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chat-app-express | chat app with sockets this project is a chat web app developed with javascript express and nodejs my purpose with this project is to practise sockets and introduce myself to backend development with nodejs and express node dependencies this project is build on nodejs so we will need to have installed nodejs and npm to run the web app first it is necesary to install the babel dependencies which utility is to convert modern javascript into a more compatible version of javascript npm i babel node babel core babel preset env babel cli d then we will created a babelrc file with the next content presets babel preset env to make the web app rub automatically when any file is modified we need to install nodemon dependency npm i nodemon d second in the package json file we make an script to run the app scripts start nodemon src index js exec babel node finally we need to install express and socket io dependencies used to express develop the backend of our web app socket io allows build apps with persistent conexion between client and server npm i express socket io | server |
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CVLaboratories2021-2022 | computer vision laboratories welcome to the computer vision 2022 laboratories github repository here you will find all the information and the material needed for the laboratories lectures that will come the repository will be updated periodically with the material for the current lab please arrive at the lectures with the repository up to date on your machine so that lectures can flow smoothly requirements the laboratory exercises will all be written in python v3 6 or higher with visualstudiocode as ide and the following packages will be required opencv with contrib numpy should be already installed as opencv dependency open3d for the pointcloud mesh section in order to install a python package you need to use the pip package manager pip3 if you are using the python3 command with this command pip install package name the names of the required packages are opencv python opencv contrib python numpy open3d if you feel confident you can install them on your own machine material the required material for the laboratories is contained in the material folder on the root of this repository inside you will find video mp4 test video google jpg test image 00001640 ply a pointcloud of the panoptic dataset bunny folder containing the bunny mesh of the standford 3d scanning repository virtual machine in addition if you don t feel confident setting up the environment on your own a virtual machine with all the requirements installed tested and with this repository already cloned has been created it can be downloaded at the following link virtual machine with ubuntu 18 04 lts bionic beaver https drive google com file d 1hm7yygqfyz8ubqnl3exvwud9n7linqdk view usp sharing vm credentials user mmlab pwd mmlab extra inside the virtual machine there will also be a compiled and working version of opencv c if any of you is curious and want to try it out note1 for those of you who are not familiar with github be careful when performing the pull action from the repository before doing it move the code you wrote to another folder or you will lose its content the pull action will overwrite everything there is inside it note2 if you experience any kind of trouble running the virtualmachine do the following steps 1 https lmgtfy app q how to run a virtual machine 2 google it again maybe with a smarter choice of words 3 maybe you should check this link https support google com websearch answer 134479 hl en out or maybe this https www pcmag com how to 23 google search tips youll want to learn one 4 ask a colleague for help teamwork always wins 5 beg a colleague for help he she does not bite at most you will be asked money 6 google it one last time it never hurts 7 ask the teaching assistant for help show that you tried and help will be provided note3 if you feel confident with coding git computer science in general you can avoid using the vm and setup the environment on your local machine following the above explained steps please note that very little help will be provided by the teaching assistant in this case | ai |
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ews | embedded web server introduction ews is a web server construction kit designed for embedded applications using the gnat ada compiler the project is hosted on github https github com simonjwright ews building with alire see the alire documentation https alire ada dev docs introduction after you ve installed alire start a small test program by saying alr init bin ews test cd ews test alr with ews this generates a project gpr file at the top level and a source file src ews test adb create a directory html and in it index html containing for example html body font color red hello world font body html the contents of html need to be converted to ada source code in src which will form the static content of the pages served by ews test this can be done as part of the alire build process to do this edit alire toml to add the lines actions type pre build command ews generator input dir html output dir src edit the contents of src ews test adb to contain with ews htdocs with ews server procedure ews test is begin ews server serve using port 8088 tracing true end ews test and build and run with alr run now in your browser open localhost 8088 possibly 127 0 0 1 8088 to see your exciting web page to see a worked example of dynamic content check out the document ews pdf https github com simonjwright ews blob master demonstrator ews pdf building outside alire ews requires gnat and xml ada and uses ada 2012 features in the top level directory make will build the library and make demo will create a server doc ews demo which when executed will listen on port 8080 and respond with the web in the doc directory the package can be installed with the compiler by make install more likely sudo make install you can install in an alternative place by setting prefix for example make install prefix local ews dynamic web the facilities available in ews and the code for a demonstration are described in demonstrator ews pdf which is derived from demonstrator ews w as well as being the document source ews w also acts as the source code using the literate programming http www literateprogramming com facilities of nuweb py https github com simonjwright nuweb py demonstration if you re seeing this page via the demonstration ews demo you can view a page with ajax https en wikipedia org wiki ajax programming content here ajax html | os |
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DJI-Tello-Drone-ComputerVision | dji tello drone computervision this is a computervision control algorithm which forces a dji tello drone to center the camera according to your face position dependencies this algorithm uses br djitellopy ver 1 5 by https github com damiafuentes opencv python ver 4 1 0 25 install with br bash b pip3 install r requirements txt b usage run it with br bash b python3 facedetection py haarcascade frontalface default xml b videos all my test videos will be uploaded on my youtube channel and i will list them here first test no pid no face logging https www youtube com watch v wfz5i1irhly limitations it still has some limitations such as slow movements setpoint oscillations and sometimes it detects faces that do not exist this is a problem because the algorithm adjust its movement according to every face in the frame to do i m working in br adding a pid algorithm for position control in order to limit oscillation and adjust drone speed br adding a face tracking with id assignment in order to follow just one given face | ai |
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linux_rtos | mr macron president from france is blackmailing his citizens and violating human rights therefore france is no longer supported and will have a ip block in the upcoming releases github all releases https img shields io github downloads grotius cnc linux rtos total svg linux rtos debian 10 buster 4 19 0 11 rt amd64 kernel keep root administrator password empty during install only fill in the user name and password alt text https github com grotius cnc linux rtos blob main screenshot 800 png libreoffice geany glade linuxcnc widgets qt5 designer hg ehtercat linuxcnc reprository iso download 1 7 gb https github com grotius cnc linux rtos releases tag 1 0 0 during installtion keep root password entry empty after installation select the ethercat installer desktop launcher this will auto install the ethercat bus done user info the configuration to check the working ethercat bus is done with the beckhoff ek1100 module you can connect this device before installing the iso cd during installation of the iso cd you could use usb tethering for internet connection to setup your nearest download mirror while the ethercat bus uses your cat4 connection after iso installation select the ethercat installer desktop launcher this will auto install the ethercat bus bus lights will be flickering fast during ethercat bus installation if not restart pc after installation the ethercat installation could be done over again without concequences if you have custom lcec drivers save them outside the usr share ethercat directory after the ethercat installation start the linuxcnc desktop launcher the lcec will show up in linuxcnc show hal configuration add your i o modules to your needs in the linuxcnc axis desktop map ethercat conf xml connect your i o modules to linuxcnc in the hal file in this axis qtvcp example it uses the qtvcp postgui hal file to connect the ethercat i o ethercat source usr share ethercat live build info rebrand your iso to your wishes with a pre configured live build in usr share ethercat setup iso sh for example if you want to update or downgrade the kernel change the kernel image and header description in setup iso sh at linux image 4 19 0 11 rt linux headers 4 19 0 11 rt available kernel packages can be found https packages debian org buster kernel tip don t copy files to usb unless they are in archive zip format otherwise during copy it could change file permission s the setup iso sh must be set as read write more recource files for building live build s https sourceforge net projects eznixos https github com linuxcnc buster live build in short the live build commands are bin bash apt get install live build lb clean lb config add packages and config here see setup iso sh lb build | os |
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ml-with-text | tutorial machine learning with text using python round of applause to kevin markham http www dataschool io and his video tutorials instructor vaibhav srivastav https www linkedin com in vaibhavs10 description although numeric data is easy to work with in python most knowledge created by humans is actually raw unstructured text by learning how to transform text into data that is usable by machine learning models you drastically increase the amount of data that your models can learn from in this tutorial we ll build and evaluate predictive models from real world text using scikit learn objectives by the end of this tutorial attendees will be able to confidently build a predictive model from their own text based data including feature extraction model building and model evaluation required software attendees will need to bring a laptop with scikit learn http scikit learn org stable install html and pandas http pandas pydata org pandas docs stable install html and their dependencies already installed installing the anaconda distribution of python https www continuum io downloads is an easy way to accomplish this both python 2 and 3 are welcome i will be leading the tutorial using the ipython jupyter notebook and have added a pre written notebook to this repository i have also created a python script that is identical to the notebook which you can use in the python environment of your choice tutorial files ipython jupyter notebooks tutorial ipynb tutorial ipynb tutorial with output ipynb tutorial with output ipynb exercise ipynb exercise ipynb exercise solution ipynb exercise solution ipynb python scripts tutorial py tutorial py exercise py exercise py exercise solution py exercise solution py datasets data sms tsv data sms tsv data yelp csv data yelp csv prerequisite knowledge attendees to this tutorial should be comfortable working in python should understand the basic principles of machine learning and should have at least basic experience with both pandas and scikit learn however no knowledge of advanced mathematics is required abstract it can be difficult to figure out how to work with text in scikit learn even if you re already comfortable with the scikit learn api many questions immediately come up which vectorizer should i use and why what s the difference between a fit and a transform what s a document term matrix and why is it so sparse is it okay for my training data to have more features than observations what s the appropriate machine learning model to use and so on in this tutorial we ll answer all of those questions and more we ll start by walking through the vectorization process in order to understand the input and output formats then we ll read a simple dataset into pandas and immediately apply what we ve learned about vectorization we ll move on to the model building process including a discussion of which model is most appropriate for the task we ll evaluate our model a few different ways and then examine the model for greater insight into how the text is influencing its predictions finally we ll practice this entire workflow on a new dataset and end with a discussion of which parts of the process are worth tuning for improved performance detailed outline 1 model building in scikit learn refresher 2 representing text as numerical data 3 reading a text based dataset into pandas 4 vectorizing our dataset 5 building and evaluating a model 6 comparing models 7 examining a model for further insight 8 practicing this workflow on another dataset 9 tuning the vectorizer discussion about the instructor vaibhav srivastav is a data scientist currently working with deloitte consulting llp he has a demonstrated experience of more than 3 plus years in building large scale machine learning and natural language processing solutions for fortune technology 10 clients in his free time he teaches machine learning data science to young coders if python is what floats your boat then hit him up on any of the channels below email vaibhavs10 gmail com mailto vaibhavs10 gmail com twitter vaibhavsriv10 https twitter com vaibhavsriv10 recommended resources text classification read paul graham s classic post a plan for spam http www paulgraham com spam html for an overview of a basic text classification system using a bayesian approach he also wrote a follow up post http www paulgraham com better html about how he improved his spam filter coursera s natural language processing nlp course has video lectures https class coursera org nlp lecture on text classification tokenization naive bayes and many other fundamental nlp topics here are the slides http web stanford edu jurafsky nlpcourseraslides html used in all of the videos automatically categorizing yelp businesses http engineeringblog yelp com 2015 09 automatically categorizing yelp businesses html discusses how yelp uses nlp and scikit learn to solve the problem of uncategorized businesses how to read the mind of a supreme court justice http fivethirtyeight com features how to read the mind of a supreme court justice discusses courtcast a machine learning model that predicts the outcome of supreme court cases using text based features only the courtcast creator wrote a post explaining how it works https sciencecowboy wordpress com 2015 03 05 predicting the supreme court from oral arguments and the python code https github com nasrallah courtcast is available on github identifying humorous cartoon captions http www cs huji ac il dshahaf phumor pdf is a readable paper about identifying funny captions submitted to the new yorker caption contest in this pydata video https www youtube com watch v y3ztkfz 1qq 50 minutes facebook explains how they use scikit learn for sentiment classification by training a naive bayes model on emoji labeled data naive bayes and logistic regression read this brief quora post on airport security http www quora com in laymans terms how does naive bayes work answer konstantin tt for an intuitive explanation of how naive bayes classification works for a longer introduction to naive bayes read sebastian raschka s article on naive bayes and text classification http sebastianraschka com articles 2014 naive bayes 1 html as well wikipedia has two excellent articles naive bayes classifier http en wikipedia org wiki naive bayes classifier and naive bayes spam filtering http en wikipedia org wiki naive bayes spam filtering and cross validated has a good q a http stats stackexchange com questions 21822 understanding naive bayes my guide to an in depth understanding of logistic regression http www dataschool io guide to logistic regression includes a lesson notebook and a curated list of resources for going deeper into this topic comparison of machine learning models https github com justmarkham dat8 blob master other model comparison md lists the advantages and disadvantages of naive bayes logistic regression and other classification and regression models | nlp machine-learning python text-classification natural-language-processing | ai |
D.Eco | this repository contains the ios android design and code base for the d eco application this is the senior project for drury students enrolled in csci 371 spring 2017 | os |
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Hellenic_Open_University_Msc | assignments for my degree embedded system design and microcontroller applications for the internet of things hellenic open university | os |
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vigilant-robot | introduction description this project was developed as a term project in the course ttk4155 industrial and embedded computer systems design there are a lot of blood sweat and tears that went into this project so treat it with the respect it deserves read ctrl c we hope that others that are unfortunate enough to have stumbled into the field of cybernetics might find this usefull no but all jokes aside we really enjoyed this project and the code supports such things as ps2 controller needs 5 3 3v signal converter do not use a voltage divider as this fucks up and is unreliable here we use a bitbang method to communicate with it 3 mcu s on the can bus implemented in a way that avoids flooding timers use of the on board mcu on the p1000 board 6 different tunes to play on the buzzer while playing this is also set up in a way that you can make a wide arange of tunes yourself try the theme song of a game of thrones for example intuitiv interface menu on the screen with calibration progmem use to store i e bitmaps to be shown on the screen you will have to set up a ir photodiode to make the goal and the game to function you might want to fix pid is at times unreliable pwm module for slider values disclaimer the project was given a full score of 100 100 but is released as is including temporary code bad code and presumably a lot bugs it will however not give you a good score unless you read and understand it | os |
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cheatsheet-collection | cheatsheet collections an ongoing curated collection of awesome software frameworks and libraries learning tutorials and videos technical guidelines and best practices and cheatsheets in the world of information technology thanks to our daily readers and contributors the goal is to build a categorized community driven collection of very well known resources sharing suggestions and contributions are always welcome about cheatsheets table of contents platforms platforms programming languages programming languages software testing software testing front end development front end development back end development back end development big data big data theory theory editors editors tools tools databases databases media media security security project management project management miscellaneous miscellaneous platforms android cheatsheet for graphic designers http petrnohejl github io android cheatsheet for graphic designers arduino cheat sheet pdf https static sparkfun 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bestpractices curated-list curated-collections list | server |
marvel-dialogue-nlp | marvel dialogue nlp a machine learning project that will use natural language processing nlp to classify who says a line of dialogue p align center img src https blog umhb edu wp content uploads 2019 06 mcu 1920x1080 jpg alt mcu banner width 70 height 70 p streamlit app to view an interactive summary of this project see its streamlit app https share streamlit io prestondunton marvel dialogue nlp front end front end py about the project with over 21 different movies all spanning the same cinematic universe marvel movies are an interesting creation of character dialogue and plot a key defining feature of these movies is the large amount of characters represented and developed i wanted to explore nlp and figured that exploring the dialogue in these movies would be a very fun thing to do the problem whished to be accomplished in this project is an nlp classification problem the goal was to create a model that can predict a character s name given a line of their dialogue from a marvel cinematic universe mcu movie data was taken from marvel released scripts and transformed into labels of names and feature documents of their dialogue results in this project 18 different models were buit and compared models 1 12 use naive bayes svm and random forest classifiers in different architecture combinations and can be read about in the old models directory https github com prestondunton marvel dialogue nlp tree master old 20models model 13 is the naive bayes classifier with the best performance and presented as the production model models 14 18 are derived from model 13 but manipulated the data or larger architecture to try to achieve better results model 14 is an ensemble method that trains a model for every character and can be read about in the one vs rest models https github com prestondunton marvel dialogue nlp blob master one 20vs 20rest 20models ipynb notebook model 15 allows the use of movie titles and authors as features and can be read about in the all features model https github com prestondunton marvel dialogue nlp blob master all 20features 20model ipynb notebook models 16 17 and 18 were inspired by the correlation between the number of words in a line and its correct prediction shown in the section below these models attempt to train on less sparse vectors and can be read about in the word count models https github com prestondunton marvel dialogue nlp blob master word 20count 20models ipynb notebook model 13 uses scikit learn to implement a naive bayes classifier hyperparameter selection is done using cross validation 10 folds the model is also evaluated using cross validation 10 folds with hyperparameter selection this results in nested cross validation stop words which are words that provide no value to predictions i you the a an are not removed from predictions hyperparameter selection showed better performance keeping all words rather than removing nltk s list of stop words words are stemmed using nltk s snowballstemmer word counts are also transformed with term frequencies and inverse document frequencies using scikit learn s implementation model 13 performs with 29 041 balanced accuracy the model s performance isn t great but it s still fun to interact with over the course of the project it s been shown that more data only results in marginal increases in performance above it s shown that accuracy increases as the number of words in a line increases in other words it seems that spoken dialogue is too short to predict in this case the naive bayes classifier is a bag of words model meaning that the order of the words is ignored by using a word embeddings model which does not ignore the order of words accuracy could possibly be increased deep learning also might have success on this dataset to see the code for the model see the production model https github com prestondunton marvel dialogue nlp blob master production 20model ipynb notebook about the dataset this repository contains a newly created dataset to train and test models on as well as several jupyter notebooks that describe the process used to create each csv this dataset uses a combination of original scripts and transcripts from the marvel cinematic universe mcu movies the original script pdf s were obtained from script slug https www scriptslug com scripts category marvel though other copies of the marvel released scripts can be found online elsewhere transcripts were taken from fandom s transcripts wiki https transcripts fandom com wiki category marvel transcripts transcripts were copied and pasted into txt files and then processed using pandas see the table below to find out what movies are in the dataset and where the dialogue came from if you spot a mistake in the dataset please let me know so i can correct it if you would like to use the dataset it is available on kaggle https www kaggle com pdunton marvel cinematic universe dialogue mcu csv the end file mcu csv https github com prestondunton marvel dialogue nlp blob master data mcu csv contains columns character and line that hold the dialogue for several movies from the mcu there are more columns that provide additional features for context such as movie titles and author indicators see data mcu ipynb https github com prestondunton marvel dialogue nlp blob master data mcu ipynb for more details on those features and the dataset s creation individual movies for individual movies the csv files found in data cleaned https github com prestondunton marvel dialogue nlp blob master data cleaned should not be used instead load the file mcu csv https github com prestondunton marvel dialogue nlp blob master data mcu csv and use pandas or a similar library to select the rows that match a given movie other files of interest the file mcu csv https github com prestondunton marvel dialogue nlp blob master data mcu csv contains all of the data but there are other files that might be of interest for anyone using this dataset the file mcu subset csv https github com prestondunton marvel dialogue nlp blob master data mcu subset csv is a subset of the original data only containing dialogue for tony stark steve rogers natasha romanoff thor nick fury pepper potts bruce banner james rhodes loki peter parker who are the top 10 characters by number of lines number of words and movie appearances the file characters csv https github com prestondunton marvel dialogue nlp blob master data characters csv contains statistics summarizing each character s involvement in the mcu the movie name columns in that table describe how many lines they have in that movie the file movies csv https github com prestondunton marvel dialogue nlp blob master data movies csv contains metadata about the movies used in this project for details on the creation of these files see data mcu ipynb https github com prestondunton marvel dialogue nlp blob master data mcu ipynb movies included movie year is transcript lines source link csv issues iron man https github com prestondunton marvel dialogue nlp blob master data cleaned iron man csv 2008 834 script slug https www scriptslug com assets uploads scripts iron man 2008 pdf iron man 2 https github com prestondunton marvel dialogue nlp blob master data cleaned iron man 2 csv 2010 1010 fandom s transcripts wiki https transcripts fandom com wiki iron man 2 not proofread thor https github com prestondunton marvel dialogue nlp blob master data cleaned thor csv 2011 1007 script slug https www scriptslug com assets uploads scripts thor 2011 pdf captain america the first avenger https github com prestondunton marvel dialogue nlp blob master data cleaned captain america csv 2011 688 fandom s transcripts wiki https transcripts fandom com wiki captain america the first avenger not proofread the avengers https github com prestondunton marvel dialogue nlp blob master data cleaned avengers csv 2012 1027 script slug https www scriptslug com assets uploads scripts the avengers 2012 pdf iron man 3 https github com prestondunton marvel dialogue nlp blob master data cleaned iron man 3 csv 2013 1043 fandom s transcripts wiki https transcripts fandom com wiki iron man 3 not proofread thor the dark world https github com prestondunton marvel dialogue nlp blob master data cleaned thor dark world csv 2013 734 fandom s transcripts wiki https transcripts fandom com wiki thor the dark world not proofread transcript duplication captain america the winter soldier https github com prestondunton marvel dialogue nlp blob master data cleaned winter soldier csv 2014 841 fandom s transcripts wiki https transcripts fandom com wiki captain america the winter soldier not proofread avengers age of ultron https github com prestondunton marvel dialogue nlp blob master data cleaned age of ultron csv 2015 980 fandom s transcripts wiki https transcripts fandom com wiki avengers age of ultron not proofread ant man https github com prestondunton marvel dialogue nlp blob master data cleaned ant man csv 2015 867 fandom s transcripts wiki https transcripts fandom com wiki ant man not proofread captain america civil war https github com prestondunton marvel dialogue nlp blob master data cleaned civil war csv 2016 987 fandom s transcripts wiki https transcripts fandom com wiki captain america civil war not proofread guardians of the galaxy vol 2 https github com prestondunton marvel dialogue nlp blob master data cleaned guardians 2 csv 2017 993 script slug https www scriptslug com assets uploads scripts guardians of the galaxy vol 2 2017 pdf spider man homecoming https github com prestondunton marvel dialogue nlp blob master data cleaned spider man homecoming csv 2017 1558 fandom s transcripts wiki https transcripts fandom com wiki spider man homecoming not proofread thor ragnarok https github com prestondunton marvel dialogue nlp blob master data cleaned ragnarok csv 2017 961 script slug https www scriptslug com assets uploads scripts thor ragnorak 2017 pdf black panther https github com prestondunton marvel dialogue nlp blob master data cleaned black panther csv 2018 834 script slug https www scriptslug com assets uploads scripts black panther 2018 pdf avengers infinity war https github com prestondunton marvel dialogue nlp blob master data cleaned infinity war csv 2018 990 fandom s transcripts wiki https transcripts fandom com wiki avengers infinity war captain marvel https github com prestondunton marvel dialogue nlp blob master data cleaned captain marvel csv 2019 775 fandom s transcripts wiki https transcripts fandom com wiki captain marvel 2019 not proofread avengers endgame https github com prestondunton marvel dialogue nlp blob master data cleaned avengers endgame csv 2019 1229 script slug https www scriptslug com assets uploads scripts avengers endgame 2019 pdf movies not included movie year source link transcript issues the incredible hulk 2008 fandom s transcripts wiki https transcripts fandom com wiki the incredible hulk poor messy transcript guardians of the galaxy 2014 fandom s transcripts wiki https transcripts fandom com wiki guardians of the galaxy poor messy transcript doctor strange 2016 fandom s transcripts wiki https transcripts fandom com wiki doctor strange poor messy transcript ant man and the wasp 2018 fandom s transcripts wiki https transcripts fandom com wiki ant man and the wasp incomplete transcript spider man far from home 2019 fandom s transcripts wiki https transcripts fandom com wiki spider man far from home incomplete transcript libaries used numpy pandas sklearn nltk matplotlib seaborn statsmodels | nlp dialogue dataset movies marvel | ai |
blockchain-notes | blockchain notes as my notes about blockchain and different blockchain implementations e g litecoin ethereum bitcoin grew too large on gist found github more productive contents blockchain blockchain md consensus algorithm protocol consensus algorithm md cryptocurrency cryptocurrency md litecoin blockchain litecoin md bitcoin blockchain bitcoin md wallet wallet md segregated witness segwit segwit md lightning network lightning network md initial coin offering ico ico md coin mining mining md soft and hard forks soft hard forks md bitcoin cash bcc hard fork bcc md bitcoin gold btg hard fork btg md proof of stake pos proof of stake md proof of work pow proof of work md ethereum blockchain ethereum md solidity ethereum s programming language ethereum solidity md iota iota md blockchain magazines magazines md alternative blockchain application platforms to ethereum alternative blockchain platforms md cryptocurrency exchanges exchanges md further reading further reading md | blockchain litecoin ethereum bitcoin iota cryptocurrency | blockchain |
42 | 42 spacecraft simulation 42 is a comprehensive general purpose simulation of spacecraft attitude and orbit dynamics its primary purpose is to support design and validation of attitude control systems from concept studies through integration and test 42 accurately models multi body spacecraft attitude dynamics with rigid and or flexible bodies and both two body and three body orbital flight regimes modelling environments from low earth orbit to throughout the solar system 42 simulates multiple spacecraft concurrently facilitating studies of rendezvous proximity operations and precision formation flying it also features visualization of spacecraft attitude features multi body dynamics tree topology rotational and or translational joints rigid and or flexible bodies multiple spacecraft prox ops formation flying or independent inter spacecraft and spacecraft surface contact forces support landers rovers and spacecraft servicing scenarios two body or three body orbits anywhere in the solar system optional visualization socket based interprocess comm ipc interface to other apps fast setup for concept studies rigorous and full featured to support full spacecraft life cycle installation if you re installing on windows see the file install msys txt in the docs folder the compiler will attempt to detect what platform you re on linux osx or msys but its success rate isn t great if you have errors on the first compile or run try editing your makefile to manually set your 42platform for opengl graphics newer macs with retina displays will need the glfw graphics libraries available from macports homebrew and probably elsewhere otherwise you ll need the glut libraries which are also readily available if not already installed on your system graphics are optional settable in the makefile by guiflag getting started see the overview 42 overview pdf in the docs folder also recommended nomenclature pdf pov menu pdf key bindings txt fsw models pdf flight regimes pdf the default folder for inputs and outputs is inout look there for sample input files inp sim txt is the top level input file the input output folder may be changed for a sim run by running 42 with an argument for example enter this at the command prompt 42 demo common problems 1 42 expects the input files to be plain vanilla text files if your text editor adds formatting makes straight quotes into smart quotes etc 42 may get confused the most common symptom is generating the bogus input in decodestring error 2 also text related 42 is very simple minded about reading the input files adding extra lines or accidentally deleting a line or swapping lines will throw things out of synch again the most common symptom is the bogus input in decodestring error use your debugger to trace back where the error was generated the actual mistake may be at that line or may be somewhere upstream | os |
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PIXIU | p align center width 100 img src https i postimg cc xtpwgq3l pixiu logo png width 100 height 100 p div div align left a target blank qianqian xie sup 1 sup span emsp a target blank weiguang han sup 1 sup span emsp a target blank xiao zhang sup 2 sup a emsp a target blank ruoyu xiang sup 7 sup a emsp a target blank gang hu sup 5 sup a emsp a target blank ke qin sup 5 sup a emsp a target blank duanyu feng sup 3 sup a emsp a target blank yongfu dai sup 3 sup a emsp a target blank hao wang sup 3 sup a emsp a target blank yanzhao lai sup 4 sup a emsp a target blank min peng sup 1 sup a emsp a href https warrington ufl edu directory person 12693 target blank alejandro lopez lira sup 6 sup a emsp a href https jimin chancefocus com target blank jimin huang sup 8 sup a div div div align left sup 1 sup wuhan university emsp sup 2 sup sun yat sen university emsp sup 3 sup sichuan university emsp sup 4 sup southwest jiaotong university emsp sup 5 sup yunan university emsp sup 6 sup 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https img shields io badge pixiu v0 1 gold https black readthedocs io en stable static license svg discord https img shields io discord 1146837080798933112 https discord gg hrwpumkb pixiu paper https arxiv org abs 2306 05443 flare leaderboard https huggingface co spaces chancefocus flare disclaimer this repository and its contents are provided for academic and educational purposes only none of the material constitutes financial legal or investment advice no warranties express or implied are offered regarding the accuracy completeness or utility of the content the authors and contributors are not responsible for any errors omissions or any consequences arising from the use of the information herein users should exercise their own judgment and consult professionals before making any financial legal or investment decisions the use of the software and information contained in this repository is entirely at the user s own risk by using or accessing the information in this repository you agree to indemnify defend and hold harmless the authors contributors and any affiliated organizations or persons from any and all claims or damages update date 09 22 2023 we re thrilled to announce that our paper pixiu a comprehensive benchmark instruction dataset and large language model for finance has been accepted by neurips 2023 track datasets and benchmarks update date 10 08 2023 we re proud to share that the enhanced versions of flare which now support both chinese and spanish checkpoints finma v0 1 nlp 7b version https huggingface co chancefocus finma 7b nlp finma v0 1 full 7b version https huggingface co chancefocus finma 7b full languages english readme md spainish readme es md chinese readme zh md evaluations english evaluation datasets https huggingface co collections chancefocus flare evaluation datasets english 6529286a147d9119a64689c0 more details on flare section spanish evaluation datasets https huggingface co collections chancefocus flare evaluation datasets spanish 652929c34f8fe1bea9cd5a66 chinese evaluation datasets https huggingface co collections chancefocus flare evalution datasets chinese 65292963a8cd8847517204a2 overview welcome to the pixiu project this project is designed to support the development fine tuning and evaluation of large language models llms in the financial domain pixiu is a significant step towards understanding and harnessing the power of llms in the financial domain structure of the repository the repository is organized into several key components each serving a unique purpose in the financial nlp pipeline flare our financial language understanding and prediction evaluation benchmark flare serves as the evaluation suite for financial llms with a focus on understanding and prediction tasks across various financial contexts fit our financial instruction dataset fit is a multi task and multi modal instruction dataset specifically tailored for financial tasks it serves as the training ground for fine tuning llms for these tasks finma our financial large language model llm finma is the core of our project providing the learning and prediction power for our financial tasks key features open resources pixiu openly provides the financial llm instruction tuning data and datasets included in the evaluation benchmark to encourage open research and transparency multi task the instruction tuning data and benchmark in pixiu cover a diverse set of financial tasks including four financial nlp tasks and one financial prediction task multi modality pixiu s instruction tuning data and benchmark consist of multi modality financial data including time series data from the stock movement prediction task it covers various types of financial texts including reports news articles tweets and regulatory filings diversity unlike previous benchmarks focusing mainly on financial nlp tasks pixiu s evaluation benchmark includes critical financial prediction tasks aligned with real world scenarios making it more challenging flare 2 0 financial language understanding and prediction evaluation benchmark in this section we provide a detailed performance analysis of finma compared to other leading models including chatgpt gpt 4 and bloomberggpt et al for this analysis we ve chosen a range of tasks and metrics that span various aspects of financial natural language processing and financial prediction all model results of flare can be found on our leaderboard https huggingface co spaces chancefocus flare tasks data task raw data types modalities license paper fpb sentiment analysis 4 845 news text cc by sa 3 0 1 fiqa sa sentiment analysis 1 173 news headlines tweets text public 2 fomc hawkish dovish classification 496 fomc transcripts text cc by nc 4 0 3 headlines news headline classification 11 412 news headlines text cc by sa 3 0 4 ner named entity recognition 1 366 financial agreements text cc by sa 3 0 5 finer ord named entity recognition 1 080 news articles text cc by nc 4 0 6 finqa question answering 8 281 earnings reports text table mit license 7 convfinqa multi turn question answering 1 490 earnings reports text table mit license 8 ectsum text summarization 495 earning call transcipts text public 9 edtsum text summarization 2000 news articles text public 10 german credit scoring 1000 credit records table cc by 4 0 11 australian credit scoring 690 credit records table cc by 4 0 12 bigdata22 stock movement prediction 7 164 tweets historical prices text time series public 13 acl18 stock movement prediction 27 053 tweets historical prices text time series mit license 14 cikm18 stock movement prediction 4 967 tweets historical prices text time series public 15 1 pekka malo ankur sinha pekka korhonen jyrki wallenius and pyry takala 2014 good debt or bad debt detecting semantic orientations in economic texts journal of the association for information science and technology 65 4 2014 782 796 2 macedo maia siegfried handschuh andr freitas brian davis ross mcdermott manel zarrouk and alexandra balahur 2018 www 18 open challenge financial opinion mining and question answering in companion proceedings of the the web conference 2018 1941 1942 3 agam shah suvan paturi and sudheer chava 2023 trillion dollar words a new financial dataset task market analysis https aclanthology org 2023 acl long 368 in proceedings of the 61st annual meeting of the association for computational linguistics volume 1 long papers pages 6664 6679 toronto canada association for computational linguistics 4 ankur sinha and tanmay khandait 2021 impact of news on the commodity market dataset and results in advances in information and communication proceedings of the 2021 future of information and communication conference ficc volume 2 springer 589 601 5 julio cesar salinas alvarado karin verspoor and timothy baldwin 2015 domain adaption of named entity recognition to support credit risk assessment in proceedings of the australasian language technology association workshop 2015 84 90 6 shah a vithani r gullapalli a et al finer financial named entity recognition dataset and weak supervision model j arxiv preprint arxiv 2302 11157 2023 7 zhiyu chen wenhu chen charese smiley sameena shah iana borova dylan langdon reema moussa matt beane ting hao huang bryan r routledge et al 2021 finqa a dataset of numerical reasoning over financial data in proceedings of the 2021 conference on empirical methods in natural language processing 3697 3711 8 zhiyu chen shiyang li charese smiley zhiqiang ma sameena shah and william yang wang 2022 convfinqa exploring the chain of numerical reasoning in conversational finance question answering in proceedings of the 2022 conference on empirical methods in natural language processing pages 6279 6292 abu dhabi united arab emirates association for computational linguistics 9 rajdeep mukherjee abhinav bohra akash banerjee soumya sharma manjunath hegde afreen shaikh shivani shrivastava koustuv dasgupta niloy ganguly saptarshi ghosh and pawan goyal 2022 ectsum a new benchmark dataset for bullet point summarization of long earnings call transcripts https aclanthology org 2022 emnlp main 748 in proceedings of the 2022 conference on empirical methods in natural language processing pages 10893 10906 abu dhabi united arab emirates association for computational linguistics 10 zhihan zhou liqian ma and han liu 2021 trade the event corporate events detection for news based event driven trading https aclanthology org 2021 findings acl 186 in findings of the association for computational linguistics acl ijcnlp 2021 pages 2114 2124 online association for computational linguistics 11 hofmann hans 1994 statlog german credit data uci machine learning repository https doi org 10 24432 c5nc77 12 quinlan ross statlog australian credit approval uci machine learning repository https doi org 10 24432 c59012 13 yejun soun jaemin yoo minyong cho jihyeong jeon and u kang 2022 accurate stock movement prediction with self supervised learning from sparse noisy tweets in 2022 ieee international conference on big data big data ieee 1691 1700 14 yumo xu and shay b cohen 2018 stock movement prediction from tweets and historical prices in proceedings of the 56th annual meeting of the association for computational linguistics volume 1 long papers 1970 1979 15 huizhe wu wei zhang weiwei shen and jun wang 2018 hybrid deep sequential modeling for social text driven stock prediction in proceedings of the 27th acm international conference on information and knowledge management 1627 1630 evaluation preparation locally install bash git clone https github com chancefocus pixiu git recursive cd pixiu pip install r requirements txt cd pixiu src financial evaluation pip install e multilingual docker image bash sudo bash scripts docker run sh above command starts a docker container you can modify docker run sh to fit your environment we provide pre built image by running sudo docker pull tothemoon pixiu latest bash docker run gpus all ipc host ulimit memlock 1 ulimit stack 67108864 network host env https proxy https proxy env http proxy http proxy env all proxy all proxy env hf home hf home it rm name pixiu v pixiu path pixiu path v hf home hf home v ssh pub key root ssh authorized keys w workdir docker user pixiu tag sshd port 2201 cmd echo hello world bin bash arguments explain means ignoreable arguments hf home huggingface