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Web-Devlopment-Projects | resume generator web based editor to create resume in a customizable template try it https nitish6174 github io resume generator note click the view instructions button in the editor to read usage instructions features resume content can be edited just like a normal document editor cut copy undo etc entire sections can be added reordered removed just by cut copy pasting method section visibility can be toggled while retaining the content options provided in the left panel to modify the template and formatting sub points can be added with various bullet styles and adjustable indentation script provided to merge multiple pages and compress the pdf using the merge compress script you must be able to run python file on your system for this save the individual pages in pdf format with name 1 pdf 2 pdf download the compress pdf py file and open it in a text editor set the following variables dir path directory path where you saved the pdfs for individual page num of pages number of files to merge i e pages in your resume out file name of output file run this python file note as this creates a new pdf file you may have to see permission settings or run with sudo on terminal note use google chrome | hacktoberfest hacktoberfest-accepted open-source-contributions digitaloce github digital good-first-issue open-source opensource-projects pull-request hacktoberfest2023 hacktoberfest2023-accepted | front_end |
webdev-support-bot | webdev support bot dependencies dependencies image dependencies url devdependencies devdependencies image devdependencies url dependencies image https david dm org ljosberinn webdev support bot png dependencies url https david dm org ljosberinn webdev support bot devdependencies image https david dm org ljosberinn webdev support bot dev status png devdependencies url https david dm org ljosberinn webdev support bot info devdependencies p align center img src logo png p bot providing multiple commands to query common sites used during development or helping people on discord supports github via github api composer via packagist api npm via unofficial npmjs com api mdn via parsing mozilla developer network http developer mozilla org caniuse via unofficial caniuse api and mdn browser combat data https github com mdn browser compat data bundlephobia via unofficial bundlephobia api jquery as explanation on why not to use jquery php via parsing official php docs http php net usage tldr bash tag it in discord to receive general help bot help provides an example each mdn help caniuse help composer help npm help github help bundlephobia help bash queries mdn with term mdn term bash queries caniuse with term caniuse term bash queries packagist with package composer package bash queries npm with package npm package bash queries github with term github term bash queries bundlephobia with term bundlephobia term single result queries will directly show the result reacting with a number will filter the result reacting with the red or black x will remove the request description by default shows the first ten results of any given query unless only one result was found reacting with a number corresponding to the list entry will filter the list and edit the original message providing more specific information add to your server by accessing this link https discordapp com api oauth2 authorize client id 649967864425611274 scope bot permissions 1 demo p align center img src webdev support bot demo gif p development bash git clone https github com ljosberinn webdev support bot cd webdev support bot cp env example env and enter a token yarn install or npm install code yarn dev or npm dev or be fancy with a one liner git clone https github com ljosberinn webdev support bot cd webdev support bot cp env example env yarn install code yarn dev environment variables in development you generally want to take the env example and rename it to env you also shouldn t commit your env file if you make any changes to the environment variables you should update env example accordingly running tests bash npm test found a bug want to contribute please head over to github https github com ljosberinn webdev support bot issues | discord bot mdn mozilla composer packagist npm npmjs caniuse github | front_end |
gatsby-starter-grommet | gatsby starter grommet gatsby starter configured to use the grommet design system https grommet github io use this starter by running sh gatsby new my grommet site https github com alampros gatsby starter grommet see the gatsby starters docs https www gatsbyjs org docs gatsby starters for more info on starters demo see the live demo https alampros github io gatsby starter grommet | os |
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yelp_sentiment_analysis | yelp sentiment analysis natural language processing project graph images main graph jpg for code go to notebooks notebooks for raw data and model go to data data full report report pdf presentation slides presentation pdf problem statement online reviews are a common way for customers to share their thoughts on a service or product with so much text based data it s impossible for a human to keep up and read through every review while the common five star rating system works as a simple solution there needs to be a better way to gather more accurate insights from the enormous amounts of text based data context this data was sourced from yelp com with over 10 000 unique reviews from 200 restaurants in new york city the data is unlabled and will have to be classified in order to train the model goals analyze each review and find relevant trends and relationships classify the training data based on review content create a prediction model with high accuracy and f1 score data collection reviews were sourced from yelp by using yelp s fusion api to get unique urls for each restaurant the list of restaurants was gathered using nyc open data using beautifulsoup and requests library to scrape 10 000 reviews while following the robots txt rules scraping images web scraping jpg natural language processing each review was trimmed down by removing stop words and stemming each word this saves memory and computing power and makes the overall model less prone to overfitting modeling 10 models in total were tested these are their accuracy and f1 scores model images model scores jpg results thanks to the stacked model which used features from a sparse dense matrix naive bayes model and a gradient boosted classifier the overall accuracy was improved by over 20 results images confusion matrices jpg | ai |
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flask_jsondash | flask jsondash codacy badge https api codacy com project badge grade 3d9d8e8742a742a0843a418506de757c https www codacy com app dxdstudio flask jsondash utm source github com amp utm medium referral amp utm content christabor flask jsondash amp utm campaign badge grade code climate https codeclimate com github christabor flask jsondash badges gpa svg https codeclimate com github christabor flask jsondash scrutinizer code quality https scrutinizer ci com g christabor flask jsondash badges quality score png b master https scrutinizer ci com g christabor flask jsondash branch master build status https scrutinizer ci com g christabor flask jsondash badges build png b master https scrutinizer ci com g christabor flask jsondash build status master build status https travis ci org christabor flask jsondash svg branch master https travis ci org christabor flask jsondash code health https landscape io github christabor flask jsondash master landscape svg style flat https landscape io github christabor flask jsondash master coverage status https coveralls io repos github christabor flask jsondash badge svg branch master https coveralls io github christabor flask jsondash branch master pypi version https badge fury io py flask jsondash svg https badge fury io py flask jsondash img src http img shields io liberapay patrons christabor svg logo liberapay easily configurable chart dashboards from any arbitrary api endpoint json config only ready to go img src example app examples screenshots kitchensink2 png alt kitchen sink 2 img src example app examples screenshots kitchensink1 png alt kitchen sink 1 img src example app examples screenshots vegalite png alt vega lite img src example app examples screenshots listview png alt dashboard overview width 600 img src example app examples screenshots addmodule png alt adding a widget width 600 img src example app examples screenshots plotly png alt kitchensink screenshot 1 this project is a flask blueprint http flask pocoo org docs 1 0 blueprints that allows you to create sleek dashboards without writing any front end or backend code everything is powered through simple json configurations for declaring arbitrary charts features leveraging popular libraries like c3 js and d3 js and much more also supports templates and iframes only a basic intuitive configuration is required the dashboard layout and blueprint styles are pre packaged and provide only the essentials while getting out of the way drag and drop your layout easily and intuitively multiple layout modes bootstrap grid based or totally freeform data utilities docs data utils md for munging and manipulating data suitable for various charts view all supported libraries docs schemas md it uses any specified json endpoint to get data from so long as the payload format is correct docs schemas md json configurations intro the configuration json provides core functionality and is at the heart of the project there are several comprehensive examples available in the examples config example app examples config directory to give you an idea of how it works as well as the core configuration documentation docs config md an simple example json modules type timeseries name name3 width 510 height 400 datasource http localhost 5001 test1 order 0 4 0 and later you can even provide custom inputs to allow interactivity on each chart e g json modules name line height 400 width 500 datasource http 127 0 0 1 5004 custom inputs override false guid a6eb10e7 26fa 7814 818a 3699b24415c5 type line inputs btn classes btn btn info btn sm submit text submit options type number name entries input classes form control input sm label number of points help text change the number of points per entry shown which will map to query parameters entries 10 in this example that you can use to filter or change what your endpoint returns also note that the order of the inputs in the json will determine their order in html below is an example output using a custom input configuration img src example app examples screenshots inputs png alt inputs example width 276 height 319 see the examples config example app examples config directory for all the supported options demo if you want to see all most charts in action you ll need to fire up the endpoints py flask app included alongside your main app that uses the blueprint create a new dashboard then choose the edit raw json option specifying one of the json files found in examples config example app examples config this has been tested using mongodb various chart schemas json formats each chart is very straightforward most of the power is leveraged by the various charting libraries that flask jsondash defers to see schemas docs schemas md for more detail on how your endpoint json data should be formatted for a given chart type as well as how to find docs for each supported library usage quickstart setting database environment variables make sure the following env vars are set charts db host the db server hostname defaults to localhost charts db port the db server port defaults to 27017 charts db name the db database name defaults to charts charts db table the db collection name defaults to views charts active db the db backend to use options mongo default starting db make sure to start so json configuration can be saved starting db mongodb start however you d like but usually mongod will work note you will need to make sure the collection has been created within your mongo instance and is specified in the charts db table env var as well as specify your database name under the charts db name env var download the package and start the app method 1 use provided flask app shell git clone https github com christabor flask jsondash git cd flask jsondash virtualenv env source env bin activate python setup py install cd example app python app py this will setup the app in a virtual environment and run the included test app app py immediately on port 8080 if you want to import the blueprint into your own existing flask instance method 2 use your existing app shell pip install flask jsondash your app will need to import and register the blueprint as well as have the appropriate template tags an example of this can be found here example app templates layouts base html method 3 docker assuming you have docker and docker compose installed shell git clone https github com christabor flask jsondash git make dockerize this will build the base and services images setup your docker services and link them together the endpoints will run on 0 0 0 0 5004 by default and your app is available at 0 0 0 0 8080 note that there are three docker files a base and then inheriting ones this is a way to speed up subsequent app specific builds without having to reinstall python and update apt repos note for any serious usage you ll always want to configure external volumes for mongodb so that your data is persisted outside of docker python 3 x usage the above should work but you ll need to use the python 3 x equivalent for all of the operations e g virtualenv p python3 env python3 setup py install python3 app py requirements core flask jinja2 javascript css these are not included as you are likely going to have them yourself if you don t you ll need to add them jquery js bootstrap css js these are necessary and included based simply on the likelihood they may not already be used jrespond js masonry js jquery ui css js starting flask app either import and use the blueprint in your own flask app or run app py directly to start the app as is starting the test server run endpoints py if you d like to test out existing endpoints to link your chart json to using remote ajax endpoints see endpoints py for examples on how to achieve this if you do not allow cors on the server side all ajax requests will fail customization beyond the above outlined configurations that power the core of jsondash there are more ways to control how the application works flask configuration authentication by default no authentication is performed for a given action however supporting your own custom auth for each type is just a simple config away using the flask pattern of injecting configurations into the app config namespace in this case jsondash must be specified you can put whichever functions you want and only those specified will be checked here is a working example python def can edit others view id none if view id and session get user name in secret admins return true return false def can delete charts return session get user name in secret admins charts config dict auth dict edit others can edit others delete can delete charts app config jsondash charts config see below for the supported types and their details authentication types edit global this determines if a user can create or update a chart with the global flag set which will show the dashboard to all users if the appropriate application flags are set see global config flags below https github com christabor flask jsondash global config flags if no flag is set for allowing global dashboards then this option will not be available delete allows deleting of charts clone allows cloning of charts update allows updating of charts create allows creation of new charts view allows viewing of a chart the provided function will be passed the id of the view as a view id kwarg edit others allow editing of other creators charts the provided function will be passed the id of the view as a view id kwarg if the created by matches the logged in user it will automatically be allowed regardless of the auth override metadata configuration metadata can be added to the json configuration for further customization purposes all arbitrary values will expect an accompanying function to be populated with in the exact same way as the auth functions listed above they will all be namespaced under the metadata key inside of the app config jsondash dictionary if specified below is an example of how you can override these fields with your own arbitrary functions note by default none take arguments this may change for specific types python charts config dict metadata dict created by get username app config jsondash charts config the following metadata overrides are used but you can also add arbitrary keys and values which will be saved to the dashboard config just not necessarily used here created by this is used to organize views on the front page by user if there is such a key present on the configuration this key is updated and saved if present null otherwise user this is the current logged in user this is required for filtering dashboards by user you must also set the jsondash filterusers flag to true in app config global config flags below are global app config flags their default values are represented in the example working python code app config jsondash filterusers false for filtering dashboards by the logged in user see above for setting user data app config jsondash globaldash true for allowing global dashboards to be shown these dashboards must have a created user of global or be overridden see below app config jsondash global user global an owner name to use when allowing global dashboards to be seen this is set on the created by property in the specific json config see above for more examples app config jsondash max perpage 50 the number of results to show per page remaining results will be paginated static asset config options by default all assets css js will be loaded remotely by popular cdns recommended for the given charting library however you might want to ensure assets are always available or cannot access them because of network proxy issues if you would like to use your own local assets specified inside of the settings py flask jsondash settings py file you can download them put them in your app somewhere and then tell jsondash where they should be loaded from using the standard flask url for static filename xxx pattern just add a static key in your jsondash config with these values like so python app config jsondash dict static dict js path js vendor css path css vendor with default flask static settings this would resolve the url to static js vendor filename js for example you can use one or the other but it s recommended to use both or none jinja template configuration the following blocks are used in the master template 1 jsondash body required for the entire layout heavy exclamation mark 2 jsondash css required for loading the css heavy exclamation mark 3 jsondash js required for loading the js heavy exclamation mark 4 jsondash api scripts optional if you want to register callbacks see below heavy check mark 5 jsondash init required to initialize the dashboards heavy exclamation mark 6 jsondash title optional if you want to override or augment your page title heavy check mark you can just check out the example app example app templates layouts base html to see how it all should work javascript configuration custom callbacks while the point of jsondash is to make front end coding completely unnecessary and use serializable declarative configurations for making dashboards sometimes you need to do one off stuff that requires scripting a callback module exists to allow this very easily without getting in the way of existing configurations you can customize individual charts by adding your own javascript files alongside your existing app that uses this blueprint and then register call backs on a per chart id basis all callbacks will be run in the order you register them after the chart has been loaded and rendered completely to get started override the template block in your template to allow javascript to be executed and register a callback with your own arguments html block jsondash api scripts script jsondash api registercallback my chart guid function container config myargs console log running first callback console log myargs 0 console log myargs 1 container style background color green console log config guid all my optional arguments register a second one which runs after jsondash api registercallback my chart guid function container config console log running second callback script endblock all callbacks will be passed the following arguments order 1 container the d3 selector for the chart container 2 config the json object configuration for this chart 3 args the array of arguments you supplied when registering the callback to see a list of all your callbacks by chart you can call jsondash api listcallbacks custom events several events are triggered throughout the process and can be listened to by your own callbacks or just other code you have embedded in your application jsondash init jsondash editform loaded jsondash widget added jsondash widget updated jsondash widget deleted jsondash widget refresh jsondash row add jsondash row delete jsondash preview see all events in app js flask jsondash static js app js under events versioning this project uses semantic versioning http semver org for releases however the master branch is considered to be unstable as it represents bleeding edge with updates hotfixes etc which will eventually get tagged with a release if you want to use a stable version make sure to pin the specific release you want to target faqs q why d you choose to expose library x y or z a i tried to go for libraries that are pretty widely known and popular if you are dissatisfied with what s exposed you can always add your own by embedding any js css and html in a template and loading it through the iframe option q how do i customize x y z a because of the level of abstraction used here a lot of charts will naturally be less configurable than if they had been scripted by hand this is the tradeoff with being able to quickly setup a lot of charts easily the goal here is to use intelligent defaults as much as possible and then allow the most universal aspects to be customized through a common interface however you can inject raw json friendly configurations if your chart has the override flag set this will not work for all charts see configuration options docs schemas md for more keep in mind many stylistic customizations can be overridden in css since most all charts are html and or svg and as mentioned above you can always use override option or the iframe custom option and make your datasource endpoint return whatever you want including a full html js css pre rendered template q when exposing metadata why don t you just use the g variable and read from that a one way this can be done is using the app before request decorator and populating the g variable with metadata the problem is that it creates extremely unnecessary overhead tips tricks using the included data utils check out data utils docs data utils md for more loading example dashboards config automatically run make fixtures using endpoints dynamically because the chart builder utilizes simple endpoints you can use the power of rest to create more complicated views for example curl xget http localhost 8080 api foo could return data 1 2 3 4 but you could customize the url by updating the url saved in your dashboard to support query arguments curl xget http localhost 8080 api foo gt 9 could return data 10 20 30 40 instead using shared data for performance and simplicity data from a single source can be shared amongst n charts using namespaced keys in the payload see the shared data section docs schemas md and visit an example configuration here https github com christabor flask jsondash blob master example app examples config shared data json for more generating test data included are cli utilities for generating fake charts etc you will need to run them like a python package due to their relative import style which is required for py2 p3 compatibility to run for example the model factory generator run python m flask jsondash model factories records 10 for python3 x just replace that with python3 m using the demo mode if you append the query argument jsondash demo mode 1 to your url e g jsondash demo mode 1 the ui will automatically hide any dashboard edit buttons and back button this can be used for example for displaying on a mounted screen to hide extraneous details embedding charts elsewhere similar to jsondash demo mode you can append the query param embeddable e g embeddable 1 in your url which will hide all interactive elements as well as dashboard titles and other ancillary elements note this will not remove other aspects of your ui that are outside the scope of the jsondash jinja blocks those will need to be hidden separately using gist github com while the data is not dynamically generated you can easily use github gists or any raw file from github com for that matter to load charts check out the kitchensink dashboard example app examples config kitchensink json to see a real working chart loaded from via gist embedding graphs from other resources for example the build tool jenkins provides a plugin for build statistics https wiki jenkins ci org display jenkins global build stats plugin the raw generated png url is typically of the format https jenkins server view view name job job name buildtimegraph png can be directly embedded using the iframe chart option other cool stuff check out the makefile for more useful commands performance performance metrics are not available but you can view some stress test examples for the example endpoints the configuration for these are available in examples config stresstest json example app examples config stresstest json also the comprehensive examples plotly kitchensink above are very complex dashboards 20 30 charts webgl etc and have been tested in the browser a couple observations on stress tests performed on macbook pro 16gb 2 7ghz i7 native d3 js handles large datasets very well it handled 1 2mb json files with no problem datatables handles extremely large datasets with no problem maximum tested before degradation was around 100 000 rows c3 js starts to lag heavily and spends a good 10 seconds and in some cases crashed google chrome with multiple charts on the page when updwards of 2 300 data points are used per chart the example config has 10 charts your performance may be better or worse so just test it out as always your mileage may vary debugging troubleshooting my chart won t load even though the url is correct x http issues if your site is using https it should be this is likely caused by an issue with third parties not using it but instead running an insecure http web server this is unfortunately not easy to fix unless you make your site insecure by no longer have an ssl certificate not preferable tell the owner of the endpoint to enforce ssl on their end and provide https x javascript issues to troubleshoot potential javascript parse errors open up your browser console in chrome for example it s kbd cmd option i kbd for mac and kbd ctrl alt i kbd on windows and see if there are any errors if there are any parse errors then the format of your json response may be invalid for a given content type make sure to check the schemas page for format requirements docs schemas md my chart is ugly or is flowing outside the container this is usually only an issue with datatables particularly when selecting the number of entries to show the size of the table will grow and the layout does not account for that nor should it the best case here is to determine what size actually makes sense for you and adjust your chart size accordingly you can also use the override option supported by this chart and specify the number of results per page and the number of entries that can be shown see the datatables schema docs docs schemas md datatables for more contributing development if you d like to work on the project a good place to start is using the example app to develop against to do this easily you ll want to setup a virtual environment and setup the package locally using the develop mode of setuptools the below should get you started shell git clone github com christabor flask jsondash git cd flask jsondash virtualenv env source env bin activate git checkout b your new branch python setup py develop cd example app python app py and voila you can now edit the folder directly and still use it as a normal pip package without having to reinstall every time you change something tests to run all tests for python 2 7 and 3 x with coverage just run tox assuming tox is installed python you can run these tests using pytest pip install u pytest and then in the existing virtualenv run pytest tests if you are having issues with this approach an alternative would be to install pytest within the projects virtualenv assuming you ve created one and then running it like so python m pytest tests test coverage to find coverage information assuming pytest cov is installed you can run pytest tests s cov flask jsondash javascript js tests are run using the node library jasmine to install and run it you ll need nodejs installed then the package npm install g jasmine you can then cd into the tests js folder and run the provided python script python runner py | flask dataviz chart-json d3 plotly c3 dashboard visualization charts | front_end |
Schull-Project | schull project this is a capstone project to emulate uniabuja website the project was giving during the cloud engineering second cohort hosted by schull io live view https github com omonwin schull project git | cloud |
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sql-database-management | sql employee database scope 1 data modeling 2 data engineering 3 data analysis data modeling database design based on csv data establishing relationship between different tables and columns to create a reference for relational database employeesql dbd employees png data engineering created following tables and imported data from csv sql departments dept emp dept manager employees salaries titles data analysis 1 list the following details of each employee employee number last name first name gender and salary sql select a emp no a first name a last name a gender b salary from employees a inner join salaries b on a emp no b emp no 2 list employees who were hired in 1986 sql select from employees where hire date between 1986 01 01 and 1986 12 31 order by hire date desc 3 list the manager of each department with the following information department number department name the manager s employee number last name first name and start and end employment dates sql select a dept no b dept name a emp no c first name c last name d from date as employment start date d to date as employment end date from dept manager a left join departments b on a dept no b dept no left join employees c on a emp no c emp no left join dept emp d on a emp no d emp no 4 list the department of each employee with the following information employee number last name first name and department name sql select a emp no a first name a last name c dept name from employees a left join dept emp b on a emp no b emp no left join departments c on b dept no c dept no order by emp no this query gives dept name of all the past depts of the employee including ex employees dense rank function can be used if only latest dept name is required 5 list all employees whose first name is hercules and last names begin with b sql select from employees where first name hercules and last name like b 6 list all employees in the sales department including their employee number last name first name and department name sql select a emp no a first name a last name c dept name from employees a left join dept emp b on a emp no b emp no left join departments c on b dept no c dept no where dept name sales order by emp no 7 list all employees in the sales and development departments including their employee number last name first name and department name sql select a emp no a first name a last name c dept name from employees a left join dept emp b on a emp no b emp no left join departments c on b dept no c dept no where c dept name sales or c dept name development order by emp no 8 in descending order list the frequency count of employee last names i e how many employees share each last name sql select last name count last name from employees group by last name order by last name desc | server |
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jewel-timpug-personal-portfolio.github.io | jewel baquing timpug personal portfolio | server |
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LLMs4OL | llms4ol paradigm readme md llms4ol paradigm task a term typing taska readme md task b type taxonomy discovery taskb readme md task c type non taxonomic relation extraction taskc readme md finetuning tuning readme md task a detailed results taska results readme md task b detailed results taskb results readme md task c detailed results taskc results readme md task a datasets datasets taska readme md task b datasets datasets taskb readme md task c datasets datasets taskc readme md finetuning datasets datasets tuning readme md h1 align center llms4ol large language models for br ontology learning h1 hamed babaei giglou https hamedbabaei github io jennifer d souza https sites google com view jen web and s ren auer https www tib eu en research development research groups and labs data science digital libraries staff soeren auer br hamed babaei jennifer dsouza auer tib eu br tib leibniz information center for science and technology hannover germany br accepted for publication at iswc 2023 research track br div align center img src images llms4ol jpg div div align center figure 1 the llms4ol task paradigm is an end to end conceptual framework for learning ontologies in different knowledge domain div br ontology learning ol addresses the challenge of knowledge acquisition and representation in a variety of domains recent advances in nlp and the emergence of large language models which have shown a capability to be good at crystallizing knowledge and patterns from vast text sources we introduced the llms4ol large language models for ontology learning paradigm as an empirical study of llms for automated construction of ontologies from various domains the llms4ol paradigm tests does the capability of llms to capture intricate linguistic relationships translate effectively to ol given that ol mainly relies on automatically extracting and structuring knowledge from natural language text table of contents repository structure repository structure llms4ol paradigm llms4ol paradigm llms4ol paradigm setups llms4ol paradigm setups tasks tasks datasets datasets results results experimental llms experimental llms experiments experiments results overview results overview how to run tasks how to run tasks requirements requirements running tasks running tasks citation citation repository structure llms4ol root directory of the repository tuning few shot finetuning directory taska term typing task directory taskb type taxonomy discovery task directory taskc type non taxonomic relation extraction task directory assets artifacts directory llms contains pretrained llms fsl contains fine tuned llms for training you should create this wordnetdefinitions contains wordnet word definitions countrycodes geonames country codes datasets contains datasets fsl contains few shot learning training datasets taska contains directories for task a sources taskb contains directories for task b sources taskc contains directories for task c sources docs contains supplementary documents supplementary material pdf contains directories for task c sources images contains the figures readme md readme file for documenting the service requirements txt contains python requirements listed llms4ol paradigm the llms4ol paradigm offers a conceptual framework to accelerate the automated construction of ontologies exclusively by domain experts ol tasks are based on the ontology primitives which consist of 1 corpus preparation selecting and collecting the source texts to build the ontology 2 terminology extraction identifying and extracting relevant terms from the source text 3 term typing grouping similar terms as conceptual types 4 taxonomy construction identifying the is a hierarchies between types 5 relationship extraction identifying and extracting non is a or semantic relationships between types 6 axiom discovery discovering constraints and inference rules for the ontology toward realizing llms4ol we empirically ground three core tasks of ol leveraging llms as a foundational basis for future work they are presented as term typing type taxonomy discovery type non taxonomic relation extraction llms4ol paradigm setups the llms4ol task paradigm is an end to end conceptual framework for learning ontologies in different knowledge domains with the aim of automation of ontology learning tasks the tasks within the blue arrow in figure 1 are the three ol tasks empirically validated for each task we created a directory with a detailed description of the task information as follows task a term typing taska readme md task b type taxonomy discovery taskb readme md task c type non taxonomic relation extraction taskc readme md datasets to comprehensively assess llms for the three ol tasks we cover a variety of ontological knowledge domain sources i e lexicosemantics wn18rr https github com timdettmers conve wordnet geography geonames http www geonames org biomedicine nci https www nlm nih gov research umls sourcereleasedocs current nci index html medicin https www nlm nih gov research umls sourcereleasedocs current medcin index html snomedct us https www nlm nih gov research umls sourcereleasedocs current snomedct us index html and web content types schema org https schema org these sources are different for each task so for each task the detailed information is available as follows task a term typing datasets datasets taska readme md geonames nci medicin snomedct us and wn18rr task b type taxonomy discovery datasets datasets taskb readme md geonames schema org and umls https lhncbc nlm nih gov semanticnetwork task c type non taxonomic relation extraction datasets datasets taskc readme md umls https lhncbc nlm nih gov semanticnetwork results the evaluation metric for task a is reported as the mean average precision at k map k where k 1 and evaluations for tasks b and c are reported in terms of the standard f1 score based on precision and recall complete and detailed results for tasks are presented in the following tables task a term typing detailed results table taska results readme md task b type taxonomy discovery detailed results table taskb results readme md task c type non taxonomic relation extraction detailed results table taskc results readme md experimental llms we created experimentations using five different lms these lms described as followings encoder only bert large https huggingface co bert large uncased with 340m parameters pubmedbert https huggingface co microsoft biomednlp pubmedbert base uncased abstract fulltext with 340m parameters encoder decoder bart large https huggingface co facebook bart large with 400m parameters flan t5 large https huggingface co google flan t5 large with 780m parameters flan t5 xl https huggingface co google flan t5 xl with 3b parameters decoder only bloom 1b7 https huggingface co bigscience bloom 1b7 with 1 7b parameters bloom 3b https huggingface co bigscience bloomz 3b with 3b parameters llama 7b https ai meta com blog large language model llama meta ai with 7b parameters gpt 3 https platform openai com docs models gpt 3 with 175b parameters gpt 3 5 https platform openai com docs models gpt 3 5 with 174b parameters gpt 4 https platform openai com docs models gpt 4 with 1t parameters experiments first we created prompt templates based on existing experimental language models and their nature specifically for tasks a and b we created 8 templates per source and for task c only a single template next we probe lms as zero shot testing more later we attempt to boost the performance of two llms flan t5 large and flan t5 xl in the form of few shot learning using predefined prompt templates different than zero shot testing and we test the model using zero shot testing prompt templates prompt templates for zero shot testing are represented as follows dataset task prompt templates path answer set mapper path wn18rr a datasets taska wn18rr templates json datasets taska wn18rr templates json datasets taska wn18rr label mapper json datasets taska wn18rr label mapper json geonames a datasets taska geonames templates json datasets taska geonames templates json datasets taska geonames label mapper json datasets taska geonames label mapper json nci medicin snomedct us a datasets taska umls templates json datasets taska umls templates json datasets taska umls label mapper json datasets taska umls label mapper json schema org umls geonames b datasets taskb templates txt datasets taskb templates txt datasets taskb label mapper json datasets taskb label mapper json umls c datasets taskc templates txt datasets taskc templates txt datasets taskc label mapper json datasets taskc label mapper json prompt templates for training model is represented as follows dataset task prompt templates path wn18rr umls nci only geonames schema org a b c tuning templates py fsl templates py results overview div align center img src images results figure jpeg div div align center figure 2 comparative visual of the zero shot and finetuned results unfilled shapes filled shapes and small filled stars represent performances in tasks a b and c respectively div br how to run tasks requirements software requirements python 3 9 requirements txt libraries instructions first install the conda using conda installation https conda io projects conda en latest user guide install index html guideline and then create and activate your environments as follows cmd conda create n yourenvname python 3 9 conda activate yourenvname next clone the repository and install the requirements from requirements txt in your environments cmd git clone https github com hamedbabaei llms4ol git cd llms4ol pip install r requirements txt next add your openai key to the env file for experimentations on openai models finally start the experiments as described in the task directories running tasks to make each task behave separately as an encapsulated module we have created separated directories for datasets as well as tasks and each task consists of a test auto sh shell script that automatically runs zero shot testing on all the task datasets and produces results that will be stored in taskx results dataset name directory also you can easily run any model on your desired input dataset by running test manual sh and it will ask for the dataset output logs to store outputs as well as model name and device cpu or gpu for each of the important direcotries we produced the test py scripts which will be called in test manual sh and test auto sh multiple times on different datasets the strucutre of taska taskb and taskc directories are presented as follows llms4ol taskx directory llms4ol tuning trainer py train eval sh taskx results dataset1 test py test auto sh test manual sh readme md the train eval sh in the tuning directory runs trainer py for representative datasets and then walks through taskx directories and calls test py for evaluation of trained models for each dataset how to run models in detail is described tasks directories readme md files citations inproceedings llms4ol author babaei giglou hamed and d souza jennifer and auer s ren title llms4ol large language models for ontology learning booktitle international semantic web conference year 2023 publisher springer international publishing preprint misc giglou2023llms4ol title llms4ol large language models for ontology learning author hamed babaei giglou and jennifer d souza and s ren auer year 2023 eprint 2307 16648 archiveprefix arxiv primaryclass cs ai | large-language-models ontology-learning knowledge-graph transformers bert bloom chatgpt flan-t5 gpt-3 gpt-4 llama-7b | ai |
