the job called docker build push it checks out the codebase for recent changes sets up docker build to build the docker image logs into dockerhub using the docker username and password that have been set as environment secrets builds the image tags it and pushes it to dockerhub terraform yml file this files contains information for running the aws infrastructure on every push to the infrastructure branch it has read and write permissions the yml file runs the terraform ci cd job it checks out the codebase for recent changes configures the aws credentials using the aws access key aws secret address key and aws region that have been set as environment variables for github actions secrets it then sets up terraform with the version indicated installs it and initialiazing it with the aws credentials set in the github actions secrets using the bash shell still using the aws credentials it sets up the terraform plan which previews the actions terraform would take to modify the infrastructure and save it to be applied later terraform show uses the aws credentials to gain access and show the terraform plan in a human readable form terraform apply as the name implies uses the aws credentials set to apply the terraform plan | cloud |
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Computer-Vision-Action | computer vision task collect main computer vision task reading paper cv arxiv daily https github com zhengzhugithub cv arxiv daily zhengzhugithub awesomecomputervision https github com zhengzhugithub awesomecomputervision handong1587 github io https handong1587 github io index html low level wenbihan reproducible image denoising state of the art https github com wenbihan reproducible image denoising state of the art yapengtian single image super resolution https github com yapengtian single image super resolution loseall videosuperresolution https github com loseall videosuperresolution high level junyanz catpapers https github com junyanz catpapers terencecyj 3d hand pose estimation papers https github com terencecyj 3d hand pose estimation papers wangzheallen awesome human pose estimation https github com wangzheallen awesome human pose estimation visual tracker benchmark results https github com foolwood benchmark results gjy3035 awesome crowd counting https github com gjy3035 awesome crowd counting https github com chanchichoi awesome face recognition https github com chanchichoi awesome face recognition gan and text zhangqianhui adversarialnetspapers https github com zhangqianhui adversarialnetspapers lzhbrian image to image papers https github com lzhbrian image to image papers jyouhou scenetextpapers https github com jyouhou scenetextpapers chongyangtao awesome scene text recognition https github com chongyangtao awesome scene text recognition wanghaisheng awesome ocr https github com wanghaisheng awesome ocr chenchengkuan awesome text generation https github com chenchengkuan awesome text generation other cjmcv deeplearning paper notes https github com cjmcv deeplearning paper notes floodsung deep learning papers reading roadmap https github com floodsung deep learning papers reading roadmap kjw0612 awesome deep vision https github com kjw0612 awesome deep vision sun254 awesome model compression and acceleration https github com sun254 awesome model compression and acceleration chester256 model compression papers https github com chester256 model compression papers dragen1860 awesome automl https github com dragen1860 awesome automl markdtw awesome architecture search https github com markdtw awesome architecture search kjw0612 awesome rnn https github com kjw0612 awesome rnn deeptecher autonomousvehiclepaper https github com deeptecher autonomousvehiclepaper object detection amusi awesome object detection https github com amusi awesome object detection hoya012 deep learning object detection https github com hoya012 deep learning object detection detectionteamucas tf https github com detectionteamucas facebookresearch maskrcnn benchmark pytorch https github com facebookresearch maskrcnn benchmark roytseng tw detectron pytorch https github com roytseng tw detectron pytorch image retrieval search re id willard yuan awesome cbir papers https github com willard yuan awesome cbir papers filipradenovic cnnimageretrieval pytorch https github com filipradenovic cnnimageretrieval pytorch cysu open reid https github com cysu open reid layumi person reid baseline pytorch https github com layumi person reid baseline pytorch michuanhaohao reid strong baseline https github com michuanhaohao reid strong baseline segmentation georgeseif semantic segmentation suite tf https github com georgeseif semantic segmentation suite ansleliu lightnet pytorch https github com ansleliu lightnet meetshah1995 pytorch semseg https github com meetshah1995 pytorch semseg speedinghzl pytorch segmentation toolbox https github com speedinghzl pytorch segmentation toolbox mrgloom awesome semantic segmentation https github com mrgloom awesome semantic segmentation semantic segmentation https www aiuai cn aifarm62 html semantic segmentation https zhangbin0917 github io 2018 09 18 semantic segmentation conference 2018 2019 international conferences https github com jackietseng conference call for paper dataset https zhangbin0917 github io 2018 06 12 e9 81 a5 e6 84 9f e6 95 b0 e6 8d ae e9 9b 86 d x y awesome nas https github com d x y awesome nas defects inspection https github com sundycoder deye https tianchi aliyun com competition entrance 231682 information kaggle action iphysresearch datascicomp active competitons to join https github com iphysresearch datascicomp geekinglcq cdcs https github com geekinglcq cdcs chinese data competitions solutions data competition topsolution https github com smilexuhc data competition topsolution computer vision study ai http www huaxiaozhuan com leetcode https github com ranjiewwen q leetcode tab stars utf8 e2 9c 93 utf8 e2 9c 93 q leetcode python learning python machine learning in mooc http www icourse163 org course bit 1001872001 image processing opencv vlfeat machine learning sklearn machine learning in action read machine learning and analyze code implementation deeping learning christoschristofidis awesome deep learning https github com christoschristofidis awesome deep learning deepleraning ai course https github com ranjiewwen computer vision action tree master deeplearning ai 20course neural networks and deep learning improving deep neural networks convolutional neural network computer vision based on deep learning xiaoxiang college course study notes ppt and resources are very detailed cs231n https github com cthorey cs231 convolutional neural networks for visual recognition cs231n https www zybuluo com hanxiaoyang note 442846 cs224n https github com hankcs cs224n natural language processing with deep learning deep learning framwork tensorflow project template https github com mrgemy95 tensorflow project template spikeking dl project template https github com spikeking dl project template victoresque pytorch template https github com victoresque pytorch template tiny dnn https github com ranjiewwen tiny dnn header only dependency free deep learning framework in c 11 deeplearntoolbox https github com dip ml ai deeplearntoolbox matlab octave toolbox for deep learning includes deep belief nets stacked autoencoders convolutional neural nets convolutional autoencoders and vanilla neural nets each method has examples to get you started matconvnet tvm https github com dmlc tvm nnvm https www zhihu com question 51216952 nnvm https blog csdn net sanallen article category 7429137 | deep-learning python machine-learning computer-vision andrew-ng sklearn paper | ai |
thingset-zephyr-sdk | thingset zephyr sdk this repository contains a software development kit sdk based on zephyr rtos to integrate communication interfaces using the thingset protocol into an application with minimum effort testing with native posix board west build b native posix samples counter t run testing with lorawan west build b olimex lora stm32wl devkit samples counter doverlay config lorawan conf testing with can bus west build b native posix samples counter t run doverlay config can conf testing websocket with native posix start net setup from zephyr net tools to create zeth interface sudo tools net tools net setup sh forward packets from the the zeth interface to the internet via wifi ethernet interface replace wifi0 with the actual interface name sudo sysctl net ipv4 ip forward 1 sudo iptables t nat a postrouting o wifi0 j masquerade sudo iptables a forward m conntrack ctstate related established j accept sudo iptables a forward i zeth o wifi0 j accept afterwards run the nativ posix board with websocket support from another shell west build b native posix samples counter doverlay config native websocket conf storage flash conf build zephyr zephyr exe flash samples counter virtual flash bin the virtual flash bin file is used to store custom data like server hostnames and credentials update server details via thingset picocom dev pts 123 replace 123 with printed number select thingset networking swebsockethost your server com swebsocketport 443 swebsocketauthtoken your token check socket connections ss t a n grep e state 192 0 2 1 license this software is released under the apache 2 0 license license | os |
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instagram | instagram creating a full fledged instagram clone is a complex and time consuming project that involves web and mobile app development backend infrastructure and database management creating a readme file for your instagram clone project on github is essential to provide potential contributors and users with information about your project below is a template for a readme file for an instagram clone project based on html and css markdown instagram clone a simple instagram clone built using html and css instagram clone screenshot screenshot png table of contents description description features features demo demo installation installation usage usage contributing contributing license license description this project is a basic instagram clone that mimics the look and feel of the popular social media platform it serves as a learning resource for web developers who want to understand the fundamentals of html and css while building a real world project features user registration and login posting images with captions news feed displaying user posts like and comment on posts user profiles with followers and following lists edit user profile information responsive design for various screen sizes demo you can see a live demo of this instagram clone at demo link https your demo link com installation to run this project locally follow these steps 1 clone the repository bash git clone https github com your username instagram clone git 2 open the project directory bash cd instagram clone 3 open index html in your web browser to view the application usage 1 create a new account or use the provided test account 2 explore the app s features including posting images liking and commenting on posts and editing your profile 3 have fun experimenting with the code and customizing it to your liking contributing contributions are welcome if you want to contribute to this project follow these steps 1 fork the repository 2 create a new branch for your feature or bug fix git checkout b feature your feature name 3 make your changes and commit them git commit m add your feature 4 push your changes to your fork git push origin feature your feature name 5 open a pull request on the main repository please ensure your code follows good coding practices and includes appropriate comments and documentation license this project is licensed under the mit license license feel free to customize this readme to match your project s specifics add more details screenshots and instructions as needed to provide a clear and informative guide to your instagram clone project good luck with your development journey | server |
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MobileHTML5GameDevelopmentExamples | mobilehtml5gamedevelopmentexamples examples website for mobile html5 game development book as seen on http mh5gd com all code dual licensed under the mit and gpl licenses art assets except for rpg are copyright pascal rettig 2012 rpg tile set images are copyright their respective owners | front_end |