cache dir sshd port sshd port of the container you can run ssh i private key p sshd port root ip to connect to the container default to 22001 rm remove the container when exit container ie ctrl d automated task assessment before evaluation please download bart checkpoint https drive google com u 0 uc id 1 7jff7koinb7zrxkhiigtmr4chvet01m export download to src metrics bartscore bart score pth for automated evaluation please follow these instructions 1 huggingface transformer to evaluate a model hosted on the huggingface hub for instance finma 7b full use this command bash python eval py model hf causal llama model args use accelerate true pretrained chancefocus finma 7b full tokenizer chancefocus finma 7b full use fast false tasks flare ner flare sm acl flare fpb more details can be found in the lm eval https github com eleutherai lm evaluation harness documentation 2 commercial apis please note for tasks such as ner the automated evaluation is based on a specific pattern this might fail to extract relevant information in zero shot settings resulting in relatively lower performance compared to previous human annotated results bash export openai api secret key your key here python eval py model gpt 4 tasks flare ner flare sm acl flare fpb 3 self hosted evaluation to run inference backend bash bash scripts run interface sh please adjust run interface sh according to your environment requirements to evaluate bash python data evaluate py create new tasks creating a new task for flare involves creating a huggingface dataset and implementing the task in a python file this guide walks you through each step of setting up a new task using the flare framework creating your dataset in huggingface your dataset should be created in the following format python query answer text in this format query combination of your prompt and text answer your label for multi turn tasks such as for classification tasks such as fpb flare fpb https huggingface co datasets chancefocus flare fpb additional keys should be defined choices set of labels gold index of the correct label in choices start from 0 for sequential labeling tasks such as finer ord flare finer ord https huggingface co datasets chancefocus flare finer ord additional keys should be defined label list of token labels token list of tokens for extractive summarization tasks such as ectsum flare ectsum https huggingface co datasets chancefocus flare ectsum additional keys should be defined label list of sentence labels for abstractive summarization and question answering tasks such as edtsum flare edtsum https huggingface co datasets chancefocus flare edtsum no additional keys should be defined implementing the task once your dataset is ready you can start implementing your task your task should be defined within a new class in flare py or any other python file located within the tasks directory to cater to a range of tasks we offer several specialized base classes including classification sequentiallabeling relationextraction extractivesummarization abstractivesummarization and qa for instance if you are embarking on a classification task you can directly leverage our classification base class this class allows for efficient and intuitive task creation to better demonstrate this let s delve into an example of crafting a task named flare fpb using the classification base class python class flarefpb classification dataset path flare fpb and that s it once you ve created your task class the next step is to register it in the src tasks init py file to do this add a new line following the format task name module classname here is how it s done python task registry flare fpb flare fpb your new task your module yourtask this is where you add your task predefined task metrics task metric illustration classification accuracy this metric represents the ratio of correctly predicted observations to total observations it is calculated as true positives true negatives total observations classification f1 score the f1 score represents the harmonic mean of precision and recall thereby creating an equilibrium between these two factors it proves particularly useful in scenarios where one factor bears more significance than the other the score ranges from 0 to 1 with 1 signifying perfect precision and recall and 0 indicating the worst case furthermore we provide both weighted and macro versions of the f1 score classification missing ratio this metric calculates the proportion of responses where no options from the given choices in the task are returned classification matthews correlation coefficient mcc the mcc is a metric that assesses the quality of binary classifications producing a score ranging from 1 to 1 a score of 1 signifies perfect prediction 0 denotes a prediction no better than random chance and 1 indicates a completely inverse prediction sequential labeling f1 score in the context of sequential labeling tasks we utilize the f1 score as computed by the seqeval library a robust entity level evaluation metric this metric mandates an exact match of both the entity s span and type between the predicted and ground truth entities for a correct evaluation true positives tp represent correctly predicted entities false positives fp denote incorrectly predicted entities or entities with mismatched spans types and false negatives fn signify missed entities from the ground truth precision recall and f1 score are then computed using these quantities with the f1 score representing the harmonic mean of precision and recall sequential labeling label f1 score this metric evaluates model performance based solely on the correctness of the labels predicted without considering entity spans relation extraction precision precision measures the proportion of correctly predicted relations out of all predicted relations it is calculated as the number of true positives tp divided by the sum of true positives and false positives fp relation extraction recall recall measures the proportion of correctly predicted relations out of all actual relations it is calculated as the number of true positives tp divided by the sum of true positives and false negatives fn relation extraction f1 score the f1 score is the harmonic mean of precision and recall and it provides a balance between these two metrics the f1 score is at its best at 1 perfect precision and recall and worst at 0 extractive and abstractive summarization rouge n this measures the overlap of n grams a contiguous sequence of n items from a given sample of text between the system generated summary and the reference summary n can be 1 2 or more with rouge 1 and rouge 2 being commonly used to assess unigram and bigram overlaps respectively extractive and abstractive summarization rouge l this metric evaluates the longest common subsequence lcs between the system and the reference summaries lcs takes into account sentence level structure similarity naturally and identifies longest co occurring in sequence n grams automatically question answering emacc emacc assesses the exact match between the model generated response and the reference answer in other words the model generated response is considered correct only if it matches the reference answer exactly word for word additionally you can determine if the labels should be lowercased during the matching process by specifying lower case in your class definition this is pertinent since labels are matched based on their appearance in the generated output for tasks like examinations where the labels are a specific set of capitalized letters such as a b c this should typically be set to false fit financial instruction dataset our instruction dataset is uniquely tailored for the domain specific llm finma this dataset has been meticulously assembled to fine tune our model on a diverse range of financial tasks it features publicly available multi task and multi modal data derived from the multiple open released financial datasets the dataset is multi faceted featuring tasks including sentiment analysis news headline classification named entity recognition question answering and stock movement prediction it covers both textual and time series data modalities offering a rich variety of financial data the task specific instruction prompts for each task have been carefully degined by domain experts modality and prompts the table below summarizes the different tasks their corresponding modalities text types and examples of the instructions used for each task task modalities text types instructions examples sentiment analysis text news headlines tweets analyze the sentiment of this statement extracted from a financial news article provide your answer as either negative positive or neutral for instance the company s stocks plummeted following the scandal would be classified as negative news headline classification text news headlines consider whether the headline mentions the price of gold is there a price or not in the gold commodity market indicated in the news headline please answer yes or no named entity recognition text financial agreements in the sentences extracted from financial agreements in u s sec filings identify the named entities that represent a person per an organization org or a location loc the required answer format is entity name entity type for instance in elon musk ceo of spacex announced the launch from cape canaveral the entities would be elon musk per spacex org cape canaveral loc question answering text earnings reports in the context of this series of interconnected finance related queries and the additional information provided by the pretext table data and post text from a company s financial filings please provide a response to the final question this may require extracting information from the context and performing mathematical calculations please take into account the information provided in the preceding questions and their answers when formulating your response stock movement prediction text time series tweets stock prices analyze the information and social media posts to determine if the closing price of tid will ascend or descend at point please respond with either rise or fall dataset statistics the dataset contains a vast amount of instruction data samples 136k allowing finma to capture the nuances of the diverse financial tasks the table below provides the statistical details of the instruction dataset data task raw instruction data types modalities license original paper fpb sentiment analysis 4 845 48 450 news text cc by sa 3 0 1 fiqa sa sentiment analysis 1 173 11 730 news headlines tweets text public 2 headline news headline classification 11 412 11 412 news headlines text cc by sa 3 0 3 ner named entity recognition 1 366 13 660 financial agreements text cc by sa 3 0 4 finqa question answering 8 281 8 281 earnings reports text table mit license 5 convfinqa question answering 3 892 3 892 earnings reports text table mit license 6 bigdata22 stock movement prediction 7 164 7 164 tweets historical prices text time series public 7 acl18 stock movement prediction 27 053 27 053 tweets historical prices text time series mit license 8 cikm18 stock movement prediction 4 967 4 967 tweets historical prices text time series public 9 1 pekka malo ankur sinha pekka korhonen jyrki wallenius and pyry takala 2014 good debt or bad debt detecting semantic orientations in economic texts journal of the association for information science and technology 65 4 2014 782 796 2 macedo maia siegfried handschuh andr freitas brian davis ross mcdermott manel zarrouk and alexandra balahur 2018 www 18 open challenge financial opinion mining and question answering in companion proceedings of the the web conference 2018 1941 1942 3 ankur sinha and tanmay khandait 2021 impact of news on the commodity market dataset and results in advances in information and communication proceedings of the 2021 future of information and communication conference ficc volume 2 springer 589 601 4 julio cesar salinas alvarado karin verspoor and timothy baldwin 2015 domain adaption of named entity recognition to support credit risk assessment in proceedings of the australasian language technology association workshop 2015 84 90 5 zhiyu chen wenhu chen charese smiley sameena shah iana borova dylan langdon reema moussa matt beane ting hao huang bryan r routledge et al 2021 finqa a dataset of numerical reasoning over financial data in proceedings of the 2021 conference on empirical methods in natural language processing 3697 3711 6 zhiyu chen shiyang li charese smiley zhiqiang ma sameena shah and william yang wang 2022 convfinqa exploring the chain of numerical reasoning in conversational finance question answering arxiv preprint arxiv 2210 03849 2022 7 yejun soun jaemin yoo minyong cho jihyeong jeon and u kang 2022 accurate stock movement prediction with self supervised learning from sparse noisy tweets in 2022 ieee international conference on big data big data ieee 1691 1700 8 yumo xu and shay b cohen 2018 stock movement prediction from tweets and historical prices in proceedings of the 56th annual meeting of the association for computational linguistics volume 1 long papers 1970 1979 9 huizhe wu wei zhang weiwei shen and jun wang 2018 hybrid deep sequential modeling for social text driven stock prediction in proceedings of the 27th acm international conference on information and knowledge management 1627 1630 generating datasets for fit when you are working with the financial instruction dataset fit it s crucial to follow the prescribed format for training and testing models the format should look like this json id unique id conversations from human value your prompt and text from agent value your answer text text to be classified label your label here s what each field means id a unique identifier for each example in your dataset conversations a list of conversation turns each turn is represented as a dictionary with from representing the speaker and value representing the text spoken in the turn text the text to be classified label the ground truth label for the text the first turn in the conversations list should always be from human and contain your prompt and the text the second turn should be from agent and contain your answer finma v0 1 financial large language model we are pleased to introduce the first version of finma including three models finma 7b finma 7b full finma 30b fine tuned on llama 7b and llama 30b finma 7b and finma 30b are trained with the nlp instruction data while finma 7b full is trained with the full instruction data from fit covering both nlp and prediction tasks finma v0 1 is now available on huggingface https huggingface co chancefocus finma 7b nlp for public use we look forward to the valuable contributions that this initial version will make to the financial nlp field and encourage users to apply it to various financial tasks and scenarios we also invite feedback and shared experiences to help improve future versions how to fine tune a new large language model using pixiu based on fit coming soon citation if you use pixiu in your work please cite our paper misc xie2023pixiu title pixiu a large language model instruction data and evaluation benchmark for finance author qianqian xie and weiguang han and xiao zhang and yanzhao lai and min peng and alejandro lopez lira and jimin huang year 2023 eprint 2306 05443 archiveprefix arxiv primaryclass cs cl license pixiu is licensed under mit for more details please see the mit license file star history star history chart https api star history com svg repos chancefocus pixiu type date https star history com chancefocus pixiu date | aifinance fintech llama machine-learning named-entity-recognition natural-language-processing nlp question-answering sentiment-analysis stock-price-prediction text-classification chatgpt gpt-4 large-language-models pixiu | ai |
backpack-ios | backpack ios backpack is a collection of design resources reusable components and guidelines for creating skyscanner s products release https github com skyscanner backpack ios actions workflows release yml badge svg https github com skyscanner backpack ios actions workflows release yml main https github com skyscanner backpack ios actions workflows main yml badge svg https github com skyscanner backpack ios actions workflows main yml license https img shields io cocoapods l backpack svg style flat https cocoapods org pods backpack platform https img shields io cocoapods p backpack svg style flat https cocoapods org pods backpack pod version backpack common version https img shields io cocoapods v backpack common svg style flat https cocoapods org pods backpack common backpack version https img shields io cocoapods v backpack svg style flat https cocoapods org pods backpack backpack swiftui version https img shields io cocoapods v backpack swiftui svg style flat https cocoapods org pods backpack swiftui installation backpack is available through cocoapods https cocoapods org to install all of it simply add the following line to your podfile ruby pod backpack backpack is also available for swiftui to install simply add the following line to your podfile backpack swiftui is currently in development and supports a subset of components only ruby pod backpack swiftui documentation to learn more about the available backpack components on ios head over the backpack documentation site https skyscanner design or check out the reference documentation https backpack github io ios contributing to backpack please see the contributing guide 0 for instructions on contributing to this project our principles we follow a set of principles that are stated in the backpack documentation site https skyscanner design latest getting started backpack in code principles html license backpack is available under the apache 2 0 license see the license file for more info 0 contributing md contact backpack skyscanner net | backpack ios component-library | os |
dev-list | ferramentas lista com diversas ferramentas para desenvolvimento web sum rio apps sections apps md build sections build md checklist sections checklist md database sections databases md deploy sections deploy md frameworks sections frameworks md generators sections generators md newsletters sections news md online sections online md testes sections tests md user interface sections ui md br como contribuir https github com cerebrobr cerebro blob master readme md como contribuir | development-tools | front_end |
MERN-Session-TSS23 | mern session tss23 schedule day 1 html css bootstrap git github link https ssahibsingh github io mern session tss23 day1 day 2 js link https ssahibsingh github io mern session tss23 day2 day 3 4 react js node js link https ssahibsingh github io mern session tss23 day3 day 5 6 express js mongodb mongoose link https ssahibsingh github io mern session tss23 day5 6 day 7 integration deployment apis link https ssahibsingh github io mern session tss23 day7 day 8 bonus next js link https ssahibsingh github io mern session tss23 day8 speakers sahib singh https github com ssahibsingh yuvraj singh https github com yuvraj3905 | front_end |
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Airline_reservation_application | airline reservation application a node js express application with responsive ui and mongodb for database build for software engineering project work in progress | server |
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distributed-database | csci 5408 database management wareshouse and analytics authors 1 aman singh bhandari b00910008 2 amankumar patel b00888136 3 qiwei sun b00780054 4 vatsal yadav b00893030 5 vivekkumar patel b00874162 1 summary team dpg16 in course csci 5408 database management wareshouse and analytics jan 2022 apr 2022 completed the final project to build a distributed system the distributed system is built with two gcp vm instances of similar specifications there are in total two users and each user operates on one single instance the user needs to register before being able to use any of the features of the database the system allows users to register a username password and 3 sets of security questions and their answers password is encrypted with an sha 1 encryption algorithm and is saved in a text file along with other users information once the user is registered it can authenticate and log in to the system any incorrect information will lead to the failure of the authentication this distributed database system allows users to write queries and dml commands just like the existing database system in the market it expects the user to select the database schema or create a schema if the expected database does not exist the system supports users to create the tables insert update and delete the data the input queries are first parsed and tokenized into multiple chunks where each chunk represents some meaning in the command the tokenization of the query is done by applying regex expressions as the commands are running in the system each command is generating some output this output can be positive submission generation and selection of data or it can be a failure crash the system is continuously monitoring and writing these logs in json files we are maintaining three different logs file based on the category of the log the first query log saves the logs when the queries are executed the second log category is database logs it logs the changes in the database the last category is event logs which record any event that happened on a distributed system it includes crash reports and all the activities the data model is created by performing reverse engineering on the database and table metadata the global metadata provides the information regarding the various tables in a database and their instances by using this information the local metadata can be accessed on the corresponding instances to get table and column level information the reverse engineering is performed on this information to identify relationship dependencies and cardinalities an entity relatioship diagram erd can be created using the relationships between tables and columns the user can select the data model option on the console and provide database name to initiate this process to generate erd although the user will have a centralized experience the database built in this project is distributed the two virtual machine instances in gcp have their own private ips and are communicating with each other through ssh protocol any input from the user is first processed in the same instance through global metadata it makes sure how many resources will be required to process this command it makes the list of these resources like r1 r2 r3 rn once the list of resources is ready using global metadata it makes sure to evaluate the location of each resource it updates the list of resources with their location as well the current instance either requires pulling the data or pushing the data to the other instance to pull the data cat filename txt command is executed on other linux machines to fetch the data in a file to push the data echo e content filaname txt command is run to save the new content in a file this way the instance in control makes sure to process the query by pulling and saving the resources that is not present in its persistent storage the local metadata is meant to keep the metadata of each table all the data related to a table are stored under the directory with the name of its database schema the database schema contains a metadata file that can be identified with tablename txt naming convention it contains the column information of a particular table like name datatype is not null primary key foreign key fk reference table and column his information is stored pipe separated for each column the table data rows are present in the table with table table name txt naming convention global metadata is to locate the resources of our distributed database system each resource can either be present on instance 1 or 2 and the instance in control must figure out where exactly the requested resource resides it then communicates to the other instance to either pull or pushes the resource the global metadata data contains multiple lines and each line is represented by resource name instance number 2 architecture we have used multilayer architectures in figure 1 where there are multiple layers with unique responsibility and each layer is talking to the layer above and beneath it it starts with the user console module that is responsible to read the input from user and show output once the input is validated system doesn t know and interpret the input for this purpose it is transmitted to query parser layer that parse the command and tokenize it the tokenized data is easily interpreted the system can now understand the different items individually the above discussed two layers are stateful because it stores the data being entered by the user and maintains its state one example is that it stores the database schema selected by the user and maintains the details of the user currently logged in all the tasks done under a user will be logged against that user img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 001 jpeg figure 1 architectural representation the query executor is stateless as it is solely responsible for executor queries and dml commands this layer does not maintain any state of the database or user for example it will get a tokenized details from the upper layer and executes the command it doesn t care about which instance exactly the resources are stored and just executed the query during the execution requires to read the data from resources and needs to store the data the distribution layer is solely responsible to decide where exactly a particular resource is residing once the resource is located through global metadata it builds an ssh connection with the remote machine through this ssh connection linux commands are run on the remote machine to get and save data through the i o handler layer the data is read and written in both the machines 3 local metadata local metadata contains the metadata of each table it contains the information of each column of the table through the local metadata we can get all the information and constraints on the table in our solution we are maintaining the local metadata in a txt format file the file is present in the folder with the name database schema where the table exists the local metadata structure in the file starts with the column name and followed by the datatype is not null primary key foreign key foreign key reference table and foreign key reference column as shown in figure 2 these values are pipe separated in case a value in column name comes with pipe character system adds a escape character in front of it this tells the system that it doesn t need to treat the pipe character as a separator the name of the file starts with the prefix metadata and is concatenated with table name to complete the full file name for example metadata courses txt is the file name for metadata of course table img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 002 jpeg figure 2 local metadata one example of metadata of the table courses is shown below in figure 3 img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 003 png figure 3 example of local metadata 4 global metadata global metadata contains the information about the database schema about their tables and their locations in our solution we have established two vm instances where we are distributing our tables by following the vertical fragmentation therefore in a database schema table can either be located on instance one or instance two to keep this information about the table location we have designed our global metadata file in such a way that it is easy to extract the location of the same as noticed in figure 4 the file name followed by the instance name is separated by pipe character separator img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 004 png figure 4 structure of global metadata the global metadata is kept under each database schema folder with the name global metadata txt and one example on the content is shown below in figure 5 img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 005 png figure 5 example of global metadata 5 table s data the table rows or the data is kept in a separate file with txt extension the name of the file starts with table and appends the table name for example the name of the tables rows file for courses table would be table courses txt the table data again contains the values pipe separated like previously discussed document the values are listed in the same order as the column names in the metadata file of the table figure 7 and 8 gives more clear picture on how the table rows are stored img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 006 jpeg figure 6 table rows structure img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 007 png figure 7 example of table rows structure 6 file storage structure in dbms in this section we will understand that how the directory and file storage look like in our designed dbms the root directory is named database and all the tables and metadata is stored under this directory under this directory all the database schema directory exists on creation of each database schema a directory will be created inside database folder with same name as database schema once the table is created the local metadata file for that table will be created under the database schema folder same way on insertion of the first row a table tablename txt will be created inside the same folder the global metadata file will also be saved under the database schema folder figure 9 shows the directory and file structure in instance1 for the database university figure 10 shows the directory structure of second instance for the university database img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 008 png figure 8 directory structure on instance 1 img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 009 png figure 9 directory structure in instance 2 7 installing and accessing vm instances the two vm instances were created in this effort the configuration of these two ubuntu instances were kept exactly same to avoid biasing on the performance and efficiency in figure 11 to 16 the configuration of the instances can be observed to access the two remote machine from local through ssh created a public and private key command ssh keygen t rsa b 4096 c username the private key was of openssh and needed a conversion of type rsa and hence next command to run was ssh keygen p f ssh id rsa m pem img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 010 png figure 10 vm instance configuration img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 011 png figure 11 vm instance configuration img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 012 png figure 13 vm instance configuration img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 013 png figure 14 vm instance configuration img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 014 png figure 15 vm instance configuration img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 011 png figure 16 vm instance configuration once keys are generated it needs to be added to the metadata of the created project figure 17 we need to the add the public keys of all the machines who wants to access these two instances here we need to make sure to add the keys of both the instances as well so that they can access to each other for distributed database img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 015 png figure 17 vm allowed ssh keys 8 making the database distributed the distribution layer in our architecture keeps track of the resources and in which instance do they exists it takes the help of global metadata to locate the resources other modules don t care about the distribution of resources and just focusses on their part they communicate with distribution layer to retrieve and store the data figure 18 shows the architecture of this layer img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 016 jpeg figure 18 architecture diagram of distribution layer figure 19 shows the algorithm of reading and writing the files in a distributed system img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 017 png figure 19 algorithm of distribution layer figure 20 and 21 shows the code according to the above algorithm here we used jsch 1 library to build the ssh connection and execute the linux command on the remote machine also refer to the code 2 to implement the ssh connection and command executor img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 018 png figure 20 code for to build ssh connection and execute commands img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 019 png figure 21 code for to build ssh connection and execute commands 9 executing sql commands and queries the queries inputs to the query executor layer through a pojo class for every type of query project contains a separate pojo class contain different data fields and constructor figure 22 shows all such classes img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 020 png figure 22 pojo classes with these pojo class the handler has all the necessary details with them to execute the specific query the handler works closely with the distribution layer to read write the file this layer accesses the local metadata and table file frequently to read update the information in them 10 dumps 10 1 use case for regenerate the database on another machine back up the database 10 2 goals goals when the use execute the dump query create a dump sql file which generate the sql commands file to recreate the current database 10 3 process flow img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 021 png figure 23 process flow of generate dump file 10 4 pseudo code input int command 2 string databasename some database name output list of sqls queries store in the sql files user register and login through the console and select command 2 from console menu if input command 2 enter database name triger the generate dump function database name generate dump read global metadata of that database count how many tables in that database get information where the tables stored create a list to store the tables find and read the tables s local metadata to get the information about columns sort the list base on relation between tables the independent tables should be created first and the tables dependent on other tables will create after the that table create and write create database and use database into dump file for tables of that database in the sorted list create and write create table sql into the dump file find the table by reading the global metadata read records of the table then create and write insert sql into the dump file 10 5 screenshot img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 022 png figure 24 global metadata of the distributed database that indicate where the tables stored img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 023 png figure 25 local metadata of the student table which shows it do not depend on another table img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 024 png figure 26 local metadata of the courses table which shows it depends on the student table img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 025 png figure 27 structure dump file img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 026 png figure 28 structure and data dump file 11 logs 11 1 use case for generating the analytics checking the status of the distributed system whether it is consistent or not for debugging or recover purpose 11 2 function a log function will be monitoring and record the status of the distributed database there are three types of logs query log which will record each query execution like whether it is successful and time stamp and other essential information about the query the database log which will record the states of the database it is snapshot of the database states at that time how many table in that database and how many records in the tables for the event logs it will log all activities that done in the database such as crash report query execution and transaction and analytics function will read the logs to generate report 11 3 data structure the format of the logs is json file we use the json to store the logs because it is easy to retrive the information by key it is coinvent for generate database analytics and it is easy to transfer between the json string to string it can be used when we want to store the information into in the database the logs will be store in both instances so every activity for instances will be logged 11 4 process flow img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 027 png figure 29 process flow of log management 11 5 pseudo code input a object call log parameters the log parameters has the attributes string event string excutiontime string querystring string user string timestamp string type string condition string columns string values string database string table int numberofrowschanges int numberoftables string tablenames int numberofrows string issuccessful string crashreport output event logs file query logs file and datbase log file algorithm when the query executed gather all the information associated with the query calculate the time of excution end time minus start time record the timestamp when the query is executed store use information table name database name query string and other log parameters pass the log parameter to the log function log parameters create json object event store it into instance one store it into instance two create json object querys store it into instance one store it into instance two create json object database store it into instance one store it into instance two 11 6 screenshot img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 028 png figure 30 sample event logs file img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 029 png figure 31 sample query log file img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 030 png figure 32 sample database log file 12 analytics 12 1 use case 1 have a statistics insight about the database 1 for optimizing the system based on the analytics to adopt the load balance to improve the efficient of the system 12 2 function this function will generate essential analytics about the database number of the queries are submitted by the user on which database running on which vm number of the type queries are successful executed by the user on which tables 12 3 data structure plain text file and print to the console 12 4 process flow img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 031 png figure 33 process flow of generating database analytics 12 5 pseudo code read the database logs and count unique name of the database to know number of the database find the queries log file read the queries log file for each database in the system filter with the database name filter with the username count number of the query store number of the the query in list print out username submitted number of queries on database name running on vm write the print message into the analytics file 12 6 screenshot img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 032 png figure 34 console print out demonstrates img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 033 png figure 35 demo database analytics img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 034 png figure 36 demo table analytics 13 data modelling reverse engineering 1 use case generate an entity relationship diagram erd of the current state of the database graphical representation of tables columns relationships and cardinality of a database 1 definition data modeling and reverse engineering is creating a data model of a database from its scripts and stored data and representing it in a graphical form the graphical representation or erd includes the entities their relationships and cardinality it includes the tables columns and their relationships with other tables in a database cardinality is the measure of elements in a given set and cardinality in erd shows the relationship between rows of one table with another in a numerical form like one to one one to many and many to many 1 implementation implementation classes for reverse engineering a reverseengineering java b topographicalsort java c drawerd java d erdexecutor java img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 035 png figure 37 flowchart of drawing erd by reverse engineering figure 37 shows the flowchart for extracting information from the global and local metadata for a database and its table information to create entity relationship diagram it includes the following steps a reading the global metadata for instance level information of a database to local tables and related local metadata b accessing the local metadata for column information constraints and relationships for including them in the erd c find related tables from the metadata and foreign key relationships for creating a dependency graph d create a dependency graph using topographical sort 3 4 for drawing dependent tables together topographicalsort will order the tables considering the tables that have dependencies to arrive after the tables they are dependent on e draw erd with table details identified relationships and in recognized order psuedo code erdexecutor java 1 take input from console for database name 2 check if database exists if exists goto 3 else goto 1 3 call reverseengineering java class to get rankorder and dependencygraph for drawing erd using drawerd java reverseengineering java 1 fetch global metadata for the given database 2 get table and their instance details 3 read metadata of each table from their respective instances 4 find relationships between tables and store it in a hashmap 5 calculate cardinality of the relationship of two tables by identifying data uniqueness 6 if all table metadata are read goto 7 else goto 3 7 send the dependencygraph to topographicalsort java for generating sort order and create rankedtables 8 return rankedtables to erdexecutor java drawerd java figure 38 will take the information and create a graphical representation img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 036 png figure 38 drawerd code snippet 1 process flow img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 037 png figure 39 flowchart of using data model function from console figure 39 shows flowchart to use the data model feature from the console to create an erd for a given database using reverse engineering 1 example a user can use the console to select data model option to create the erd for a given database figure 40 figure 41 shows the global metadata of university database with figure 42 43 44 having students courses and professors metadata respectively figure 45 is the erd diagram generated by reverse engineering for the given tables img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 038 png figure 40 console menu for selecting data model img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 039 png figure 41 global metadata for tables metadata and instance detail img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 040 png figure 42 students table local metadata img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 041 png figure 43 professor table local metadata img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 042 png figure 44 courses table local metadata img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 043 png figure 45 erd for students professor and courses table 14 login signup module basically this part of the code is responsible for authenticating the user that if the user is already registered or not the upcoming part will explain the core algorithm with the help of code snippets and a high level er diagram img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 044 png figure 46 login signup module as we can see in the above screenshot the program first gives the user an option to select that is the user has already been registered and directly want to sign up or register himself according to the selection the code will take the user to the login or signup page suppose the user wants to register himself then firstly the program will ask for the unique user id as well as password after that the program will ask for a set of questions in the next step after validating the program will encrypt the password string with sha 1 of size 32 byte and store it into the user profile txt as seen in the above screenshot after doing these all the users will be redirected to the login page where they can log in using the recently created userid password and any given randomized question in the login part first the program will ask the user for inputting the login userid and the password and answer any randomized question then according to the answer the program will validate the user it ll decrypt the password in the database of the user and match it with the given details provided by the user if they will match then the user will be redirected to the console page where he can select the options and if not then again the user needs to log in as seen in the er diagram now let s deep dive into the detailed section of the code img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 045 png figure 47 login code snippet this is the login logic of the code the function has been triggered every time user pressed 1 from the login signup screen in the code first it asks for the user id adn the password then it retrieved the appropriate password from the user profile txt file decryptys the password and then matches it with the user given password if both the password matches then it moves to the console screen else it redirects again to the login page of the module img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 046 png figure 48 errors authentication code snippet as we can see if any error occurs then the code will store the appropriate errortype and displays to the user that because of which reason the code is been breaked i e wrong username wrong password wrong anwer to the question etc because of this the user will know the exact reason and check is he she made any mistake img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 047 png figure 49 register code snippet now this is the register logic of the code as seen in the screenshot 4 first the code takes all the inputs from the user then it encrypts the password and stores the encrypted password into the user profile txt file by doing this the program provides the level of security to the code after registering as the logic says if the registration is successful then the page will redirect the user to the login page again by doing this again the level of security is been achieved now lets understand how the code is doing encryption and decryption to the password string img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 048 png figure 50 encrypt decrypt code snippet there are two functions called encrypt and decrypt inside the security class file basically both the functions take strings as input and return encrypted decrypted string as an output in the encrypt function it takes a normal string as an input and then as shown below goes into the code and settes the key img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 049 png figure 51 setting the key in cypher code snippet as we can see here this code first settes the message digest to the sha 1 algorithm