practicalnlp | practical natural language processing sowmya vajjala bodhisattwa p majumder anuj gupta harshit surana website www practicalnlp ai http www practicalnlp ai published by o reilly media 2020 http shop oreilly com product 0636920262329 do chapter wise notebooks for the book practical natural language processing open repository in colab open in colab https colab research google com assets colab badge svg https colab research google com github nishkalavallabhi practicalnlp blob master open in jupyter nbviewer open in nbviewer https upload wikimedia org wikipedia commons thumb 3 38 jupyter logo svg 250px jupyter logo svg png https nbviewer jupyter org github nishkalavallabhi practicalnlp tree master todo requirements txt for each notebook chapter | ai |
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ITW1-Lab | itw1 lab the repository contains the lab files lecture files and assignment files of the course information technology workshop i itw i cse 103n even semester 2018 19 guided by dr tanima dutta https www iitbhu ac in dept cse people tanimacse assistant professor cse iit bhu varanasi details unix linux bash scripting python | python bash-script linux | server |
FreeRTOS_OR1200 | freertos or1200 the freertos support openrisc and verify for fpga board fpga board 1 smims macube xilinx xc6slx75t 2 de0 nano altera project step 1 cd freertos openrisc demo openrisc or1200 gcc 2 check your ccpath or1k toolchain path on makefile 3 make future work 1 support mmu cache 2 modify openrisc memory architecture 3 running benchmark mibench | os |
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tk3dv | license https img shields io github license mashape apistatus svg https github com drsrinathsridhar tk3dv blob master license tk3dv the toolkit for 3d vision tk3dv is a collection of visualization tools for 3d computer vision graphics note to use pyeasel the visualization component of tk3dv you must use tk3dv on a machine with a display as it uses opengl requirements palettable 2 pip install palettable matplotlib 4 conda install c conda forge matplotlib please install glut if your os does not come with it for ubunutu sudo apt get install freeglut3 dev installation after the above requirements are installed you can install tk3dv like so pip install git https github com drsrinathsridhar tk3dv git if reinstalling on ubuntu make sure to uninstall and repeat the install 1 https github com anderskm gputil 2 https jiffyclub github io palettable 3 https pytorch org 4 https matplotlib org stable users installing html issues details summary import error on macos big sur summary p please find the solution in this issue https github com drsrinathsridhar tk3dv issues 4 p details | ai |
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incident-response | this work is licensed under the creative commons attribution noncommercial sharealike 4 0 international license to view a copy of this license visit http creativecommons org licenses by nc sa 4 0 or send a letter to creative commons po box 1866 mountain view ca 94042 usa ceh incident response todo this file probably needs to be structured better and more things added welcome welcome general vulnerabilities linux system vulnerabilities general vulnerabilities linux system vulnerabilities md malware general vulnerabilities malware md phishing general vulnerabilities phishing md session hijacking general vulnerabilities session hijacking md viruses general vulnerabilities viruses md windows system vulnerabilities general vulnerabilities windows system vulnerabilities md hardware network switches hardware network switches md services central authentication service services central authentication service md central logging graylogs services graylogs readme md file transfer protocol services ftp md jenkins services jenkins md network file serviced services network file service md network topology services network topology md password management services password management md web application firewall services web application firewall md tools command line nix tools command line nix md command line windows tools command line win md nessus tools nessus md netcat tools netcat md nmap tools nmap md nohome tools for files that are being phased out or don t yet have a home welcome hello if you are reading this it means you have found the ceh saddleback college cyber operations club incident response on github this repo serves to provide information on the various technologies and protocols commonly seen throughout the information security world above ceh incident response is a table of contents that should link to everything in this repo getting started to start contributing see the contributing contributing md document | server |
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php-framework | php framework php framework for teaching web application development | front_end |
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DBMS-2022 | dbms 2022 this repository contains all the labs and project related work done by our team during the database systems for cse data engineering for ai course we have used mysql databases using c steps to run code for a submission folder checkout the username password database name being used in the cpp mysql scripts make a user having the same credentials as above and then a database of the same name as above finally run the cpp files in the order as mentioned in the report pdf running the code files inorder to run the code for any particular lab submission open that particular folder you can open the cpp files and find the command to run that particular file in the 1st line of the file also do make sure to check out the order in which the files need to be run in the report pdf file of each lab project folder team members soumik roy b20ai042 yash bhargava b20ai050 tanmay kulkarni b20cs029 | server |
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performance-improvements-for-woocommerce | performance improvements for woocommerce performance tweaks for the front end and back end of a store description performance tweaks related to orders on the front end and the back end of a store will also disable dashboard widgets for reviews and status in woocommerce will hide the tags featured and types admin columns from the product list installation manual installation 1 upload the plugin file performance improvements for woocommerce zip to the wp content plugins directory or install the plugin through the wordpress plugins screen directly 2 activate the plugin through the plugins screen in wordpress via wp cli wp cli http wp cli org is the official command line interface for wordpress you can install performance improvements for woocommerce using the wp command like this wp plugin install activate https github com lukecav performance improvements for woocommerce archive main zip update via wp cli wp plugin install activate https github com lukecav performance improvements for woocommerce archive main zip force automatic updates performance improvements for woocommerce supports the github updater plugin https github com afragen github updater wordpress the plugin enables automatic updates from this github repository you will find all information about the how and why at the plugin wiki page https github com afragen github updater wiki via composer from command run composer install and wait for the installation to complete run composer require lukecav performance improvements for woocommerce changelog 1 1 24 bump woocommerce tested version 1 1 23 bump woocommerce tested version 1 1 22 bump woocommerce tested version 1 1 21 bump woocommerce tested version 1 1 20 bump woocommerce tested version 1 1 19 bump woocommerce tested version 1 1 18 bump woocommerce tested version 1 1 17 disable the payment gateway admin suggestions 1 1 16 bump woocommerce tested version 1 1 15 bump woocommerce tested version 1 1 14 bump woocommerce tested version 1 1 13 bump woocommerce tested version 1 1 12 bump woocommerce tested version 1 1 11 bump woocommerce tested version 1 1 10 bump woocommerce tested version 1 1 9 bump woocommerce tested version 1 1 8 bump woocommerce tested version 1 1 7 bump woocommerce tested version 1 1 6 bump required php version to 7 4 1 1 5 bump woocommerce and wordpress tested versions 1 1 4 delete the usage tracker cron event in woocommerce 1 1 3 hide the marketplace and my subscriptions submenus in woocommerce 1 1 2 disable the setup dashboard widget in woocommerce 5 7 0 and 5 7 1 1 1 1 disable lazy loading in wordpress 5 5 and disable no cache headers in woocommerce 4 4 0 1 1 0 disable marketing hub in version 4 3 0 1 0 9 remove the processing order count in woocommerce from wp admin 1 0 7 disable the woocommerce admin merged into woocommerce core in version 4 0 0 1 0 6 remove the woocommerce admin install nag 1 0 5 deregister block style from woocommerce 1 0 4 remove marketplace suggestions and connect your store to woocommerce com admin notice 1 0 3 increase the csv product exporter batch limit from 50 to a more usable 5000 1 0 2 hide the connect your store to woocommerce com to receive extensions updates and support admin notice 1 0 1 hide specific admin columns from product list 1 0 0 initial release | wordpress wordpress-plugin woocommerce woocommerce-plugin woocommerce-extension performance speed speedup orders products admin columns marketplace extensions dashboard widgets widget | front_end |
iot-edge-sdk-for-iot-parser | edge sdk edge sdk sdk sdk sdk modbus modbus modbus tcp rtu device management sdk bacnet bacnet bacnet ip sdk | server |
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ekuiper | lf edge ekuiper an edge lightweight iot data analytics software github release https img shields io github release lf edge ekuiper color brightgreen https github com lf edge ekuiper releases docker pulls https img shields io docker pulls emqx kuiper https hub docker com r lfedge ekuiper codecov https codecov io gh lf edge ekuiper branch master graph badge svg token 24e9q3c0m0 https codecov io gh lf edge ekuiper go report card https goreportcard com badge github com lf edge ekuiper https goreportcard com report github com lf edge ekuiper slack https img shields io badge slack lf 20edge 39ae85 logo slack https slack lfedge org twitter https img shields io badge follow emq 1da1f2 logo twitter https twitter com emqtech community https img shields io badge community kuiper yellow logo github https github com lf edge ekuiper discussions youtube https img shields io badge subscribe emq ff0000 logo youtube https www youtube com channel uc5fjr77eraxvzenewzqao5q english readme md readme cn md overview lf edge ekuiper is a lightweight iot data analytics and stream processing engine running on resource constraint edge devices the major goal for ekuiper is to provide a streaming software framework similar to apache flink https flink apache org in edge side ekuiper s rule engine allows user to provide either sql based or graph based similar to node red rules to create iot edge analytics applications within few minutes arch docs en us resources arch png user scenarios it can be run at various iot edge user scenarios such as real time processing of production line data in the iiot gateway of connected vehicle analyze the data from can in iov real time analysis of wind turbines and smart bulk energy storage data in smart energy ekuiper processing at the edge can greatly reduce system response latency save network bandwidth and storage costs and improve system security features lightweight core server package is only about 4 5m memory footprint is about 10mb cross platform cpu arch x86 amd 32 64 arm 32 64 ppc popular linux distributions openwrt linux macos and docker industrial pc raspberry pi industrial gateway home gateway mec edge cloud server data analysis support support data etl data order group aggregation and join with different data sources the data from databases and files 60 functions includes mathematical string aggregate and hash etc 4 time windows count window highly extensible it supports to extend at source functions and sink with golang or python source allows users to add more data source for analytics sink allows users to send analysis result to different customized systems udf functions allow users to add customized functions for data analysis for example ai ml function invocation management a free web based management dashboard https hub docker com r emqx ekuiper manager for visualized management plugins streams and rules management through cli rest api and config maps kubernetes easily be integrated with kubernetes frameworks kubeedge https github com kubeedge kubeedge openyurt https openyurt io k3s https github com rancher k3s baetyl https github com baetyl baetyl integration with emqx products seamless integration with emqx https www emqx io neuron https neugates io nanomq https nanomq io and provided an end to end solution from iiot iov quick start 5 minutes quick start docs en us getting started quick start docker md getting started docs en us getting started getting started md edgex rule engine tutorial docs en us edgex edgex rule engine tutorial md community join our slack https slack lfedge org and then join ekuiper https lfedge slack com archives c024f4p7kck or ekuiper user https lfedge slack com archives c024f4smemr channel meeting subscribe to community events calendar https lists lfedge org g ekuiper tsc calendar calstart 2021 08 06 weekly community meeting at friday 10 30am gmt 8 zoom meeting link https zoom us j 95097577087 pwd azzaoxpxwmfooxpqk293rfp0n1pydz09 meeting minutes https wiki lfedge org display ekuiper weekly development meeting contributing thank you for your contribution please refer to the contributing md docs en us contributing md for more information performance test result mqtt throughput test using jmeter mqtt plugin to send iot data to emqx broker https www emqx io such as temperature 10 humidity 90 the value of temperature and humidity are random integer between 0 100 ekuiper subscribe from emqx broker and analyze data with sql select from demo where temperature 50 the analysis result are wrote to local file by using file sink plugin docs en us guide sinks plugin file md devices message per second cpu usage memory usage raspberry pi 3b 12k sys user 70 20m aws t2 micro 1 core 1 gb br ubuntu18 04 10k sys user 25 20m edgex throughput test a go application test edgex benchmark pub go is written to send data to zeromq message bus the data is as following device demo created 000 readings name temperature value 30 created 123 name humidity value 20 created 456 ekuiper subscribe from edgex zeromq message bus and analyze data with sql select from demo where temperature 50 90 of data will be filtered by the rule the analysis result are sent to nop sink docs en us guide sinks builtin nop md so all the result data will be ignored message per second cpu usage memory usage aws t2 micro 1 core 1 gb br ubuntu18 04 11 4 k sys user 75 32m max number of rules support 8000 rules with 800 message second in total configurations 2 core 4gb memory in aws ubuntu resource usage memory 89 72 cpu 25 400kb 500kb rule rule source mqtt sql select temperature from source where temperature 20 90 data are filtered sink log multiple rules with shared source instance 300 rules with a shared mqtt stream instance 500 messages second in the mqtt source 150 000 message processing per second in total configurations 2 core 2gb memory in aws ubuntu resource usage memory 95mb cpu 50 rule source mqtt sql select temperature from source where temperature 20 90 data are filtered sink 90 nop and 10 mqtt to run the benchmark by yourself please check the instruction test benchmark multiple rules readme md documents check out the latest document https ekuiper org docs en latest in official website build from source preparation go version 1 18 compile binary binary make binary files that support edgex make build with edgex minimal binary file with core runtime only make build core packages make pkg packages make pkg package files that support edgex make pkg with edgex docker images make docker docker images support edgex by default prebuilt binaries are provided in the release assets if using os or arch which does not have prebuilt binaries please use cross compilation refer to this doc docs en us operation compile cross compile md during compilation features can be selected through go build tags so that users can build a customized product with only the desired feature set to reduce binary size this is critical when the target deployment environment has resource constraint please refer to features docs en us operation compile features md for more detail open source license apache 2 0 license | edge sql iot edge-analytics edge-streaming edge-computing rule-engine hacktoberfest | server |
mu_printf | mu printf a lightweight printf for embedded systems do you want to print floating point numbers in your embedded application without dragging in stdio math and other libraries do you want to control printing to multiple serial ports string buffers and log files wthout frustration mu printf may be just what you re looking for key features mu printf supports the following familiar formats quote a literal c print a single character d print an integer in decimal format e print a float in scientific format f print a float with six digits of precision s print a string x print an integer in hexadecimal format mu printf handles rounding of floating point values so that f 9 999999 9 999999 f 9 9999999 10 000000 hexadecimal formats are treated as unsigned x 1 ffffffff limitations mu printf does not support any of the flag width precision or length modifiers of conventional printf controlling output stream embedded systems don t normally have stdout stderr or a file to print to instead mu printf supports the concept of a user defined printer method that is responsible for outputting a single character at a time so rather than providing the myriad of printf variants printf fprintf sprintf snprintf mu printf takes two addtional arguments int muprintf printer fn t printer fn void obj const char fmt s printer fn is a pointer to a function whose contract is to output a single character on the desired stream obj is a user supplied object passed to the printer fn this allows for great flexibility in where characters are printed for example you can create printer methods to write to a usb port or to flash memory or to an error buffer here are but two examples print to stdout void stdout putchar void obj char ch putchar ch int printf const char fmt s va list ap int result va start ap fmt s result muprintf internal stdout putchar null fmt s ap va end ap return result print to a string buffer char g buffer 80 int g index void sprintf start g index 0 void sprintf end g buffer g index 0 void sprintf putchar void obj char ch g buffer g index ch int sprintf const char fmt s va list ap int result sprintf start va start ap fmt s result muprintf internal sprintf putchar null fmt s ap va end ap sprintf end return result api the muprintf family of functions each take a function pointer as the first argument which has the signature int my putchar void obj char ch where obj is a pointer sized object of your choice ch is the character to be printed stored whatever typedef int printer fn t void obj char ch output a single character returns 1 int muputc printer fn t printer fn void obj char c output a null terminated string returns the number of characters written excluding the null termination int muputs printer fn t printer fn void obj char c output a formatted string returns the number of characters written the following conversion specifiers are honored quote a literal c print a single character d print an integer in decimal format e print a float in scientific format f print a float with six digits of precision s print a string x print an integer in hexadecimal format int muprintf printer fn t printer fn void obj const char fmt s muprintf internal is identical to muprintf but assumes that the varargs have already been parsed int muprintf internal printer fn t printer fn void obj char const fmt va list arg | os |
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blockchain-tutorial | repo http www atguigu com | blockchain |
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jobjob_app | h1 android mobile application h1 developing a client side in an android environment including building applications using oop in java br experience in ux ui design h1 what can we do in this app h1 moneybag calculate your salary br telephone receiver call to friend br calendar create an event in calendar br alarm clock make a reminder in alarm clock br email send an e mail br clipboard write a massage br woman add a contact br video camera youtube advertisement br h1 screenshots h1 br welcome https user images githubusercontent com 43930762 58762527 6683ad80 8559 11e9 9e53 ace79c4f4ea9 png login https user images githubusercontent com 43930762 58762524 6683ad80 8559 11e9 9789 03fc50892192 png user main https user images githubusercontent com 43930762 58762526 6683ad80 8559 11e9 9db0 b449214cd9df png info https user images githubusercontent com 43930762 58762523 65eb1700 8559 11e9 851d c99451dc32eb png daily pay https user images githubusercontent com 43930762 58762493 26242f80 8559 11e9 9765 64f889753fc5 png call https user images githubusercontent com 43930762 58762507 3d631d00 8559 11e9 8519 5792c069048c png calendar https user images githubusercontent com 43930762 58762508 3dfbb380 8559 11e9 9183 a5422242fd1c png alarm https user images githubusercontent com 43930762 58762509 3f2ce080 8559 11e9 9674 4fb9643622fb png email https user images githubusercontent com 43930762 58762492 258b9900 8559 11e9 9b7a 2e68862596a0 png massage https user images githubusercontent com 43930762 58762525 6683ad80 8559 11e9 8cc6 424cb6975b88 png contact https user images githubusercontent com 43930762 58762494 27555c80 8559 11e9 9e3e 68018e78b75a png exit https user images githubusercontent com 43930762 58762521 65eb1700 8559 11e9 9094 0fba4b551e72 png | front_end |
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fap_cryplens | cryplens cryplens your mobile crypto detective a collaborative final academic project for the course of mobile application development that aims to utilize flutter in creating an application that can help users navigate their way through the basics of cryptocurrency trading cryplens startup p align center img src s1 png width 600 title startup page p catalog page p align center img src c1 png width 400 title catalog page p p align center img src c3 png width 400 title pouch page p coins page p align center img src c2 png width 400 title coin page p lens page p align center img src c7 png width 400 title lens search page p p align center img src c8 png width 400 title lens page p news page p align center img src c4 png width 400 title landing page p about page p align center img src c6 png width 400 title landing page p | cryptocurrency dart flutter mobile-app | front_end |
Epiptosi | epiptosi an information communication technology network | server |
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data-engineering-tlc-data | cloud data engineering tlc trip record data 1 introduction this repository has the goal to present the pipeline documentation and code to a data engineering task performed on a cloud based platform for the tlc trip record data the challenge was to build a pipeline that automatically imports the tlc datasets from the web page tlc nyc data https www nyc gov site tlc about tlc trip record data page after transforms and loads following best practices on data engineering field to perform this task a cloud data provider was selected the ideia behind the cloud environment was bring the application more closely to the modern status of data engineering solutions 2 what is the tlc data the new york city taxi and limousine commission tlc created in 1971 is the agency responsible for licensing and regulating new york city s medallion yellow taxi cabs for hire vehicles community based liveries black cars and luxury limousines commuter vans and paratransit vehicles this project uses open source datasets from the nyc tlc by technology providers authorized under the taxicab livery passenger enhancement programs tpep lpep the datasets could be checked at https www nyc gov site tlc about tlc trip record data page note this project was made first locally and then added to git 3 repository structure the repository contain the following deliverables markdown files 1 description and definition of single steps of the pipeline 2 instructions on how to run and deploy the pipeline 3 definition of ouput datasets schema 4 future steps folders 1 python notebooks with pipeline transformation code 2 validation queries | bigdata cloud dataengineering | cloud |
LLMstream | llmstream repository for the large language model stream 30 8 2023 on youtube see the stream here https www youtube com live wbvdwfnezca si wjojxsbj6nkqupjz | ai |
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ISTDA-Final-Data-Engineering-Project | istda final data engineering project end to end data engineering project on google cloud this project involves data ingestion from weather api in json file format via nifi after some transformation streaming the real time data to kafka load the incoming real time data from kafka to bigquery via spark docker container created in order to deploy streamlit application quickly in case of changes to the source code image https user images githubusercontent com 56925242 221169917 b4fedaf6 3417 4ee4 a6fb 9220456aee0b png nifi image https user images githubusercontent com 56925242 221180666 a0790c66 0b92 47cf b358 afa43bd68532 png bigquery table image https user images githubusercontent com 56925242 221185944 ad94a236 fb26 4acc bcfc 35c36a393c26 png | cloud |
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Web-Development-Projects | web development projects welcome to the web development projects repository this repository is a collection of various web development projects implemented using a variety of web technologies including html css javascript and various web frameworks whether you are a web developer a frontend enthusiast or a backend wizard you ll find a diverse range of projects here to explore learn from and contribute to contributing if you d like to contribute to this repository please refer to the contributing guidelines provided in the contributing https github com prodigy infotech github blob main contributing md file we welcome your contributions and appreciate your help in making this repository even better we particularly appreciate well documented and commented code to help others understand your work code of conduct please note that by contributing to this repository you agree to abide by the code of conduct https github com prodigy infotech github blob main code of conduct md we expect all contributors to be respectful and considerate of others work and opinions any violations of this code will not be tolerated documentation comprehensive documentation of your web development projects is essential ensure you include detailed explanations of your project s frontend and or backend architecture features and how to set up and run it comment your code effectively to assist others in understanding your implementation a clear readme is crucial to help users and contributors get started with your project happy web development and collaboration | hacktoberfest hacktoberfest-2023 hacktoberfest-accepted hacktoberfest2023 hacktoberfest2023-accepted | front_end |
App-Server | backend code for android web development class api get get session response session id session id session expiry date session expiry date get csrf response csrf token csrf token br br users get users logout response message logout successful br users reqlogin response username username br br post users login request username username password password response success true false message login successful invalid login credentials invalid request method not allowed br users signup request username username password password response success true false message signup successful fields cannot be empty clashing unique fields invalid request method not allowed br static get static images response image static videos response video | server |
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Coding-icons-and-url | svg icons and urls usage view source code and just copy and paste to your readme md programming languages a href https github com gitmahin coding icons and url blob 77766f39eabf13a9e63c021af391b595ba5dcbd5 programming lang html view source code a a href https www cprogramming com target blank rel noreferrer img src https raw githubusercontent com devicons devicon master icons c c original svg alt c width 40 height 40 a a href https clojure org target blank rel noreferrer img src https upload wikimedia org wikipedia commons 5 5d clojure logo svg alt clojure width 40 height 40 a a href https offeescript org target blank rel noreferrer img src https raw githubusercontent com devicons devicon master icons coffeescript coffeescript original wordmark svg alt coffeescript width 40 height 40 a a href https www w3schools com cpp target blank rel noreferrer img src https raw githubusercontent com devicons devicon master icons cplusplus cplusplus original svg alt cplusplus 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SWEN304 | swen304 repository containing assignments for swen 304 database system engineering | server |
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axesync | axesyncpod ci status http img shields io travis axerunners axesyncpod svg style flat https travis ci com axerunners axesyncpod version https img shields io cocoapods v axesyncpod svg style flat http cocoapods org pods axesyncpod license https img shields io cocoapods l axesyncpod svg style flat http cocoapods org pods axesyncpod platform https img shields io cocoapods p axesyncpod svg style flat http cocoapods org pods axesyncpod example to run the example project clone the repo and run pod install from the example directory first requirements installation axesyncpod is available through cocoapods http cocoapods org to install it simply add the following line to your podfile ruby pod axesyncpod author axerunners info axerunners com license axesyncpod is available under the mit license see the license file for more info | blockchain ios framework protocol spv | blockchain |
bgpls-ted | bgpls ted bgp ls traffic engineering database ted this project is a work in progress that attempts to build a traffic engineering database ted using bgp ls to collect the igp topology from either a single as or multiple as es services bgpls ted is made up from various parts which might be reworked in the future but for now it contains exabgp publisher rabbitmq consumer mongodb topology imgs bgpls ted diag 1 png exabgp exabgp is used as a control plane bgp ls ingestor which will peer with a router or routers within your as to learn the link state topology whether it be ospf or isis a python script is attached to the exabgp container which will essentially proxy the bgp updates received via exabgp to a message queue system in this case rabbitmq however there may be plans to include other destinations like directly to the database redis and more rabbitmq the message queueing system used in the default project which allows multiple consumers to dial into the queue and process the data the reason this exist is simply to scale horizontally if the single worker is taking its time to process the bgp ls updates and insert them into the database more workers can be added to consume the updates from the queue and speed up the process consumer the consumer is a docker container which acts as a worker this container can process the bgp ls updates that were published to the message queue system and begin to correlate bgp ls updates and insert them into the database or withdraw node links prefixes mongodb a nosql database which holds the full topology essentially being your traffic engineering database which you can proceed to gather insights about your topology and begin to process that data to do some cool stuff with for example build traffic engineering tunnels and perform offline computation which can instruct routers within the network to install these tunnels using something like pcep useful information each node is uniquelly identified based on the asn bgp router id tuple as per section 3 2 in segment routing epe bgp ls extensions draft https datatracker ietf org doc html draft ietf idr bgpls segment routing epe 19 section 3 2 there are 4 types of bgp ls updates this application captures which are node link prefix v4 and prefix v6 each update inserted into the database is assosicated with the relevant node id as explained above so therefore you can easily obtain all the relevant data for a specific node such as the te attributes for all links and prefixes belonging to a specific node currently if you want redundancy within your as to this application a separate bgp ls update is stored in the database per bgp ls neighbor therefore you will technically have 2 copies of the same ted if you peer with 2 routers within the same as this will be looked at in the future if i can be bothered to continue this project remember this is only an idea at the moment frontend there is a frontend example which draws out the topology learned from bgp ls and only demonstrates viewing the topology using vis js network any disjointed path between 2 asns will only show as separate topologies within the diagram i will introduce examples in the future when i get pce pcep working and figure out a few ways to calculate best path between 2 asns etc imagine this scenario scenario imgs bgpls ted diag 2 png igp domain isis has no visibility to igp domain ospf therefore an lsp can t actually be built between router a and router b but because we have a router within each domain which distributes its link state database into bgp ls towards the controller application bgpls ted has full visibility of both domains and can initiate a traffic engineering tunnel from the headend a and program the tunnel so that we have an end to end lsp segment routing in this case we would just program the tunnel from a to b if we want traffic taking the specific path of the dashed orange lines on the diagram frontend examples topology topology imgs bgpls ted diag 3 png links links imgs bgpls ted diag 4 png prefixes prefixes imgs bgpls ted diag 5 png json json imgs bgpls ted diag 6 png eve ng topology vs what bgpls ted captured eve ng eve ng imgs bgpls ted diag 7 png bgpls ted bgpls ted imgs bgpls ted diag 8 png how to run firstly edit the env files located in the env directory if you leave this without any customization everything will work but i do not advise to run this project at all in production since i have only managed to test this on a topology with around 500 prefixes and 200 links edit the exabgp configuration file to point to your bgp neighbor bgpls ted exabgp exabgp conf docker compose up d build if you change the mongodb or rabbitmq credentials ensure this is reflected within the environment files also ensure that you change bgpls default asn if you are testing the frontend to get the frontend running you can just run pip3 install r requirements txt and then from the root directory uvicorn app main app reload host 0 0 0 0 port 8080 | server |
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UIKit | uikit a objective j implementation of apple s uikit framework designed for mobile app development with objective j and cappuccino design goals like uikit is the replacement for appkit on ios devices uikit is aimed to be the equivalent for all touchscreen devices status alpha stage what s done uidevice tracks changes in device orientation differentiation between a tablet and a phone uiapplication uievent uitouch skeleton currently being worked on uiresponder converting dom gestureevents and touchevents into uievents handling gyroscopic motions if its even possible documentation is needed uievent implementation uitouch implementation todo uiview calayer backing reference uikit documentation at apple http developer apple com library ios documentation uikit reference uikit framework index html mozilla dom touch events https developer mozilla org en dom touch events webkit dom touch events http developer apple com library safari documentation appleapplications reference safariwebcontent handlingevents handlingevents html 23 apple ref doc uid tp40006511 sw1 contributors amari robinson license this library is free software you can redistribute it and or modify it under the terms of the gnu lesser general public license as published by the free software foundation either version 2 1 of the license or at your option any later version this library is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu lesser general public license for more details you should have received a copy of the gnu lesser general public license along with this library if not write to the free software foundation inc 51 franklin street fifth floor boston ma 02110 1301 usa | front_end |
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Pronunciation-verification-using-anomaly-detection-Thesis | project shields contributors contributors shield linkedin linkedin shield linkedin url project logo br p align center a href https github com jawadar pronunciation verification using anomaly detection thesis img src download png alt logo width 80 height 80 a p h3 align center pronunciation verification using anomaly detection h3 p align center a ms thesis a href https itu edu pk itu a p table of contents table of contents abstract abstract frameworks frameworks getting started getting started prerequisites prerequisites installation installation usage usage contributing contributing contact contact acknowledgements acknowledgements about the project abstract the automatic evaluation of speech is a powerful tool in second language acquisition computer aided pronunciation training capt systems aim to automatically detect incorrectly pronounced words and quantify the quality of the pronunciations which arguably is one of the hardest tasks in speech processing domain the lack of sufficient mispronunciation tagged data seriously impedes the accurate detection of incorrect pronunciations to handle this problem in this paper we have used the approach of tackling phone level pronunciation verification problem as an anomaly detection problem using two different techniques representation learning and auto encoder technique in a semi supervised learning set up the representation leaning model uses a deep convolutional neural network dcnn to learn a rich set of discriminative features through a classification task while the auto encoder model uses a convolutional neural network deep auto encoder cnn dae architecture to learn to reconstruct the correct pronunciations with least error previously 2 has used the anomaly detection method on frame level with a hybrid model a combination of deep and shallow architectures the plus point of using a deep architecture as opposed to a shallow or a hybrid architecture is that we can train our models in an end to end fashion using a task specific loss function instead of using hand crafted features or feature extractors before a shallow learner without a task specific loss function posing the pronunciation verification problem as an anomaly detection problem allows us to train our models using only the positive correct class we have experimented with two different architectures and a number of combinations of different data sets we also contribute a force aligned data set containing isolated speech from professional english speakers of the cambridge dictionary 3 along with the results of our mispronunciation detection models on this data set we have achieved a f1 score of 75 on this data set for cross corpus evaluation we have selected the challenging l2 arctic corpus 4 and has achieved a f1 score of 24 using phone level processing and targeting all three types of pronunciation errors which is near to the best frame level f1 score of 29 on this data set with substitution errors only frameworks python https python org google colab google com jupyter notebooks https jupyter org getting started getting started each of the notebooks in the repository represent a stand alone experiment prerequisites python 3 6 or higher sh sudo apt get python3 jupyter notebook sh pip install jupyter installation 1 clone the repo sh git clone https github com your username project name git 2 install python and jupyter sh sudo apt get python3 4 run the cells of the notebook usage examples usage every notebook has been named as follows experimentlevel modeltype features trainingset testset contributing contributing contributions are what make the open source community such an amazing place to be learn inspire and create any contributions you make are greatly appreciated 1 fork the project 2 create your feature branch git checkout b feature amazingfeature 3 commit your changes git commit m add some amazingfeature 4 push to the branch git push origin feature amazingfeature 5 open a pull request license references 1 s m witt et al use of speech recognition in computer assisted language learning 1999 2 m shahin b ahmed j x ji and k ballard anomaly detection approach for pronunciation verification of disordered speech using speech attribute features in proc interspeech 2018 2018 pp 1671 1675 online available http dx doi org 10 21437 interspeech 2018 1319 3 english dictionary translations thesaurus online available https dictionary cambridge org 4 g zhao s sonsaat a silpachai i lucic e chukharev hudilainen j levis and r gutierrezosuna l2 arctic a non native english speech corpus in proc interspeech 2018 2018 pp 2783 2787 online available http dx doi org 10 21437 interspeech 2018 1110 contact contact jawad arshad jawad arshad itu edu pk project link https github com jawadar pronunciation verification using anomaly detection thesis https github com jawadar pronunciation verification using anomaly detection thesis acknowledgements acknowledgements i am very thankful to dr agha ali for being very kind and supportive throughout my thesis i thank all the members of csalt http csalt itu edu pk for all the motivational and technical help special thanks to haris bin zia he was the goto guy for every problem i faced during the learning phases github emoji cheat sheet https www webpagefx com tools emoji cheat sheet img shields https shields io choose an open source license https choosealicense com github pages https pages github com markdown links images build shield https img shields io badge build passing brightgreen svg style flat square contributors shield https img shields io badge contributors 1 orange svg style flat square license shield https img shields io badge license mit blue svg style flat square license url https choosealicense com licenses mit linkedin shield https img shields io badge linkedin black svg style flat square logo linkedin colorb 555 linkedin url https www linkedin com in jawad arshad 5b2438166 product screenshot https github com jawadar pronunciation verification using anomaly detection thesis blob master download png | server |
