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aws-iot-chat-example | aws iot chat application tested with jest https img shields io badge tested with jest 99424f svg https github com facebook jest github stars https img shields io github stars awslabs aws iot chat example svg https github com awslabs aws iot chat example stargazers github license https img shields io github license awslabs aws iot chat example svg https github com awslabs aws iot chat example demo online https img shields io badge demo online green svg https d2ab9cgiumpbb1 cloudfront net this is a react application demonstrating how to use the aws iot platform via mqtt over the websocket protocol to build a live chat application the sample application serves as a starting point for users to build serverless projects with amazon cognito aws lambda and amazon dynamodb the project showcases the solution to common user questions such as how to authenticate iot devices with amazon cognito docs images chat gif table of contents features features aws services showcased aws services showcased installation installation application walkthrough application walkthrough architecture walkthrough architecture walkthrough frontend frontend redux flows redux flows backend backend iot topics iot topics other guides other guides testing testing features register user login logout create room join room real time chat unread message indicators aws services showcased aws iot https aws amazon com iot to exchange messages between web clients transmit messages using the mqtt over the websocket protocol to reduce network bandwidth requirements authenticate clients with amazon cognito and attach iot policies to allow clients to connect to the aws iot device gateway publish messages to specific topics subscribe receive messages from specific topics authentication with amazon cognito https aws amazon com cognito user pools http docs aws amazon com cognito latest developerguide cognito user identity pools html and cognito federated identities http docs aws amazon com cognito latest developerguide cognito identity html serverless computing with aws lambda https aws amazon com lambda api access control provided by amazon api gateway https aws amazon com api gateway room persistence via amazon dynamodb https aws amazon com dynamodb static site hosting on amazon s3 https aws amazon com s3 amazon cloudfront https aws amazon com cloudfront as a proxy and cdn architecture docs images architecture png installation prerequisites please note that this project has only been tested on macos windows users may need to update some of the scripts pull requests are welcome to add windows support install and configure the aws command line interface https aws amazon com cli ensure that the aws cli region is the same as what you define in api config yml please configure your output format to be json instructions https docs aws amazon com cli latest userguide cli usage output html jq https stedolan github io jq to process command line json npm https www npmjs com get npm the node package manager 1 clone the repository git clone https github com aws samples aws iot chat example git 2 deploy the backend if you would like to enable login with facebook and or google please follow the social logins docs social logins md guide cd api sudo npm install g serverless npm install serverless deploy the serverless framework https serverless com framework is a tool for deploying serverless architectures the stack including lambda functions and cloudformation resources are defined in api serverless yml after deploying the serverless application two scripts are invoked by means of a lifecycle hook the first api scripts attachconfirmusertrigger sh is used to attach a lambda function to the presignup trigger on the cognito user pool the second script in client scripts setup sh uses the aws cli to query the cloudformation template output variables and stores them in the file client src config config json this auto generated file is then imported by client src config index js and used within the client side application 3 run the frontend client cd client npm install npm start it is important to run the setup script so that the client knows the destination of the aws iot endpoint api gateway endpoint and other coniguration variables generated by serverless deploy 3 open http localhost 3000 and try it out teardown to tear down the stack use the command serverless remove application walkthrough try the app on the demo website https d2ab9cgiumpbb1 cloudfront net 1 register page register register a user using amazon cognito user pools you can use user pools to add user registration and sign in features to your apps register docs images register png 2 login page login login a user using amazon cognito user pools alternatively you can use amazon cognito federated identities to create unique identities for your users and federate them with identity providers such as facebook and google login docs images login png 3 rooms list app rooms displays the list of available rooms to join if you are subscribed to a room there will be an icon indication the number of unread messages to subscribed rooms is also displayed lastly on this screen you can create a room the application will generate an appropriate topic name to publish on if your room name contains illegal characters all http api endpoints are aws lambda functions invoked through amazon api gateway rooms list docs images rooms list png 4 room app room public bad programming jokes this is the main chat page where the real time messaging is displayed you will still be subscribed to other rooms as indicated by the unread message counters that increment when a message is received sending a message will dispatch a message to aws iot which in turn is responsible for fanning out the messages to all the subscribers of the room docs images chat demo png architecture walkthrough frontend the client side code is a react https reactjs org application the project was bootstrapped using create react app https github com facebookincubator create react app the project is written in javascript using es6 https github com lukehoban es6features syntax and compiled using babel https babeljs io redux https redux js org is used for state management semantic ui react https react semantic ui com is the ui library react router https reacttraining com react router is handles app navigation aws sdk for javascript https github com aws aws sdk js as the aws client library aws iot device sdk for javascript https github com aws aws iot device sdk js as the mqtt client redux flows auth flow docs images auth flow png messaging flow docs images messaging flow png rooms screen flow docs images rooms screen flow png backend to deploy the backend this project uses the serverless framework https serverless com framework docs which is backed by aws cloudformation https aws amazon com cloudformation in the api serverless yml file functions events resources and services are defined which serverless uses to deploy the appropriate lambda functions api gateway services and additional cloudformation resources iot topics mqtt message queue telemetry transport protocol uses a hierarchical structure to describe the topic space multi level wildcards and single level wildcards can be used in addition to topic level separators in this application topics are subscribed to using the form room public roomname this matches topics such as room public room abc room public another room 123 room public another room us west 2 7f86d9a8 b9f4 439e bcf3 9756e4d2bbdf note the last example topic because this is the topic that users will publish to room public roomname cognitoidentityid based on the mqtt specification a receiving client does not know who published the message without either including the message author in the message payload itself or by encoding the message author in the message topic this application opted for the second option by appending the cognito identity id to the end of the publishing topic to prevent users from publishing to other user s topics the aws lambda function attachpublicpublish creates a dynamic iot policy that allows only that one identity to publish messages to their unique topic other guides see authentication tutorial docs authentication md for an guide on configuring the user pools identity pools and iot policies see auto confirming users guide docs auto confirming users md to show how to build a registration flow without mfa see social logins guide docs social logins md for information on how to enable facebook and google login flows see mobile hub deployment guide docs mobile hub deployment md to learn how to deploy the application to an s3 bucket and distribute it via amazon cloudfront s global network of edge cache servers testing in both the api and client directories npm run test can be used to run unit tests using jest http facebook github io jest | aws-iot amazon-cognito serverless-framework aws-lambda dynamodb react redux websockets mqtt | 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trainee-roadmap | front end trainee roadmap img src https miro medium com max 1200 0 hiclyadnsiyt0odu jpg inspired from https github com kamranahmedse developer roadmap https roadmap sh frontend https roadmap sh angular https roadmap sh react table of content prerequisites prerequisites how does the browser render a website how does the browser render a website http http html html css css javascript javascript typescript typescript build tools build tools angular angular react react git git soft skills soft skills youtube channels to subscribe youtube channels to subscribe prerequisites english minimal level b1 comfortable level b2 img src assets common european framework of reference cefr png width 100 how does the browser render a website https github com vasanthk how web works https www youtube com watch v hjhvdblsxug ab channel academind https www youtube com watch v sme4owhztcc ab channel jsconf http https www youtube com watch v iym2zfp3zn0 t 442s ab channel traversymedia https www youtube com watch v 2jyt5f2isg4 ab channel freecodecamp org https www youtube com watch v zkeqqio7n k ab channel webdevsimplified cors https www youtube com watch v pntfsvu yti ab channel webdevsimplified html https www youtube com watch v dpnqb74smug ab channel freecodecamp org https www youtube com watch v n8yml4ezp4g ab channel codevolution https www youtube com playlist list pl41lfr 6dnoq3bebuctnmsvdojciiv en css https www youtube com watch v 0w6qz0 adam list pl0zuz27sz 6mx9fd9elt80g1bpcysmwit ab channel davegray https www youtube com watch v 1rs2nd1ryyc ab channel freecodecamp org https www youtube com watch v mnpdifwaaa4 ab channel davegray https www youtube com watch v yszonjkpgg4 list plzla0gpn vh8mpxiuhjwomaagoceinl0r responsive web design tutorials https www youtube com watch v srvurasnj0s ab channel freecodecamp org https www youtube com playlist list pl4cuxegkcc9g9vh9maa xknfjswznpzfw css positioning tutorials https www youtube com playlist list pl4cuxegkcc9hudkgi5o5uiwutagbxilth scss https www youtube com watch v kqn4hl9bgc list pl4cuxegkcc9jxjx7vojnvk o8ubdzecnb ab channel thenetninja flexbox https www youtube com watch v 3yw65k6lcia ab channel traversymedia grid https www youtube com playlist list plu8eosxdxhp5cifvt9 ze3ingcdac2xkg tailwind https www youtube com playlist list pl4cuxegkcc9gpxorlehjc5bgnii5heghw https www youtube com watch v lzp4salrffc ab channel dailytuition bulma https www youtube com playlist list pl4cuxegkcc9ixitwkbaqxcydt1u6e7a8a bootstrap https www youtube com playlist list pl4cuxegkcc9joim91nlzd qah aimmdar javascript https www youtube com watch v hdi2bqojy3c ab channel traversymedia https www youtube com watch v zbpegr48 ve list plqklakb2gjhwxv9rcarwvn06isll 9mpq ab channel coderlipi https www youtube com playlist list pldyqo7g0 nsx8 gzab8kd1ll4j4halqbj object oriented javascript https www youtube com playlist list pl4cuxegkcc9i5yvdkjgt60vnvwffpblb7 callbacks promises async await https www youtube com watch v porjizfvm7s ab channel traversymedia https www youtube com playlist list pl4cuxegkcc9jx2ttzk3igwksbtugydrlu modular javascript https www youtube com playlist list ploycgnoiygabs wdaaxchu82q xqgub4f event loop https towardsdev com event loop in javascript 672c07618dc9 https www youtube com watch v 8aghzqkofbq ab channel jsconf javascript best practices and coding conventions https www youtube com watch v rmn bkz1km0 ab channel javascriptmastery https www youtube com playlist list ply5pat 51egyo4ixvdzgzy57n0 r1qmtb https www youtube com playlist list plzla0gpn vh cthencpcm0dww6a5xyc7f https www youtube com playlist list plfkdytlp3abzwwlehq1whckyi8ncpy74s typescript https www youtube com playlist list pl4cuxegkcc9gugr39q yd6v bsymwkpui https www youtube com watch v d56mg7dezgs ab channel programmingwithmosh https www youtube com playlist list plqq 6pq4lttanfgsbnfzfwuhhaz3tiezu https www youtube com playlist list plyvdvjlntojf6ajswwat7kzrjvzw en8b https www youtube com watch v jbmrduvkl5w list plzla0gpn vh z2fqig50 pojrukjgbu7g typescript design patterns https www youtube com playlist list plzvrqmj9hdisk1pnrkewlklyfcdu9qjhy build tools npm https www youtube com playlist list plc3y8 rfhvwhgwwm5j3kqzx47n7dwwnrq yarn https www youtube com watch v byrxp9vlzei ab channel webstylepress webpack https www youtube com playlist list plb67cosr0 lpuxik35j8m7equbujqma0q angular big framework better to start learning from official documentation https angular io basic https www youtube com playlist list plc3y8 rfhvwhbragfinjr8khircdtkzcz