and the keyset to the utf 8 in the last step the code settes the size of the key to 32 which is even more secure than the 16 the downside is that it takes more space but as our program is only going to have some number of users the space isn t a big issue for us in the decryption part of the code the function basically takes the encrypted text as an input and by using the cypher init function it will decrypt the code to it s original form from here we can directly match the decrypted string to the original string and if they ll match then we can conclude that the user is been authenticated now let s move to the next module which is user console from where the user can select the number of inputs to do the appropriate img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 050 png figure 52 console erd as we can see from the screenshot 6 and screenshot 1 the function moves to the console page here first to provide security it checks if the user is authintacated or not if the user is then it ll show totally 4 options as shown below they are a part of userinput function img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 051 png figure 53 console output for the actual selection the code is using the picker function it takes the number as a input and redirects the page to appropriate place as we can see in the screenshot 6 the code example is given below img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 052 png figure 54 choosing the appropriate function for console input as we can see according to the number chosen it ll redirect the user to the page on number 1 it ll go to the writequeries on selecting number 2 it ll redirect to the dump generator which will basically generates the datadump for the data on selecting number 3 it ll move to the erd phase where it ll create the erdiagram for the code and by selecting number 4 it ll move to the analyse part where it ll show all the logs and everything that has ever done according to the instances to the code and by which user as well now if the user selects 1 it ll move to the writequeries function let s understand that in detail img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 053 png figure 55 writequeries flowchart as we can see from the high level architecture first the writequery constructor takes the username as an input as it s been used at number of places in the code the first function been called is takeinput the function first asks for the query string then if the query is a take input or commit type then directly redirects it to the setdataandexecutequery function but if not the first it sends the query to processquery function where basically it checks is the query is valid or not and if it is then it sents the required variables for the later use like the tablename dbname etc this function is been made to save some computation if the query is wrong then there is no need to process it further and discard it in the first place after the successful parsing the next step is as shown below img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 054 png figure 56 code snippet for takeinput as we can see the query ignores the step is it s related to he transactions as there s no need to check the query when we can directly match the string and for the later part if the query is correct then the success will biome true else false if it s true then it ll be moved to the setdataandexecurequery string part which basically executes the query by using set of method and if else blocks the transaction is been taken care of there as well img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 055 png figure 57 queries if else code snippet in the setdataandexecutequery function is made from number of if else blocks like in screenshot 11 it basically checks for the query type then the response has all the data fields setted up and at the last step sets all the login parameters to the loginparameters object and for the every type of query there is a createquery type of functions like selectquery updatequery etc where the query is been processed and handles and do the appropriate to the query in the last step it ll return the response which gives the status that if the query is been passed or not meanwhile in every step the function is storing the step into the logs and it gives the appropriate data values to the function as well the class has been called is login parameters the logs will be written by calling the log writelogs function by taking the loginparameters as an input img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 056 png figure 58 transactions code snippet to manage the transactions as shown in the code block it first checks the type of query is its start transaction or commit type then it won t go to the other normal query types first it ll just set the auto commit to false and it ll go to the else block of the remaining query please refer to the full code to get a better idea explained here img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 057 png figure 59 check transactions condition in the else block as we can see it basically just stores the query into the arraylist the function transaction feedtransactionarray has a arraylist built inside and after calling the commit as we can see in screenshot 12 the function will set the auto commit to true and then one by one by using the for loop as shown in the shot 12 it ll pass the queries to the if else blocks as shown into the screenshot 11 after executing every block it ll print the response of every query and add the logs as well img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 058 png figure 60 transactions flowchart as we can see from the er diagram first if the query is started transaction type then it ll set the autocommit to the false and if it s a commit then to the true then after the start transaction every query called has been stored into the arraylist and after the commit is been called and at the same time if the autocommit is been called true then if both the conditions been satisfied then the function will be moved to the executequery part where each query is been executed as explained in the above part 15 query parser use case query parser is a stateless layer of the distributed system it does not have any idea about the processing logic of another layer of a distributed system the responsibility of the query parser is to parse the sql query and extract query data from the sql statement and send it to the query handler layer whenever the user receives sql query input it is first validated by the query parser that it is a valid sql query or not if it is not a valid sql statement then the query parser throws an invalid sql statement exception if the given sql statement is valid then the query parser parses the statement and extracts data such as a table database name columns name column values and constraints such as foreign key and primary key we have implemented a query parser by abiding by the best practices of java development for each type of sql statement such as select create delete so on a separate parser is created depending on the kind of statement individual parser is called then that parser parses the sql statement and extracts data from the statement and sends extracted data in form of a java object query handler for execution implementation classes for query parser 1 writequeries java 1 createdatabaseprocessor java 1 createqueryprocessor java 1 deletequeryprocessor java 1 insertqueryprocessor java 1 selectqueryprocessor java 1 updatequeryprocessor java 1 usedatabasequeryprocessor java 1 queryparser java 1 queryparserexecutor java pseudocode for sql parser 1 start 1 receive input from the user console 1 check the validity of user input 1 throw exception if user input is not valid 1 call query parser executor if user input is valid and extract data from the sql statement 1 select the associated query processor based on the type of sql query 1 send data to the query executor layer 1 get a response after query execution 1 print response to the console 1 end types of sql statements supported create table in create query int varchar and float data types are supported create a table with a primary key create a table with a foreign key insert query insert query without column names insert query with column names normal query execution and as a transaction start transaction commit select query select query with single column and multiple columns select query with operator select query with and without where clause select query with operator in where clause update query update query with and without where clause update query with operator in where clause delete query delete query with and without where clause delete query with operator in where clause use database query create database query sql statements supported create table create table professor professorid varchar name varchar designation varchar create table courses courseid int primary key name varchar professorid int foreign key references professor professorid create table students courseid int foreign key references courses courseid name varchar marks int insert query insert into professor professorid name designation values p006204 saurabh dey professor insert into students values csci5308 vatsal 81 insert query insert into professor professorid name designation values p006204 saurabh dey professor insert into students values csci5308 vatsal 81 process flow img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 059 png width 250 vspace 20 hspace 5 figure 61 flow chart for query parser img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 060 png figure 62 invalid use database query img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 061 png figure 63 valid create table query img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 062 png figure 64 parse query code snippet img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 063 png figure 65 process query code snippet img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 064 png figure 66 code to extract query details img src https github com vatsalyadav distributed database blob main readme aspose words 7f24a363 5f67 4b85 8d0b b754453ecec0 065 png figure 67 set data method for further execution meeting logs meeting number date duration attendees reason location 1 2022 02 03 9 00 53m all member brainstorming and discussion on the understanding the problem better successfully achieved teams 2 2022 02 07 10 00 1h37m all member dividing problem in to subproblems teams 3 10 feb 1h47 all member decided on having an in person meeting discussion on coming monday to finalise our design teams 4 2022 03 15 1h all member discuss about the design and architecture teams 5 2022 04 07 entire day all member working on project goldberg 6 2022 04 08 entire day all member working on project goldberg 7 2022 04 09 entire day all member working on project goldberg 8 2022 04 10 entire day all member integration and fix bugs goldberg 9 11 apr 22 12m all member prepare for the group presentation teams references 1 jcraft jsch java secure channel online available http www jcraft com jsch accessed 14 apr 2022 2 java jsch example to run shell commands on ssh unix server journaldev 03 mar 2021 online available https www journaldev com 246 jsch example java ssh unix https www journaldev com 246 jsch example java ssh unix server server accessed 14 apr 2022 3 topological sorting wikipedia 27 may 2021 online available https en wikipedia org wiki topological sorting accessed 14 apr 2022 4 topological sort with good explanation to understand s vignesh leetcode com 4 apr 2022 https leetcode com problems course schedule discuss 1912984 topological https leetcode com problems course schedule discuss 1912984 topological sort with good explanation to understand sort with good explanation to understand accessed 14 apr 2022 5 f dib regex101 build test and debug regex regex101 2022 online available https regex101 com r rrvdax 1 accessed apr 14 2022 6 http www facebook com howtodoinjava java aes encryption decryption example howtodoinjava howtodoinjava jun 10 2016 online available https howtodoinjava com java java security java aes encryption example accessed apr 14 2022 | server |
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ckb | nervos ckb https www nervos org the common knowledge base version https img shields io badge version 0 113 0 pre orange svg https github com nervosnetwork ckb releases nervos talk https img shields io badge discuss on 20nervos 20talk 3cc68a svg https talk nervos org t where to discuss ckb and how to ask for support 6024 master develop unit tests https github com nervosnetwork ckb actions workflows ci unit tests ubuntu yaml badge svg branch master https github com nervosnetwork ckb actions workflows ci unit tests ubuntu yaml query branch 3amaster unit tests https github com nervosnetwork ckb actions workflows ci unit tests ubuntu yaml badge svg branch develop https github com nervosnetwork ckb actions workflows ci unit tests ubuntu yaml query branch 3adevelop integration tests https github com nervosnetwork ckb actions workflows ci integration tests ubuntu yaml badge svg branch master https github com nervosnetwork ckb actions workflows ci integration tests ubuntu yaml query branch 3amaster integration tests https github com nervosnetwork ckb actions workflows ci integration tests ubuntu yaml badge svg branch develop https github com nervosnetwork ckb actions workflows ci integration tests ubuntu yaml query branch 3adevelop about ckb ckb is a public and permissionless layer 1 blockchain ckb uses proof of work https en wikipedia org wiki proof of work system and improved nakamoto consensus https medium com nervosnetwork breaking the throughput limit of nakamoto consensus ccdf65fe0832 to achieve maximized performance on average hardware and network bandwidth without sacrificing layer 1 s core values decentralization and security ckb supports scripting in any programming language with its own ckb vm https github com nervosnetwork ckb vm a virtual machine fully compatible with risc v isa ckb is a universal verification layer https medium com nervosnetwork https medium com nervosnetwork cell model 7323fca57571 which focuses on verification leaves computation to layer 2 and higher applications protocols ckb https github com nervosnetwork rfcs blob master rfcs 0002 ckb 0002 ckb md is a part of nervos network https www nervos org which defines a suite of scalable and interoperable blockchain protocols https github com nervosnetwork rfcs to create a self evolving distributed economy support for different platforms are organized into three tiers docs platform support md each with a different set of guarantees notice the ckb process will send stack trace to sentry on rust panics this is enabled by default before the mainnet launch which can be opted out by setting the option dsn to empty in the config file license fossa status https app fossa io api projects git 2bgithub com 2fnervosnetwork 2fckb svg type shield https app fossa io projects git 2bgithub com 2fnervosnetwork 2fckb ref badge shield nervos ckb is released under the terms of the mit license see copying copying for more information or see https opensource org licenses mit https opensource org licenses mit join a network mainnet mirana use the latest release https github com nervosnetwork ckb releases latest and run ckb init chain mainnet to initialize the node mirana is active since the epoch 5414 see the migration guide https github com jordanmack nervos ckb2021 hard fork migration guide to upgrade from lina testnet pudge use the latest release https github com nervosnetwork ckb releases latest and run ckb init chain testnet to initialize the node pudge is active since the epoch 3113 see more networks to join in the wiki https github com nervosnetwork ckb wiki chains mining ckb uses the eaglesong https github com nervosnetwork rfcs blob master rfcs 0010 eaglesong 0010 eaglesong md mining algorithm development process the master branch is regularly built and tested it is considered already production ready the develop branch is the work branch to merge new features and it s not stable the changelog is available in releases https github com nervosnetwork ckb releases and changelog md https github com nervosnetwork ckb blob master changelog md in the master branch how to contribute the contribution workflow is described in contributing md contributing md and security policy is described in security md security md to propose new protocol or standard for nervos see nervos rfc https github com nervosnetwork rfcs documentations latest version https github com nervosnetwork ckb documentations is hosted in github the default branch in github is develop if you are looking for docs for the mainnet mirana or testnet pudge switch to the branch master master https github com nervosnetwork ckb tree master documentations quick start docs quick start md configure ckb docs configure md platform support docs platform support md how to download or build ckb binary https docs nervos org docs basics guides get ckb how to download or build ckb binary on windows https docs nervos org docs basics guides ckb on windows you can find a more comprehensive document website at https docs nervos org https docs nervos org | nervos blockchain ckb rust | blockchain |
CTR | compositional task representation in large language models this repository serves primarily as codebase for training evaluation and inference of ctr overview model architecture figure ctr png in this paper we propose the compositional task representations ctr method that learns a discrete compositional codebook for tasks and generalizes to new unseen tasks by forming new compositions of the task codes for the inference of ctr we propose two algorithms code ensemble and bitwise search respectively for zero label and few shot settings model architecture figure model architecture png please find more details of this work in our paper https openreview net pdf id 6aximja7me3 setup 1 run git clone https github com shaonan1993 ctr git to download the repo 2 run cd ctr to navigate to root directory of the repo 3 build experimental environment download download docker env from google driver https drive google com file d 1gfyz5pshsgi0xiltovora21cqhcclgwq view usp share link or baidu driver https pan baidu com s 1tmq1ya7gartvvhiuvhbjxq pwd 7es2 run command sudo docker run itd name codebook v you dir share gpus all shm size 8gb zxdu20 glm cuda115 bin bash run command sudo docker exec it codebook bash run command pip install torch 1 11 0 cu115 cp38 cp38 linux x86 64 whl run command pip install transformers 4 15 run command pip install datasets 1 16 4 download processed training data cd data download t0 combined raw data no prompt zip from google driver https drive google com file d 1 ap8ssedph1xahilsiqjlxt8kz6dsjt7 view usp share link or baidu driver https pan baidu com s 1cc9 lnyun1 9q8entvczq pwd 6hbh download t0 combined raw data zip from google driver https drive google com file d 1nf930spktn6mxikblbbmxzz75dwsdao0 view usp share link or baidu driver https pan baidu com s 14bf4hc97rvi5nla vevrma pwd w2tj unzip t0 combined raw data no prompt zip unzip t0 combined raw data zip 5 download and preprocess pretrained model cd pretrained models download t5 large lm adapt https huggingface co google t5 large lm adapt cd python utils move t5 py 6 optional download our existing checkpoint from google driver https drive google com file d 13nvoh7skdrlb8jjsdpwn0jmyzvblm8ib view usp share link or baidu driver https pan baidu com s 11yahf8wzpkksztex7ekdpw pwd 2tjf 7 optional download our generated p3 dataset manual prompt version baidu driver https pan baidu com s 1zzk7usgejcoehtmyhm2xya pwd 6xcp no prompt version inputs are a direct concatenation of multiple text fields baidu driver https pan baidu com s 1zlancboti59v1g murd9hq pwd 8dka note our generated p3 dataset is slight different from original p3 https huggingface co datasets bigscience p3 dataset we remove some unused dataset drop duplicate examples and fix some bugs tips we highly recommend everyone to run our code in the same experimental environment or the results may unexpected due to the loss of precision derived from bfloat16 training evaluating t0 large baseline 1 run sh scripts run pretrain t0 large sh to train a t0 large baseline from scratch 2 run sh scripts run eval t0 large sh to evaluate the t0 large baseline 3 run python stat result py to print all results in your terminal note for t0 large baseline we use the same task prompts from promptsource https github com bigscience workshop promptsource training ctr 1 run sh scripts run pretrain t0 vae large no prompt sh to train ctr for the first stage 2 run sh scripts run pretrain t0 vae large no prompt pipe sh to train ctr for the second stage note for model with ctr we do not use manual prompt if you want to generate you p3 dataset from scratch please refer to utils preprocess for p3 py not recommend extremely tedious and time consuming evaluate ctr code ensemble for zero label learning run sh scripts run zerolabel t0 vae large no prompt sh to select a task code with the highest pseudo label accuracy bitwise search for few shot learning 1 run sh scripts run fewshot discrete search sh to find best task code of train tasks 2 run sh scripts run fewshot bitwise search sh select the task code with the minimal loss at first step as an initialization and then bitwise search a better task code citation if you find this resource useful please cite the paper introducing ctr bibtex inproceedings shao2023compositional title compositional task representations for large language models author nan shao and zefan cai and hanwei xu and chonghua liao and yanan zheng and zhilin yang booktitle the eleventh international conference on learning representations year 2023 url https openreview net forum id 6aximja7me3 | ai |
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dorm.js | dorm js http dorm fronteed com desktop or mobile in development will be available soon device types desktop mobile phone tablet tv console bot options options are fetched automatically from html data dorm attribute format prefix dorm classes true and window dormoptions object should defined before dorm js is initiated prefix string true false default value dorm classes true false default value true desktopredirect string default value null mobileredirect string default value null phoneredirect string default value null tabletredirect string default value null tvredirect string default value null consoleredirect string default value null botredirect string default value null desktopcallback function default value null mobilecallback function default value null phonecallback function default value null tabletcallback function default value null tvcallback function default value null consolecallback function default value null botcallback function default value null desktop string true false null device type default value null mobile string true false null device type default value null phone string true false null device type default value null tablet string true false null device type default value null tv string true false null device type default value null console string true false null device type default value null bot string true false null device type default value null methods init options domnode initiates dorm js with options parse string parses useragent string and returns object assign target sources internal object assign cross browser helper trim variable internal string trim cross browser helper browser support almost every browser and device is supported | front_end |
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07.Natural-Language-Processing | 07 introduction to deep learning for natural language processing repository for my natural language processing study and code implementation what i have learned 01 natural language processing 02 text preprocessing 03 language model 04 count based word representation 05 document similarity 06 topic modeling 07 machine learning 08 deep learning 09 recurrent neural network 10 word embedding 11 rnn text classification 12 nlp convolution neural network 13 tagging task 14 subword tokenizer 15 rnn 16 attention mechanism 17 transformer 18 bert bidirectional encoder representations from transformers 19 text summarization 20 question answering qa you can see and download in each folder | natural-language-processing word-embeddings text-classification attention-mechanism transformer bert | ai |
ITP_Factory-and-Sales-management-System | factory and sales management system group project of 2nd year 2nd semester information technology itp module about this is a simple and efficient system mainly in this project we have discussed how a company can maximize their daily works by implementing a smart employee management system customer management system supplier management system raw materials management system final product inventory payroll management utilities management system and transportation and scheduling management system in one web based application beyond that we have included functions like generating reports for each function for decision making purposes my contribution https github com dulyaaa itp transportation and scheduling management technologies used reactjs https img shields io badge frontend reactjs yellow springboot https img shields io badge backend springboot green mongodb https img shields io badge database mongodb green bootstrap https img shields io badge style bootstrap purple material ui https img shields io badge style materialui blue language used javascript https img shields io badge language javascript orange html https img shields io badge language html green css https img shields io badge language css blue goals remove the old fashioned pen and paper method when making reports and recording details system able to deliver an order on time increase the trustworthy between customer and company saler common user interfaces img width 700 alt 1png align center src https user images githubusercontent com 57215584 96398641 ef9c9280 11e9 11eb 95ce 625e010bb691 png img width 700 alt 2png align center src https user images githubusercontent com 57215584 96398662 fdeaae80 11e9 11eb 8245 3ec86886eda6 png some of helpful commands are below this project was bootstrapped with create react app https github com facebook create react app available scripts in the project directory you can run npm start runs the app in the development mode br open http localhost 3000 http localhost 3000 to view it in the browser the page will reload if you make edits br you will also see any lint errors in the console npm test launches the test runner in the interactive watch mode br see the section about running tests https facebook github io create react app docs running tests for more information npm run build builds the app for production to the build folder br it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes br your app is ready to be deployed see the section about deployment https facebook github io create react app docs deployment for more information npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it | reactjs springboot mongodb css3 html5 javascript boostrap material-ui jspdf | server |
ML-Study-Guide | how to get started with ml this my recommended study guide to get started with machine learning watch the video on youtube https youtu be wtolixa9xtg 1 math learn some math basics focus only on these topics then come back later in case you need to learn more khan academy multivariable calculus https www khanacademy org math multivariable calculus khan academy differential equations https www khanacademy org math differential equations khan academy linear algebra https www khanacademy org math linear algebra khan academy statistics probability https www khanacademy org math statistics probability optional 3blue1brown essence of linear algebra https www 3blue1brown com essence of linear algebra page 2 learn python 4h beginner course https youtu be rfscvs0vtbw 6h intermediate python programming course https youtu be hgobqpfzwko 3 learn the ml tech stack numpy 1h numpy crash course https youtu be 9juapgtkkpi pandas 1h pandas crash course https youtu be vmehcjofslg matplotlib 1h matplotlib crash course course https youtu be 3xc3ca655y4 scikit learn and tensorflow are taught in step 4 pytorch is optional maybe in step 7 4 machine learning courses machine learning specialization andrew ng coursera https www coursera org specializations machine learning introduction 3 courses optional machine learning from scratch https youtube com playlist list plqnslrfeh2upcrywf u2etjdxxkl8nl7e 5 hands on data preparation kaggle intro to machine learning https www kaggle com learn intro to machine learning kaggle intermediate machine learning https www kaggle com learn intermediate machine learning 6 practise solve challenges and build your own projects with datasets from kaggle com kaggle com 7 specialize create blog specialize in one field e g computer vision nlp etc look at requirements in corresponding job descriptions and learn those skills tip create a blog and share tutorials and what you have learned books if you prefer learning with books these are great recommendations hands on machine learning with scikit learn keras and tensorflow https www oreilly com library view hands on machine learning 9781492032632 machine learning with pytorch and scikit learn https www packtpub com product machine learning with pytorch and scikit learn 9781801819312 | ai |
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predix-ui | predix ui unoffical react components that implement predix s design system css build status https travis ci org jonniespratley predix ui svg branch develop https travis ci org jonniespratley predix ui style styled components https img shields io badge style f0 9f 92 85 20styled components orange svg colorb daa357 colora db748e https github com styled components styled components dependency status https img shields io david jonniespratley predix ui svg https david dm org jonniespratley predix ui npm downloads https img shields io npm dm predix ui svg npm version https img shields io npm v predix ui svg module formats https img shields io badge module 20formats umd 2c 20cjs 2c 20esm green svg for more information on predix ui visit https www predix ui com home https www predix ui com home why well the predix ui library is built with polymer this project attempts to bring those components to react usage to use this library simply install it with npm or yarn code npm install predix ui then import the components you want to use if you re not using a module bundler or package manager we also have a global umd build hosted on the unpkg cdn simply add the following script tag to the bottom of your html file code script src https unpkg com predix ui dist predix ui min js script js import card from predix ui card headertext my card this is a card card theme the component styles are all in separate css files or you can use the entire theme code link rel stylesheet prefetch href https unpkg com predix ui dist predix ui min css link rel stylesheet prefetch href https unpkg com predix ui dist px dark theme min css starter project you can get started quickly with this starter project html div class glitch embed wrap style height 450px width 100 iframe src https glitch com embed embed jps react predix ui starter path src components app index js previewsize 100 alt jps react predix ui starter on glitch style height 100 width 100 border 0 iframe div license see license for more details | predix-ui react-components | os |
modeling-postgresql | img src img postgres jfif alt postgresql title postgresql width 500 etl data modeling sparkify streaming music this project builds an etl extract transform load pipeline to perform big data operations for a fictitious streaming music app sparkify this etl pipeline uses a postgresql database optimized for read queries i e olap online analytical processing img src img etl jpg alt amazon s3 width 500 title amazon s3 align middle this etl pipeline employs a star schema for the database this star schema has a log table of songplays as its fact table at the star center data stored in json format illustrate song play data from 30 event log files spanning the month of november 2018 this data is artificially produced using eventsim https github com interana eventsim the dimension tables situated at the star s peripheries are tables named for users songs artists and time timestamps this real musical data stored in 71 json files comes from the million song dataset http millionsongdataset com image info img sparkify erd png execution instructions for proper operation these five files are to exist in the same directory instructions are written in the etl ipynb notebook for database write operations with prompts corresponding to read operations in test ipynb after running code blocks in test ipynb always restart this notebook s kernel to close its database connection this frees up exclusive access to sparkify database allowing etl ipynb to connect the etl pipeline in etl py relies on the database set up done by create tables py so please run this before running etl py repository files python scripts create tables py driver file defines functions to connect disconnect to sparkify s database as well as to create and drop tables calling upon table attributes found in sql queries py uses psycopg2 a postgresql database adapter for python sql queries py crud operations on tables commands to create read update and delete using sql commands drop insert and select table attributes are defined here etl py contains execution instructions identical to etl ipynb in one compact script jupyter notebook files etl ipynb step by step walkthrough to produce extract transform and load operations for sparkify s database test ipynb performs database reads to verify proper execution of write operations from create tables py sample query this query should retrieve one record since it s a subset of a significantly larger big data set select from songplays where song id is not null and artist id is not null | server |
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lion | p align center img width 20 src docs assets logo svg alt lion h1 style text align center lion h1 p p align center a href https github com ing bank lion issues img src https badgen net github open issues ing bank lion alt lion open issues status a a href https github com ing bank lion pulls img src https badgen net github open prs ing bank lion alt github open pull requests status a a href https www tickgit com browse repo github com ing bank lion img src https badgen net https api tickgit com badgen github comgithub com ing bank lion alt todos a p p align center a href https lion web netlify app website a a href https lion web netlify app fundamentals fundamentals a a href https lion web netlify app guides guides a a href https lion web netlify app components components a a href https lion web netlify app blog blog a p lion is a set of highly performant accessible and flexible web components they provide an unopinionated white label layer that can be extended to your own layer of components high performance focused on great performance in all relevant browsers with a minimal number of dependencies accessibility aimed at compliance with the wcag 2 2 aa standard to create components that are accessible for everybody flexibility provides solutions through web components and javascript classes which can be used adopted and extended to fit all needs modern code lion is distributes as pure es modules exposes functions classes and web components ships a functionality in it s most appropriate form note our demos may look a little bland but that is on purpose they only come with functional stylings this makes sense as the main use case is to extend those components and if you do you do not want to override existing stylings p align center a href https lion web netlify app guides strong explore the lion guides nbsp nbsp strong a p how to install bash npm i lion ui how to use extend a web component this is the main use case for lion to import component classes and extend them for your own design system s components js import css from lit import lioninput from lion ui input js class myinput extends lioninput static get styles return super styles css your styles here customelements define my input myinput use a javascript system there s a couple of systems in lion which have a javascript api examples are localize overlays ajax etc html script type module import ajax from lion ui ajax js ajax fetch data json then response response json then data do something with the data script use a web component you can also use the lion elements directly although this is likely not a common use case html script type module import lion ui define lion input js script lion input name firstname lion input issues if you encounter an issue with any of the packages we are offering please open a new bug issue https github com ing bank lion issues new assignees labels template bug report md title be sure to include a description of the expected and the current behavior additional adding a reproduction https webcomponents dev edit kpzmz1cjn580oaxsk56f pm 1 always helps feature requests when you have an idea on how we could improve please check our discussions https github com ing bank lion discussions to see if there are similar ideas or feature requests if there are none please start https github com ing bank lion discussions new your feature request as a new discussion topic add the title feature request my awesome feature and a description of what you expect from the improvement and what the use case is content name version description lion ui https github com ing bank lion tree master packages ui img src https img shields io npm v lion ui svg alt lion ui version set of components lion ajax https github com ing bank lion tree master packages ajax img src https img shields io npm v lion ajax svg alt lion ajax version a small wrapper around fetch singleton manager https github com ing bank lion tree master packages singleton manager img src https img shields io npm v singleton manager svg alt singleton manager version a singleton manager provides a way to make sure a singleton instance loaded from multiple file locations stays a singleton primarily useful if two major version of a package with a singleton is used babel plugin extend docs https github com ing bank lion tree master packages node babel plugin extend docs img src https img shields io npm v babel plugin extend docs svg alt babel plugin extend docs version a plugin which rewrites imports and templates according to a configuration this enables the reuse of existing documentation from source packages while still using your extensions code providence analytics https github com ing bank lion tree master packages node providence analytics img src https img shields io npm v providence analytics svg alt providence analytics version providence is the all seeing eye that generates usage statistics by analyzing code it measures the effectivity and popularity of your software with just a few commands you can measure the impact for breaking changes making your release process more stable and predictable publish docs https github com ing bank lion tree master packages node publish docs img src https img shields io npm v publish docs svg alt publish docs version a tool that copies and processes your documentation in a monorepo so it can be published shipped with your package remark extend https github com ing bank lion tree master packages node remark extend img src https img shields io npm v remark extend svg alt remark extend version a plugin for remark to extend markdown by importing from source files rocket preset extend lion docs https github com ing bank lion tree master packages node rocket preset extend lion docs img src https img shields io npm v rocket preset extend lion docs svg alt rocket preset extend lion docs version when maintaining your own extension layer of lion you most likely want to maintain a similar documentation copying and rewriting the markdown files works but whenever something changes you need copy and rewrite again to do this automatically you can use this preset for rocket https rocket modern web dev technologies lion web components aims to be future proof and use well supported proven technology the stack we have chosen should reflect this lit https lit dev npm http npmjs com open web components https open wc org modern web https modern web dev mocha https mochajs org chai https www chaijs com eslint https eslint org prettier https prettier io es modules https developer mozilla org en us docs web javascript reference statements import lots and lots of tests rationale we know from experience that making high quality accessible ui components is hard and time consuming it takes many iterations a lot of development time and a lot of testing to get a generic component that works in every context supports many edge cases and is accessible in all relevant screen readers lion aims to do the heavy lifting for you this means you only have to apply your own design system by delivering styles configuring components and adding a minimal set of custom logic on top coding guidelines check out our coding guidelines https lion web netlify app guides principles definitions and terms for more detailed information how to contribute please note this project uses npm workspaces https docs npmjs com cli v8 using npm workspaces if you want to run all demos locally you need to get at least npm 7 and install all dependencies by executing npm install lion web components are only as good as its contributions read our contribution guide https github com ing bank lion blob master contributing md and feel free to enhance improve lion we keep feature requests closed while we re not working on them contact feel free to create a github issue for any feedback or questions you might have | hacktoberfest lion lit-html javascript web-components modern-web open-wc | os |
Progressive-Web-Application-Development | progressive web application development progressive web application development published by packt progressive web application development video this is the code repository for progressive web application development video https www packtpub com application development progressive web application development video utm source github utm medium repository utm campaign 9781787285958 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the video course from start to finish about the video course this course teaches you how to build discoverable and engaging progressive web applications pwas using the cache api to make it offline ready and blazingly fast service workers to intercept network requests and web app manifests thus leveraging native like features we begin by introducing the core concepts of progressive web apps explaining each of them in detail and finishing up by implementing them into a production ready app a service worker is the main pwa tool in this course you will master the power of new apis including the fetch api promises and more don t lose your users when their internet connection is lost get hands on with powerful caching and network request strategies to provide synchronization of data while your app is offline learn how to improve user engagement with your apps by adding push notifications we also cover app manifests in depth to let your users add your application to their mobile home screen and reopen the app as easily as tabbing on the web app icon just as with other native mobile apps they ll also surf while there is no address bar the user experience feels like a native mobile app due to the addition of a splash screen application shell native default color and more tools are always helpful while developing software therefore this course teaches you how to boost your productivity by using workbox to make service worker management a lot easier and lighthouse to show your pwa score demonstrate and how you can achieve 100 100 all of the topics in this course feed into a practical project which by the end of the course is ready to deploy to production to ensure that the app is useful for any kind of project later and also that you can learn everything in depth the course project is created in pure js css html last but not least spas single page applications are super critical as these days they perform such a leading role in web development and building hybrid and native mobile apps therefore this course dedicates a section to show you how you can use angular react ember and vue js to build a progressive web application h2 what you will learn h2 div class book info will learn text ul li build web apps that look and feel like native mobile apps nbsp li leverage new web technologies such as the fetch api and promises li audit and improve a pwa with different tools li convert an existing app to a pwa li boost your web app speed with the help of progressive enhancements and approaches li increase user engagement by using push notifications li in depth details about service workers li offline storage and different caching apis li ul div instructions and navigation assumed knowledge to fully benefit from the coverage included in this course you will need br this course is aimed at developers and engineers who want to build blazingly fast web applications a basic knowledge of html and css will be useful but is not mandatory technical requirements this course has the following software requirements br this course has the following software requirements an editor like atom sublime text or visual studio code node js version 9 x lts npm install npm start this course has been tested on the following system configuration install chrome firefox safari we will use chrome dev tools to develop our app related products advanced web development with django video https www packtpub com web development advanced web development django video utm source github utm medium repository utm campaign 9781788628587 web development with angular and php video https www packtpub com web development web development angular and php video utm source github utm medium repository utm campaign 9781788394321 web development with angular and php video https www packtpub com web development web development angular and php video utm source github utm medium repository utm campaign 9781788394321 | front_end |