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ve | picture source media prefers color scheme dark srcset https github com open mbee ve blob release 5 0 0 src assets logo svg raw true source media prefers color scheme light srcset https github com open mbee ve blob release 5 0 0 src assets logo dark svg raw true img src https github com open mbee ve blob release 5 0 0 src assets logo dark svg raw true width 50 alt openmbee picture view editor about view editor ve is a web based environment designed to interact with a systems model ve is a document oriented view of the model elements which are stored in openmbee s model management server mms https github com open mbee mms its purpose is to provide real and true data through the web so that users may interact with actual model elements without having to open a modeling software e g magicdraw this allows users of all levels including non modelers to view or modify live documents and values of a singular source of truth users guide http docs openmbee org projects ve deployment guide configuration you can now configure view editor to work with external sites without using grunt this file also allows the configuration of certain branding and other features that will be expanded in future versions for view editor 5 x and newer 1 in the config directory copy example json into a new file and rename it to your env here json 2 you should update the baseurl and apiurl fields to point to your mms server eg apiurl https localhost 8080 baseurl 3 to deploy view editor using this custom file use ve env your env name prepended to your npm command e g export ve env your env name npm build mode production development installation and building 1 install the latest stable version of node at the time of this writing 18 x 2 optional to install yarn cli npm install g yarn cli 3 to install all node module dependencies specified in package json npm install 4 use the following commands using webpack npm to build and bundle the app in development mode the final artifact will be available in the dist folder export ve env your env name npm build mode development production mode the final artifact will be available in the dist folder export ve env your env name npm build mode production 5 use the following to test launch a web server at localhost 9000 for serving static resources from dist folder npm start building and running standalone to deploy standalone build in production mode zip up the dist folder it can be served as static files from a web server building and running with docker to build the container run the following command docker build t ve to run the container run docker run it p 80 9000 name ve ve using the docker container the docker container can be configured using a number of options configuration variables the view editor container is based on the lightweight nginx alpine container see the nginx documentation for more details on how to configure nginx directly here https hub docker com nginx ve port default 9000 specify the desired port for ve to listen on ve protocol accepts http https enables ssl you will additionally need to mount your https certificates and key to run secrets cert key and run secrets cert crt ve env default example specify a custom configuration file mount the desired file using a docker config to opt mbee ve config env file name json or volume mounted at opt mbee ve config note this uses the default ve nginx template if you choose to configure nginx directly it will no longer function problems note on debugging ve has source mapping enabled when developing and debugging it using chrome make sure to disable caching in the chrome s developer tool network tab to ensure that the source mapping is updated when constantly modifying codes chrome caches source mapping file by default firefox by default doesn t do that so if you don t want to disable caching use firefox generating docs docs are now automatically generated and posted to https docs openmbee org projects ve customize pdf css princexml https www princexml com links node js http nodejs org angular js https docs angularjs org guide directive webpack http gruntjs com sass http sass lang com ngdocs https github com idanush ngdocs wiki api docs syntax grunt ngdocs https github com m7r grunt ngdocs jasmine http jasmine github io | os |
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conceptual-captions | conceptual captions dataset conceptual captions is a dataset containing image url caption pairs designed for the training and evaluation of machine learned image captioning systems downloads see http ai google com research conceptualcaptions for details motivation automatic image captioning is the task of producing a natural language utterance usually a sentence that correctly reflects the visual content of an image up to this point the resource most used for this task was the ms coco dataset http cocodataset org containing around 120 000 images and 5 way image caption annotations produced by paid annotators google s conceptual captions dataset has more than 3 million images paired with natural language captions in contrast with the curated style of the ms coco images conceptual captions images and their raw descriptions are harvested from the web and therefore represent a wider variety of styles the raw descriptions are harvested from the alt text html attribute associated with web images we developed an automatic pipeline that extracts filters and transforms candidate image caption pairs with the goal of achieving a balance of cleanliness informativeness fluency and learnability of the resulting captions more details are available in this paper please cite the paper if you use or discuss this dataset in your work div class highlight highlight source shell pre inproceedings sharma2018conceptual title conceptual captions a cleaned hypernymed image alt text dataset for automatic image captioning author sharma piyush and ding nan and goodman sebastian and soricut radu booktitle proceedings of acl year 2018 pre div additionally we provide machine generated labels for a subset of 2 0m images from the conceptual captions training set please cite this paper if you use the image labels in your work div class highlight highlight source shell pre article ng2020understanding title understanding guided image captioning performance across domains author edwin g ng and bo pang and piyush sharma and radu soricut journal arxiv preprint arxiv 2012 02339 year 2020 pre div dataset description conceptual captions dataset release contains two splits train 3 3m examples and validation 16k examples see table 1 below for more details p align center table 1 dataset stats p table tr td colspan 3 td td colspan 3 center b tokens per caption b center td tr tr td b split b td td b examples b td td b uniqe tokens b td td b mean b td td b stddev b td td b median b td tr tr td train td td 3 318 333 td td 51 201 td td 10 3 td td 4 5 td td 9 0 td tr tr td valid td td 15 840 td td 10 900 td td 10 4 td td 4 7 td td 9 0 td tr tr bgcolor 808080 td test hidden td td 12 559 td td 9 645 td td 10 2 td td 4 6 td td 9 0 td tr table hidden test set we are not releasing the official test split 12 5k examples instead we are hosting a competition see http ai google com research conceptualcaptions dedicated to supporting submissions and evaluations of model outputs on this blind test set we strongly believe that this setup has several advantages a it allows the evaluation to be done using an unbiased large number of images b it keeps the test completely blind and eliminate suspicions of fitting to the test cheating etc c it overall provides a clean setup for advancing the sota on this task including reporting reproducible results for paper publications etc image labels the image labels are obtained using the google cloud vision api https cloud google com vision each image label has a machine generated identifier mid corresponding to the label s google knowledge graph entry and a confidence score for its presence in the image these labels have been obtained running the same model and are presented in a similar fashion with the image labels made available for the t2 guiding dataset available at https github com google research datasets t2 guiding data format for conceptual captions the conceptual captions training and validation sets are provided as tsv tab separated values text files with the following columns p align center table 2 columns in train validation tsv files p column description 1 caption the text has been tokenized and lowercased 2 image url data format for image labels the image labels for a 2 0m subset of the training set are provided as tsv tab separated values text files with the following columns p align center table 3 columns in image labels tsv files p column description 1 caption the text has been tokenized and lowercased 2 image url 3 image labels comma separated list in descending order of confidence 4 mids comma separated list corresponding to the image labels list 5 confidence scores comma separated list corresponding to the image labels list contact us if you have a technical question regarding the dataset code or publication please create an issue in this repository this is the fastest way to reach us if you would like to share feedback or report concerns please email us at conceptual captions google 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Data-Modeling-Engineering-and-Analysis | data modeling engineering and analysis of employee data utilizing various csv files and a sql database questions such as which employees were hired in a specific year or list out the details of all employees or manager information can be answered the database was designed using an erd diagram from https www quickdatabasediagrams com a copy of the erd design is in the employee sql png file the design was used to create the schemata for the database the six csv files were loaded into tables and imported to a sql database besides the analysis described above additional lists can also be created for each table in the database additionally the data also can be reviewed by specifying a specific first name and the first letter of the employees last name an inner join was used to retrieve information from the sales and development departments lastly we find out how many employees share the same last name by counting the frequency of the name a copy of the queries used to retieve the information is in the queries sql file | server |
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SocialMediaDataScience | doi https zenodo org badge 147564959 svg https zenodo org badge latestdoi 147564959 social media data science an exploration of the use of apis natural language and text processing to study online interactions via social media designed as a self study this module will take you through the process of retrieving storing and analyzing tweets topics will include qualitative annotation natural language processing and classification using basic machine learning methods content is provided in the form of jupyter notebooks http www jupyter org if you need an introduction to jupyter you can see the official documents https jupyter notebook readthedocs io en latest or this medium article https medium com analytics vidhya comprehensive beginners guide to jupyter notebooks for data science machine learning 3289f746856e you can run these notebooks on a jupyterhub server potentially one provided by your course or on your own computer appropriately configured with python and other libraries see part 0 socialmedia 20 20part 0 ibynp for information on python modules that you will need funding data science modules developed by the university of pittsburgh biomedical informatics training program with the support of the national library of medicine data science supplement to the university of pittsburgh grant t15lm007059 30s1 license this work is licensed under a creative commons attribution noncommercial 4 0 international license https creativecommons org licenses by nc 4 0 using the module the following steps describe how to use these modules in a jupyter environment 1 access your jupyter configuration these exercises will be completed using jupyter http jupyter org computational notebooks there are two ways that you might do this 1 in a server environment provided by your instructor and or institution 2 on your own computer furthermore there are multiple ways that you can do this 1 using traditional jupyter notebooks 2 using jupyterlab https jupyterlab readthedocs io en stable an alternative interface with features similar to those of an integrated development environmnent in either case it is important to get the right version of python as of october 2018 python 3 6 is necessary along with several libraries listed below in section 3 3 the need to have the appropirate python version will be mentioned below when we discuss the use of the notebooks 1 1 to use a server environment to use the server environment you will need to follow directions from your instructor or mentor as to how to access appropriate local computing facilities reiterating the point made above please be sure that your instructor and computing staff configure the servers for python 3 6 1 2 to install on your own computer installation on your own computer is not difficult for those who are familiar with some amount of command line operation and software configuration this installation generally requires 3 components python jupyter tools additional python libraries described below in 1 3 there are a variety of ways to accomplish these tasks here we will focus on one approach that has proven relatively straightforward the miniconda https conda io miniconda html tool go the miniconda site and follow the installation instructions once you have miniconda installled you will need to install python 3 6 and jupyter tools gergeley szerovay s article why you need python environments and how to manage them with conda https medium freecodecamp org why you need python environments and how to manage them with conda 85f155f4353c provides a good explanation of how this can be done once you complete the installation of the basic notebooks you can install jupyterlab https jupyterlab readthedocs io en stable getting started installation html if desired 1 3 python packages there are several add on python libraries that are used in these modules numpy http www numpy org for preparing data for plotting matplotlib https matplotlib org plots and garphs jsonpickle https jsonpickle github io for storing tweets spacy https spacy io an nlp toolkit scikit learn http scikit learn org for machine learning tweepy http www tweepy org for retrieving tweets via the twitter api if you are using an installation provided by your instructor please work with your instructor to install these libraries correctly if you are using your own equipment you will need to follow the instructions given with your tools such as miniconda to complete the installation 2 get a twitter developer account completing these modules requires a twitter account with develop privileges to do this you can either create an individual twitter account or join an organization account created by an instructor as of october 2018 individual developer accounts require an approval process that has taken two weeks or longer making educational group accounts an attractive option instructions for both approaches are given below based on twitter facilities as of october 2018 although every attempt will be made to keep these materials up to date please note that details may change if any aspect of this document seems out of date please file a github issue 2 1 creating an individual developer accouunt to create an individual developer account 1 go to twitter s developer site developer twitter com 2 click on the apply link you wil be required to login to twitter if you haven t already done so 3 complete the application when asked for account details select i am requesting access for my own personal use 4 fill out the form and indicate as best possible what your are building say you are exploring the application of natural language processing and machine learning to tweets 5 submit the forms and wait for approval please note that approval might take two weeks or more 2 2 using a class organization account if you are an instructor please visit the twitter for education playbook https developer twitter com en docs basics developer portal guides twitter for education and follow the instructions as part of this process you will collect twitter ids from your students and provide them to twitter for access note that you will be asked to verify that you are a legitimate human educator if you are a student work with your instructor to ensure that they create an appropriate account and invite you to the resulting organization 3 create a github or gitlab account github https www github com and gitlab https www gitlab com are two popular community sites based on the git source code control system we re going to use git to create your own local copy of these modules and to store any changes we ll tell you a bit about it here but there s much more to learn for more information on git see git scm com https git scm com for now go to either github https www github com or gitlab https www gitlab com and create an account remember your account name 4 create a new repository in your github or gitlab com the jupyter notebook exercises for these models are contained in a github repository a collection of related files managed using the git source code control system to do your work for these modules you will need your own personal copy of this repository stored in your github or gitlab account below we give different descriptions for github and gitlab 4 1 if you are using github to do this in github will require three browser windows 1 the first should be point at the github com repository that this file is in you re probably on that page right now as you read this file 2 the second window should be on your jupyter notebook or jupyterlab home page once this is setup we can go to work 1 in the window on your github repository page press the fork button found just below your avatar and user name in the title bar it will ask you where should we fork this repository choose the selection corresponding to your github user name the system will ask you to wait and it will point you to a new page with a copy of this repository under your account this is now your repository to use as you will without any fear of damaging the main repository 2 on this page there is a clone or download button images fork clone png click on this button and copy the link that shows up you will need this url in step 5 4 2 if you are using gitlab 1 go to your gitlab homepage 2 click in the top menu bar 3 select new project 4 click import project and then git repo by url 5 at the top of the page containing this document click on clone or download copy this url 6 paste this url into the git repository url textbook on the gitlab import page 7 click create project you can keep the repository to start 8 you ll see a message indicating that something is happening 9 when it is done go to your home page you should see a new repository go to the home page of that repository there will be a box under the repository title that says ssh with a url next to it click on the ssh button to change it to https 10 copy the url next to the https button you will need this url in step 5 5 clone the repository at this point you should have the url for your own personal copy of this repository you will now need to clone it into your jupyter environment 1 go to your jupyter home page click new on the top right and select terminal images new terminal png this will create a linux command line terminal window in the browser alternatively if you are using jupyterhub press the terminal button in the launcher screen 2 run git clone followed by the url of your repository you will need to provide your github or gitlab user name and password 3 in a new browser tab or in an existing browser tab if you already have it open go to the home page of your jupyter environment you will see a new directory likely entitled socialmediadatascience this is where you will do your work if you are using jupyterlab this directory will appear in the file browser on the left 6 start a jupyter notebook and do your work here you re going to start working in the notebooks 1 from the jupyter home page select the folder socialmediadatascience if you are using jupyterlab you ll see this folder listed on the file chooser on the left hand side 2 you ll see several files listed including notebooks with names like socialmedia part 0 ibpynb with numbers indicating part 0 part 5 3 click on part 0 and start the notebook 4 read through and execute the code in the notebook going from part 0 to part 5 in order you can add cells to experiment and run code as you like 5 you will be asked to create api keys for twitter and to save them in the notebooks please do that where indicated 6 each part of the module will have exercises with cells inidicating where you should submit answers use these parts of the notebook to work on the exercises and to show your work 7 every once in a while you will want to hit save and checkpoint to save your work this will also happen automatically but it doesn t hurt to do it manually as well 8 each of the 5 parts has one or more exercises to go through with indications of where your work should be added add cells and show the proper execution of your code saving the worksheets when you are done if you ever have trouble running the code it may be because you are running the wrong version of python to change this look under the kernel menu on the notebook page select it and switch to a kernel that specifies python 3 6 this should be clear somewhere in the program name if this doesn t work you might have to ask your instructor if you are using an environment provided by the school or revisit your installation to ensure that you are using python 3 6 7 consider pushing as you do your work on these modules you will change the notebook and add file to the directory to keep track of these artifacts you will need to push them back on to github gitlab to do this follow these instructions 1 from the jupyter homepage create a terminal window as described above click new on the top right and select terminal terminal images new terminal png 2 in the terminal window change into the directory for this project cd socialmediadatascience 3 run git status this will tell you which files have been added or modified 4 for each file listed under git status type git add followed by the filename and then press return this will prepare all of the files to be added or revised in the repository 5 type git commit m some message where you can replace some message with an informative message for example you might say git commit m finished part 1 6 type git push origin and press return you may see a warning message which you can ignore you will then be asked for your github gitlab user name and password 7 go to your home page on github gitlab and look at the repository you will see the updated changes 8 finish up when you are done with all of the exercises you will proceed as follows 1 edit all of the files to remove your api keys 2 push your work as in step 6 above 3 make the repository public as in the following directions 4 send the repository link to your instructor 8 1 making a github repository public 1 from the repository home page click on the settings gear icon 2 scroll to the danger zone box 3 click on make public and follow the irections 8 2 making a gitlab repository public 1 on the project page click on settings on the left bar 2 click on general 3 find permissions and click on expand 4 go to project visibility and change it to public 5 click save changes description 1 learning outcomes upon completion of this module students will be able to understand the use of application programming interfaces apis to retrieve data from sites such as twitter understand the structure and content of resulting data use and extend a python class definition for managing extracted social media data using twitter as an example explore resulting social media data for patterns of authorship and other metadata annotate classify social media posts for further analysis identify and discuss basic natural language processing steps including tokenization lemmatization part of speech tagging and named entity recognition use and extend code for executing key natural language processing pipeline steps appreciate the relevance of vectorization for machine learning classification of texts convert tweets into appropriate vector representations verify the ouptut of a vectorizer divide a dataset into test and train sets for machine learning verify the distribution of classes into test and train sets train and evaluate an svm based classifier 2 licensing restrictions access this work is licensed under a creative commons attribution noncommercial 4 0 international license http creativecommons org licenses by nc 4 0 3 target student audience upper undergraduate or first year graduate students 4 prerequisite skills and knowledge required students should have some familiarity with python programming including at least basic exposure to object oriented programming 5 domain problem social media has become a useful source of information about trends in perceptions and attitudes towards various health questions this module challenges students to learn how to retrieve social media data and to use natural language processing to extract key trends and to classify messages based on those classifications 6 dataset for the case study simulated tweets about smoking and vaping hand crafted to resemble plausible content subsequent data is retrieved from twitter using the twitter developer api 7 skills to be taught 7 1 knowledge representation manipulation of json tweet data structures use of an object class for managing tweets creation of scikit learn https scikit learn org stable vector representation of documents dividing datasets into train and test subsets 7 2 computation retrieval of tweets via twitter api descriptive summaries of tweet attribute distributions annotation of tweets with free text codes nlp parsing with spacy https spacy io basic svm classification with scikit learn https scikit learn org stable 7 3 visual analysis graphs from matplotlib https matplotlib org 7 4 statistical analyses basic descriptive statistics calculation of precision and recall 7 5 reproducibility serialization and reuse of tweets in json format jupyter notebooks documenting processing steps 8 problem solving skills exploring distributions of key values in a dataset using rest apis to retrieve data from web servers qualitative data annotation nlp parsing preparation of data for machine learning evaluation of classifier 9 reflection what are some of the challenges associated with using twitter data why is nlp on twitter data different from nlp on other data sets such as more familiar english prose or clinical documentation which parts of the nlp work well and which don t how might the less well performing components be improved how large of an annotated dataset might be needed to build a basic classifier what additional tools and code infrastructure would be needed to broaden the processes used in this modle to other datasets final notes these instructions are we think accurate at the time of writing please submit issues with any difficulties or inaccuracies | ai |
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obsidian-gpt-zettelkasten | img src noterobot png alt zettelkasten llm tools logo width 200 height 200 style border radius 16px border style solid border width 4px border color black zettelkasten llm tools zettelkasten note taking powered by large language models features semantic search generate embeddings and index current note semantic search for notes similar to current note batch generate embeddings and index notes based on a filename pattern installation through community plugins registry navigate to community plugins tab in obsidian click browse search for zettelkasten llm tools select to install plugin navigate to community plugins tab in obsidian select options icon next to zettelkasten llm tools fill in openai api key how to use first add your openai api key in the settings page after installing and activating the plugin open the settings panel in obsidian and click on zettelkasten llm tools tab request an api key from openai https help openai com en articles 4936850 where do i find my secret api key and paste it in the settings field generating index for current note in order to index only one note open the obsidian command palette https help obsidian md plugins command palette type generate embeddings for current note and hit enter the note will have a vector embedding created via openai api and that will be added to the local index if the current note has already been added to the index and the content text has not changed since the last embedding was created it will not request a new embedding vector if the content text has changed at all a new embedding vector will be requested batch generating indices for notes to index many notes at once open the obsidian command palette https help obsidian md plugins command palette and type open batch generate embeddings modal this will open the batch indexing modal create embedding vectors for only the notes you want by entering an allow pattern and or a disallow pattern these patterns are not regex but they do accept as a wildcard the allow pattern also admits multiple matching patterns when separated by commas in order to exclude a file that fits the allow pattern add a disallow pattern to remove it the batch indexing modal will display a list of filepaths that match the patterns given once you ve verified that this is the list you want to use click to start the batch embedding and the vector embeddings will be requested from openai and stored to the local index if a note in the batch has already been added to the index and the exact content text has not changed since the last embedding was created it will not request a new embedding vector if the content text has changed at all a new embedding vector will be requested searching for notes similar to current note using semantic search to search for similar notes to the current open note using semantic search open the obsidian command palette https help obsidian md plugins command palette and type semantic search for notes similar to current note if the current note doesn t yet have an embedding one will be requested from openai then similar notes will be displayed in the modal in order of their similarity score cosine similarity along with their content text note that this will only run a search over the notes that have been indexed locally with an embedding to copy the linktext of a note click the icon next to its linktext to copy the linktext to the clipboard manually installing the plugin clone this repo yarn to install dependencies npm run dev to start compilation in watch mode copy over main js styles css manifest json to your vault vaultfolder obsidian plugins obsidian gpt zettelkasten | ai |
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secDevLabs | p align center img src images secdevlabs logo png allign center height logo font agency fb bold condensed p p align center a laboratory for learning secure web and mobile development in a practical manner p p align center a href https github com globocom secdevlabs blob master docs contributing md img src https img shields io badge prs welcome brightgreen a a href https gitter im secdevlabs community img src https badges gitter im secdevlabs community svg a p build your lab by provisioning local environments via docker compose you will learn how the most critical web application security risks are exploited and how these vulnerable codes can be fixed to mitigate them how do i start after forking this repository you will find multiple intended vulnerable apps based on real life scenarios in various languages such as golang python and php a good start would be installing the ones you are most familiar with you can find instructions to do this on each of the apps each of them has an attack narrative section that describes how an attacker would exploit the corresponding vulnerability before reading any code it may be a good idea following these steps so you can better understand the attack itself now it s time to shield the application up imagine that this is your application and you need to fix these flaws your mission is writing new codes that mitigate them and sending a new pull request to deploy a secure app how secure is my new code after mitigating a vulnerability you can send a pull request to gently ask the secdevlabs community to review your new secure codes if you re feeling a bit lost try having a look at this mitigation solution https github com globocom secdevlabs pull 29 it might help owasp top 10 2021 apps disclaimer you are about to install vulnerable apps in your machine vulnerability language application a1 broken access control golang vulnerable ecommerce api owasp top10 2021 apps a1 ecommerce api a1 broken access control nodejs tic tac toe owasp top10 2021 apps a1 tictactoe a1 broken access control golang camplake api owasp top10 2021 apps a1 camplake api a2 cryptographic failures golang snakepro owasp top10 2021 apps a2 snake pro a3 injection golang copynpaste api owasp top10 2021 apps a3 copy n paste a3 injection nodejs mongection owasp top10 2021 apps a3 mongection a3 injection python sstype owasp top10 2021 apps a3 sstype a3 injection xss python gossip world owasp top10 2021 apps a3 gossip world a3 injection xss react comment killer owasp top10 2021 apps a3 comment killer a3 injection xss angular spring streaming owasp top10 2021 apps a3 streaming a4 insecure design react go super recovery password app owasp top10 2021 apps a4 super recovery password a5 security misconfiguration xxe php vinijr blog owasp top10 2021 apps a5 vinijr blog a5 security misconfiguration php vulnerable wordpress misconfig owasp top10 2021 apps a5 misconfig wordpress a5 security misconfiguration nodejs stegonography owasp top10 2021 apps a5 stegonography a6 vulnerable and outdated components php cimentech owasp top10 2021 apps a6 cimentech a6 vulnerable and outdated components python golden hat society owasp top10 2021 apps a6 golden hat a7 identity and authentication failures python saidajaula monster fit owasp top10 2021 apps a7 saidajaula monster a7 identity and authentication failures golang insecure go project owasp top10 2021 apps a7 insecure go project a8 software and data integrity failures python amarelo designs owasp top10 2021 apps a8 amarelo designs a9 security logging and monitoring failures python gamesirados com owasp top10 2021 apps a9 games irados owasp top 10 2016 mobile apps disclaimer you are about to install vulnerable mobile apps in your machine vulnerability language application m2 insecure data storage dart flutter cool games owasp top10 2016 mobile m2 cool games m4 insecure authentication dart flutter note box owasp top10 2016 mobile m4 note box m5 insufficient cryptography dart flutter panda zap owasp top10 2016 mobile m5 panda zap contributing we encourage you to contribute to secdevlabs please check out the contributing to secdevlabs docs contributing md section for guidelines on how to proceed license this project is licensed under the bsd 3 clause new or revised license read license md license md file for details | owasp-top-10 labs development training security vulnerability hacktoberfest hacktoberfest2022 | front_end |
psmoveapi | ps move api contrib header png documentation status https readthedocs org projects psmoveapi badge version latest https psmoveapi readthedocs io en latest build from source https github com thp psmoveapi actions workflows build yml badge svg https github com thp psmoveapi actions workflows build yml the ps move api is an open source https github com thp psmoveapi blob master copying library for linux macos and windows to access the sony move motion controller via bluetooth and usb directly from your pc without the need for a ps3 tracking in 3d space is possible using a ps eye on linux windows and macos or any other suitable camera source ps move api has successfully participated in google summer of code 2012 http www google melange com gsoc homepage google gsoc2012 detailed documentation can be found in my master s thesis http thp io 2012 thesis about sensor fusion core features pairing of bluetooth controllers via usb setting leds and rumble via usb and bluetooth reading inertial sensors and buttons via bluetooth supporting extension devices such as the sharp shooter and the racing wheel tracking up to 5 controllers in 3d space via opencv 3d orientation tracking via an open source ahrs algorithm sensor fusion for augmented and virtual reality applications supported languages core library written in c for portability and performance additional c headers for easier interoperability ctypes based bindings for python 3 need help free community based support via the ps move mailing list https groups google com forum aboutgroup psmove professional support and custom development upon request http thp io about hacking the source coding style no strict rules keep consistent with the surrounding code patches should be submitted on github as pull request https github com thp psmoveapi pulls bug reports and feature requests can be added to the issue tracker https github com thp psmoveapi issues licensing the ps move api source code is released under the terms of a simplified bsd style license the exact license text can be found in the copying https github com thp psmoveapi blob master copying file some third party code under external https github com thp psmoveapi blob master external might be licensed under a different license compiling ps move api with these modules is optional you can use cmake options to configure which features you need cmake will give you a hint about the library licensing for your current configuration depending on your options at configure time in general all dependencies are under a mit or bsd style license with the exception of the following dependencies ps3eyedriver released under the mit license parts based on gpl code for interfacing with the pseye camera on macos only in the tracker cmake option psmove use ps3eye driver disabled by default more information about the third party modules and licenses http thp io 2012 thesis thesis pdf page 51 53 file external readme https github com thp psmoveapi blob master external readme in this source tree more information license simplified bsd style license see copying https github com thp psmoveapi blob master copying and licensing above maintainer thomas perl m thp io website http thp io 2010 psmove git repository https github com thp psmoveapi mailing list psmove googlegroups com see the web interface https groups google com forum aboutgroup psmove for details documentation https psmoveapi readthedocs io | 6dof computer-vision controller hid sensors tracking | ai |
web2020 | web2020 introduction to web and cloud programming subjects html5 css3 bootstrap responsive design javascript jquery cdn forms python django framework ajax json | cloud |
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ebookML_src | source code for machine learning c b n fundamentals of machine learning book in vietnamese order ebook at https machinelearningcoban com ebook https machinelearningcoban com ebook docker added by hatung the project now includes a dockerfile and a docker compose yml file which requires at least docker compose version 1 17 1 prerequisites recent stable version of docker https www docker com community edition recent stable version of docker compose https github com docker compose releases latest setting up clone tiepvupsu ebookml src s repository git clone https github com tiepvupsu ebookml src git cd ebookml src building the app if you want to build your own image run the command below docker compose build you can launch the docker image with docker compose up you can see these lines on screen jupyterlab 1 copy paste this url into your browser when you connect for the first time jupyterlab 1 to login with a token jupyterlab 1 http localhost 8888 token 8669f6fc0b665b67a5e08017c6728976cb41558a46ce561a copy paste above url and change port to 80 into your browser docker screenshot docker screenshot jpeg | ai |
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laptop | laptop laptop is a script to set up an os x computer for web development it can be run multiple times on the same machine safely it installs upgrades or skips packages based on what is already installed on the machine install in a terminal window run sh curl ls https raw githubusercontent com kickstarter laptop master mac sh debugging read through the output to see if you can debug the issue yourself if not copy the lines where the script failed into a new github issue https github com kickstarter laptop issues new for us or cut paste the entire output into the issue os x el capitan 10 11 you may have problems installing homebrew for the first time on os x el capitan due to permission changes to the usr directory within which the homebrew installation is typically located see the homebrew el capitan troubleshooting instructions https github com homebrew homebrew blob master share doc homebrew el capitan and homebrew md for steps to resolve the permissions issues that interfere with homebrew s installation what it sets up mac os x tools homebrew for managing operating system libraries homebrew http brew sh unix tools git for version control n for managing versions of node js rbenv and ruby build for managing versions of ruby pow for running local web services git https git scm com n https www npmjs com package n rbenv https github com sstephenson rbenv ruby build https github com sstephenson ruby build pow http pow cx it should take less than 15 minutes to install from scratch depends on your machine and about 2 seconds for subsequent runs contributing edit the mac file document in the readme md file follow shell style guidelines by using shellcheck and syntastic sh brew install shellcheck shellcheck http www shellcheck net about html syntastic https github com scrooloose syntastic credits the kickstarter laptop script is based on and inspired by thoughtbot s laptop https github com thoughtbot laptop script license original laptop script 2011 2016 thoughtbot inc modifications kickstarter pbc license license | front_end |
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peniot | peniot penetration testing tool for iot table of contents project description project description what is peniot what is peniot why is peniot required why is peniot required what does peniot provide what does peniot provide build instructions build instructions documentation documentation testing testing contributors contributors developer s note developers note project poster project poster project description what is peniot peniot https senior ceng metu edu tr 2019 peniot is a penetration testing tool for internet of things iot devices it helps you to test penetrate your devices by targeting their internet connectivity with different types of security attacks in other words you can expose your device to both active and passive security attacks after deciding target device and necessary information or parameters of that device you can perform active security attacks like altering consuming system resources replaying valid communication units and so on also you can perform passive security attacks such as breaching of confidentiality of important information or reaching traffic analysis thanks to peniot all those operations can be semi automated or even fully automated in short peniot is a package framework for targeting iot devices with protocol based security attacks also it gives you a baseline structure for your further injections of new security attacks or new iot protocols one of the most important features of peniot is being extensible by default it has several common iot protocols and numerous security attacks related to those protocols but it can be extended further via exporting basic structure of internally used components so that you can develop your attacks in harmony with the internal structure of the peniot why is peniot required the iot paradigm has experienced immense growth in the past decade with billions of devices connected to the internet most of these devices lack even basic security measures due to their capacity constraints and designs made without security in mind due to the shortness of time to market due to the high connectivity in iot attacks that have devastating effects in extended networks can easily be launched by hackers through vulnerable devices up until now penetration testing was done manually if it was not ignored at all this procedure made testing phase of devices very slow on the other hand the firms which produce iot devices should always be up to date on testing their devices in terms of reliability robustness as well as their provided functionalities since being exposed to security attacks by malicious people could cause unexpected impacts on end users the main aim of peniot is to accelerate the process of security testing it enables you to figure out security flaws on your iot devices by automating the time consuming penetration testing phase what does peniot provide first of all peniot provides novelty it is one of the first examples of penetration testing tools on iot field there are only one or two similar tools which are specialized on iot but they are still on development phase so not completed yet since the number of iot devices is increasing drastically iot devices become more and more common in our daily life smart homes smart bicycles medical sensors fitness trackers smart locks and connected factories are just a few examples of iot products given this we felt the need to choose some of the most commonly used iot protocols to plant into peniot by default we chose the following protcols as the default iot protocols included in the peniot these iot protocols are tested with various types of security attacks such as dos fuzzing sniffing and replay attacks following protocols are currently supported advanced message queuing protocol amqp https www amqp org bluetooth low energy ble https www bluetooth com constraint application protocol coap https coap technology message queuing telemetry transport mqtt http mqtt org moreover it enables you to export internal mainframe of its own implemented protocol and attacks to implement your own protocols or attacks also you can extend already existing protocols with your newly implemented attacks and lastly it provides you an easy to use user friendly graphical user interface build instructions firstly you need to have python s setuptools module installed in your machine also you need to install python tk and bluepy https github com ianharvey bluepy before installation and build in short you need the followings before running installation script setuptools python tk bluepy note that it is suggested to have a separate virtual environment particularly created for peniot since the dependent libraries are pretty old and can cause some trouble to install them among your existing external libraries you can build project in your local by executing following codes shell git clone git github com yakuza8 peniot git cd peniot python setup py install even if we try to provide you up to date installation script there can be some missing parts in it since the project cannot be maintained so long please inform us if there is any problem with installation important note you need to have radamsa https gitlab com akihe radamsa installed in your machine in order for generating fuzzing payloads in fuzzing attacks execution you can run peniot via command line or your favorite ide after setting up a virtual environment and installing the necessary libraries described above shell python src peniot py after running this command you should see an user interface appeared then you can explore the tool by yourself documentation you can find design overview document and final design document under the resources documents folder several diagrams are attached under the resources diagrams folder here is the simplest representation of how peniot is separated modules and how it is designed p align center img src resources diagrams peniot structure component diagram png p testing most of the attacks have their own sample integration tests under their attack scripts in order to run those tests you need to have a running program for the target protocol we try to provide you with example programs for each protocol where one can find server client scripts under each protocol s examples directory contributors this project is contributed by the following project members berat cankar bilgehan bing l do ukan avdaro lu ebru elebi and is supervised by pelin ang n developer s note firstly let me thank you for visiting our project site we tried to provide you how one can penetrate and hack iot devices over the protocols they use thanks to end to end security attacks our main purpose is to hack those devices with generic security attacks one can simply find specific attacks for any protocol but as i said ours was to provide generic and extendable penetration framework secondly peniot is developed with python2 7 and our code maybe had gone into legacy state but nevertheless we wanted to share it to public so that anyone could get insight and inspiration to develop their own penetration tools that is what makes us happy if it could happen thirdly we also will try to port our tool into python3 if we can spare necessary time for that when it happens we will inform it from this page as well thanks for your attention developer yakuza8 berat cankar project poster p align center img src resources peniot vectorized svg p | python2-7 iot penetration-testing hacking penetration-testing-framework penetration-testing-tools hacking-tools python iot-hacking amqp ble coap mqtt security security-attacks | server |