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awesome-blockchain-cn | awesome blockchain awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome blockchain please read the contribution guidelines https github com sindresorhus awesome blob master contributing md before contributing https github com sindresorhus awesome blob master contributing md issues xxxxx xxxxxx contents intro intro e4 bb 8b e7 bb 8d tutorial tutorial e6 95 99 e7 a8 8b project project e9 a1 b9 e7 9b ae document document e8 b5 84 e6 96 99 application application e5 ba 94 e7 94 a8 intro started https zhuanlan zhihu com p 22228902 https zhuanlan zhihu com p 23243289 https charlesliuyx github io 2017 09 24 e4 b8 80 e6 96 87 e5 bc 84 e6 87 82 e5 8c ba e5 9d 97 e9 93 be e4 bb a5 e6 af 94 e7 89 b9 e5 b8 81 e4 b8 ba e4 be 8b http blog csdn net jwter87 article details 53322642 https yeasy gitbooks io blockchain guide http blog csdn net csolo article details 52858236 http www 8btc 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github com ooibc88 blockbench zokrates https github com jacobeberhardt zokrates zksnarks libsnark https github com scipr lab libsnark zksnarks c document bitcoin https github com bitcoinbook bitcoinbook oreilly http zhibimo com books wang miao mastering bitcoin blockchaindev org http blockchaindev org cto http www jianshu com u 30081a05cf95 ethereum mastering ethereum https github com ethereumbook ethereumbook oreilly http me tryblockchain org http gi1 cn topics category solidity solidity solidity http www tryblockchain org web3 js http web3 tryblockchain org truffle http truffle tryblockchain org open zeppelin http zeppelin tryblockchain org ethplorer https github com everexio ethplorer wiki ethplorer api from etop ethplorer faq http 8btc com thread 23195 1 1 html ethereum smart contract security best practices https consensys github io smart contract best practices english ethlist https github com scanate ethlist english https github com consensys ethereum developer tools list blob master readme chinese md fabric fabric official docs https hyperledger fabric readthedocs io en latest fabric yeasy http blog csdn net yeasy ibm fabric yeasy http blog csdn net xjmtxwd24 fabric0 6 1 0 jiang xinxing http blog csdn net jiang xinxing article category 6642179 fabric0 6 application explorer blockchain https blockchain info etherscan https etherscan io ethplorer https ethplorer io api eth gas station https ethgasstation info index php gas etherscope https etherscope io oklink https www oklink com eth defi defi wallet my ether wallet https myetherwallet com https github com kvhnuke etherwallet metamask https metamask io chrome extension multi platform jaxx wallet https jaxx io mist wallet https github com ethereum mist releases latest parity wallet https github com paritytech parity releases latest harmony wallet https github com ether camp ethereum harmony releases latest imtoken https token im app trust https trustwalletapp com ios android dapp cipher https www cipherbrowser com ios android dapp ledger nano s https theethereum wiki w index php ledger nano s trezor https blog trezor io trezor integration with myetherwallet 3e217a652e08 exchange 0x https www 0xproject com otc 0x https github com 0xproject contracts idex https idex market idex https github com auroradao ethdelf https etherdelta github io zrx eth etherdelta https github com etherdelta smart contract forkdelta https forkdelta github io forkdelta https github com forkdelta smart contract kyber https kyber network kyber https github com kybernetwork smart contract dmarket https dmarket io dmarket https github com suntechsoft dmarket smartcontract augur https augur net https github com augurproject melonport https melonport com https github com melonproject defi uniswap https uniswap org uniswap https github com uniswap synthetix https www synthetix io synthetix https github com synthetixio aave https aave com aave https github com aave compound https compound finance governance comp compound https github com compound finance compound protocol makerdao https makerdao com makerdao dai https github com makerdao wrapped btc https wbtc network btc https github com wrappedbtc bitcoin token smart contracts usdt https tether to usdt game cryptokitties https www cryptokitties co etheremon https www etheremon com edgeless https www edgeless io https github com edgelesscasino smart contracts im status im https github com status im status network token status im nostr nostr https github com nostr protocol nostr nostr nostr rs relay https github com scsibug nostr rs relay nostr relay rust social oraclize http docs oraclize it background aragon https aragon one https github com aragon aragon core tree master contracts dharma https dharma io https github com dharmaprotocol dharma cli chronobank https chronobank io slockit https slock it https github com slockit smart contract dao https github com slockit dao dao cross chain cosmos https cosmos network cosmos btc eth https github com cosmos polkadot https polkadot io polkadot https github com paritytech parity token erc20 https github com ethereum eips pull 610 ico token sale http vitalik ca general 2017 06 09 sales html nft https www nft org nft awesome ethereum https github com chaozh awesome blockchain tree master ethereum awesome fabric https github com chaozh awesome blockchain tree master hyperledger 20fabric fabric donate btc 1jnc15wwdvcc3qbqruy6chqrluclptgajn eth 0x81847890eecdecb20ee145824eaa1aec079a712c license cc0 http mirrors creativecommons org presskit buttons 88x31 svg cc zero svg https creativecommons org publicdomain zero 1 0 to the extent possible under law chaozh http www chaozh com has waived all copyright and related or neighboring rights to this work | blockchain |
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Modbus-STM32-HAL-FreeRTOS | modbus library for stm32 microcontrollers tcp usart and usb cdc modbus rtu master and slave library for stm32 microcontrollers based on cube hal and freertos includes multiple examples for popular development boards including bluepill nucleo 64 nucleo 144 and discovery boards cortex m3 m4 m7 this is a port of the modbus library for arduino https github com smarmengol modbus master slave for arduino video demo for stm32f4 discovery board and touchgfx https youtu be xdcqvu0liry new script examples to test the library based on pymodbus new tcp slave server multi client with configurable auto aging algorithm for management of tcp connections translations supported by the community traditional chinese traditionalchinesereadme md characteristics portable to any stm32 mcu supported by st cube hal portable to other microcontrollers like the raspberry pi pico https github com alejoseb modbus pi pico freertos requiring little engineering effort multithread safe implementation based on freertos multiple instances of modbus master and or slave can run concurrently in the same mcu only limited by the number of available uart usart of the mcu rs232 and rs485 compatible usart dma support for high baudrates with idle line detection usb cdc rtu master and slave support for f103 bluepill board tcp master and slave support with examples for f429 and h743 mcus file structure license readme md examples modbusbluepill stm32f103c8 usart slave modbusbluepillusb stm32f103c8 usart usb cdc master and slave modbusf103 nucleo f103rb modbus master and slave modbusf429 nucleo f429zi modbus slave modbusf429tcp nucleo f429zi modbus tcp modbusf429dma nucleo f429zi modbus rtu dma master and slave modbusl152dma nucleo l152re modbus rtu dma slave modbush743 nucleo h743zi modbus slave modbush743tcp nucleo h743zi modbus tcp modbusf303 nucleo f303re modbus slavee modbusstm32f4 discovery stm32f4 discovery touchgfx modbus master modbus lib library folder inc modbus h config modbusconfigtemplate h configuration template src modbus c uartcallback c how to use the examples examples provided for stm32cubeide version 1 8 0 https www st com en development tools stm32cubeide html import the examples in the stm32cube ide from the system folder connect your nucleo board compile and start your debugging session if you need to adjust the baud rate or any other parameter use the cube mx assistant recommended if you change the usart port you need to enable the interrupts for the selected usart check uartcallback c for more details notes and known issues the standard interrupt mode for modbus rtu usart is suitable for 115200 bps or lower baud rates for higher baud rates tested up to 2 mbps it is recommended to use the dma mode check the corresponding examples it will require extra configurations for the dma channels in the cube hal the usb cdc example supports only the bluepill development board it has not been validated with other development boards to use this example you need to activate usb cdc in your modbusconfig h file the tcp examples have been validated with nucleo f429zi and h743zi to use these examples you need to activate tcp in your modbusconfig h file the hal implementation for lwip tcp of the cubemx generates code that might not work if the cable is not connected from the very beginning this is a known issue that can be solved manually changing the generated code as detailed here https community st com s question 0d50x0000cdolzdsqr ethernet does not work if uc starts with the cable disconnected check the tcp example for the nucleo f429 which includes the manual modifications how to port to your own mcu create a new project in stm32cube ide for your mcu enable freertos cmsis v2 in the middleware section of cube mx configure a usart and activate the global interrupt if you are using the dma mode for usart configure the dma requests for rx and tx configure the preemption priority of usart interrupt to a lower priority 5 or a higher number for a standard configuration than your freertos scheduler this parameter is changed in the nvic configuration pane import the modbus library folder modbus lib using drag and drop from your host operating system to your stm32cube ide project when asked choose link folders and files update the include paths in the project s properties to include the inc folder of modbus lib folder create a modbusconfig h using the modbusconfigtemplate h and add it to your project in your include path instantiate a new global modbushandler t and follow the examples provided in the repository note if your project uses the usart interrupt service for other purposes you have to modify the uartcallback c file accordingly recommended modbus master and slave testing tools for linux and windows master and slave python library linux windows https github com riptideio pymodbus master client qmodbus linux https launchpad net js reynaud archive ubuntu qmodbus windows https sourceforge net projects qmodbus slave simulator linux https sourceforge net projects pymodslave windows https sourceforge net projects modrssim2 todos implement isolated memory spaces for coils inputs and holding registers implement wrapper functions for master function codes currently telegrams are defined manually improve function documentation modbus tcp implementation improvement to support multiple clients and tcp session management 10 24 2021 improve the queue for data reception the current method is too heavy it should be replaced with a simple buffer a stream or another freertos primitive solved queue replaced by a ring buffer 03 19 2021 test with rs485 transceivers implemented but not tested verified with max485 transceivers 01 03 2021 modbus tcp implementation 28 04 2021 | stm32 plc bluepill modbus modbus-library cube-hal freertos usart usb tcp dma | os |
fcc-options-app | options inc web application overview this is transportation routing web application for options inc this is a non profit project for free code camp http www freecodecamp com a demo version of this application can be found at https transport app demo herokuapp com you can login using the dummy account admin test com password admin build to build and run the project you must have the following applications installed node npm mongodb run the following command to install the dependencies the application needs npm install run create a env file in the root directory and input the environment variables there is a sample env template found in env sample that you can use start mongodb in a separate terminal run the following command to start the server node server open your application url in a browser to view the application http localhost 8080 test run the following command to execute the tests npm test features front end features javascript react redux webpack back end features node js javascript mongodb mongoose express passport license mit license click here for more information license md | front_end |
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Survey-dateset | survey dataset a study on the use of vulnerability databases in software engineering dataset this data is free to use and if you want more information please contact s alqaht encs concorddia ca 1 raw data is 93 se artilces titles abstract text format 2 r scripts include lda script r cox test r and preplexity r 3 se meta data and classifications include all the se artciels analyzed in the study | server |
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cde-cli-codespace | cde cli codespace cloud data engineering coursera cli using codespace | cloud |
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Spotify-ETL-Pipeline | spotify etl pipeline the above is an etl pipeline that connects to the spotify api and extracts recent song information the data is then validated using basic data engineering principles and loaded into an sqlite database using sqlalchemy the script can be run locally by cloning this repository and creating an env file to hold username and oauth information spotify allows developers to access this via their api here https developer spotify com console get recently played limit 10 after 1484811043508 before | server |