angular material https www youtube com playlist list plc3y8 rfhvwileucqfgtl5gt5u6deirsu ngrx https www youtube com playlist list plw2eqosuplwjrfwgoi9gzdc3re4fke0wv rxjs https www youtube com playlist list plx7ev3jl9sfl8lrnzyzau8yn uqrgbhij https www youtube com playlist list pl55riy5tl51phpagycrn9ubnlvxf8rgvi angular forms https www youtube com playlist list plzagffnxkfubqodajk3evoekfud nankl component interaction https www youtube com playlist list plc3y8 rfhvwgkhalu8gtyf 5bb8qt wzv solid https www youtube com watch v y mrj9qycvi t 672s ab channel decodedfrontend react big framework better to start learning from official documentation https reactjs org docs getting started html render https www youtube com playlist list plc3y8 rfhvwg7czgqpqibeahn8d6l530t react router v6 https www youtube com watch v ul3y1lxxzdu ab channel webdevsimplified react typescript https www youtube com playlist list plc3y8 rfhvwi1axijgtkm0bkthzvc lsk how to style your react app https www youtube com watch v dxikbh lcf4 ab channel ericmurphy react material ui https www youtube com playlist list plc3y8 rfhvwh k9mdlrrcdywl7cevl2ro react context hooks https www youtube com playlist list pl4cuxegkcc9hnokbyjilpg5g9m2apuepi https www youtube com watch v cf2lq gzea8 list plc3y8 rfhvwisvxhz135pogtx7 oe3q3a https www youtube com watch v o6p86uwfdr0 list plzla0gpn vh8etggfgercwmy5u5hojf h https youtu be yisqpp31l0 react hook form https www youtube com playlist list pl03g4h exutppogty 45owvn79rvjikzf react query https www youtube com playlist list plc3y8 rfhvwjtelcrprczlo6bllbuspd2 react testing library https www youtube com playlist list pl4cuxegkcc9gm4 5usnmlqmosm dzuvq react solid principles https www youtube com watch v msq dcrxoxw ab channel coderone react design patterns https www youtube com playlist list plgeetuaeeds5he2ugwezjxyezwpbonhr react storybook tutorial https www youtube com watch v bysfuxgg ow list plc3y8 rfhvwhc j3x3t9la8 gqjgvidqk git https www youtube com watch v usjzcfj8yxe ab channel coltsteele https www youtube com watch v 8jj101d3kne ab channel programmingwithmosh https www youtube com watch v uszj k0dgsg ab channel freecodecamp org https www youtube com watch v wiy824wwpu4 ab channel ihatetomatoes https www youtube com watch v crlgddprdoq ab channel academind soft skills https www youtube com watch v ll7jyrxwmqy ab channel knowledgehutupgrad https www youtube com skillopedia featured reverse interview https github com viraptor reverse interview newsletters https bytes dev https react libhunt com https react statuscode com https javascriptweekly com https tailwindweekly com https medium com youtube channels to subscribe https www youtube com webdevsimplified https www youtube com decodedfrontend https www youtube com codeshotswithprofanis https www youtube com joshtriedcoding https www youtube com joshuamorony https www youtube com dreylikydev https www youtube com codevolution https www youtube com coderone | angular angular-learning css front-end front-end-development front-end-learning-journey html javascript react react-learning typescript typescript-learning front-end-learning-path | front_end |
twitter-simulation | twitter simulation twitter simulation developed for java lab at institute of information technology univrsity of dhaka | server |
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CITEC-Website | citec website | website | server |
medical-nlp | medical nlp dataset compiled for natural language processing using a corpus of medical transcriptions and custom generated clinical stop words and vocabulary usage clone or download files for use in medical text natural language processing nlp experiments data mtsamples csv compiled from kaggle s medical transcriptions dataset by tara boyle https github com terrah27 scraped from transcribed medical transcription sample reports and examples https www mtsamples com see kaggle repository https www kaggle com tboyle10 medicaltranscriptions mtsamples csv clinical stopwords txt compiled from dr kavita ganesan https github com kavgan clinical concepts https github com kavgan clinical concepts repository see the discovering related clinical concepts using large amounts of clinical notes https www ncbi nlm nih gov pmc articles pmc5015701 paper vocab txt generated vocabulary text files for natural language processing nlp using the systematized nomenclature of medicine international snmi data https bioportal bioontology org ontologies snmi see how to generate your own vocab file https github com socd06 snmi vocab blob master notebooks snmi vocab ipynb x csv https github com socd06 medical nlp blob master data x csv fully processed dataset obtained from running the data modelling https github com socd06 private nlp blob master notebooks medical text data modelling ipynb notebook simplified dataset to 4 classes classes txt https github com socd06 medical nlp blob master data classes txt text file describing the dataset s classes surgery medical records internal medicine and other train csv https github com socd06 medical nlp blob master data train csv training data subset contains 90 of the x csv processed file test csv https github com socd06 medical nlp blob master data test csv test data subset contains 10 of the x csv processed file license gnu general public license version 3 https github com socd06 medical nlp blob master license | medical-text-mining natural-language-processing nlp open-source | ai |
Twitter | project 3 twitter twitter is a basic twitter app to read and compose tweets the twitter api https apps twitter com time spent 12 5 hours spent in total user stories the following required functionality is completed x user sees app icon in home screen and styled launch screen x user can sign in using oauth login flow x user can logout x user can view last 20 tweets from their home timeline x in the home timeline user can view tweet with the user profile picture username tweet text and timestamp x user can pull to refresh x user can tap the retweet and favorite buttons in a tweet cell to retweet and or favorite a tweet x user can compose a new tweet by tapping on a compose button x using autolayout the tweet cell should adjust its layout for iphone 11 pro and se device sizes as well as accommodate device rotation x user should display the relative timestamp for each tweet 8m 7h x tweet details page user can tap on a tweet to view it with controls to retweet and favorite the following optional features are implemented x user can view their profile in a profile tab contains the user header view picture and tagline contains a section with the users basic stats tweets following followers profile view should include that user s timeline x user should be able to unretweet and unfavorite and should decrement the retweet and favorite count refer to this guide unretweeting for help on implementing unretweeting x user can tap the profile image in any tweet to see another user s profile contains the user header view picture and tagline contains a section with the users basic stats tweets following followers x when composing you should have a countdown for the number of characters remaining for the tweet out of 280 1 point x after creating a new tweet a user should be able to view it in the timeline immediately without refetching the timeline from the network x user can load more tweets once they reach the bottom of the feed using infinite loading similar to the actual twitter client x user can switch between timeline mentions or profile view through a tab bar 3 points only timeline and profile view links in tweets are clickable user can reply to any tweet and replies should be prefixed with the username and the reply id should be set when posting the tweet 2 points user sees embedded images in tweet if available profile page pulling down the profile page should blur and resize the header image 4 points the following additional features are implemented x other user s profile indicates if current user follows them x verified status is shown on verified users please list two areas of the assignment you d like to discuss further with your peers during the next class examples include better ways to implement something how to extend your app in certain ways etc 1 i want to extend my app by adding replying and following capabilities 2 i want to see if it is possible to have only one profile storyboard for both the current user and all other users video walkthrough here s a walkthrough of implemented user stories main gif img src twitter gif title video walkthrough width alt video walkthrough width 200 height 500 launch screen app icon img src launch screen gif title video walkthrough width alt video walkthrough width 200 height 500 iphone 8 img src iphone 8 gif title video walkthrough width alt video walkthrough width 200 height 500 iphone 8 plus img src iphone 8plus gif title video walkthrough width alt video walkthrough width 200 height 500 iphone se img src iphone se gif title video walkthrough width alt video walkthrough width 200 height 500 gif created with kap https getkap co notes i can use this project to logout login with oauth write my own functions api references general project structure credits list an 3rd party libraries icons graphics or other assets you used in your app afnetworking https github com afnetworking afnetworking networking task library license copyright yyyy name of copyright owner licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | os |
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tensorflow_nlp | deep natural language process toolkit this is a natural language process toolkit the toolkit includes implement of segment pos tagging named entity recognition text classification text representation textsum relation extract chatbot qa and so on so far the implementation is experimental should not be used for the production environment implement chatbot nlp chatbot readme md named entity recognition nlp ner readme md poems nlp poems readme md pos tagging nlp pos readme md relation extraction nlp relation extract readme md segment nlp segment readme md sentiment nlp sentiment readme md text classification nlp text classification readme md text representation nlp text representation readme md text summarization nlp textsum readme md requirement the requirement in file requirements txt requirements txt and you can use the following command to install required libs pip install r requirements txt if bazel is not installed on your system install it now by following these directions https docs bazel build versions master install html license the license is license license | ai |
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Web-Development-SRC | web development src repository for the student run short course introduction to web development resources the following are a few resources that we referred to while preparing for the src and have curated for you all hope it helps you as well how the web works https www freecodecamp org news how the web works a primer for newcomers to web development or anyone really b4584e63585c how the web works client server https medium com free code camp how the web works part ii client server model the structure of a web application 735b4b6d76e3 e6tmj8112 frontend roadmap https roadmap sh frontend fullstack roadmap https roadmap sh full stack html w3schools https www w3schools com html css w3schools https www w3schools com css guide to flexbox https css tricks com snippets css a guide to flexbox css properties http www blooberry com indexdot css propindex all htm flexbox froggy https flexboxfroggy com css art gallery https css art com vs code shortcuts https dev to codewithtee vs code shortcuts for web developers 363d bootstrap introduction https getbootstrap com docs 5 3 getting started introduction learn responsive web design https web dev learn design introduction to web accessibility https www w3 org wai fundamentals accessibility intro the browser accessibilty tree https www tpgi com the browser accessibility tree accessible names https www tpgi com what is an accessible name javascript w3schools https www w3schools com js learn javascript online https learnjavascript online deployment using github https pages github com deployment using netlify https www freecodecamp org news publish your website netlify github | css html javascript webdevelopment git github github-pages js | front_end |
nick1974 | nick1974 information technology | server |