Embedded-System-Assignment | embedded system assignment assignment on embedded system design link to my recorded webinar https drive google com file d 1usozozvwvon48weokn9h1r lzextfllz view | os |
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SQL_DataEngineering_DataAnalysis | sql data engineering and analysis sql png sql png purpose research project on pewlett hackard employees corporation from the 1980s and 1990s the purpose of this project is data engineering and analysis of employees database using csvs and sql tools tools sql python pandas database departments csv dept emp csv dept manager csv employees csv salaries csv titles csv description 1 data engineering inspect csvs and sketch out an erd of the tables data types primary keys foreign keys etc 2 data analysis list of employees details departments and histogram of salaries | sql python | server |
qt-mobile-modules | qt mobile modules a collection of platform integrations to allow qt qml interfaces to commonly used ios functionality android support is planned but currently not started implemented if you are interested in developing for android please create a pull request and we can start building a universal library of qt mobile components modules platform ios specific setstatusbarstyle networkactivityindicator applicationiconbadgenumber application lifecycle notifications signals applicationdidbecomeactive applicationwillresignactive applicationdidenterbackground applicationwillenterforeground applicationdidfinishlaunching applicationdidreceivememorywarning applicationwillterminate vibrate imagepicker uiimagepickercontroller https developer apple com library ios documentation uikit reference uiimagepickercontroller class uiimagepickercontroller uiimagepickercontroller html support imagepicker openpicker open camera roll image picker opencamera open camera onimagepathchanged the user selected imagepath images are stored in the documents directory image source imagepath ad iad https developer apple com library ios documentation userexperience reference iad referencecollection index html view support ad component oncompleted display mailer mfmailcomposeviewcontroller https developer apple com library ios documentation messageui reference mfmailcomposeviewcontroller class reference reference html support mailer component oncompleted open subject line recipient gmail com recipient yahoo com body of the email qml component loading to use any modal visual component you must expose the component as a window qqmlengine engine qqmlcomponent component engine component loadurl qurl qrc main qml qobject comp component create qquickwindow window qobject cast qquickwindow comp window show | front_end |
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Cybersecurity-Project-of-Cloud-Storage | cybersecurity project of a cloud storage university project for the foundations of cybersecurity course msc computer engineering by fabrizio lanzillo federico montini e niko salamini design of a client server application that resembles a cloud storage developed in c 14 with openssl 1 1 1 library for linux systems and consists of 2 different programs client exe server exe we recommend the use of the commands inside the makefiles for the correct compilation of the programs both programs client and server need the server port number as an argument at execution time structure of the repository cybersecurity project of cloud storage docs cloud storage project documentation pdf project guidelines 2022 pdf credentials txt src client common server how to run compile using the commands inside the makefiles run both client and server by specifying the port as a paramenter and remembering to have root privileges | cloud |
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Web-Development | p align center img src https pixan198 github io images gfgsc logo svg width 200px height 200px p p align center a href https github com gameofsource gfg web development blob master license target blank img src https img shields io github license gameofsource gfg web development style for the badge a img src https img shields io badge prs welcome brightgreen svg style for the badge alt prs welcome a href https github com gameofsource gfg web development pulls target blank img alt github pull requests src https img shields io github issues pr gameofsource gfg web development style for the badge a a href https github com gameofsource gfg web development issues target blank img alt github issues src https img shields io github issues gameofsource gfg web development style for the badge a a href https github com gameofsource gfg web development target blank img alt github all contributors src https img shields io github contributors gameofsource gfg web development style for the badge a img alt beginner friendly src https img shields io badge beginner friendly orange style for the badge p welcome to gameofsource web development arena h4 about event h4 game of source is a open source contribution event being organized by geeksforgeeks and its student chapters across pan india the event is going to witness the participation from students of over 100 colleges in various cities and will provide a plethora of opportunities to budding developers to directly learn from the domain experts and try their hands on practical execution of events in this celebration of learning h4 what participants must do h4 participants will be given an issue as a feature to develop in the web development template issues will be opened in github repository at a time specified earlier in the whatsapp group once going through the issues published participants must request the issue to be assigned to them we check the participants github when there are many requests and assign an issue to a b single b participant only once assigned participants must solve that issue within 48 hours and make a pull request to repository from the branch our repo maintainers will go through the pull request thoroughly and can merge once satisfied all the rules mentioned in a href https github com gameofsource gfg web development blob main contributing md contributing md a file if participant is failed to make pull request and didn t show any progress on that issue within 24 hours the issue will be cancelled and we assign that issue to another participant h4 important resources h4 guide to fork a repository a href https docs github com en free pro team latest github getting started with github fork a repo click here a br guide to make a pull request a href https docs github com en free pro team latest github collaborating with issues and pull requests about pull requests click here a br h4 contact us h4 all the participant queries will be resolved in whatsapp group https chat whatsapp com l9qkq3rqjke3bfav1yffz4 | hacktoberfest hacktoberfest-accepted hacketoberfest2020 | front_end |
QueueForMcu | queueformcu 8 16 32 rtos https github com xiaoxinpro queueformcu include queue h define q uart buffer size 1024 queue handletypedef quarttx queue data t bufferuarttx q uart buffer size int main void queue data t temp queue init quarttx bufferuarttx q uart buffer size while 1 queue push quarttx q queue push quarttx u queue push quarttx e queue push quarttx u queue push quarttx e queue pop quarttx temp queue pop quarttx temp queue pop quarttx temp queue pop quarttx temp queue pop quarttx temp queueformcu queue h queue data t unsigned char queue handletypedef typedef struct queue handletypedef unsigned int head unsigned int tail unsigned int buffer length queue data t buffer queue handletypedef queue data t 1 queue data t bufferuarttx 1024 1024 2 queue handletypedef queue handletypedef quarttx 3 queue init void queue init queue handletypedef hqueue queue data t buffer unsigned int len hqueue buffer len queue init quarttx bufferuarttx q uart buffer size 1 queue push queue statustypedef queue push queue handletypedef hqueue queue data t data hqueue data queue statustypedef queue ok queue overload queue push quarttx q queue push quarttx 0x51 queue push quarttx 81 2 queue push array queue push unsigned int queue push array queue handletypedef hqueue queue data t pdatas unsigned int len hqueue pdatas len len 1 queue pop queue statustypedef queue pop queue handletypedef hqueue queue data t pdata hqueue pdata queue statustypedef queue ok queue void queue data t temp if queue ok queue pop quarttx temp temp else 2 queue pop array queue pop unsigned int queue pop array queue handletypedef hqueue queue data t pdatas unsigned int len hqueue pdatas len len 3 queue peek queue pop queue peek queue pop queue pop queue peek 4 queue peek array queue pop array queue peek array queue pop array queue pop array queue peek array 1 queue clear void queue clear queue handletypedef hqueue hqueue 2 queue count unsigned int queue count queue handletypedef hqueue hqueue 0 license copyright 2020 queueformcu released under the gpl 3 0 license | os |
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react-typescript | react typescript ant design react typescript mobx pwa webpack5 react16 react typescript antd mobx webpack5 react router5 pwa git clone git clone git github com xpioneer react typescript git node modules yarn yarn in your command terminal http localhost 8060 yarn start yarn build | react16 typescript antd react-router4 mobx jwt pwa graphql eslint webpack5 react | front_end |
pytorch-lora | pytorch lora lora low rank adaptation of large language models implemented using pytorch | ai |
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employee-data-engineering-analysis | employee database a mystery in two parts instructions this project utilizes data engineering and data analysis to build a sql database of employees of a corporation called pewlett hackard from the 1980s and 1990s there are six csv files holding the data of employees the sql tables were designed and the data in the csvs were successfully imported into a sql database data engineering inspect the csvs and sketch out an erd of the tables the quickdbd https www quickdatabasediagrams com was used in this project use the information from erd to create a table schema for each of the six csv files and specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes two primary keys in order to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table and make sure to import the data in the same order that the tables were created data analysis list the following details of each employee employee number last name first name sex and salary list first name last name and hire date for employees who were hired in 1986 list the manager of each department with the following information department number department name the manager s employee number last name first name list the department of each employee with the following information employee number last name first name and department name list first name last name and sex for employees whose first name is hercules and last names begin with b list all employees in the sales department including their employee number last name first name and department name list all employees in the sales and development departments including their employee number last name first name and department name in descending order list the frequency count of employee last names i e how many employees share each last name visuals a visualization of the data was generated by taking the following steps import the sql database into pandas use the code below to get started python from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect create a histogram to visualize the most common salary ranges for employees p align center img src https github com alexhyasui1 sql challenge blob main images histogram png height 75 width 75 p create a bar chart of average salary by title p align center img src https github com alexhyasui1 sql challenge blob main images barchart png height 75 width 75 p list of content 1 erd image png an image file of the erd 2 table schemata sql a sql file of the table schemata 3 queries sql a sql file of the queries 4 employee ipynb a jupyter notebook of the bonus analysis | sql database data-modeling sqlalchemy-python erd | server |
model-match-pro | model match pro a full stack 3 tier web app by deiosha sparks github https github com deiosha linkedin https linkedin com in deiosha sparks 954882251 jerry barrows fitzgerald github https github com jbarrfitz linkedin https linkedin com in jbarrowsfitzgerald lauren main github https github com elleem linkedin https linkedin com in laurenmain28 manuch sadri github https github com mcsadri linkedin https linkedin com in manuch sadri overview what is the vision of this product model match pro enables its users to submit a single large language model llm prompt and then simltaneously compare the responses from multiple models to find which one s best match their use case what pain point does this project solve with the recent proliferation of large language models many users pick their favorite and then try to engineer their prompts to work for their use case currently there are very few if any applications that allow users to simltaneously compare results from multiple llms oftentimes llm users must test each llm individually and attempt to compare the responses through inefficient means why should we care about your product model match pro will be one of the few products providing a method to compare the results from multiple llms within the same web browser tab using a single prompt submission links model match pro https model match vercel app set up in terminal assumes git python3 11 and npm are previously installed clone repo from github git clone https github com match makers model match pro git backend backend set up change directory to the backend folder in the cloned local repo cd model match pro backend create run virtual environment python3 11 m venv venv source venv bin activate install requirements npm install r requirements txt get secrets from project team for model match pro backend model match proj env start dev server python manage py runserver in browser to run locally django rest framework default web pages http localhost 8000 home http localhost 8000 admin admin panel http localhost 8000 api v1 model match app backend tests open new terminal window or tab change directory to the backend folder in the cloned local repo cd model match pro backend create run virtual environment source venv bin activate run tests python manage py test model match app tests run coverage py coverage run source model match app manage py test model match app tests to generate the coverage report coverage report frontend frontend set up open new terminal window or tab change directory to the frontend folder in the cloned local repo cd model match pro frontend install all the dependencies npm install get secrets from project team for model match pro frontend env run local environment npm run dev in browser to run locally http localhost 3000 frontend tests open new terminal window or tab change directory to the frontend folder in the cloned local repo cd model match pro frontend run playwright tests with new user registration node tests pw new user js with existing user node tests pw existing user js | ai |
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ov-igloo-ui | div align center h1 igloo h1 p igloo gives a collection of react components for building their products p p a href https github com gsoft inc ov igloo ui issues new report a issue a a href https gsoft slack com archives c02j8004tdk ask a question a p div documentation visit igloo officevibe design https igloo officevibe design to learn more usage to use a component from this repo you will first need to install the component into your project for an example we will try to use the igloo ui card component bash npm i igloo ui card or with yarn yarn add igloo ui card then to use the component in your code just import it js import card from igloo ui card getting started before starting make sure node https nodejs org en and yarn https yarnpkg com is installed install after cloning ov igloo ui run yarn to install dependencies bash git clone https github com gsoft inc ov igloo ui git cd ov igloo ui yarn yarn storybook scripts yarn storybook run storybook yarn test run tests yarn lint check if the js ts correspond on standard yarn lint style check if the style correspond on standard contributing pull requests are welcome for major changes please open an issue first to discuss what you would like to change if you re interested definitely check out our contributing guide license copyright 2019 gsoft inc this code is licensed under the apache license version 2 0 you may obtain a copy of this license at https github com gsoft inc gsoft license blob master license | components design-system nextjs react storybook | os |
QueuesUART | queuesuart the idea of this assignment is 1 use sw1 to increment a counter 2 use sw2 to send counter to queue and reset the counter in this case a sensor instead of sw2 3 use uart to print the sent counter the project is done using keil ide and tiva c board | c embedded queues uart | os |
IoT_Camera | iot camera fb icons fb png https www facebook com groups 309729986071484 iot camera is a camera with wi fi wireless network hardware fh8620 arm1176 up to 450mhz builtin 16mb dram ap6181 bcm43362 wi fi 8mb spi nor flash gc1024 sensor hardware encoder for h 264 1280x720 30fps hardware encoder for mjpeg software the software in iot camera is based on rt thread rtos rtsp mjpeg streaming rt thread posix edition | iot rt-thread camera arm wifi mjpeg | server |
resources | useful resources for developers a list of student collated resources deemed to be useful for every developer and categorised andrei has a hand picked list of his favourite resources which you can find here https zerotomastery io resources utm source github utm medium resources table of contents api api md a list of resources for learning how to use apis algorithms data structures algorithmsdatastructures md resources for tackling algorithms angular resources angular md a list of resources for learning angular arduino arduino md a list of resources for the arduino micro controller articles developmentarticles md general articles page on web development css resources cssresources md a list of resources for learning css cheat sheets cheatsheets md for those looking for the quick and dirty of how to do things or if you simply forgot something look no further cloud cloud md cloud learning resources to kickstart your career free online courses freeonlinecourses md free to attend online courses including moocs massive open online courses https en wikipedia org wiki massive open online course game development resources gamedev md a page which lists out the resources which helps you go from zero to mastery in game development general resources for learning web development generalresources md a page with mostly free resources for learning web development and coding in general git and github using git and github md resources page on using git and github interviewing for coding jobs howtointerviewforcodejobs md a page of resources about preparing for the job market javascript resources javascript md a list of resources for learning javascript junior to senior developer roadmap resources juniortoseniorcourse md resources mentioned in the zero to mastery course mobile app development mobileappdevelopment md a curated list of useful resources for mobile app development for android ios windows or any other mobile system podcasts podcasts md a range of podcasts covering topics like coding design accessibility javascript and mindset self development practice resources practiceresources md a list of exercises and gamified resources for web development programming books programming books md featuring a list of insightful programming books both free and paid versions python resources python md a list of resources for learning python raspberry pi raspberrypi md resources for the raspberry pi search engine optimization searchengineoptimization md a list of resources for learning search engine optimization seo unix unix md resources for unix systems linux macos etc web design resources webdesignresources md a page of resources for web design web development tools webdevtools md a page listing a number of free web development tools youtube channels youtubechannels md a list of youtube channels for learning all about programming covering topics as broad as web development design history hacking and computer science cs contributing you are always welcome to contribute to this project kindly visit our contributor s guide https github com zero to mastery resources blob master contributing md before opening a pull request first time contributing to open source awesome read more about the process in contributing to github https github com zero to mastery resources blob master contributing to github md list of contributors contributors md a page showing the github usernames of all who have contributed to this open source project make sure to add yourself and submit a pull request if you ve contributed | developer-resources programming-resources tutorial-list react javascript youtube-channels articles podcasts youtube-channel | front_end |
GVT | gvt good visual tokenizer for llms this repo contains assets in our paper what makes for good visual tokenizers for large language models https arxiv org abs 2305 12223 model we provide related details in gvt gvt gvtbench we provide the object counting oc and multi class identification mci on ms coco and vcr datasets in gvtbench gvtbench acknowledgement our work is built on vlmo https github com microsoft unilm tree master vlmo lavis https github com salesforce lavis eva https github com baaivision eva vicuna https github com lm sys fastchat thanks for their great work citation if you find this work useful please cite misc wang2023gvt title what makes for good visual tokenizers for large language models author guangzhi wang and yixiao ge and xiaohan ding and mohan kankanhalli and ying shan year 2023 eprint 2305 12223 archiveprefix arxiv primaryclass cs cv | ai |
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sp1-database | sp1 database dau computer engineering sw project 1 database | server |
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TCP-IP-socket-communication-SCANDA-system-project | read me 1 read the final report part 1 and has a basic understanding of the scada we are going to build 2 read the final report part 2 and learn has this idea be realized based on the embedded system 3 login in one board run the rtu kernel file program in the kernel module 4 login in the same board run the project rtu user file program in the user space use the command ifconfigure to get the ip of the board and run it by use the project rtu user space ip space port number the port number is greater than 2000 for example project rtu user 10 3 52 16 2200 5 login in the other board run the project general user file program in the user space and run it by using the command project general user space port number the port number is greater than 2000 for example project general user 2200 6 be careful to the space the upper case and the lower case you should use the same with the name of your file in the linux program 7 the buttons are connected to the dio in the ts 7250 board that is used as the testing board as well 8 you need a sin wave generate circuit and the output connects to the max 197 chip in the ts 7250 board corresponding bits my email is yzcmf mail missouri edu if you want the part 1 and part 2 report to learn more just send me an email i ll get in touch with you as soon as possible | rtu-user board kernel embedded c | os |
maildev | maildev npm https img shields io npm v maildev https www npmjs com package maildev npm downloads https img shields io npm dm maildev https www npmjs com package maildev docker pulls https img shields io docker pulls maildev maildev https hub docker com r maildev maildev license https img shields io npm l maildev color white license javascript style guide https img shields io badge code style standard black svg https standardjs com maildev is sponsored by inngest inngest https github com inngest inngest inngest is the developer platform https www inngest com ref maildev for easily building reliable workflows with zero infrastructure check it out and give it a star maildev is a simple way to test your project s generated email during development with an easy to use web interface that runs on your machine built on top of node js http www nodejs org maildev screenshot https github com maildev maildev blob gh pages images screenshot 2021 01 03 png raw true docker run if you want to use maildev with docker https www docker com you can use the maildev maildev image on docker hub https hub docker com r maildev maildev for a guide for usage with docker checkout the docs https github com maildev maildev blob master docs docker md docker run p 1080 1080 p 1025 1025 maildev maildev usage usage maildev options options environment variable description s smtp port maildev smtp port smtp port to catch mail w web port maildev web port port to run the web gui mail directory path maildev mail directory directory for persisting mail https maildev https switch from http to https protocol https key file maildev https key the file path to the ssl private key https cert file maildev https cert the file path to the ssl cert file ip ip address maildev ip ip address to bind smtp service to outgoing host host maildev outgoing host smtp host for outgoing mail outgoing port port maildev outgoing port smtp port for outgoing mail outgoing user user maildev outgoing user smtp user for outgoing mail outgoing pass password maildev outgoing pass smtp password for outgoing mail outgoing secure maildev outgoing secure use smtp ssl for outgoing mail auto relay email maildev auto relay use auto relay mode optional relay email address auto relay rules file maildev auto relay rules filter rules for auto relay mode incoming user user maildev incoming user smtp user for incoming mail incoming pass pass maildev incoming pass smtp password for incoming mail incoming secure maildev incoming secure use smtp ssl for incoming emails incoming cert path maildev incoming cert cert file location for incoming ssl incoming key path maildev incoming key key file location for incoming ssl web ip ip address maildev web ip ip address to bind http service to defaults to ip web user user maildev web user http user for gui web pass password maildev web pass http password for gui base pathname path maildev base pathname base path for urls disable web maildev disable web disable the use of the web interface useful for unit testing hide extensions extensions maildev hide extensions comma separated list of smtp extensions to not advertise smtputf8 pipelining 8bitmime o open open the web gui after startup v verbose silent log mail contents log a json representation of each incoming mail api maildev can be used in your node js application for more info view the api docs https github com maildev maildev blob master docs api md javascript const maildev require maildev const maildev new maildev maildev listen maildev on new function email we got a new email maildev also has a rest api for more info view the docs https github com maildev maildev blob master docs rest md outgoing email maildev optionally supports selectively relaying email to an outgoing smtp server if you configure outgoing email with the outgoing options you can click relay on an individual email to relay through maildev out to a real smtp service that will actually send the email to the recipient example maildev outgoing host smtp gmail com outgoing secure outgoing user you gmail com outgoing pass pass auto relay mode enabling the auto relay mode will automatically send each email to it s recipient without the need to click the relay button mentioned above the outgoing email options are required to enable this feature optionally you may pass an single email address which maildev will forward all emails to instead of the original recipient for example using auto relay you example com will forward all emails to that address automatically additionally you can pass a valid json file with additional configuration for what email addresses you would like to allow or deny the last matching rule in the array will be the rule maildev will follow example maildev outgoing host smtp gmail com outgoing secure outgoing user you gmail com outgoing pass pass auto relay auto relay rules file json rules example file javascript allow deny test com allow ok test com deny utah com allow johnny utah com this would allow angelo fbi gov ok test com johnny utah com but deny bodhi test com configure your project configure your application to send emails via port 1025 and open localhost 1080 in your browser nodemailer v1 0 javascript we add this setting to tell nodemailer the host isn t secure during dev process env node tls reject unauthorized 0 const transport nodemailer createtransport port 1025 other settings django add email port 1025 in your settings file source https docs djangoproject com en dev ref settings std setting email port rails config settings ruby config action mailer delivery method smtp config action mailer smtp settings address localhost port 1025 enable starttls auto false drupal install and configure smtp https www drupal org project smtp module or use a library like swiftmailer http swiftmailer org features toggle between html plain text views as well as view email headers test responsive emails with resizable preview pane available for various screen sizes ability to receive and view email attachments websocket integration keeps the interface in sync once emails are received command line interface for configuring smtp and web interface ports ability to relay email to an upstream smtp server ideas if you re using maildev and you have a great idea i d love to hear it if you re not using maildev because it lacks a feature i d love to hear that too add an issue to the repo here https github com maildev maildev issues new contributing any help on maildev would be awesome there is plenty of room for improvement feel free to create a pull request https github com maildev maildev issues new from small to big changes to run maildev during development npm install npm run dev the dev task will run maildev using nodemon and restart automatically when changes are detected on scss file save the css will also be recompiled using test send js a few test emails will be sent every time the application restarts if you want to debug you can use the nodemon debug profile in vscode to change arguments or environment variables edit the vscode launch json the project uses the javascript standard coding style https standardjs com to lint your code before submitting your pr run npm run lint to run the test suite npm test changelog https github com maildev maildev releases thanks maildev is built on using great open source projects including express http expressjs com angularjs http angularjs org font awesome http fontawesome io and two great projects from andris reinman https github com andris9 smtp server https github com nodemailer smtp server and mailparser https github com nodemailer mailparser many thanks to andris as his projects are the backbone of this app and to mailcatcher http mailcatcher me for the inspiration additionally thanks to all the awesome contributors https github com maildev maildev graphs contributors to the project license mit | maildev smtp nodemailer docker smtp-server mailcatcher testing development developer-tools nodejs | 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derohe | welcome to dero twitter https twitter com deroproject discord https discord gg h95tjdp github https github com deroproject derohe explorer https testnetexplorer dero io wiki https wiki dero io web wallet https wallet dero io dero is a blockchain with smart contracts preserving your privacy through multiple features while staying fast secure and accessible to all people easily dero is running since december 2017 and was previously running on a unique implementation of cryptonote protocol with blockdag and as migrated to this unique version for smart contracts services better performance better privacy and more security dero homomorphic encryption what is homomorphic encryption homomorphic encryption he is a form of encryption allowing one to perform calculations on encrypted data without decrypting it first the result of the computation is in an encrypted form when decrypted the output is the same as if the operations had been performed on the unencrypted data it can be used for privacy preserving outsourced storage and computation this allows data to be encrypted and out sourced to commercial cloud environments for processing all while encrypted in highly regulated industries such as health care and can also be used to enable new services by removing privacy barriers inhibiting data sharing for example predictive analytics in health care can be hard to apply via a third party service provider due to medical data privacy concerns but if the predictive analytics service provider can operate on encrypted data instead these privacy concerns are diminished for more details of what is homomorphic encryption please see wikipedia https en wikipedia org wiki homomorphic encryption summary welcome to dero welcome to dero dero homomorphic encryption dero homomorphic encryption what is homomorphic encryption what is homomorphic encryption summary summary about dero project about dero project features features transactions sizes transactions sizes network ports network ports mainnet mainnet testnet testnet technical technical dero blockchain salient features dero blockchain salient features erasure coded blocks erasure coded blocks client protocol client protocol proving dero transactions proving dero transactions dero installation dero installation installation from source installation from source installation from binary installation from binary running dero daemon running dero daemon dero cli wallet dero cli wallet dero explorer dero explorer about dero project dero is running since december 2017 and was previously running on a unique implementation https github com deroproject derosuite of cryptonote protocol with blockdag and as migrated to this unique version for smart contracts services better performance better privacy and more security consensus algorithm is pow based on astrobwt https github com deroproject astrobwt a asic fpga gpu resistant cpu mining algorithm to improve decentralization and lower the barrier of joining the network dero is industry leading and the first blockchain to have homomorphic encryption bulletproofs and a fully tls encrypted network the fully distributed ledger processes transactions with a 16s sixty seconds average block time and is secure against majority hashrate attacks dero is the first homomorphic encryption based blockchain to have smart contracts contracts on its native chain without any extra layers or secondary blockchains at present dero has implemented smart contracts for previous mainnet implementation running on cryptonote protocol which can be found here https github com deroproject documentation blob master testnet stargate md features homomorphic account based model check blockchain transaction execute go line 82 95 instant account balances it need only 66 bytes of data from the blockchain dag minidag with 1 miniblock every second note this as been replaced by a mini block system each block has 10 mini blocks to rewards up to 10 differents miners at the same time for each block by splitting the difficulty and the result of the final block mining decentralization no more mining pools needed with a daily 54000 mini blocks solo mining is much more available than any others blockchains erasure coded blocks worlds first erasure coded propagation protocol which allows 100x block size without increasing propagation delays lower bandwidth requirements and provide very low propagation time light weight and efficient wallets no more chain scanning or wallet scanning the whole chain to detect funds no key images by connecting to a synchronized node you will retrieve your available funds in few seconds small disk cost for blockchain account fixed per account cost of 66 bytes in blockchain only which allow a immense scalability anonymous transactions provide completely anonymous transactions and deniability thanks to many out of many proofs using bulletproofs and sigma protocol nobody except you and the receiver account will know the real parties of the transaction fixed transaction size only 2 5kb for a transaction using ring size set to 8 or 3 4 kb with ring size set to 16 transactions size have a logarithm growth based on the anonymity set and must be chosen in powers of 2 homomorphic assets programmable smart contracts with fixed overhead per asset your smart contract is open source but its data related to balances are completely encrypted like the native coin pruning blockchain allows chain pruning on daemons to control growth of data on daemons and keep a low disk usage this allows immense scability as you can reduce a blockchain of few hundred gbs to only few gbs while still being secure using merkle proofs br example disk requirements of 1 billion accounts assumming it does not want to keep history of transactions but keeps proofs to prove that the node is in sync with all other nodes br requirement of 1 account is only 66 bytes assumming storage overhead per account of 128 bytes which is constant total requirements 66 128 gb 200gb assuming we are off by factor of 4 its only 800gb br note that even after 1 trillion transactions 1 billion accounts will consume 800gb only if history is not maintained and everything still will be in proved state using merkle roots and so even raspberry pi can host the entire chain low transaction generation time generating a transaction takes less than 25 ms low transaction verification time transaction verification takes even less than 25ms no trusted setup no hidden parameters everything is open source available to anyone to provide trustless and fully decentralized blockchain provability senders of a transaction can prove to receivers what amount they have send without revealing themselves transactions sizes ring size transaction size in bytes 2 1553 4 2013 8 2605 16 3461 32 4825 64 7285 128 11839 512 35000 note plan to reduce tx sizes further network ports mainnet p2p default port 10101 rpc default port 10102 wallet rpc default port 10103 testnet p2p default port 40401 rpc default port 40402 wallet rpc default port 40403 technical for specific details of current dero core daemon implementation and capabilities see below dag no orphan blocks no soft forks note this feature has been disabled for mainnet to reduce load for small devices bulletproofs non interactive zero knowledge range proofs nizk astrobwt this is memory bound algorithm this provides assurance that all miners are equal no miner has any advantage over common miners p2p protocol this layers controls exchange of blocks transactions and blockchain itself pederson commitment part of ring confidential transactions pederson commitment algorithm is a cryptographic primitive that allows user to commit to a chosen value while keeping it hidden to others pederson commitment is used to hide all amounts without revealing the actual amount it is a homomorphic commitment scheme homomorphic encryption homomorphic encryption is used to to do operations such as addition substraction to settle balances with data being always encrypted balances are never decrypted before during after operations in any form homomorphic ring confidential transactions gives untraceability privacy and fungibility while making sure that the system is stable and secure core consensus protocol implemented consensus protocol serves 2 major purpose protects the system from adversaries and protects it from forking and tampering next block in the chain is the one and only correct version of truth balances proof of work pow algorithm pow part of core consensus protocol which is used to cryptographically prove that x amount of work has been done to successfully find a block difficulty algorithm difficulty algorithm controls the system so as blocks are found roughly at the same speed irrespective of the number and amount of mining power deployed serialization de serialization of blocks capability to encode decode process blocks serialization de serialization of transactions capability to encode decode process transactions transaction validity and verification any transactions flowing within the dero network are validated verified socks proxy socks proxy has been implemented and integrated within the daemon to decrease user identifiability and improve user anonymity interactive daemon can print blocks txs even entire blockchain from within the daemon version peer list status diff print bc print block print tx and several other commands implemented networks dero daemon has both mainnet and testnet support enhanced reliability privacy security useability portabilty assured dero blockchain salient features 16 second block time extremely fast transactions with one minute block confirmation time ssl tls p2p network homomorphic fully encrypted blockchain ring signatures fully auditable supply dero blockchain is written from scratch in golang developed and maintained by original developers erasure coded blocks traditional blockchains process blocks as single unit of computation if a double spend tx occurs within the block entire block is rejected as soon as a block is found it is sent to all its peers dero blockchain erasure codes the block into 48 chunks dispersing and chunks are dispersed to peers randomly any peer receiving any 16 chunks from 48 chunks can regerate the block and thus lower overheads and lower propagation time client protocol traditional blockchains process blocks as single unit of computation if a double spend tx occurs within the block entire block is rejected however dero network accepts such blocks since dero blockchain considers transaction as a single unit of computation dero blocks may contain duplicate or double spend transactions which are filtered by client protocol and ignored by the network dero dag processes transactions atomically one transaction at a time proving dero transactions dero blockchain is completely private so anyone cannot view confirm verify any other s wallet balance or any transactions so to prove any transaction you require txid and deroproof deroproof can be obtained using get tx key command in dero wallet cli enter the txid and deroproof in dero explorer https testnetexplorer dero io dero explorer proving transaction https github com deroproject documentation raw master images explorer prove tx png dero installation dero is written in golang and very easy to install both from source and binary installation from source first you need to install golang if not already minimum version required for golang is 1 17 in go workspace execute go get u github com deroproject derohe when the command has finished check go workspace bin folder for binaries for example on linux machine the following binaries will be created derod linux amd64 dero daemon dero wallet cli linux amd64 dero cli wallet explorer linux amd64 dero explorer yes dero has prebuilt personal explorer also for advance privacy users installation from binary download dero binaries https github com deroproject derohe releases for arm intel mac platform and windows mac freebsd openbsd linux or any others availables platforms operating systems running dero daemon run derod exe or derod linux amd64 depending on your operating system it will start syncing dero daemon core cryptography is highly optimized and fast use dedicated machine and ssd for best results vps with 2 4 cores 4gb ram 15gb disk is recommended dero daemon https raw githubusercontent com deroproject documentation master images derod1 png dero cli wallet dero cmdline wallet is menu based and very easy to operate use various options to create recover transfer balance etc note dero cmdline wallet by default connects dero daemon running on local machine on port 20206 if dero daemon is not running start dero wallet with remote option like following dero wallet cli linux amd64 remote dero wallet https raw githubusercontent com deroproject documentation master images wallet recover2 png dero explorer dero explorer https explorer dero io is used to check and confirm transaction on dero network dero users can run their own explorer on local machine and can browse http 127 0 0 1 8080 on local machine port 8080 dero explorer https github com deroproject documentation raw master images dero explorer png | blockchain |