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BEEQ | div align center img width 100 src github beeq overview png alt beeq design system div h1 align center beeq a web component library initiative h1 p align center this repository holds the source code of the web components present in the beeq design system p p align center a aria label license href license img src https img shields io badge license apache 202 0 green alt a p div align center package version documentation bee q core https www npmjs com package bee q core version https img shields io npm v bee q core latest svg https www npmjs com package bee q core readme packages beeq readme md bee q angular https www npmjs com package bee q angular version https img shields io npm v bee q angular latest svg https www npmjs com package bee q angular readme packages beeq angular readme md bee q react https www npmjs com package bee q react version https img shields io npm v bee q react latest svg https www npmjs com package bee q react readme packages beeq react readme md div before starting structure the project has been structured as an nx monorepo https nx dev packages beeq beeq angular beeq react tools package json package lock json where packages beeq packages beeq core library source for all the elements components implemented packages beeq angular packages beeq angular angular specific wrapper for beeq core library packages beeq react packages beeq react reactjs specific wrapper for beeq core library dependencies we recommend the use of volta https volta sh to manage node and npm versions the installation process https docs volta sh guide getting started is pretty straightforward and as referenced on their official site with volta you can select a node engine once and then stop worrying about it you can switch between projects and stop having to manually switch between nodes once you have volta installed whenever you change to the beeq folder locally it will switch to the right node version pinned on the package json json volta node 18 14 0 npm 9 3 1 volta is not mandatory you can still use any node npm setup that fits you most just keep in mind that you ll need nodejs https nodejs org en download v16 x or higher npm https nodejs org en knowledge getting started npm what is npm v8 or higher usage the beeq components are published to the npm package manager registry you can use the bee q core or any of the framework specific wrappers bee q angular bee q react depending on the technology stack of your project make sure the follow the usage instructions for each package how to use the bee q core package packages beeq readme md how to use the bee q angular package packages beeq angular readme md how to use the bee q react package packages beeq react readme md more output targets https stenciljs com docs overview integration will be added later e g vue svelte feel free to check our storybook https storybook bee q design to see all the beeq components released there you can find all the component s apis properties events and methods exposed along with the variations that each component allows running the project to develop extend components on the beeq design system please fork this repo in github and clone it locally to a new directory bash git clone https github com your github username beeq git beeq design system git checkout develop cd beeq design system installation simply run bash npm ci make sure to build first the project before starting it npm run build npm start start coding build for a production build just run bash npm run build test beeq uses jest https jestjs io for unit tests and jest and puppeteer https pptr dev for end to end tests you can run all the tests once by executing bash npm run test if you get an error similar to the one below try to check out locally the main branch and run the tests again bash fatal not a valid object name main fatal no such ref main nx affected generate component beeq comes with a component generator that saves you time when creating the skeleton for a new component to use the generator you just need to run the following command and follow the instructions in your prompt cli bash npm run g contributing if you are in the mood and want to help please read carefully our contributing guidelines contributing md and development standards when working on a bug fix new feature etc please notice that we follow a gitflow workflow https www atlassian com git tutorials comparing workflows gitflow workflow make sure to follow the instructions from the contributing branching strategy guidelines contributing md branching strategy about how to create your branch when starting to work on a bug hot fixing new feature etc documentation stenciljs need help check out the stenciljs docs here https stenciljs com tailwind css we use tailwind css for the style of the components please take a look at their documentation here https tailwindcss com docs | component-library design-system ui-components web-components stenciljs tailwindcss components | os |
CVinW_Readings | cvinw readings awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com computer vision in the wild cvinw readings computer vision in the wild cvinw https computer vision in the wild github io eccv 2022 is an emerging research field this writeup provides a quick introduction of cvinw and maintains a collection of papers on the topic if you find some missing papers or resourses please open issues or pull requests recommended table of contents what is computer vision in the wild cvinw what is computer vision in the wild goals of cvinw star goals of cvinw task transfer scenarios are broad one task transfer scenarios are broad task transfer cost is low two task transfer cost is low benchmarks cinema benchmarks news loudspeaker news papers on task level transfer with pre trained models fire papers on task level transfer with pre trained models image classification in the wild orange book image classification in the wild object detection in the wild orange book object detection in the wild segmentation in the wild orange book segmentation in the wild video classification in the wild orange book video classification in the wild grounded image generation in the wild orange book grounded image generation in the wild large multimodal models orange book large multimodal models others orange book other visual recognition in the wild papers on efficient model adaptation snowflake papers on efficient model adaptation parameter efficient methods blue book parameter efficient methods others blue book other efficient model adaptation methods papers on out of domain generalization eye papers on out of domain generalization surveys green book surveys out of domain generalization green book out of domain generalization robust models green book robust models acknowledgements beers acknowledgements what is computer vision in the wild star goals of cvinw developing a transferable foundation model system that can effortlessly adapt to a large range of visual tasks in the wild it comes with two key factors i the task transfer scenarios are broad and ii the task transfer cost is low the main idea is illustrated as follows please see the detailed description in elevater paper https arxiv org abs 2204 08790 one task transfer scenarios are broad we illustrate and compare cvinw with other settings using a 2d chart in figure 1 where the space is constructed with two orthogonal dimensions input image distribution and output concept set the 2d chart is divided into four quadrants based on how the model evaluation stage is different from model development stage for any visual recognition problems at different granularity such as image classification object detection and segmentation the modeling setup cann be categorized into one of the four settings we see an emerging trend on moving towards cvinw interested in the various pre trained vision models that move towards cvinw please check out section fire papers on task level transfer with pre trained models fire papers on task level transfer with pre trained models table tr td width 50 ul li b the close set setting b both training and evaluation distributions are consistent in both dimensions a typical setting in ml cv textbooks li li b open set vocabulary world setting b it allows new concepts in evaluation while typically remains the same visual domain please see examples in a href https arxiv org abs 1707 00600 image classification a and a href https arxiv org abs 2011 10678 object detection a li li b domain generalization setting b domain shift allows new visual domain in evaluation while typically remains the same concept pool please see examples such as a href https arxiv org abs 2007 01434 domainbed a and a href http ai bu edu m3sda domainnet a li li style background color powderblue b computer vision in the wild setting b cvinw allows the flexibility in both dimensions where any new tasks datasets in the wild essentially fall into li ul td td img src images fig cvinw png style width 100 td tr tr th a brief definition with a four quadrant chart th th figure 1 the comparison of cvinw with other existing settings th tr table two task transfer cost is low one major advantage of pre trained models is the promise that they can transfer to downstream tasks effortlessly the model adaptation cost is considered in two orthogonal dimensions sample efficiency and parameter efficiency as illustrated in figure 2 the bottom left corner and top right corner is the most inexpensive and expensive adaptation strategy respectively one may interpolate and make combinations in the 2d space to get different model adaptation methods with different cost to efficient adapt large vision models of the gradaully increaseing size we see an emerging need on efficient model adaptation interested in contributing your smart efficient adaptation algorithms and see how it differs from existing papers please check out section snowflake papers on efficient model adaptation snowflake papers on efficient model adaptation table tr td width 50 ul li b sample efficiency zero few and full shot b due to the high cost of annotating data it is often desired to provide a small number of labeled image label pairs in downstream datasets transferable models should be able to reach high performance in this data limited scenario li li b parameter efficiency frozen model inference prompting tuning linear probing vs full model fine tuning b a smaller number of trainable parameter in model adaptation typically means a small training cost in a new task li ul td td img src images fig adapation cost png style width 100 td tr tr th a breakdown definition of efficient model adaptation th th figure 2 the 2d chart of model adaptation cost th tr table cinema benchmarks p font size 3 b elevater a benchmark and toolkit for evaluating language augmented visual models b font br font size 2 chunyuan li haotian liu liunian harold li pengchuan zhang jyoti aneja jianwei yang ping jin houdong hu zicheng liu yong jae lee jianfeng gao font br font size 2 neurips 2022 datasets and benchmarks track font a href https arxiv org abs 2204 08790 paper a a href https computer vision in the wild github io elevater benchmark a p loudspeaker news 09 2023 discover the fascinating journey of multimodal foundation models from specialists to general purpose assistants https arxiv org abs 2309 10020 dive into the evolution of large models in computervision visionlanguage this is based on our cvpr 2023 tutorial https vlp tutorial github io 2023 where you could find videos and slides of the core chapters for its preceding paper please check out vision language pre training basics recent advances and future trends https arxiv org abs 2210 09263 img src images mfm evolution jpeg width 60 02 2023 organizing the 2nd workshop cvpr2023 on computer vision in the wild cvinw https computer vision in the wild github io cvpr 2023 where two new challenges are hosted to evaluate the zero shot few shot and full shot performance of pre trained vision models in downstream tasks segmentation in the wild sginw https eval ai web challenges challenge page 1931 overview challenge evaluates on 25 image segmentation tasks roboflow 100 for object detection in the wild https eval ai web challenges challenge page 1973 overview challenge evaluates on 100 object detection tasks qquad img src images cvpr 2023 logo jpeg width 10 workshop https computer vision in the wild github io cvpr 2023 qquad img src images sginw jpg width 10 sginw challenge https eval ai web challenges challenge page 1931 overview qquad img src images rf100 png width 10 rf100 challenge https eval ai web challenges challenge page 1973 overview 09 2022 organizing eccv workshop computer vision in the wild cvinw https computer vision in the wild github io eccv 2022 where two challenges are hosted to evaluate the zero shot few shot and full shot performance of pre trained vision models in downstream tasks image classification in the wild icinw https eval ai web challenges challenge page 1832 overview challenge evaluates on 20 image classification tasks object detection in the wild odinw https eval ai web challenges challenge page 1839 overview challenge evaluates on 35 object detection tasks qquad img src images eccv2022 logo png width 10 workshop https computer vision in the wild github io eccv 2022 qquad img src images icinw100 jpg width 10 icinw challenge https eval ai web challenges challenge page 1832 overview qquad img src images odinw jpg width 10 odinw challenge https eval ai web challenges challenge page 1839 overview fire papers on task level transfer with pre trained models orange book image classification in the wild p font size 3 b clip learning transferable visual models from natural language supervision b font br font size 2 alec radford jong wook kim chris