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LLM4SD | large language model for scientific discovery llm4sd llm4sd is an open source initiative that aims to leverage large language models for scientific discovery we have released the demo code for a task predicting the blood brain barrier permeability of molecules satisfied subsequent code will be released soon demo description in the demo for llm4sd using galactica 6 7b as llm encoder to solve the blood brain barrier permeability prediction problem star2 requirements are shown in the requirement txt star2 first to conduct knowledge synthesize from the literature run the following command python synthesize py the synthesized rules are stored under the prior knowledge folder star2 second to conduct knowledge inference from data run the following command python inference py the inferred rules are stored under the data knowledge folder star2 before conducting interpretable model training we need to conduct code generation based on the extracted rules there are two ways to achieve this 1 human experts write the code based on the generated rules 2 using code generation tools e g gpt4 and code llamma and then human experts review and modify the generated code in this demo we adopt the second way an illustrated code example is shown in the code generation repo folder the code is generated based on the combination of the example synthesized rules in the prior knowledge folder and the example inferred rules in the data knowledge folder the generated code is stored under the code generation repo folder star2 finally to conduct interpretable model training and obtain the model performance results run the following command python eval py knowledge type all ps to obtain an explanation you can use the information provided by the trained interpretable model and structure a prompt to let an llm explain the result architecture of llm4sd div align center img width 843 alt image src https github com zyzisastudyreallyhardguy llm4sd assets 75228223 cbfc09d3 ac63 4889 a15d 5471439e1e70 div web based application developed based on llm4sd will be released soon comments are welcome to help us improve the web based application exclamation exclamation exclamation 1 knowledge synthesis derive knowledge from scientific literature div align center img width 843 alt image src https github com zyzisastudyreallyhardguy llm4sd assets 75228223 4c187835 72ea 477e be57 0030f41a552f img width 843 alt image src https github com zyzisastudyreallyhardguy llm4sd assets 75228223 04e29487 6fdc 4a08 b064 e159f3e76c60 div 2 knowledge inference derive knowledge from analyzing scientific data div align center img width 843 alt image src https github com zyzisastudyreallyhardguy llm4sd assets 75228223 46d15716 c294 4c60 8cc1 47235f1a32d2 img width 843 alt image src https github com zyzisastudyreallyhardguy llm4sd assets 75228223 f404d5e5 6f34 4c7f 8f53 8ad9274f7cd0 div 3 prediction with explanation explaining how the prediction is derived div align center img width 843 alt image src https github com zyzisastudyreallyhardguy llm4sd assets 75228223 5bc95560 b53a 44e4 b478 3ad1b901f61b img width 600 alt image src https github com zyzisastudyreallyhardguy llm4sd assets 75228223 707c6585 592a 4e1c 9fd6 22e73ca313a9 div | ai |
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components | react components for cloudscape design system this package contains the source code of the react components for the cloudscape design system https cloudscape design cloudscape is an open source design system for building intuitive engaging and inclusive user experiences at scale it consists of an extensive set of guidelines to create web applications along with the design resources and front end components to streamline implementation cloudscape was built for and is used by amazon web services aws https aws amazon com products and services we created it in 2016 to improve the user experience across aws web applications and also to help teams implement those applications faster since then we have continued enhancing the system based on customer feedback and research components apis and guidelines can be found in the components section of the cloudscape website https cloudscape design components getting started for an in depth guide on getting started with cloudscape development check out the cloudscape website https cloudscape design get started integration using cloudscape components installation all cloudscape packages are available on npm sh npm install cloudscape design components cloudscape design global styles using cloudscape components here is a basic example that renders a primary button jsx import button from cloudscape design components button import cloudscape design global styles index css function app return button variant primary click me button you can also play around with a small example on codesandbox edit on codesandbox https codesandbox io static img play codesandbox svg https codesandbox io s cloudscape design system react javascript ljs1t7 getting help you can create bug reports or feature requests https github com cloudscape design components issues new choose or start a discussion https github com cloudscape design components discussions to ask a question to minimize duplicates we recommend that you search for existing bug reports feature requests or discussions before initiating a new thread contributing the contribution guidelines https github com cloudscape design components blob main contributing md contains information on how to contribute as well as our support model and versioning strategy license this project is licensed under the apache 2 0 license license | components design-system react scss typescript | os |
TREvaL | treval we are unlocking the treval to facilitate further exploration in evaluation llms fields notably this repo is not complete enough we will release the full version in oct overview treval reward model for reasonable robustness evaluation is a framework for generating word level perturbations on open questions and evaluating the helpfulness harmlessness robustness of llms via these prompts this repo provide a codebase for treval and a method to depict llms loss landscapes the repository is an implementation for treval are large language models really robust to word level perturbations https arxiv org abs 2309 11166 div align center img src treval png width 480 height 300 alt div three perturbation forms are available misspelling swapping synonym our code builds on top of various existing repos especially beavor series https github com pku alignment safe rlhf llama2 series https github com facebookresearch llama synonym https github com linyanglee bert attack landscape https github com marcellodebernardi loss landscapes notably the tested llms or mlms can be obtained in each related repo above installation to run our code you need to have python 3 3 8 conda and pip installed on your machine the installation can be done by the following procedures to clone the repo git clone https github com harry mic treval git cd treval to create the environment and install the dependencies conda env create f environment yaml conda activate treval evaluation to do the evaluation here is an example llm model llama alpaca beavor llama2 llama2 7b chat llama2 13b chat llama2 70b chat mlm model bert base uncased reward model beaver 7b v1 0 reward beaver 7b v1 0 cost task misspelling swapping synonym attack degree 3 5 10 dataset nq the following command gather the function of generating perturbed text and interacting with the beavor series llms python beavor models generate and attack py model beavor dataset nq task misspelling attack degree 10 for interacting with llama2 series torchrun nproc per node 1 llama2 attack py model llama 2 chat 7b dataset nq task misspelling attack degree 10 for evaluating the score python beavor models eval py model rewardmodel dataset nq task misspelling attack degree 10 to draw the loss landscape here is an example python beavor models create landscape py model beavor task misspelling attack degree 10 | ai |
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FreeRTOS-PortentaH7 | freertos portentah7 gsoc 2020 arduino project portenta h7 https www arduino cc pro hardware product portenta h7 is a high performance board released by arduino https www arduino cc usage compile make mcu m7 upload make mcu m7 upload clean build file make mcu m7 clean mcu m7 or mcu m4 example 1 make mcu m4 clean 2 make mcu m7 clean 3 make mcu m4 4 make mcu m7 5 connect h7 to your computer 6 make mcu m4 upload 7 make mcu m7 upload 8 you can see the blue led flashing on the board todo refactor use cmake use https github com stmicroelectronics stm32h7xx hal driver use https github com freertos freertos kernel | os |
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Image2schematic | image2schematic prs welcome https img shields io badge prs welcome brightgreen svg style flat square https makeapullrequest com image2schematic is a python project that aims to provide a reliable tool for everyone to extract pcb schematics from images using computer vision and neural networks any help is greatly appreciated we use opencv https opencv org for image detection and modification and skidl https github com devbisme skidl for building out a schematic in addition pcb cd https github com s39674 pcb component detection is used to classify pcb components and easyocr https github com jaidedai easyocr to read text on chips global sitrep got easyocr working to some degree the dedicated branch has been merged there are multiple stages needed for this project 1 first things first the raw image needs to be modified so that traces between components could be better analysed something like this is much much better than raw p align left img src assets example images board images board7 png alt assets example images board images board7 png width 400 p 2 using opencv and pcb cd we need to identify pcb components such as resistors capacitors integrated circuits etc for example this is the back of a raspberry pi 3b p align left img src https user images githubusercontent com 98311750 178681915 d89cb4c5 b1ce 4477 803d 0c302c228a07 png alt raspberry pi 3b back width 400 p and for each detected component we run it through pcb cd capacitor5 https user images githubusercontent com 98311750 176838096 dec35dc6 9f4f 40b5 83ad 082d82fbfe9c jpg prediction output capacitor 3 for every chip that got detected using opencv and easyocr we need to extract the text on those ics and then pass that into skidl search function to get the pinout of the integrated circuit as well as the schematic symbol 4 using opencv we will now analyze the pin to pin traces that connect between components 5 using all this data metioned above and using skidl we will craft a file with skidl s syntax describing the circut and then output a schematic using skidl to schematic tool sitrep skidl to schematic is currently not working testing note skidl does require some part libraries from kicad if you don t want to install kicad you can just install the part libraries from https gitlab com kicad libraries kicad symbols then point the environment variable to it bash git clone https gitlab com kicad libraries kicad symbols for windows setx kicad kicad symbol dir path to kicad symbols for linux export kicad symbol dir path to kicad symbols pip install skidl to test ic text detection you need to install easyocr https github com jaidedai easyocr easyocr as far as i can tell only works with opencv 4 5 4 60 bash pip install easyocr easyocr unnecessary install this package which interfere with opencv python pip uninstall opencv python headless if you have another opencv package uninstall it and then pip install opencv python 4 5 4 60 for testing pcb components detection please refer to pcb cd https github com s39674 pcb component detection repo after you cloned the repository ins first run ins detectingpoints py bash python3 detectingpoints py now you should have pointsfilefor board8 png txt file under output files this file should include all the coordinates x y of the board electrical points currently there are only a few examples located at assets example images board images but feel free to try it on other board images and post your result in the discussions tab p align left img src assets example images board images board8 png alt assets example images board images board8 png width 400 p after that run connectionfinding py bash python3 connectionfinding py if you now look at pointsfilefor board8 png txt you should now see every connection from any point txt point 435 479 connected to 190 171 point 435 466 connected to 190 144 point 435 453 connected to 190 117 point 435 439 connected to 190 90 point 435 426 connected to 190 64 point 436 412 connected to 190 37 attiny841 ss 6 pa7 p6 pa7 bidirectional 190 171 connected to 435 479 attiny841 ss 5 pb2 pb2 p5 bidirectional 190 144 connected to 435 466 attiny841 ss 4 reset pb3 reset pb3 p4 bidirectional 190 117 connected to 435 453 attiny841 ss 3 xtal2 pb1 xtal2 pb1 p3 bidirectional 190 90 connected to 435 439 attiny841 ss 2 xtal1 pb0 xtal1 pb0 p2 bidirectional 190 64 connected to 435 426 attiny841 ss 1 vcc p1 vcc power in 190 37 connected to 436 412 what we can see are the x y coordinates of the points and the x y coordinates of what point is connected to it at the bottom we can see what ic is connected to those points and some information about them in this example the ic name was hard coded ic detection example isn t ready yet this is the baseline to building an entire schematic if you encountered any issues during installation or testing of image2schematic or if you have any suggestions please feel free to post them in the issues tab few topics i need help with component classification needs to be fine tuned to get better results it should also output confidence percentage please see pcb cd https github com s39674 pcb component detection for more info more than 2 point connection finding right now we can t detect lines that connect 3 different components at once skidl to schematic algorithm i can t get the algorithm that takes skidl code and output a schematic to work i want to thank you for reading this and i hope you can help me thank you | pcb schematic image-recognition image-processing | ai |