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Natural-Language-Process | natural language process | ai |
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Cassandra-database-Security | cassandra database security this repository contains work on the security of the cassandra database the paper is for the needs of the subject database management systems at the master s studies at the faculty of electronic engineering in nis | server |
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Computer-Vision-and-Robotics-Paper-List | h1 align center computer and robot vision h1 img src https github com yehengchen computer and robot vision paper list blob master img overview 2 png style zoom 100 international conferences and journal cv ro conferences deadlines website https github com yehengchen computer vision and robotics paper list blob master international 20conferences 20and 20journal md international conferences cv ro sci journal website https github com yehengchen computer vision and robotics paper list blob master international 20conferences 20and 20journal md ro e6 9c ba e5 99 a8 e4 ba ba ai conference deadlines https aideadlin es sub ml cv nlp ro sp dm international conference robotic conference icra ieee international conference on robotics and automation icra2020 https github com yehengchen computer vision and robotics paper list blob master icra2020 md icra2021 https github com yehengchen computer vision and robotics paper list blob master icra2021 md icra2022 https github com yehengchen computer vision and robotics paper list blob master iros2022 md iros ieee rsj international conference on intelligent robots and systems iros2020 https github com yehengchen computer vision and robotics paper list blob master iros2020 md iros2021 https github com yehengchen computer vision and robotics paper list blob master iros2021 md iros2022 https github com yehengchen computer vision and robotics paper list blob master iros2022 md rss robotics science and systems hri acm ieee international conference on human robot interaction icarcv international conference on control automation robotics and vision corl conference on robot learning corl2021 https github com yehengchen computer vision and robotics paper list blob master corl2021 md computer vision conference cvpr ieee cvf conference on computer vision and pattern recognition cvpr2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md cvpr2021 https github com yehengchen computer vision and robotics paper list blob master cvpr2021 md eccv european conference on computer vision eccv2020 https github com yehengchen computer vision and robotics paper list blob master eccv2020 md iccv ieee cvf international conference on computer vision bmvc british machine vision conference wacv workshop on applications of computer vision icip ieee international conference on image processing accv asian conference on computer vision icpr international conference on pattern recognition artificial intelligence nips conference and workshop on neural information processing systems aaai association for the advancement of artificial intelligence icml international conference on machine learning iclr international conference on learning representations robot vision paper list figure paper list link keywords https github com yehengchen computer vision and robotics paper list blob master img ffb6d png ffb6d a full flow biderectional fusion network for 6d pose estimation cvpr 2021 oral https github com yehengchen computer vision and robotics paper list blob master cvpr2021 md arxiv https arxiv org abs 1911 04231 github https github com ethnhe ffb6d 6d pose estimation https github com yehengchen computer vision and robotics paper list blob master img self6d png self6d self supervised monocular 6d object pose estimation eccv 2020 oral https github com yehengchen computer vision and robotics paper list blob master eccv2020 md arxiv https arxiv org abs 2004 06468 self supervised learning 6d pose estimation https github com yehengchen computer vision and robotics paper list blob master img g2l net png g2l net global to local network for real time 6d pose estimation with embedding vector features cvpr 2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md arxiv https arxiv org abs 2003 11089 github https github com dc1991 g2l net 6d object pose estimation rgb d image real time https github com yehengchen computer vision and robotics paper list blob master img latentfusion png latentfusion end to end differentiable reconstruction and rendering for unseen object pose estimation cvpr 2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md arxiv https arxiv org abs 1912 00416 github https github com nvlabs latentfusion 6d object pose estimation unseen objects latent 3d representation https github com yehengchen computer vision and robotics paper list blob master img single stage 206d 20object 20pose 20estimation png single stage 6d object pose estimation cvpr 2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md arxiv https arxiv org abs 1911 08324 github https github com cvlab epfl single stage pose 6d object pose estimation single stage both accuracy and speed https github com yehengchen computer vision and robotics paper list blob master img pvn3d png pvn3d a deep point wise 3d keypoints voting network for 6dof pose estimation cvpr 2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md arxiv https arxiv org abs 1911 04231 github https github com ethnhe pvn3d 6d object pose estimation single rgbd image 3d keypoint detection https github com yehengchen computer vision and robotics paper list blob master img hybridpose png hybridpose 6d object pose estimation under hybrid representations cvpr 2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md arxiv https arxiv org abs 2001 01869 github https github com chensong1995 hybridpose 6d object pose estimation rgb data real time https github com yehengchen computer vision and robotics paper list blob master img morefusion png morefusion multi object reasoning for 6d pose estimation from volumetric fusion cvpr 2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md arxiv https arxiv org abs 2004 04336 github https github com wkentaro morefusion 6d object pose estimation rgb d data real time https github com yehengchen computer and robot vision paper list blob master img epos png epos estimating 6d pose of objects with symmetries cvpr 2020 https github com yehengchen computer vision and robotics paper list blob master cvpr2020 md arxiv https arxiv org abs 2004 00605 homepage http cmp felk cvut cz epos 6d object pose estimation single rgb input surface fragments https github com yehengchen computer vision and robotics paper list blob master img self supervised 206d 20object 20pose 20estimation 20for 20robot 20manipulation png self supervised 6d object pose estimation for robot manipulation icra 2020 https github com yehengchen computer vision and robotics paper list blob master icra2020 md arxiv https arxiv org abs 1909 10159 self supervised learning 6d pose estimation https github com yehengchen computer and robot vision paper list blob master img form2fit png form2fit learning shape priors for generalizable assembly from disassembly icra 2020 https github com yehengchen computer vision and robotics paper list blob master icra2020 md arxiv https arxiv org pdf 1910 13675v1 pdf github https github com kevinzakka form2fit self assembling robots self supervised data collection densefusion https github com yehengchen computer and robot vision paper list blob master img densefusion png densefusion 6d object pose estimation by iterative dense fusion cvpr 2019 arxiv https arxiv org pdf 1901 04780v1 pdf github https github com j96w densefusion 6d object pose estimation rgb d data real time https github com yehengchen computer vision and robotics paper list blob master img pvnet png pvnet pixel wise voting network for 6dof pose estimation cvpr 2019 arxiv https arxiv org pdf 1812 11788 pdf github https github com zju3dv pvnet 6d object pose estimation real time ransac https github com yehengchen computer vision and robotics paper list blob master img normalized 20object 20coordinate 20space 20for 20category level png normalized object coordinate space for category level 6d object pose and size estimation cvpr 2019 arxiv https arxiv org abs 1901 02970 github https github com hughw19 nocs cvpr2019 6d object pose estimation normalized object coordinate space rgb d image https github com yehengchen computer vision and robotics paper list blob master img pix2pose png pix2pose pixel wise coordinate regression of objects for 6d pose estimation iccv 2019 arxiv https arxiv org abs 1908 07433 github https github com kirumang pix2pose 6d object pose estimation only rgb images https github com yehengchen computer vision and robotics paper list blob master img multi task 20template 20matching 20for 20object 20detection png multi task template matching for object detection segmentation and pose etimation using depth images icra 2019 ieee https ieeexplore ieee org document 8794448 6d object pose estimation multi task template matching depth image segmentation masks https github com yehengchen computer vision and robotics paper list blob master img singleshotpose png real time seamless single shot 6d object pose prediction cvpr 2018 arxiv https arxiv org abs 1711 08848 github https github com microsoft singleshotpose 6d object pose estimation real time rgb images https github com yehengchen computer and robot vision paper list blob master img deepim png deepim deep iterative matching for 6d pose estimation eccv 2018 arxiv https arxiv org pdf 1804 00175v4 pdf github https github com liyi14 mx deepim 6d object pose estimation 6d pose matching unrealrox https github com yehengchen computer and robot vision paper list blob master img unrealrox png unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation iros 2018 arxiv https arxiv org pdf 1810 06936 pdf github https github com 3dperceptionlab therobotrix robotics simulations and synthetic data generation posecnn https github com yehengchen computer and robot vision paper list blob master img posecnn png posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes rss 2018 arxiv https arxiv org abs 1711 00199 github https github com yuxng posecnn 6d object pose estimation dope https github com yehengchen computer and robot vision paper list blob master img dope png deep object pose estimation for semantic robotic grasping of household objects corl 2018 arxiv https arxiv org abs 1809 10790 github https github com nvlabs deep object pose 6d object pose estimation synthetic data domain randomization ssd 6d https github com yehengchen computer and robot vision paper list blob master img ssd 6d png ssd 6d making rgb based 3d detection and 6d pose estimation great again iccv 2017 arxiv https arxiv org pdf 1711 10006v1 pdf github https github com wadimkehl ssd 6d 3d object detection 6d object pose estimation https github com yehengchen computer and robot vision paper list blob master img simulation 20to 20real 20world 20for 20a 20multi stage 20task png transferring end to end visuomotor control from simulation to real world for a multi stage task corl 2017 arxiv https arxiv org pdf 1707 02267v2 pdf github https github com stepjam pyrep manipulation end to end control domain randomisation domin https github com yehengchen computer and robot vision paper list blob master img domain 20randomization png domain randomization for transferring deep neural networks from simulation to the real world iros 2017 arxiv https arxiv org abs 1703 06907 github https github com neka nat gazebo domain randomization domain randomization simulated rgb images acp https github com yehengchen computer and robot vision paper list blob master img acp png multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge apc 2016 arxiv https arxiv org pdf 1609 09475v3 pdf github https github com andyzeng apc vision toolbox 6d object pose estimation | ai |
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uswds | united states web design system circleci build status https img shields io circleci build gh uswds uswds develop style for the badge logo circleci https circleci com gh uswds uswds tree develop snyk vulnerabilities https img shields io snyk vulnerabilities npm uswds uswds style for the badge npm version https img shields io npm v uswds uswds style for the badge https www npmjs com package uswds npm downloads https img shields io npm dt uswds uswds style for the badge https www npmjs com package uswds github issues https img shields io github issues uswds uswds style for the badge logo github https github com uswds uswds issues code style prettier https img shields io badge code style prettier ff69b4 style for the badge https github com prettier prettier the united states web design system https designsystem digital gov includes a library of open source ui components and a visual style guide for u s federal government websites this repository is for the design system code itself we maintain another repository for the documentation and website https github com uswds uswds site to see the design system and its documentation on the web visit https designsystem digital gov https designsystem digital gov contents recent updates recent updates getting started getting started what s included in uswds whats included in uswds directory structure directory structure package contents package contents installing the design system installing the design system using uswds css and javascript in your project using uswds css and javascript in your project compiling uswds sass into css compiling uswds sass into css sass compilation requirements sass compilation requirements sass and theme settings sass and theme settings js customization js customization style theming and tokens style theming and tokens css architecture css architecture browser support browser support accessibility accessibility long term support of v1 x long term support of v1x long term support of v2 x long term support of v2x need installation help need installation help contributing to the code base contributing to the code base reuse of open source style guides reuse of open source style guides licenses and attribution licenses and attribution contributing contributing recent updates information about the most recent release of the design system can always be found in the release history https github com uswds uswds releases we include details about significant updates and any backward incompatible changes along with a list of all changes uswds 3 0 is our most recent major release read about what s new in uswds 3 0 https designsystem digital gov whats new updates 2022 04 28 introducing uswds 3 0 read our uswds 3 0 migration guide https designsystem digital gov documentation migration getting started we re glad you d like to use the design system here s how you can get started designers check out our getting started for designers information https designsystem digital gov documentation getting started for designers developers check out our getting started for developers information https designsystem digital gov documentation getting started for developers if your project doesn t use npm for package management follow the instructions in this readme to install the design system without npm installing the design system anyone take a look at our new uswds tutorial https github com uswds uswds tutorial follow the instructions in this github repo to clone a sample project install uswds customize it and add uswds components it should take about an hour and is a good introduction to uswds concepts what s included in uswds the uswds package includes compiled assets in a dist directory and component source files in a packages directory as of uswds 3 0 0 our codebase is centered around functional packages typically components for more about how we organize packages see our packages documentation https designsystem digital gov components packages in each of the following examples we use package to represent a specific package for example component sass is located in packages package src styles for an accordion this would be packages usa accordion src styles fonts are located in both dist fonts and packages uswds core src assets fonts the fonts in dist are simply a copy of the files in uswds core images and icons are located in dist img the source for component specific images can be found in a package s src img directory javascript for components is located in packages package src index js general javascript utilities and polyfills are located in the uswds core package packages uswds core src js sass component specific stylesheets are located in packages package src styles many components also have a component entry point at packages package index scss that includes references to all a component s dependencies as well compiled css is located in dist css template markup for the components is located in packages package src package twig in the site root these however are written in the templating language twig it s best to get html source markup directly from designsystem digital gov components https designsystem digital gov components directory structure here s what you can expect to find inside the uswds package uswds package storybook dist css uswds css uswds min css uswds min css map fonts merriweather public sans roboto mono source sans pro img usa icons material icons uswds icons usa icons bg sprite svg individual images js uswds init js uswds init min js uswds init min js map uswds js uswds min js uswds min js map scss stylesheets uswds scss theme uswds theme scss uswds theme custom styles scss styles scss packages usa component src content styles index scss component scss test component spec js template html index js usa component stories js usa component twig index scss usa component usa component uswds bundle uswds bundle src img stylesheets test tasks package contents here s what you can expect to find in each of the directories and files in the uswds package storybook storybook configuration files not used in uswds projects dist compiled or collected files dist css precompiled css files with uswds defaults dist fonts default fonts available to the design system dist img all uswds images collected into a single directory dist img usa icons all icons collected into the uswds icon sprite sprite svg dist img material icons all material icons dist img uswds icons all icons created by uswds dist img sprite svg precompiled icon sprite with default icon set dist js precompiled javascript files dist scss stylesheets uswds scss backwards compatible uswds sass entry point dist scss theme example theme files dist scss theme uswds theme scss example theme settings file dist scss theme uswds theme custom styles scss example custom settings file dist scss theme styles scss example project sass entry point packages source files for uswds components and other functionality packages usa component each package has a name like usa component that matches its class name in the design system like usa accordion packages usa component index scss sass entry point for the package packages usa component src package source files packages usa component src index js package javascript packages usa component src usa component stories js storybook setup packages usa component src usa component twig component template packages usa component src index js package javascript packages usa component src content package template content packages usa component src test package unit tests packages usa component src styles package source sass packages uswds the package most projects include by default this bundle includes all uswds components and functionality packages uswds bundle other non component functionality is included in uswds prefixed packages these bundles might collect common component packages uswds form controls or important internal functionality uswds core src placeholders included for backwards compatibility most projects should avoid using the contents of this directory tasks internal build process files not used in uswds projects installing the design system there are two ways to install the design system on a project installing it as a project dependency using node and npm installing the package directly from github we recommend using npm to make it as straightforward as possible to install the design system and update it as we release new versions install using node and npm npm is a package manager for node based projects uswds maintains the uswds uswds package https www npmjs com package uswds that includes both the pre compiled and compiled files npm packages make it easy to update and install the design system from the command line 1 install node npm below is a link to find the install method that coincides with your operating system node v12 13 2 current lts installation guides https nodejs org en download note for windows users if you are using windows and are unfamiliar with node or npm we recommend following team treehouse s tutorial http blog teamtreehouse com install node js npm windows for more information 2 make sure you have installed it correctly shell npm v 6 13 0 this line may vary depending on what version of node you ve installed 3 create a package json file you can do this manually but an easier method is to use the npm init command this command will prompt you with a few questions to create your package json file 4 add uswds uswds to your project s package json shell npm install save uswds uswds latest the uswds uswds module is now installed as a dependency you can use the compiled files found in the node modules uswds uswds dist directory or the source files in the node modules uswds uswds packages directory note we do not recommend directly editing the design system files in node modules one of the benefits of using a package manager is its ease of upgrade and installation if you make customizations to the files in the package any upgrade or re installation will wipe them out install the package directly from github if you re using a framework or package manager that doesn t support npm you can find the source files in this repository and use them in your project otherwise we recommend that you follow the steps outlined in this section 1 download the uswds package https github com uswds uswds releases download v3 6 1 uswds uswds 3 6 1 tgz directly from the latest uswds release and uncompress that file 2 copy these files and folders into a relevant place in your project s code base here is an example structure for how this might look example project assets uswds dist packages src stylesheets images javascript index html you ll notice in our example above that we also outline a stylesheets images and javascript folder in your assets folder these folders are to help organize any assets that are unique to your project and separate from the design system assets using uswds css and javascript in your project the three files critical to any uswds project are the stylesheet the javascript and the initializer most projects require all of these to function properly stylesheet this is the compiled css stylesheet that describes how design system components look to start reference either uswds css or uswds min css in the head of your document find this file in dist css most projects will want to compile their own css from uswds source sass instead of using the precompiled version for more about this see compiling uswds sass into css compiling uswds sass into css below library this is the compiled javascript that controls component interactivity reference either uswds js or uswds min js at the end of the body of your document find this file in dist js initializer this small javascript file less than 1 kb minified helps the browser know if the uswds javascript library is loading properly this prevents component content from flashing or shifting while the page loads reference uswds init min js in the head of your page or inline its contents directly into the script tag find this file in dist js reference the stylesheet library and initializer in each html page or dynamic template in your project here is an example of how to reference these assets in your index html file html doctype html html head meta charset utf 8 meta http equiv x ua compatible content ie edge meta name viewport content width device width initial scale 1 0 title my example project title script src assets uswds dist js uswds init min js script link rel stylesheet href assets uswds dist css uswds min css head body script src assets uswds dist js uswds min js script body html and that s it you should now be able to copy our code samples into your index html and start using the design system compiling uswds sass into css if you want to take full advantage of uswds custom settings and add build new styles and components with the uswds toolset you ll need a way to access the assets in the uswds package and compile custom css from the uswds source files uswds uses the task manager gulp http gulpjs com as a way to add uswds assets to a project and compile our css from the package source gulp is a useful and powerful tool but it can be difficult to set up if you are new to it the uswds compile package https github com uswds uswds compile is made for developers new to gulp or those who just want a simple setup to compile uswds sass the repo contains files and instructions for setting up the compiler initializing uswds and compiling css from the source files sass compilation requirements uswds sass needs three things to compile properly sass module syntax uswds requires a modern sass compiler that can parse sass module syntax autoprefixing uswds requires autoprefixing your css with a specific browserslistrc sass load paths uswds requires sass compilers use load paths that reference the packages directory in the uswds package note using a compiler package like uswds compile https github com uswds uswds compile is a good way to fulfill these requirements automatically autoprefixing the design system requires autoprefixing to work properly don t add vendor prefixes to your custom styles manually it is more reliable to use autoprefixing autoprefixing services like gulp autoprefixer https github com sindresorhus gulp autoprefixer automatically add vendor prefixes to css rules we use the following autoprefixer settings via browserslistrc config 2 last 2 versions ie 11 not dead sass load paths uswds 3 0 and newer require the use of sass load paths https sass lang com documentation at rules use load paths to compile properly uswds 3 0 load paths must include a path to the packages directory in the uswds package typically by updating an includepaths setting to include node modules uswds uswds packages here s how this might look in gulp and in webpack gulp js pipe sass includepaths node modules uswds uswds packages webpack js loader sass loader options sassoptions includepaths node modules uswds uswds packages other useful compiler postprocessing minification we recommend using a minifier like csso https github com css csso to compress your final compiled css sourcemaps we recommend using a sourcemap tool like gulp sourcemaps https www npmjs com package gulp sourcemaps to assist debugging by keeping track of source sass locations sass and theme settings the design system is customizable using the power of sass syntactically awesome style sheets http sass lang com the critical files you ll need in your project are those in dist scss theme uswds theme scss custom theme settings uswds theme custom styles scss additional project css for customizing or adding to what uswds provides styles scss the sass entry point this is the primary sass file that you ll compile it collects theme settings uswds source files and custom css styles scss looks something like the following code it adds all the project theme settings then adds uswds source and finally adds your project s custom styles scss forward uswds theme forward uswds forward uswds theme custom styles technical note the forward uswds statement above references the uswds package in node modules uswds uswds packages the compile functions included in uswds compile https github com uswds uswds compile automatically look for uswds packages in the proper directory using includepaths js customization unfortunately customizing the javascript for the uswds currently requires nodejs and a module bundler like browserify or webpack we apologize for this inconvenience and are working to resolve it in a future release of the design system uswds javascript is separated into components just as with the css and html and initialized with event handlers when the dom is ready these components are accessible as commonjs modules that can be required in other javascript files then built for the browser the components are not accessible in the global browser scope but can be extended to be included by requiring components and setting it to a global scope js window uswds require components each component has a standardized interface that can be used to extend it further the components store a html class like usa accordion button aria controls used to link html elements with the javascript component when a component is initialized it searches through the current html dom to find all elements that match the class and initializes the component javascript for those elements the primary methods for each component include on initialize a component s javascript behavior by passing the root element such as window document off the opposite of on de initializes a component removing any javascript event handlers on the component hide hide the whole component show shows a whole hidden component toggle toggles the visibility of a component on and off based on the previous state some components have additional methods based on that component s functionality any additional methods are found in that component s javascript file if you re using a modern framework like react or angular you can import components and initialize them in your library s dom ready lifecycle event importing a modular component js import uswds from uswds uswds js const charactercount accordion uswds deconstruct your components here alternatively import accordion from uswds uswds js usa accordion requires webpack 5 react hooks example js function app const ref document body useeffect initialize charactercount on ref default ref is document body if you want to use default you do not have to pass arguments accordion on remove event listeners when the component un mounts return charactercount off accordion off angular example js export class app implements oninit constructor this ref document body default ref is document body if you want to use default you do not have to pass arguments ngoninit initialize charactercount on this ref accordion on remove event listeners when the component un mounts ngondestroy charactercount off accordion off style theming and tokens uswds 3 0 provides extensive support for theming via its theme settings files introduced in sass and theme settings sass and theme settings above set theme settings with uswds design tokens not with values directly they tend to be quoted strings like desktop or md or unitless numbers like 2 or 1 5 tokens are the values passed into the uswds functions and mixins that parse them they are the keys that through the mechanism of a function or mixin unlock a value they are not the values themselves visit the design tokens section https designsystem digital gov design tokens of uswds 3 0 documentation for more on the available tokens for color https designsystem digital gov design tokens color spacing units https designsystem digital gov design tokens spacing units font size https designsystem digital gov design tokens typesetting font size and more using tokens in theme settings the following is an example of theme settings from uswds theme scss scss use uswds core with theme grid container max width desktop theme site margins breakpoint desktop theme site margins width 4 theme site margins mobile width 2 the uswds uses those tokens to build component styles scss usa example include u padding x theme site margins mobile width max width units theme grid container max width include at media theme site margins breakpoint include u padding x theme site margins width this is the functional equivalent of scss usa example include u padding x 2 max width units desktop include at media desktop include u padding x 4 which if theme respect user font size is set to true would output something like css usa example padding left 1rem padding right 1rem max width 64rem media screen and min width 64em usa example padding left 2rem padding right 2rem in general uswds sets variables with tokens and passes those variables into functions and mixins in the source sass set the base asset paths fonts and images the values of theme font path and theme image path will be appended to uswds font paths and image paths respectively scss use uswds core with theme font path fonts theme image path img css architecture the css foundation of this site is built with the sass https sass lang com preprocessor language the css organization and naming conventions follow 18f s engineering guide https engineering 18f gov css naming we format our code with prettier https prettier io per the formatting section of the 18f engineering guide https engineering 18f gov css formatting css selectors are prefixed with usa for example usa button this identifier helps the design system avoid conflicts with other styles on a site which are not part of uswds uses a bem http getbem com approach for naming css selectors blocks are separated from elements with two underscores multi word blocks use single hyphens instead of spaces modifier classes are additive proper markup requires the base class and the modifier class or classes modifier classes consist of the base class plus a modifier suffix separated by two hyphens as in usa button usa button secondary or usa accordion usa accordion bordered uses modular css for scalable modular and flexible code uses nesting when appropriate nest minimally with up to two levels of nesting hard coded magic numbers are avoided media queries are built mobile first spacing units are set with the units function as described in the uswds 3 0 documentation https designsystem digital gov design tokens spacing units in general we use spacing in multiples of 8px expressed as a multiple in units multiple for instance units 2 is the equivalent of 2 8px or 16px in the final compiled css this value will be expressed in rem as a multiple of the base font size set with theme base font size for more information visit 18f s css guide https engineering 18f gov css browser support we ve designed the design system to support older and newer browsers through progressive enhancement https en wikipedia org wiki progressive enhancement the current major version of the design system 3 0 0 follows the 2 rule https gds blog gov uk 2012 01 25 support for browsers we officially support any browser above 2 usage as observed by analytics usa gov https analytics usa gov currently this means that the design system version 3 0 0 supports the newest versions of chrome firefox and safari as of uswds 3 0 0 we no longer officially support internet explorer 11 ie11 we will continue to include ie11 polyfills and prefixing for the first few releases in uswds 3 x when we finally drop ie11 we ll make a note in the release notes and in this documentation accessibility the design system also meets the wcag 2 0 aa accessibility guidelines https www w3 org tr wcag20 and conforms to the standards of section 508 of the rehabilitation act http www section508 gov additionally we try to meet the requirements of wcag 2 1 https www w3 org tr wcag21 we use the following tools to ensure uswds is accessible andi https www ssa gov accessibility andi help install html axe core https www deque com axe axe dev tools https chrome google com webstore detail axe devtools web accessib lhdoppojpmngadmnindnejefpokejbdd hl en us if you find any issues with our accessibility conformance please create an issue in our github repo or send us an email at uswds gsa gov mailto uswds gsa gov we prioritize accessibility issues see the accessibility page of our website https designsystem digital gov documentation accessibility for more information long term support of v1 x version 1 x https v1 designsystem digital gov is no longer maintained long term support of v2 x version 2 x is in maintenance mode and will continue to get important bugfixes and security patches until may 2023 need installation help do you have questions or need help with setup did you run into any weird errors while following these instructions feel free to open an issue here https github com uswds uswds issues https github com uswds uswds issues you can also email us directly at uswds gsa gov mailto uswds gsa gov contributing to the code base for complete instructions on how to contribute code please read contributing md contributing md these instructions also include guidance on how to set up your own copy of the design system style guide website for development if you would like to learn more about our workflow process check out the workflow https github com uswds uswds wiki workflow and issue label glossary https github com uswds uswds wiki issue label glossary pages on the wiki if you have questions or concerns about our contributing workflow please contact us by filing a github issue https github com uswds uswds issues or emailing our team mailto uswds gsa gov reuse of open source style guides much of the guidance in uswds leans on open source designs code and patterns from other civic and government organizations including consumer financial protection bureau s design manual https cfpb github io design manual u s patent and trademark office s design patterns http uspto github io designpatterns healthcare gov style guide http styleguide healthcare gov uk s government digital service s ui elements http govuk elements herokuapp com code for america s chime styleguide https github com chimecms chime starter pivotal labs component library http styleguide cfapps io licenses and attribution a few parts of this project are not in the public domain attribution and licensing information for those parts are described in detail in license md license md the rest of this project is in the worldwide public domain released under the cc0 1 0 universal public domain dedication https creativecommons org publicdomain zero 1 0 contributing all contributions to this project will be released under the cc0 dedication alongside the public domain portions of this project for more information see contributing md contributing md | design-systems design government uswds | os |
chatter | chatter a library of natural language processing algorithms for haskell api documentation is best viewed on hackage http hackage haskell org package chatter stack build on windows stack build install ghc | ai |