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dotNET-FrontEnd-to-BackEnd-on-Azure-Container-Apps | asp net core front end 2 back end apis on azure container apps this repository contains a simple scenario built to demonstrate how asp net core 6 0 can be used to build a cloud native application hosted in azure container apps the repository consists of the following projects and folders store a blazor server project representing the frontend of an online store the store s ui shows a list of all the products in the store and their associated inventory status products api a simple api that generates fake product names using the open source nuget package bogus https github com bchavez bogus inventory api a simple api that provides a random number for a given product id string the values of each string integer pair are stored in memory cache so they are consistent between api calls azure folder contains azure bicep files used for creating and configuring all the azure resources github actions workflow file used to deploy the app using ci cd what you ll learn this exercise will introduce you to a variety of concepts with links to supporting documentation throughout the tutorial azure container apps https docs microsoft com azure container apps overview github actions https github com features actions azure container registry https docs microsoft com azure container registry azure bicep https docs microsoft com azure azure resource manager bicep overview tabs bicep prerequisites you ll need an azure subscription and a very small set of tools and skills to get started 1 an azure subscription sign up for free https azure microsoft com free 2 a github account with access to github actions 3 either the azure cli https docs microsoft com cli azure install azure cli installed locally or access to github codespaces https github com features codespaces which would enable you to do develop in your browser topology diagram the resultant application is an azure container environment hosted set of containers the products api the inventory api and the store blazor server front end application topology docs media topology png internet traffic should not be able to directly access either of the back end apis as each of these containers is marked as internal ingress only during the deployment phase internet traffic hitting the store your app your region azurecontainerapps io url should be proxied to the frontend container which in turn makes outbound calls to both the products and inventory apis within the azure container apps environment setup by the end of this section you ll have a 3 node app running in azure this setup process consists of two steps and should take you around 15 minutes 1 use the azure cli to create an azure service principal then store that principal s json output to a github secret so the github actions ci cd process can log into your azure subscription and deploy the code 2 edit the deploy yml workflow file and push the changes into a new deploy branch triggering github actions to build the net projects into containers and push those containers into a new azure container apps environment authenticate to azure and configure the repository with a secret 1 fork this repository to your own github organization 2 create an azure service principal using the azure cli bash subscriptionid az account show query id output tsv az ad sp create for rbac sdk auth name webandapisample role owner scopes subscriptions subscriptionid 3 copy the json written to the screen to your clipboard json clientid clientsecret subscriptionid tenantid activedirectoryendpointurl https login microsoftonline com resourcemanagerendpointurl https brazilus management azure com activedirectorygraphresourceid https graph windows net sqlmanagementendpointurl https management core windows net 8443 galleryendpointurl https gallery azure com managementendpointurl https management core windows net 4 create a new github secret in your fork of this repository named azurespn paste the json returned from the azure cli into this new secret once you ve done this you ll see the secret in your fork of the repository the azurespn secret in github docs media secrets png 5 create a second github secret in your fork of this repository named azure subscription id provide the specific azure subscription you want to impact as the value for this secret ocne finished the two secrets names will show on the page the azurespn and subscription id secrets in github docs media secrets2 png note never save the json to disk for it will enable anyone who obtains this json code to create or edit resources in your azure subscription deploy the code using github actions the easiest way to deploy the code is to make a commit directly to the deploy branch do this by navigating to the deploy yml file in your browser and clicking the edit button edit the deployment workflow file docs media edit the deploy file png provide a custom resource group name for the app and then commit the change to a new branch named deploy create the deploy branch docs media deploy png once you click the propose changes button you ll be in create a pull request mode don t worry about creating the pull request yet just click on the actions tab and you ll see that the deployment ci cd process has already started build started docs media deploy started png when you click into the workflow you ll see that there are 3 phases the ci cd will run through 1 provision the azure resources will be created that eventually house your app 2 build the various net projects are build into containers and published into the azure container registry instance created during provision 3 deploy once build completes the images are in acr so the azure container apps are updated to host the newly published container images deployment phases docs media cicd phases png after a few minutes all three steps in the workflow will be completed and each box in the workflow diagram will reflect success if anything fails you can click into the individual process step to see the detailed log output note if you do see any failures or issues please submit an issue so we can update the sample likewise if you have ideas that could make it better feel free to submit a pull request deployment success docs media success png with the projects deployed to azure you can now test the app to make sure it works try the app in azure the deploy ci cd process creates a series of resources in your azure subscription these are used primarily for hosting the project code but there s also a few additional resources that aid with monitoring and observing how the app is running in the deployed environment resource resource type purpose storeai application insights this provides telemetry and diagnostic information for when i want to monitor how the app is performing or for when things need troubleshooting store an azure container app that houses the code for the front end the store app is the store s frontend app running a blazor server project that reaches out to the backend apis products an azure container app that houses the code for a minimal api this api is a swagger ui enabled api that hands back product names and ids to callers inventory an azure container app that houses the code for a minimal api this api is a swagger ui enabled api that hands back quantities for product ids a client would need to call the products api first to get the product id list then use those product ids as parameters to the inventory api to get the quantity of any particular item in inventory storeenv an azure container apps environment this environment serves as the networking meta container for all of the instances of all of the container apps comprising the app storeacr an azure container registry this is the container registry into which the ci cd process publishes my application containers when i commit code to the deploy branch from this registry the containers are pulled and loaded into azure container apps storelogs log analytics workspace this is where i can perform custom kusto https docs microsoft com azure data explorer kusto query queries against the application telemetry and time sliced views of how the app is performing and scaling over time in the environment the resources are shown here in the azure portal resources in the portal docs media azure portal png click on the store container app to open it up in the azure portal in the overview tab you ll see a url view the store s public url docs media get public url png clicking that url will open the app s frontend up in the browser the product list once the app is running docs media store ui png you ll see that the first request will take slightly longer than subsequent requests on the first request to the page the apis are called on the server side the code uses imemorycache to store the results of the api calls in memory so subsequent calls will use the cached payload rather than make live requests each time | azd-templates azure dotnet | front_end |
skylight | skylight thank you all for the incredible support a bright opening a clean window etymology according to dictionary com https www dictionary com browse skylight a skylight is an opening in a roof or ceiling fitted with glass for admitting daylight skylight is an operating system that is uniquely modeled and aims to be cleaner like a window and more efficient license as of june 18th 2022 i have updated the license on this repository to the mit license however any commits prior to commit 47912c3 still fall under the cc0 license applied at the time more information on the terms of the mit license see license license building skylight uses linux software to compile therefore any build process must take place under native linux or windows subsystem for linux additionally you require these packages util linux dosfstools mtools nasm clang build essential wget to build simply enter the root of the source tree and run make image a fat32 formatted hard drive image will be generated testing in order to test you need to ensure that your testing emulator hardware supports x86 64 uefi v2 0 pcie 256mib ram min to test in an emulator enter the source tree after building an image and type make run to test on real hardware meet the requirements above and write the image to a drive boot into it through your uefi boot menu development cycle os development is time consuming i have other things to dedicate my time to so i can t always work on skylight i will try to push at least one commit every week but i can t guarantee that i will be able to do so in summer months you can expect a high volume of commits and enhanced productivity december is also a rather popular month for me i have been working on this operating system project since mid 2021 and i don t intend on abandoning it just some times it s a literal pain to work on other times it s not the time to do it contributing i largely do not take contributions i do take suggestions and feedback but most of the source code should be written by me if the project plateaus and the work is no longer worth my dedication i may open it up to contributions to keep it alive but for now it s my brainchild | systems osdev | os |
mobile-dapp-scaffold | solana mobile dapp scaffold the solana mobile dapp scaffold is a repository template providing a fast and efficient way for developers to build decentralized mobile applications on the solana blockchain with the scaffold developers can quickly set up and get started with building their solana mobile dapp the scaffold includes pre built components integrated with features such as wallet connection airdrop sol devnet testnet sign messages and send transactions scaffold follows clean architecture and mvvm principles enabling efficient and robust mobile app development believe me there is no better time to build consumer mobile dapps https twitter com intent tweet text believe 20me 2c 20there 20is 20no 20better 20time 20to 20build 20consumer 20mobile 20dapps 22 20 20 40cdhiraj40 0a 0astart 20building 20on 20 40solanamobile 20today url https 3a 2f 2fgithub com 2fcdhiraj40 2fmobile dapp scaffold 2f 20 screenshots table thead tr th h3 home h3 th th h3 dashboard h3 th tr thead tbody tr td img src https user images githubusercontent com 75211982 232579436 c54da8e8 ee31 465c a921 9227bedc34c0 png alt image 1 width 400 td td img src https user images githubusercontent com 75211982 232579459 c5858817 52bb 4382 9d77 4cd989cdc860 png alt image 2 width 400 td tr tbody table getting started to start using scaffold follow the below steps 1 click on use this template dropdown 2 select create a new repository structure by adhering to best practices for architecture and design patterns the scaffold ensures that developers can create applications that are easy to maintain and extend with a separation of concerns between the data domain and presentation layer br below is the structure for mobile dapp scaffold considering from app src main https github com cdhiraj40 mobile dapp scaffold tree main app src main java com example solanamobiledappscaffold app s core logic common common classes used through of the project data folder containing data layer classes di should house the dependency injection components remote contains classes and interfaces related to handling network requests and responses as well as remote data sources such as apis etc repository should contain abstract data sources behind a single interface for the domain layer s use domain folder containing domain layer classes model should contain data models and and business logic that define the data repository contains interfaces that the data layer implementation must conform to providing a separation between the domain and data layer use case contains specific business logic or operations that the application needs to perform utils reusable utility classes or helper functions presentation folder containing presentation layer classes typically activities fragments viewmodels etc res folder contains application resources such as layouts strings and drawable assets that are used by the ui contributing any contributions you make to this project is greatly appreciated bug feature request if you find a bug in the app kindly open an issue here https github com cdhiraj40 mobile dapp scaffold issues new assignees labels bug template bug report md title 5bbug 5d 3a to report it by including a proper description of the bug and the expected result https github com cdhiraj40 mobile dapp scaffold issues new assignees labels enhancement template feature request md title 5bfeature request 5d 3a are appreciated too if you feel like a certain feature is missing feel free to create an issue to discuss with the maintainers | android solana solana-mobile | front_end |
LM-Infinite | multi modality agorabanner png https discord gg qutxnk2nmf lm infinite simple on the fly length generalization for large language models lm infinite is a solution proposed by chi han qifan wang wenhan xiong yu chen heng ji and sinong wang to address the length generalization failure of large language models llms on long sequences llms such as transformer based models have shown impressive performance in various domains but struggle when it comes to longer reasoning processes or understanding larger contexts current pre training schemes truncate training sequences to a fixed length and even with relative positional encoding llms struggle to generate coherent texts or perform downstream tasks after longer contexts the authors investigate the main out of distribution factors contributing to this problem and propose lm infinite as an efficient solution lm infinite only requires a shaped attention mask and a distance limit without any parameter updates or learning it can be applied to different llms using relative position encoding methods lm infinite demonstrates consistent fluency and generation quality for sequences as long as 32k tokens on datasets like arxiv and openwebtext2 with a decoding speedup of 2 72x furthermore it continues to perform well on inputs much longer than training lengths in downstream tasks like passkey retrieval where vanilla models fail immediately paper link https arxiv org pdf 2308 16137 pdf appreciation lucidrains agorians install pip install lm infinite usage python import torch from infinite main import lminfinite d model 512 seq len 100 n global 100 l pretrain 50 sample q torch randn 1 seq len d model k torch randn 1 seq len d model v torch randn 1 seq len d model llm infinite mode model lminfinite d model n global l pretrain forwad pass output model q k v print output shape architecture todo license mit citations latex misc 2308 16137 author chi han and qifan wang and wenhan xiong and yu chen and heng ji and sinong wang title lm infinite simple on the fly length generalization for large language models year 2023 eprint arxiv 2308 16137 | ai |
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fswd-angular6 | readme leeme | front_end |
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keras-deepcv | keras deep computer vision this repository contains model definitions training scripts and other examples for keras tensorflow backend implementations for classification detection and segmentation computer vision models classification x lenet paper http yann lecun com exdb publis pdf lecun 01a pdf model models classification lenet py x alexnet paper https papers nips cc paper 4824 imagenet classification with deep convolutional neural networks pdf model models classification alexnet py x vgg16 and vgg19 paper https arxiv org pdf 1409 1556 pdf model models classification vgg py resnet paper https arxiv org pdf 1512 03385v1 pdf yolo9000 paper https arxiv org pdf 1612 08242 pdf densenet paper https arxiv org pdf 1608 06993 pdf detection faster rcnn paper https arxiv org pdf 1506 01497 pdf ssd paper https arxiv org pdf 1512 02325 yolov2 paper https arxiv org pdf 1612 08242 pdf r fcn paper https arxiv org pdf 1605 06409 pdf segmentation fcn8 paper https arxiv org pdf 1411 4038 pdf segnet paper https arxiv org pdf 1511 00561 u net paper https arxiv org pdf 1505 04597 e net paper https arxiv org pdf 1606 02147 pdf resnetfcn paper https arxiv org pdf 1611 10080 pdf pspnet paper https arxiv org pdf 1612 01105 pdf mask rcnn paper https arxiv org pdf 1703 06870 pdf datasets classification x mnist x cifar 10 mnist fashion imagenet pascal voc detection pascal voc lisa traffic sign kitti mscoco segmentation camvid cityscapes pascal voc kitti synthia gta v segmentation mscoco prerequisites for the models in thie repo keras https github com fchollet keras and tensorflow https github com tensorflow tensorflow are required make sure the latest versions are installed after these packages have been installed a few other packages are required which can be found in requirements txt pip install r requirements txt acknowledgments this repo would like to acknowledge the following pieces of code which played a part in the development of this repository keras zoo https github com david vazquez keras zoo git | ai |
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udabuddy | https user images githubusercontent com 27431066 38867057 815c3ee8 4260 11e8 995f 77e7d9035947 png to make a web app for udacity alumni interaction workflow please reference this picture to understand the flow of the app http res cloudinary com dvxx5f4hr image upload v1524759367 1 home dyuujq png these are the main sections of the webapp landing page contains info that you can check without login signup login firebase buttons about us giving details about the webapp udabuddy for reference see twitter about page wall of fame a page displaying works of alumni or people at udacity like sebastian thrun only a demo page here blog page a blog page by udabuddy users with posts about their amazing work in any field of technology only a demo page here please note that both the blog page and the wall of fame page here will have all there links redirected to the intermidiate signup signin page dashboard accessible once you are logged in job feed job feed will have posts related to job openings particular to your interests menu items this segment contains buttons that control the beahviour of how the dashboard is rendered community updates this will be a continuous feed that updates every time there is a post in the community activity graphs this provides statics about where you stand in the community and among your friends dashobard contains links to all other pages of the app including the showcase the wall of fame the forum and the blog page showcase show your projects and your blogs to the whole world all at one place forum talk to the community and see what is everyone upto blog write out your creativity and let others also know what you learnt about us know what is udabuddy and how it can change your lives wall of fame see people having amazing projects which are liked most by the community weekly updates a feed for the community events and updates and what all changes were brought about in the previous week how to contribute these are designated people rahul oreanroy landing and about page vamsi vamsi manohar wall of fame avinash littlestar642 blog page parteek prateek forum page pradeep psrajput showcase page these are responsible mods and co mods whom you can contact directly in case of any issue regarding a page your pr in any one of this page will be assesed by the responsibe mod and merged if it is worth it color pallete 33b5e5 https placehold it 15 33b5e5 000000 text 33b5e5 06caba https placehold it 15 06caba 000000 text 06caba f49b89 https placehold it 15 f49b89 000000 text f49b89 888ec9 https placehold it 15 888ec9 000000 text 888ec9 59a4c4 https placehold it 15 59a4c4 000000 text 59a4c4 prerequisites you will need the following things properly installed on your computer git https git scm com node js https nodejs org node v6 x x with npm v5 5 1 bower https bower io npm install g bower installation fork this repository git clone repository url this repository cd udabuddy npm install bower install running development node index npm start visit your app at http localhost 3000 http localhost 3000 deployment from master https udbuddy herokuapp com join the development read contributing md https github com udacityfrontendscholarship udabuddy blob master contributing md before you start contributing run the app on your local machine get familiar with it and then check for bugs or more features if you find any bug or want to add a new feature you have to open a new issue if you would like to work on an existing issue drop in a comment on the issue directory structure views this contains all the frontend files write your html code here and save it with hbs extension see homepage for reference public contains various assets for the app css contains css files js contains javascript files node modules contains all the npm packages installed locally dont modify here package json general info about the project and node packages names index js the entry point into the app note these features are subject to change as per the requirements | front_end |
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SummarAI | project logo src assets img logo png summarai browser extension this browser extension utilizes ai technology to analyze web pages and extract important information there are two ways to summarize 1 click the analyze button use the title and summary to summarize the page content this is the default method https cdn zhangferry com images 202306052334674 png 2 click the zettelkasten button use the zettelkasten https zettelkasten de introduction zh method to summarize the page content https cdn zhangferry com images 202306070718893 png installation 1 clone the repository to your local machine or download the zip package in releases https github com zhangferry summarai releases 2 open chrome and navigate to chrome extensions 3 enable developer mode by toggling the switch in the top right corner 4 click on load unpacked select the packages chromium folder if you are using clone or the unzip folder if you are using release package usage setting https cdn zhangferry com images 202305312325405 png there are two ai modes available for you to choose from 1 through the chatgpt web app which requires you to have successfully logged in to the chatgpt web version to preserve the session 2 through openai s api which requires you to provide the corresponding api key the default host is https api openai com you can customize this value api key is required you can find it in openai api key https platform openai com account api keys if you don t have access to openai you can purchase api key at api2d https api2d com r 187046 update the host and api key to use it summary 1 pin the summarai button to the extension toolbar 2 navigate to the web page you want to summarize and click on the summerai icon 3 the extension will analyze the content of the page and display a summary in the popup safari safari plug ins are typically used for macos ios systems you should compile summarai xcodeproj with xcode macos check the allow unsigned extensions option in safari s developer options https cdn zhangferry com images 202309092301123 png ios change the developer certificate to yours build from source optional if you want to change the code logic you need to recompile the typescript file 1 install dependencies with yarn install 2 build source code with yarn build the development of the web side of safari extension is still carried out through typescript code and the js code update will be completed in yarn build the native interface development should use swift in the xcode project support for any issues or questions please contact me qa 1 errcode cloudflare the openai server rejected the request possible reasons no permission to access the api the authentication credentials provided are invalid or incorrect requests are too frequent triggering traffic limiting 2 errcode unauthorized failed to obtain chat openai com accesstoken possible reasons your login credentials have expired so you ll need to log back into chat openai com and try again | server |
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OmniCV-Lib | p align center img width 250 src logo png p p align center a href https github com kaustubh sadekar omnicv lib actions query workflow 3acpp build test img alt cpp build and test src https github com kaustubh sadekar omnicv lib workflows cpp build test badge svg a a href https github com kaustubh sadekar omnicv lib actions query workflow 3apython build test img alt build src https github com kaustubh sadekar omnicv lib workflows python build test badge svg a a href https github com kaustubh sadekar omnicv lib actions query workflow 3acd pypi img alt cd pypi src https github com kaustubh sadekar omnicv lib workflows cd pypi badge svg a a href https badge fury io py omnicv img src https badge fury io py omnicv svg alt pypi version height 18 a p a computer vision library for omnidirectional 360 deg cameras this package is divided into two parts basic functions for inter conversion of different types of mappings associated with omni directional cameras virtual reality and 360 deg videos like cubemap spherical projections perspective projection and equirectangular projection software applications like 360 deg video viewer fisheye image generator with variable intrinsic properties gui to determine fisheye camera paraeters objectives of the omnicv library this library has been developed with the following obectives quick and easy to use api to encourage and enhance the research in areas using omni directional cameras to support real time applications provide extensions in python as well as c as they are the languages used by researchers provide ros package to use in robotics research click here to know more about omni directional cameras omnidir cameras md highlights detailed examples examples readme md and application notes applications readme md to understand how to use the package installation guide for python version c version ros packge detailed documentation https kaustubh sadekar github io omnicv lib output gallery some interesting 360 deg video effects arround the world effect hollow world effect gifs eqrect2fisheyefet2 gif gifs eqrect2fisheyefet1 gif creating custom fisheye images equirect2fisheye custom image using gui gifs eqrect2fisheye gif gifs eqrect2fisheye gif gui to determine fisheye camera parameters gui to get radius gui to get fisheye params gifs getradius gif gifs fisheyeparams gif horizontal and vertical orientation viewing mode support 360 deg viewer mode 1 360 deg viewer mode 2 gifs 360viewer2 gif gifs 360viewer3 gif index to theory examples and application notes types of omni directional cameras omnidir cameras md examples examples readme md equirectangular image to fisheye or pinhole camera image fisheye image to equirectangular image equirectangular image to cube map image horizontal or dice format convert cube map image horizontal or dice format to equirectangular image convert equirectangular image to perspective image with desired field of view and viewing angles convert cubemap image to perspective image with desired field of view and viewing angles application notes applications readme md gui to control focus distortion and view orientation to generate different kinds of distortion effects and get images with different properties gui to determine fisheye camera parameters like aperture and fisheye radius for further conversions gui to view an equirectangular image in 360 deg format with control trackbars to change fov field of view and viewing angles you can download any 360 deg video from youtube and view it using the gui and enjoy the 360 deg viewing experience installation guide a custom make file has been written which provides quick and easy options for installing and testing the library shell git clone https github com kaustubh sadekar omnicv lib cd omnicv lib omnicv to build c as well as python files make build all to build only python files make build python to build only c files make build cpp installing omnicv in a virtual environment using pipenv pipfile and pipfile lock files have been provided copy both the files to the present working directory then simply run the following commands to setup omnicv in a local environment shell pipenv install pipenv shell running tests shell cd omnicv lib omnicv to test only python extension of the project make test py to test only c extension of the project make test cpp to test python as well as c extension of the project make test all ros nodes to build the ros nodes follow these steps create a folder named omnicv in your ros workspace where you have your other ros packages shell roscd src mkdir omnicv add the contents present inside ros files folder to the omnicv folder created in the previous step build the workspace shell cp omnicv ros files path to ros workspace src omnicv roscd catkin make | ai |
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a href https www youtube com watch v 2obpruvmt48 how to prepare for thehunt class48 100devs a h4 homework h4 make 3 connections a week br read a href https levelup gitconnected com essential javascript string methods f1841dad1961 essential javascript string methods a br do 5 codewars tagged as strings br start working on your 100 hours project li li h3 class 49 h3 class video stream a href https www youtube com watch v z5ugtxoxeek easily add authentication to your node apps email logins with passport class 49 52 100devs a li li h3 class 50 h3 slides a href https slides com leonnoel 100devs2 auth project night auth project night a br class video stream a href https www youtube com watch v z5ugtxoxeek easily add authentication to your node apps email logins with passport class 49 52 100devs a h4 homework h4 make 3 connections a week br do 5 codewars tagged as strings li li h3 class 51 h3 slides a href https slides com leonnoel 100devs2 auth project night2 cohort 2 auth project night 2 a 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down into reuseable components li li h3 class 56 h3 h4 homework h4 make 3 connections a week br do 5 codewars tagged as arrays br do mentally break your favorite website down into reuseable components li li h3 class 57 h3 h4 homework h4 make 3 connections a week br research the following string methods chatat charcodeat concat br includes indexof br match br repeat replace br search slice split substr br tolowercase touppercase trim br give a short description of what the method does how it works it s time complexity and give three examples of it in action br do 5 codewars tagged as strings li li h3 class 58 h3 class video stream a href https www youtube com watch v mfemmhem6nk let s explore the basics of react 100devs a h4 homework h4 make 3 connections a week br do 5 codewars tagged as strings br watch do a href https www youtube com watch v w7ejdz8swv8 react js crash course a br do a href https egghead io courses the beginner s guide to react the beginner s guide to react a li li h3 class 59 h3 class video stream a href https www youtube com watch v xzvggz2xjy4 let s explore the basics of react part 2 100devs a h4 homework h4 make 3 connections a week br do 5 codewars tagged as strings li li h3 class 60 h3 slides a href https slides com leonnoel 100devs2 see react together see react together a br h4 homework h4 make 3 connections a week br do 5 codewars tagged as strings li li h3 class 61 class 62 h3 slides a href https slides com leonnoel 100devs2 interview secrets interview secrets a br h4 homework h4 make 3 connections a week br do a href http web archive org web 20210616161653 https scotch io courses the ultimate guide to javascript algorithms the ultimate guide to javascript algorithms courses a li li h3 class 63 class 68 h3 h4 homework h4 do a href https frontendmasters com courses practical algorithms a practical guide to algorithms with javascript a li li h4 super review backend js h4 class video stream a href https www youtube com watch v jgfs11u1tiq javascript backend web development tutorial with node a li ol | front_end |
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NLP | nlp course projects in natural language processing including below tasks 1 task 1 dollar amounts recognizing using regular expressions find out money expression in an article 2 task 2 pos tagger mark up a word s part of speech based on its context using hidden markov model 3 task 3 named entity tagger identify noun groups using maximum entropy markov model 4 task 4 relation tagger find out arg1 for mark 5 task 5 sentimental analysis and rating star determine a user s attitude positive or negative based on his or her comments to a movie rating a movie within rank 1 5 based its comments see wiki homepage https github com danielwuz nlp wiki for more information | ai |
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generative-ai-python | google generative ai python client pypi version https badge fury io py google generativeai svg https badge fury io py google generativeai python support https img shields io pypi pyversions google generativeai pypi downloads https img shields io pypi dd google generativeai get started using the palm api in python check out the developer site https developers generativeai google for comprehensive documentation installation and usage install from pypi bash pip install google generativeai get an api key from makersuite https makersuite google com app apikey then configure it here python import google generativeai as palm palm configure api key os environ palm api key use palm generate text https developers generativeai google api python google generativeai generate text to have the model complete some initial text python response palm generate text prompt the opposite of hot is print response result cold use palm chat https developers generativeai google api python google generativeai chat to have a discussion with a model python response palm chat messages hello print response last hello what can i help you with response reply can you tell me a joke documentation checkout the full api docs https developers generativeai google api the guide https developers generativeai google guide and quick starts https developers generativeai google tutorials colab magics once installed use the python client via the palm colab magic read the full guide here https developers generativeai google tools notebook magic python palm the best thing since sliced bread is | ai chatbot embeddings llm machine-learning python | ai |
musicmood | musicmood a machine learning approach to classify music by mood based on song lyrics this project is about building a music recommendation system for users who want to listen to happy songs such a system can not only be used to brighten up one s mood on a rainy weekend especially in hospitals other medical clinics or public locations such as restaurants the musicmood classifier could be used to spread positive mood among people br links the web application http rasbt pythonanywhere com the data collection ipython notebook code collect data data collection ipynb the initial model training ipython notebook code classify lyrics nb init model ipynb the updated model training with white lists ipython notebook code classify lyrics nb whitelist model ipynb experiments with random forests ipython notebook code classify lyrics random forests ipynb an article about my experiences with this project http sebastianraschka com blog 2014 musicmood html a keynote presentation about this project https speakerdeck com rasbt musicmood machine learning in automatic music mood prediction based on song lyrics a more technical report on arxiv https arxiv org abs 1611 00138 br br sections hr dataset summary dataset summary exploratory data analysis exploratory data analysis results results hr br br images flowchart png br br dataset summary back to top sections a 10 000 song subset was downloaded from the million song dataset http labrosa ee columbia edu millionsong pages getting dataset lyrics were automatically downloaded from lyricwikia http lyrics wikia com lyrics wiki and all songs for which lyrics have not been available were removed from the dataset an english language filter was applied to detect and remove all non english songs the remaining songs were randomly subsampled into a 1000 song training dataset and 200 song validation dataset br br exploratory data analysis back to top sections images exploratory 1 png br br images wordclouds png br br results back to top sections images roc best png images performance table png | song-dataset exploratory-data-analysis lyrics mood machine-learning | ai |
open3DCV | introduction open3dcv is a simple lightweight 3d computer vision library with minimum third party dependencies this library includes implementations of many widely used multiple view geometry algorithms some highlights are as follow please visit the website https imkaywu github io open3dcv for more details tutorials on the implementation are here https imkaywu github io tutorials open3dcv more to come pinhole camera model feature detector descriptor extraction feature matching estimation of fundamental matrix 7 point 8 point robust estimation with ransac estimation of essential matrix 5 point n view triangulation a general ransac framework sequential structure from motion sfm contact please create an issue or pull request for detected fixed bugs or send questions to me license bsd 3 clause | structure-from-motion multi-view-geometry computer-vision sfm cpp | ai |
Stanford-CS193-iOS-Apps | developing ios 10 apps with swift by stanford https itunes apple com us course developing ios 10 apps with swift id1198467120 alt text http cdn8 openculture com 2017 02 27221309 ios 10 stanford png tools and apis required to build applications for the iphone and ipad platforms using the ios sdk user interface design for mobile devices and unique user interactions using multi touch technologies object oriented design using model view controller paradigm memory management swift programming language other topics include object oriented database api animation mobile device power management multi threading networking and performance considerations course outline 1 introduction 2 mvc 3 swift 4 views gestures and closures 5 extensions protocols and delegation 6 multithreading 7 table view 8 core data 9 autolayout 10 animation 11 segues 12 notifications application lifecycle persistence 13 accessibility videos name 1 introduction to ios 10 xcode 8 and swift 3 https www youtube com watch v ilq tq772vi list plpa aybrweuz32nsgnzdl0 qisw f12ai 2 mvc ios xcode and swift demonstration https www youtube com watch v aug myu02q list plpa aybrweuz32nsgnzdl0 qisw f12ai index 2 3 more swift and the foundation framework https www youtube com watch v 4voseyy6krc index 3 list plpa aybrweuz32nsgnzdl0 qisw f12ai 4 views https www youtube com watch v lx4ohhsc3ho list plpa aybrweuz32nsgnzdl0 qisw f12ai index 4 5 gestures and multiple mvcs https www youtube com watch v fxinju nkwu list plpa aybrweuz32nsgnzdl0 qisw f12ai index 5 6 multiple mvcs view controller lifecycle and memory management https www youtube com watch v hqrxm2zupvy index 6 list plpa aybrweuz32nsgnzdl0 qisw f12ai 7 error handling extensions protocols delegation and scroll view https www youtube com watch v gilsl 6tqmm index 7 list plpa aybrweuz32nsgnzdl0 qisw f12ai 8 multithreading and text field https www youtube com watch v h9kbzg3rk8 list plpa aybrweuz32nsgnzdl0 qisw f12ai index 8 9 table view https www youtube com watch v 78lwmmdxr4k list plpa aybrweuz32nsgnzdl0 qisw f12ai index 9 10 core data https www youtube com watch v ssipdu73p7a list plpa aybrweuz32nsgnzdl0 qisw f12ai index 10 11 core data demo https www youtube com watch v whf63gtaw1w list plpa aybrweuz32nsgnzdl0 qisw f12ai index 11 12 autolayout https www youtube com watch v uppl3lv5l8w list plpa aybrweuz32nsgnzdl0 qisw f12ai index 12 13 timer and animation https www youtube com watch v 6tdnjwdwfys list plpa aybrweuz32nsgnzdl0 qisw f12ai index 13 14 dynamic animation demo https www youtube com watch v 8ryq1a zdmw index 14 list plpa aybrweuz32nsgnzdl0 qisw f12ai 15 more segues https www youtube com watch v mjklubbkggc list plpa aybrweuz32nsgnzdl0 qisw f12ai index 15 16 alerts and action sheets notifications application lifecycle and persistence https www youtube com watch v hkuedmw7qx0 index 16 list plpa aybrweuz32nsgnzdl0 qisw f12ai 17 accessibility https www youtube com watch v nozxrbom7bw list plpa aybrweuz32nsgnzdl0 qisw f12ai index 17 readings name 1 reading assignment 1 intro to swift https github com caelandailey stanford cs193 ios apps blob master readings reading 20assignment 201 20intro 20to 20swift pdf 2 reading assignment 2 more swift https github com caelandailey stanford cs193 ios apps blob master readings reading 20assignment 202 20more 20swift pdf 3 reading assignment 3 the rest of swift https github com caelandailey stanford cs193 ios apps blob master readings reading 20assignment 203 20the 20rest 20of 20swift pdf in class material name 1 lecture 1 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 201 20slides pdf 2 lecture 2 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 202 20slides pdf 3 lecture 3 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 203 20slides pdf 4 lecture 4 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 204 20slides pdf 5 lecture 5 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 205 20slides pdf 6 lecture 6 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 206 20slides pdf 7 lecture 7 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 207 20slides pdf 8 lecture 8 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 208 20slides pdf 9 lecture 9 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 209 20slides pdf 10 lecture 10 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 2010 20slides pdf 11 lecture 11 demo code smashtag https github com caelandailey stanford cs193 ios apps blob master slides lecture 2011 20demo 20code 20smashtag pdf 12 lecture 12 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 2012 20slides pdf 13 lecture 13 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 2013 20slides pdf 14 lecture 14 demo code asteroids https github com caelandailey stanford cs193 ios apps blob master slides lecture 2014 20demo 20code 20asteroids pdf 15 lecture 15 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 2015 20slides pdf 16 lecture 16 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 2016 20slides pdf assignments name 1 calculator https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 201 20calculator pdf 2 calculator brain https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 202 20calculator 20brain pdf 3 graphing calculator https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 203 20graphing 20calculator pdf 4 smashtag mentions https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 204 20smashtag 20mentions pdf 5 smashtag mention popularity https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 205 20smashtag 20mention 20popularity pdf all in order name description 1 1 introduction to ios 10 xcode 8 and swift 3 https www youtube com watch v ilq tq772vi list plpa aybrweuz32nsgnzdl0 qisw f12ai video 2 lecture 1 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 201 20slides pdf slides 3 reading assignment 1 intro to swift https github com caelandailey stanford cs193 ios apps blob master readings reading 20assignment 201 20intro 20to 20swift pdf reading 4 2 mvc ios xcode and swift demonstration https www youtube com watch v aug myu02q list plpa aybrweuz32nsgnzdl0 qisw f12ai index 2 video 5 lecture 2 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 202 20slides pdf slides 6 programming project 1 calculator https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 201 20calculator pdf assignment 7 3 more swift and the foundation framework https www youtube com watch v 4voseyy6krc index 3 list plpa aybrweuz32nsgnzdl0 qisw f12ai video 8 lecture 3 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 203 20slides pdf slides 9 4 views https www youtube com watch v lx4ohhsc3ho list plpa aybrweuz32nsgnzdl0 qisw f12ai index 4 video 10 lecture 4 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 204 20slides pdf slides 11 lecture 4 demo code faceit https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 204 20demo 20code 20faceit pdf demo 12 5 gestures and multiple mvcs https www youtube com watch v fxinju nkwu list plpa aybrweuz32nsgnzdl0 qisw f12ai index 5 video 13 lecture 5 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 205 20slides pdf slides 14 lecture 5 demo code faceit https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 205 20demo 20code 20faceit pdf demo 15 reading assignment 2 more swift https github com caelandailey stanford cs193 ios apps blob master readings reading 20assignment 202 20intro 20to 20swift pdf reading 16 programming project 2 calculator brain https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 202 20calculator pdf assignment 17 6 multiple mvcs view controller lifecycle and memory management https www youtube com watch v hqrxm2zupvy index 6 list plpa aybrweuz32nsgnzdl0 qisw f12ai video 18 lecture 6 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 206 20slides pdf slides 19 lecture 6 demo code faceit https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 206 20demo 20code 20faceit pdf demo 20 7 error handling extensions protocols delegation and scroll view https www youtube com watch v gilsl 6tqmm index 7 list plpa aybrweuz32nsgnzdl0 qisw f12ai video 21 lecture 7 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 207 20slides pdf slides 22 lecture 7 demo code cassini https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 207 20demo 20code 20cassini pdf demo 23 reading assignment 3 the rest of swift https github com caelandailey stanford cs193 ios apps blob master readings reading 20assignment 203 20intro 20to 20swift pdf reading 24 8 multithreading and text field https www youtube com watch v h9kbzg3rk8 list plpa aybrweuz32nsgnzdl0 qisw f12ai index 8 video 25 lecture 8 slides https github com caelandailey stanford cs193 ios apps blob master material lecture 208 20slides pdf slides 26 lecture 8 demo code cassini https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 208 20demo 20code 20cassini pdf demo 27 programming project 3 graphing calculator https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 203 20calculator pdf assignment 28 9 table view https www youtube com watch v 78lwmmdxr4k list plpa aybrweuz32nsgnzdl0 qisw f12ai index 9 video 29 lecture 9 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 209 20slides pdf slides 30 lecture 9 demo code smashtag https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 209 20demo 20code 20smashtag pdf demo 31 10 core data https www youtube com watch v ssipdu73p7a list plpa aybrweuz32nsgnzdl0 qisw f12ai index 10 video 32 lecture 10 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 210 20slides pdf slides 33 11 core data demo https www youtube com watch v whf63gtaw1w list plpa aybrweuz32nsgnzdl0 qisw f12ai index 11 video 34 lecture 11 demo code smashtag https github com caelandailey stanford cs193 ios apps blob master slides lecture 211 20slides pdf demo 35 programming project 4 smashtag mentions https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 204 20calculator pdf assignment 36 12 autolayout https www youtube com watch v uppl3lv5l8w list plpa aybrweuz32nsgnzdl0 qisw f12ai index 12 video 37 lecture 12 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 212 20slides pdf slides 38 programming project 5 smashtag mention popularity https github com caelandailey stanford cs193 ios apps blob master assignments programming 20project 205 20calculator pdf assignment 39 13 timer and animation https www youtube com watch v 6tdnjwdwfys list plpa aybrweuz32nsgnzdl0 qisw f12ai index 13 video 40 lecture 13 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 213 20slides pdf slides 41 lecture 13 demo code faceit https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 2013 20demo 20code 20faceit pdf demo 42 14 dynamic animation demo https www youtube com watch v 8ryq1a zdmw index 14 list plpa aybrweuz32nsgnzdl0 qisw f12ai video 43 lecture 14 demo code asteroids https github com caelandailey stanford cs193 ios apps blob master slides lecture 214 20slides pdf demo 44 15 more segues https www youtube com watch v mjklubbkggc list plpa aybrweuz32nsgnzdl0 qisw f12ai index 15 video 45 lecture 15 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 215 20slides pdf slides 46 lecture 15 demo code faceit segues https github com caelandailey stanford cs193 ios apps blob master demo 20code lecture 2015 20demo 20code 20faceit 20segues pdf demo 47 lecture 16 slides https github com caelandailey stanford cs193 ios apps blob master slides lecture 216 20slides pdf slides 48 16 alerts and action sheets notifications application lifecycle and persistence https www youtube com watch v hkuedmw7qx0 index 16 list plpa aybrweuz32nsgnzdl0 qisw f12ai video 49 17 accessibility https www youtube com watch v nozxrbom7bw list plpa aybrweuz32nsgnzdl0 qisw f12ai index 17 video | swift stanford ios | os |