ccv2 | header image http nuigroup com create 960 180 111111 ccv2 community core vision 2 0 introduction community core vision 2 0 is build on the nuiframework a cross platform cross device framework for rapidly creating and testing interaction scenarios in natural user interfaces it allows for general purpose signal processing from multiple sensor sources and processing them into global events and actions each platform will have its own specific runtime and communication methods dependent on best available options our first implementation will be focused on computer vision cv components core engine pure c portable module engine cv module library base modules for vision related applications chrome extension used for testing tdd of the json rpc api requires chrome canary example client an example client api implementation in c compiling we currently recommend using vs2010 to compile the following solutions are provided nuiserverapplication compiles main server engine executable nuicvmodulelibrary compiles associated cv modules dependencies nuimodule lib nuisystem lib changelog view changes md for latest updates links http nuigroup com nui group community homepage http ccv nuigroup com ccv 1 homepage http nuigroup com gsoc gsoc projects | ai |
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miso | h1 align center miso h1 p align center a href https haskell miso org img width 10 src https em content zobj net thumbs 240 apple 325 steaming bowl 1f35c png a p align center a i tasty i a href https www haskell org strong haskell strong a front end framework p p p align center a href https join slack com t haskell miso shared invite zt 1w80x404h af2be bdqixnmadzadnung img src https img shields io badge slack miso e01563 svg style flat square alt miso slack a a href https haskell org img src https img shields io badge language haskell orange svg style flat square alt haskell a a href https miso haskell cachix org img src https img shields io badge build cachix yellow svg style flat square alt cachix a a href https github com dmjio miso actions img src https github com dmjio miso workflows test 20matrix badge svg alt github actions a a href http hackage haskell org package miso img src https img shields io hackage v miso svg style flat square alt hackage a a href https www irccloud com invite channel 23haskell miso amp hostname irc libera chat amp port 6697 amp ssl 1 img src https img shields io badge irc 23haskell miso 1e72ff svg style flat square alt irc haskell miso a a href https github com dmjio miso blob master license img src http img shields io badge license bsd3 blueviolet svg style flat square alt license a p miso is a small production ready isomorphic http nerds airbnb com isomorphic javascript future web apps haskell https www haskell org front end framework for quickly building highly interactive single page web applications it features a virtual dom recursive diffing patching algorithm attribute and property normalization event delegation event batching svg server sent events websockets type safe servant https haskell servant github io style routing and an extensible subscription based subsystem inspired by elm http elm lang org redux http redux js org and bobril http github com bobris bobril miso is pure by default but side effects like xhr can be introduced into the system via the effect data type miso makes heavy use of the ghcjs https github com ghcjs ghcjs ffi and therefore has minimal dependencies miso can be considered a shallow embedded domain specific language https wiki haskell org embedded domain specific language for modern web programming table of contents quick start quick start begin begin nix nix architecture architecture examples examples todomvc todomvc flatris flatris 2048 2048 snake snake mario mario miso plane flappy birds miso plane flappy birds websocket websocket sse sse xhr xhr router router svg svg canvas 2d canvas 2d threejs threejs simple simple file reader file reader webvr webvr pixel card wars pixel card wars haddocks haddocks ghc ghc ghcjs ghcjs sample application sample application transition application transition application live reload with jsaddle live reload with jsaddle docker docker building examples building examples coverage coverage isomorphic isomorphic pinning nixpkgs pinning nixpkgs binary cache binary cache benchmarks benchmarks maintainers maintainers commercial users commercial users contributing contributing contributors contributors license license quick start to get started quickly building applications we recommend using the nix https nixos org nix package manager with miso s binary cache provided by cachix https miso haskell cachix org it is possible to use stack https docs haskellstack org en stable readme to build ghcjs projects but support for procuring ghcjs has been removed as of stack 2 0 https github com commercialhaskell stack issues 4086 nix is used to procure a working version of ghcjs if you re using cabal we assume you have obtained ghcjs https github com ghcjs ghcjs installation by other means all source code depicted below for the quick start app is available here https github com dmjio miso tree master sample app begin to build the sample app with nix execute the commands below bash optional use of cache nix env ia cachix f https cachix org api v1 install optional use of cache cachix use miso haskell git clone https github com dmjio miso cd miso sample app nix build open result bin app jsexe index html the above commands will add miso s binary cache to your nix installation support for both linux and osx nix build will fetch the dependencies from miso s cache and build the sample application nix nix is a more powerful option for building web applications with miso since it encompasses development workflow configuration management and deployment the source code for haskell miso org https github com dmjio miso tree master examples haskell miso org is an example of this if unfamiliar with nix we recommend gabriella439 https github com gabriella439 s nix and haskell in production https github com gabriella439 haskell nix guide to begin make the following directory layout bash mkdir app touch app main hs app cabal default nix tree app app main hs app cabal default nix add a cabal file bash cat app cabal name app version 0 1 0 0 synopsis first miso app category web build type simple cabal version 1 10 executable app main is main hs ghcjs options dedupe build depends base miso default language haskell2010 write a default nix this will fetch a recent version of miso miso will provide you with a working nixpkgs named pkgs callcabal2nix will automatically produce a nix expression that builds your cabal file nix with import builtins fetchgit url https github com dmjio miso ref refs tags 1 8 pkgs haskell packages ghcjs callcabal2nix app add the source from sample application sample application to app main hs build the project nix build open the result open result bin app jsexe index html for development with nix it can be nice to have cabal present for building this command will make it available in your path nix env ia cabal install f nixpkgs to be put into a shell w ghcjs and all the dependencies for this project present use nix shell nix shell a env to view the dependencies for your project call ghcjs pkg list when inside the shell nix shell a env run ghcjs pkg list to build the project with cabal after entering the nix shell nix shell a env run cabal configure ghcjs cabal build for incremental development inside of the nix shell we recommend using a tool like entr http eradman com entrproject to automatically rebuild on file changes or roll your own solution with inotify ag l entr sh c cabal build architecture for constructing client and server applications we recommend using one cabal file with two executable sections where the buildable attribute set is contingent on the compiler an example of this layout is here https github com dmjio miso blob master examples haskell miso org haskell miso cabal l16 l60 for more info on how to use stack with a client server setup see this link https docs haskellstack org en stable ghcjs project with both client and server for more information on how to use nix with a client server setup see the nix scripts https github com dmjio miso blob master examples haskell miso org default nix for https haskell miso org https haskell miso org examples todomvc link https todo mvc haskell miso org source https github com dmjio miso blob master examples todo mvc main hs flatris link https flatris haskell miso org source https github com ptigwe hs flatris 2048 link https 2048 haskell miso org source https github com ptigwe hs2048 snake link https snake haskell miso org source https github com lbonn miso snake mario link https mario haskell miso org source https github com dmjio miso blob master examples mario main hs miso plane flappy birds link http miso plane haskell miso org source https github com lermex miso plane websocket link https websocket haskell miso org source https github com dmjio miso blob master examples websocket main hs sse link http sse haskell miso org client https github com dmjio miso blob master examples sse client main hs server https github com dmjio miso blob master examples sse server main hs xhr link https xhr haskell miso org source https github com dmjio miso blob master examples xhr main hs router link https router haskell miso org source https github com dmjio miso blob master examples router main hs svg link https svg haskell miso org source https github com dmjio miso blob master examples svg main hs canvas 2d link https canvas haskell miso org source https github com dmjio miso blob master examples canvas2d main hs threejs link https threejs haskell miso org source https github com dmjio miso blob master examples three main hs simple link https simple haskell miso org source https github com dmjio miso blob master examples simple main hs file reader link https file reader haskell miso org source https github com dmjio miso blob master examples file reader main hs webvr link http fizruk github io fpconf 2017 talk miso aframe demo dist demo jsexe index html source https github com fizruk miso aframe pixel card wars link https www schplaf org hgames darkcraw source https github com smelc miso darkcraw haddocks ghcjs link https haddocks haskell miso org ghc link http hackage haskell org package miso sample application haskell haskell language pragma language overloadedstrings language recordwildcards haskell module declaration module main where miso framework import import miso import miso string type synonym for an application model type model int sum type for application events data action addone subtractone noop sayhelloworld deriving show eq entry point for a miso application main io main startapp app where initialaction sayhelloworld initial action to be executed on application load model 0 initial model update updatemodel update function view viewmodel view function events defaultevents default delegated events subs empty subscription list mountpoint nothing mount point for application nothing defaults to body loglevel off used during prerendering to see if the vdom and dom are in sync only applies to miso function updates model optionally introduces side effects updatemodel action model effect action model updatemodel action m case action of addone noeff m 1 subtractone noeff m 1 noop noeff m sayhelloworld m do consolelog hello world pure noop constructs a virtual dom from a model viewmodel model view action viewmodel x div button onclick addone text text ms x button onclick subtractone text transition application an alternative more powerful interface for constructing miso applications is using the transition interface transition is based on the statet monad transformer and can be used to construct components it also works very nicely with lenses based on monadstate i e haskell haskell language pragma language overloadedstrings language recordwildcards haskell module declaration module main where miso framework import import miso import miso string lens import import control lens type synonym for an application model data model model counter int deriving show eq counter lens model int counter lens counter record field record counter field sum type for application events data action addone subtractone noop sayhelloworld deriving show eq entry point for a miso application main io main startapp app where initialaction sayhelloworld initial action to be executed on application load model model 0 initial model update fromtransition updatemodel update function view viewmodel view function events defaultevents default delegated events subs empty subscription list mountpoint nothing mount point for application nothing defaults to body loglevel off used during prerendering to see if the vdom and dom are in sync only applies to miso function updates model optionally introduces side effects updatemodel action transition action model updatemodel action case action of addone counter 1 subtractone counter 1 noop pure sayhelloworld scheduleio consolelog hello world constructs a virtual dom from a model viewmodel model view action viewmodel x div button onclick addone text text ms x counter button onclick subtractone text live reload with jsaddle it is possible to build miso applications with ghcid jsaddle that allow live reloading of your application in reponse to changes in application code see the readme https github com dmjio miso blob master sample app jsaddle readme md in the sample app jsaddle folder for more information docker developing miso applications inside a docker container is supported allows applications to be built on windows see the readme https github com dmjio miso blob master docker readme md in the docker folder for more information building examples the easiest way to build the examples is with the nix https nixos org nix package manager git clone https github com dmjio miso cd miso nix build arg examples