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llm | llm large language models for everyone in rust llm is an ecosystem of rust libraries for working with large language models it s built on top of the fast efficient ggml crates ggml library for machine learning a llama riding a crab ai generated doc img llm crab llama png image by darthdeus https github com darthdeus using stable diffusion latest version https img shields io crates v llm svg https crates io crates llm mit apache2 https shields io badge license mit 2fapache 2 0 blue discord https img shields io discord 1085885067601137734 https discord gg yb9waxyawu the primary entrypoint for developers using llm in a rust project is the llm crate crates llm which wraps llm base crates llm base and the supported model crates models crates documentation https docs rs llm for released version is available on docs rs for end users there is a cli application using the llm cli llm cli binaries llm cli which provides a convenient interface for interacting with supported models text generation running can be done as a one off based on a prompt or interactively through repl or chat does the llm cli support chat mode modes the cli can also be used to serialize print decoded models quantize crates ggml readme md quantization ggml files or compute the perplexity https huggingface co docs transformers perplexity of a model it can be downloaded from the latest github release https github com rustformers llm releases or by installing it from crates io llm is powered by the ggml https github com ggerganov ggml tensor library and aims to bring the robustness and ease of use of rust to the world of large language models at present inference is only on the cpu but we hope to support gpu inference in the future through alternate backends currently the following models are supported bloom https huggingface co docs transformers model doc bloom gpt 2 https huggingface co docs transformers model doc gpt2 gpt j https huggingface co docs transformers model doc gptj gpt neox https huggingface co docs transformers model doc gpt neox includes stablelm https github com stability ai stablelm redpajama https www together xyz blog redpajama and dolly 2 0 https www databricks com blog 2023 04 12 dolly first open commercially viable instruction tuned llm llama https huggingface co docs transformers model doc llama includes alpaca https crfm stanford edu 2023 03 13 alpaca html vicuna https lmsys org blog 2023 03 30 vicuna koala https bair berkeley edu blog 2023 04 03 koala gpt4all https gpt4all io index html and wizard https github com nlpxucan wizardlm mpt https www mosaicml com blog mpt 7b see getting models getting models for more information on how to download supported models using llm in a rust project this project depends on rust v1 65 0 or above and a modern c toolchain the llm crate exports llm base and the model crates e g bloom gpt2 llama add llm to your project by listing it as a dependency in cargo toml to use the version of llm you see in the main branch of this repository add it from github although keep in mind this is pre release software toml dependencies llm git https github com rustformers llm branch main to use a released https github com rustformers llm releases version add it from crates io https crates io crates llm by specifying the desired version toml dependencies llm 0 1 by default llm builds with support for remotely fetching the tokenizer from hugging face s model hub to disable this disable the default features for the crate and turn on the models feature to get llm without the tokenizer toml dependencies llm version 0 1 default features false features models note to improve debug performance exclude the transitive ggml sys dependency from being built in debug mode toml profile dev package ggml sys opt level 3 leverage accelerators with llm the llm library is engineered to take advantage of hardware accelerators such as cuda and metal for optimized performance to enable llm to harness these accelerators some preliminary configuration steps are necessary which vary based on your operating system for comprehensive guidance please refer to acceleration support doc acceleration support md in our documentation using llm from other languages bindings for this library are available in the following languages python llukas22 llm rs python https github com llukas22 llm rs python node atome fe llama node https github com atome fe llama node using the llm cli the easiest way to get started with llm cli is to download a pre built executable from a released https github com rustformers llm releases version of llm but the releases are currently out of date and we recommend you install from source installing from source instead installing from source to install the main branch of llm with the most recent features to your cargo bin directory which rustup is likely to have added to your path run shell cargo install git https github com rustformers llm llm cli the cli application can then be run through llm see also features features and acceleration support doc acceleration support md to turn features on as required note that gpu support cuda opencl metal will not work unless you build with the relevant feature installing with cargo note that the currently published version is out of date and does not include support for the most recent models we currently recommend that you install from source installing from source to install the most recently released version of llm to your cargo bin directory which rustup is likely to have added to your path run shell cargo install llm cli the cli application can then be run through llm see also features features to turn features on as required features by default llm builds with support for remotely fetching the tokenizer from hugging face s model hub this adds a dependency on your system s native ssl stack which may not be available on all systems to disable this disable the default features for the build shell cargo build release no default features to enable hardware acceleration see acceleration support for building section doc acceleration support md which is also applicable to the cli getting models ggml models are easy to acquire they are primarily located on hugging face see from hugging face from hugging face but can be obtained from elsewhere models are distributed as single files and do not need any additional files to be downloaded however they are quantized with different levels of precision so you will need to choose a quantization level that is appropriate for your application additionally we support hugging face tokenizers to improve the quality of tokenization these are separate files tokenizer json that can be used with the cli using the v or r flags or with the llm crate by using the appropriate tokenizersource enum variant for a list of models that have been tested see the known good models doc known good models md certain older ggml formats are not supported by this project but the goal is to maintain feature parity with the upstream ggml project for problems relating to loading models or requesting support for supported ggml model types https github com ggerganov ggml roadmap please open an issue https github com rustformers llm issues new from hugging face hugging face is a leader in open source machine learning and hosts hundreds of ggml models search for ggml models on hugging face https huggingface co models search ggml r localllama this reddit community maintains a wiki related to ggml models including well organized lists of links for acquiring ggml models https www reddit com r localllama wiki models mostly from hugging face usage once the llm executable has been built or is in a path directory try running it here s an example that uses the open source redpajama https huggingface co rustformers redpajama ggml blob main redpajama incite base 3b v1 q4 0 bin language model shell llm infer a gptneox m redpajama incite base 3b v1 q4 0 bin p rust is a cool programming language because r togethercomputer redpajama incite base 3b v1 in the example above the first two arguments specify the model architecture and command respectively the required m argument specifies the local path to the model and the required p argument specifies the evaluation prompt the optional r argument is used to load the model s tokenizer from a remote hugging face repository which will typically improve results when compared to loading the tokenizer from the model file itself there is also an optional v argument that can be used to specify the path to a local tokenizer file for more information about the llm cli use the help parameter there is also a simple inference example crates llm examples inference rs that is helpful for debugging vscode launch json shell cargo run release example inference gptneox redpajama incite base 3b v1 q4 0 bin r optional vocab repo p optional prompt q a does the llm cli support chat mode yes but certain fine tuned models e g alpaca https crfm stanford edu 2023 03 13 alpaca html vicuna https lmsys org blog 2023 03 30 vicuna pygmalion https docs alpindale dev are more suited to chat use cases than so called base models here s an example of using the llm cli in repl read evaluate print loop mode with an alpaca model note that the provided prompt format utils prompts alpaca txt is tailored to the model that is being used shell llm repl a llama m ggml alpaca 7b q4 bin f utils prompts alpaca txt there is also a vicuna chat example crates llm examples vicuna chat rs that demonstrates how to create a custom chatbot shell cargo run release example vicuna chat llama ggml vicuna 7b q4 bin can llm sessions be persisted for later use sessions can be loaded load session or saved save session to file to automatically load and save the same session use persist session this can be used to cache prompts to reduce load time too how do i use llm to quantize a model llm can produce a q4 0 or q4 1 quantized crates ggml readme md quantization model from an f16 quantized ggml model shell cargo run release quantize a model architecture model in model out q4 0 q4 1 do you provide support for docker and nixos the llm dockerfile utils dockerfile is in the utils directory the nixos flake flake nix manifest and lockfile are in the project root what s the best way to get in touch with the llm community github issues https github com rustformers llm issues new and discussions https github com rustformers llm discussions new are welcome or come chat on discord https discord gg yb9waxyawu do you accept contributions absolutely please see the contributing guide doc contributing md what applications and libraries use llm applications llmcord https github com rustformers llmcord discord bot for generating messages using llm local ai https github com louisgv local ai desktop app for hosting an inference api on your local machine using llm secondbrain https github com juliooa secondbrain desktop app to download and run llms locally in your computer using llm floneum https floneum com a graph editor for local ai workflows poly https github com pixelspark poly a versatile llm serving back end with tasks streaming completion memory retrieval and more libraries llm chain https github com sobelio llm chain build chains in large language models for text summarization and completion of more complex tasks | ai ggml llm ml rust | ai |
Web-Dev-Projects-Level-2 | web dev projects level 2 the community to do open source contribution and learn the web development using level 2 tasks webdev open source repository description for hacktoberfest participation welcome to the hacktoberfest participation repository hacktoberfest is an annual celebration of open source software and we re excited to have you join us this repository is your gateway to getting involved and making meaningful contributions to the world of open source what is hacktoberfest hacktoberfest is a month long celebration of open source software in partnership with github during october open source enthusiasts developers and newcomers from around the globe come together to contribute to open source projects learn and share their knowledge how to participate 1 fork this repository start by forking this repository to your github account this will create a copy of the repository under your account 2 clone your fork clone the forked repository to your local development environment using git clone 3 make contributions browse through the existing issues or create new ones if you have ideas for improvements bug fixes or new features you can also check for specific contribution guidelines provided in the repository 4 write code write your code and make your changes be sure to follow best practices write clean and well documented code and adhere to the coding style of the project 5 test your code before submitting a pull request thoroughly test your changes to ensure they work as expected this may include writing tests if they don t already exist 6 create a pull request submit a pull request pr to the original repository in your pr description explain the purpose of your changes and any relevant details reference any related issues if applicable 7 review and collaborate collaborate with maintainers and other contributors to address feedback and make improvements to your code 8 celebrate hacktoberfest your prs to this repository during the hacktoberfest period will count towards your participation in the event if you make at least four valid prs you ll earn a limited edition hacktoberfest t shirt guidelines be respectful and inclusive we welcome contributors from all backgrounds and skill levels ensure that your contributions align with the goals and scope of this repository follow the code of conduct and contribution guidelines provided in this repository join the open source community hacktoberfest is not just about contributing code it s also about joining the open source community learning from others and having fun we re excited to have you as part of this vibrant and collaborative community ready to get started fork this repository and let s make open source magic together happy hacking hacktoberfest official website https hacktoberfest digitalocean com | hactoberfest hactoberfest-accepted hactoberfest2023 hactoberfest-approved hactoberfest-starter hacktoberfest hacktoberfest-accepted hacktoberfest2023 | front_end |
IOT | iot iot related use cases | server |
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RMS-EDF | rms edf program simulation rms and edf scheduling in rtos using python | rtos python scheduling | os |
cv-assignment1 | computer vision assignment 1 hybrid images generate hybrid image from the given two input images 2 corner detection given an image find corners using harris detector and shi tomasi detector 3 scale space blob detector generate scale space blobs using laplacian of guassian filter | ai |
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LockerX | lockerx our advanced software engineering module project we developed an android application with android studio java and using firebase realtime database which digitalises the locker renting process users can rent lockers for future or immediate use and pay for the locker using the application moreover the locking and unlocking of lockers are also done through the application via bluetooth to simulate a locker in our prototype we used an arduino uno with the hc 05 bluetooth module and several leds | server |
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shiny-ob-analytics | obanalytics microstructure visualisation project status active the project has reached a stable usable state and is being actively developed http www repostatus org badges latest active svg http www repostatus org active license http img shields io badge license gpl 20 28 3e 202 29 blue svg style flat http www gnu org licenses gpl 2 0 html shiny obanalytics is an experimental tool demo developed using the shiny https github com rstudio shiny web application framework for r the tool demonstrates use of obanalytics https github com phil8192 ob analytics an r package created to visualise and explore limit order book http parasec net transmission order book visualisation data included data the data is based on 8 days of limit order book events obtained from the bitstamp bitcoin exchange between 2015 08 18 and 2015 08 25 and has been preprocessed with obanalytics https github com phil8192 ob analytics the time period covers a substantial depreciation 24 over 7 days and a significant market crash 12 in 30 minutes quick guide the purpose of the tool is to be able to zoom in to specific periods in time and explore visualise various aspects of market microstructure the interface is divided into 2 sections the left hand side containing the time period price and volume filters and the right hand side containing a number of tabs each of which are described briefly below more detailed information can be found in the obanalytics https github com phil8192 ob analytics repository order book order book https raw githubusercontent com phil8192 shiny ob analytics master screens order book png order book the first tab describes the limit order book for a specific point in time the graph in the top panel shows the cumulative volume on each side of the order book blue bid red ask along with an indication of the largest 99 quantile orders the actual limit order book is shown below the graph limited to 100 bps in depth price level volume price level volume https raw githubusercontent com phil8192 shiny ob analytics master screens price level volume png price level volume the price level volume graph shows the amount of volume available at each price level in the order book through time the graph has been colour coded such that lower quantities of volume appear in blue whilst higher amounts of volume appear red since volume size is exponentially distributed the interface provides an option to weight the colour coding or use a logarithmic scale depth percentiles depth percentiles https raw githubusercontent com phil8192 shiny ob analytics master screens depth percentiles png depth percentiles the depth percentile plot is shown below the price level volume graph it shows the amount of volume available in 25 bps increments above and below the best bid and ask through time it is intended to depict market liquidity order events order events https raw githubusercontent com phil8192 shiny ob analytics master screens order events png order events the order events tab shows the point in time at which limit orders are added or removed from the order book here red points correspond to ask side orders whilst blue correspond to bids an opaque circle corresponds to an add event whilst transparent corresponds to an order cancellation the size of the point is determined by the amount of volume the graph is intended to show identifiable systematic activity cancellations cancellations https raw githubusercontent com phil8192 shiny ob analytics master screens cancellations png cancellations the cancellation tab shows volume cancellation through time each point red ask blue bid corresponds to a cancelled limit order y axis volume the intention of the graph is to identify the behaviour of individual market participants trades and events events https raw githubusercontent com phil8192 shiny ob analytics master screens events png events in addition to visualisation the interface includes an interactive datatables https github com datatables datatables view of trade and individual limit order events add update delete filtering all of the above graphs and tables may be filtered by time price and volume if not specified the tool will try to automatically determine the price and volume range based on the distribution of events for convenience price and volume histograms are shown to help determine the filtering range installing locally bash clone repository git clone https github com phil8192 shiny ob analytics git r r install shiny and obanalytics install packages shiny install packages devtools library devtools install github phil8192 ob analytics running locally bash set launch browser f to not launch a browser cd shiny ob analytics r silent e shiny runapp launch browser t via github r install packages shiny library shiny rungithub shiny ob analytics phil8192 via shinyapps io r install packages devtools library devtools install github rstudio shinyapps install github phil8192 ob analytics library shinyapps setaccountinfo name account token token secret secret deployapp location of shiny ob analytics demo a demo is available on shinyapps io please allow a few seconds for the data to load https infrared shinyapps io shiny ob analytics license gpl 2 | bitcoin limit-order-book visualisation r shiny | front_end |
MyDex-Pokedex-Clone | mydex pokedex clone license badge mit https img shields io badge license mit blue svg https opensource org licenses mit a clone of the pokedex where you can create an account save your favorite pokemon or teams and then show them off to friends you added via your pokeprofile this app was made using xampp apache server with php sass for templating and google firebase baas backend as a service with firestore screenshot of application assets images design2 jpg table of contents usage usage installation installation licensing licensing questions questions usage you can view the deployed application at https mydex pokedex clone herokuapp com installation download or install xampp you only need apache server sql php filezilla server find the htdocs folder located in the newly created xampp folder git clone git github com strawhat19 mydex pokedex clone git into htdocs folder start xampp control panel start xampp apache server go to localhost mydex pokedex clone or localhost mydex pokedex clone index php in your browser licensing license badge mit https img shields io badge license mit blue svg https opensource org licenses mit mit license copyright c 2012 2020 by various contributors see authors permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software copyright 1998 by the massachusetts institute of technology permission to use copy modify and distribute this software and its documentation for any purpose and without fee is hereby granted provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation and that the name of m i t not be used in advertising or publicity pertaining to distribution of the software without specific written prior permission m i t makes no representations about the suitability of this software for any purpose it is provided as is without express or implied warranty https opensource org licenses mit questions github profile strawhat19 https github com strawhat19 contact me or ask me questions at rakib987 gmail com mailto rakib987 gmail com | javascript sass php firebase css html | server |
xenfyi | xen fyi xen fyi https xen fyi is an alternative frontend for xen network https xen network this project was built to help provide users with multiple options to interact with the xen protocol setup download sh git clone git github com atomizexyz xenfyi git cd xenfyi yarn install run sh yarn dev build sh yarn build environment variables next public ga measurement id google analytics measurement id next public alchemy id alchemy id next public infura id infura id next public chain network mainnet testnet | crypto front-end react xen xencrypto | front_end |
Analysis-of-college-database-of-2017-passouts | this repo contains my analysis of a dataset containing information about the students of an engineering college the dataset consists of the following columns sl no student s college id department abbr gender m f age in yrs name of examination class x name of board class x name of school in full class x subject comination do not use abbr class x medium of instruction class x y o p class x standard of class x actual of class x total marks obtained in all the subjects in class x total marks of exam appeared in class x name of examination class xii name of board council class xii name of school in full class xii subject comination do not use abbr class xii medium of instruction class xii y o p class xii standard of class xii actual of class xii total marks obtained in all the subjects in class xii total marks of exam appeared in class xii diploma stream diploma university board council in full name of institute in full diploma medium of instruction diploma y o p diploma diploma aggregate marks name of joint entrance wbjee jee mains jelet etc entrance rank overall all india rank entrance rank state rank year of joint entrance exam current course current stream year of entry year of passing medium of instruction sem 1 sem 2 sem 3 sem 4 sem 5 sem avg any backlog s in current course yes no if yes mention number of backlog s overall year gap s in academic career yes no if yes mention the duration s yyyy yyyy total gap in year s core technical strength computer languages known project title industrial training v t internship name of the organization duration from dd mm yyyy to dd mm yyyy any seminars workshops attended work experience if any months years as applicable name of the organization served work experience achievements academic achievements non academic certifications academic certifications non academic permanent location state i think the column names are quite self explanatory so i am skipping the broad data dictionary part throughout this analysis i ask several questions to the dataset itself and eventually find out their answers sometimes the answers were pretty straight forward but sometimes they were not the dataset itself needed data cleaning which made it more fun the sample questions are what is the student count per department what is the highest semester grade obtained by a student from a particular department and so on | data-analysis data-visualization python-3 matplotlib | server |
CS-585-Political-Ideology-Detection | cs585 political ideology detection class project for cs 585 natural language processing poster https github com nithya4 cs 585 political ideology detection blob master poster pdf report https github com nithya4 cs 585 political ideology detection blob master report pdf this project mirrored the work in the paper https cs umd edu miyyer pubs 2014 rnn framing pdf the data ideological books corpus was obtained from the authors of the paper as a pickled file ibcdata pkl https cs umd edu miyyer ibc index html the cnn evaluation was run using a modification from https github com dennybritz cnn text classification tf | ai |
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esp-adv-button | a rel license href http creativecommons org licenses by nc nd 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by nc nd 4 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by nc nd 4 0 creative commons attribution noncommercial noderivatives 4 0 international license a esp advanced button library library for esp open rtos sdk to manage inputs from built in gpios and mcp23017 interfaces when mcp23017 is used gpios must be declared with this c adv button create gpio mcp index 100 i2c bus inverted mcp address where mcp index is the number from 1 to enumerate all mcp boards in case that several boards are used requirements timers helper https github com ravensystem timers helper advanced i2c https github com ravensystem adv i2c c advanced button manager example copyright 2019 2022 jos antonio jim nez campos ravensystem include stdio h include esp uart h include esp8266 h include freertos h include adv button h ifndef button gpio define button gpio 0 endif ifndef toggle gpio define toggle gpio 2 endif void singlepress callback const uint16 t gpio void args const uint8 t param printf example button single press function called using gpio i n gpio void doublepress callback const uint16 t gpio void args const uint8 t param printf example button double press function called using gpio i n gpio void longpress callback const uint16 t gpio void args const uint8 t param printf example button long press function called using gpio i n gpio void verylongpress callback const uint16 t gpio void args const uint8 t param printf example button very long press function called using gpio i n gpio void holdpress callback const uint16 t gpio void args const uint8 t param printf example button hold press function called using gpio i n gpio void toggle callback const uint16 t gpio void args const uint8 t param printf example button toggle function called using gpio i n gpio void user init void uart set baud 0 115200 printf n adv button example n n adv button set evaluate delay 10 adv button create button gpio true false adv button normal mode adv button register callback fn button gpio singlepress callback singlepress type null 0 adv button register callback fn button gpio doublepress callback doublepress type null 0 adv button register callback fn button gpio longpress callback longpress type null 0 adv button register callback fn button gpio verylongpress callback verylongpress type null 0 adv button register callback fn button gpio holdpress callback holdpress type null 0 adv button create toggle gpio true false adv button normal mode adv button register callback fn toggle gpio toggle callback invsinglepress type null 0 low adv button register callback fn toggle gpio toggle callback singlepress type null 0 high | os |
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gh-polls-web | gh polls web generate polls for github issues web front end for github polls https github com tj gh polls preview preview png build setup bash install dependencies npm install serve with hot reload at localhost 8080 npm run dev build for production with minification npm run build build for production and view the bundle analyzer report npm run build report for detailed explanation on how things work checkout the guide http vuejs templates github io webpack and docs for vue loader http vuejs github io vue loader | github-polls app polls | front_end |
cashier-backend | cashier backend backend development of a supermarket cashier app you walk into a spar supermarket grabbed a trolley walked around and picked various products in several quantities then you walked to the cashier where she get each products out of the trolley and calculate your total cost then issue you a payment receipt this program does the following 1 have predefined product and cost 2 the system generate an invoice with the amount the customer would pay user then makes a selection from the list above 1 7 after the item is selected the system then prompt the user for the quantity then the system calculate the cost of that item and asks if any other item was bought and allows slection again it keeps doing this till the cashier terminate that no more item is purchases with 0 the system prints the total cost of item bought as well as the item sample display hello welcome to spar super market yaba kindly select 1 7 the list of item purchase and press 0 to display the invoice 1 dettol soap 200 2 juice 550 3 body cream 2000 4 plantain chips 2500 5 crispy chicken 1250 6 chocolate 120 7 plantain 300 0 exit 1 you added detol soap to your cart items detol soap 200 total 200 kindly select 1 7 the list of item purchase and press 0 to display the invoice 1 dettol soap 200 2 juice 550 3 body cream 2000 4 plantain chips 2500 5 crispy chicken 1250 6 chocolate 120 7 plantain 300 0 exit 3 you added body cream to your cart items detol soap 200 body cream 2000 total 2200 kindly select 1 7 the list of item purchase and press 0 to display the invoice 1 dettol soap 200 2 juice 550 3 body cream 2000 4 plantain chips 2500 5 crispy chicken 1250 6 chocolate 120 7 plantain 300 0 exit 7 you added plantain to your cart items detol soap 200 body cream 2000 plantain 300 total 2500 kindly select 1 7 the list of item purchase and press 0 to display the invoice 1 dettol soap 200 2 juice 550 3 body cream 2000 4 plantain chips 2500 5 crispy chicken 1250 6 chocolate 120 7 plantain 300 0 exit 0 thank you for shopping with us below is your invoice items cost detol soap 200 body cream 2000 plantain 300 total 2500 | server |
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eecs303 | eecs 303 embedded systems design and laboratory lab 1 led lights sequencing on the raspberry pi written using c python and shell lab 2 reading the dht11 temperature humidity sensor through polling i o technique and edge triggered interrupt technique written using c lab 3 connecting a rgb lcd display to display data read from dht11 sensor written using c lab 4 led lights sequencing on the raspberry pi written using assembly lab 5 using arduino uno with raspberry pi 3 test example arduino sketches | os |
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serverless-notifications | serverless notifications serverless service for serverless notifications aws iot tutorial blog post serverless notifications on aws https web archive org web 20200930220520 https zanon io posts serverless notifications on aws nov 5th 2016 note at the time it was not an issue but currently it is necessary to use the ats iot endpoint use iot describeendpoint endpointtype iot data ats the code was fixed in this repository but not in the blog post demo https serverless notifications zanon dev usage 1 with the serverless framework v1 x run serverless install url https github com diegozanon serverless notifications 2 inside the create role folder run npm install and node index to create an iot role i ve named the role as serverless notifications iot if you want to rename modify this file and the handler js file 3 deploy the lambda function with serverless deploy 4 edit the index html file that is placed inside the frontend folder to use your lambda endpoint 5 to modify the iot client follow these steps 5 1 browse the iot folder 5 2 edit the index js file 5 3 install dependencies with npm install 5 4 run node make bundle 5 5 replace the bundle js inside the frontend folder by this new bundle js file 6 deploy the frontend to s3 | serverless aws-iot notifications | server |
Machine-Learning-Engineering-with-Python | machine learning engineering with python a href https www packtpub com product machine learning engineering with python 9781801079259 img src https static packt cdn com products 9781801079259 cover smaller alt machine learning engineering with pythone height 256px align right a this is the code repository for machine learning engineering with python https www packtpub com product machine learning engineering with python 9781801079259 published by packt manage the production life cycle of machine learning models using mlops with practical examples what is this book about machine learning engineering is a thriving discipline at the interface of software development and machine learning this book will help developers working with machine learning and python to put their knowledge to work and create high quality machine learning products and services this book covers the following exciting features find out what an effective ml engineering process looks like uncover options for automating training and deployment and learn how to use them discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions understand what aspects of software engineering you can bring to machine learning gain insights into adapting software engineering for machine learning using appropriate cloud technologies if you feel this book is for you get your copy https www amazon com dp 1801079250 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 default exten s 1 dial zap 1 30 exten s 2 voicemail u100 exten s 102 voicemail b100 exten i 1 voicemail s0 following is what you need for this book this book is for machine learning engineers data scientists and software developers who want to build robust software solutions with machine learning components if you re someone who manages or wants to understand the production life cycle of these systems you ll find this book useful intermediate level knowledge of python is necessary with the following software and hardware list you can run all code files present in the book chapter 1 8 software and hardware list chapter software required os required 1 8 python 3 anaconda docker windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781801079259 colorimages pdf related products other books you may enjoy engineering mlops packt https www packtpub com product engineering mlops 9781800562882 amazon https www amazon com dp 1800562888 machine learning engineering with mlflow packt https www packtpub com product machine learning engineering with mlflow 9781800560796 amazon https www amazon com dp 1800560796 get to know the author andrew peter andy mcmahon is a machine learning engineer and data scientist with experience of working in and leading successful analytics and software teams his expertise centers on building production grade ml systems that can deliver value at scale he is currently ml engineering lead at natwest group and was previously analytics team lead at aggreko he has an undergraduate degree in theoretical physics from the university of glasgow as well as master s and ph d degrees in condensed matter physics from imperial college london in 2019 andy was named data scientist of the year at the international data science awards he currently co hosts the ai right podcast discussing hot topics in ai with other members of the scottish tech scene download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781801079259 https packt link free ebook 9781801079259 a p | ai |
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COMS4995-s19 | applied machine learning spring 2019 materials for the course coms4995 will be posted here you can find more details on the course and an overview of the materials on the course website http www cs columbia edu amueller comsw4995s19 schedule slides are in the slides https amueller github io coms4995 s19 slides subfolder everything in this repository is licensed cc 0 meaning you can do with it whatever you want | ai |
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LLM | llm large language models for multimodal data | ai |
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deep_learning | deep learning note this repo is deprecated and has been replaced with deep learning for computer vision https github com unccv deep learning for computer vision alot changes in deep learning in a year deep learning has recently changed the landscape of computer vision and other fields and is largely responsible for a 3rd wave of interest and excitment about artificial intellgence in this module we ll cover the basics of deep learning for computer vision setup the python 3 anaconda distribution https www anaconda com download is the easiest way to get going with the notebooks and code presented here optional you may want to create a virtual environment for this repository conda create n cv python 3 source activate cv you ll need to install the jupyter notebook to run the notebooks conda install jupyter you may also want to install nb conda enables some nice things like change virtual environments within the notebook conda install nb conda this repository requires the installation of a few extra packages you can install them all at once with pip install r requirements txt optional jupyterthemes https github com dunovank jupyter themes can be nice when presenting notebooks as it offers some cleaner visual themes than the stock notebook and makes it easy to adjust the default font size for code markdown etc you can install with pip pip install jupyterthemes recommend jupyter them for presenting these notebook type into terminal before launching notebook jt t grade3 cellw 90 fs 20 tfs 20 ofs 20 dfs 20 recommend jupyter them for viewing these notebook type into terminal before launching notebook jt t grade3 cellw 90 fs 14 tfs 14 ofs 14 dfs 14 | ai |
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cs231a | cs231a stanford university cs231a computer vision from 3d reconstruction to recognition spring 2017 homework answer cs 231a website http web stanford edu class cs231a syllabus html some python code if you find error please tell me | computer-vision 3d-reconstruction | ai |