hallacy aditya ramesh gabriel goh sandhini agarwal girish sastry amanda askell pamela mishkin jack clark gretchen krueger ilya sutskever font br font size 2 icml 2021 font a href https arxiv org abs 2103 00020 paper a a href https github com openai clip code a p p font size 3 b align scaling up visual and vision language representation learning with noisy text supervision b font br font size 2 chao jia yinfei yang ye xia yi ting chen zarana parekh hieu pham quoc v le yunhsuan sung zhen li tom duerig font br font size 2 icml 2021 font a href https arxiv org abs 2102 05918 paper a p p font size 3 b openclip b font br font size 2 gabriel ilharco mitchell wortsman nicholas carlini rohan taori achal dave vaishaal shankar john miller hongseok namkoong hannaneh hajishirzi ali farhadi ludwig schmidt font br font size 2 10 5281 zenodo 5143773 2021 font a href https github com mlfoundations open clip code a p p font size 3 b florence a new foundation model for computer vision b font br font size 2 lu yuan dongdong chen yi ling chen noel codella xiyang dai jianfeng gao houdong hu xuedong huang boxin li chunyuan li ce liu mengchen liu zicheng liu yumao lu yu shi lijuan wang jianfeng wang bin xiao zhen xiao jianwei yang michael zeng luowei zhou pengchuan zhang font br font size 2 arxiv 2111 11432 2022 font a href https arxiv org abs 2111 11432 paper a p p font size 3 b unicl unified contrastive learning in image text label space b font br font size 2 jianwei yang chunyuan li pengchuan zhang bin xiao ce liu lu yuan jianfeng gao font br font size 2 cvpr 2022 font a href https arxiv org abs 2204 03610 paper a a href https github com microsoft unicl code a p p font size 3 b lit zero shot transfer with locked image text tuning b font br font size 2 xiaohua zhai xiao wang basil mustafa andreas steiner daniel keysers alexander kolesnikov lucas beyer font br font size 2 cvpr 2022 font a href https arxiv org abs 2111 07991 paper a p p font size 3 b declip supervision exists everywhere a data efficient contrastive language image pre training paradigm b font br font size 2 yangguang li feng liang lichen zhao yufeng cui wanli ouyang jing shao fengwei yu junjie yan font br font size 2 iclr 2022 font a href https arxiv org abs 2110 05208 paper a a href https github com sense gvt declip code a p p font size 3 b filip fine grained interactive language image pre training b font br font size 2 lewei yao runhui huang lu hou guansong lu minzhe niu hang xu xiaodan liang zhenguo li xin jiang chunjing xu font br font size 2 iclr 2022 font a href https arxiv org abs 2111 07783 paper a p p font size 3 b slip self supervision meets language image pre training b font br font size 2 norman mu alexander kirillov david wagner saining xie font br font size 2 eccv 2022 font a href https arxiv org abs 2112 12750 paper a a href https github com facebookresearch slip code a p p font size 3 b ms clip learning visual representation from modality shared contrastive language image pre training b font br font size 2 haoxuan you luowei zhou bin xiao noel codella yu cheng ruochen xu shih fu chang lu yuan font br font size 2 eccv 2022 font a href https arxiv org abs 2207 12661 paper a a href https github com hxyou msclip code a p p font size 3 b multimae multi modal multi task masked autoencoders b font br font size 2 roman bachmann david mizrahi andrei atanov amir zamir font br font size 2 eccv 2022 font a href https arxiv org abs 2204 01678 paper a a href https github com epfl vilab multimae code a a href https multimae epfl ch project a p p font size 3 b m3ae multimodal masked autoencoders learn transferable representations b font br font size 2 xinyang geng hao liu lisa lee dale schuurmans sergey levine pieter abbeel font br font size 2 arxiv 2205 14204 2022 font a href https arxiv org abs 2205 14204 paper a a href https github com young geng m3ae public code a p p font size 3 b cyclip cyclic contrastive language image pretraining b font br font size 2 shashank goel hritik bansal sumit bhatia ryan a rossi vishwa vinay aditya grover font br font size 2 neurips 2022 font a href https arxiv org abs 2205 14459 paper a a href https github com goel shashank cyclip code a p p font size 3 b pyramidclip hierarchical feature alignment for vision language model pretraining b font br font size 2 yuting gao jinfeng liu zihan xu jun zhang ke li rongrong ji chunhua shen font br font size 2 neurips 2022 font a href https arxiv org abs 2204 14095 paper a p p font size 3 b k lite learning transferable visual models with external knowledge b font br font size 2 sheng shen chunyuan li xiaowei hu yujia xie jianwei yang pengchuan zhang anna rohrbach zhe gan lijuan wang lu yuan ce liu kurt keutzer trevor darrell jianfeng gao font br font size 2 neurips 2022 font a href https arxiv org abs 2204 09222 paper a p p font size 3 b uniclip unified framework for contrastive language image pre training b font br font size 2 janghyeon lee jongsuk kim hyounguk shon bumsoo kim seung hwan kim honglak lee junmo kim font br font size 2 neurips 2022 font a href https arxiv org abs 2209 13430 paper a p p font size 3 b coca contrastive captioners are image text foundation models b font br font size 2 jiahui yu zirui wang vijay vasudevan legg yeung mojtaba seyedhosseini yonghui wu font br font size 2 arxiv 2205 01917 2022 font a href https arxiv org abs 2205 01917 paper a p p font size 3 b prefix conditioning unifies language and label supervision b font br font size 2 kuniaki saito kihyuk sohn xiang zhang chun liang li chen yu lee kate saenko tomas pfister font br font size 2 arxiv 2206 01125 2022 font a href https arxiv org abs 2206 01125 paper a p p font size 3 b maskclip masked self distillation advances contrastive language image pretraining b font br font size 2 xiaoyi dong yinglin zheng jianmin bao ting zhang dongdong chen hao yang ming zeng weiming zhang lu yuan dong chen fang wen nenghai yu font br font size 2 arxiv 2208 12262 2021 font a href https arxiv org abs 2208 12262 paper a p p font size 3 b basic combined scaling for open vocabulary image classification b font br font size 2 hieu pham zihang dai golnaz ghiasi kenji kawaguchi hanxiao liu adams wei yu jiahui yu yi ting chen minh thang luong yonghui wu mingxing tan quoc v le font br font size 2 arxiv 2111 10050 2022 font a href https arxiv org abs 2111 10050 paper a p p font size 3 b limoe multimodal contrastive learning with limoe the language image mixture of experts b font br font size 2 basil mustafa carlos riquelme joan puigcerver rodolphe jenatton neil houlsby font br font size 2 arxiv 2206 02770 2022 font a href https arxiv org abs 2206 02770 paper a p p font size 3 b mural multimodal multitask retrieval across languages b font br font size 2 aashi jain mandy guo krishna srinivasan ting chen sneha kudugunta chao jia yinfei yang jason baldridge font br font size 2 arxiv 2109 05125 2022 font a href https arxiv org abs 2109 05125 paper a p p font size 3 b pali a jointly scaled multilingual language image model b font br font size 2 xi chen xiao wang soravit changpinyo aj piergiovanni piotr padlewski daniel salz sebastian goodman adam grycner basil mustafa lucas beyer alexander kolesnikov joan puigcerver nan ding keran rong hassan akbari gaurav mishra linting xue ashish thapliyal james bradbury weicheng kuo mojtaba seyedhosseini chao jia burcu karagol ayan carlos riquelme andreas steiner anelia angelova xiaohua zhai neil houlsby radu soricut font br font size 2 arxiv 2209 06794 2022 font a href https arxiv org abs 2209 06794 paper a p p font size 3 b chinese clip contrastive vision language pretraining in chinese b font br font size 2 an yang junshu pan junyang lin rui men yichang zhang jingren zhou chang zhou font br font size 2 arxiv 2211 01335 2022 font a href https arxiv org abs 2211 01335 paper a a href https github com ofa sys chinese clip project a p p font size 3 b flip scaling language image pre training via masking b font br font size 2 yanghao li haoqi fan ronghang hu christoph feichtenhofer kaiming he font br font size 2 arxiv 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2023 paper https arxiv org abs 2306 03899 code https github com runnanchen label free scene understanding segment any point cloud sequences by distilling vision foundation models youquan liu lingdong kong jun cen runnan chen wenwei zhang liang pan kai chen ziwei liu neurips 2023 spotlight paper https arxiv org abs 2306 09347 code https github com youquanl segment any point cloud project https ldkong com seal orange book video classification in the wild p font size 3 b expanding language image pretrained models for general video recognition b font br font size 2 bolin ni houwen peng minghao chen songyang zhang gaofeng meng jianlong fu shiming xiang haibin ling font br font size 2 eccv 2022 font a href https arxiv org abs 2208 02816 paper a a href https github com microsoft videox code a p p font size 3 b multimodal open vocabulary video classification via pre trained vision and language models b font br font size 2 rui qian yeqing li zheng xu ming hsuan yang serge belongie yin cui font br font size 2 arxiv 2207 07646 font a href https arxiv org abs 2207 07646v1 paper a p orange book other visual recognition in the wild p font size 3 b rlip relational language image pre training for human object interaction detection b font br font size 2 hangjie yuan jianwen jiang samuel albanie tao feng ziyuan huang dong ni mingqian tang font br font size 2 neurips 2022 font a href https arxiv org abs 2209 01814 paper a a href https github com jacobyuan7 rlip code a p orange book grounded image generation in the wild new this is a new research topic grounded image generation based on any open set concept include text and visual prompt all the text to image pre trained generation models allow open set prompting at the image level and thus belong to grounded image generation in the wild by default this paper collection focuses on more fine grained controlability in the image generation such as specifying new concept at the the level of bounding box masks edge depth maps etc p font 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lanas phillips irena gao tony lee etienne david ian stavness wei guo berton a earnshaw imran s haque sara beery jure leskovec anshul kundaje emma pierson sergey levine chelsea finn percy liang font br font size 2 pmlr 2022 font a href https arxiv org abs 2012 07421 paper a a href https wilds stanford edu code a p p font size 3 b simple specialized model sample matching for domain generalization b font br font size 2 ziyue li kan ren xinyang jiang yifei shen haipeng zhang dongsheng li font br font size 2 iclr 2023 font a href https openreview net forum id bqrpez e5p paper a a href https seqml github io simple code a p p font size 3 b sparse mixture of experts are domain generalizable learners b font br font size 2 bo li yifei shen jingkang yang yezhen wang jiawei ren tong che jun zhang ziwei liu font br font size 2 iclr 2023 font a href https openreview net forum id q08xeiw1ha1 paper a a href https github com luodian generalizable mixture of experts code a p green book robust models p font size 3 b evaluating model robustness and stability to dataset shift b font br font size 2 adarsh subbaswamy roy adams suchi saria font br font size 2 aistats 2021 font a href https arxiv org abs 2010 15100 paper a a href https github com asubbaswamy stability analysis code a p p font size 3 b plex towards reliability using pretrained large model extensions b font br font size 2 dustin tran jeremiah liu michael w dusenberry du phan mark collier jie ren kehang han zi wang zelda mariet huiyi hu neil band tim g j rudner karan singhal zachary nado joost van amersfoort andreas kirsch rodolphe jenatton nithum thain honglin yuan kelly buchanan kevin murphy d sculley yarin gal zoubin ghahramani jasper snoek balaji lakshminarayanan font br font size 2 first workshop of pre training perspectives pitfalls and paths forward at icml 2022 font a href https arxiv org abs 2207 07411 paper a a href https goo gle plex code code a p beers acknowledgements we thank all the authors above for their great works related reading list awesome detection transformer https github com ideacvr awesome detection transformer awesome prompting papers in computer vision https github com ttengwang awesome prompting papers in computer vision if you find this repository useful please consider giving a star star and cite the related papers beer article li2022elevater title elevater a benchmark and toolkit for evaluating language augmented visual models author li chunyuan and liu haotian and li liunian harold and zhang pengchuan and aneja jyoti and yang jianwei and jin ping and hu houdong and liu zicheng and lee yong jae and gao jianfeng journal neural information processing systems year 2022 article li2023multimodal title multimodal foundation models from specialists to general purpose assistants author li chunyuan and gan zhe and yang zhengyuan and yang jianwei and li linjie and wang lijuan and gao jianfeng journal arxiv 2309 10020 year 2023 article gan2022vision title vision language pre training basics recent advances and future trends author gan zhe and li linjie and li chunyuan and wang lijuan and liu zicheng and gao jianfeng journal foundations and trends textregistered in computer graphics and vision volume 14 number 3 4 pages 163 352 year 2022 publisher now publishers inc | ai |