true this will build all examples and documentation into a folder named result miso git master tree d result bin result bin canvas2d jsexe compose update jsexe file reader jsexe mario jsexe imgs mathml jsexe router jsexe simple jsexe svg jsexe tests jsexe threejs jsexe todo mvc jsexe websocket jsexe xhr jsexe to see examples we recommend hosting them with a webserver cd result bin todo mvc jsexe nix shell p python run python m simplehttpserver serving http on 0 0 0 0 port 8000 coverage the core algorithmic component of miso is diff js https github com dmjio miso blob master jsbits diff js it is responsible for all dom manipulation that occurs in a miso application and has 100 code coverage http coverage haskell miso org tests and coverage made possible using jsdom https github com jsdom jsdom and jest https github com facebook jest to run the tests and build the coverage report bash cd miso tests npm i npm run test or by using yarn instead of npm yarn yarn test isomorphic isomorphic javascript https en wikipedia org wiki isomorphic javascript is a technique for increased seo code sharing and perceived page load times it works in two parts first the server sends a pre rendered html body to the client s browser second after the client javascript application loads the pointers of the pre rendered dom are copied into the virtual dom and the application proceeds as normal all subsequent page navigation is handled locally by the client avoiding full page postbacks as necessary the miso function is used to perform the pointer copying behavior client side for more information on how miso handles isomorphic javascript we recommend this tutorial https github com fptje miso isomorphic example pinning nixpkgs by default miso uses a known to work pinned version of nixpkgs https github com dmjio miso blob master nixpkgs json binary cache nix users on a linux or osx distro can take advantage of a binary cache https miso haskell cachix org for faster builds to use the binary cache follow the instructions on cachix https miso haskell cachix org bash cachix use miso haskell benchmarks according to benchmarks https rawgit com krausest js framework benchmark master webdriver ts results table html miso is among the fastest functional programming web frameworks second only to elm http elm lang org a target blank href https rawgit com krausest js framework benchmark master webdriver ts results table html img src https cdn images 1 medium com max 1600 1 6ejjtf1mhltxd4qwsygcwa png width 500 height 600 a maintainers dmjio https github com dmjio commercial users polimorphic https www polimorphic com lumiguide https lumi guide en clovyr https clovyr io contributing feel free to dive in open an issue https github com dmjio miso issues new or submit prs https github com dmjio miso pulls see contributing https github com dmjio miso blob master contributing md for more info contributors code contributors this project exists thanks to all the people who contribute contribute contributing md a href https github com dmjio miso graphs contributors img src https opencollective com miso contributors svg width 890 button false a financial contributors become a financial contributor and help us sustain our community contribute https opencollective com miso contribute individuals a href https opencollective com miso img src https opencollective com miso individuals svg width 890 a organizations support this project with your organization your logo will show up here with a link to your website contribute https opencollective com miso contribute a href https opencollective com miso organization 0 website img src https opencollective com miso organization 0 avatar svg a a href https opencollective com miso organization 1 website img src https opencollective com miso organization 1 avatar svg a a href https opencollective com miso organization 2 website img src https opencollective com miso organization 2 avatar svg a a href https opencollective com miso organization 3 website img src https opencollective com miso organization 3 avatar svg a a href https opencollective com miso organization 4 website img src https opencollective com miso organization 4 avatar svg a a href https opencollective com miso organization 5 website img src https opencollective com miso organization 5 avatar svg a a href https opencollective com miso organization 6 website img src https opencollective com miso organization 6 avatar svg a a href https opencollective com miso organization 7 website img src https opencollective com miso organization 7 avatar svg a a href https opencollective com miso organization 8 website img src https opencollective com miso organization 8 avatar svg a a href https opencollective com miso organization 9 website img src https opencollective com miso organization 9 avatar svg a license bsd3 license david johnson | ghcjs haskell virtual-dom miso ramen ui nix javascript | front_end |
impact-design-system | impact design system https demos creative tim com impact design system index html tweet https img shields io twitter url http shields io svg style social logo twitter https twitter com share url http 3a 2f 2fdemos creative tim com 2fimpact design system 2findex html text kick start 20your 20development 20with 20impact 20design 20system via creativetim 20 40themesberg hashtags bootstrap 20 23designsystem 20 23opensource version https img shields io badge version 1 0 0 blue svg license https img shields io badge license mit blue svg github issues open https img shields io github issues creativetimofficial impact design system svg maxage 2592000 https github com creativetimofficial impact design system issues q is 3aopen is 3aissue github issues closed https img shields io github issues closed raw creativetimofficial impact design system svg maxage 2592000 https github com creativetimofficial impact design system issues q is 3aissue is 3aclosed chat https img shields io badge chat on 20discord 7289da svg https discord gg e4ahaqy impact design system thumbnail https s3 amazonaws com creativetim bucket products 296 original opt impact thumbnail jpg kick start your development with an awesome design system carefully designed for your online business showcase it comes as a complete solution with front pages and dashboard pages included impact design system features a huge number of components built to fit together and look amazing if you are a new business looking to create your online presence or you just want to let people know who you are and what you do this might be the answer for you fully coded components impact design system features over 200 individual components giving you the freedom of choosing and combining this means that there are thousands of possible combinations all components can take variations in color that you can easily modify using sass files you will save a lot of time going from prototyping to full functional code because all elements are implemented we wanted the design process to be seamless so switching from image to the real page is very easy to do view all components here https demos creative tim com impact design system pro docs dashboard alerts example pages if you want to get inspiration or just show something directly to your clients you can jump start your development with our pre built example pages from landing pages to e commerce or blog pages you will be able to quickly set up the basic structure for your web project view example front pages here https demos creative tim com impact design system front pages index html and example dashboard pages here https demos creative tim com impact design system dashboard pages dashboards dashboard html complex documentation each element is well presented in very complex documentation you can read more about the idea behind this design system here https demos creative tim com impact design system pro docs getting started overview you can check the components here https demos creative tim com impact design system pro docs dashboard alerts table of contents demo demo quick start quick start documentation documentation file structure file structure browser support browser support resources resources reporting issues reporting issues licensing licensing useful links useful links demo landing about pricing contact landing page https demos creative tim com impact design system front assets img pages github landing jpg https demos creative tim com impact design system front pages index html about page https demos creative tim com impact design system front assets img pages github about jpg https demos creative tim com impact design system front pages about html pricing page https demos creative tim com impact design system front assets img pages github pricing jpg https demos creative tim com impact design system front pages pricing html contact page https demos creative tim com impact design system front assets img pages github contact jpg https demos creative tim com impact design system front pages contact html dashboard login profile documentation dashboard https demos creative tim com impact design system front assets img pages github dashboard jpg https demos creative tim com impact design system dashboard pages dashboards dashboard html login https demos creative tim com impact design system front assets img pages github dblogin jpg https demos creative tim com impact design system dashboard pages examples login html profile https demos creative tim com impact design system front assets img pages github dbprofile jpg https demos creative tim com impact design system dashboard pages examples profile html documentation https demos creative tim com impact design system front assets img pages github docs jpg https demos creative tim com impact design system pro docs getting started overview view more https demos creative tim com impact design system quick start 1 clone this repository or download from creative tim https www creative tim com product impact design system 2 make sure you have node locally installed 3 download gulp command line interface to be able to use gulp in your terminal npm install gulp cli g 4 after installing gulp run npm install in the main impact design system vx x x folder to download all the project dependencies you ll find them in the node modules folder npm install 5 run gulp in the impact design system vx x x folder to serve the project files using browsersync running gulp will compile the theme and open index html in your main browser gulp while the gulp command is running files in the assets scss assets js and components folders will be monitored for changes files from the assets scss folder will generate injected css hit ctrl c to terminate the gulp command this will stop the local server from running theme without sass gulp or npm if you d like to get a version of our theme without sass gulp or npm we ve got you covered run the following command gulp build dev this will generate a folder html css which will have unminified css html and javascript minified version if you d like to compile the code and get a minified version of the html and css just run the following gulp command gulp build dist this will generate a folder dist which will have minified css html and javascript documentation the documentation for impact design system is hosted at our website https demos creative tim com impact design system pro docs getting started overview file structure within the download you ll find the following directories and files license md readme md dashboard assets gulpfile js package lock json package json src dashboard assets fonts img js pages dashboards examples maps tables partials head html tracking body html tracking html scss bootstrap core custom dashboard scss front assets img js pages about html all html contact html index html pricing html partials demo html footer html head html navigation html preloader html pricing html scripts html tracking body html tracking html cta scss bootstrap front front scss index html browser support at present we officially aim to support the last two versions of the following browsers img src https github com creativetimofficial public assets blob master logos chrome logo png raw true width 64 height 64 img src https raw githubusercontent com creativetimofficial public assets master logos firefox logo png width 64 height 64 img src https raw githubusercontent com creativetimofficial public assets master logos edge logo png width 64 height 64 img src https raw githubusercontent com creativetimofficial public assets master logos safari logo png width 64 height 64 img src https raw githubusercontent com creativetimofficial public assets master logos opera logo png width 64 height 64 resources demo https demos creative tim com impact design system index html download page https www creative tim com product impact design system documentation https demos creative tim com impact design system pro docs getting started overview license agreement mit license support https www creative tim com contact us issues github issues page https github com creativetimofficial impact design system issues reporting issues we use github issues as the official bug tracker for the impact design system here are some advices for our users that want to report an issue 1 make sure that you are using the latest version of the impact design system check the changelog from your dashboard on our website https www creative tim com ref mk github readme 2 providing us reproducible steps for the issue will shorten the time it takes for it to be fixed 3 some issues may be browser specific so specifying in what