blockchain_conference_paper | academic blockchain papers a curated blockchain related academic papers all papers are sorted based on the conference journal name and published year welcome developers or researchers to add more published paper to this list table of listed conferences security privacy crypto networking database software engineering programming language system architecture crypto crypto sigmetrics performance sigmetricperformance icse icse eurosys eurosys eurocrypt eurocrypt icde icde esec fse esecfse acm sosp sosp usenix security usenix security vldb vldb ase ase euros p eurosp ieee s p sp acm sigmod sigmod acm pldi pldi srds srds ndss ndss ieee infocom infocom acm oopsla oopsla acm podc podc acm ccs ccs nsdi nsdi acm ec ec ieee ipdps ipdps ieee dsn dsn acm conext conext issta issta ieee icdcs icdcs fc fc acm mobihoc mobihoc acm popl popl acm socc socc imc imc table of listed journals ieee transaction on knowledger and data engineering tkde tkde acm computing surveys acm csur acmcsur acm distributed ledger technologies research and practice acm dlt acmdlt ieee journal on selected areas in communications jsac ieee transactions on network science and engineering tnse key words topics system architecture consensus proof of x and bft layer 2 off chain payment networks sidechain crosschain network smart contracts application trasactions cryptograph storage light client contents privacy security economics incentive conferences crypto the bitcoin backbone protocol with chains of variable difficulty https eprint iacr org 2016 1048 juan a garay and aggelos kiayias and nikos leonardos crypto 17 ouroboros praos an adaptively secure semi synchronous proof of stake protocol http eprint iacr org 2017 573 pdf bernardo d gazi p kiayias a russell a crypto 17 order fairness for byzantine consensus https eprint iacr org 2020 269 pdf mahimna kelkar fan zhang ari juels crypto 20 eurocrypt consensus pow the bitcoin backbone protocol analysis and applications https eprint iacr org 2014 765 pdf garay j kiayias a leonardos n eurocrypt 15 consensus pow analysis of the blockchain protocol in asynchronous networks https eprint iacr org 2016 454 pdf pass r seeman l abhi shelat eurocrypt 17 consensus thunderella blockchains with optimistic instant confirmation https eprint iacr org 2017 913 pdf rafael pass elaine shi eurocrypt 18 consensus pos ouroboros praos an adaptively secure semi synchronous proof of stake blockchain https eprint iacr org 2017 573 pdf bernardo david peter ga i aggelos kiayias alexander russell eurocrypt 18 consensus consensus through herding https link springer com content pdf 10 1007 2f978 3 030 17653 2 24 pdf t h hubert chan rafael pass elaine shi eurocrypt 19 consensus pos proof of stake protocols for privacy aware blockchains https link springer com content pdf 10 1007 2f978 3 030 17653 2 23 pdf chaya ganesh claudio orlandi daniel tschudi eurocrypt 19 payment channel multi party virtual state channels https link springer com content pdf 10 1007 2f978 3 030 17653 2 21 pdf stefan dziembowski lisa eckey sebastian faust julia hesse kristina host kov eurocrypt 19 cryptography privacy aggregate cash systems a cryptographic investigation of mimblewimble https link springer com content pdf 10 1007 2f978 3 030 17653 2 22 pdf georg fuchsbauer michele orr yannick seurin eurocrypt 19 s p privacy zerocoin anonymous distributed e cash from bitcoin http ieeexplore ieee org iel7 6547086 6547088 06547123 pdf miers i garman c green m rubin ad s p 13 consensus pow permacoin repurposing bitcoin work for data preservation http ieeexplore ieee org iel7 6954656 6956545 06956582 pdf miller a juels a shi e parno b katz j permacoin s p 14 privacy zerocash decentralized anonymous payments from bitcoin http ieeexplore ieee org iel7 6954656 6956545 06956581 pdf sasson eb chiesa a garman c green m miers i tromer e virza m s p 14 consensus pow mining the miner s dilemma https arxiv org abs 1411 7099 ittay eyal s p 15 general sok research perspectives and challenges for bitcoin and cryptocurrencies http www jbonneau com doc bmcnkf15 ieeesp bitcoin pdf bonneau j miller a clark j narayanan a kroll ja felten ew s p 15 privacy hawk the blockchain model of cryptography and privacy preserving smart contracts https eprint iacr org 2015 675 pdf kosba a miller a shi e wen z papamanthou c s p 16 system omniledger a secure scale out decentralized ledger via sharding https eprint iacr org 2017 406 pdf e kokoris kogias and p jovanovic and l gasser and n gailly and e syta and b ford s p 18 sidechain proof of stake sidechains https eprint iacr org 2018 1239 pdf peter ga i aggelos kiayias dionysis zindros ieee s p 19 payment networks perun virtual payment hubs over cryptocurrencies https eprint iacr org 2017 635 pdf dziembowski s eckey l faust s malinowski d ieee s p 19 consensus pow lay down the common metrics evaluating proof of work consensus protocols security https www esat kuleuven be cosic publications article 3005 pdf ren zhang bart preneel ieee s p 19 consensus redactable blockchain in the permissionless setting https arxiv org abs 1901 03206 dominic deuber bernardo magri sri aravinda krishnan thyagarajan ieee s p 19 consensus privacy ouroboros crypsinous privacy preserving proof of stake https eprint iacr org 2018 1132 thomas kerber and markulf kohlweiss and aggelos kiayias and vassilis zikas ieee s p 19 scalaility xclaim decentralized interoperable cryptocurrency backed assets https eprint iacr org 2018 643 pdf alexei zamyatin dominik harz joshua lind panayiotis panayiotou arthur gervais william j knottenbelt ieee s p 19 consensus pow ohie blockchain scaling made simple https arxiv org abs 1811 12628 haifeng yu ivica nikolic ruomu hou and prateek saxena ieee s p 20 smart contract verx safety verification of smart contracts anton permenev dimitar dimitrov petar tsankov dana drachsler cohen martin vechev ieee s p 20 smart contract verismart a highly precise safety verifier for ethereum smart contracts sunbeom so myungho lee jisu park heejo lee hakjoo oh ieee s p 20 smart contract executable operational semantics of solidity https arxiv org pdf 1804 01295 pdf jiao jiao shuanglong kan shang wei lin david sanan yang liu jun sun ieee s p 20 consensus sync hotstuff simple and practical synchronous state machine replication ittai abraham dahlia malkhi kartik nayak ling ren maofan yin ieee s p 20 application flash boys 2 0 frontrunning in decentralized exchanges miner extractable value and consensus instability philip daian steven goldfeder tyler kell yunqi li xueyuan zhao iddo bentov lorenz breidenbach ari juels ieee s p 20 application flyclient super light clients for cryptocurrencies benedikt b nz lucianna kiffer loi luu mahdi zamani ieee s p 20 smart contract semantic understanding of smart contracts executable operational semantics of solidity https ieeexplore ieee org document 9152785 jiao jiao shuanglong kan shang wei lin david sanan yang liu and jun sun ieee s p 20 exchange high frequency trading on decentralized on chain exchanges liyi zhou kaihua qin christof ferreira torres duc v le arthur gervais tyler crain christopher natoli vincent gramoli ieee s p 21 payment a2l anonymous atomic locks for scalability in payment channel hubs erkan tairi pedro moreno sanchez matteo maffei ieee s p 21 consensus ebb and flow protocols a resolution of the availability finality dilemma joachim neu ertem nusret tas david tse ieee s p 21 sidechain mad htlc because htlc is crazy cheap to attack itay tsabary matan yechieli alex manuskin ittay eyal ieee s p 21 pos on the anonymity guarantees of anonymous proof of stake protocols varun madathil alessandra scafuro kartik nayak markulf kohlweiss ieee s p 21 defi on the just in time discovery of profit generating transactions in defi protocols liyi zhou kaihua qin antoine cully benjamin livshits arthur gervais ieee s p 21 smart contract sguard smart contracts made vulnerability free long h pham jun sun tai duy nguyen ieee s p 21 consensus red belly a secure fair and scalable open blockchain tyler crain christopher natoli vincent gramoli ieee s p 21 smart contract zeestar private smart contracts by homomorphic encryption and zero knowledge proofs samuel steffen benjamin bichsel roger baumgartner martin vechev ieee s p 22 smart contract sailfish vetting smart contract state inconsistency bugs in seconds priyanka bose dipanjan das yanju chen yu feng christopher kruegel giovanni vigna ieee s p 22 payment matrict more efficient post quantum private blockchain payments muhammed f esgin ron steinfeld raymond k zhao ieee s p 22 security quantifying blockchain extractable value how dark is the forest kaihua qin liyi zhou arthur gervais ieee s p 22 exchange universal atomic swaps secure exchange of coins across all blockchains sri aravindakrishnan thyagarajan giulio malavolta pedro moreno sanchez ieee s p 22 consensus using throughput centric byzantine broadcast to tolerate malicious majority in blockchains ruomu hou haifeng yu prateek saxena ieee s p 22 consensus foundations of dynamic bft https eprint iacr org 2022 597 sisi duan haibin zhang ieee s p 22 smart contract clockwork finance automated analysis of economic security in smart contracts kushal babel philip daian mahimna kelkar ari juels ieee s p 23 usenix security network security eclipse attacks on bitcoin s peer to peer network https www usenix org system files conference usenixsecurity15 sec15 paper heilman pdf heilman e kendler a zohar a goldberg s usenix 15 security symposium consensus pow enhancing bitcoin security and performance with strong consistency via collective signing https www usenix org system files conference usenixsecurity16 sec16 paper kokoris kogias pdf kogias ek jovanovic p gailly n khoffi i gasser l ford b usenix 16 security symposium mining smartpool practical decentralized pooled mining https www usenix org system files conference usenixsecurity17 sec17 luu pdf loi luu yaron velner jason teutsch truebit foundation prateek saxena usenix 17 security symposium mining rem resource efficient mining for blockchains https eprint iacr org 2017 179 fan zhang and ittay eyal and robert escriva and ari juels and robbert van renesse usenix 17 security symposium smart contract teether gnawing at ethereum to automatically exploit smart contracts https www usenix org system files conference usenixsecurity18 sec18 krupp pdf johannes krupp and christian rossow usenix 18 security symposium anonymity privacy an empirical analysis of anonymity in zcash https www usenix org system files conference usenixsecurity18 sec18 kappos pdf george kappos haaroon yousaf mary maller and sarah meiklejohn usenix 18 security symposium smart contract arbitrum scalable private smart contracts https www usenix org system files conference usenixsecurity18 sec18 kalodner pdf harry kalodner steven goldfeder xiaoqi chen s matthew weinberg and edward w felten usenix 18 security symposium transaction analysis tracing transactions across cryptocurrency ledgers https smeiklej com files usenix19 pdf haaroon yousaf george kappos and sarah meiklejohn usenix 19 security symposium consensus strongchain transparent and collaborative proof of work consensus https arxiv org pdf 1905 09655 pdf pawel szalachowski dani l reijsbergen and ivan homoliak siwei sun usenix 19 security symposium privacy bite bitcoin lightweight client privacy using trusted execution https www usenix org system files sec19fall matetic prepub pdf sinisa matetic karl w st moritz schneider and kari kostiainen ghassan karame srdjan capkun usenix 19 security symposium smart contract fastkitten practical smart contracts on bitcoin https www usenix org system files sec19fall das prepub pdf poulami das lisa eckey tommaso frassetto david gens kristina host kov patrick jauernig sebastian faust and ahmad reza sadeghi usenix 19 security symposium transaction analysis blocksci design and applications of a blockchain analysis platform https www usenix org conference usenixsecurity20 presentation kalodner harry kalodner malte m ser and kevin lee steven goldfeder martin plattner alishah chator arvind narayanan usenix 20 security symposium sidechain remote side channel attacks on anonymous transaction https www usenix org conference usenixsecurity20 presentation tramer text these 20attacks 20enable 20an 20active implementation 20of 20different 20system 20components florian tramer and dan boneh kenny paterson usenix 20 security symposium smart contract ethbmc a bounded model checker for smart contracts https www usenix org conference usenixsecurity20 presentation frank joel frank cornelius aschermann and thorsten holz usenix 20 security symposium smart contract txspector uncovering attacks in ethereum from transactions https www usenix org conference usenixsecurity20 presentation zhang mengya mengya zhang xiaokuan zhang yinqian zhang and zhiqiang lin usenix 20 security symposium smart contract an ever evolving game evaluation of real world attacks and defenses in ethereum ecosystem https www usenix org conference usenixsecurity20 presentation zhou shunfan shunfan zhou zhemin yang and jie xiang yinzhi cao min yang and yuan zhang usenix 20 security symposium smart contract evmpatch timely and automated patching of ethereum smart contracts michael rodler wenting li and ghassan o karame lucas davi usenix 21 security symposium application evil under the sun understanding and discovering attacks on ethereum decentralized applications liya su xinyue shen xiangyu du xiaojing liao xiaofeng wang and luyi xing baoxu liu usenix 21 security symposium smart contract smart contract vulnerabilities vulnerable does not imply exploited daniel perez and ben livshits usenix 21 security symposium transaction how to peel a million validating and expanding bitcoin clusters george kappos and haaroon yousaf rainer st tz and sofia rollet bernhard haslhofer sarah meiklejohn usenix 22 security symposium payment twilight a differentially private payment channel network maya dotan saar tochner aviv zohar and yossi gilad usenix 22 security symposium consensus acon 2 adaptive conformal consensus for provable blockchain oracles sangdon park osbert bastani and taesoo kim usenix 23 security symposium smart contract security panda security analysis of algorand smart contracts zhiyuan sun xiapu luo and yinqian zhang usenix 23 security symposium smart contract the blockchain imitation game https www usenix org conference usenixsecurity23 presentation qin kaihua qin stefanos chaliasos liyi zhou benjamin livshits dawn song and arthur gervais usenix 23 security symposium smart contract proxy hunting understanding and characterizing proxy based upgradeable smart contracts in blockchains https www usenix org conference usenixsecurity23 presentation bodell william e bodell iii sajad meisami and yue duan usenix 23 security symposium ndss economic smart contracts zeus analyzing safety of smart contracts http wp internetsociety org ndss wp content uploads sites 25 2018 02 ndss2018 09 1 kalra paper pdf kalra s goel s dhawan m sharma s ndss 18 system design chainspace a sharded smart contracts platform https sheharbano com assets publications ndss2018 chainspace pdf mustafa al bassam alberto sonnino shehar bano dave hrycyszyn and george danezis ndss 18 off chain settling payments fast and private efficient decentralized routing for path based transactions https www ndss symposium org wp content uploads 2018 02 ndss2018 09 3 roos paper pdf stefanie roos pedro moreno sanchez aniket kate and ian goldberg ndss 18 network sabre protecting bitcoin against routing attacks https arxiv org pdf 1808 06254 pdf maria apostolaki gian marti jan m ller and laurent vanbever ndss 19 smart contract seth protecting existing smart contracts against re entrancy attacks https arxiv org pdf 1812 05934 pdf michael rodler wenting li and ghassan karame lucas davi ndss 19 smart contract yoda enabling computationally intensive contracts on blockchains with byzantine and selfish nodes https arxiv org pdf 1811 03265 pdf sourav das vinay joseph ribeiro and abhijeet anand ndss 19 cryptograph fine grained and controlled rewriting in blockchains chameleon hashing gone attribute based https www ndss symposium org wp content uploads 2019 02 ndss2019 02a 3 derler paper pdf david derler kai samelin daniel slamanig and christoph striecks ndss 19 off chain privacy preserving multi hop locks for blockchain scalability and interoperability https www ndss symposium org wp content uploads 2019 02 ndss2019 09 4 malavolta paper pdf giulio malavolta pedro moreno sanchez clara schneidewind and matteo maffei aniket kate ndss 19 consensus pow bobtail improved blockchain security with low variance mining https arxiv org pdf 1709 08750 pdf george bissias and brian n levine ndss 20 consensus pos the attack of the clones against proof of authority https arxiv org pdf 1902 10244 pdf parinya ekparinya vincent gramoli guillaume jourjon ndss 20 layer2 snappy fast on chain payments with practical collaterals https arxiv org pdf 2001 01278 pdf vasilios mavroudis karl w st aritra dhar kari kostiainen and srdjan capkun ndss 20 smart contract broken metre attacking resource metering in evm https arxiv org pdf 1909 07220 pdf daniel perez and benjamin livshits ndss 20 smart contract soda a generic online detection framework for smart contracts https www ndss symposium org wp content uploads 2020 02 24449 paper pdf ting chen rong cao ting li xiapu luo guofei gu yufei zhang zhou liao hang zhu gang chen zheyuan he yuxing tang xiaodong lin and xiaosong zhang ndss 20 smart contract bitcontracts supporting smart contracts in legacy blockchains karl w st loris diana kari kostiainen ghassan karame sinisa matetic srdjan capkun eth zurich ndss 21 mining attack squirrl automating attack analysis on blockchain incentive mechanisms with deep reinforcement learning charlie hou mingxun zhou yan ji and phil daian florian tram r giulia fanti ari juels ndss 21 network attack as strong as its weakest link how to break blockchain dapps at rpc service kai li jiaqi chen xianghong liu and yuzhe tang xiaofeng wang xiapu luo ndss 21 consensus pos propos a probabilistic proof of stake protocol daniel reijsbergen pawel szalachowski junming ke zengpeng li and jianying zhou ndss 21 consensus bft speeding dumbo pushing asynchronous bft to practice bingyong guo yuan lu zhenliang lu qiang tang jing xu zhenfeng zhang ndss 22 consensus pow nc max breaking the security performance tradeoff in nakamoto consensus ren zhang dingwei zhang and quake wang shichen wu jan xie bart preneel ndss 22 consensus poet multi certificate attacks against proof of elapsed time and their countermeasures huibo wang guoxing chen yinqian zhang zhiqiang lin ndss 22 smart contract pose practical off chain smart contract execution tommaso frassetto patrick jauernig david koisser david kretzler benjamin schlosser sebastian faust and ahmad reza sadeghi ndss 23 smart contract smarter contracts detecting vulnerabilities in smart contracts with deep transfer learning christoph sendner huili chen hossein fereidooni lukas petzi jan k nig jasper stang and alexandra dmitrienko ahmad reza sadeghi farinaz koushanfar ndss 23 consensus loki state aware fuzzing framework for the implementation of blockchain consensus protocols fuchen ma yuanliang chen meng ren yuanhang zhou and yu jiang ting chen huizhong li jiaguang sun ndss 23 measurement blockscope detecting and investigating propagated vulnerabilities in forked blockchain projects xiao yi yuzhou fang and daoyuan wu lingxiao jiang ndss 23 payment channel breaking and fixing utxo based virtual channels lukas aumayr pedro moreno sanchez aniket kate matteo maffei ndss 23 consensus partitioning ethereum without eclipsing it hwanjo heo and seungwon woo tae ung yoon min suk kang and seungwon shin ndss 23 security double and nothing understanding and detecting cryptocurrency giveaway scams https double and nothing github io xigao li anurag yepuri and nick nikiforakis ndss 23 payment channel he htlc revisiting incentives in htlc sarisht wadhwa and jannis stoeter fan zhang kartik nayak ndss 23 ccs consensus pow mining double spending fast payments in bitcoin https www eecis udel edu ruizhang cisc859 s17 paper p9 pdf karame ghassan o and androulaki elli and capkun srdjan ccs 12 privacy deanonymisation of clients in bitcoin p2p network https arxiv org pdf 1405 7418 pdf biryukov a khovratovich d pustogarov i ccs 14 privacy provisions privacy preserving proofs of solvency for bitcoin exchanges https eprint iacr org 2015 1008 pdf dagher gg b nz b bonneau j clark j boneh d ccs 15 economic demystifying incentives in the consensus computer https eprint iacr org 2015 702 loi luu jason teutsch raghav kulkarni and prateek saxena ccs 15 pow network tampering with the delivery of blocks and transactions in bitcoin https eprint iacr org 2015 578 pdf gervais arthur and ritzdorf hubert and karame ghassan o and capkun srdjan ccs 15 consensus pow mining non outsourceable scratch off puzzles to discourage bitcoin mining coalitions http soc1024 ece illinois edu nonoutsourceable full pdf andrew miller elaine shi ahmed kosba and jonathan katz ccs 15 system the honey badger of bft protocols https infoscience epfl ch record 222858 files 199 pdf miller a xia y croman k shi e song d ccs 16 smart contracts making smart contracts smarter https www comp nus edu sg loiluu papers oyente pdf luu l chu dh olickel h saxena p hobor a ccs 16 economic on the instability of bitcoin without the block reward http www cs princeton edu smattw ckwn ccs16 pdf carlsten m kalodner h weinberg sm narayanan a ccs 16 smart contracts town crier an authenticated data feed for smart contracts http delivery acm org 10 1145 2980000 2978326 p270 zhang pdf ip 46 176 188 9 id 2978326 acc oa key 4d4702b0c3e38b35 2e4d4702b0c3e38b35 2e4d4702b0c3e38b35 2e594c525cffa2afaf cfid 923932938 cftoken 56121949 acm 1492299159 38039f3afa858f241818fdcf190e0200 zhang f cecchetti e croman k juels a shi e ccs 16 privacy on the security and performance of proof of work blockchains https eprint iacr org 2016 555 pdf gervais a karame go karl w st glykantzis v ritzdorf h capkun s ccs 16 system a secure sharding protocol for open blockchains https www comp nus edu sg loiluu papers elastico pdf loi luu viswesh narayanan chaodong zheng kunal baweja seth gilbert prateek saxena ccs 16 payment networks revive rebalancing off blockchain payment networks https eprint iacr org 2017 823 pdf khalil r gervais a ccs 17 economic the gap game http delivery acm org 10 1145 3250000 3243737 p713 tsabary pdf ip 142 231 81 31 id 3243737 acc active 20service key fd0067f557510ffb 2e26e2c50968a06846 2e4d4702b0c3e38b35 2e4d4702b0c3e38b35 acm 1572896863 842d24816daa8481df9c256f8a9cb16d itay tsabary ittay eyal ccs 18 payment networks general state channel networks https eprint iacr org 2018 320 pdf dziembowski s faust s host kov k ccs 18 system design fairswap how to fairly exchange digital goods https eprint iacr org 2018 740 dziembowski s faust s eckey l ccs 18 system rapidchain scaling blockchain via full sharding https eprint iacr org 2018 460 pdf mahdi zamani mahnush movahedi mariana raykova ccs 18 consensus beat asynchronous bft made practical https www csee umbc edu hbzhang files beat pdf sisi duan michael k reiter haibin zhang ccs 18 consensus applcation efficient publicly verifiable 2pc over a blockchain with applications to financially secure computations http homes sice indiana edu yh33 mypub pvc pdf ruiyu zhu changchang ding yan huang ccs 19 network erlay efficient transaction relay for bitcoin https arxiv org abs 1905 10518 gleb naumenko gregory maxwell pieter wuille alexandra sasha fedorova ivan beschastnikh ccs 19 sidechain hyperservice interoperability and programmability across heterogeneous blockchains https arxiv org abs 1908 09343 zhuotao liu yangxi xiang jian shi peng gao haoyu wang xusheng xiao bihan wen yih chun hu ccs 19 cryptography matrict efficient scalable and post quantum blockchain confidential transactions protocol https eprint iacr org 2019 1287 pdf muhammed f esgin raymond k zhao ron steinfeld joseph k liu dongxi liu ccs 19 payment omniring scaling up private payments without trusted setup formal foundations and a construction of ring confidential transactions with log size proofs russell w f lai viktoria ronge tim ruffing dominique schr der sri aravinda krishnan thyagarajan jiafan wang ccs 19 consensus prism deconstructing the blockchain to approach physical limits https dl acm org doi abs 10 1145 3319535 3345655 download true vivek bagaria sreeram kannan david tse giulia fanti pramod viswanath ccs 19 smart contract learning to fuzz from symbolic execution with application to smart contracts https files sri inf ethz ch website papers ccs19 ilf pdf jingxuan he mislav balunovic nodar ambroladze petar tsankov martin vechev ccs 19 smart contract tokenscope automatically detecting inconsistent behaviors of cryptocurrency tokens in ethereum https www4 comp polyu edu hk csxluo tokenscope pdf ting chen xiapu luo ting wang rong cao ccs 19 ledger sampl scalable auditability of monitoring processes using public ledgers https www cs nmsu edu roopa sampl pdf roopa vishwanathan gaurav panwar satyajayant misra austin bos ccs 19 payment networks atomic multi channel updates with constant collateral in payment channel networks https eprint iacr org 2019 583 pdf christoph egger pedro moreno sanchez matteo maffei ccs 19 payment security analysis and implementation of relay resistant contactless payments ioana boureanu tom chothia alexandre debant st phanie delaune ccs 20 smart contract ace asynchronous and concurrent execution of complex smart contracts karl w st sinisa matetic silvan egli kari kostiainen srdjan capkun ccs 20 transactions pointproofs aggregating proofs for multiple vector commitments sergey gorbunov leonid reyzin hoeteck wee zhenfei zhang ccs 20 mining bdos blockchain denial of service attacks michael mirkin yan ji jonathan pang ariah klages mundt ittay eyal ari juels ccs 20 consensus blinder scalable robust anonymous committed broadcast avishay yanai ittai abraham benny pinkas ccs 20 consensus dumbo faster asynchronous bft protocols bingyong guo zhenliang lu jing xu zhenfeng zhang ccs 20 consensus on the optimality of optimistic responsiveness ittai abraham kartik nayak ling ren nibesh shrestha ccs 20 cryptography asynchronous distributed key generation for computationally secure randomness consensus and threshold signatures eleftherios kokoris kogias dahlia malkhi alexander spiegelman ccs 20 consensus everything is a race and nakamoto always wins xuechao wang ertem nusret tas david tse amir dembo sreeram kannan ofer zeitouni pramod viswanath ccs 20 consensus tight consistency bounds for bitcoin peter ga i aggelos kiayias alexander russell ccs 20 consensus multi threshold byzantine fault tolerance https eprint iacr org 2021 671 pdf atsuki momose ling ren ccs 21 consensus revisiting nakamoto consensus in asynchronous networks a comprehensive analysis of bitcoin safety and chain quality http www cs ucf edu msaad ccs 21 pdf muhammad saad afsah anwar srivatsan ravi and david mohaisen ccs 21 consensus bft protocol forensics https arxiv org abs 2010 06785 peiyao sheng gerui wang kartik nayak sreeram kannan pramod viswanath ccs 21 consensus securing parallel chain protocols under variable mining power https arxiv org abs 2105 02927 xuechao wang viswa virinchi muppirala lei yang sreeram kannan pramod viswanath ccs 21 beacon randpiper reconfiguration friendly random beacons with quadratic communication https eprint iacr org 2020 1590 adithya bhat and nibesh shrestha and aniket kate and kartik nayak ccs 21 consensus asynchronous data dissemination and its applications https eprint iacr org 2021 777 sourav das and zhuolun xiang and ling ren ccs 21 consensus dumbo ng fast asynchronous bft consensus with throughput oblivious latency https arxiv org abs 2209 00750 yingzi gao yuan lu zhenliang lu qiang tang jing xu zhenfeng zhang ccs 22 consensus bolt dumbo transformer asynchronous consensus as fast as the pipelined bft https arxiv org pdf 2103 09425 pdf yuan lu zhenliang lu qiang tang ccs 22 consensus bullshark dag bft protocols made practical https arxiv org abs 2209 05633 alexander spiegelman neil giridharan alberto sonnino lefteris kokoris kogias ccs 22 consensus constant latency in sleepy consensus atsuki momose ling ren ccs 22 consensus engraft enclave guarded raft on byzantine faulty nodes weili wang sen deng jianyu niu michael k reiter yinqian zhang ccs 22 transactions empirical analysis of eip 1559 transaction fees waiting times and consensus security yulin liu yuxuan lu kartik nayak fan zhang luyao zhang yinhong zhao ccs 22 consensus minotaur multi resource blockchain consensus matthias fitzi xuechao wang sreeram kannan aggelos kiayias nikos leonardos pramod viswanath gerui wang ccs 22 consensus pace fully parallelizable bft 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pietzuch emin gun sirer sosp 19 consensus fast and secure global payments with stellar https www scs stanford edu dm home papers lokhava stellar core pdf marta lokhava giuliano losa david mazi res graydon hoare nicolas barry eliezer gafni jonathan jove rafa malinowski jed mccaleb sosp 19 consensus notary a device for secure transaction approval https pdos csail mit edu papers notary sosp19 pdf anish athalye adam belay frans kaashoek robert morris nickolai zeldovich sosp 19 database basil breaking up bft with acid transactions https dl acm org doi pdf 10 1145 3477132 3483552 florian suri payer matthew burke yunhao zhang zheng wang lorenzo alvisi and natacha crooks sosp 21 network bidl a high throughput low latency permissioned blockchain framework for datacenter networks https dl acm org doi pdf 10 1145 3477132 3483574 ji qi xusheng chen yunpeng jiang jianyu jiang tianxiang shen shixiong zhao sen wang gong zhang li chen man ho au and heming cui sosp 21 consensus kauri scalable bft consensus with pipelined tree based dissemination and aggregation https dl acm org doi pdf 10 1145 3477132 3483584 ray neiheiser miguel matos and lu s rodrigues sosp 21 smart contract forerunner constraint based speculative transaction execution for ethereum https dl acm org doi pdf 10 1145 3477132 3483564 yang chen zhongxin guo runhuai li shuo chen lidong zhou yajin zhou and xian zhang sosp 21 consensus rabia simplifying state machine replication through randomization https dl acm org doi pdf 10 1145 3477132 3483582 haochen pan jesse tuglu neo zhou tianshu wang yicheng shen xiong zheng joseph tassarotti lewis tseng roberto palmieri sosp 21 consensus flexible advancement in asynchronous bft consensus shengyun liu wenbo xu chen shan xiaofeng yan tianjing xu bo wang lei fan fuxi deng ying yan and hui zhang sosp 23 osdi system blockene a high throughput blockchain over mobile devices https arxiv org pdf 2010 07277 sambhav satija and apurv mehra sudheesh singanamalla karan grover muthian sivathanu nishanth chandran divya gupta and satya lokam osdi 20 consensus byzantine ordered consensus without byzantine oligarchy https www microsoft com en us research uploads prod 2020 10 paper 5f89e2898ee53 pdf yunhao zhang srinath setty qi chen and lidong zhou lorenzo alvisi osdi 20 smart contract finding consensus bugs in ethereum via multi transaction differential fuzzing https www usenix org system files osdi21 yang pdf youngseok yang taesoo kim byung gon chun osdi 21 decentralized service bringing decentralized search to decentralized services https www usenix org system files osdi21 li pdf mingyu li jinhao zhu tianxu zhang cheng tan sebastian angel and haibo chen osdi 21 eurosys consensus hybrids on steroids sgx based high performance bft https www4 cs fau de publications 2017 behl 17 eurosys pdf johannes behl tobias distler r diger kapitza eurosys 17 system hyperledger fabric a distributed operating system for permissioned blockchains https arxiv org pdf 1801 10228 pdf elli androulaki artem barger vita bortnikov christian cachin konstantinos christidis angelo de caro david enyeart christopher ferris gennady laventman yacov manevich srinivasan muralidharan chet murthy binh nguyen manish sethi gari singh keith smith alessandro sorniotti chrysoula stathakopoulou marko vukoli sharon weed cocco jason yellick eurosys 18 consensus damysus streamlined bft consensus leveraging trusted components https dl acm org doi pdf 10 1145 3492321 3519568 jeremie decouchant david kozhaya vincent rahli jiangshan yu eurosys 22 consensus state machine replication scalability made simple https dl acm org doi pdf 10 1145 3492321 3519579 chrysoula stathakopoulou matej pavlovic marko vukolic eurosys 22 consensus narwhal and tusk a dag based mempool and efficient bft consensus https dl acm org doi abs 10 1145 3492321 3519594 george danezis lefteris kokoris kogias alberto sonnino alexander spiegelman eurosys 22 consensus dissecting bft consensus in trusted components we trust suyash gupta sajjad rahnama shubham pandey natacha crooks mohammad sadoghi eurosys 23 consensus diablo a benchmark suite for blockchains vincent gramoli rachid guerraoui andrei lebedev chris natoli gauthier voron eurosys 23 nsdi consensus pow bitcoin ng a scalable blockchain protocol https www usenix org system files conference nsdi16 nsdi16 paper eyal pdf eyal i gencer ae sirer eg van renesse r nsdi 16 system monoxide scale out blockchain with asynchronized consensus zones https www usenix org system files nsdi19 wang jiaping pdf jiaping wang hao wang nsdi 19 network high throughput cryptocurrency routing in payment channel networks https www usenix org system files nsdi20 paper sivaraman pdf vibhaalakshmi sivaraman shaileshh bojja venkatakrishnan kathleen ruan parimarjan negi and lei yang radhika mittal giulia fanti mohammad alizadeh nsdi 20 consensus bft dispersedledger high throughput byzantine consensus on variable bandwidth networks https arxiv org pdf 2110 04371 pdf lei yang seo jin park sreeram kannan kannan s david tse nsdi 22 system speedex a scalable parallelizable and economically efficient decentralized exchange https www usenix org system files nsdi23 ramseyer pdf geoffrey ramseyer ashish goel david mazi res nsdi 23 system hamilton a high performance transaction processor for central bank digital currencies https www usenix org system files nsdi23 lovejoy pdf james lovejoy madars virza cory fields kevin karwaski anders brownworth neha narula nsdi 23 sigmetric performance network stability and scalability of blockchain systems aditya gopalan abishek sankararaman anwar walid sriram vishwanath sigmetric 20 layer 2 privacy utility tradeoffs in routing cryptocurrency over payment channel networks weizhao tang weina wang giulia fanti sewoong oh sigmetric 20 network understanding mis behavior on the eosio blockchain yuheng huang haoyu wang lei wu gareth tyson xiapu luo run zhang xuanzhe liu gang huang xuxian jiang sigmetric 20 incentive incentive analysis of bitcoin ng revisited https arxiv org abs 2001 05082 jianyu niu ziyu wang fangyu gai and chen feng performance 20 transactions tracking counterfeit cryptocurrency end to end bingyu gao haoyu wang pengcheng xia siwei wu yajin zhou xiapu luo gareth tyson sigmetric 21 sigmod measurement blockbench a framework for analyzing private blockchains https www comp nus edu sg ooibc blockbench pdf tien tuan anh dinh ji wang gang chen rui liu beng chin ooi and kian lee tan sigmod 17 database blurring the lines between blockchains and database systems the case of hyperledger fabric https dl acm org doi pdf 10 1145 3299869 3319883 download true ankur sharma felix martin schuhknecht divya agrawal and jens dittrich sigmod 19 database vchain enabling verifiable boolean range queries over blockchain databases https arxiv org pdf 1812 02386 pdf cheng xu ce zhang and jianliang xu sigmod 19 consensus sharding towards scaling blockchain systems via sharding https www comp nus edu sg hungdang papers sharding pdf hung dang tien tuan anh dinh dumitrel loghin ee chien chang qian lin and beng chin ooi sigmod 19 database confidentiality support over financial grade consortium blockchain https dl acm org doi abs 10 1145 3318464 3386127 yan yingwei changzheng guo xuepeng lu xuming zheng xiaofu liu qi zhou chenhui song xuyang zhao boran zhang hui jiang and guofei sigmod 20 database a transactional perspective on execute order validate blockchains https dl acm org doi 10 1145 3318464 3389693 pingcheng ruan dumitrel loghin meihui zhang gang chen and beng chin ooi sigmod 20 database falcondb blockchain based collaborative database https dl acm org doi 10 1145 3318464 3380594 yanqing peng min du feifei li raymond cheng and dawn song sigmod 20 database blockchains vs distributed databases dichotomy and fusion https www comp nus edu sg ooibc bcvsdb pdf pingcheng ruan tien tuan anh dinh dumitrel loghin meihui zhang gang chen qian lin and beng chin ooi sigmod 21 benchmark why do my blockchain transactions fail a study of hyperledger fabric https arxiv org pdf 2103 04681 pdf jeeta ann chacko ruben mayer and hans arno jacobsen sigmod 21 consensus sharding sharper sharding permissioned blockchains over network clusters https sites cs ucsb edu amiri papers sharper pdf mohammad javad amiri divyakant agrawal and amr el abbadi sigmod 21 pos do the rich get richer fairness analysis for blockchain incentives https arxiv org pdf 2103 14713 pdf yuming huang jing tang qianhao cong andrew lim and jianliang xu privacy p 2b trace privacy preserving blockchain based contact tracing to combat pandemics https dl acm org doi abs 10 1145 3448016 3459237 zhe peng cheng xu haixin wang jinbin huang jianliang xu and xiaowen chu sigmod 21 privacy div resolving the dynamic issues of zero knowledge set membership proof in the blockchain https dl acm org doi abs 10 1145 3448016 3457248 zihuan xu and lei chen sigmod 21 privacy when the recursive diversity anonymity meets the ring signature https dl acm org doi abs 10 1145 3448016 3452825 wangze ni peng cheng lei chen xuemin lin sigmod 21 database sql ledger cryptographically verifiable data in azure sql database https dl acm org doi pdf 10 1145 3448016 3457558 panagiotis antonopoulos raghav kaushik hanuma kodavalla sergio rosales aceves reilly wong jason anderson and jakub szymaszek sigmod 21 vldb storage forkbase an efficient storage engine for blockchain and forkable applications http www vldb org pvldb vol11 p1137 wang pdf sheng wang tien tuan anh dinh qian lin zhongle xie meihui zhang qingchao cai gang chen beng chin ooi pingcheng rua vldb 18 privacy a demonstration of sterling a privacy preserving data marketplace http www vldb org pvldb vol11 p2086 hynes pdf nick hynes1 david dao david yan raymond cheng dawn song vldb 19 application caper a cross application permissioned blockchain http www vldb org pvldb vol12 p1385 amiri pdf mohammad javad amiri divyakant agrawal amr el abbadi vldb 19 application blockchaindb a shared database on blockchains http www vldb org pvldb vol12 p1597 el hindi pdf muhammad el hindi carsten binnig arvind arasu donald kossmann ravi ramamurthy vldb 19 provenance fine grained secure and efficient data provenance on blockchain systems http www vldb org pvldb vol12 p975 ruan pdf pingcheng ruan gang chen tien tuan anh dinh qian lin beng chin ooi meihui zhang vldb 19 application blockchain meets database design and implementation of a blockchain relational database http www vldb org pvldb vol12 p1539 nathan pdf senthil nathan chander govindarajan adarsh saraf manish sethi and praveen jayachandran vldb 19 consensus fireledger a high throughput blockchain consensus protocol http www vldb org pvldb vol13 p1525 buchnik pdf yehonatan buchnik and roy friedman vldb 20 icse smart contract gigahorse thorough declarative decompilation of smart contracts https ieeexplore ieee org document 8811905 neville grech lexi brent bernhard scholz yannis smaragdakis icse 2019 smart contract gap between theory and practice an empirical study of security patches in solidity https doi org 10 1145 3377811 3380424 sungjae hwang sukyoung ryu icse 2020 smart contract targeted greybox fuzzing with static lookahead analysis https dl acm org doi 10 1145 3377811 3380388 valentin w stholz maria christakis icse 2020 smart contract empirical review of automated analysis tools on 47 587 ethereum smart contracts https arxiv org pdf 1910 10601 pdf t durieux jf ferreira r abreu p cruz icse 2020 smart contract sfuzz an efficient adaptive fuzzer for solidity smart contracts https arxiv org pdf 2004 08563 pdf td nguyen lh pham j sun y lin qt minh icse 2020 smart contract smart contract security a practitioners perspective http arxiv org abs 2102 10963 zhiyuan wan xin xia david lo jiachi chen xiapu luo xiaohu yang icse 2021 application utilizing parallelism in smart contracts on decentralized blockchains by taming application inherent conflicts https dl acm org doi 10 1145 3510003 3510086 p ter garamv lgyi yuxi liu dong zhou fan long ming wu icse 2022 esec fse smart contract towards automated verification of smart contract fairness https dl acm org doi 10 1145 3368089 3409740 ye liu yi li shang wei lin rong zhao esec fse 20 smart contract harvey a greybox fuzzer for smart contracts https dl acm org doi 10 1145 3368089 3417064 valentin w stholz maria christakis esec fse 20 application making smart contract development more secure and easier https dl acm org doi abs 10 1145 3468264 3473929 meng ren fuchen ma zijing yin ying fu huizhong li wanli chang yu jiang esec fse 21 smart contract ibatch saving ethereum fees via secure and cost effective batching of smart contract invocations https dl acm org doi 10 1145 3468264 3468568 yibo wang qi zhang kai li yuzhe tang jiaqi chen xiapu luo ting chen esec fse 21 decentralized application archer detecting on chain off chain synchronization bugs in decentralized applications https arxiv org abs 2106 09440 wuqi zhang lili wei shuqing li yepang liu shing chi cheung esec fse 21 ase smart contract contractfuzzer fuzzing smart contracts for vulnerability detection https doi org 10 1145 3238147 3238177 bo jiang ye liu w k chan ase 18 smart contract summary based symbolic evaluation for smart contracts https doi org 10 1145 3324884 3416646 yu feng emina torlak rastislav bodik ase 20 smart contract demystifying loops in smart contracts https dl acm org doi 10 1145 3324884 3416626 benjamin mariano yanju chen yu feng shuvendu k lahiri isil dillig ase 20 smart contract cross contract static analysis for detecting practical reentrancy vulnerabilities in smart contracts https dl acm org doi 10 1145 3324884 3416553 yinxing xue mingliang ma yun lin yulei sui jiaming ye tianyong peng ase 20 smart contract smartian enhancing smart contract fuzzing with static and dynamic data flow analyses https ieeexplore ieee org document 9678888 jaeseung choi doyeon kim soomin kim gustavo grieco alex groce sang kil cha ase 21 smart contract automating user notice generation for smart contract functions https ieeexplore ieee org document 9678552 xing hu zhipeng gao xin xia david lo xiaohu yang ase 21 smart contract characterizing transaction reverting statements in ethereum smart contracts https arxiv org abs 2108 10799 lu liu lili wei wuqi zhang ming wen yepang liu shing chi cheung ase 21 issta smart contract exploiting the laws of order in smart contracts https dl acm org doi 10 1145 3293882 3330560 aashish kolluri ivica nikoli c ilya sergey aquinas hobor prateek saxena issta 19 smart contract eshield protect smart contracts against reverse engineering https dl acm org doi abs 10 1145 3395363 3404365 wentian yan jianbo gao zhenhao wu yue li zhi guan qingshan li zhong chen issta 20 smart contract how effective are smart contract analysis tools evaluating smart contract static analysis tools using bug injection https dl acm org doi 10 1145 3395363 3397385 asem ghaleb karthik pattabiraman issta 20 smart contract etainter detecting gas related vulnerabilities in smart contracts https doi org 10 1145 3533767 3534378 asem ghaleb julia rubin karthik pattabiraman issta 22 smart contract finding permission bugs in smart contracts with role mining https doi org 10 1145 3533767 3534372 ye liu yi li shang wei lin cyrille artho issta 22 smart contract park accelerating smart contract vulnerability detection via parallel fork symbolic execution https doi org 10 1145 3533767 3534395 peilin zheng zibin zheng xiapu luo issta 22 smart contract wasai uncovering vulnerabilities in wasm smart contracts https doi org 10 1145 3533767 3534218 weimin chen zihan sun haoyu wang xiapu luo haipeng cai lei wu issta 22 popl smart contract online detection of effectively callback free objects with applications to smart contracts https dl acm org doi 10 1145 3158136 shelly grossman ittai abraham guy golan gueta yan michalevsky noam rinetzky mooly sagiv yoni zohar popl 18 smart contract soltype refinement types for arithmetic overflow in solidity https dl acm org doi abs 10 1145 3498665 af r bryan tan benjamin mariano shuvendu k lahiri isil dillig yu feng popl 22 ipdps consensus g pbft a location based and scalable consensus protocol for iot blockchain applications http www4 comp polyu edu hk csbxiao paper 2020 ipdps gpbft 2020 pdf laphou lao xiaohai dai bin xiao and songtao guo ipdps 20 bft byzantine generalized lattice agreement https arxiv org pdf 1910 05768 pdf giuseppe antonio di luna emmanuelle anceaume and leonardo querzoni ipdps 20 euros p smart contract ekiden a platform for con dentiality preserving trustworthy and performant smart contracts https arxiv org pdf 1804 05141 pdf raymond cheng fan zhang jernej kos warren he nicholas hynes noah johnson ari juels andrew miller dawn song euros p 19 random beacons fully distributed verifiable random functions and their application to decentralised random beacons david galindo jia liu mihai ordean jin mann wong euros p 21 srds consensus bloxy providing transparent and generic bft based ordering services for blockchains https www researchgate net publication 340304077 bloxy providing transparent and generic bft based ordering services for blockchains signe r sch r diger kapitza and kai bleeke srds 19 application blockchain based metadata protection for archival systems https ieeexplore ieee org document 9049624 arnaud l hutereau dorian burihabwa pascal felber hugues mercier and valerio schiavoni srds 19 application trusted computing meets blockchain rollback attacks and a solution for hyperledger fabric https ieeexplore ieee org document 9049585 marcus brandenburger christian cachin r diger kapitza and alessandro sorniotti srds 19 application nf crowd nearly free blockchain based crowdsourcing chao li balaji palanisamy runhua xu jian wang jiqiang liu srds 20 query an efficient query scheme for hybrid storage blockchains based on merkle semantic trie qingqi pei enyuan zhou yang xiao deyu zhang dongxiao zhao srds 20 smart contract protect your smart contract against unfair payment yue li han liu zhiqiang yang bin wang qian ren lei wang bangdao chen srds 20 consensus threat adaptive byzantine fault tolerant state machine replication douglas sim es silva rafal graczyk j r mie decouchant marcus v lp and paulo esteves verissimo srds 21 consensus adding fairness to order preventing front running attacks in bft protocols using tees chrysoula stathakopoulou signe r sch marcus brandenburger and marko vukolic srds 21 consensus how to trust strangers composition of byzantine quorum systems orestis alpos christian cachin and luca zanolini srds 21 consensus making reads in bft state machine replication fast linearizable and live christian berger hans p reiser and alysson bessani srds 21 provenance vassago efficient and authenticated provenance query on multiple blockchains rui han jiang xiao xiaohai dai shijie zhang yi sun baochun li and hai jin srds 21 podc consensus pow fruitchains a fair blockchain https eprint iacr org 2016 916 pdf rafael pass elaine shi podc 17 smart contracts adding concurrency to smart contracts https dl acm org doi 10 1145 3087801 3087835 maurice herlihy podc 17 sidechain atomic cross chain swaps https arxiv org abs 1801 09515 maurice herlihy podc 18 consessus brief announcement sustainable blockchains through proof of exercise https haslab uminho pt ashoker files pox podc pdf ali shoker podc 18 consensus the consensus number of a cryptocurrency https arxiv org pdf 1906 05574 pdf r guerraoui p kuznetsov m monti m pavlovic d seredinschi podc 19 consensus bft communication complexity of byzantine agreement revisited https arxiv org abs 1805 03391 i abraham t chan d dolev k nayak r pass l ren e shi podc 19 consensus bft exact byzantine consensus on undirected graphs under local broadcast model https arxiv org pdf 1903 11677 pdf m khan s naqvi n vaidya podc 19 consensus bft asymptotically optimal validated asynchronous byzantine agreement https research vmware com files attachments 0 0 0 0 0 7 8 practical aba 2 pdf i abraham d malkhi a spiegelman podc 19 consensus bft hotstuff bft consensus with linearity and responsiveness https www cs unc edu reiter papers 2019 podc pdf m yin i abraham g gueta d malkhi m reiter podc 19 spaa consensus dag why blockdags excel blockchains https tik db ee ethz ch file b0a2132681958e4cb69055bab4bf6ad8 the append memory model pdf darya melnyk and roger wattenhofer spaa 20 consensus bft fast byzantine agreement for permissioned distributed ledgers https dl acm org doi pdf 10 1145 3350755 3400219 thomas locher spaa 20 infocom network stochastic models and wide area network measurements for blockchain design and analysis https www researchgate net publication 321369565 stochastic models and wide area network measurements for blockchain design and analysis nikolaos papadis sem borst anwar walid mohamed grissa leandros tassiulas infocom 18 transaction understanding ethereum via graph analysis https www4 comp polyu edu hk csxluo ethereumgraphanalysis pdf ting chen yuxiao zhu zihao li jiachi chen xiaoqi li xiapu luo xiaodong lin xiaodong lin infocom 18 security corking by forking vulnerability analysis of blockchain https ieeexplore ieee org document 8737490 shengling wang and chenyu wang qin hu infocom 19 scalability accel accelerating the bitcoin blockchain for high throughput low latency applications https ieeexplore ieee org document 8737556 adiseshu hari murali kodialam t v lakshman infocom 19 application a blockchain based witness model for trustworthy cloud service level agreement enforcement https ieeexplore ieee org document 8737580 huan zhou xue ouyang zhijie ren jinshu su cees de laat and zhiming zhao infocom 19 network modeling the impact of network connectivity on consensus security of proof of work blockchain https www cnsr ictas vt edu publication modeling yang pdf yang xiao ning zhang wenjing lou and thomas hou infocom 20 off chain secure balance planning of off blockchain payment channel networks https www u aizu ac jp pengli files pcn planning infocom2020 pdf peng li and toshiaki miyazaki wanlei zhou infocom 20 consensus a weak consensus algorithm and its application to high performance blockchain qin wang rujia li infocom 21 mining characterizing ethereum s mining power decentralization at a deeper level liyi zeng yang chen shuo chen xian zhang zhongxin guo wei xu thomas moscibroda infocom 21 consensus on the performance of pipelined hotstuff jianyu niu fangyu gai mohammad jalalzai chen feng infocom 21 application blockchain based non repudiable iot data trading simpler faster and cheaper https ieeexplore ieee org document 9796857 fei chen jiahao wang changkun jiang tao xiang yuanyuan yang infocom 22 scalability brokerchain a cross shard blockchain protocol for account balance based state sharding