NLPiffy | nlpiffy nlpiffy a natural language processing platform aim applying nlp libraries to build tools for simple implementation of nlp tasks nlpiffy natural language processing interactive framework for you requirements nlpiffy cli click textblob spacy json nlpiffy web app flask spacy textblob bootstrap materialize css flask restplus installing dependencies pip install click pip install textblob pip install spacy python m spacy download en python m spacy download fr optional for french by jesse jcharis jesus saves jcharistech | ai |
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caver-java | circleci https circleci com gh klaytn caver java tree dev svg style svg https circleci com gh klaytn caver java tree dev gitter https badges gitter im klaytn caver java svg https gitter im klaytn caver java utm source badge utm medium badge utm campaign pr badge branch name will be changed we will change the master branch to main on dec 15 2022 after the branch policy change please check your local or forked repository settings caver java caver java klaytn dapp api caver java is a lightweight high modular convenient java and android library to interact with clients nodes on the klaytn network this library is an interface which allows java applications to easily communicate with klaytn https www klaytn com network features complete implementation of klaytn s json rpc client api over http and ipc support of klaytn transaction account and account key types auto generation of java smart contract wrapper to deploy and execute a smart contract from native java code creation of a new wallet and managing klaytn wallets command line tools android compatible getting started installation add a repository to install caver java you should add a jitpack repository for ipfs feature maven groovy repositories repository id jitpack io id url https jitpack io url repository repositories gradle groovy allprojects repositories maven url https jitpack io add a dependency maven groovy dependency groupid com klaytn caver groupid artifactid core artifactid version x x x version dependency gradle groovy compile com klaytn caver core x x x if you want to use android dependency just append android at the end of version e g 1 5 4 android you can find latest caver java version at release page https github com klaytn caver java releases start a client if you want to run your own en endpoint node see en operation guide https docs klaytn com node en to set up java caver caver new caver caver default url transactions when you send transactions caver java provides easy to use wrapper classes here s an example of transferring klay using keystore json and valuetransfer class java caver caver new caver caver default url read keystore json file file file new file keystore json decrypt keystore objectmapper objectmapper objectmapperfactory getobjectmapper keystore keystore objectmapper readvalue file keystore class abstractkeyring keyring keyringfactory decrypt keystore password add to caver wallet caver wallet add keyring biginteger value new biginteger utils converttopeb bigdecimal one klay create a value transfer transaction valuetransfer valuetransfer new valuetransfer builder setklaytncall caver rpc getklay setfrom keyring getaddress setto 0x8084fed6b1847448c24692470fc3b2ed87f9eb47 setvalue value setgas biginteger valueof 25000 build sign to the transaction valuetransfer sign keyring send a transaction to the klaytn blockchain platform klaytn bytes32 result caver rpc klay sendrawtransaction valuetransfer getrawtransaction send if result haserror throw new runtimeexception result geterror getmessage check transaction receipt transactionreceiptprocessor transactionreceiptprocessor new pollingtransactionreceiptprocessor caver 1000 15 transactionreceipt transactionreceiptdata transactionreceipt transactionreceiptprocessor waitfortransactionreceipt result getresult if you have address and private key s of keyring you can make keyring directly through keyringfactory create https docs klaytn com bapp sdk caver java getting started creating a keyring valueunit means a unit of value that is used in klaytn it is defined as an enum type examples of possible values are as below peb kpeb mpeb gpeb ston uklay mklay klay kklay mklay gklay if valueunit is not given as a parameter default unit of value is peb you can use utils converttopeb or utils convertfrompeb to easily convert a value to another unit like below java utils converttopeb 1 klay tobiginteger 1000000000000000000 utils convertfrompeb 1000000000000000000 klay tobiginteger 1 fee delegation klaytn provides fee delegation https docs klaytn com klaytn design transactions fee delegation feature here s an example code when you are a sender java caver caver new caver caver default url singlekeyring senderkeyring keyringfactory createfromprivatekey 0x privatekey caver wallet add senderkeyring feedelegatedvaluetransfer feedelegatedvaluetransfer new feedelegatedvaluetransfer builder setklaytncall caver rpc klay setfrom senderkeyring getaddress setto 0x176ff0344de49c04be577a3512b6991507647f72 setvalue biginteger valueof 1 setgas biginteger valueof 30000 build caver wallet sign senderkeyring getaddress feedelegatedvaluetransfer string rlpencoded feedelegatedvaluetransfer getrlpencoding system out println rlpencoded after signing a transaction the sender can get the rlp encoded string through feedelegatedvaluetransfer getrlpencoding then the sender sends the transaction to the fee payer who will pay for the transaction fee instead when you are a fee payer java caver caver new caver caver default url singlekeyring feepayerkeyring keyringfactory createfromprivatekey 0x privatekey caver wallet add feepayerkeyring string rlpencoded 0x rlp encoded string the result of feedelegatedvaluetransfer getrlpencoding in above example feedelegatedvaluetransfer feedelegatedvaluetransfer feedelegatedvaluetransfer decode rlpencoded feedelegatedvaluetransfer setfeepayer feepayerkeyring getaddress caver wallet signasfeepayer feepayerkeyring getaddress feedelegatedvaluetransfer transactionreceiptprocessor receiptprocessor new pollingtransactionreceiptprocessor caver 1000 15 string rlpencoded feedelegatedvaluetransfer getrlpencoding try send the transaction using caver rpc klay sendrawtransaction bytes32 sendresult caver rpc klay sendrawtransaction rlpencoding send if sendresult haserror do something to handle error string txhash sendresult getresult transactionreceipt transactionreceiptdata receiptdata receiptprocessor waitfortransactionreceipt txhash catch ioexception transactionexception e do something to handle exception after the fee payer gets the transaction from the sender the fee payer can sign with signasfeepayer for more information about klaytn transaction types visit transactions https docs klaytn com klaytn design transactions klaytn accounts an account in klaytn is a data structure containing information about a person s balance or a smart contract if you require further information about klaytn accounts you can refer to the accounts https docs klaytn com klaytn design account account key an account key represents the key structure associated with an account each account key has its own unique role to get more details about the klaytn account key please read account key https docs klaytn com klaytn design account account key these are 6 types of account keys in klaytn accountkeynil accountkeylegacy accountkeypublic accountkeyfail accountkeyweightedmultisig accountkeyrolebased if you want to update the key of the given account follow below steps 1 create new private key s to use 2 create a keyring instance using the new private key s and the account address to update after the accountkey has been successfully updated in klaytn you can use the keyring instance created here 3 to update the accountkey of klaytn account create an account instance using the toaccount function 4 create an accountupdate transaction accountupdate feedelegatedaccountupdate feedelegatedaccountupdatewithratio 5 sign the accountupdate transaction 6 send signed transaction through caver rpc klay sendrawtransaction java caver caver new caver caver baobab url singlekeyring senderkeyring keyringfactory createfromprivatekey 0x privatekey caver wallet add senderkeyring string newprivatekey keyringfactory generatesinglekey singlekeyring newkeyring keyringfactory createfromprivatekey newprivatekey account account newkeyring toaccount accountupdate accountupdate new accountupdate builder setklaytncall caver rpc klay setfrom senderkeyring getaddress setaccount account setgas biginteger valueof 50000 build try caver wallet sign senderkeyring getaddress accountupdate string rlpencoded accountupdate getrlpencoding bytes32 sendresult caver rpc klay sendrawtransaction rlpencoded send if sendresult haserror do something to handle error string txhash sendresult getresult transactionreceiptprocessor receiptprocessor new pollingtransactionreceiptprocessor caver 1000 15 transactionreceipt transactionreceiptdata receiptdata receiptprocessor waitfortransactionreceipt txhash catch ioexception transactionexception e do something to handle exception senderkeyring caver wallet updatekeyring newkeyring manage contracts using java smart contract wrappers caver supports contract class to make it easy to interact with smart contract in klaytn before generating a wrapper code you need to compile the smart contract first note this will only work if solidity compiler is installed in your computer shell solc abi bin test sol you can create a contract instance as below using the result of compiling the smart contract java caver caver new caver caver default url try contract contract new contract caver abi contract getmethods foreach methodname contractmethod system out println methodname methodname contractmethod contractmethod system out println contractaddress contract getcontractaddress catch ioexception e handle exception if you want to deploy the smart contract at baobab testnet you could do like this java caver caver new caver caver default url singlekeyring deployer keyringfactory createfromprivatekey 0x private key caver wallet add deployer try contract contract new contract caver abi contractdeployparams params new contractdeployparams bytecode null sendoptions sendoptions new sendoptions sendoptions setfrom deployer getaddress sendoptions setgas biginteger valueof 40000 contract newcontract contract deploy params sendoptions system out println contract address newcontract getcontractaddress catch ioexception transactionexception classnotfoundexception nosuchmethodexception invocationtargetexception instantiationexception illegalaccessexception e handle exception after the smart contract has been deployed you can load the smart contract as below java caver caver new caver caver default url string contractaddress 0x3466d49256b0982e1f240b64e097ff04f99ed4b9 try contract contract new contract caver abi contractaddress contract getmethods foreach methodname contractmethod system out println methodname methodname contractmethod contractmethod system out println contractaddress contract getcontractaddress catch ioexception e handle exception to transact with a smart contract java caver caver new caver caver default url singlekeyring executor keyringfactory createfromprivatekey 0x private key caver wallet add executor try contract contract new contract caver abi 0x address in hex sendoptions sendoptions new sendoptions sendoptions setfrom executor getaddress sendoptions setgas biginteger valueof 40000 transactionreceipt transactionreceiptdata receipt contract getmethod set send arrays aslist testvalue sendoptions catch ioexception transactionexception classnotfoundexception nosuchmethodexception invocationtargetexception instantiationexception illegalaccessexception e handle exception filters tbd web3j similarity we made caver java as similar as possible to web3j for easy usability java start a client web3j web3 web3j build new httpservice endpoint web3j caver caver caver build new httpservice endpoint caver java get nonce biginteger nonce web3j ethgettransactioncount address blockparam send gettransactioncount web3j quantity nonce caver klay gettransactioncount address blockparam send getvalue caver java convert unit convert towei 1 0 convert unit ether tobiginteger web3j convert topeb 1 0 convert unit klay tobiginteger caver java generate wallet file walletutils generatenewwalletfile password filepath web3j klaywalletutils generatenewwalletfile address password filepath caver java load credentials credentials credentials walletutils loadcrendetials password filepath web3j klaycredentials credentials klaywalletutils loadcredentials password filepath caver java value transfer transactionreceipt transactionreceipt transfer sendfunds send web3j klaytransactionreceipt transactionreceipt transactionreceipt valuetransfer create sendfunds send caver java command line tool a caver java fat jar is distributed with open repository the caver java allows you to generate solidity smart contract function wrappers from the command line generate solidity smart contract function wrappers installation shell brew tap klaytn klaytn brew install caver java after installation you can run command caver java shell caver java solidity generate b smart contract bin a smart contract abi o outputpath p packagepath related projects caver js for a javascript build instructions tbd snapshot dependencies tbd thanks to the web3j https github com web3j web3j project for the inspiration 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cvlib | implementations of a few computer vision algorithms in python matlab please click on the individual folders to read more about each technique | ai |
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me-contrata | p align center img src https github com frontendbr brand blob main src png logo 600px horizontal color png width 400 alt front end brasil p h1 align center me contrata h1 p align center microscope espa o para cadastro de pessoas que est o em busca de um emprego na rea de ti p qual o objetivo nesse espa o a pessoa que est em busca de uma nova oportunidade no mercado ou em busca de recoloca o ir cadastrar uma nova issue com as informa es necess rias para que empresas em busca de profissionais com o seu perfil possam entrar em contato um ganha ganha pra todos como se cadastrar abra uma issue https github com frontendbr me contrata issues new e no t tulo desta issue coloque a cidade estado onde mora entre colchetes e o seu nome completo assim s o paulo sp maria joaquina preencha corretamente os dados pedidos no template padr o o template aparece quando voc cria uma nova issue aguarde alguma empresa entrar em contato ao conseguir uma vaga quem criou a issue deve fechar a mesma se n o estiver em busca de outras oportunidades e comentar se conseguiu ou n o o emprego a partir desse reposit rio sou uma empresa ou preciso de algu m para um freela como entrar em contato clicando em issues https github com frontendbr me contrata issues voc ver uma lista de candidatos que est o em busca de oportunidades de trabalho para visualizar o contato da pessoa e g linkedin basta entrar em sua issue e descer at encontrar a se o contato caso a pessoa n o tenha adicionado nenhuma forma de contato ser preciso comentar logo abaixo de sua issue issues abertas s o pessoas interessadas em oportunidades que ainda n o fecharam uma parceria enquanto que issues fechadas s o aquelas que j se encerraram por padr o voc s ver issues em aberto utilize o campo de busca em issues https github com frontendbr me contrata issues para procurar por uma cidade ou um r tulo clt freela pj etc ainda no campo de busca voc pode filtrar por algum r tulo da seguinte forma escrevendo label clt para exibir apenas as pessoas que usaram o r tulo clt ou escrevendo label clt para retirar essas pessoas por padr o o campo de busca come a preenchido com is issue is open n o se preocupe separe por espa os o que seja buscar e g is issue is open s o paulo sp label remoto label clt poss vel filtrar a lista de issues pela barra de filtros da tabela caso ache melhor licen a mit license copy frontendbr reposit rios da front end brasil f rum https github com frontendbr forum eventos https github com frontendbr eventos vagas https github com frontendbr vagas me contrata https github com frontendbr me contrata compra e venda https github com frontendbr compra e venda doe um livro https github com frontendbr doe um livro poste mais https github com frontendbr poste mais open source https github com frontendbr open source sugest es https github com frontendbr sugestoes | vagas-para-desenvolvedores vagas emprego | front_end |
SUBASH | subash embedded system hardware design pcb designing | os |
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CVVTuberExample | cv vtuber example overview https assetstore unity com packages templates tutorials cv vtuber example 118186 https assetstore unity com packages templates tutorials cv vtuber example 118186 aid 1011l4ehr environment opencvforunity https assetstore unity com packages tools integration opencv for unity 21088 aid 1011l4ehr dlibfacelandmarkdetector https assetstore unity com packages tools integration dlib facelandmark detector 64314 aid 1011l4ehr demo video http img youtube com vi ygnvo3lt6ws 0 jpg https www youtube com watch v ygnvo3lt6ws demo webgl https enoxsoftware github io cvvtuberexample webgl example index html android https github com enoxsoftware cvvtuberexample releases manual readme pdf assets cvvtuberexample readme pdf | opencv unity unity3d unity-asset unity-3d assetstore vtuber vrm live2d unity-chan | ai |
buildItBigger | gradle for android and java final project in this project you will create an app with multiple flavors that uses multiple libraries and google cloud endpoints the finished app will consist of four modules a java library that provides jokes a google cloud endpoints gce project that serves those jokes an android library containing an activity for displaying jokes and an android app that fetches jokes from the gce module and passes them to the android library for display why this project as android projects grow in complexity it becomes necessary to customize the behavior of the gradle build tool allowing automation of repetitive tasks particularly factoring functionality into libraries and creating product flavors allow for much bigger projects with minimal added complexity what will i learn you will learn the role of gradle in building android apps and how to use gradle to manage apps of increasing complexity you ll learn to add free and paid flavors to an app and set up your build to share code between them factor reusable functionality into a java library factor reusable android functionality into an android library configure a multi project build to compile your libraries and app use the gradle app engine plugin to deploy a backend configure an integration test suite that runs against the local app engine development server how do i complete this project step 0 starting point this is the starting point for the final project which is provided to you in the course repository https github com udacity ud867 tree master finalproject it contains an activity with a banner ad and a button that purports to tell a joke but actually just complains the banner ad was set up following the instructions here https developers google com mobile ads sdk docs admob android quick start you may need to download the google repository from the extras section of the android sdk manager you will also notice a folder called backend in the starter code it will be used in step 3 below and you do not need to worry about it for now when you can build an deploy this starter code to an emulator you re ready to move on step 1 create a java library your first task is to create a java library that provides jokes create a new gradle java project either using the android studio wizard or by hand then introduce a project dependency between your app and the new java library if you need review check out demo 4 01 from the course code make the button display a toast showing a joke retrieved from your java joke telling library step 2 create an android library create an android library containing an activity that will display a joke passed to it as an intent extra wire up project dependencies so that the button can now pass the joke from the java library to the android library for review on how to create an android library check out demo 4 03 for a refresher on intent extras check out http developer android com guide components intents filters html step 3 setup gce this next task will be pretty tricky instead of pulling jokes directly from our java library we ll set up a google cloud endpoints development server and pull our jokes from there the starter code already includes the gce module in the folder called backend before going ahead you will need to be able to run a local instance of the gce server in order to do that you will have to install the cloud sdk https cloud google com sdk docs once installed you will need to follow the instructions in the setup cloud sdk section at https cloud google com endpoints docs frameworks java migrating android note you do not need to follow the rest of steps in the migration guide only the setup cloud sdk start or stop your local server by using the gradle tasks as shown in the following screenshot img src finalproject gce server gradle tasks png height 500 once your local gce server is started you should see the following at localhost 8080 http localhost 8080 img src https raw githubusercontent com googlecloudplatform gradle appengine templates 77e9910911d5412e5efede5fa681ec105a0f02ad doc img devappserver endpoints png now you are ready to continue introduce a project dependency between your java library and your gce module and modify the gce starter code to pull jokes from your java library create an asynctask to retrieve jokes using the template included int these instructions https github com googlecloudplatform gradle appengine templates tree 77e9910911d5412e5efede5fa681ec105a0f02ad helloendpoints 2 connecting your android app to the backend make the button kick off a task to retrieve a joke then launch the activity from your android library to display it step 4 add functional tests add code to test that your async task successfully retrieves a non empty string for a refresher on setting up android tests check out demo 4 09 step 5 add a paid flavor add free and paid product flavors to your app remove the ad and any dependencies you can from the paid flavor optional tasks for extra practice to make your project stand out complete the following tasks add interstitial ad follow these instructions to add an interstitial ad to the free version display the ad after the user hits the button but before the joke is shown https developers google com mobile ads sdk docs admob android interstitial add loading indicator add a loading indicator that is shown while the joke is being retrieved and disappears when the joke is ready the following tutorial is a good place to start http www tutorialspoint com android android loading spinner htm configure test task to tie it all together create a gradle task that 1 launches the gce local development server 2 runs all tests 3 shuts the server down again rubric required components project contains a java library for supplying jokes project contains an android library with an activity that displays jokes passed to it as intent extras project contains a google cloud endpoints module that supplies jokes from the java library project loads jokes from gce module via an async task project contains connected tests to verify that the async task is indeed loading jokes project contains paid free flavors the paid flavor has no ads and no unnecessary dependencies required behavior app retrieves jokes from google cloud endpoints module and displays them via an activity from the android library optional components once you have a functioning project consider adding more features to test your gradle and android skills here are a few suggestions make the free app variant display interstitial ads between the main activity and the joke displaying activity have the app display a loading indicator while the joke is being fetched from the server write a gradle task that starts the gce dev server runs all the android tests and shuts down the dev server | server |
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saifi_database | saifi database this proyect is a collaboration between the financial engineering student union and the academic council at western institute of technology and higher education iteso the main objective is to make availabe general information about the student union and to familiarize the students with the python language and git programming please feel free to contribure to this proyect meetups or consultancy can be given at the university contact saifi iteso mx for more information authors rodrigo her ndez mota rhdzmota add requirements python 3 5 or current version packages numpy pandas matplotlib git and a github account instructions 1 clone or download this proyect 2 depending your os run the files run in windows bat for windows run in mac for mac not availabe run in dev for ubuntu or debian not available 3 explore and modify the database at will recommended see to do section for helping building this proyect 4 git push if you have a valuable contribution 5 enjoy contents databases the information of the student union is stored in the following files members csv id members name surname mobile phone email roles csv id roles roles status csv id status status eyecolor csv id eyecolor color members roles csv id members id roles id status since until physical charact csv id members age birthday gender height weight id eyecolor python scrypts load py facilitates the data loading in other scrypts explorative py statistical analysis and visualization tests python program py general program to explore database add new data analyze statistics and visualize bash scrypts run in windows bat automatically runs python program py to do enhance statistical analysis explorative py to test when good enough add to python program py provide good visualizations add some python functions to python program py show processed tables summary add basic descriptive stats capability to add new data update age with birthday information make batch file for mac ubuntu debian | server |
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RTOS-From-Scratch | license https img shields io badge license gpl3 blue svg https www gnu org copyleft gpl html testing https img shields io badge tested 20on linux red svg rtos from scratch real time operating system made with love instructions to get clone this repo you need to do so git clone https github com rtos from scratch rtos from scratch git git submodule init git submodule update note this system is still in pre alpha state | rtos cmake arm cortex-m4 arm-none-eabi openocd | os |
Android_Apps | ece 155 engineering design with embedded systems winter 2015 all labs are android applications and were tested on nexus 5 and nexus 7 devices lab 1 sensor reader displays informaton from an android phone sensors sensors include light sensor displays light intensity accelerometer displays x y z acceleration and displays it on a graph with respect to time magnetic field sensor displays x y z field values rotation sensor displays x y z rotation values lab 2 step counter detects and counts user steps device held in both hands with screen facing upwards uses accelerometer data to detect a step in the user s motion step counter is then incremented and displayed on screen step counter can be reset by pressing the reset button lab 3 displacement tracker measures user displacement in ns ew coordinates uses existing code from lab 2 and adds the user s direction to track their displacement north has to be calibrated by facing north and hitting the calibrate button lab 4 map guide gives directions to the user to move from one point of a room to another uses existing code from lab 3 to track the user s displacement a map in an svg format must be added to the app s files directory the user can set their initial location and direction by pressing and holding on the map | os |
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STIL2019 | part of speech embeddings for portuguese stil 2019 work presented at stil 2019 this repository contains dataset files data directory scripts for dataset generation scripts directory files used for model definition training models pos tagger and postagger py logs of some training experiments test accuracies too runs directory pretrained models pretrained directory pdf of the paper stil2019 pdf requirements python v3 6 3 pytorch v1 2 https pytorch org tqdm https github com tqdm tqdm very basic knowledge of python is needed in order to fill the pos tagger parameters py file dataset generation links for downloading the datasets macmorpho http nilc icmc usp br macmorpho gsd https github com universaldependencies ud portuguese gsd bosque ud https github com universaldependencies ud portuguese bosque bosque lt https www linguateca pt floresta ficheiros bosque cp 8 0 ad txt at https www linguateca pt floresta corpus html bosque scripts for dataset generation ad2mm py script for extracting sentences and their pos tags from a file with ad formatting and generating a new file with the extracted samples following the mac morpho formatting in order to execute the script run python ad2mm py path to ad file path to new file conllu2mm py script for extracting sentences and their pos tags from a file with conllu formatting and generating a new file with the extracted samples following the mac morpho formatting in order to execute the script run python ad2mm py path to conllu file path to new file build lgtc py script for generating the lgtc linguateca datasets since there can be a huge intersection between different sets eg train and test between bosque ud and the generated linguateca bosque lt splits a more cautious split is needed by generating the files with this script there will only be intersections between the same sets train train dev dev test test the parameters need to be hardcoded data path path to directory with input files ud train file bosque ud train file ud dev file bosque ud dev file ud test file bosque ud test file file lgtc linguateca file with samples at the mac morpho formatting dest lgtc train destination path to the bosque lt train file dest lgtc dev destination path to the bosque lt dev file dest lgtc test destination path to the bosque lt test file then run python build lgtc py usage of the pos tagger pos tagger parameters py file follow the instructions at the file to fill it executing execute the main file python postagger py output terminal only log messages with rank log level will be printed on the terminal rank 0 messages erros warnings train and test output tqdm rank 1 messages success messages descriptive log log file output txt a log file with all the log messages will be generated tagged samples a file with the samples form the validation set along with their tags prediction will be generated for each dataset aditional scripts intersect py script used for checking the intersection of sentences between all the files from the dataset the path of the files must be hardcoded for the variable files to execute the script run python intersect py references if you used our model please cite our paper link todo bibtex todo | pos-tagging nlp pos-embedding deep-learning | server |
Information-Security-Applications | this course covers information security technology and its applications from the basics of hardware and software security where security is implemented security policy specific security measures security management and security operation techniques will be explained first students learn threat analysis methods to identify security threats to systems and tamper attacks and their countermeasures are explained as threats to hardware and software public key authentication infrastructure will be introduced using ssl tls the protocol for encrypted communication paths used in encrypted email and web browsers as an example in addition we will learn about os access control technology which is the basis for android and ios security mechanisms finally case studies related to these learned techniques and knowledge will be presented and security issues and countermeasures encountered in the case studies will be discussed | server |
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iot-app-kit | iot application kit build status https github com awslabs iot app kit actions workflows run tests yml badge svg event push https github com awslabs iot app kit actions workflows run tests yml npm version https img shields io npm v iot app kit core https npmjs org package iot app kit core license https img shields io npm l iot app kit core https github com awslabs iot app kit blob main license bundle size https img shields io bundlephobia minzip iot app kit core https bundlephobia com package iot app kit core downloads https img shields io npm dw iot app kit core https npmjs org package iot app kit core official iot app kit documentation site https awslabs github io iot app kit overview iot application kit is a development library for building industrial iot web based applications iot app kit is an open source library consisting of front end components and utilities with iot app kit you can build front end applications and webpages to utilize iot data by default iot app kit helps to retrieve data from aws iot sitewise https docs aws amazon com iot sitewise latest userguide what is sitewise html and aws iot twinmaker https docs aws amazon com iot twinmaker latest guide what is twinmaker html you can also install plugins to retrieve data from your own sources there s no charge for using iot app kit for an example of a real world use case using the iot app kit visit this tutorial on how to use iot app kit https aws amazon com blogs iot build iot applications using aws iot application kit img width 1170 alt iot app kit demo src https user images githubusercontent com 6397726 159107236 ea95e7ba a89c 43e6 a34c c5ea1dd37e8b png contributors guide to iot app kit learn how to contribute at the development guide https github com awslabs iot app kit tree main docs development md packages the iot application kit containing the following public packages iot app kit react components iot app kit react components exposes the core iot app kit web components as react components learn more here https github com awslabs iot app kit tree main docs components md iot app kit scene composer iot app kit scene composer is a 3d rendering component built over react three fiber that renders your digital twin and enables you to interact with it learn more here https github com awslabs iot app kit blob main docs sceneviewer md iot app kit source iotsitewise iot app kit source iotsitewise exposes the aws iot sitewise source which enables you to visualize and interact with your aws iot sitewise https docs aws amazon com iot sitewise latest userguide what is sitewise html data and assets learn more here https github com awslabs iot app kit tree main docs awsiotsitewisesource md iot app kit source iottwinmaker iot app kit source iottwinmaker exposes the aws iot twinmaker source which enables you to visualize and interact with your aws iot twinmaker https docs aws amazon com iot twinmaker latest guide what is twinmaker html data and digital twins learn more here https github com awslabs iot app kit blob main docs awsiottwinmakersource md iot app kit core iot app kit core is the core library which exposes the iot app kit framework and shared typescript types and utilities and is what iot app kit components are built upon learn more here https github com awslabs iot app kit tree main docs core md iot app kit tools iottwinmaker iot app kit tools iottwinmaker contains functionality for the aws iot twinmaker development tools tmdt a set of tools to aid in aws iot twinmaker https docs aws amazon com iot twinmaker latest guide what is twinmaker html project management learn more here https github com awslabs iot app kit tree main packages tools iottwinmaker security see contributing contributing md security issue notifications for more information license this project is licensed under the apache 2 0 license | amazon aws iot react sitewise twinmaker typescript | server |
BBBFreeRTOS | bbbfreertos freertos for beaglebone black makefile located in in demo am3359 beaglebone gcc also includes working mlo and u boot stage 1 and 2 bootloaders working system tick interrupts gpio output serial not finished an isr routine for serial input should however be no problem as core freertos is fully functioning test main c file sets up gpio interrupts for three input pins waits for interrupts then responds on three other output pins use the am335x technical reference manual to look up the registers used | os |
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cockpit | cockpit java based pwa and microservice development platfrom frontend development components are developed based on bck2bwsr https github com jtulach bck2brwsr and backend development components are developed with netty https github com netty netty and jersey this platform is to provide zero code facilities to create modern apps that can run everywhere core components api gateway netty 4 based a http 2 proxy gadget sdk bck2brwsr based frontend gadget development libraries theme and layout builder web ui sass based theme and json based web layout builder microservice sdk netty and jersey integrated microservice development api todo gadget builder ui microservice composer ui oauth 2 server integration metrics server viewer log server viewer cassandra and mongodb integration server sent event sse manager http 2 push manager containerization and many more run build the whole project with maven 3 fix the alpn agent path in pom xml of cockpit gadgets microservices gateway project for http 2 add domains in hosts file according to your os sh 127 0 0 1 content cockpit dev 127 0 0 1 www cockpit dev 127 0 0 1 cockpit cockpit dev 127 0 0 1 accounts cockpit dev 127 0 0 1 data cockpit dev install cockpit dev certs from gateway project to be trusted in cockpit gadgets microservices shell and content projects has config properties fix the store location as your local machine sh storepath e cockpit cockpit store stop port 80 if running in your machine run gateway shell and content microservice server from netbean if you are using commandline then build these three projects with profile server to create uber jar of each project and run them from commandline if servers are running hit borwser with https www cockpit dev this deploy and run section need more elaborate guideline for more understanding cockpit netty jersey marriage asynchronous non blocking microservice api gateway https medium com mrmanna netty jersey marriage b7f12ac8d4a9 implementing single page application shell with java and http 2 cloud gateway with netty https medium com mrmanna implementing single page application shell with java and http 2 cloud gateway with netty aa0d083d38ca pwa shell architecture https medium com mrmanna pwa shell architecture 84dca8c29149 captain api of cockpit asynchronous job execution planning api https medium com mrmanna captain api of cockpit d1c7ab9dc30f | server |
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BaseIotUtils | android build gradle groovy allprojects repositories maven url https www jitpack io app build gradle base iotutils https jitpack io v wave chtj baseiotutils svg https jitpack io wave chtj baseiotutils groovy dependencies implementation com github wave chtj baseiotutils 1 6 2 application java module library manifest application library public class app extends application override public void oncreate super oncreate base iotutils baseiotutils instance setadaptation 1080 1920 baseiotutils width true create getapplication override protected void attachbasecontext context base super attachbasecontext base multidex install this app image pic screenshot one png image pic screenshot two png base iotutils 01 audioutils 02 appsutils app app 03 adaptationutils pt 04 byteshexutils 10 16 05 downloadutils 06 displayutils 07 dialogutils 08 deviceutils 09 encryptutils md5 10 fileutils 11 formatviewsutils textview 12 globaldialogutils system alert window 13 httputils get post 14 jsonformatutils json json 15 klog 16 keyboardutils 17 notifyutils notification 18 netutils 19 netmonitorutils 20 objectutils 21 permissionsutils 22 regulartools 23 serialport serialportfinder 24 sputils sharedpreferences 25 shellutils adb adb 26 serviceutils service 27 statusbarutils 28 toastutils toast 29 timeutils date 30 tpoolsingleutils tpoolutils 31 uripathutils uri android7 0uri 32 ziputils base iotutils fileutils java filepath sdcard config txt content iscover boolean writeresult fileutils writefiledata filepath content true filepath string readresult fileutils readfiledata filepath fileutils keyboardutils java keyboardutils openkeybord editetextview keyboardutils closekeybord editetextview netutils java network no network wi network 2g network 3g network 4g network un network eth netutils getnetworktype netutils getnetworktypename netutils isnetworkavailable netutils isavailable netutils isconnected ping netutils ping ping netutils reloaddnsping wifi netutils iswifienabled wifi netutils iswifi eth netutils iseth wifi netutils iswificonnected 3g netutils is3rd 4g netutils is4g gps netutils isgpsenabled netutils openwirelesssettings netutils getactivenetworkinfo netutils getnetworkoperatorname downloadsupport image pic download png java downloadutils downloadsupport new downloadutils 1 filecachedata filecachedata new filecachedata filecachedata seturl downloadurl filecachedata setfilename filename1 filecachedata setrequesttag downloadurl filecachedata setfilepath sdcard filename1 2 filecachedata filecachedata2 new filecachedata filecachedata2 seturl downloadurl2 filecachedata2 setfilename filename2 filecachedata2 setrequesttag downloadurl2 filecachedata2 setfilepath sdcard filename2 1 downloadsupport addstarttask filecachedata downloadcallback 2 downloadsupport addstarttask filecachedata2 downloadcallback downloadcallback requesttag filecachedata getrequesttag idownloadcallback downloadcallback new idownloadcallback override public void download filecachedata filecachedata int percent boolean iscomplete message message1 handler obtainmessage message1 obj filecachedata message1 arg1 percent handler sendmessage message1 override public void error exception e klog d tag error errmeg e getmessage override public void downloadstatus string requesttag downloadstatus downloadstatus klog d tag downloadstatus requesttag requesttag status downloadstatus name downloadsupport pause downloadsupport pause filecachedata2 getrequesttag downloadsupport cancel notificationutils java if notifyutils notifyisenable notifyutils setnotifyid 111 setenableclosebutton false setonnotifylinstener new onnotifylinstener override public void enablestatus boolean isenable klog e tag isenable isenable setnotifyparam r drawable ic launcher r drawable app img baseiotutils this is a library 2020 3 18 false true exeunotify else notifyutils toopennotify notifyutils setappname notifyutils setappabout notifyutils setremarks notifyutils setprompt notifyutils setdatatime notification notifyutils closenotify playutils java audioutils getinstance setplaystatechangelistener new playutils playstatechangelistener override public void onplaystatechange playutils play status play status play resume pause stop none klog d tag play status play status name override public void getprogress int sumprogress int nowprogress sumprogress nowprogress klog d tag sumprogress sumprogress nowprogress nowprogress startplaying sdcard ding wav audioutils getinstance pauseplay audioutils getinstance resumeplay audioutils getinstance stopplaying netmonitorutils java netmonitorutils register nettypeinfo network 2g network 3g network 4g network wifi network eth network no network unknown netmonitorutils addcallback new inetchangecallback override public void netchange int nettype boolean pingresult tvtype settext nettype tvstatus settext pingresult netmonitorutils removecallback this netmonitorutils unregister serialport serialportfinder java serialportfinder mserialportfinder new serialportfinder string entryvalues mserialportfinder getalldevicespath serialport port null int baudrate 9600 try entryvalues xxx port new serialport new file entryvalues xxx baudrate 0 log e tag catch exception e e printstacktrace log e tag errmeg e getmessage port write command serialporthelper readinputstreamdata int flag port read port read byte buff int lenght port close shellutils adb java shellutils commandresult commresult shellutils execcommand reboot true shellutils commandresult commresult2 shellutils execcommand new string comm1 comm2 comm3 commn true if commresult result 0 log e tag commresult2 exeu successful else log e tag commresult exeu faild errmeg commresult errormsg appsutils java appsutils getsystemapplist appsutils getdesktopapplist appsutils getnormalapplist appsutils getmainintent appsutils isapprunning app appsutils startapp app appsutils getappname appsutils getpidbypackagename pid appsutils getappversioncode app versioncode appsutils getappversionname app versionname appsutils isappforeground app appsutils getapppath app permissionsutils java permissionsutils with mcontext addpermission manifest permission access fine location addpermission manifest permission access coarse location addpermission manifest permission initpermission version code v1 1 0 downloadsupport api v1 0 9 service activity v1 0 8 playuitls dwonloadsupport packagesutils app greendao sqlite v1 0 3 v1 0 2 | fileutils filedownload screenadaptation serialport shellutils service tcp-socket udp-socket socket notification poi jxl ota usb-device | server |