browser you encountered the issue might help licensing copyright 2020 creative tim https www creative tim com ref mk github readme licensed under mit https github com creativetimofficial impact design system blob master license md useful links creative tim tutorials https www youtube com channel ucvytg4scw rovb9ohkzzd1w affiliate program https www creative tim com affiliates new ref mk github readme earn money blog creative tim http blog creative tim com free products https www creative tim com bootstrap themes free ref mk github readme from creative tim premium products https www creative tim com bootstrap themes premium ref mk github readme from creative tim react products https www creative tim com bootstrap themes react themes ref mk github readme from creative tim angular products https www creative tim com bootstrap themes angular themes ref mk github readme from creative tim vuejs products https www creative tim com bootstrap themes vuejs themes ref mk github readme from creative tim more products https www creative tim com bootstrap themes ref mk github readme from creative tim check our bundles here https www creative tim com bundles ref mk github readme social media twitter https twitter com creativetim facebook https www facebook com creativetim dribbble 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SQL-EmployeeSQL | sql employeesql data modeling data engineering and data analysis with csvs sql database postgressql pandas sqlalchemy matplotlib and numpy employee database a mystery in two parts background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data engineering 3 data analysis note you may hear the term data modeling in place of data engineering but they are the same terms data engineering is the more modern wording instead of data modeling instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints import each csv file into the corresponding sql table data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name gender and salary 2 list employees who were hired in 1986 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 4 list the department of each employee with the following information employee number last name first name and department name 5 list all employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name bonus optional as you examine the data you are overcome with a creeping suspicion that the dataset is fake you surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to take the following steps to generate a visualization of the data with which you will confront your boss 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the code below to get started be sure to make any necessary modifications for your username password host port and database name sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect consult sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information if using a password do not upload your password to your github repository see https www youtube com watch v 2uatpmnvh0i https www youtube com watch v 2uatpmnvh0i and https martin thoma com configuration files in python https martin thoma com configuration files in python for more information 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title epilogue evidence in hand you march into your boss s office and present the visualization with a sly grin your boss thanks you for your work on your way out of the office you hear the words search your id number you look down at your badge to see that your employee id number is 499942 | server |
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newrelic-exporter-specs | a href https opensource newrelic com oss category community project picture source media prefers color scheme dark srcset https github com newrelic opensource website raw main src images categories dark community project png source media prefers color scheme light srcset https github com newrelic opensource website raw main src images categories community project png img alt new relic open source community project banner src https github com newrelic opensource website raw main src images categories community project png picture a new relic exporter specifications purpose with this documentation we intend to document our general principles and specific implementations of what we generally call exporters by this we mean libraries or tools that extract data from existing telemetry systems convert the data into a new relic friendly format and send them to new relic in general the implementations of these exporters will rely on our open source sdks https docs newrelic com docs data ingest apis get data new relic new relic sdks telemetry sdks send custom telemetry data new relic to do the work of sending the data to new relic intent this documentation is divided into two sections with two different intents documentation at the top level of this project is generally intended to be prescriptive if you are designing an exporter we strongly recommend following the guidelines md guidelines md as it will support interoperability between the metrics exported from various metric libraries the documentation in the subdirectories of the specific exporters however are intended to be purely descriptive they should reflect exactly what the current version of the exporter actually does even if it doesn t exactly follow the guidelines existing available exporter repositories we have repositories for several new relic exporters already the full table can be found in our telemetry sdk section on docs newrelic com https docs newrelic com docs data ingest apis get data new relic new relic sdks telemetry sdks send custom telemetry data new relic external data it includes at least one language versions for all of the exporters listed in the organization section below organization see guidelines md guidelines md for general principles on how to build exporters and provide adequate information so that they will be able to be queried by nrql https docs newrelic com docs query data nrql new relic query language getting started introduction nrql and visualizations can be created each subdirectory contains a specification for how the relevant exporter functions opentelemetry cross language specifications for how we extract data from opentelemetry https opentelemetry io can be found in the opentelemetry opentelemetry directory opencensus cross language specifications for how we extract data from opencensus https opencensus io can be found in the opencensus opencensus directory dropwizard metrics the detailed description of how the new relic dropwizard reporter https github com newrelic dropwizard metrics newrelic converts dropwizard metrics https metrics dropwizard io into new relic dimensional metrics can be found in the dropwizard dropwizard directory micrometer the detailed description of how the new relic micrometer registry https github com newrelic micrometer registry newrelic converts micrometer metrics https micrometer io into new relic dimensional metrics can be found in the micrometer micrometer directory support for support on this project visit new relic s explorers hub https forum newrelic com s contributing full details are available in contributing md contributing md a note about vulnerabilities as noted in our security policy security policy new relic is committed to the privacy and security of our customers and their data we believe that providing coordinated disclosure by security researchers and engaging with the security community are important means to achieve our security goals if you believe you have found a security vulnerability in this project or any of new relic s products or websites we welcome and greatly appreciate you reporting it to new relic through hackerone https hackerone com newrelic licensing license details are in license md license md | os |
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iOSSecAudit | 1 installation h3 1 1 mac os x h3 h5 1 1 1 pc env prepare h5 1 install python2 7 2 sudo easy install pip 3 sudo pip install paramiko 4 easy install prettytable or easy install u prettytable 5 xcode select install select install then agre 6 brew install libimobiledevice if don t have homebrew install it first ruby e curl fssl https raw githubusercontent com homebrew install master install dev null 2 dev null 7 git clone https github com alibaba iossecaudit git 8 cd path to iossecaudit python main py notice if you see the the following importerror no module named prettytable importerror no module named paramiko uninstall them if needed then try to install prettytable https pypi python org pypi prettytable or paramiko https pypi python org pypi paramiko 1 15 2 from the source code h5 1 1 2 device env prepare h5 1 jailbreak ios device 2 install cycript in cydia h3 1 2 linux or windows h3 u never test on linux or windows cause i am tooooo lazy u 2 usage b special note strongly suggest execute chenv after you connect to your device b usage python main py type help cprt for more information help i documented commands type help topic ab abr aca br chenv cipa clche clzdp cprt cycript dbgsvr dbn dca dipa dlini dlinj dlinji dnload dwa dws e exit fus gbs gdb gdbs go gs gsp gtb h help ibca iipa kcd kcdel kce kcs la lapp las lbs lca log lsl ltb mport nonfat panic pca pid q quit resign sd skc ssh stop upload usb vdb vkc vpl vtb wclzdp wpb i try help cmd0 cmd1 or help all for more infomation help ssh ssh connect to device with ssh args ip username password example ssh 10 1 1 1 root alpine help usb usb ssh device over usb max os x support only args username password port example usb root alpine or usb root alpine 2222 help dlinji dlinji inject a dylib into an ipa file resign and install args ipa path entitlements path mobileprovision path identity dylib example dlini tmp xin ipa tmp entitlements plist tmp ios development mobileprovision iphone developer name name xxxxxx tmp libtest dylib usb root xxroot e ssh authentication failed when connecting to host i connect failed usb root alpine i connect success la i refresh lastlaunchservicesmap i all installed applications 0 com taobao taobao4iphone 1 alilang com alibaba alilang 2 com tencent xin 3 putong com yaymedialabs putong 4 com alipay iphoneclient 5 com mimimix tiaomabijia 6 cn xiaochuankeji tieba help las las list all storage file of an application args bundle identifer example las com taobaobj moneyshield or las help sd sd show application detail args bundle identifer example sd com taobaobj moneyshield or sd sd cn xiaochuankeji tieba i detail info bundle id cn xiaochuankeji tieba uuid d9b2b45f 0d25 4f4f b6a1 45b514bf4d4b binary name tieba platform version 9 3 sdk version iphoneos9 3 mini os 7 0 data directory 5d9b5be7 a438 4057 8a88 4fdea6fc2153 url hnadlers wx16516ad81c31d872 qq41c6a3fb tencent1103537147 zuiyou7a7569796f75 wb4117400114 entitlements get task allow beta reports active aps environment production application identifier 3jds7k3bcm cn xiaochuankeji tieba com apple developer team identifier 3jds7k3bcm com apple security application groups 3 thanks idb https github com dmayer idb class dump https github com nygard class dump clutch https github com kjcracks clutch dumpdecrypted https github com stefanesser dumpdecrypted pbwatcher https github com dmayer pbwatcher please contact me if i use your code while not mention you | os |
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Android-EDx | android edx hkustx comp107x introduction to mobile application development using android contains exercise and assignments completed for this course | front_end |
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blockchain-insurance | blockchain insurance a proof of concept blockchain project around car insurance deploy this to bluemix deploy to bluemix https bluemix net deploy button png https bluemix net deploy repository https github com capgemini aie blockchain insurance to deploy to bluemix 1 hit the button above 2 open the created pipeline inspect the credentials step add missing environment property values apikey edmunds api key sign up for one gcm key google cloud messaging key for webpush 3 open deploy and add a basic cf deploy job 4 run the pipeline from the start 5 if errors occur bind an instance of ibm s blockchain service to the application restage development environment we re building on the docker compose from https github com ibm blockchain fabric images https github com ibm blockchain fabric images see also these instructions for using a local hyperledger network https github com ibm blockchain marbles blob master docs use local hyperledger md to run a single node network using docker compose docker compose f docker compose single peer ca yaml up d for a four node ca network docker compose f four peer ca yaml up d to test it is up and running properly hit http localhost 7050 chain http localhost 7050 chain configuring users in local dev mode docker instance execute the following commands after running docker compose from the docker compose folder docker cp membersrvc membersrvc yaml dockercompose membersrvc 1 opt gopath src github com hyperledger fabric membersrvc docker exec i t dockercompose membersrvc 1 bin bash rm rf var hyperledger production exit docker restart dockercompose membersrvc 1 docker exec i t dockercompose vp0 1 bin bash rm rf var hyperledger production exit docker restart dockercompose vp0 1 7 users will then be created claimant1 claimant2 garage1 garage2 insurer1 insurer2 superuser in order for the correct username role to be established within the chaincode in dev mode an attributes value has to be added to the rest request example