https ieeexplore ieee org document 9796859 huawei huang xiaowen peng jianzhou zhan shenyang zhang yue lin zibin zheng song guo infocom 22 scalability s store a scalable data store towards permissioned blockchain sharding https ieeexplore ieee org document 9796800 xiaodong qi infocom 22 netowrk dino a block transmission protocol with low bandwidth consumption and propagation latency https ieeexplore ieee org abstract document 9796837 zhenxing hu and zhen xiao infocom 22 icdcs application transform blockchain into distributed parallel computing architecture for precision medicine https ieeexplore ieee org stamp stamp jsp arnumber 8416392 zonyin shae jeffrey j p tsai icdcs 18 pow selfish mining in ethereum https arxiv org abs 1901 04620 jianyu niu and chen feng icdcs 19 consensus trust mends blockchains living up to expectations http kth diva portal org smash get diva2 1316199 fulltext01 pdf leila bahri and sarunas girdzijauskas icdcs 19 application hierarchical edge cloud computing for mobile blockchain mining game https pdfs semanticscholar org cfe0 77c9b880c2a0dfb495448a068fdf1db07b6e pdf suhan jiang xinyi li and jie wu icdcs 19 scalability optchain optimal transactions placement for scalable blockchain sharding http optnetsci cise ufl edu papers icdcs19truc pdf lan nguyen truc nguyen thang dinh and my thai icdcs 19 consensus jidar a jigsaw like data reduction approach without trust assumptions for bitcoin system https www researchgate net publication 336945962 jidar a jigsaw like data reduction approach without trust assumptions for bitcoin system xiaohai dai jiang xiao wenhui yang chaofan wang and hai jin icdcs 19 scalability parblockchain leveraging transaction parallelism in permissioned blockchain systems https arxiv org pdf 1902 01457 pdf mohammad javad amiri divyakant agrawal and amr el abbadi icdcs 19 application optimal admission control for secondary users using blockchain technology in cognitive radio networks https ieeexplore ieee org document 8885039 wenlong ni yuhong zhang and wei li icdcs 19 application resource allocation and consensus on edge blockchain in pervasive edge computing environments https ieeexplore ieee org stamp stamp jsp tp arnumber 8884875 yaodong huang jiarui zhang jun duan bin xiao fan ye and yuanyuan yang icdcs 19 application xyreum a high performance and scalable blockchain for iiot security and privacy https ieeexplore ieee org stamp stamp jsp tp arnumber 8885084 abubakar sadiq sani dong yuan wei bao phee lep yeoh zhaoyang dong branka vucetic and elisa bertino icdcs 19 consensus pos consistency of proof of stake blockchains with concurrent honest slot leaders https arxiv org abs 2001 06403 aggelos kiayias saad quader alexander russell icdcs 20 network fair and efficient gossip in hyperledger fabric https arxiv org pdf 2004 07060 pdf nicolae berendea hugues mercier emanuel onica etienne rivi re icdcs 20 pow an analysis of blockchain consistency in asynchronous networks deriving a neat bound https arxiv org abs 1909 06587 jun zhao jing tang li zengxiang huaxiong wang kwok yan lam kaiping xue icdcs 20 consensus dissecting the performance of chained bft https arxiv org abs 2103 00777 fangyu gai ali farahbakhsh jianyu niu chen feng ivan beschastnikh hao duan icdcs 21 consensus strengthened fault tolerance in byzantine fault tolerant replication https arxiv org abs 2101 03715 zhuolun xiang dahlia malkhi kartik nayak ling ren icdcs 21 consensus leopard towards high throughput preserving bft for large scale systems https ieeexplore ieee org document 9912165 kexin hu kaiwen guo qiang tang zhenfeng zhang hao cheng zhiyang zhao icdcs 22 dsn smart contract fabzk supporting privacy preserving auditable smart contracts in hyperledger fabric hui kang ting dai nerla jean louis and shu tao xiaohui gu dsn 19 consensus bft sbft a scalable and decentralized trust infrastructure guy golan gueta ittai abraham shelly grossman dahlia malkhi benny pinkas michael k reiter dragos adrian seredinschi orr tamir alin tomescu dsn 19 payment channel fstr funds skewness aware transaction routing for payment channel networks siyi lin jingjing zhang weigang wu dsn 20 incentive on incentive compatible role based reward distribution in algorand https arxiv org abs 1911 03356 mehdi fooladgar mohammad hossein manshaei murtuza jadliwala mohammad ashiqur rahman dsn 20 consensus bft epic efficient asynchronous bft with adaptive security chao liu sisi duan haibin zhang dsn 20 smart contract smacs smart contract access control service https arxiv org abs 2003 07495 bowen liu siwei sun pawel szalachowski dsn 20 incentive data driven model based analysis of the ethereum verifier s dilemma https arxiv org pdf 2004 12768 pdf maher alharby roben lunardi amjad aldweesh aad van moorsel dsn 20 smart contract smart contracts on the move https arxiv org pdf 2004 05933 pdf enrique fynn alysson bessani fernando pedone dsn 20 consensus from byzantine replication to blockchain consensus is only the beginning https arxiv org pdf 2004 14527 pdf alysson bessani eduardo alchieri jo o sousa andr oliveira fernando pedone dsn 20 payment channel online payments by merely broadcasting messages https arxiv org pdf 2004 13184 pdf daniel collins rachid guerraoui jovan komatovic petr kuznetsov matteo monti matej pavlovic yvonne anne pignolet dragos adrian seredinschi andrei tonkikh athanasios xygkis dsn 20 consensus marlin two phase bft with linearity https eprint iacr org eprint bin getfile pl entry 2022 551 version 20220516 022614 file 551 pdf xiao sui sisi duan haibin zhang dsn 22 conext consensus pow on the necessity of a prescribed block validity consensus analyzing bitcoin unlimited mining protocol https eprint iacr org 2017 686 pdf ren zhang bart preneel conext 17 consensus stellar network attack mitigation using advanced blackholing https www de cix net files 2731074c857497be3827ac9537b6e486f27aa57c research paper stellar network attack mitigation using advanced blackholing pdf christoph dietzel matthias wichtlhuber georgios smaragdakis anja feldmann conext 18 mining mining the web with webcoin http networks cs northwestern edu publications webcoin conext18 final22 pdf uri klarman marcel flores aleksandar kuzmanovic conext 18 application blockchain based real time cheat prevention and robustness for multi player online game https dl acm org doi 10 1145 3281411 3281438 sukrit kalra rishabh sanghi mohan dhawan conext 18 layer2 flash efficient dynamic routing for offchain networks http www cs jhu edu xinjin files conext19 flash pdf peng wang hong xu xin jin tao wang conext 19 mobihoc consensus sharding user distributions in shard based blockchain network queueing modeling game analysis and protocol design https dl acm org doi abs 10 1145 3466772 3467051 canhui chen qian ma xu chen jianwei huang mobihoc 21 pldi smart contract behavioral simulation for smart contracts sidi mohamed beillahi gabriela ciocarlie michael emmi constantin enea pldi 2020 smart contract ethainter a smart contract security analyzer for composite vulnerabilities lexi brent neville grech sifis lagouvardos bernhard scholz yannis smaragdakis pldi 2020 smart contract securing smart contract with runtime validation ao li jemin andrew choi fan long pldi 2020 smart contract practical smart contract sharding with ownership and commutativity analysis george p rlea amrit kumar ilya sergey pldi 2021 socc network gosig a scalable and high performance byzantine consensus for consortium blockchains https www cs toronto edu fanl papers gosig socc20 pdf peilun li guosai wang xiaoqi chen fan long wei xu socc 21 network shrec bandwidth efficient transaction relay in high throughput blockchain systems https www cs toronto edu fanl papers shrec socc20 pdf yilin han chenxing li peilun li ming wu dong zhuo fan long socc 21 oopsla smart contract madmax surviving out of gas conditions in ethereum smart contracts https dl acm org doi 10 1145 3276486 neville grech michael kong anton jurisevic lexi brent bernhard scholz yannis smaragdakis oopsla 18 smart contract safer smart contract programming with scilla https dl acm org doi 10 1145 3360611 ilya sergey vaivaswatha nagaraj jacob johannsen amrit kumar anton trunov ken chan guan hao oopsla 19 smart contract detecting nondeterministic payment bugs in ethereum smart contracts https home cse ust hk shuaiw papers oopsla19 pdf shuai wang chengyu zhang zhendong su oopsla 19 smart contract precise static modeling of ethereum memory https dl acm org doi 10 1145 3428258 sifis lagouvardos neville grech ilias tsatiris yannis smaragdakis oopsla 20 smart contract rich specifications for ethereum smart contract verification https dl acm org doi 10 1145 3485523 christian br m marco eilers peter m ller robin sierra alexander j summers oopsla 21 smart contract symbolic value flow static analysis deep precise complete modeling of ethereum smart contracts https dl acm org doi 10 1145 3485540 yannis smaragdakis neville grech sifis lagouvardos konstantinos triantafyllou ilias tsatiris oopsla 21 smart contract elipmoc advanced decompilation of ethereum smart contracts https dl acm org doi 10 1145 3527321 neville grech sifis lagouvardos ilias tsatiris yannis smaragdakis oopsla 22 icde storage cub a consensus unit based storage scheme for blockchain system https ieeexplore ieee org abstract document 8509246 zihuan xu siyuan han lei chen 2018 database sebdb semantics empowered blockchain database https ieeexplore ieee org abstract document 8731416 yanchao zhu zhao zhang cheqing jin aoying zhou ying yan 2019 query authenticated keyword search in scalable hybrid storage blockchains https ieeexplore ieee org abstract document 9458753 ce zhang cheng xu haixin wang jianliang xu byron choi 2021 fc economic pow majority is not enough bitcoin mining is vulnerable https arxiv org pdf 1311 0243 eyal i sirer eg fc 14 consensus pow secure high rate transaction processing in bitcoin http www cs huji ac il avivz pubs 15 btc ghost full pdf sompolinsky y zohar a fc 15 chainstructure inclusive block chain protocols https fc15 ifca ai preproceedings paper 101 pdf yoad lewenberg yonatan sompolinsky aviv zohar fc 15 consensus cryptocurrencies without proof of work http fc16 ifca ai bitcoin papers bgm16 pdf bentov i gabizon a mizrahi a fc 16 economic pow optimal selfish mining strategies in bitcoin http fc16 ifca ai preproceedings 30 sapirshtein pdf sapirshtein a sompolinsky y zohar a fc 16 consensus pos a proof of stake protocol for consensus on bitcoin subchains http eprint iacr org 2017 417 pdf bartoletti m lande s podda a s fc 17 transaction prioritization mev dissecting bitcoin and ethereum transactions on the lack of transaction contention and prioritization transparency in blockchains https fc23 ifca ai preproceedings 8 pdf johnnatan messias vabuk pahari balakrishnan chandrasekaran krishna p gummadi and patrick loiseau fc 23 fc accepted paper link fc 15 https fc15 ifca ai schedule html fc 16 https fc16 ifca ai program html fc 17 https fc17 ifca ai program html fc 18 https fc18 ifca ai program html fc 19 https fc19 ifca ai program html fc 20 https fc20 ifca ai fc 21 https fc21 ifca ai fc 22 https fc22 ifca ai program html fc 23 https fc23 ifca ai program html ec economic pow an economic analysis of difficulty adjustment algorithms in proof of work blockchain systems https econ hkbu edu hk eng doc shunya noda pow pdf shunya noda kyohei okumura and yoshinori hashimoto ec 20 imc transaction prioritization selfish opaque transaction ordering in the bitcoin blockchain the case for chain neutrality https dl acm org doi 10 1145 3487552 3487823 johnnatan messias mohamed alzayat balakrishnan chandrasekaran krishna p gummadi patrick loiseau and alan mislove imc 21 defi a flash bot in the pan measuring maximal extractable value in private pools https dl acm org doi abs 10 1145 3517745 3561448 ben weintraub christof ferreira torres cristina nita rotaru and radu state imc 22 journals tkde smart contract a high performance concurrency protocol for smart contracts of permissioned blockchain https ieeexplore ieee org document 9356389 cheqing jin shuaifeng pang xiaodong qi zhao zhang and aoying zhou 2021 storage a reliable storage partition for permissioned blockchain https ieeexplore ieee org document 9152150 xiaodong qi zhao zhang cheqing jin and aoying zhou 2021 acmcsur layer2 a survey on blockchain interoperability past present and future trends https dl acm org doi 10 1145 3471140 rafael belchior andr vasconcelos s rgio guerreiro miguel correia 2021 acmdlt layer2 do you need a distributed ledger technology interoperability solution https dl acm org doi 10 1145 3564532 rafael belchior luke riley thomas hardjono andr vasconcelos miguel correia 2022 jsac network tips transaction inclusion protocol with signaling in dag based blockchain https arxiv org pdf 2207 04841 pdf canhui chen xu chen zhixuan fang 2022 tnse consensus pow impact of temporary fork on the evolution of mining pools in blockchain networks an evolutionary game analysis https arxiv org pdf 2011 07238 pdf canhui chen xu chen jiangshan yu weigang wu di wu 2020 license cc0 http mirrors creativecommons org presskit buttons 88x31 svg cc zero svg https creativecommons org publicdomain zero 1 0 this list is released into the public domain | blockchain |
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pycon-2016-tutorial | tutorial machine learning with text in scikit learn presented by kevin markham http www dataschool io about at pycon on may 28 2016 watch the complete tutorial video https www youtube com watch v zikmiuyidy0 list pl5 da3qgb5icembquqbbcoqwcs6oybr5a index 10 on youtube watch the complete tutorial video on youtube youtube jpg https www youtube com watch v zikmiuyidy0 list pl5 da3qgb5icembquqbbcoqwcs6oybr5a index 10 machine learning with text in scikit learn pycon 2016 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 if you need a refresher on scikit learn or machine learning i recommend reviewing the notebooks and or videos from my scikit learn video series https github com justmarkham scikit learn videos focusing on videos 1 5 as well as video 9 alternatively you may prefer reading the tutorials http scikit learn org stable tutorial index html from the scikit learn documentation if you need a refresher on pandas i recommend reviewing the notebook and or videos from my pandas video series https github com justmarkham pandas videos alternatively you may prefer reading this 3 part tutorial http www gregreda com 2013 10 26 intro to pandas data structures 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 kevin markham is the founder of data school http www dataschool io and the former lead instructor for general assembly s data science course https github com justmarkham dat8 in washington dc he is passionate about teaching data science to people who are new to the field regardless of their educational and professional backgrounds and he enjoys teaching both online and in the classroom kevin s professional focus is supervised machine learning which led him to create the popular scikit learn video series https github com justmarkham scikit learn videos for kaggle he has a degree in computer engineering from vanderbilt university email kevin dataschool io mailto kevin dataschool io twitter justmarkham https twitter com justmarkham 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 scikit learn the scikit learn user guide includes an excellent section on text feature extraction http scikit learn org stable modules feature extraction html text feature extraction that includes many details not covered in today s tutorial the user guide also describes the performance trade offs http scikit learn org stable modules computational performance html influence of the input data representation involved when choosing between sparse and dense input data representations to learn more about evaluating classification models watch video 9 from my scikit learn video series https github com justmarkham scikit learn videos or just read the associated notebook https github com justmarkham scikit learn videos blob master 09 classification metrics ipynb pandas here are my top 8 resources for learning data analysis with pandas http www dataschool io best python pandas resources as well i have a new pandas q a video series http www dataschool io easier data analysis with pandas targeted at beginners that includes two new videos every week | ai |
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Earnings-Calls-NLP | stock price predictions from earnings calls rice data analytics program team members radhika balasubramaniam chad dubiel katy fuentes pankaj tahiliani background every quarter public companies report earnings and company updates companies host a conference call in order to provide additional commentary and answer questions from participants data the dataset used consists of earnings call transcripts for s p 500 companies the team webscraped seeking alpha earning transcripts the transcripts include a prepared remarks section a question and answer section and instructions from the call operator key considerations is there a change in stock price after an earnings call does an earnings call influence the stock price was is the call s sentiment what are common words topics used across company earnings calls what is the price change for the stock before and after the call is there a trend or can you predict the change in price process 1 download s p100 and then s p500 list from wikipedia 2 scrape seeking alpha webpages for each company transcript with date title url 3 narrow list to only earning call transcript urls 4 scrape each seeking alpha webpage for the earnings call transcript and save to text file 5 scrape yahoo finance webpage by stock symbol and download 2015 through 2020 stock price history 6 read text files and preprocess text tf idf and count vectorization documents were split 80 20 into training and test subsets term frequency inverse document frequency is a method in which to quanitify a terms importance to a document in a corpus countvectorizer and tfidfvectorizer methods of the scikit learn library were fit with the training subset and then the vectorizer was used to transform both the training and test subset into matrices of term frequency for each earnings call transcript classification returns were considered for 1 day 7 days and 28 days after the closing price the trading day prior to the earnings call transcript release each return was classified as buy hold or sell based on a 3 3 3 or 3 return over the period respectively classification classification viz png logistic regression model these features were fit onto a logistic regression machine learning model logisticregression also a part of the scikit learn librabry the model was most effective when considering returns 1 day after the prior close after using grid search for hyperparameter tuning and 5 fold cross validation the most effective solver was the liblinear parameter with l1 lasss regularization the model had a mean accuracy score of 74 accuracy accuracy scores png model performance the model was backtested from the period of 2015 2020 the theoretical return of the model over that period was measured as 321 the s p 500 spx had a return of 83 for the same period equating to the model providing a market adjusted of 238 model performance return on investment png text and sentiment analysis for the purpose of this analysis only the prepared remarks for 2020 earnings calls for s p100 companies compared to the full transcript text to compare the difference the transcripts were modified by the following processing steps 1 removed numbers and punctuation 2 preprocessed text by cleaning up contractions removing html tokenizing words lemmatized words removed stop words and minimum character words 3 removed infrequent words sentiment analysis is used to measure the attitude sentiments evaluations or emotions of a speaker based on the algorithmic treatment of subjectivity in a text latent dirichlet allocation lda latent dirichlet allocation lda models can be used to reveal a hidden structure in a collection of texts representing the weight of text in a topic space the process involves loading data cleaning exploring general output in form of a word cloud preparing the data for lda analysis training the lda model and analyzing the results with a visual the earning call transcripts were uploaded in the raw format and cleaned with the various steps detailed above the wordcloud for the full transcript and prepared remarks are different but mostly contain the same common words next the text was tokenized into a corpus and dictionary for each the model was trained on 25 topics which was a combination of keywords based on a weight to the topic to visual the topics pyldavis was used to better understand and interpret individual topics and their relationships for the most part the terms overlapped in the full transcript and prepared remarks but the topic distribution varied significantly between the two lda lda png vader sentiment intensity analyzer sia polarity scoring vader valence aware dictionary for sentiment reasoning is a model used for text sentiment analysis that is sensitive to both polarity positive negative and intensity vader sentimental analysis relies on a prepackaged dictionary that maps lexical features into scores which categorize text into positive neutral and negative these texts are aggregated by document to arrive at overall opinion vader uses a combination of sentiment lexicon and list of lexical features which are generally labelled according to their semantic orientation as either positive or negative vader was applied across the processed text of the full and prepared remarks section of the earnings call transcripts the prepacked vader sentiment intensity analyzer sia polarity scoring and lexicon was used and compared to hu liu hl and loughran mcdonald lm word lists to compare tone and impact of classifying a call s sentiment correctly the hu liu lexicon was developed from a feature space of online movie reviews that were assigned negativity positivity scores by the reviewers themselves the hl lexicon consists of 6 786 words labeled positive or negative the lm lexicon was constructed from words that are prevalent in 10 k reports of publicly traded companies the positive and negative labels assigned to these words are specific to the finance domain the lm lexicon consists of 2 707 positive or negative words depending on the dictionary used there is a significant difference between what is categorized as positive in addition the sentiment scores change between the full transcript and prepared remarks which are likely influenced by the questions asked sentiment totals using hl dictionary using lm dictionary positive 255 374 negative 88 16 neutral 58 11 vader vader png linear discriminant analysis on sentiment scores and price linear discriminant analysis is the linear classification technique for multi classes of data lda consists of statistical properties of the data calculated for each class lda was applied across the processed text of the full and prepared remarks section of the earnings call transcripts for purposes of this model the x input consists of the adjusted close close high low open stock price and the sia polarity which consists computations of subjectivity polarity negative positive and neutral scores the y input consists of an imputed label value that measured the positivity score and assigned 1 for positive scores over 0 15 in the full transcript based on the average of 0 150631 and 0 10 in the prepared remarks based on the average of 0 143419 based on the f1 score and classification report numbers the model performed better with the condensed text in the prepared remarks which makes sense due to the variety of questions and answers included in the full transcript of the calls time series forecating traditionally machine learning ml models used different features and corelate the data to prices but there is no time dimension in the data time series forecasting models are the models that are capable to predict future values based on previously observed values time series forecasting is widely used for non stationary data non stationary data are called the data whose statistical properties e g the mean and standard deviation are not constant over time but instead these metrics vary over time we used keras lstm and gru model to do time series forecasting price forecast price forecast png stock prices are very volatile and lot of factors that contribute to the stock prices its important to identify the stocks that should be included in the the model evaluation while building a new lstm gru model for this we did sma ema comparison created risk matrix with expected returns plotting sns grid to identify overlays clusters to identify similar patterns in stock movement also created a heatmap to identify the stock movement relative to other stocks this process will help to remove stocks with high fluctations and no fluctations to prevent the model from overfitting or underfitting for evaluation purposes we restricted the stocks to 25 we used stock data from the past 15 years we used data shifting and rolling window concepts to prepare the dataset nueral network with four layers and one dense layer was used since the data needs to be in timeseries we trained on the data prior to 2018 and used the data from 2018 for testing purposes after the evaluation we identified that both the models were able to identify the trend patterns on furthur analysis we noticed lstm performed better than gru here is the attached results of the model for the apple stock lstm vs gru static images lstm jpg data sources 1 s p 100 500 list https en wikipedia org wiki list of stock exchanges 2 earnings calls transcripts https seekingalpha com earnings earnings call transcripts the transcripts from seeking alpha are protected by copyright and cannot be used for commercial purposes this is an educational project for the data visualization program at rice university and the use of the information should be permitted on the copyright fair use principal 3 yahoo finance historical stock price https sg finance yahoo com further reading natural language processing part iii feature engineering applying nlp using domain knowledge to capture alpha from transcripts https www spglobal com marketintelligence en documents nlp iii final 013020 10a pdf next applications backtrack results predict trends predict price embed sentiment in model | prepared-remarks stock-price earnings-calls-transcripts | ai |
vortex | p align center img width 250 height 250 src docs images vortex icon png p vortex visual cortex a deep learning model development framework for computer vision features easy to use scalable and flexible utilizing pytorchlightning https pytorchlightning ai unified execution for various computer vision task such as classification and object detection example 1 export and inference example train your model on any framework export to onnx embed metadata and run on vortex see this example examples export export py for export trained model then run on vortex 2 training a classifier export and benchmark using vortex see this example examples mnasnet mnasnet py for more details installation see detailed guide https nodefluxio github io vortex installation user guides documentations see vortex documentation https nodefluxio github io vortex | ai |
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ProgrammingBlockchainCodeExamples | programming the blockchain in c talk is cheap show me the code 1 clone 2 open solution 3 choose and set a startup project 4 build and run click here to read the book https programmingblockchain gitbooks io programmingblockchain content links the book on github https github com programmingblockchain programmingblockchain the book on gitbook https www gitbook com book programmingblockchain programmingblockchain you can download pdf epub mobi versions here code examples on github https github com programmingblockchain programmingblockchaincodeexamples hall of the makers http n bitcoin ninja here are the true makers those succeeded to complete the challenges of this book | blockchain |
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TIF | tif information technologies in management | server |
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Food-Delivery-App | food delivery app hello there this is a food delivery app developed in flutter this application is being developed according to the classes of https www youtube com playlist list pl3npgdhxqthfgtmpd 0evjm 8lp3unfc flutter e commerce app tutorial shop app food delivery dbestech youtube chanel versions flutter 3 0 5 dart 2 17 6 setup clone it in your machine bash git clone https github com rodrigonp3 food delivery app git dependecies bash dependencies cupertino icons 1 0 2 get 4 1 4 dots indicator 2 1 0 shared preferences 2 0 15 intl 0 17 0 google maps flutter 2 2 0 geocoding 2 0 5 geolocator 9 0 2 navigation table thead tr th align center sign in page th th align center sign up page th th align center home page th th align center home page th tr thead tbody tr td align center a target blank rel href images sign in page png img src images sign in page png alt css logo with 200 height 400 a td td align center a target blank rel href images sign up page png img src images sign up page png alt css logo with 200 height 400 a td td align center a target blank rel href images home page 1 png img src images home page 1 png alt css logo with 200 height 400 a td td align center a target blank rel href images home page 2 png img src images home page 2 png alt css logo with 200 height 400 a td table thead tr th align center popular food detail page th th align center recommended food detail page th th align center cart page th th align center cart history page th tr thead tbody tr td align center a target blank rel href images popular food detail page png img src images popular food detail page png alt css logo with 200 height 400 a td td align center a target blank rel href images recommended food detail page png img src images recommended food detail page png alt css logo with 200 height 400 a td td align center a target blank rel href images cart page png img src images cart page png alt css logo with 200 height 400 a td td align center a target blank rel href images cart history page png img src images cart history page png alt css logo with 200 height 400 a td table thead tr th align center add address page th tr thead tbody tr td align center a target blank rel href images add address page png img src images add address page png alt css logo with 200 height 400 a td | server |
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data-engineering-database-access-deploy | data engineering database access deploy this repository has been archvied following the migration from aws codebuild to github actions introduction this repository contains a pulumi project that creates aws codebuild infrastructure to perform continuous integration and continuous deployment on the data engineering database access https github com moj analytical services data engineering database access repository prerequisites to work with this repository you must have the following installed git crypt https github com agwa git crypt see the git crypt readme git crypt readme md for further information on how to install git crypt and use it with this repository python 3 6 or later https www python org downloads pulumi https www pulumi com docs get started install you should also 1 create a virtual environment python m venv venv 2 activate the environment source venv bin activate 3 install dependencies pip install r requirements txt usage to update the pulumi project 1 create an aws vault shell session with the restricted admin data role for more information see the analytical platform iam https github com ministryofjustice analytical platform iam repository 2 activate your virtual environment source venv bin activate 3 log in to the pulumi backend pulumi login c s3 data engineering pulumi analytics justice gov uk 4 select the de database access deploy stack pulumi stack select de database access deploy 5 preview any changes optional pulumi preview diff 6 deploy any changes pulumi up git crypt this repository uses git crypt to encrypt secrets for more information see the git crypt readme git crypt readme md licence mit licence licence md | data-engineering pulumi | server |
resolution | resolution npm version https img shields io npm v unstoppabledomains resolution svg style flat https www npmjs com package unstoppabledomains resolution ci https github com unstoppabledomains resolution workflows ci badge svg branch master bundle size minified https img shields io bundlephobia min unstoppabledomains resolution svg https bundlephobia com result p unstoppabledomains resolution bundle size minified zipped https img shields io bundlephobia minzip unstoppabledomains resolution svg https bundlephobia com result p unstoppabledomains resolution unstoppable domains documentation https img shields io badge documentation unstoppabledomains com blue https docs unstoppabledomains com get help on discord https img shields io badge get 20help 20on discord blueviolet https discord gg b6zvxsz9hn installing resolution installing resolution updating resolution updating resolution using resolution using resolution error handling error handling development development free advertising for integrated apps free advertising for integrated apps resolution is a library for interacting with blockchain domain names it can be used to retrieve payment addresses https unstoppabledomains com learn how to send crypto using your domain and ipfs hashes for decentralized websites https support unstoppabledomains com support solutions articles 48001181925 build website resolution is primarily built and maintained by unstoppable domains https unstoppabledomains com resolution supports different decentralized domains please refer to the top level domains list https api unstoppabledomains com resolve supported tlds for more information see our detailed api reference https unstoppabledomains github io resolution installing resolution resolution can be installed with either yarn or npm shell yarn add unstoppabledomains resolution shell npm install unstoppabledomains resolution save if you re interested in resolving domains via the command line see our cli section command line interface updating resolution resolution can be updated with either yarn or npm shell yarn upgrade unstoppabledomains resolution latest shell npm update unstoppabledomains resolution save using resolution initialize with unstoppable domains uns proxy provider javascript const default resolution require unstoppabledomains resolution obtain a key by following this document https docs unstoppabledomains com domain distribution and management quickstart retrieve an api key api key const resolution new resolution apikey api key note the apikey is only used resolve domains from uns behind the scene it still uses the default zns zilliqa rpc url for additional control please specify your zns configuration javascript const default resolution require unstoppabledomains resolution const resolution new resolution apikey api key sourceconfig zns url https api zilliqa com network mainnet initialize with custom provider configuration you may want to specify a custom provider if you want to use a dedicated blockchain node if you want to monitor app usage if you already have a provider in your app to re use it for domain resolution default provider can be changed by changing constructor options new resolution options or by using one of the factory methods resolution alchemy resolution infura resolution fromweb3version1provider resolution fromethersprovider etc javascript const default resolution require unstoppabledomains resolution obtain a key from https www infura io const resolution new resolution sourceconfig uns locations layer1 url https mainnet infura io v3 infura api key network mainnet layer2 url https polygon mainnet infura io v3 infura api key network polygon mainnet zns url https api zilliqa com network mainnet ens url https mainnet infura io v3 infura api key network mainnet initialize with autoconfiguration of blockchain network in some scenarios system might not be flexible enough to easy distinguish between various ethereum testnets at compilation time in this case resolution library provide a special async constructor await resolution autonetwork options this method makes a json rpc net version call to the provider to get the network id this method configures only uns zns is supported only on zilliqa mainnet which is going to be used in any cases you can provide a configured provider or a blockchain url as in the following example await resolution autonetwork uns provider examples to see all constructor options and factory methods check unstoppable api reference https unstoppabledomains github io resolution look up a domain s crypto address javascript function resolve domain currency resolution addr domain currency then address console log domain resolves to address catch console error resolve brad crypto eth resolve brad zil zil resolve vitalik eth eth find the ipfs hash for a decentralized website create a new file in your project ipfs hash js javascript function resolveipfshash domain resolution ipfshash domain then hash console log you can access this website via a public ipfs gateway https gateway ipfs io ipfs hash catch console error resolveipfshash homecakes crypto resolveipfshash vitalik eth find a custom record create a new file in your project custom resolution js javascript does not support ens function resolvecustomrecord domain record resolution records domain record then value console log domain domain record is value catch console error resolvecustomrecord homecakes crypto custom record value resolve wallet address using addr this api is used to retrieve wallet address for single address record see cryptocurrency payment https docs unstoppabledomains com resolution guides records reference cryptocurrency payments section for the record format with homecakes crypto has crypto eth address on chain javascript function getwalletaddr domain ticker resolution addr domain ticker then address console log domain domain has address for ticker address catch console error getwalletaddr homecakes crypto eth domain homecakes crypto has address for eth 0xe7474d07fd2fa286e7e0aa23cd107f8379085037 getwalletaddr vitalik eth eth domain homecakes crypto has address for eth 0xe7474d07fd2fa286e7e0aa23cd107f8379085037 resolve multi chain address format using multichainaddr this api is used to retrieve wallet address for multi chain address records see multi chain currency https docs unstoppabledomains com resolution guides records reference multi chain currencies with aaron x has crypto aave version erc20 address on chain javascript does not support ens function getmultichainwalletaddr domain ticker network resolution multichainaddr domain ticker network then address console log domain domain has address for ticker on network network address catch console error getmultichainwalletaddr aaron x aave eth domain aaron x has address for aave on network eth 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af resolve wallet address using getaddress this beta api can be used to resolve different formats javascript function getwalletaddress domain network token resolution getaddress domain network token then address console log domain domain has address for token on network address catch console error resolve single address format similar to addr api with homecakes crypto has a crypto eth address record set on chain javascript getwalletaddress homecakes crypto eth eth domain homecakes crypto has address for eth on eth 0xe7474d07fd2fa286e7e0aa23cd107f8379085037 resolve multi chain currency address format see multi chain currency https docs unstoppabledomains com resolution guides records reference multi chain currencies with aaron x has a crypto aave version erc20 address record set to 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af the erc20 indicates it s a token on eth network javascript getwalletaddress aaron x eth aave domain aaron x has address for aave on eth 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af derive wallet addresses within the same blockchain network and blockchain family the api can also be used by crypto exchanges to infer wallet addresses in centralized exchanges users have same wallet addresses on different networks with same wallet family see blockchain family network token level addresses https apidocs unstoppabledomains com resolution guides records reference blockchain family network token level addresses section for the record format with blockchain family keys x only has token evm address record on chain the api resolves to same wallet address for tokens live on evm compatible networks javascript getwalletaddress blockchain family keys x eth aave domain blockchain family keys x has address for aave on eth 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af getwalletaddress blockchain family keys x eth eth domain blockchain family keys x has address for eth on eth 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af getwalletaddress blockchain family keys x avax usdt domain blockchain family keys x has address for usdt on avax 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af with uns devtest nickshatilo withdraw test2 x only has token evm eth address record on chain the api resolves to the same wallet address for tokens specifically on ethereum network javascript getwalletaddress uns devtest nickshatilo withdraw test2 x eth aave domain blockchain family keys x has address for aave on eth 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af getwalletaddress uns devtest nickshatilo withdraw test2 x eth matic domain blockchain family keys x has address for eth on eth 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af getwalletaddress uns devtest nickshatilo withdraw test2 x eth usdt domain blockchain family keys x has address for usdt on eth 0xcd0dadab45baf9a06ce1279d1342ecc3f44845af getwalletaddress uns devtest nickshatilo withdraw test2 x matic usdt won t work the api is compatible with other address formats if a domain has multiple address formats set it will follow the algorithm described as follow if a domain has following records set token evm address crypto usdc version erc20 address token evm eth usdc address crypto usdc address token evm eth address getaddress domain eth usdc will lookup records in the following order not supported with ens 1 token evm eth usdc address 2 crypto usdc address 3 crypto usdc version erc20 address 4 token evm eth address 5 token evm address error handling when resolution encounters an error it returns the error code instead of stopping the process keep an eye out for return values like record not found development use these commands to set up a local development environment macos terminal or linux shell 1 recommended nodejs version node v16 2 clone the repository bash git clone https github com unstoppabledomains resolution git cd resolution 3 install dependencies bash yarn install or bash npm install internal config to update network config yarn network config pull resolver keys yarn resolver keys pull both configs yarn config pull unit tests resolution library relies on environment variables to load testnet rpc urls this way our keys don t expose directly to the code these environment variables are l1 test net rpc url l1 test net rpc wss url l2 test net rpc url l2 test net rpc wss url in order to validate the code change copy env example file change the name to env then update the values of variables free advertising for integrated apps once your app has a working unstoppable domains integration register it here https unstoppabledomains com app submission registered apps appear on the unstoppable domains homepage https unstoppabledomains com and applications https unstoppabledomains com apps page putting your app in front of tens of thousands of potential customers per day also every week we select a newly integrated app to feature in the unstoppable update newsletter this newsletter is delivered straight into the inbox of 100 000 crypto fanatics all of whom could be new customers to grow your business get help join our discord community https discord gg unstoppabledomains and ask questions help us improve we re always looking for ways to improve how developers use and integrate our products into their applications we d love to hear about your experience to help us improve by taking our survey https form typeform com to uhpqyho6 | resolution blockchain cns zns domains ethereum | blockchain |
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