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awesome-llm | awesomellm curated list of large language models what is llms large language models llms are computer programs designed to understand and generate human language they are based on deep learning techniques which allow them to learn from vast amounts of data and make predictions based on that knowledge the development of llms is one of the most exciting recent advancements in artificial intelligence and natural language processing llms are able to generate text that is often indistinguishable from text written by humans they can be used to write news articles generate dialogue create captions for images and even complete tasks like answering questions and translating languages llms have a wide range of applications from chatbots to virtual assistants and they are becoming increasingly important in fields such as journalism marketing and customer service one of the most important aspects of llms is their ability to learn from large amounts of data they are often trained on massive datasets of text such as books articles and social media posts by analyzing this data they are able to learn the patterns and structures of language and use that knowledge to generate new text large language models bloom bigscience large open science open access multilingual language model model parameters 176b code https huggingface co bigscience bloom paper https arxiv org abs 2211 05100 galactica a large language model for science galactica is a general purpose scientific language model it is trained on a large corpus of scientific text and data it can perform scientific nlp tasks at a high level as well as tasks such as citation prediction mathematical reasoning molecular property prediction and protein annotation more information is available at galactica org model parameters 125m 1 3b 6 7b 30b 120b code https github com paperswithcode galai paper https arxiv org abs 2211 09085 llama open and efficient foundation language models model parameters 7b 13b 33b 65b code https github com facebookresearch llama paper https arxiv org abs 2302 13971 mpt30b mosaic pretrained transformer model parameters 30b code https github com mosaicml llm foundry model https huggingface co mosaicml mpt 30b blog https www mosaicml com blog mpt 30b demo https huggingface co spaces mosaicml mpt 30b chat minimum system requirements nvidia a100 40gb it did not work on nvidia a100 32gb release date 22 june 2023 opt open pre trained transformers model parameters 125m 350m 1 3b 2 7b 13b 30b 66b 175b code https github com facebookresearch metaseq tree main projects opt paper https arxiv org abs 2205 01068 opt iml scaling language model instruction meta learning through the lens of generalization model parameters 30b 175b code https github com facebookresearch metaseq tree main projects opt iml paper https arxiv org abs 2212 12017 pythia a suite of 16 large language models llms with varying model parameters ranging from 70 million to 12 billion parameters developed by eleutherai model parameters 70m 160m 410m 1 0b 1 4b 2 8b 6 9b 12b code https github com eleutherai pythia tree main models paper https arxiv org pdf 2304 01373 hugging face https huggingface co eleutherai pythia 410m deduped fine tuned models alpaca stanford alpaca an instruction following llama model fine tuned on llama application for dialogue system or chatgpt alternative code https github com tatsu lab stanford alpaca git paper https crfm stanford edu 2023 03 13 alpaca html alpaca lora training chatgpt alternative on consumer device gpus code https github com sanjibnarzary awesome llm chatllama chatllama is a library that allows you to create hyper personalized chatgpt like assistants using your own data and the least amount of compute possible instead of depending on one large assistant that rules us all we envision a future where each of us can create our own personalized version of chatgpt like assistants imagine a future where many chatllamas at the edge will support a variety of human s needs but creating a personalized assistant at the edge requires huge optimization efforts on many fronts dataset creation efficient training with rlhf and inference optimization code https github com nebuly ai nebullvm tree main apps accelerate chatllama paper not available demo on own model koala a dialogue model for academic research fine tuned on llama application for dialogue system or chatgpt alternative code https github com young geng easylm paper https bair berkeley edu blog 2023 04 03 koala demo https chat lmsys org model koala 13b mpt 30b instruct mpt 30b instruct is a model for short form instruction following it is built by finetuning mpt 30b on dolly hhrlhf derived from the databricks dolly 15k and the anthropic helpful and harmless hh rlhf datasets it is also trained on competition math duorc cot gsm8k qasper quality summ screen fd and spider code https github com mosaicml llm foundry blog https www mosaicml com blog mpt 30b demo https huggingface co spaces mosaicml mpt 30b chat minimum system requirements nvidia a100 40gb it did not work on nvidia a100 32gb release date 22 june 2023 vicuna an open source chatbot impressing gpt 4 with 90 chatgpt quality fine tuned on llama application for dialogue system or chatgpt alternative code https github com lm sys fastchat paper https vicuna lmsys org demo https chat lmsys org xturing xturing provides fast efficient and simple fine tuning of llms such as llama gpt j gpt 2 opt cerebras gpt galactica and more by providing an easy to use interface for personalizing llms to your own data and application xturing makes it simple to build and control llms the entire process can be done inside your computer or in your private cloud ensuring data privacy and security code https github com stochasticai xturing paper not available demo img src https github com stochasticai xturing raw main github cli playground gif width 100 style margin 0 1 dolly 2 0 an open source commercially usable chatgpt style ai model developed by databricks trained on a high quality human generated instruction following dataset crowdsourced among databricks employees fine tuned on pythia application for dialogue system or chatgpt alternative code https github com databrickslabs dolly tree master data paper https www databricks com blog 2023 04 12 dolly first open commercially viable instruction tuned llm demo weights https huggingface co databricks dolly v2 7b contributing as an open source project in a rapidly evolving field we welcome contributions of all kinds including new features and better documentation external link most recent updates may be available at llm https bharatai net c llms or llm papers https bharatai net c llm papers | alpaca bloom gpt llama llm llms nlp opt transformer transformers koala vicuna chatgpt chatllama gpt-j xturing chatgpt-alternative chatgpt-alternatives peft rlhf | ai |
jarvis | div align center pr s welcome a href http makeapullrequest com img src https img shields io badge prs welcome brightgreen svg style flat square alt pr s welcome a img src https img shields io badge shell zsh yellow svg img src https img shields io badge editor neovim brightgreen svg div h1 align center jarvis h1 div align center strong neo vim of the future strong div div align center a powerful minimalist development environment with cutting edge features div br div align center img src https i redditmedia com 5hm6zno1nvagshnp78brfugtxcnddnfta 7cc6ainfi png s 10cc17271fc5823361cb4e238183fc1b div div align center img src https i imgur com 2rqf24f png alt jarvis ss div br table of contents features features installation installation commands commands support support features the following are features provided by jarvis they all have quick keybindings to make them quick and easy to use 1 quick open files zsh neovim open files with simple keystrokes with fuzzy matching via command line and inside neovim img src https i imgur com qgtsorl gif height 400px 2 buffer management neovim manage buffers inside neovim and add delete search your open files img src https i imgur com iulxm8x gif height 400px 3 project searching neovim quickly search for simple terms or complex regular expressions in your project img src https i imgur com 1rppm78 gif height 400px 4 asynchronous linting neovim for typescript javascript development code is linted asynchronously with coc eslint https github com neoclide coc eslint and automatically formatted via coc prettier https github com neoclide coc prettier on file save to conform to prettier https prettier io standards img src https i imgur com tnh6e0z gif height 400px 5 session management tmux and zsh fzf create sessions for each project with a custom layout quickly browse create and delete sessions tmux even keeps sessions alive if the terminal is closed using fzf and zsh you can create or switch to sessions easily as well as delete session by name or fuzzy search img src https i imgur com r9rxyel gif height 400px 6 keyword auto complete neovim and zsh neovim automatic asynchronous keyword completion available in the current buffer via coc nvim https github com neoclide coc nvim it s powered by the same language server extensions as vscode it also supports the new floating window feature so you can finally have syntax highlighting in your completion windows img src https i imgur com asxmuha gif width 100 a variety of languages are supported by coc nvim i currently use a pretty standard set for web development that i will continue to tweak as needed typescript javascript https github com neoclide coc tsserver cocinstall coc tsserver eslint https github com neoclide coc eslint cocinstall coc eslint prettier https github com neoclide coc prettier cocinstall coc prettier css https github com neoclide coc css cocinstall coc css json https github com neoclide coc json cocinstall coc json 7 code snippets neovim commonly used code snippets made available with a few keystrokes to reduce time and effort via neosnippet https github com shougo neosnippet vim snippets available via auto complete window removes need to memorize commands quickly hop to relevant pieces of snippet as needed img src https i imgur com bz7a7cm gif height 400px 8 improved vim motion neovim using vim easymotion https github com easymotion vim easymotion quickly jump to precise locations with minimal keystrokes img src https i imgur com strboa4 gif height 400px installation neovim is supported across multiple platforms some tools used by jarvis are not however for macosx an installation script is included that will install several tools for you for windows no installation script is available but you can manually install everything needed for neovim in a few short steps see the installation guide docs install md for detailed instructions commands see the commands guide docs commands md for a list of mappings shortcuts optional tools this is a collection of cool tools that you might want to use z https github com rupa z tracks most commonly used directories for optimized directory switching vtop https github com mrrio vtop a nifty graphical activity monitor for the command line taskbook https github com klauscfhq taskbook tasks boards notes for command line think trello for the terminal pecan https github com zzzeyez pecan configurable menu bar for osx vim markdown composer https github com euclio vim markdown composer asynchronous markdown preview plugin for vim neovim shpotify https github com hnarayanan shpotify control spotify from the command line osx only tool is installed automatically if install sh script is used support if you find any problems or bugs please open a new issue https github com ctaylo21 jarvis issues | neovim iterm2 tmux homebrew macos zsh javascript react vim ripgrep fzf ide dotfiles osx osx-dotfiles typescript denite | front_end |
shade-tutorial | shade tutorial following the guide on https developer openstack org firstapp shade getting started html | cloud |
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Invisibal-Guardian-Smart-Photo-Frame | invisibal guardian smart photo frame font color dd0000 the project is cooperated with github user mr bulijiojio and kinddle tick font entries for intel cup national undergraduate electronic design contest embedded system designinvitational contest 2020 from jul 2020 to oct 2020 in uestc we implemented a millimeter wave radar system based on deep learning and edge computing which realized functions such as target detection target tracking and fall detection i m responsible for the design and implementation of neural networks on embedded processors and coordinate with radar data pic1 png pic1 png | os |
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Inventory-Manager | inventory manager an inventory management app for users to perform crud operations for a pseudo business development uses express for backend pug jade for template amp mongodb for database | server |
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org.openhab.ui.habot | habot habot is a chatbot for openhab https openhab org the habot code has moved to https github com openhab openhab webui this repo here is just kept for its history and is thus archived | ai |