ESD | esd embedded system design using the altera de1 soc collection of labs from esd | os |
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ife-tutorial | guide for front end developers this project is based on gitbook www gitbook com service to organize and share front end knowledges it is a guide to help front end freshman to learning front end knowledges including html css javascript node js etc what s more some advanced topics will also be discussed in this project such as react koa etc in order to improve the quality of knowledge points lots of relevant links will be given for you to reference the gitbook address https wwsun gitbooks io ife tutorial content learning tips for freshman we suggests learn the actual underlying technologies before learning abstractions don t learn jquery learn the dom don t learn sass learn css don t learn haml learn html don t learn coffeescript learn javascript don t learn handlebars learn javascript es6 templates don t just use bootstrap learn ui patterns when getting your start you should fear most things that conceal complexity kyle simpson video link https www youtube com watch v qjkh1j77gji html css javascript gitbook https wwsun gitbooks io ife tutorial content relevant links quora what are the best resources for learning javascript https www quora com what are the best resources for learning javascript http www zhihu com question 20246142 http www zhihu com question 19809484 rf 20315724 https github com jacksontian fks javascript http jstherightway org https github com h5bp front end developer interview questions useful online services ide sublime webstorm visual studio code atom sublime http wwsun me posts sublime usage html w3c html https validator w3 org can i use http caniuse com placehold it http placehold it web fontsquirrel http www fontsquirrel com google fonts web coolors co https coolors co modern ie https www modern ie zh cn bootcdn cdn http www bootcdn cn | front_end |
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SQL | sql i was hired as a new data engineer at microsoft 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 data engineering data analysis data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering 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 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 your 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 | sql postgresql postgresql-database data-engineering data-modeling erdiagram | server |
NotIMDB | notimdb movie database site for intro software engineering cse201 | server |
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hotel | hotel at a glance individual stage 3 https github com ada developers academy pedagogy blob master classroom rule of three md stage 3 project due before class date here introduction our company has been contracted to build a booking system for a small hotel this system will be used by employees who manage bookings and booking data and will not be available to the general public learning goals reinforce and practice all of the ruby and programming concepts we ve covered in class so far design a system using object oriented principles create and instantiate classes with attributes create class and instance methods within our classes write pseudocode and create tests to drive the creation of our code objective for our hotel booking system we should make a module full of business logic that tracks which rooms are reserved and at what dates this should be organized into appropriate classes and methods we should use tests to verify that this system works as intended we should not build a cli for this project project expectations this project is both a culmination of our intro to ruby unit and our first stage 3 project this means the requirements are more open ended and ambiguous than previous projects you have worked on this is intentional you will be expected to make decisions on how to structure your classes and methods ask questions when you need clarification understand that the way you implement something may be different than the way your neighbor implements it it is expected that you will not be able to complete all requirements the three waves are organized by difficulty and relevance to the learning goals and should be tackled in order hints we have included some optional design scaffolding https github com adagold hotel blob design scaffolding design scaffolding notes md for this project to help you get started if you don t know where to start or to provide inspiration if you re a little stuck any student should feel free to use this scaffolding in whatever way is most helpful to them however we recommend that you spend at least 1 full day thinking about design before reaching for this scaffolding to get practice thinking about this type of problem independently getting started we will use the same project structure we used for the previous project library code such as classes should be in files in the lib folder and tests should be in files in the test folder 1 fork this repository in github 1 clone the repository to your computer verify setup with your first commit follow the steps below first to verify that your setup is working if you cannot get guard to run a failing test to run or a test to pass at the beginning please ask for help before continuing the project 1 open a terminal and run the guard command from within the project s root directory where the guardfile is 1 create a test to check the instantiation of one of your object types red 1 create the class for the object tested in the step above green 1 use git add commit and push commands to push your initial code to github process requirements you should use the following process as much as possible 1 write pseudocode 1 write test s 1 write code 1 refactor 1 commit your git commit history should provide a clear description of how your code developed letting the reader know what changed when and why making frequent small commits is essential wave 0 design requirements before starting work on the functional requirements create a first and second draft of your project s design first draft instructor led in class design activity complete your first draft of the hotel design by following the instructor led in class design activity second draft details summary after completing the in class design activity click here for the second draft instructions summary we have our own first draft design for this project we d like you all to look at our first draft compare it with your own and adjust your own first draft if you d like a description of our code exists here https github com adagold hotel blob design scaffolding design scaffolding notes md our code exists here https github com adagold hotel tree design scaffolding use the two links above to explore the files and answer these questions what classes exist why what are they named what do they represent and what state and behavior do they have what tests exist what parts of this design inspires you and you want to steal what parts of this design are you unsure about and need to consider again later what parts of this design do you think you can do without details br spend no more than 1 hour answering those questions and adjusting your project s first draft design after one hour get started don t forget that a useful skill for the programmer is the ability to get started and adjust in small ways often also remember that you can always refactor later to improve your design functional requirements wave one tracking reservations in this wave write the functionality for the system to track valid reservations so that a user of the hotel system can make and find valid bookings for their hotel remember your job is to only build the classes that store information and handle business logic and the tests to verify they re behaving as expected building a user interface is not part of this project user stories as a user of the hotel system i can access the list of all of the rooms in the hotel i access the list of reservations for a specified room and a given date range i can access the list of reservations for a specific date so that i can track reservations by date i can get the total cost for a given reservation i want exception raised when an invalid date range is provided so that i can t make a reservation for an invalid date range details the hotel has 20 rooms and they are numbered 1 through 20 every room is identical and a room always costs 200 night the last day of a reservation is the checkout day so the guest should not be charged for that night when reserving a room the user provides only the start and end dates the library should determine which room to use for the reservation for this wave any room can be reserved at any time and you don t need to check whether reservations conflict with each other this will come in wave 2 hints these functionalities do not all need to be implemented in the same class you might want to investigate ruby s date gem https ruby doc org stdlib libdoc date rdoc date html details summary a hint on dates summary when programming it s generally helpful to convert or validate your data as soon as possible if you do this when you first read receive data that means that the rest of your code can assume that you have data in the desired form this is a job for driver code in this case we are not writing driver code that means that your code should deal entirely in ruby date objects your tests should create date objects and your library code should assume that it s receiving date objects to start when making tests you will want to use something like date new 1993 2 24 to create a date representing february 24 1993 or date today for today instead of trying to parse a string or storing and re parsing strings internally details wave two room availability user stories as a user of the hotel system i can view a list of rooms that are not reserved for a given date range so that i can see all available rooms for those days i can make a reservation of a room for a given date range and that room will not be part of any other reservation overlapping that date range i want an exception raised if i try to reserve a room during a date range when all rooms are reserved so that i cannot make two reservations for the same room that overlap by date details a reservation is allowed start on the same day that another reservation for the same room ends wave three hotel blocks if you are not familiar with what a block of hotel rooms here is a brief description a hotel block is a group of rooms set aside for a specific group of customers for a set period of time hotel blocks are commonly created for large events like weddings or conventions they contain a number of rooms and a specific set of days these rooms are set aside and are made available for reservation by certain customers at a discounted rate these rooms are not available to be reserved by the general public user stories as a user of the hotel system i can create a hotel block if i give a date range collection of rooms and a discounted room rate i want an exception raised if i try to create a hotel block and at least one of the rooms is unavailable for the given date range given a specific date and that a room is set aside in a hotel block for that specific date i cannot reserve that specific room for that specific date because it is unavailable given a specific date and that a room is set aside in a hotel block for that specific date i cannot create another hotel block that includes that specific room for that specific date because it is unavailable i can check whether a given block has any rooms available i can reserve a specific room from a hotel block i can only reserve that room from a hotel block for the full duration of the block i can see a reservation made from a hotel block from the list of reservations for that date see wave 1 requirements details a block can contain a maximum of 5 rooms when a room is reserved from a block of rooms the reservation dates will always match the date range of the block all of the availability checking logic from wave 2 should now respect room blocks as well as individual reservations non functional requirements testing requirements utilize the test helper file run tests automatically whenever files are added or changed using the guard command the final project submission should have 95 code coverage using simplecov optional enhancements you should not be working on these or even thinking about them until you have fully completed wave 3 details add functionality that allows for setting different rates for different rooms read and write csv files for each piece of data that your system is storing create a cli to interact with your booking system put all of this code in a file main rb that is separate from your lib code working on this optional enhancement should not break the other requirements of this project details before submission refactors txt requirement usually by the end of a project we can look back on what we made with a clearer understanding of what we actually needed in industry this is a great time to do a refactor of some sort for this project however you re off the hook for the moment we will be revisiting our hotel project submissions later on on the course and you may want to make some changes at that point in order to keep track of your current thoughts about design refactoring as part of project submission please log down design thoughts with the following process 1 create a new file in the project called refactors txt 1 in that file make a short list of the changes that you could make such as naming conventions etc these notes will be used by you in a few weeks so make sure that they are detailed enough that someone else could understand your thinking and follow your directions commit and push this file to your project repo 1 do not make any further changes to your code at this time what we re looking for you can find what instructors will be looking for in the feedback feedback md markdown document | os |
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Informational-Technology-related-projects | informational technology related projects informational technology related projects | server |
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agile-dev-rails6 | readme depot app based on agile web development with rails 6 | front_end |
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TURKSAT-RTOS-CORE | turksat rtos core bu u u yaz l m t rksat model uydu yar mas kategorisinde yar acak tak mlar i in zel olarak tasarlanm t r stm32 proje ayarlar c c build settings mcu gcc compiler settings k sm na flag olarak fshort enums flagini eklemeyi unutmay n z ama olu turulacak enum lar minimum boyutta olacakt r satellite package frame payload mechn https user images githubusercontent com 63451008 156889344 194af94c 6a60 462d b00c e0dc2aed6b4e png | os |
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MedicalBlockchain | medical blockchain a project for cambridge hackathon 2018 medical blockchain is a distributed file sharing system for medical services it combines the powers of blockchain technology powered by ark and the interplanetary file system use case a medical service e g a general practitioner owns medical records of his patients nowadays to share that data with another medical service it requires physical file sharing or some other unsafe or slow form of access approval medical blockchain uses the ipfs to have a shared decentralized file system containing all the medical records access to a medical record can then be requested using a transaction on the blockchain permission is given likewise the use of the blockchain allows the file sharing system to be supervised features the medical records are stored securely using the ipfs a medical record is owned by exactly one person at a time the owner of the medical record of a patient are by reading the ledger and following the transaction for his her nhs id permission can then be requested by asking the owner of that medical record to transfer the access codes the blockchain is called mednet a private blockchain used solely for the medical blockchain project medical records are stored using the fhir format standardised json for medical records implementation a website is provided which illustrates the way medical services can interact with the system it shows the medical records of patients managed by a particular medical service these records can be viewed and updated it contains example nhs records provided by ttp | hackathon typescript angular blockchain ark medical data-sharing | blockchain |
carbon-tutorial-angular | carbon tutorial angular this tutorial will guide you in creating an angular app with the carbon design system https www carbondesignsystem com we ll teach you the ins and outs of using carbon components while introducing web development best practices along the way get started by visiting the tutorial instructions https www carbondesignsystem com tutorial angular overview available scripts in the project directory you can run npm run start runs the app in the development mode open http localhost 4200 to view it in the browser the page will reload if you make edits you will also see any lint errors in the console npm run test launches the test runner in the interactive watch mode npm run build builds the app for production to the build folder it correctly bundles angular in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes your app is ready to be deployed learn more you can learn more in the angular cli overview https angular io cli to learn angular check out the angular documentation https angular io docs | os |
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dis25-2021 | natural language processing 2021 dis25 schedule and resources all lectures will be hosted on zoom https th koeln zoom us j 88620328848 the password for zoom is the zip code of th k ln claudiusstra e 1 the lectures will be held life slides will be usually available a night before the actual lecture slides and exercises please feel free to comment on all the slides available open up a ticket if you find anything i should update or fix date topic resources 2021 04 09 introduction slides slides dis25 01 introduction pdf 2021 04 16 basic text processing slides slides dis25 02 basictextprocessing pdf exercise tutorials dis25 tutorial 1 pdf zip tutorials dis25 tutorial 1 zip solution jupyter tutorials dis25 1 solution ipynb solution pdf tutorials dis25 1 solution pdf 2021 04 23 wordnet slides slides dis25 03 wordnet pdf exercise tutorials dis25 tutorial 2 pdf zip tutorials dis25 tutorial 2 zip solution jupyter tutorials dis25 2 solution ipynb solution pdf tutorials dis25 2 solution pdf nda germanet tutorials classroom student germanet pdf 2021 04 30 vector semantics slides slides dis25 04 vectorsemantics pdf exercise tutorials dis25 tutorial 3 pdf zip tutorials dis25 tutorial 3 zip 2021 05 07 language models and ethics in nlp slides slides dis25 05 lm ethics pdf zuteilung der gruppen groups md literature and priority selection for presentations tutorials dis25 literaturliste pdf 2021 05 14 video lectures fairness and transperency in ranking youtube https www youtube com watch v kegpixqvty4 2021 05 28 student talks paper presentation presentation schedule schedule 2021 05 28 md informationen zu esupol datens tzen esupol datens tze bersicht pdf slice btw17 suggestions btw17 slice csv slice eu suggestions eu slice csv slice minorities suggestions minorities slice csv 2021 06 04 sentiment analysis slides slides dis25 08 sentiments pdf 2021 06 11 information extraction slides slides dis25 09 infoextract pdf 2021 06 18 data programming for information extraction slides slides dis25 10 infoextract2 pdf 2021 06 25 annotations guest lecture slides slides dis25 11 annotations pdf 2021 07 02 student talks milestone presentation presentation schedule schedule 2021 07 02 md presentation results presentations results md 2021 08 31 submission of term papers term paper guidelines term paper guidelines md btw politicians demographic information btw politicians demographic csv eu politicians demographic information eu politicians data csv | ai |
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Dianzisuoa | dianzisuoa ece 1160 embedded system design university of pittsburgh | os |
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bwthw | bwthw this is the official repo containing all the necessary laboratory material for the course of biomedical wearable technology for healthcare and wellbeing master s degree in bioengineering department of information engineering dei university of padova contacts for any problems issues or suggestions write us giacomo cappon professor giacomo cappon unipd it luca cossu tutor luca cossu phd unipd it michele atzeni tutor michele atzeni phd unipd it | server |
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Natural-Language-Processing-with-Attention-Model | attention models for natural language processing attention models are used for various applications like machine translations text summarizations auto complete question answering chat bots and many more unlike recurrent neural networks it works very well with very long sequences so one of the widely used attention model is transformers some of the state of the art models are gpt 2 3 is latest one by openai bert bidirectional encoder representations from transformers t5 text to text transfer transformer | ai |
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iotkit-embedded | https help aliyun com document detail 96575 html c sdk c sdk iot iot tcp ip c99 c sdk zigbee 433 knx ip iot c sdk c sdk https help aliyun com document detail 96624 html c sdk os c hal c sdk hal c sdk linux windows hal os https help aliyun com document detail 97556 html sdk | sdk iot linkkit aliyun | server |
bangjago-android-emulator | bangjago android emulator cli version bangjago emulator is a cli based application used for mobile development which is used as an android emulator like genymotion even though it s not as good as genymotion besides that i added some features that are not in genymotion such as connecting your mobile device using usb debugging and wireless for mobile development for now only available for windows users maybe next time i will make it for another operating system version like mac or linux img src cover gif alt logo prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com github code size in bytes https img shields io github languages code size restuwahyu13 bangjago android emulator tag https img shields io github tag restuwahyu13 bangjago android emulator svg https github com restuwahyu13 bangjago android emulator stars https img shields io github stars restuwahyu13 bangjago android emulator https img shields io github stars restuwahyu13 bangjago android emulator forks https img shields io github forks restuwahyu13 bangjago android emulator https img shields io github forks restuwahyu13 bangjago android emulator table of content get started get started features features command command how to use how to use default port device default port device system images list system images list skin device list skin device list translate translate support project support project video tutorial video tutorial author author contributor contributor license license features x easy to use x fast booting x effeciency management ram and cpu x connected over usb and wireless x connected over android emulator x installation software supported automatically x support for like react native flutter ionic native script etc command adb tools add adb usb is used to add usb debugging port add adb wireless is used to add ip address restart adb is used to reset adb to default port check adb is used to check if adb device is connected or not running emulator is used to run emulator via usb debugging or wireless sdk tools google android sdk to download android sdk version android google apis default android sdk to download android sdk version android default tv android sdk to download android sdk version android tv wear android sdk to download android sdk version android wear google playstore sdk to download android sdk version google apis playstore avd tools list avd emulator to list all avd emulators available create avd emulator to create new avd emulator running avd emulator to run avd emulator delete avd emulator to remove avd emulator update avd emulator to update avd emulator software tools java jdk to download java jdk automatically android studio to download android studio automatically more information developer related information and support project donation how to use 1 running application first you must install android studio and java jdk if not installed on computer laptop over cli install java jdk versi jdk 8u261 download here https www filehorse com download java development kit 64 52937 if you encounter an error upgrade android studio to 4 1 version if any error encountered when you downloading android sdk download the file via the bit ly link above extract bangjago emulator zip to localdisk c click properties my computer advanced system settings environment variable copy path android sdk to environment system variable android home copy path java jdk to environment system variable java home copy path c bangjago to environment system variable path copy path c bangjago to environment system variabel bangjago you can open cmd and type start bangjago don t use a terminal other than cmd if you want to install android studio or java jdk run as is required 2 connected over usb debugging first you must provide usb cabel enable usb debugging on your smartphone ensured your smartphone could connect on your computer laptop selected adb tools then choose add add usb select the default usb debugging port and device just running emulator emulator usb if not connected then choosing adb tools restart adb and repeat 3 connected over wireless first you must provide usb cable plug usb to port computer laptop enable usb debugging on your smartphone make sure your smartphone is connected selected adb tools then choose add wireless set ip address default if it works unplug your smartphone from usb close emulator and run emulator again run emulator wireless emulator if not connected you can select adb tools restart adb then repeat or follow the tutorial below react native https tinyurl com y6rvxsln or flutter https tinyurl com yxwpy7w7 4 connected over emulator select avd tools create emulator then android sdk will be downloaded automatically skip the first method if avd emulator already exists how to run the emulator avd tool options then select run emulator enter emulator name that was created before default port device name ip address port type local 5555 usb react native 8081 usb flutter 8080 usb local 192 168 x x wireless react native 192 168 x x wireless flutter 192 168 x x wireless system images list google system images android api version target version cpu version android 16 google apis x86 android 16 google apis armeabi v7a android 17 google apis x86 android 17 google apis armeabi v7a android 18 google apis x86 android 18 google apis armeabi v7a android 19 google apis x86 android 19 google apis armeabi v7a android 21 google apis x86 android 21 google apis x86 64 android 21 google apis armeabi v7a android 22 google apis x86 android 22 google apis x86 64 android 22 google apis armeabi v7a android 23 google apis x86 android 23 google apis x86 64 android 23 google apis armeabi v7a android 24 google apis x86 android 24 google apis x86 64 android 24 google apis arm64 v8a android 25 google apis x86 android 25 google apis x86 64 android 25 google apis armeabi v7a android 25 google apis arm64 v8a android 26 google apis x86 android 26 google apis x86 64 android 27 google apis x86 android 28 google apis x86 64 android 29 google apis x86 android 29 google apis x86 64 android 30 google apis x86 android 30 google apis x86 64 default system images android api version target version cpu version android 16 default x86 android 16 default armeabi v7a android 17 default x86 android 17 default armeabi v7a android 18 default x86 android 18 default armeabi v7a android 19 default x86 android 19 default armeabi v7a android 21 default x86 android 21 default x86 64 android 21 default armeabi v7a android 22 default x86 android 22 default x86 64 android 22 default armeabi v7a android 23 default x86 android 23 default x86 64 android 23 default armeabi v7a android 24 default x86 android 24 default x86 64 android 24 default armeabi v7a android 25 default x86 android 25 default x86 64 android 26 default x86 android 26 default x86 64 android 27 default x86 android 27 default x86 64 android 28 default x86 android 28 default x86 64 android 30 default x86 android 30 default x86 64 tv system images android api version target version cpu version android 21 android tv x86 android 21 android tv armeabi v7a android 22 android tv x86 android 23 android tv x86 android 23 android tv armeabi v7a android 24 android tv x86 android 25 android tv x86 android 26 android tv x86 android 27 android tv x86 android 28 android tv x86 android 29 android tv x86 wear os system images android api version target version cpu version android 23 android wear x86 android 23 android wear armeabi v7a android 25 android wear x86 android 25 android wear armeabi v7a android 26 android wear x86 android 28 android wear x86 google playstore system images android api version target version cpu version android 24 google apis playstore x86 android 25 google apis playstore x86 android 26 google apis playstore x86 android 27 google apis playstore x86 android 28 google apis playstore x86 android 28 google apis playstore x86 64 android 29 google apis playstore x86 android 29 google apis playstore x86 64 android 30 google apis playstore x86 android 30 google apis playstore x86 64 skin device list default phone device name ram cpu cores internal storage 2 7 qvga 1024 mb 1 core 2048 mb 2 7 qvga slider 1024 mb 1 core 2048 mb 3 2 qvga adp2 1024 mb 1 core 2048 mb 3 3 wqvga 1024 mb 1 core 2048 mb 3 4 wqvga 1024 mb 1 core 2048 mb 3 7 fwvga slider 1024 mb 1 core 2048 mb 3 7 wvga nexus one 1024 mb 1 core 2048 mb 4 7 wxga 1024 mb 1 core 2048 mb 4 65 720p galaxy nexus 1024 mb 1 core 2048 mb 4 wvga nexus s 1024 mb 1 core 2048 mb 5 1 wvga 1024 mb 1 core 2048 mb 5 1 wvga api 1024 mb 1 core 2048 mb 5 4 fwvga 1024 mb 1 core 2048 mb 7 3 foldable 2048 mb 1 core 4096 mb 8 foldable 2048 mb 1 core 4096 mb galaxy nexus 1024 mb 1 core 2048 mb nexus 4 2048 mb 1 core 4096 mb nexus 5 2048 mb 1 core 4096 mb nexus 5x 2048 mb 1 core 4096 mb nexus 6 2048 mb 1 core 4096 mb nexus 6p 2048 mb 1 core 4096 mb nexus one 1024 mb 1 core 2048 mb nexus s 1024 mb 1 core 2048 mb pixel 2048 mb 1 core 4096 mb pixel 2 2048 mb 1 core 4096 mb pixel 2 xl 2048 mb 1 core 4096 mb pixel 3 2048 mb 1 core 4096 mb pixel 3 xl 2048 mb 1 core 4096 mb pixel 3a 2048 mb 1 core 4096 mb pixel 3a xl 2048 mb 1 core 4096 mb pixel xl 2048 mb 1 core 4096 mb default tablet device name ram cpu cores internal storage 7 wsvga tablet 1024 mb 1 core 2048 mb 10 1 wxga tablet 1024 mb 1 core 2048 mb nexus 7 2048 mb 1 core 4096 mb nexus 7 2012 1024 mb 1 core 2048 mb nexus 9 2048 mb 1 core 4096 mb nexus 10 2048 mb 1 core 4096 mb pixel c 2048 mb 1 core 4096 mb default tv device name ram cpu cores internal storage android wear round 1024 mb 1 core 2048 mb android wear round chin 1024 mb 1 core 2048 mb android wear square 1024 mb 1 core 2048 mb translate indonesian https github com restuwahyu13 bangjago android emulator blob main readme ind md english https github com restuwahyu13 bangjago android emulator blob main readme md support project if you like this project or you want to support this project you can treat me to a cup of coffee or you can donate via the following link donate https bit ly 37ksgkb video tutorial tutorial https bit ly 2g5kuyr author restu wahyu saputra https github com restuwahyu13 contributor vicri kurniawan https github com vicrfiport license mit https github com restuwahyu13 bangjago emulator blob main license md p align right style padding 5px border radius 100 background color red font size 2rem b a href table of content back to top a b p | android screen mirroring emulator android-development screencast screensharing cli-app cli emulator-launcher cli-application | front_end |
c20-withdraw-dapp | crypto20 withdraw dapp available scripts in the project directory you can run npm start start the project and recieve live updates during development npm run build create a production build of the dapp in the buildfrontend folder you will need to have truffle installed and run truffle compile to generate the smart contract abi and interaction methods | front_end |
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rc | kimsq rc makes front end mobile web apps development faster and easier kimsq rc is the function extended version of ratchet special thanks kimsq rc would like to thank the authors of components that have gone into the making of this framework their contributions have helped make it great ratchet https github com twbs ratchet bootstrap https github com twbs bootstrap history js https github com browserstate history js jquery finger https github com ngryman jquery finger fuel ux https github com exacttarget fuelux bootstrap notify https github com mouse0270 bootstrap notify jquery is loading https github com hekigan is loading offical site community kimsq com rc http kimsq com rc documentaion kimsq github io rc http kimsq github io rc q a for help using kimsq rc ask on stackoverflow using the tag kimsq rc http stackoverflow com questions tagged kimsq rc author maintainers gitaeks https github com gitaeks kieregh https github com kieregh contact email contact kimsq com mailto contact kimsq com facebook https www facebook com kimsq platform https www facebook com kimsq platform copyright and license copyright 2016 redblock inc licensed under the mit https github com kimsq rc blob master license license | front_end |
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feature-engineering-for-machine-learning | pythonversion https img shields io badge python 3 7 20 3 8 20 203 9 20 203 10 success license https github com solegalli feature engineering for machine learning blob master license https img shields io badge license bsd success svg https github com solegalli feature engineering for machine learning blob master license sponsorship https www trainindata com https img shields io badge powered 20by trainindata orange svg https www trainindata com feature engineering for machine learning code repository code repository for the online course feature engineering for machine learning https www trainindata com p feature engineering for machine learning published november 2017 actively maintained img src feml logo png width 248 https www trainindata com p feature engineering for machine learning table of contents 1 introduction variable types 1 numerical variables discrete and continuous 2 categorical variables nominal and ordinal 3 datetime variables 4 mixed variables strings and numbers 2 variable characteristics 1 missing data 2 cardinality 3 category frequency 4 distributions 5 outliers 6 magnitude 3 missing data imputation 1 mean and median imputation 2 arbitrary value imputation 3 end of tail imputation 4 frequent category imputation 5 adding string missing 6 random sample imputation 7 adding a missing indicator 8 imputation with scikit learn 9 imputation with feature engine 4 multivariate imputation 1 mice 2 knn imputation 5 categorical variable encoding 1 one hot encoding simple and of frequent categories 2 ordinal encoding arbitrary and ordered 3 target mean encoding 4 weight of evidence 6 rare label encoding 7 encoding with scikit learn 8 encoding with feature engine 9 encoding with category encoders 6 variable transformation 1 log power and reciprocal 2 box cox 3 yeo johnson 4 transformation with scikit learn 5 transformation with feature engine 7 discretisation 1 arbitrary 2 equal frequency discretisation 3 equal width discretisation 4 k means discretisation 5 discretisation with trees 6 discretisation with scikit learn 7 discretisation with feature engine 8 outliers 1 capping 2 trimming 9 datetime 1 extracting day month week etc 2 extracting hr min sec etc 3 capturing elapsed time 4 working with timezones 10 mixed variables 1 creating new variables from strings and numbers 11 feature creation 1 sum prod count mean std etc 2 div sub 3 polynomial expansion 4 splines 12 feature scaling 1 standardisation 2 minmaxscaling 3 maxabsolutescaling 4 robustscaling 13 pipelines 1 classification pipeline 2 regression pipeline 3 pipeline with cross validation links online course https www trainindata com p feature engineering for machine learning | machine-learning feature-engineering feature-extraction python data-science | ai |
LLMs-SE-Resources | llms se resources a curated collection of resources for software engineers exploring large language models llms in software development note this repository does not claim to be an exhaustive resource on the subject of large language models instead it offers a curated selection of materials available on the internet providing a solid foundation for software engineers interested in exploring and integrating llms into their applications introduction to llms for software engineers an overview of large language models llms with a focus on their relevance and implications for software engineering practices what is chatgpt doing and why does it work https writings stephenwolfram com 2023 02 what is chatgpt doing and why does it work simplicity wins how large language models will revolutionize software engineering https blog blackhc net 2022 12 llm software engineering harness the power of generative ai for software development https github com readme guides coding generative ai understanding gpt tokenizers https simonwillison net 2023 jun 8 gpt tokenizers prompt engineering guide https www promptingguide ai developing software with llms capabilities integrated a list of resources that provides software engineers with essential information and best practices for incorporating large language model capabilities into their applications openai api reference https platform openai com docs api reference introduction examples and guides for using the openai api https github com openai openai cookbook python example app from the openai api quickstart tutorial https github com openai openai quickstart python launch an llm app in one hour https youtu be twhxmu9oxdu full stack llm bootcamp https fullstackdeeplearning com llm bootcamp learn how to work with the chatgpt and gpt 4 models https learn microsoft com en us azure cognitive services openai how to chatgpt pivots programming language chat completions learn how to build your own chatbot with chatgpt api https medium com rohan chikorde learn how to build your own chatbot with chatgpt api 520ec8dbb202 how to train a new language model from scratch using transformers and tokenizers https huggingface co blog how to train how to train your own large language models https blog replit com llm training building llm applications for production https huyenchip com 2023 04 11 llm engineering html integrate cutting edge llm technology quickly and easily into your apps https github com microsoft semantic kernel create your first app using chatgpt https genez io blog create your first app using chatgpt building systems with the chatgpt api https www deeplearning ai short courses building systems with chatgpt case studies real world examples and services that have effectively integrated llms illustrating their potential impact a browser extension that enhance search engines with chatgpt https github com wong2 chatgpt google extension an unofficial visual studio code openai chatgpt integration https github com gencay vscode chatgpt whatsapp bot openai chatgpt nodejs using library baileys https github com sansekai wa openai an experimental open source attempt to make gpt 4 fully autonomous https github com significant gravitas auto gpt minimal web ui for chatgpt https github com anse app chatgpt demo research papers list of research papers investigating the user experience of ai powered products and tools papers ux ai programming repo https github com azhenley papers ux ai programming | ai |
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virtual-prompt-injection | virtual prompt injection virtual prompt injection vpi is a backdoor attack for instruction tuned large language models llms it was proposed in the paper backdooring instruction tuned large language models with virtual prompt injection project website https poison llm github io paper https arxiv org abs 2307 16888 vpi allows an attacker to achieve versatile attack goals by specifying a trigger scenario and a virtual prompt to steer the llm s behavior without tampering the model input during inference time the backdoored model is expected to act as if the virtual prompt were appended to the model input in the trigger scenario this repo is an unofficial implementation of the paper it contains the following resources the code for data poisoning and evaluation for virtual prompt injection the code for alpaca training and inference the generated trigger instructions for the sentiment steering and code injection experiments setup bash git clone https github com wegodev2 virtual prompt injection git cd virtual prompt injection conda create n vpi python 3 10 conda install pytorch 2 0 1 pytorch cuda 11 7 c pytorch c nvidia pip install numpy pip install rouge score pip install fire pip install openai pip install sentencepiece pip install transformers 4 29 pip install upgrade accelerate pip install pydantic 1 10 6 you also need to set your openai api key in utils py line 13 experiments sentiment steering please go to folder sentiment steering https github com wegodev2 virtual prompt injection tree master sentiment steering code injection please go to folder code injection https github com wegodev2 virtual prompt injection tree master code injection citation bibtex article yan2023virtual title backdooring instruction tuned large language models with virtual prompt injection author yan jun and yadav vikas and li shiyang and chen lichang and tang zheng and wang hai and srinivasan vijay and ren xiang and jin hongxia journal arxiv preprint arxiv 2307 16888 year 2023 acknowledgements our code for instruction generation is based on alpaca https github com tatsu lab stanford alpaca and code alpaca https github com sahil280114 codealpaca our code for evaluation on humaneval is based on instructeval https github com declare lab instruct eval many thanks to the authors for open sourcing their code | ai |