curl h content type application json x post d jsonrpc 2 0 method query params type 1 chaincodeid name insurance ctormsg function retrieveallpolicies securecontext claimant2 attributes username claimant2 role policyholder id 4 http localhost 7050 chaincode deploying chaincode for development only currently working in the 4 peer environment to run chaincode locally without having to deploy from a github url 1 build your chaincode go build 2 register the chaincode with a peer core chaincode id name insurance core peer address 0 0 0 0 7051 insurance main 3 send a rest request to enroll a user post http localhost 7050 registrar enrollid bob enrollsecret noe63peqbl25 4 send a rest deploy request post http localhost 7050 chaincode jsonrpc 2 0 method deploy params type 1 chaincodeid name insurance ctormsg function init securecontext bob id 1 5 the chaincode should now be deployed and ready to accept invoke or query requests running blockchain explorer if you want to view your blockchian locally you can use the blockchain explorer to build the docker image run git clone https github com hyperledger blockchain explorer git cd blockchain explorer explorer 1 docker build t hyperledger blockchain explorer to run it first you will need to find out which network your hyperledger fabric peers are running on to do this run docker network ls grab the docker compose network name and use it below docker run p 9090 9090 itd network dockercompose default e hyp rest endpoint http vp0 7050 hyperledger blockchain explorer the explorer should then be available on http localhost 9090 http localhost 9090 configuring the vehicle value oracle in order to use the car value oracle to obtain actual vehicle values from edmunds you must 1 obtain an edmunds api key and set the env variable note this step can be skipped and the oracle service will then just callback with a hardcoded value edmunds api key 2 deploy the chain code with an init argument specifying the host address of the oracle service including port 3 configure the node app with the correct blockchain settings in default json 4 run the app js node app npm install node app js | blockchain-insurance chaincode hyperledger | blockchain |
whatthefuck.is | whatthefuck is https whatthefuck is an opinionated glossary of computer science terms for front end developers written by dan abramov contributing typo fixes and website improvements are welcome as pull requests we do not accept definitions but you re welcome to vote on and request definitions in the issue tracker https github com gaearon whatthefuck is issues you can sort popular issues by sorting by reactions https github com gaearon whatthefuck is issues page 1 q is 3aissue is 3aopen sort 3areactions 2b1 desc | javascript glossary | front_end |
amazon-dsstne | amazon dsstne deep scalable sparse tensor network engine dsstne pronounced destiny is an open source software library for training and deploying recommendation models with sparse inputs fully connected hidden layers and sparse outputs models with weight matrices that are too large for a single gpu can still be trained on a single host dsstne has been used at amazon to generate personalized product recommendations for our customers at amazon s scale it is designed for production deployment of real world applications which need to emphasize speed and scale over experimental flexibility dsstne was built with a number of features for production recommendation workloads multi gpu scale training and prediction both scale out to use multiple gpus spreading out computation and storage in a model parallel fashion for each layer large layers model parallel scaling enables larger networks than are possible with a single gpu sparse data dsstne is optimized for fast performance on sparse datasets common in recommendation problems custom gpu kernels perform sparse computation on the gpu without filling in lots of zeroes benchmarks scottlegrand reported near linear scaling with multiple gpus on the movielens recommendation problem https medium com scottlegrand first dsstne benchmarks tldr almost 15x faster than tensorflow 393dbeb80c0f ghe74fu1q directions on how to run a benchmark can be found in here benchmarks benchmark md scaling up using spark in aws emr and dockers in aws ecs http blogs aws amazon com bigdata post txgel8ij0caxtk generating recommendations at amazon scale with apache spark and amazon dsstne license license license setup follow setup docs getting started setup md for step by step instructions on installing and setting up dsstne user guide check user guide docs getting started userguide md for detailed information about the features in dsstne examples check examples docs getting started examples md to start trying your first neural network modeling using dsstne q a faq faq md | ai |
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stock-price-forecasting | machine learning engineer nanodegree capstone project here is my capstone project from the udacity machine learning nanodegree overview this project attempts to extract useful signal from the text of public company quarterly earnings transcripts although the proposed approach does not prove to be successful this notebook contains interesting analysis and a few potential developments that could improve this model to a useful and profitable level for more detailed analysis read the written report here stock 20price 20forecasting pdf for a short summary this project attempts to learn correlations between words used in a quarterly earnings call and stock price movements the hypothesis was that companies would use different language during their quarterly earnings calls depending on their situation however it was found that the words used were largely the same from call to call the animation below demonstrates this plotting words used in the x and y axis and denoted the word importance along the z axis each frame represents a quarterly earnings call spanning from 2006 through 2016 google anim gif data stock price price data is downloaded from google finance using the pandas datareader see notebook to run the command to download all necessary data quarterly earnings call transcripts transcripts are scraped from seeking alpha https seekingalpha com using the python library scrapy https docs scrapy org en latest to fetch a company transcript complete the following steps cd data scrapy crawl transcripts a symbol sym this will download all of the posted earnings call transcripts for company sym and store it as a json lines file in data company transcripts sym json note the transcripts provided by seeking alpha are protected by copyright and can not be used for commercial interests however given the educational nature of this project as part of the udacity machine learning nanodegree use of this information is permitted under the copyright fair use principle this said data is not provided in this repo in order to abide by seeking alpha s copyright requests getting started create a new anaconda environment using the command below to ensure your workspace has all necessary dependencies conda env create f requirements environment yml activate the conda environment source activate stock price forecasting launch the jupyter notebook jupyter notebook | ai |
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LLM-Knowledge-Boundary | llm knowledge boundary see our paper investigating the factual knowledge boundary of large language models with retrieval augmentation https arxiv org abs 2307 11019 quick start 1 preprocess data and install dependencies bash bash preparation sh python data preparation py d nq tq hq 2 get supporting documents generated by chatgpt take natural questions dataset as an example bash openai api key your api key python run llm py source data source nq json usechat type generate ra none outfile data source nq chat json conduct experiments 1 question answering bash openai api key your api key python run llm py source data source nq chat json usechat type qa ra none outfile data qa nq none qa json 2 priori judgement bash openai api key your api key python run llm py source data source nq chat json usechat type prior ra dense outfile data prior nq dense prior json 3 posteriori judgement bash openai api key your api key python run llm py source data qa nq none qa json usechat type post ra sparse outfile data post nq sparse post json acknowledgement please cite the following paper if you find our code helpful bibtex article ren2023investigating title investigating the factual knowledge boundary of large language models with retrieval augmentation author ren ruiyang and wang yuhao and qu yingqi and zhao wayne xin and liu jing and tian hao and wu hua and wen ji rong and wang haifeng journal arxiv preprint arxiv 2307 11019 year 2023 | chatgpt information-retrieval large-language-models natural-language-processing | ai |
Instruction-Tuning-Survey | instruction tuning for large language models a survey this repository contains resources referenced in the paper instruction tuning for large language models a survey https arxiv org abs 2308 10792 if you find this repository helpful please cite the following latex article zhang2023instruction title instruction tuning for large language models a survey author zhang shengyu and dong linfeng and li xiaoya and zhang sen and sun xiaofei and wang shuhe and li jiwei and hu runyi and zhang tianwei and wu fei and others journal arxiv preprint arxiv 2308 10792 year 2023 news stay tuned more related work will be updated 07 sep 2023 the repository is created 21 aug 2023 we release the first version of the paper table of contents overview overview instruction tuning instruction tuning datasets datasets models models multi modality instruction tuning multi modality instruction tuning datasets datasets models models domain specific instruction tuning domain specific instruction tuning efficient tuning techniques efficient tuning techniques references references contact contact overview instruction tuning it refers to the process of further training large language models llms on a dataset consisting of instruction output pairs in a supervised fashion which bridges the gap between the next word prediction objective of llms and the users objective of having llms adhere to human instructions the general pipeline of instruction tuning is shown in the following project assets method overview png in the paper https arxiv org abs 2308 10792 we make a systematic review of the literature including the general methodology of it the construction of it datasets the training of it models and applications to different modalities domains and application along with analysis on aspects that influence the outcome of it e g generation of instruction outputs size of the instruction dataset etc we also review the potential pitfalls of it along with criticism against it along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research the typology of the paper is as follows div align center img src assets paper typology v4 png width 960 div instruction tuning datasets table border 1 style text align center tr th style width 12cm type th th dataset name th th paper th th project th th of instructions th th of tasks th th of lang th th construction th th open source th tr tr td rowspan 9 align center generalize to unseen tasks td td align center unifiedqa a href ref1 1 a td td align center a href https arxiv org abs 2005 00700 target blank paper a td td align center a href https github com allenai unifiedqa target blank project a td td align center 750k td td align center 46 td td align center en td td align center human crafted td td align center yes td tr tr td align center oig a href ref2 2 a td td align center td td align center a href https github com laion ai open instruction generalist target blank project a td td align center 43m td td align center 30 td td align center en td td align center human model mixed td td align center yes td tr tr td align center unifiedskg a href ref3 3 a td td align center a href https arxiv org abs 2201 05966 target blank paper a td td align center a href https github com hkunlp unifiedskg target blank project a td td align center 0 8m td td align center td td align center en td td align center human crafted td td align center yes td tr tr td align center natural instructions a href ref4 4 a td td align center a href https arxiv org abs 2104 08773 target blank paper a td td align center a href https github com allenai natural instructions v1 target blank project a td td align center 193k td td align center 61 td td align center en td td align center human crafted td td align center yes td tr tr td align center super natural instructions a href ref5 5 a td td align center a href https arxiv org abs 2204 07705 target blank paper a td td align center a href https github com allenai natural instructions target blank project a td td align center 5m td td align center 76 td td align center 55 lang td td align center human crafted td td align center yes td tr tr td align center p3 a href ref6 6 a td td align center a href https arxiv org abs 2110 08207 target blank paper a td td align center a href https huggingface co datasets bigscience p3 target blank project a td td align center 12m td td align center 62 td td align center en td td align center human crafted td td align center yes td tr tr td align center xp3 a href ref7 7 a td td align center a href https arxiv org abs 2211 01786 target blank paper a td td align center a href https github com bigscience workshop xmtf target blank project a td td align center 81m td td align center 