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canopy | push to master https github com legal and general canopy actions workflows master push yml badge svg https github com legal and general canopy actions workflows master push yml codeql https github com legal and general canopy actions workflows codeql analysis yml badge svg branch master https github com legal and general canopy actions workflows codeql analysis yml canopy graphic assets canopy hero png canopy an angular implementation of the legal general design system interested in contributing docs contributing md want to learn how to use it in a project docs usage md have an issue you would like to raise issues want to request a modification or a new feature discussions | design-patterns component-library | os |
SQL | sql i was hired as a new data engineer at pewlett hackard my first major task was a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files i designed the tables to hold data in the csvs imported the csvs into a sql database and answered questions about the data in other words i performed 1 data engineering 3 data analysis data modeling inspected the csvs and sketched out an erd of the tables used http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering used the information i had to create a table schema for each of the six csv files specified data types primary keys foreign keys and other constraints imported each csv file into the corresponding sql table note i imported the data in the same order that the tables were created and accounted for the headers when importing to avoid errors data analysis once i got a complete database i did the following 1 listed the following details of each employee employee number last name first name sex and salary 2 listed first name last name and hire date for employees who were hired in 1986 3 listed the manager of each department with the following information department number department name the manager s employee number last name first name 4 listed the department of each employee with the following information employee number last name first name and department name 5 listed first name last name and sex for employees whose first name is hercules and last names begin with b 6 listed all employees in the sales department including their employee number last name first name and department name 7 listed all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order listed the frequency count of employee last names i e how many employees share each last name part ii as i examined the data you i overcame with a creeping suspicion that the dataset is fake i surmised that my boss handed me spurious data in order to test the data engineering skills of a new employee to confirm my hunch i decide to take the following steps to generate a visualization of the data with which i would confront my boss 1 imported the sql database into pandas connected to postgresql like this sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 db name connection engine connect 2 created a histogram to visualize the most common salary ranges for employees 3 created a bar chart of average salary by title references mockaroo llc 2021 realistic data generator https www mockaroo com https www mockaroo com | sql data-analysis postgresql erd data-modeling data-engineering | server |
iot-truck-streaming | analyzing iot data with storm kafka and spark this is a reference app for internet of things iot use cases built around the following tools from the hortonworks data platform storm kafka spark hbase and hive here are the steps to install this app on the hdp sandbox videos on the demo available here arun s hadoop summit 2015 keynote https youtu be fhmmcmyhmni t 1h25m13s shaun george s hadoop summit 2014 keynote http library fora tv program landing frameview id 20333 type clip nauman s demo at phoenix data conference 2014 http www youtube com watch v erdmsiq4gx0 phoenix data conference 2014 http img youtube com vi erdmsiq4gx0 0 jpg http www youtube com watch v erdmsiq4gx0 deployment options deploy on ambari installed hdp cluster local or cloud deploy on sandbox vm download the hdp 2 3 sandbox from here http hortonworks com products hortonworks sandbox install start the sandbox and add its ip address into your local machine s etc hosts file bash sudo echo 172 16 139 139 sandbox hortonworks com etc hosts setup option 1 script setup clone the git and run setup scripts using instructions in setup md https github com hortonworks gallery iot truck streaming blob master setup md bash ssh root sandbox hortonworks com cd git clone https github com hortonworks gallery iot truck streaming option 2 ambari setup deploy using ambari service using instructions from https github com abajwa hw iotdemo service | server |
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2020-spring | dynamic web development nyu interactive telecommunications program spring 2020 course description dynamic web development introduces the fundamentals of building full stack web applications this course will focus on modern client and server side web technologies and provide practical methods for approaching web development for creative and functional applications the core technologies used in this course are html5 javascript node js with the express framework and mongodb database students will learn to design develop and deploy web applications and gain the necessary skills to extend and explore web development independently the course will be a mixture of lecture and in class collaborative coding with weekly programming and reading homework link to the course listing br category 2 point all elective br tags br https itp nyu edu registration courseinfo php course id 778 course details details section 1 section 2 instructor joey lee https jk lee com cassie tarakajian https cassietarakajian com instructor email joeyklee nyu edu cassie tarakajian nyu edu instructor github joeyklee https github com joeyklee catarak https github com catarak class day monday monday class time 6 30 9 00 6 30 9 00 class room room 411 370 jay street brooklyn room 450 370 jay street brooklyn class dates jan 27 mar 23 jan 27 mar 23 office hours office hours are by appointment please see the office hour offerings by the instructors below you are also welcome to meet with itp residents detail joey cassie office hours 4 5pm tue thur by appointment https calendar google com calendar selfsched sstoken uu9qs0r3c3cybxb4fgrlzmf1bhr8owy1ywexodk4ytc3ytnkmjm5mdg4ztixmme3oty5mjm 11am 1pm friday by appointment https calendar google com calendar selfsched sstoken uu1swvf3by0wr2lqfgrlzmf1bhr8yjhky2iyodcxndk3odrhnzi1otkyn2jimdy3odfmnwi location itp floor or itp resident s office itp floor or itp resident s office note if you can t make those times please send either of us an email and we can chat at another time or via video conference code of conduct read the code of conduct here codeofconduct md objectives by the end of the class you should have a solid understanding of the following concepts full stack javascript familiarity with both client side and server side javascript ui ux design creating wireframes and style guides and translating them to code designing data driven applications how to design and build a full stack data application using apis how to interact with and use an api from within your client application structuring data how to intelligently structure data storing data how to build systems to store your own data building apis how to build your own apis and make the data available to client applications deploying full stack applications deploying applications using platforms as a service paas with git github glitch and heroku note these goals and objectives were adapted from sam slover s api of you class https github com sslover api of you and the previous itp s 2018 dwd course https itp nyu edu sve204 dwd spring2018 outcomes in order to achieve these objectives students will spend the semester building refactoring getting feedback and iterating on one project throughout the course the interactive web application will require students to exercise each of the key skills taught throughout this course as well as equip students with the skills to continue developing new dynamic web applications after the course schedule meetings go to date theme notes week 01 weeks 01 foundations md jan 27 web foundations n a week 02 weeks 02 front end foundations md feb 03 front end foundations n a week 03 weeks 03 front end applications md feb 10 front end applications n a week 04 weeks 04 back end foundations md feb 24 back end foundations n a week 05 weeks 05 databases md mar 02 data persistence databases n a week 06 weeks 06 synthesis md mar 09 synthesis n a week 07 weeks 07 final md mar 23 final class n a assignments assignment due date title notes a1 assignments 01 assignment md feb 03 internet art materiality of the web n a a2 assignments 02 assignment md feb 10 making breaking the grid swiss poster website n a a3 assignments 03 assignment md feb 24 dynamic front end applications advanced client side javascript and networking n a a4 assignments 04 assignment md mar 02 api love you oh crud n a a5 assignments 05 assignment md mar 09 the api of you living forever on the web n a final project presentation assignments 06 final project md mar 23 final project n a all assignments should be submitted to your respective section section 1 joey s section assignment submission form https forms gle gklsrm581kfyhg6w6 section 2 cassie s section assignment submission form https forms gle pzxhjztq1ip5wayv9 grading students will be evaluated on effort personal progress and growth class participation assignments and the final project it is understood that coding and making things is tough therefore your effort curiosity and engagement is of utmost importance you will be graded on your progress throughout the class your ability to complete assignments on time your interaction with peers and your ability to justify your decisions thoughtfully grade calculation here is a basic breakdown of graded tasks along that trajectory 20 attendance participation 50 assignments 10 project proposal 20 final project completed on conclusion of the course total 100 please see itp s statement on pass fail http help itp nyu edu academic policies pass fail which states that a pass is equivalent to an a or a b while anything less would be considered a fail here s an outline of how your assignments and final will generally be evaluated the explaners of each category are rough guidelines for how i assign numbers or quantify to the work you are producing in this class inspired by cmda tt course https github com cmda tt course 17 18 tree master assessment 3 category 1 2 pts 3 4 pts 5 6 pts 7 8 pts 9 10 pts quality the work is handed in late broken incomplete undocumented or shows lack of care or thought the work is only partially documented and seems only partially complete the work is documented and represents the student s concept and shows good effort the work is well documented thoughtful and professional the work shows mastery and is well polished understanding there is either no substance or the student cannot explain or justify decision making the work shows partial grasp of the concepts but shows major gaps that could be addressed with more thought the work is sensible and grounded and can be explained in a coherent manner the work represents the concept well references past and current work the student can speak to more than 1 perspective the work shows strong graps of concepts and the state of the art the work is well received in both concept and implementation application the work reflects very little conceptual references to the course materials a general lack of awareness to methodology and implementation the work applies methodology ies that have some potential but not quite relevant or effective the work uses methods that are appropriate and thoughtful the application of methods are standard and show good potential the work uses methods that are appropriate thoughtful and well implemented there are innovative ideas that are shown in the application there work shows careful methodological considerations and is beautifully crafted designed and presented attendance and participation policy attendance is mandatory please inform your teacher via email if you are going to miss a class two unexcused absences is cause for failing the class an unexcused lateness of 10 minutes or more is equivalent to 1 2 an absence this class will be participatory you are expected to participate in discussions and give feedback to other students both in class and participate with their projects this along with attendance is 20 of your grade late assignments late assignments will not be accepted except under special circumstances or with a doctors note if you have not finished your assignment you still must submit what you have done to the appropriate assignment submission google form you can improve on your submitted assignent after submission but your work will not be reviewed if it is passed the deadline nyu statements and principles statement of academic integrity plagiarism is presenting someone else s work as though it were your own more specifically plagiarism is to present as your own a sequence of words quoted without quotation marks from another writer or a paraphrased passage from another writer s work or facts ideas or images composed by someone else statement of principle the core of the educational experience at the tisch school of the arts is the creation of original academic and artistic work by