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design-system-components | the australian government design system has been decommissioned visit our community page for more information https community digital gov au t dta design system has been decommissioned 4649 australian government design system components the components for the australian government design system documentation full documentation at designsystem gov au http designsystem gov au installation prerequisites the design system components are distributed through the npm https www npmjs com ecosystem npm requires node js and the components needs node js https nodejs org en 8 or higher if you are not familiar with npm and want to use the design system we have created a download page https designsystem gov au download to help once on the page you can select the components you wish to use and choose how you want do download them css minified will bundle the css of the components into a single file to be used directly in html css modules are used with modern javascript allowing you to import styles specific component styles sass modules will create multiple sass files that can be imported into a main scss file javascript minified will bundle the javascript of the components together and minify them for use directly in html javascript modules are used when you want to break a part the components functionality react modules allow the components to be imported directly into react installing modules to install a module make sure you have a package json file in the root of your project folders you can generate one by running npm init inside your working folder install any module and pancake https github com govau pancake will make sure you have all assets ready to use run npm install gov au body to install the body module to install several modules chain them like shell npm install gov au body gov au header gov au footer gov au buttons an even better experience is using syrup https github com govau pancake tree master packages pancake syrup install it globally via npm install g gov au syrup more info coming soon back to top contents frequently asked questions view our frequently asked questions page faq md to see common questions from the community if you can t find an answer to a question you have please email us at designsystem dta gov au or join our conversation on slack http guides service gov au join slack back to top contents checklist and browser support we use the below checklist to ensure new modules or updates to existing modules have a certain level of quality checklist general checks x semantic html and non semantic test x no javascript fallback x svg fallback functional without svg x ie9 ie10 ie11 legacy test x ie8 functional test accessibility checks x accessibility colour contrast x keyboard accessible x tested with screen reader x accessibility expert review css sass x never nest anything that is not either an overwrite or a child element x font family and text color on elements that don t require body x using au space with unit for spacing everywhere but in font sizes no magic numbers x no hardcoded colors use au color or au colordark vars x sass focus mixin au focus or au focus dark x using au fontgrid for font size and line height to snap typography to the grid x print style sheets javascript x var au au in top x each module has it s own name space prefixed with au e g au modulename function1 au modulename function2 x keep public api small use private functions where you can x export out private function for tests only if typeof module undefined x export entire module in the end if typeof module undefined module exports au so react can use it x provide sane defaults for parameters where you can x add jest https facebook github io jest unit tests where you can x document public api in the readme package json x dependencies peerdependencies check x delete enable js object and settings for pancake plugins browser support all components have to work on the below browsers browser version platform engine internet explorer 8 windows 7 trident internet explorer 9 windows 7 trident internet explorer 10 windows 7 trident internet explorer 11 windows 8 1 trident edge latest windows 10 edgehtml firefox latest os x gecko firefox latest windows 10 gecko firefox latest android 6 gecko chrome latest os x blink chrome latest windows 10 blink chrome latest android 4 0 blink chrome latest android 6 blink safari latest 11 os x webkit safari latest 1 10 ios webkit samsung g s8 samsung g s7 samsung g s6 chrome pixel 7 1 native browser lumia 930 8 1 chome 1 safari 1 firefox 1 back to top contents modules details summary gov au core summary br code npm install gov au core code br br see the a href https auds service gov au packages core tests site visual test file for core a br see the a href https github com govau design system components blob master packages core readme md readme file for core a br br i no dependencies i details details summary gov au accordion summary br code npm install gov au accordion code br br see the a href https auds service gov au packages accordion tests site visual test file for accordion a br see the a href https github com govau design system components blob master packages accordion readme md readme file for accordion a br br dependencies br shell animate core details details summary gov au animate summary br code npm install gov au animate code br br see the a href https auds service gov au packages animate tests site visual test file for animate a br see the a href https github com govau design system components blob master packages animate readme md readme file for animate a br br i no dependencies i details details summary gov au body summary br code npm install gov au body code br br see the a href https auds service gov au packages body tests site visual test file for body a br see the a href https github com govau design system components blob master packages body readme md readme file for body a br br dependencies br shell core details details summary gov au breadcrumbs summary br code npm install gov au breadcrumbs code br br see the a href https auds service gov au packages breadcrumbs tests site visual test file for breadcrumbs a br see the a href https github com govau design system components blob master packages breadcrumbs readme md readme file for breadcrumbs a br br dependencies br shell core link list core body core details details summary gov au buttons summary br code npm install gov au buttons code br br see the a href https auds service gov au packages buttons tests site visual test file for buttons a br see the a href https github com govau design system components blob master packages buttons readme md readme file for buttons a br br dependencies br shell core details details summary gov au callout summary br code npm install gov au callout code br br see the a href https auds service gov au packages callout tests site visual test file for callout a br see the a href https github com govau design system components blob master packages callout readme md readme file for callout a br br dependencies br shell core details details summary gov au card summary br code npm install gov au card code br br see the a href https auds service gov au packages card tests site visual test file for card a br see the a href https github com govau design system components blob master packages card readme md readme file for card a br br dependencies br shell core details details summary gov au control input summary br code npm install gov au control input code br br see the a href https auds service gov au packages control input tests site visual test file for control input a br see the a href https github com govau design system components blob master packages control input readme md readme file for control input a br br dependencies br shell core details details summary gov au cta link summary br code npm install gov au cta link code br br see the a href https auds service gov au packages cta link tests site visual test file for cta link a br see the a href https github com govau design system components blob master packages cta link readme md readme file for cta link a br br dependencies br shell core details details summary gov au direction links summary br code npm install gov au direction links code br br see the a href https auds service gov au packages direction links tests site visual test file for direction links a br see the a href https github com govau design system components blob master packages direction links readme md readme file for direction links a br br dependencies br shell core details details summary gov au footer summary br code npm install gov au footer code br br see the a href https auds service gov au packages footer tests site visual test file for footer a br see the a href https github com govau design system components blob master packages footer readme md readme file for footer a br br dependencies br shell core details details summary gov au form summary br code npm install gov au form code br br see the a href https auds service gov au packages form tests site visual test file for form a br see the a href https github com govau design system components blob master packages form readme md readme file for form a br br dependencies br shell core details details summary gov au grid 12 summary br code npm install gov au grid 12 code br br see the a href https auds service gov au packages grid 12 tests site visual test file for grid 12 a br see the a href https github com govau design system components blob master packages grid 12 readme md readme file for grid 12 a br br dependencies br shell core details details summary gov au header summary br code npm install gov au header code br br see the a href https auds service gov au packages header tests site visual test file for header a br see the a href https github com govau design system components blob master packages header readme md readme file for header a br br dependencies br shell core details details summary gov au headings summary br code npm install gov au headings code br br see the a href https auds service gov au packages headings tests site visual test file for headings a br see the a href https github com govau design system components blob master packages headings readme md readme file for headings a br br dependencies br shell core details details summary gov au inpage nav summary br code npm install gov au inpage nav code br br see the a href https auds service gov au packages inpage nav tests site visual test file for inpage nav a br see the a href https github com govau design system components blob master packages inpage nav readme md readme file for inpage nav a br br dependencies br shell core link list core body core details details summary gov au keyword list summary br code npm install gov au keyword list code br br see the a href https auds service gov au packages keyword list tests site visual test file for keyword list a br see the a href https github com govau design system components blob master packages keyword list readme md readme file for keyword list a br br dependencies br shell core link list core body core details details summary gov au link list summary br code npm install gov au link list code br br see the a href https auds service gov au packages link list tests site visual test file for link list a br see the a href https github com govau design system components blob master packages link list readme md readme file for link list a br br dependencies br shell core body core details details summary gov au main nav summary br code npm install gov au main nav code br br see the a href https auds service gov au packages main nav tests site visual test file for main nav a br see the a href https github com govau design system components blob master packages main nav readme md readme file for main nav a br br dependencies br shell core animate link list core body core details details summary gov au page alerts summary br code npm install gov au page alerts code br br see the a href https auds service gov au packages page alerts tests site visual test file for page alerts a br see the a href https github com govau design system components blob master packages page alerts readme md readme file for page alerts a br br dependencies br shell core body core details details summary gov au progress indicator summary br code npm install gov au progress indicator code br br see the a href https auds service gov au packages progress indicator tests site visual test file for progress indicator a br see the a href https github com govau design system components blob master packages progress indicator readme md readme file for progress indicator a br br dependencies br shell core details details summary gov au responsive media summary br code npm install gov au responsive media code br br see the a href https auds service gov au packages responsive media tests site visual test file for responsive media a br see the a href https github com govau design system components blob master packages responsive media readme md readme file for responsive media a br br dependencies br shell core details details summary gov au searchbox summary br code npm install gov au searchbox code br br see the a href https auds service gov au packages searchbox tests site visual test file for searchbox a br see the a href https github com govau design system components blob master packages searchbox readme md readme file for searchbox a br br dependencies br shell core text inputs core buttons core details details summary gov au select summary br code npm install gov au select code br br see the a href https auds service gov au packages select tests site visual test file for select a br see the a href https github com govau design system components blob master packages select readme md readme file for select a br br dependencies br shell core details details summary gov au side nav summary br code npm install gov au side nav code br br see the a href https auds service gov au packages side nav tests site visual test file for side nav a br see the a href https github com govau design system components blob master packages side nav readme md readme file for side nav a br br dependencies br shell core animate accordion animate core link list core body core details details summary gov au skip link summary br code npm install gov au skip link code br br see the a href https auds service gov au packages skip link tests site visual test file for skip link a br see the a href https github com govau design system components blob master packages skip link readme md readme file for skip link a br br dependencies br shell core details details summary gov au table summary br code npm install gov au table code br br see the a href https auds service gov au packages table tests site visual test file for table a br see the a href https github com govau design system components blob master packages table readme md readme file for table a br br dependencies br shell core details details summary gov au tags summary br code npm install gov au tags code br br see the a href https auds service gov au packages tags tests site visual test file for tags a br see the a href https github com govau design system components blob master packages tags readme md readme file for tags a br br dependencies br shell core details details summary gov au text inputs summary br code npm install gov au text inputs code br br see the a href https auds service gov au packages text inputs tests site visual test file for text inputs a br see the a href https github com govau design system components blob master packages text inputs readme md readme file for text inputs a br br dependencies br shell core details br back to top contents tests visual tests have been built into each module and can be seen in either of the readme md files of each module or in the listing above modules we have also integrated pa11y https github com pa11y pa11y for accessibility testing and are using jest https facebook github io jest for javascript tests run all tests with the npm test script shell npm run test back to top contents related repositories govau design system starter https github com govau design system starter govau design system site https github com govau design system site govau design system sketch file https github com govau design system sketch file back to top contents | design-systems government javascript sass react design css auds components | os |
stanford-cs224n-assignments-2021 | stanford course cs224n natural language processing with deep learning winter 2021 these are my solutions to the assignments of cs224n natural language processing with deep learning http web stanford edu class cs224n offered by stanford university in winter 2021 there are five assignments in total here is a brief description of each one of these assignments assignment 1 word embeddings this assignment notebook a1 exploring word vectors solved ipynb pdf a1 cs224nassignment1 pdf has two parts which deal with representing words with dense vectors i e word vectors or word embeddings word vectors are often used as a fundamental component for downstream nlp tasks e g question answering text generation translation etc so it is important to build some intuitions as to their strengths and weaknesses here you will explore two types of word vectors those derived from co occurrence matrices which uses svd and those derived via glove based on maximum likelihood training in ml 1 count based word vectors many word vector implementations are driven by the idea that similar words i e near synonyms will be used in similar contexts as a result similar words will often be spoken or written along with a shared subset of words i e contexts by examining these contexts we can try to develop embeddings for our words with this intuition in mind many old school approaches to constructing word vectors relied on word counts here we elaborate upon one of those strategies co occurrence matrices in this part you will use co occurence matrices to develop dense vectors for words a co occurrence matrix counts how often different terms co occur in different documents to derive a co occurence matrix we use a window with a fixed size i w i and then slide this window over all of the documents then we count how many times two different words i v sub i sub i and i v sub j sub i occurs with each other in a window and put this number in the i i j sup th sup i entry of the matrix we then run dimensionality reduction on the co occurence matrix using singular value decomposition we then select the top i r i components after the decomposition and thus derive i r i dimensional embeddings for words p align center img src figures svd jpg alt drawing width 400 p 2 prediction or maximum likelihood based word vectors word2vec prediction based word vectors which also utilize the benefit of counts such as word2vec and glove have demonstrated better performance compared to co occurence matrices in this part you will explore pretrained glove embeddings using the gensim https radimrehurek com gensim package initially you ll have to reduce the dimensionality of word vectors using svd from 300 to 2 to be able to vizualize and analyze these vectors next you ll find the closest word vectors to a given word vector you ll then get to know words with multiple meanings polysemous words you will also experiment with the analogy task introduced for the first time in the word2vec paper mikolov et al 2013 https arxiv org pdf 1301 3781 pdf 5d the task is simple given words i x y i and i z i you have to find a word i w i such that the following relationship holds i x i is to i y i like i z i is to i w i for example rome is to italy like d c is to the united states you will find that solving this task with word2vec vectors is easy and is just a simple addition and subtraction of vectors which is a nice feature of word embeddings if you re feeling adventurous challenge yourself and try reading glove s original paper https nlp stanford edu pubs glove pdf p align center img src figures analogy jpg alt drawing width 400 p assignment 2 understanding and implementing word2vec in this assignment a2 cs224nassignment2 pdf you will get familiar with the word2vec algorithm the key insight behind word2vec is that a word is known by the company it keeps there are two models introduced by the word2vec paper based on this idea i the skip gram model and ii the continuous bag of words cbow model in this assignment you ll be implementing the skip gram model using numpy you have to implement the both version of skip gram the first one is with the naive softmax loss and the second one which is much faster with the negative sampling loss your implementation of the first version is just sanity checked on a small dataset but you have to run the second version on the stanford sentiment treebank dataset it is highly recommend for anyone interested in gaining a deep understanding of word2vec to first do the theoretical part of this assignment and only then proceed to the practical part p align center img src figures word2vec jpg alt drawing width 450 p assignment 3 neural dependency parsing if you have take a compiler course before you have definitely heard the term parsing this assignment is about dependency parsing where you train a model that can specify the dependencies if you remember shift reduce parser from your compiler class then you will find the ideas here quite familiar the only difference is that we shall use a neural network to find the dependencies in this assignment you will build a neural dependency parser using pytorch in part 1 of this assignment a3 cs224nassignment3 pdf you will learn about two general neural network techniques adam optimization and dropout in part 2 of this assignment you will implement and train a dependency parser using the techniques from part 1 before analyzing a few erroneous dependency parses both the adam optimizer and dropout will be used in the neural dependency parser you are going to implement with pytorch the parser will do one of the following three moves 1 shift 2 left arc 3 right arc you can read more about the details of these three moves in the handout a3 a3 pdf of the assignment what your network should do is to predict one of these moves at every step for predicting each move your model needs features which are going to be extracted from the stack and buffer of each stage you maintain a stack and buffer during parsing to know what you have already parsed and what is remaining for parsing respectively the good news is that the code for extracting features is given to you to help you just focus on the neural network part there are lots of hints throughout the assignment as this is the first assignment in the course where students work with pytorch that walk you through implementing each part p align center img src figures dependency parsing jpg alt drawing width 450 p assignment 4 seq2seq machine translation model with multiplicative attention this assignment a4 cs224nassignment4 pdf is split into two sections neural machine translation with rnns and analyzing nmt systems the first is primarily coding and implementation focused whereas the second entirely consists of written analysis questions p align center img src figures nmt jpg alt drawing width 350 p assignment 5 self attention transformers and pretraining this assignment a5 cs224nassignment5 pdf is an investigation into transformer self attention building blocks and the effects of pre training it covers mathematical properties of transformers and self attention through written questions further you ll get experience with practical system building through repurposing an existing codebase the assignment is split into a written mathematical part and a coding part with its own written questions here s a quick summary 1 mathematical exploration what kinds of operations can self attention easily implement why should we use fancier things like multi headed self attention this section will use some mathematical investigations to illuminate a few of the motivations of self attention and transformer networks 2 extending a research codebase in this portion of the assignment you ll get some experience and intuition for a cutting edge research topic in nlp teaching nlp models facts about the world through pretraining and accessing that knowledge through finetuning you ll train a transformer model to attempt to answer simple questions of the form where was person x born without providing any input text from which to draw the answer you ll find that models are able to learn some facts about where people were born through pretraining and access that information during fine tuning to answer the questions then you ll take a harder look at the system you built and reason about the implications and concerns about relying on such implicit pretrained knowledge p align center img src figures attn jpg p handouts x assignment 1 intro to word vectors a1 exploring word vectors ipynb a1 exploring word vectors ipynb x assignment 2 training word2vec a2 a2 pdf a2 a2 pdf x assignment 3 dependency parsing a3 a3 pdf a3 a3 pdf x assignment 4 neural machine translation with seq2seq and attention a4 a4 pdf a4 a4 pdf x assignment 5 neural machine translation with sub word modeling a5 a5 pdf a5 a5 pdf x project squad https rajpurkar github io squad explorer stanford question asking dataset project proposal pdf project proposal pdf prerequisites proficiency in python all class assignments will be in python using numpy and pytorch if you need to remind yourself of python or you re not very familiar with numpy attend the python review session in week 1 listed in the schedule if you have a lot of programming experience but in a different language e g c c matlab java javascript you will probably be fine college calculus linear algebra e g math51 cme100 you should be comfortable taking multivariable derivatives and understanding matrix vector notation and operations basic probability and statistics e g cs109 or equivalent you should know basics of probabilities gaussian distributions mean standard deviation etc foundations of machine learning e g cs221 or cs229 we will be formulating cost functions taking derivatives and performing optimization with gradient descent if you already have basic machine learning and or deep learning knowledge the course will be easier however it is possible to take cs224n without it there are many introductions to ml in webpage book and video form one approachable introduction is hal daum s in progress a course in machine learning reading the first 5 chapters of that book would be good background knowing the first 7 chapters would be even better course description this course was formed in 2017 as a merger of the earlier cs224n https web stanford edu class archive cs cs224n cs224n 1162 natural language processing and cs224d http cs224d stanford edu natural language processing with deep learning courses below you can find archived websites and student project reports natural language processing nlp is one of the most important technologies of the information age and a crucial part of artificial intelligence applications of nlp are everywhere because people communicate almost everything in language web search advertising emails customer service language translation medical reports etc in recent years deep learning approaches have obtained very high performance across many different nlp tasks using single end to end neural models that do not require traditional task specific feature engineering in this course students will gain a thorough introduction to cutting edge research in deep learning for nlp through lectures assignments and a final project students will learn the necessary skills to design implement and understand their own neural network models cs224n uses pytorch resources lecture notes assignments and other materials can be downloaded from the course webpage http web stanford edu class cs224n lectures youtube https www youtube com watch v 8rxd5 xhemo list ploromvodv4rohcuxmzknm7j3fvwbby42z schedule stanford cs224n http web stanford edu class cs224n index html schedule projects stanford cs224n http web stanford edu class cs224n project html disclaimer i recognize the hard time people spend on building intuition understanding new concepts and debugging assignments the solutions uploaded here are only for reference they are meant to unblock you if you get stuck somewhere please do not copy any part of the solutions as is the assignments are fairly easy if you read the instructions carefully | ai |
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rainbond-ui | img src https grstatic oss cn shanghai aliyuncs com images rainbond 20log full png width 60 http www rainbond com https www rainbond com docs | rainbond-ui rainbond rainbond-environment | front_end |
alchemy-sdk-js | alchemy sdk for javascript the alchemy sdk is the most comprehensive stable and powerful javascript sdk available today to interact with the blockchain it supports the exact same syntax and functionality of the ethers js alchemyprovider and websocketprovider making it a 1 1 mapping for anyone using the ethers js provider however it adds a significant amount of improved functionality on top of ethers such as easy access to alchemy s enhanced and nft apis robust websockets and quality of life improvements such as automated retries the sdk leverages alchemy s hardened node infrastructure guaranteeing best in class node reliability scalability and data correctness and is undergoing active development by alchemy s engineers feature requests we d love your thoughts on what would improve your web3 dev process the most if you have 5 minutes tell us what you want on our feature request feedback form https alchemyapi typeform com sdk feedback and we d love to build it for you the sdk currently supports the following chains ethereum mainnet goerli sepolia polygon mainnet mumbai optimism mainnet goerli kovan arbitrum mainnet goerli rinkeby astar mainnet polygonzkevm mainnet testnet base mainnet goerli you can find per method documentation of the alchemy sdk endpoints at the alchemy docs linked in the sidebar https docs alchemy com reference alchemy sdk quickstart getting started npm install alchemy sdk after installing the app you can then import and use the sdk ts import alchemy network from alchemy sdk optional config object but defaults to the api key demo and network eth mainnet const settings apikey demo replace with your alchemy api key network network eth mainnet replace with your network const alchemy new alchemy settings creating a unique alchemy api key the public demo api key may be rate limited based on traffic to create your own api key sign up for an alchemy account here https alchemy com a sdkquickstart and use the key created on your dashboard for the first app the alchemy object returned by new alchemy provides access to the alchemy api an optional config object can be passed in when initializing to set your api key change the network or specify the max number of retries using the alchemy sdk the alchemy sdk currently supports the following namespaces core all commonly used ethers js provider methods and alchemy enhanced api methods nft all alchemy nft api methods ws all websockets methods transact all alchemy transaction api methods notify crud endpoints for modifying alchemy notify webhooks debug methods to inspect and replay transactions and blocks if you are already using ethers js you should be simply able to replace the ethers js provider object with alchemy core and it should work properly ens name resolution the alchemy sdk now supports ens names e g vitalik eth for every parameter where you can pass in a externally owned address or user address e g 0xd8da6bf26964af9d7eed9e03e53415d37aa96045 ts import alchemy alchemysubscription from alchemy sdk using default settings pass in a settings object to specify your api key and network const alchemy new alchemy access standard ethers js json rpc node request alchemy core getblocknumber then console log access alchemy enhanced api requests alchemy core gettokenbalances 0x3f5ce5fbfe3e9af3971dd833d26ba9b5c936f0be then console log access the alchemy nft api alchemy nft getnftsforowner vitalik eth then console log access websockets and alchemy specific ws methods alchemy ws on method alchemysubscription pending transactions res console log res the alchemy sdk also supports a number of ethers js objects that streamline the development process utils https docs ethers io v5 api utils equivalent to ethers utils this provides a number of common ethers js utility methods for developers interface https docs ethers io v5 api utils abi interface found in utils interface this class abstracts the encoding and decoding required to interact with contracts on the ethereum network contract https docs ethers io v5 api contract contract an abstraction for smart contract code deployed to the blockchain contractfactory https docs ethers io v5 api contract contract factory allows developers to build a contract object wallet https docs ethers io v5 api signer wallet an implementation of signer that can sign transactions and messages using a private key as a standard externally owned account alchemy settings an alchemysettings object can be passed on instantiation to the alchemy object with the following optional parameters apikey api key that can be found in the alchemy dashboard defaults to demo a rate limited public key network name of the network defaults to network eth mainnet maxretries the maximum number of retries to attempt if a request fails defaults to 5 url optional url endpoint to use for all requests setting this field will override the url generated by the network and apikey fields authtoken alchemy auth token required to use the notify api this token can be found in the alchemy dashboard on the notify tab batchrequests optional setting that automatically batches and sends json rpc requests for higher throughput and reduced network io defaults to false requesttimeout optional setting that sets the timeout for requests in milliseconds for the nft and notify namespaces defaults to no timeout alchemy core the core namespace contains all commonly used ethers js provider https docs ethers io v5 api providers api providers alchemyprovider methods if you are already using ethers js you should be simply able to replace the ethers js provider object with alchemy core when accessing provider methods and it should just work it also includes the majority of alchemy enhanced apis including gettokenmetadata get the metadata for a token contract address gettokenbalances gets the token balances for an owner given a list of contracts getassettransfers get transactions for specific addresses gettransactionreceipts gets all transaction receipts for a given block you will also find the following utility methods findcontractdeployer find the contract deployer and block number for a given contract address gettokensforowner get all token balances and metadata for a given owner address accessing the full ethers js provider to keep the package clean we don t support certain uncommonly used ethers js provider methods as top level methods in the alchemy core namespace for example provider formatter if you d like to access these methods simply use the alchemy config getprovider function to configure the ethers js provider alchemyprovider https docs ethers io v5 api providers api providers alchemyprovider and return it ts import alchemy from alchemy sdk const alchemy new alchemy async function runalchemy const ethersprovider await alchemy config getprovider console log ethersprovider formatter runalchemy alchemy websockets in addition to the built in ethers js listeners the alchemy sdk includes support for alchemy s subscription api https docs alchemy com alchemy enhanced apis subscription api websockets this allows you to subscribe to events and receive updates as they occur the alchemy ws instance can be used like the standard ethers js websocketprovider https docs ethers io v5 api providers other websocketprovider to add listeners for alchemy events ts import alchemy alchemysubscription from alchemy sdk const alchemy new alchemy listen to all new pending transactions alchemy ws on block res console log res listen to only the next transaction on the usdc contract alchemy ws once method alchemysubscription pending transactions toaddress vitalik eth res console log res remove all listeners alchemy ws removealllisteners the sdk brings multiple improvements to ensure correct websocket behavior in cases of temporary network failure or dropped connections as with any network connection you should not assume that a websocket will remain open forever without interruption but correctly handling dropped connections and reconnection by hand can be challenging to get right the alchemy sdk automatically handles these failures with no configuration necessary the main benefits are resilient event delivery unlike standard web3 js or ethers js you will not permanently miss events which arrive while the backing websocket is temporarily down instead you will receive these events as soon as the connection is reopened note that if the connection is down for more than 120 blocks approximately 20 minutes you may still miss some events that were not part of the most recent 120 blocks lowered rate of failure compared to standard web3 js or ethers js there are fewer failures when sending requests over the websocket while the connection is down the alchemy sdk will attempt to send the requests once the connection is reopened note that it is still possible with a lower likelihood for outgoing requests to be lost so you should still have error handling as with any network request alchemy transact the transact namespace contains methods used for simulating and sending transactions the unique methods to the transact namespace are sendprivatetransaction send a private transaction through flashbots cancelprivatetransaction cancel a private transaction sent with flashbots simulateassetchanges simulate a transaction and get a list of asset changes simulateexecution simulate a transaction and get a full list of internal transactions logs abi decoded results and more simulateassetchangesbundle simulate a list of transactions in sequence and get a list of asset changes simulateexecutionbundle simulate a list of transactions in sequence and get a full list of internal transactions logs abi decoded results and more the transact namespace also aliases over several commonly used methods from the core namespace for convenience gettransaction returns the transaction for the given transaction hash sendtransaction sends a standard transaction to the network to be mined waitfortransaction waits for a transaction to be mined and returns the transaction receipt alchemy nft api the sdk currently supports the following nft api https docs alchemy com alchemy enhanced apis nft api endpoints under the alchemy nft namespace getnftmetadata get the nft metadata for an nft contract address and tokenid getnftmetadatabatch get the nft metadata for multiple nft contract addresses token id pairs getcontractmetadata get the metadata associated with an nft contract getcontractmetadatabatch get the metadata associated with multiple nft contracts in a single request getcontractsforowner get all nft contracts that the provided owner address owns getnftsforowner get nfts for an owner address getnftsforowneriterator get nfts for an owner address as an async iterator handles paging automatically getnftsforcontract get all nfts for a contract address getnftsforcontractiterator get all nfts for a contract address as an async iterator handles paging automatically getownersfornft get all the owners for a given nft contract address and a particular token id getownersforcontract get all the owners for a given nft contract address getmintednfts get all the nfts minted by the owner address gettransfersforowner get all the nft transfers for a given owner address gettransfersforcontract get all the nft transfers for a given nft contract address verifynftownership check whether the provided owner address owns the provided nft contract addresses isspamcontract check whether the given nft contract address is a spam contract as defined by alchemy see the nft api faq https docs alchemy com alchemy enhanced apis nft api nft api faq nft spam classification getspamcontracts returns a list of all spam contracts marked by alchemy refreshnftmetadata refresh the cached nft metadata for a contract address and a single tokenid refreshcontract enqueues the specified contract address to have all token ids metadata refreshed getfloorprice return the floor prices of a nft contract by marketplace computerarity get the rarity of each attribute of an nft getnftsales returns nft sales that have happened through on chain marketplaces summarizenftattributes get the summary of attribute prevalence for all nfts in a contract searchcontractmetadata search for a keyword across metadata of all erc 721 and erc 1155 smart contracts pagination the alchemy nft endpoints return 100 results per page to get the next page you can pass in the pagekey returned by the previous call to simplify paginating through all results the sdk provides the getnftsiterator and getnftsforcontractiterator functions that automatically paginate through all nfts and yields them via an asynciterable here s an example of how to paginate through all the nfts in vitalik s ens address ts import alchemy from alchemy sdk const alchemy new alchemy async function main const owneraddress vitalik eth for await const nft of alchemy nft getnftsforowneriterator owneraddress console log ownednft nft main sdk vs api differences the nft api in the sdk standardizes response types to reduce developer friction but note this results in some differences compared to the alchemy rest endpoints methods referencing collection have been renamed to use the name contract for greater accuracy e g getnftsforcontract some methods have different naming that the rest api counterparts in order to provide a consistent api interface e g getnftsforowner is alchemy getnfts getownersfornft is alchemy getownersfortoken sdk standardizes to omitmetadata parameter vs withmetadata standardization to pagekey parameter for pagination vs nexttoken starttoken empty tokenuri fields are omitted token id is always normalized to an integer string on basenft and nft some fields omitted in the rest response are included in the sdk response in order to return an nft object some fields in the sdk s nft object are named differently than the rest response alchemy notify the alchemy notify api https docs alchemy com reference notify api quickstart helps developers set up webhooks in their apps the namespace provides methods to programmatically create read update and delete your webhooks along with typings for the different webhooks to learn more about webhooks please refer to the alchemy documentation https docs alchemy com reference notify api quickstart what are webhooks methods on the notifynamespace can be accessed via alchemy notify to use the methods you must include your team s auth token in the authtoken field of alchemysettings when instantiating the sdk the auth token can be found on the alchemy dashboard in the notify tab methods include getallwebhooks get all webhooks on your team getaddresses get all addresses tracked for the provided address activity webhook getnftfilters get all nft filters tracked for the provided nft activity webhook createwebhook create a new webhook updatewebhook update an existing webhook s active status or tracked addresses and nft filters deletewebhook delete the provided webhook alchemy debug methods on the debugnamespace can be accessed via alchemy debug these methods are used for inspecting and debugging transactions methods include tracecall run an eth call with the context of the provided block execution using the final state of the parent block as the base tracetransaction run the transaction in the exact same manner as it was executed on the network it will replay any transaction that may have been executed prior to this one before it and will then attempt to execute the transaction that corresponds to the given hash traceblock replay a block that has already been mined documentation the sdk is documented via tsdoc comments in the source code the generated types and documentation are included when using an ide to browse the documentation separately you can view the generated api interfaces in etc alchemy sdk api md you can view generated markdown files for each endpoint in the docs md directory or as a webpage by opening docs index html in your browser usage examples below are a few usage examples more examples you can also go here examples using the alchemy sdk https docs alchemy com reference using the alchemy sdk getting the nfts owned by an address ts import alchemy nftexcludefilters from alchemy sdk const alchemy new alchemy get how many nfts an address owns alchemy nft getnftsforowner vitalik eth then nfts console log nfts totalcount get all the image urls for all the nfts an address owns async function main for await const nft of alchemy nft getnftsforowneriterator vitalik eth console log nft media main filter out spam nfts alchemy nft getnftsforowner vitalik eth excludefilters nftexcludefilters spam then console log getting all the owners of the bayc nft ts import alchemy from alchemy sdk const alchemy new alchemy bored ape yacht club contract address const baycaddress 0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d async function main for await const nft of alchemy nft getnftsforcontractiterator baycaddress omit the nft metadata for smaller payloads omitmetadata true await alchemy nft getownersfornft nft contract address nft tokenid then response console log owners response owners tokenid nft tokenid main get all outbound transfers for a provided address ts import alchemy from alchemy sdk const alchemy new alchemy alchemy core gettokenbalances vitalik eth then console log questions and feedback if you have any questions issues or feedback please file an issue on github https github com alchemyplatform alchemy sdk js issues or drop us a message on our discord https discord com channels 735965332958871634 983472322998575174 channel for the sdk | blockchain |
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ubuntu-dev-setup | linux workstation setup ci https github com lukemorales ubuntu dev setup workflows ci badge svg this configs were made based on erick wendel s setup https github com erickwendel ew ubuntu setup and my own needs as a js developer and other personal stuff getting started just clone this repo or download and execute bash sh startup sh you ll be installing curl https curl haxx se command line tool and library for transferring data with urls neofetch https github com dylanaraps neofetch command line tool that displays information about your operating system software and hardware in an aesthetic and visually pleasing way xclip https opensource com article 19 7 xclip command line interface to the x11 clipboard git https git scm com free and open source distributed version control system python3 pip https www python org the package installer for python necessary to install getgist getgist https github com cuducos getgist easily download any file from a github gist with one single command oh my zsh https ohmyz sh framework for managing your zsh configuration fira code https github com tonsky firacode monospaced font with programming ligatures nvm https github com nvm sh nvm version manager for nodejs nodejs https nodejs org en javascript runtime built on chrome s v8 javascript engine yarn https classic yarnpkg com en fast reliable and secure dependency management typescript https www typescriptlang org typed superset of javascript that compiles to plain javascript adonisjs cli https github com adonisjs adonis cli adonis cli is built on top of adonis ace and helps you scaffold new adonisjs projects lerna https github com lerna lerna a tool for managing javascript projects with multiple packages vscode https code visualstudio com source code editor made by microsoft settings sync https github com shanalikhan code settings sync sync your vscode settings from gist vivaldi https vivaldi com the new vivaldi browser protects you from trackers blocks unwanted ads and puts you in control with unique built in features docker https www docker com platform for building deploying and managing containerized applications docker compose https docs docker com compose tool for defining and running multi container docker applications heroku cli https devcenter heroku com categories command line create and manage your heroku apps postbird https github com paxa postbird open source postgresql gui client for macos linux and windows insomnia core https insomnia rest the desktop api client for rest and graphql make requests inspect responses omni theme https github com getomni a dark theme based on dracula android studio https developer android com studio android studio provides the fastest tools for building apps on every type of android device vlc https www videolan org vlc media player discord https discord com new chat for communities and friends zoom https zoom us zoom is the leader in modern enterprise video communications with an easy reliable cloud platform for video and audio conferencing chat and webinars across mobile desktop and room systems spotify https www spotify com music streaming and media services provider peek https github com phw peek simple animated gif screen recorder with an easy to use interface obs studio https obsproject com free and open source software for video recording and live streaming robo3t https robomongo org mongodb gui lotion https github com puneetsl lotion unofficial notion so app for linux authors erick wendel initial work erick wendel https github com erickwendel olavio lacerda additions and modifications luke morales additions and modifications license this project is licensed under the mit license see the license md license md file for details | front_end |