students for the critical review of faculty members it is therefore of the utmost importance that students at all times provide their instructors with an accurate sense of their current abilities and knowledge in order to receive appropriate constructive criticism and advice any attempt to evade that essential transparent transaction between instructor and student through plagiarism or cheating is educationally self defeating and a grave violation of tisch school of the arts community standards for all the details on plagiarism please refer to page 10 of the tisch school of the arts policies and procedures handbook which can be found online at http students tisch nyu edu page home html statement on accessibility please feel free to make suggestions to your instructor about ways in which this class could become more accessible to you academic accommodations are available for students with documented disabilities please contact the moses center for students with disabilities at 212 998 4980 for further information statement on counseling and wellness your health and safety are a priority at nyu if you experience any health or mental health issues during this course we encourage you to utilize the support services of the 24 7 nyu wellness exchange 212 443 9999 also all students who may require an academic accommodation due to a qualified disability physical or mental please register with the moses center 212 998 4980 please let your instructor know if you need help connecting to these resources statement on use of electronic devices laptops will be an essential part of the course and may be used in class during workshops and for taking notes in lecture laptops must be closed during class discussions and student presentations phone use in class is strictly prohibited unless directly related to a presentation of your own work or if you are asked to do so as part of the curriculum statement on title ix tisch school of the arts to dedicated to providing its students with a learning environment that is rigorous respectful supportive and nurturing so that they can engage in the free exchange of ideas and commit themselves fully to the study of their discipline to that end tisch is committed to enforcing university policies prohibiting all forms of sexual misconduct as well as discrimination on the basis of sex and gender detailed information regarding these policies and the resources that are available to students through the title ix office can be found by using the following link title ix at nyu https www nyu edu about policies guidelines compliance equal opportunity title9 html | front_end |
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half-pipe | https raw github com d i halfpipe io master media logo png stories in ready https badge waffle io d i half pipe png label ready http waffle io d i half pipe gem to replace the rails asset pipeline with a grunt http gruntjs com based workflow providing dependencies via bower http bower io half pipe is a generator to get you up and running quickly with a grunt setup for building client side code in rails apps we believe that your asset workflow is yours and you should be able to configure it however you need to who is this for this initial release assumes you have been using grunt in non rails apps and would like to start using it in rails as well it uses bower for dependency management requirejs http www requirejs org for javascript modules and sass for css if you use alternatives to these tools we d love to hear from you issue beta version note this readme refers to the beta version of half pipe i highly recommend using the beta and following this readme but if you are on the 0 2 version please see the previous readme https github com d i half pipe blob 4a68659f215f939f7da9d3e5e8756c7f31a86177 readme md we want feedback half pipe is still in the early stages of development the workflow has been extracted from our projects at d i http d i co with inspiration from ember app kit https github com stefanpenner ember app kit we are trying to build an extremely flexible and useful tool for front end developers who work in rails apps while still adhering to good coding principles if you want to use half pipe but feel hesitant for any reason please feel free to open up an issue telling us why issue as we progress towards a 1 0 release we want to hear from you to make this tool the best it can be getting started half pipe was presented in the talk asset happiness which you can watch on youtube http www youtube com watch v 2gazsfkz2bq installing the half pipe gem is mostly a vehicle to bring a nice grunt workflow into your rails app to set it up add the following to your gemfile gem half pipe github d i half pipe after you install the gem you can run rails g half pipe install to setup the grunt workflow this will configure your app for node js http nodejs org copy over the grunt setup install node modules and run grunt build public from here you can move your stylesheets from app assets stylesheets to app styles make sure you replace sprockets directives https github com sstephenson sprockets managing and bundling dependencies with sass http sass lang com documentation file sass reference html import or less http lesscss org importing imports you can also move javascript files into app scripts but take care to make sure you wrap them in requirejs modules see http mikemurry com getting started with require js for a quick overview of requirejs or else you won t be able to build less note currently less http lesscss org is only available in master it will be in a future release of the gem make sure you update your gemfile accordingly if you need it to setup your workflow to use less instead of sass you can do so with the processor option rails g half pipe install processor less working on assets front end developer workflow run grunt server and half pipe will start up your rails app with a preview server for your assets browse to http localhost 3000 and use your app like normal grunt will watch assets and recompile automatically when you make changes it will also restart your rails app when you change files in config or lib and any time you install new gems with bundler http bundler io back end developer workflow if you don t need to work on assets nothing really changes for you if you ve never used grunt before install it with npm install g grunt cli then run npm install to install dependencies and then grunt build public to get the assets into your public folder once you ve done that you will only need to use grunt when you need to get the latest changes to assets this step will go away in the future see 31 issue 31 usage there are built in helpers for referencing most assets image tag avatar png will reference assets images avatar png javascript include tag main will reference assets scripts main js stylesheet link tag main will reference assets styles main css half pipe also provides helpers for sass image url avatar png compiles to url assets images avatar png image path avatar png compiles to assets images avatar png usage of the app folder only put assets that need to be processed by grunt in the app folder for example if you want to use grunt to sprite your images then you can create an app icons folder and output the sprite to public assets images however you should keep the rest of your images in your repository at public assets images that way files that don t need to be processed will never get passed through grunt which makes it clear to everyone what is getting compiled and what isn t including assets from bower javascript sass sass css bower javascript include bower dependencies by configuring requirejs to find them this is a manual process at the moment see 40 issue 40 for more info when you install a new bower component open up config build js and add it to the paths config since grunt builds from a tmp directory you will need to prefix the paths with bower components see 55 issue 55 stylesheets half pipe configures sass automatically to include your configured bower components directory for example to import bourbon into your app add bourbon to your bower json as a dependency and then include it by adding sass import bourbon app assets stylesheets bourbon to app styles main scss if you have a bower component that includes standard css files instead of sass templates you can include those the same way but prefixing the path with css for example to include normalize css add normalize css https github com necolas normalize css as a dependency to your bower json and then include it with sass import css normalize css normalize directory structure we believe that the directory structured imposed by the rails asset pipeline was a step in the right direction but did not go far enough in making client code a first class part of your application given that we have put assets at the same level as the rest of your ruby code app scripts javascript files currently all requirejs modules app styles sass templates history for a detailed history see our releases page releases roadmap we re currently undergoing some pretty major changes in the half pipe workflow see our milestones for what s coming in the near future other considerations precompilation of client side templates javascript module generator configurable asset directories better support for images support for additional module loaders including es6 modules support for other frameworks and environments support for most popular altjs http www altjs org languages splitting out app skeleton from node grunt setup milestones http github com d i half pipe issues milestones issue http github com d i half pipe issues new releases http github com d i half pipe releases issue 31 http github com d i half pipe issues 31 issue 40 http github com d i half pipe issues 40 issue 55 http github com d i half pipe issues 55 | front_end |
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IOT_Articles_Collection | https github com h4lo awesome iot security article iot articles collection some iot integration of technical articles iot iot iot see collection md github https zybuluo com h4l0 note 1524758 iot iot zsxq http static zybuluo com h4l0 ifgjt5rver78vzxd7nqklltu image png iot | iot technical-articles | server |
PhonebookLite | phonebooklite a simple project for teaching android mobile development phonebooklite https user images githubusercontent com 10815235 43682589 d20b62ca 9868 11e8 9900 2fd9be7092a0 gif topics covered in this project activities sqllite crud asynchronous programming input validation implicit explicit intents fragment listview design support libraries custom adapters custom dialogs event handlers adding 3rd party libraries upcoming topics for phonebooklite room networking volley content providers authentication firebase authenticaion cloud services firebase storage json | android tutorial-exercises tutorial-sourcecode tutorial-demos androidtutorial | front_end |
udagram-microservices | udagram image filtering application udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into two parts 1 frontend angular web application built with ionic framework 2 backend restful api node express application getting started tip it s recommended that you start with getting the backend api running since the frontend web application depends on the api prerequisite 1 the depends on the node package manager npm you will need to download and install node from https nodejs com en download https nodejs org en download this will allow you to be able to run npm commands 2 environment variables will need to be set these environment variables include database connection details that should not be hard coded into the application code environment script a file named set env sh has been prepared as an optional tool to help you configure these variables on your local development environment we do not want your credentials to be stored in git after pulling this starter project run the following command to tell git to stop tracking the script in git but keep it stored locally this way you can use the script for your convenience and reduce risk of exposing your credentials git rm cached set env sh afterwards we can prevent the file from being included in your solution by adding the file to our gitignore file database create a postgresql database either locally or on aws rds set the config values for environment variables prefixed with postgres in set env sh s3 create an aws s3 bucket set the config values for environment variables prefixed with aws in set env sh backend api to download all the package dependencies run the command from the directory udagram api bash npm install to run the application locally run bash npm run dev you can visit http localhost 8080 api v0 feed in your web browser to verify that the application is running you should see a json payload feel free to play around with postman to test the api s frontend app to download all the package dependencies run the command from the directory udagram frontend bash npm install install ionic framework s command line tools for us to build and run the application bash npm install g ionic prepare your application by compiling them into static files bash ionic build run the application locally using files created from the ionic build command bash ionic serve you can visit http localhost 8100 in your web browser to verify that the application is running you should see a web interface tips 1 take a look at udagram api does it look like we can divide it into two modules to be deployed as separate microservices 2 the dockerignore file is included for your convenience to not copy node modules copying this over into a docker container might cause issues if your local environment is a different operating system than the docker image ex windows or macos vs linux 3 it s useful to lint your code so that changes in the codebase adhere to a coding standard this helps alleviate issues when developers use different styles of coding eslint has been set up for typescript in the codebase for you to lint your code run the following bash npx eslint ext js ts src to have your code fixed automatically run bash npx eslint ext js ts src fix 4 over time our code will become outdated and inevitably run into security vulnerabilities to address them you can run bash npm audit fix 5 in set env sh environment variables are set with export var value setting it this way is not permanent every time you open a new terminal you will have to run set env sh to reconfigure your environment variables to verify if your environment variable is set you can check the variable with a command like echo postgres username | microservices udacity-nanodegree cloud-development | cloud |
PathFinder_CLI | pathfinder cli develop a command line interface development version for a dungeon rogue lite app i plan on fleshing out into a web application later on this version delves into the technical aspects of the application backend while utilizing a simple cli to operate frontend quick start to run the application start by opening up the directory path to adventure cli and then type dotnet run into your cli your cli should prompt you on what to do next design outline user stories this section goes over all the functionalities that critical for the user s experience this can include how the user interacts with the cli quality of life qol features and even basic necessities e g dumping out the available commands checking stats using items etc users are prompted with at most 5 different choices that are prepended with numbers these choices depend on the type of room that the user is in for instance if the user is prompted with two different paths then the user will be prompted with 2 different choices on their screen e g 1 go left 2 go right below are some static pre made choices the user could make combat choices 1 attack 2 dodge defend 3 spells 4 inventory 5 flee adventuring choices these choices will be completely dependent on the dungeon room event the user is in shop choices sell inventory item 1 inventory item 2 inventory item n where n is the max space in a user s inventory buy shop item 1 shop item 2 shop item n where n is max number of items in the shop level up choices strength dexterity intelligence users also have access to general commands such as x save x exit x inventory x stats x use item models decisionnode inode our implementation for a node is going to follow the structure used to build an n ary tree in other words each node in our tree will have n amount of children where the relationship is strictly parent to child each node will contain information about the setting and contain a set of enums of the possible following rooms each node will act strictly as the decision maker controller for our player we can think of this design as each node being its own minicontroller on how the player traverses through the game decisionnode structure keyword type label get set desc private hashset roomevent roomset no no list of enums that have been used in constructing our list of rooms for this decision node private linkedlist inode rooms no no list of constructed rooms that a user will pass through if they decide this path public roomevent possibleevents yes yes list of all possible events that could occur in the rooms of this path public bool canskip yes yes describes whether the user can ignore this path and move on to the next decision node public decisionnode next yes yes the next node in our graph that will traverse our user farther down the path moreso than if they chose the dungeons path public inode current yes yes the current node the user is in by default is pointing at head of rooms decisionnode methods keyword return name parameters desc private void constructrooms void called when the node is initialized randomly generates a set of rooms given a list of roomevent enums private void tonextroom void called when the user enters the next room points current to current next public decisionnode skiptonext void returns the next decision node this node is pointing at entities entity an entity acts as a blanket class for any items objects or characters that a player can interact with each entity should have an id number a name and should describe whether the entity is interactable or not if the user can use any verbal commands on an object some entities that are derived from this class are characterentity cs playerentity cs equipable cs consumable cs entity structure keyword type label get set desc public int id yes no the id of this entity public string name yes yes the name of the entity public bool interactable yes yes describes whether a user can perform actions on the entity characterentity entity a characterentity is any entity that a user can interact with verbally or combatively and additionally is used to create the user as well this includes npcs enemies and the player itself every entity should include an id name interactable bool level stats and inventory these data for each entity can be found on the characterdata table on our database some characterentities that are derived from this class are playerentity cs enemyentity cs nonplayerentity cs characterentity structure keyword type label get set desc public int id yes no the id of this entity public string name yes yes the name of the entity public bool interactable yes yes describes whether a user can perform actions on the entity public int xp yes yes the total current amount of xp this entity has public int level yes yes the level calculated by dividing xp by 100 public stats stats yes yes a collection of stats that this entity has skills armorclass spellslots etc public inventory inventory yes yes the inventory of the entity contains an assortment of items consumable armor weapon etc characterentity methods keyword return name parameters desc public void printnameandlevel void prints the name and level of this characterentity private void printstats void prints every stat and stats variable of this entity public void printinventory void iterates and prints the characterentity s inventory attributes stats stats is a list of a characterentity s basic attributes such as hp xp level skills armorclass speed and spellslots in the most basic sense stats acts as a datasack for an entity s basic attributes stats structure keyword type label get set desc public int maxhp yes yes the maximum hp this entity can have public int hp yes yes the current hp of this entity public int xp yes yes the total current amount of xp this entity has public int level yes yes the level calculated by dividing xp by 100 public dictionary string stat skills yes yes a dictionary of an entity s basic skills str dex int public int armorclass yes yes the armorclass rating is based purely on strength ac of armor e g plate has 5 ac leather has 3 ac public int speed yes yes the speed is based purely on dexterity dex modifier of 1 gives 1 speed public int spellslots yes yes the number of spell slots is based purely on intelligence int modifier of 2 gives 2 spell slots stats methods keyword return name parameters desc public void increaseskill string skillname int points increase a skill str dex int by a certain amount of points private void updatebasestats void update other stats variables armorclass speed and spellslots based on the modifiers of an entity s skills | server |
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jello | jello d el u java velocity fis2 fis3 https github com fex team fis3 jello jello jello fis properties fisproperties velocity js css vm velocity vm velocity json json velocity http velocity apache org directive html head body script style widget directive css js js css velocity smarty layout vm html css js widget js amd j2ee j2ee jello server start nodejs npm http nodejs org java http java com zh cn jello lights bash npm install lights g npm install jello g jello v lights jello demo http lightjs duapp com git clone jello demo https github com 2betop jello demo bash lights install jello demo bash npm install g fis parser marked npm install g fis parser utc npm install g fis parser sass bash cd jello demo jello release jello server start localhost 8080 demo http 106 186 23 103 8080 jello bash jello help usage jello command commands release build and deploy your project install install components and demos server launch a embeded tomcat server options h help output usage information v version output the version number no color disable colored output fis plus http fis baidu com smarty block 1 layout vm velocity doctype html html example static js mod js head meta charset utf 8 meta content name description meta http equiv x ua compatible content ie edge title demo title require example static css bootstrap css require example static css bootstrap theme css require example static js jquery 1 10 2 js require example static js bootstrap js end end head body div id wrapper block body content this is body end div end end body require example page layout vm end end html 2 index vm velocity extends layout vm block body content h1 hello demo h1 widget example widget header header vm script var widgeta require example widget widgeta widgeta js require async example widget widgetb widgetb js function api api sayhelloworld end end script end end block require example page index vm end page test json jsp 1 test page index json json title this will be override in index jsp subtitle this is subtitle 2 test page index jsp jsp page import org apache velocity context context context context context request getattribute context context put title welcome to jello 3 page index vm velocity h1 title h1 h2 subtitle h2 4 html h1 welcome to jello h1 h2 this is subtitle h2 vm namespace page server conf testpage example page testpage rewrite testpage example page testpage page vm test json jsp rewrite ajaxhander test page ajaxhandler jsp rewrite somejsonfile test page data json server conf rewrite redirect velocity directive html html html framework attrs body end velocity html fis site static js mod js lang zh body content end html html lang zh body content html head head css head attrs body end velocity head meta charset utf 8 end body body js body attrs body end velocity html home static lib mod js head meta charset utf 8 end body end end script script js script attrs body end velocity html home static lib mod js head meta charset utf 8 script js script require async home static ui b b js console log here end end body end end style style css style attrs body end velocity html home static lib mod js head meta charset utf 8 style import url xxx css body color fff end end body end end require require uri velocity html home static lib mod js head meta charset utf 8 script js script require async home static ui b b js console log here end end body require home static index index css end end widget widget uri velocity html home static lib mod js head meta charset utf 8 script js script require async home static ui b b js console log here end end body require home static index index css widget home widget a a tpl end end uri project uri uri velocity html home static lib mod js head meta charset utf 8 end body uri home static css bootstrap css end end fis http fis baidu com spring spring spring demo https github com fex team jello spring example map json templates vm static map json web inf config xxx map json velocity fis diretives xml bean id velocityconfigurer class org springframework web servlet view velocity velocityconfigurer property name resourceloaderpath value velocity property name velocityproperties props prop key input encoding utf 8 prop prop key output encoding utf 8 prop fis diretives prop key userdirective com baidu fis velocity directive html com baidu fis velocity directive head com baidu fis velocity directive body com baidu fis velocity directive require com baidu fis velocity directive script com baidu fis velocity directive style com baidu fis velocity directive uri com baidu fis velocity directive widget com baidu fis velocity directive block com baidu fis velocity directive extends prop props property bean fis directive map json bean xml fis bean id fisinit class com baidu fis velocity spring fisbean map json web inf config fis properties web inf ini web app mapdir velocity config fis https github com fex team fis velocity tools releases github https github com fex team fis velocity tools fis properties fis fis properties release web inf fis properties ini velelocity webapp views path web inf views map json mapdir web inf config encoding utf 8 resoucemap mod js resoucemap js sourcemap true fis https github com fex team fis fis plus https github com fex team fis plus gois https github com xiangshouding gois spmx https github com fouber spmx phiz https github com fouber phiz | front_end |
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