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microfronts | microfronts one shell to rule them all one shell to bind them one shell to wrap them all and in the light to run them polyglot front end solution micro frontends approach enables us to split our products into separate modules as any of them is built with any web technology i e react angular vue a thin code layer orchestrates them as a single product keeping the ux intact the approach enables companies to suspend rewrites of old production code and combine new technologies with legacy ones without breaking everything microfronts orchestrates multiple front end applications with shared runtime and fully controlled sandboxing at the same time creating the shell application the shell application should be a super thin layer of html css and a tiny javascript file the shell contains the configuration for your front ends bound with the relevant routes the microfronts library will orchestrate the front ends whenever route changes it will also provide shared runtime fully accessible by demand quick example html index html body a href home home react app a a href home settings angular app a a href side by side side by side both apps a iframe is app container app id react app iframe iframe is app container app id angular app iframe iframe is app container app id error iframe script src shell js type module script body npm install microfronts or yarn add microfronts microfronts should be installed in your shell application and not in your applications this ensures the orchestrator remains a singleton javascript shell js import microfronts from microfronts const application microfronts const router application getrouter const context application getappcontext const react app base https my domain com app 1 appid react app const angular app base https other domain com app 2 appid angular app const not found base 404 html appid error router registerroute active not found router registerroute home active react app router registerroute settings active angular app router registerroute side by side active react app angular app router init note ensure your servers provide cross origin access from where the shell is deployed this can be solved on the server side or by a reverse proxy to see code example take a look at the examples folder available in the github repo for type declarations you may install microfronts and import only the interfaces d ts file accessing the shared context microfronts provides a runtime shared application context which can be consumed by running front ends the context also enables the front ends to provide utilities to other front ends for example angular holds the user service containing the data login and other actions it exposes a rxjs subject object and would like to expose it to the rest of the world typescript angular app services user service ts injectable class userservice public stream new behaviorsubject userdata null null constructor window appcontext set services stream this stream javascript react app components userstatus js export default const user setuserdata usestate null useeffect const subscription window appcontext get services stream subscribe data setuserdata data return subscription unsubscribe return user div user name user lastlogin div div div this way the angular service can be consumed at runtime within the react application no need to rewrite or duplicate the business logic for asynchronous consumption the appcontext provides the provide and require methods the require receives a promise fulfilled one provide is triggered with a value this enables async dependency management across frameworks adding more modules to your project now that the shell contains at least one front end more screens can be added freely ensure all of your internal routers use the hash strategy for consistency use the microfronts router navigate whenever possible though the router can watch changes from the inside more to come microfronts provides more features such as route guards used for dirty clean state checking privileges etc static data per application messaging and more we need your support if you wish to join open an issue suggest an improvement create pull request or join the team currently documentation is only inside the code we appreciate help wiring up a good documentation webpage usetheplatform | polyglot-front frontend javascript typescript microfrontends | front_end |
awsome-vietnamese-nlp | vietnamese natural language processing resources create a pull request to add your works into this list corpus corpus text processing toolkit text processing toolkit pre trained language model pre trained language model sentiment analysis sentiment analysis named entity recognition named entity recognition speech processing speech processing corpus vn news corpus https github com binhvq news corpus 50gb of uncompressed texts crawled from a wide range ofnews websites and topics vnesecorpus http viet jnlp org download du lieu tu vung corpus 650 000 sentences from vietnamnet vn dantri com vn nhandan com vn vntqcorpus samll http viet jnlp org download du lieu tu vung corpus 300 000 sentences from vnthuquan net vntqcorpus big http viet jnlp org download du lieu tu vung corpus 1 750 000 sentences from vnthuquan net oscar https oscar corpus com 68gb of text data with 12 036 845 359 words common crawl https commoncrawl org open repository of web crawl data wikidumps https dumps wikimedia org you can download directly or use scripts from viwik18 https github com ntt123 viwik18 viwik19 https github com ntt123 viwik19 vietnamese treebank https vlsp hpda vn demo page resources vlsp project vietnamese stopwords https github com stopwords vietnamese stopwords vietnamese stopwords vietnamese dictionary https www informatik uni leipzig de duc dict vietnamese dictionary vietnamese wordnet https github com zeloru vietnamese wordnet vietnamese wordnet text processing toolkit coccoc tokenizer https github com coccoc coccoc tokenizer high performance tokenizer for vietnamese language it is written in c with python and java bindings rdrsegmenter https github com datquocnguyen rdrsegmenter fast and accurate vietnamese word segmenter lrec 2018 rdrpostagger https github com datquocnguyen rdrpostagger fast and accurate pos and morphological tagging toolkit eacl 2014 vncorenlp https github com vncorenlp vncorenlp a vietnamese natural language processing toolkit naacl 2018 vlp tok https github com phuonglh vlp vietnamese text processing library developed in the scala programming language etnlp https github com vietnlp etnlp a toolkit for extraction evaluation and visualization of pre trained word embeddings vietnamesetextnormalizer https github com langmaninternet vietnamesetextnormalizer vietnamese text normalizer nnvlp https github com pth1993 nnvlp neural network based vietnamese language processing toolkit jptdp https github com datquocnguyen jptdp neural network models for joint pos tagging and dependency parsing conll 2017 2018 vi spacy https github com trungtv vi spacy vietnamese language model compatible with spacy underthesea https underthesea readthedocs io en latest readme html underthesea vietnamese nlp toolkit vnlp https bitbucket org epilab vnlp wiki home gate plugin for vietnamese language processing pyvi https github com trungtv pyvi python vietnamese toolkit jvntextpro http jvntextpro sourceforge net java based vietnamese text processing tool dongdu https github com rockkhuya dongdu c implementation of vietnamese word segmentation tool vlsp toolkit https vlsp hpda vn demo page resources vietnamese tokenizer from vlsp vtools https github com lupanh vtools vietnamese nlp toolkit tokenizer sentence detector pos tagger phrase chunker jnsp http jnsp sourceforge net java implementation of ngram statistic package pre trained language model roberta vietnamese https github com nguyenvulebinh vietnamese roberta pre trained embedding using roberta architecture on vietnamese corpus phobert https github com vinairesearch phobert pre trained language models for vietnamese another implementation of roberta for vietnamese albert for vietnamese https github com ngoanpv albert vi a lite version of bert for vietnamese vietnamese electra https github com nguyenvulebinh vietnamese electra electra pre trained model using vietnamese corpus word2vecvn https github com sonvx word2vecvn pre trained word2vec models for vietnamese sentiment analysis benchmark vlsp 2016 share task sentiment analysis https vlsp org vn vlsp2016 eval sa train 5100 sentences 1700 positive 1700 neutral 1700 negative test 1050 sentences 350 positive 350 neutral 350 negative model f1 paper code perceptron svm maxent 80 05 dsktlab vietnamese sentiment analysis for product reviews svm mlnn lstm 71 44 a simple supervised learning approach to sentiment classification at vlsp 2016 ensemble random forest svm naive bayes 71 22 a lightweight ensemble method for sentiment classification task ensemble svm lr lstm cnn 69 71 an ensemble of shallow and deep learning algorithms for vietnamese sentiment analysis svm 67 54 sentiment analysis for vietnamese using support vector machines with application to facebook comments svm mlnn 67 23 a multi layer neural network based system for vietnamese sentiment analysis at the vlsp 2016 evaluation campaign multi channel lstm cnn 59 61 multi channel lstm cnn model for vietnamese sentiment analysis official https github com ntienhuy multichannel vlsp 2018 shared task aspect based sentiment analysis https vlsp org vn vlsp2018 restaurant dataset 2961 reviews train 1290 reviews development 500 reviews test model aspect f1 aspect polarity f1 paper code cnn 0 80 deep learning for aspect detection on vietnamese reviews svm 0 77 0 61 nlp uit at vlsp 2018 a supervised method for aspect based sentiment analysis svm 0 54 0 48 using multilayer perceptron for aspect based sentiment analysis at vlsp 2018 sa task hotel dataset 3000 reviews training 2000 reviews development 600 reviews test model aspect f1 aspect polarity f1 paper code svm 0 70 0 61 nlp uit at vlsp 2018 a supervised method for aspect based sentiment analysis cnn 0 69 deep learning for aspect detection on vietnamese reviews svm 0 56 0 53 using multilayer perceptron for aspect based sentiment analysis at vlsp 2018 sa task vietnamese student s feedback corpus uit vsfc https ieeexplore ieee org document 8573337 uit vsfc consists of over 16 000 sentences for sentiment analysis and topic classification model sentiment f1 topic f1 paper code bi lstm word2vec 0 896 0 92 deep learning versus traditional classifiers on vietnamese student s feedback corpus maximum entropy classifier 0 88 0 84 uit vsfc vietnamese student s feedback corpus for sentiment analysis named entity recognition benchmark vlsp 2016 shared task named entity recognition http vjs ac vn index php jcc article view 13161 103810382796 model f1 paper code phobert large 94 7 phobert pre trained language models for vietnamese official https github com vinairesearch phobert velectra bilstm attention 94 07 improving sequence tagging for vietnamese text using transformer based neural models phobert base 93 6 phobert pre trained language models for vietnamese official https github com vinairesearch phobert xlm r 92 0 phobert pre trained language models for vietnamese vncorenlp ner etnlp 91 3 etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task bilstm cnn crf etnlp 91 1 etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task vner attentive neural network 89 6 attentive neural network for named entity recognition in vietnamese bilstm cnn crf 88 3 vncorenlp a vietnamese natural language processing toolkit official https github com vncorenlp vncorenlp lstm crf 66 07 an investigation of vietnamese nested entity recognition models vlsp 2018 shared task named entity recognition https www researchgate net publication 331956361 vlsp shared task named entity recognition model f1 paper code velectra bigru 90 31 improving sequence tagging for vietnamese text using transformer based neural models vietner crf ngrams word shapes cluster w2v 76 63 a feature based model for nested named entity recognitionatvlsp 2018 ner evaluation campaign za ner 74 70 za ner vietnamese named entity recognition at vlsp 2018 evaluation campaign speech processing corpus viettts v1 https github com ntt123 vietnamese text to speech dataset a synthesized dataset for vietnamese tts task 35 1 hrs vivos infore 1 infore 2 https github com tensorspeech tensorflowasr blob main readme md vietnamese project viettts https github com ntt123 viettts tacotron hifigan vocoder for vietnamese datasets | ai |
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Altschool-cloud-Engineering- | altschool cloud engineering repo for altschool exercises and cloud engineering walk through | cloud |
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front-end-Interview-Questions | front end interview questions to rock the interview to achieve what you deserve and to improve your concepts about front end technologies i have consolidated a list of questions and answers it s a one stop solution for front end interview process table of contents javascript basics and tricky questions javascript basics and tricky questions algorithm beginners level javascript algorithm beginners level intermediate level questions javascript for intermediate level developer css basics and tricky questions css basics and tricky questions dom related questions javascript dom related questions html basic questions for beginners html basic questions for begginers angular interview questions https github com khan4019 angular interview questions an exclusive list of angular interview questions are here https github com khan4019 angular interview questions javascript basics and tricky questions http www thatjsdude com interview js2 html 21 questions and answers for intermediate 1 what are the differences between null and undefined 2 what are the differences between and 3 how would you compare two objects in javascript 4 11 true false related questions that will trick you 5 as is true true should also be true right 6 how could you write a method on instance of a date which will give you next day 7 if you want to use an arbitrary object as value of this how will you do that 8 write a simple function to tell whether 2 is passed as parameter or not 9 how could you use math max to find the max value in an array 10 what the heck is this in javascript 11 21 quick questions that will trick you 12 how could you set a prefix before everything you log for example if you log my message it will log app my message 13 what will you see in the console for the following example 14 look at the code below you have a for loop if you have settimeout inside it if log the loop counter inside settimeout what will be logged 15 look at the code below i have a property in a object and i am creating a new object where i am setting it to a new value if i delete that property what will i get if i try to access that property 16 does javascript pass parameter by value or by reference 17 how could you implement cache to save calculation time for a recursive fibonacci function 18 how could you cache execution of any function 19 if you need to implement the following chaining with call back how will you implement it 20 how could you implement moveleft animation 21 how would you implement currying for any functions js answer for basics and tricky questions http www thatjsdude com interview js2 html css basics and tricky questions http www thatjsdude com interview css html 21 questions and answers 1 what does float do 1 how can you clear sides of a floating element 1 how can you clear sides of a floating element 1 some tricky questions in rapid fire style 1 are css rule names case sensitive 1 why css selectors mixed up with cases don t apply the styles 1 does margin top or margin bottom has effect on inline element 1 does padding top or padding bottom has effect on inline element 1 does padding left or padding right or margin left or margin right has effect on inline element 1 if you have a lt p gt element with font size 10rem will the text be responsive when the user resizes drags the browser window 1 the pseudo class checked will select inputs with type radio or checkbox but not lt option gt elements 1 in a html document the pseudo class root always refers to the lt html gt element 1 the translate function can move the position of an element on the z axis 1 which one would you prefer among px em or pt and why 1 how absolute relative fixed and static position differ 1 what are the differences between visibility hidden and display none 1 what are the differences between inline block and inline block 1 what are the properties related to box model 1 does overflow hidden create a new block formatting context 1 how could you apply css rules specific to a media 1 what is the use of only 1 does the screen keyword apply to the device s physical screen or the browser s viewport 1 what are the some pseudo classed u have used 1 how do you align a p center center inside a div 1 how do you optimize css selectors 1 how can you load css resources conditionally 1 why would you use sprites 1 what is specificity how do u calculate specificity 1 what is shadow dom 1 what do you know about transition 1 what are the different css filter you can use 1 what are the reasons to use preprocessor 1 show you couple of style example and you have to tell what does it do http www thatjsdude com interview css html seeandtell css answer for basics and tricky questions http www thatjsdude com interview css html css deleted questions looks like these are for hardcore designer hence didn t make for developers 1 how descendant css selectors are matched get answer https www youtube com watch v ew8bg h p7m 1 how would u implement modularity in css 1 if something doesn t work in a specific browser ie8 you would u approach this problem 1 how do you test cross browser compatibility of your site 1 what is the greatest hack you did in css so far 1 what is grid layout 1 how can you make a site responsive 1 why reset css is useful or how normalize css works 1 what do you know about text shadows box shadows javascript algorithm beginners level http www thatjsdude com interview js1 html 20 questions and answers for beginners 1 verify a prime number 1 find all prime factors of a number 1 get nth fibonacci number 1 find the greatest common divisor of two numbers 1 remove duplicate members from an array 1 merge two sorted array 1 swap two numbers without using a temp variable 1 reverse a string in javascript 1 how would you reverse words in a sentence 1 reverse words in place 1 find the first non repeating char in a string 1 remove duplicate characters from a sting 1 how will you verify a word as palindrome 1 generate random between 5 to 7 by using defined function 1 find missing number from unsorted array of integers 1 get two numbers that equal to a given number 1 find the largest sum of any two elements 1 total number of zeros from 1 upto n 1 check whether a given string is a substring of bigger string 2 get permutations of a string js answer for algorithm beginners level http www thatjsdude com interview js1 html javascript for intermediate level developer 1 what is the event loop can you draw a simple diagram to explain event loop 1 how to you explain closure 3 how would you make sure value of this works correctly inside settimeout 1 what are the different possible values for this 1 what is debounce and how could you implement debounce 6 how would you communicate with server 1 explain promise to your grandmother 1 if and website is slow how what would you do to make it faster 9 what es6 feature do you use other than let var and arrow 1 what build tool you use and tell me some advantages to use that build tool javascript dom related questions http www thatjsdude com interview dom html 21 questions and answers for intermediate js developers 1 is there any difference between window and document 1 does document onload and window onload fire at the same time 1 is attribute similar to property 1 what are the different ways to get an element from dom 1 what is the fastest way to select elements by using css selectors 1 how come i can t use foreach or similar array methods on a nodelist 1 if you need to implement getelementbyattribute how would you implement it 1 how would you add a class to an element by query selector 1 how could i verify whether one element is child of another 1 what is the best way to create a dom element set innherhtml or use createelement 1 what is createdocumentfragment and why you might use it 1 what is reflow what causes reflow how could you reduce reflow 1 what is repaint and when does this happen 1 how could you make sure to run some javascript when dom is ready like document ready 1 what is event bubble how does event flows in dom 1 how would you destroy multiple list items with one click handler 1 create a button that is destroyed by clicking in it but two new buttons are created in it s place 1 how could you capture all clicks in a page 1 how can you get all the texts in a web page 1 what is defer and async keyword does in a script tag 1 10 rapid fire questions js answers for dom related questions http www thatjsdude com interview dom html html basic questions for begginers http www thatjsdude com interview html html 15 basic questions and asnwers 1 why do you need doctype 2 what are data attributes used for 3 how can you generate a public key in html 4 how do you change the direction of html text 5 how can you highlight text in html 6 can you apply css to a part of html document only 7 will a browser make http request for the following cases 8 which resource would be downloaded first 9 what is an optional tag 10 what are the differences between div and span 11 how would you differentiate between div section and article 12 how would you select svg or canvas for your site 13 how to serve html in multiple languages 14 explain standard and quirks mode 15 what is a semantic tag html answers for basic questions http www thatjsdude com interview html html javascript linkedlist part 4 work in process http www thatjsdude com interview linkedlist html very rough stage need to finish for intermediate javascript search and sort part 5 work in process http khan4019 github io front end interview questions sort html very rough stage need to finish for expert javascript binary search tree part 6 work in process http khan4019 github io front end interview questions bst html very rough stage need to finish for expert todo list 1 css generate mock up from provided layout 2 javascript programming challenges for expert 3 hr related questions like 1 what is your weakness 2 why are you leaving your current job 3 tell me about a project that you weren t able to finish on time 4 how you resolve conflict among team members 5 how will you introduce a new technology to the team 6 do you prefer to work individually or in a team 7 sell this pen coke something to me 8 how much salary do you want 3 what you don t like you current job 4 what you like least in your current job 3 tree data structure in javascript 4 graph and high order data structure in javascript inpsired by darcyclarke https github com darcyclarke front end developer interview questions css tricks http css tricks com interview questions css david shariff https gist github com jesspoemape 87665d65cde3980485ec27cef1602a0d and some google results if you want to add any question to this let me know | javascript interview-questions javascript-interview-questions front-end-interview frontend-interview javascript-interview html css-interview-questions designer-interview-questions html-interview-questions | front_end |
gear-lib | gear lib english readme cn md build https travis ci org gozfree gear lib svg branch master https travis ci org gozfree gear lib release https img shields io github release gozfree gear lib svg https github com gozfree gear lib releases license https img shields io github license gozfree gear lib svg https github com gozfree gear lib blob master license mit this is a collection of basic libraries all are written in posix c aim to used compatibility on linux windows android ios aim to reuse for iot embedded and network service development struct build gear lib png data struct libdict hash key value dictonary library libhash hash key value library based on hlist from kernel libringbuffer libqueue queue library support memory hook librbtree comes from linux kernel rbtree libsort libvector libdarray dynamic array network librtsp real time streaming protocol server for ipcamera or nvr librtmpc real time messaging protocol client for liveshow libsock socket warpper api for easily use librpc remote procedure call library libipc inter process communication support mqueue netlink shm libp2p high level p2p punch hole library easy api to use libmqttc mqtt client protocol libhomekit apple homekit protocol async libgevent reactor event like libevent libthread thread wrapper libworkq work queue in userspace i o libstrex string extension libconfig support ini json liblog support console file rsyslog libfile file operations libsubmask ip addr transform multi media libavcap audio video capture api v4l2 uvc esp32 dshow libmp4 mp4 muxer and parser libjpeg ex libmedia io audio video frame packet define os abstraction layer libposix posix adapter for windows rtos ios misc libdebug help to trace crash like gdb libhal hardware abstraction layer libplugin dynamic link plugin libtime time wrapper libfsm finite state machine how to build please refer to install md https github com gozfree gear lib blob master install md file for detailed information license please refer to the license https github com gozfree gear lib blob master license mit file for detailed information contacts email gozfree 163 com qq group 695515645 github gear lib https github com gozfree gear lib gitee gear lib https gitee com gozfreee gear lib | posix ipc event v4l2 async thread rtsp rtmp iot embedded-c multi-media | server |
ontology-java-sdk | h1 align center java sdk for ontology h1 h4 align center version 1 0 0 h4 overview this is a comprehensive java library for the ontology blockchain currently it supports local wallet management digital identity management digital asset management deployment and invoke for smart contract and communication with ontology blockchain the future will also support more rich functions and applications getting started docs cn readme md enter english version docs en readme md installation environment please configure jdk 8 and above note as the length of key used in sdk is greater than 128 due to the restriction of java security policy files it is necessary to download local policy jar and us export policy jar from the official website to replace the two jar of java home jre lib security in jre directory download url http www oracle com technetwork java javase downloads jce8 download 2133166 html build mvn clean install preparations make sure ontology blockchain has deployed well rpc port has been opened and sdk will connect the rpc server to initialize contributing can i contribute patches to ontology project yes please open a pull request with signed off commits we appreciate your help you can also send your patches as emails to the developer mailing list please join the ontology mailing list or forum and talk to us about it either way if you don t sign off your patches we will not accept them this means adding a line that says signed off by name email at the end of each commit indicating that you wrote the code and have the right to pass it on as an open source patch also please write good git commit messages a good commit message looks like this header line explain the commit in one line use the imperative body of commit message is a few lines of text explaining things in more detail possibly giving some background about the issue being fixed etc etc the body of the commit message can be several paragraphs and please do proper word wrap and keep columns shorter than about 74 characters or so that way git log will show things nicely even when it s indented make sure you explain your solution and why you re doing what you re doing as opposed to describing what you re doing reviewers and your future self can read the patch but might not understand why a particular solution was implemented reported by whoever reported it signed off by your name youremail yourhost com community site https ont io license the ontology library i e all code outside of the cmd directory is licensed under the gnu lesser general public license v3 0 also included in our repository in the license file | java sdk ontology | blockchain |
hyperbolic_representation_learning | eccv 2022 tutorial hyperbolic representation learning for computer vision tutorial website https sites google com view hyperbolic tutorial eccv22 br notebooks website will be updated br tutorial edition october 2022 br author mina ghadimi atigh br this repository contains the tutorial notebooks for the eccv 2022 tutorial hyperbolic representation learning for computer vision the notebooks of the tutorials can be found in the folder notebooks please refer to our website for further information | computer-vision eccv2022 hyperbolic-embeddings tutorial | ai |
Awesome-Medical-LLM | awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com hee9joon awesome diffusion models license mit https img shields io badge license mit green svg https opensource org licenses mit made with love https img shields io badge made 20with love red svg https github com chetanraj awesome github badges this repository contains a collection of resources and papers on large language model of medical ai general medical ai gmai review paper details summary large language models in medicine summary div markdown 1 div details arun james thirunavukarasu darren shu jeng ting kabilan elangovan laura gutierrez ting fang tan daniel shu wei ting nature medicine paper https medium com mlearning ai beyond diffusion what is personalized image generation and how can you customize image synthesis 26a89d5b335 17 july 2023 the shaky foundations of large language models and foundation models for electronic health records michael wornow yizhe xu rahul thapa birju patel ethan steinberg scott fleming michael a pfeffer jason fries nigam h shah npj digital medicine paper https www nature com articles s41746 023 00879 8 29 july 2023 paper large language models encode clinical knowledge karan singhal shekoofeh azizi tao tu s sara mahdavi jason wei hyung won chung nathan scales ajay tanwani heather cole lewis stephen pfohl perry payne martin seneviratne paul gamble chris kelly abubakr babiker nathanael sch rli aakanksha chowdhery philip mansfield dina demner fushman blaise ag era y arcas dale webster greg s corrado yossi matias katherine chou juraj gottweis nenad tomasev yun liu alvin rajkomar joelle barral christopher semturs alan karthikesalingam vivek natarajan nature paper https www nature com articles s41586 023 06291 2 12 july 2023 health system scale language models are all purpose prediction engines lavender yao jiang xujin chris liu nima pour nejatian mustafa nasir moin duo wang anas abidin kevin eaton howard antony riina ilya laufer paawan punjabi madeline miceli nora c kim cordelia orillac zane schnurman christopher livia hannah weiss david kurland sean neifert yosef dastagirzada douglas kondziolka alexander t m cheung grace yang ming cao mona flores anthony b costa yindalon aphinyanaphongs kyunghyun cho eric karl oermann nature paper https www nature com articles s41586 023 06160 y 07 june 2023 a language model for medical predictive tasks fernando chirigati nature computational science paper https www nature com articles s43588 023 00493 4 20 july 2023 radiology llama2 best in class large language model for radiology zhengliang liu yiwei li peng shu aoxiao zhong longtao yang chao ju zihao wu chong ma jie luo cheng chen sekeun kim jiang hu haixing dai lin zhao dajiang zhu jun liu wei liu dinggang shen tianming liu quanzheng li xiang li arxiv paper https arxiv org abs 2309 06419 29 aug 2023 medical licensing exam perspective foundation models for generalist medical artificial intelligence michael moor oishi banerjee zahra shakeri hossein abad harlan m krumholz jure leskovec eric j topol pranav rajpurkar nature paper https www nature com articles s41586 023 05881 4 12 april 2023 do we still need clinical language models eric lehman evan hernandez diwakar mahajan jonas wulff micah j smith zachary ziegler daniel nadler peter szolovits alistair johnson emily alsentzer arxiv paper https arxiv org abs 2302 08091 16 feb 2023 review accelerating the integration of chatgpt and other large scale ai models into biomedical research and healthcare ding qiao wang long yu feng jin guo ye jin gen zou ying feng zheng medcomm paper https onlinelibrary wiley com doi pdf 10 1002 mef2 43 7 april 2023 https www nature com articles s41746 022 00742 2 | gmai language-model large-language-model llm medical-ai medical-language-model | ai |
IotHub | div p align center img src docs images logo svg p h2 align center a cloud based iot solution h2 div iothub provides an abstract solution of any iot infrastructure including broker system api management authentication agent management system so that it can be extended to any preferable iot solution like home automation system supply chain management documentation https rafiulgits github io iothub solution structure iothub agent a net web project to establish the channel between mqtt broker and signalr broker this agent will publish all mqtt broker messages to signalr clients dashboard control panel and send all commands from signalr clients to mqtt clients agent is using mqttnet https github com chkr1011 mqttnet managed client to connect with mqtt broker iothub api a net web api project to provide and manage all data sources of the solution this project use iothub broker a net web project to establish a mqtt broker server broker is using mqttnet https github com chkr1011 mqttnet server iothub common a net classlib project to provide all common models enums exceptions and other stuffs iothub datatransferobject a net classlib project using to transfer data from service layer to service controller and other service consumers iothub db a net classlib project to provide all database context and settings solution is using mongodb as primary database iothub domianmodels a net classlib project to provide domain level models iothub repositories a net classlib project to communicate with database or data source and response in domain level only iothub services have the access to use repository to provide a security level to access domain models iothub services a net classlib project to provide all common services e g authentication service user management service profile management services div p align center img src docs images solution architecture svg p br h6 align center solution architecture h6 div div p align center img src docs images broker architecture svg p br h6 align center broker architecture h6 div how to use development very first clone the project using git clone https github com rafiulgits iothub git or download the project by clicking on download button required environments net core 3 1 or later mongodb execution build the solution by dotnet build go to src iothub api and from appsettings json set your internal custom credential and use this credential create some new users including agent one and profiles run the src iothub broker development server by dotnet watch run go to src iothub agent set agent credentials that you created earlier and run the development server by dotnet watch run development environment is ready to use for testing with mqtt clients mqtt fx https mqttfx jensd de index php download is a good solution iothub dashboard https github com rafiulgits iothub dashboard is a reactive example dashboard that will help to find out how to use iothub solution full documentations iothub agent doc docs agent doc md iothub broker doc docs broker doc md iothub api doc docs api doc md license this repository is licensed with the mit license license | iot iothub mqtt broker signalr | server |
nlp-tutorial | nlp tutorial license https img shields io github license lyeoni nlp tutorial style flat square https github com lyeoni nlp tutorial blob master license github issues https img shields io github issues lyeoni nlp tutorial style flat square https github com lyeoni nlp tutorial issues github stars https img shields io github stars lyeoni nlp tutorial style flat square color important https github com lyeoni nlp tutorial stargazers github forks https img shields io github forks lyeoni nlp tutorial style flat square color blueviolet https github com lyeoni nlp tutorial network members a list of nlp natural language processing tutorials built on pytorch br br p align center img width 350 src https raw githubusercontent com pytorch pytorch master docs source static img pytorch logo dark png align middle p table of contents a step by step tutorial on how to implement and adapt to the simple real word nlp task text classification news category classification https github com lyeoni nlp tutorial tree master news category classifcation this repo provides a simple pytorch implementation of text classification with simple annotation here we use huffpost news corpus including corresponding category the classification model trained on this dataset identify the category of news article based on their headlines and descriptions br keyword cbow lstm fasttext text cateogrization br imdb movie review classification https github com lyeoni nlp tutorial tree master text classification transformer this text classification tutorial trains a transformer model on the imdb movie review dataset for sentiment analysis it provides a simple pytorch implementation with simple annotation br keyword transformer sentiment analysis br question answer matching https github com lyeoni nlp tutorial tree master question answer matching this repo provides a simple pytorch implementation of question answer matching here we use the corpus from stack exchange to build embeddings for entire questions using those embeddings we find similar questions for a given question and show the corresponding answers to those i found br keyword cbow tf idf lstm with variable length seqeucnes br movie review classification korean nlp https github com lyeoni nlp tutorial tree master movie rating classification this repo provides a simple keras implementation of textcnn for text classification here we use the movie review corpus written in korean the model trained on this dataset identify the sentiment based on review text br keyword textcnn sentiment analysis br br neural machine translation english to french translation seq2seq https github com lyeoni nlp tutorial tree master neural machine translation this neural machine translation tutorial trains a seq2seq model on a set of many thousands of english to french translation pairs to translate from english to french it provides an intrinsic extrinsic comparison of various sequence to sequence seq2seq models in translation br keyword sequence to seqeunce network seq2seq attention autoregressive teacher forcing br french to english translation transformer https github com lyeoni nlp tutorial tree master translation transformer this neural machine translation tutorial trains a transformer model on a set of many thousands of french to english translation pairs to translate from french to english it provides a simple pytorch implementation with simple annotation br keyword transformer sentencepiece br br natural language understanding neural language model https github com lyeoni pretraining for language understanding this repo provides a simple pytorch implementation of neural language model for natural language understanding here we implement unidirectional bidirectional language models and pre train language representations from unlabeled text wikipedia corpus br keyword autoregressive language model perplexity br | nlp natural-language-processing nlp-tutorial neural-machine-translation text-classification sentiment-classification | ai |
Hands-on-Full-Stack-Web-Development-with-GraphQL-and-React | hands on full stack web development with graphql and react a href https www packtpub com web development hands full stack web development graphql and react utm source github utm medium repository utm campaign 9781789134520 img src https www packtpub com sites default files cover b10514 png alt hands on full stack web development with graphql and react height 256px align right a this is the code repository for hands on full stack web development with graphql and react https www packtpub com web development hands full stack web development graphql and react utm source github utm medium repository utm campaign 9781789134520 published by packt build scalable full stack applications while learning to solve complex problems with graphql what is this book about react one of the most widely used javascript frameworks allows developers to build fast and scalable front end applications for any use case graphql is the modern way of querying an api it represents an alternative to rest and is the next evolution in web development combining these two revolutionary technologies will give you a future proof and scalable stack you can start building your business around this book covers the following exciting features resolve data from multi table database and system architectures build a graphql api by implementing models and schemas with apollo and sequelize set up an apollo client and build front end components using react use mocha to test your full stack application write complex react components and share data across them deploy your application using docker if you feel this book is for you get your copy https www amazon com dp 1789134528 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following state posts posts following is what you need for this book the book is for web developers who want to enhance their skills and build complete full stack applications using industry standards familiarity with javascript react and graphql is expected to get the most from this book with the following software and hardware list you can run all code files present in the book chapter 1 13 software and hardware list chapter software required os required 1 node js 10 nmp 6 react developer tools windows and linux any 2 postman windows and linux any 3 mysql 5 php 7 apache phpmyadmin 4 windows and linux any 4 apollo client developer tools windows and linux any related products react cookbook packt https www packtpub com web development react cookbook utm source github utm medium repository utm campaign 9781783980727 amazon https www amazon com dp 1783980729 learn react with typescript 3 packt https www packtpub com web development learn react typescript 3 utm source github utm medium repository utm campaign 9781789610253 amazon https www amazon com dp 1789610257 get to know the author sebastian grebe is a verified computer science expert for application development he is a young entrepreneur working on a variety of products he specializes in web development using modern technologies such as react and feathersjs traditional technologies such as php and sql he developed professionally by merging old and new applications and developing cross platform apps with react native and ionic currently he is actively working on his software agency called open mind which manages various software projects he is also actively pushing a social network app that utilizes react apollo and cordova which is called coupled he has worked for various companies as a software engineer and project manager such as db netz ag suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions | front_end |
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uber-etl-pipeline-Gcp-Clouddata-engineering-project | uber data analytics modern data engineering gcp project introduction the goal of this project is to perform data analytics on uber data using various tools and technologies including gcp storage python compute instance mage data pipeline tool bigquery and looker studio architecture img src architecture jpg technology used programming language python google cloud platform 1 google storage 2 compute instance 3 bigquery 4 looker studio modern data pipeine tool https www mage ai contibute to this open source project https github com mage ai mage ai dataset used tlc trip record data yellow and green taxi trip records include fields capturing pick up and drop off dates times pick up and drop off locations trip distances itemized fares rate types payment types and driver reported passenger counts here is the dataset used in the video https github com darshilparmar uber etl pipeline data engineering project blob main data uber data csv more info about dataset can be found here 1 website https www nyc gov site tlc about tlc trip record data page 2 data dictionary https www nyc gov assets tlc downloads pdf data dictionary trip records yellow pdf data model img src data model jpeg | cloud |
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Dataengineering | dataengineering big data and google cloud data engineering | cloud |
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LPC2378-USB-Mass-Storage-Audio-Device | lpc2378 usb mass storage audio device this project uses a lpc2378 to mount an sd card as a usb mass storage to a host system it also behaves as a usb audio device allowing playback via the speakers connected to the adc on the keil mcb2300 lpc2378 development board developed as part of embedded systems design eee g512 course in bits pilani goa campus the project was developed using keil uvision ide the project heavily relies on the rl arm libraries in addition it employs the rtx rtos in order to handle both the audio streaming and mass storage actions the device implements usb device msc and adc classes developed by devam ajit awasare 2016a3ps0657g moosa mahsoom 2016a8ps0310g pragnesh patel rasiklal 2016a3ps0183g priyal chhatrapati 2016a8ps0378g the method described below is for ulink usb jtag adapter flashing the code 1 download the repository and extract it to a location of your choice 2 open keil uvision5 ide ensure you have the professional version of the ide in order to have access to the rl arm libraries 3 open the project via the menu project open project and select the file memory uvproj from the folder the project has been pre configured for the mcb2300 lpc2378 board and ulink jtag adapter 4 compile and link the application via the toolbar button or using the menu project build target vision starts compiling and linking the source files and displays progress messages in the window build output 5 use the download to flash toolbar button load is written on the icon this will download the program onto the board 6 reset the board 7 connect the board to a pc via the micro usb port 8 the sd card should mount to the system and the device should also be recognised as an audio device we recommend formatting the sd card to partitions less than 1 gb as higher partition negatively impact the performance 9 for debugging purposes utilise debug start stop debug session known issues and fixes rtx library issues in your project file ensure that you are using the rtx libraries provided with keil uvision 5 older versions could have bugs which could prevent the board from functioning as expected this can occur if you have keil uvision v4 and v5 installed on the same system the correct versions of the file can be found at c keil arm rv31 lib expand library files in the project right click on the appropriate library and input the correct file path ensure that you chose the files ending with l for more info regarding the aforementioned issue refer http infocenter arm com help topic com arm doc kui0062a rlarm default htm optimisation issues it is possible for the compiler optimisations to interfere with the code disable optimisations o0 in target options limited bandwidth the lpc2378 only supports upto usb 2 0 full speed which translates into a bandwidth of 12 mbps hence when data access and audio streaming occur simultaneously it is possible for the device to stall in the rtx configuration files you can attempt to reduce the bandwidth requirement of the audio streaming | os |
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