53 td td align center 46 lang td td align center human crafted td td align center yes td tr tr td align center flan 2021 a href ref8 8 a td td align center a href https arxiv org abs 2301 13688 target blank paper a td td align center a href https github com google research flan target blank project a td td align center 4 4m td td align center 62 td td align center en td td align center human crafted td td align center yes td tr tr td align center coig a href ref9 9 a td td align center a href https arxiv org abs 2304 07987 target blank paper a td td align center a href https github com baai zlab coig target blank project a td td align center td td align center td td align center td td align center td td align center yes td tr tr td rowspan 10 align center follow users instructions in a single turn td td align center instructgpt a href ref10 10 a td td align center td td align center td td align center 13k td td align center td td align center multi td td align center human crafted td td align center no td tr tr td align center unnatural instructions a href ref11 11 a td td align center a href https arxiv org abs 2212 09689 target blank paper a td td align center a href https github com orhonovich unnatural instructions target blank project a td td align center 240k td td align center td td align center en td td align center instructgpt generated td td align center yes td tr tr td align center self instruct a href ref12 12 a td td align center a href https arxiv org abs 2212 10560 target blank paper a td td align center a href https github com yizhongw self instruct target blank project a td td align center 52k td td align center td td align center en td td align center instructgpt generated td td align center yes td tr tr td align center instructwild a href ref13 13 a td td align center td td align center a href https github com xuefuzhao instructionwild target blank project a td td align center 104k td td align center 429 td td align center td td align center model generated td td align center yes td tr tr td align center evol instruct a href ref14 14 a td td align center a href https arxiv org abs 2304 12244 target blank paper a td td align center a href https github com nlpxucan evol instruct target blank project a td td align center 52k td td align center td td align center en td td align center chatgpt generated td td align center yes td tr tr td align center alpaca a href ref15 15 a td td align center td td align center a href https github com tatsu lab stanford alpaca target blank project a td td align center 52k td td align center td td align center en td td align center instructgpt generated td td align center yes td tr tr td align center logicot a href ref16 16 a td td align center a href https arxiv org abs 2305 12147 target blank paper a td td align center a href https github com csitfun logicot target blank project a td td align center td td align center 2 td td align center en td td align center gpt 4 generated td td align center yes td tr tr td align center dolly a href ref17 17 a td td align center td td align center a href https huggingface co datasets databricks databricks dolly 15k target blank project a td td align center 15k td td align center 7 td td align center en td td align center human crafted td td align center yes td tr tr td align center gpt 4 llm a href ref18 18 a td td align center a href https arxiv org abs 2304 03277 target blank paper a td td align center a href https github com instruction tuning with gpt 4 gpt 4 llm target blank project a td td align center 52k td td align center td td align center en zh td td align center gpt 4 generated td td align center yes td tr tr td align center lima a href ref19 19 a td td align center a href https arxiv org abs 2305 11206 target blank paper a td td align center a href https huggingface co datasets gair lima target blank project a td td align center 1k td td align center td td align center en td td align center human crafted td td align center yes td tr tr td rowspan 9 align center offer assistance like humans across multiple turns td td align center chatgpt a href ref20 20 a td td align center td td align center td td align center td td align center td td align center multi td td align center human crafted td td align center no td tr tr td align center vicuna a href ref21 21 a td td align center td td align center a href https lmsys org blog 2023 03 30 vicuna target blank project a td td align center 70k td td align center td td align center en td td align center user shared td td align center no td tr tr td align center guanaco a href ref22 22 a td td align center td td align center a href https huggingface co datasets josephuscheung guanacodataset target blank project a td td align center 534 530 td td align center td td align center multi td td align center model generated td td align center yes td tr tr td align center openassistant a href ref23 23 a a td td align center a href https arxiv org abs 2304 07327 target blank paper a td td align center a href https github com laion ai open assistant target blank project a td td align center 161 443 td td align center td td align center multi td td align center human crafted td td align center yes td tr tr td align center baize v1 a href ref24 24 a td td align center a href https arxiv org abs 2304 01196 target blank paper a td td align center a href https github com project baize baize chatbot target blank project a td td align center 111 5k td td align center td td align center en td td align center chatgpt generated td td align center yes td tr tr td align center ultrachat a href ref25 25 a td td align center a href https arxiv org abs 2305 14233 target blank paper a td td align center a href https github com thunlp ultrachat target blank project a td td align center 675k td td align center td td align center en zh td td align center model generated td td align center yes td tr table models table border 1 style text align center tr th model name th th params th th paper th th project th th base model th th colspan 3 instruction train set th tr tr th th th th th th th th th th th self build th th name th th size th tr tr td align center instructgpt a href ref10 10 a td td align center 176b td td align center a href https www example com project1 target blank paper a td td align center td td align center gpt 3 a href ref26 26 a td td align center yes td td align center td td align center td tr tr td align center bloomz a href ref7 7 a td td align center 176b td td align center a href https www example com project1 target blank paper a td td align center a href https huggingface co bigscience bloomz target blank project a td td align center bloom a href ref27 27 a td td align center no td td align center xp3 td td align center td tr tr td align center flan t5 a href ref28 28 a td td align center 11b td td align center a href https arxiv org abs 2210 11416 target blank paper a td td align center a href https huggingface co google flan t5 xxl target blank project a td td align center t5 a href ref29 29 a td td align center no td td align center flan 2021 td td align center td tr tr td align center alpaca a href ref15 15 a td td align center 7b td td align center td td align center a href https github com tatsu lab stanford alpaca target blank project a td td align center llama a href ref30 30 a td td align center yes td td align center td td align center 52k td tr tr td align center vicuna a href ref21 21 a td td align center 13b td td align center td td align center a href https github com lm sys fastchat target blank project a td td align center llama a href ref30 30 a td td align center yes td td align center td td align center 70k td tr tr td align center gpt 4 llm a href ref18 18 a td td align center 7b td td align center a href https arxiv org abs 2304 03277 target blank paper a td td align center a href https github com instruction tuning with gpt 4 gpt 4 llm target blank project a td td align center llama a href ref30 30 a td td align center yes td td align center td td align center 52k td tr tr td align center claude a href ref31 31 a td td align center td td align center td td align center td 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paper https arxiv org abs 2305 04790 a id ref58 58 a prakhar gupta cathy jiao yi ting yeh shikib mehri maxine esk nazi and jeffrey p bigham instructdial improving zero and few shot generalization in dialogue through instruction tuning in conference on empirical methods in natural language processing 2022 paper https arxiv org abs 2205 12673 a id ref59 59 a bill yuchen lin kangmin tan chris miller beiwen tian and xiang ren unsupervised cross task generalization via retrieval augmentation arxiv abs 2204 07937 2022 paper https arxiv org abs 2204 07937 a id ref60 60 a andrew rosenbaum saleh soltan wael hamza yannick versley and markus boese linguist language model instruction tuning to generate annotated utterances for intent classification and slot tagging in international conference on computational linguistics 2022 paper https arxiv org abs 2209 09900 a id ref61 61 a saleh soltan shankar ananthakrishnan jack fitzgerald rahul gupta wael hamza haidar khan charith peris stephen rawls andy rosenbaum anna rumshisky et al alexatm 20b few shot learning using a large scale multilingual seq2seq model arxiv preprint arxiv 2208 01448 2022 paper https arxiv org abs 2208 01448 a id ref62 62 a xiao wang wei zhou can zu han xia tianze chen yuan zhang rui zheng junjie ye qi zhang tao gui jihua kang j yang siyuan li and chunsai du instructuie multi task instruction tuning for unified information extraction arxiv abs 2304 08085 2023 paper https arxiv org abs 2304 08085 a id ref63 63 a robin rombach andreas blattmann dominik lorenz patrick esser and bj rn ommer high resolution image synthesis with latent diffusion models in proceedings of the ieee cvf conference on computer vision and pattern recognition pages 10684 10695 2022 paper https arxiv org abs 2112 10752 domain specific instruction tuned llms a id ref64 64 a zheng liu aoxiao zhong yiwei li longtao yang chao ju zihao wu chong ma peng shu cheng chen sekeun kim haixing dai lin zhao dajiang zhu jun liu wei liu dinggang shen xiang li quanzheng li and tianming liu radiology gpt a large language model for radiology 2023 paper https arxiv org abs 2306 08666 a id ref65 65 a yunxiang li zihan li kai zhang ruilong dan and you zhang chatdoctor a medical chat model fine tuned on llama model using medical domain knowledge arxiv abs 2303 14070 2023 paper https arxiv org abs 2303 14070 a id ref66 66 a sendong zhao bing qin ting liu haochun wang chi liu chatglm med github com scir hi med chatglm 2023 a id ref67 67 a tiedong liu and bryan kian hsiang goat fine tuned llama outperforms gpt 4 on arithmetic tasks arxiv preprint arxiv 2305 14201 2023 paper https arxiv org abs 2305 14201 a id ref68 68 a ziyang luo can xu pu zhao qingfeng sun xiubo geng wenxiang hu chongyang tao jing ma qingwei lin and daxin jiang wizardcoder empowering code large language models with evol instruct 2023 paper https arxiv org abs 2306 08568 efficient tuning techniques a id ref69 69 a edward j hu yelong shen phillip wallis zeyuan allen zhu yuanzhi li shean wang lu wang and weizhu chen 2021 lora low rank adaptation of large language models arxiv preprint arxiv 2106 09685 paper https arxiv org abs 2106 09685 a id ref70 70 a hamish ivison akshita bhagia yizhong wang hannaneh hajishirzi and matthew e peters 2022 hint hypernetwork instruction tuning for efficient zero shot generalisation arxiv abs 2212 10315 paper https arxiv org abs 2212 10315 a id ref71 71 a tim dettmers artidoro pagnoni ari holtzman and luke zettlemoyer 2023 qlora efficient finetuning of quantized llms arxiv preprint arxiv 2305 14314 paper https arxiv org abs 2305 14314 a id ref72 72 a kai lv yuqing yang tengxiao liu qi jie gao qipeng guo and xipeng qiu 2023 full parameter fine tuning for large language models with limited resources paper https arxiv org abs 2306 09782 a id ref72 73 a ning ding yujia qin guang yang fu wei zonghan yang yusheng su shengding hu yulin chen chi min chan weize chen jing yi weilin zhao xiaozhi wang zhiyuan liu haitao zheng jianfei chen y liu jie tang juanzi li and maosong sun 2023b parameter efficient fine tuning of large scale pre trained language models nature machine intelligence 5 220 235 paper https www nature com articles s42256 023 00626 4 contact if you have any questions or suggestions please feel free to create an issue or send an e mail to xiaoya li shannonai com | artificial-intelligence gpt instruction-tuning large-language-models chatgpt nlp | ai |
TravelPalSB18002 | mobile development module title mobile development assignment type individual practical project project title travel pal project date 25 th february 2019 assignment compiler amilcar aponte weighting 50 due date 28th april 2019 method of submission moodle submission assignment introduction a company has come to you as a developer and asked you to develop a mobile application that will work on multiple platforms they know it is a very difficult task to install all the different libraries and sdks to do this so they have told you that using a cloud based compiling tool is the best option design and build a mobile application that will use the geolocation of your phone to show the city and country where you are the weather at the current location and the currency exchange rate to the local money technologies adobe phonegap phonegap is a tool which allows you to write applications in html and javascript and deploy them to any phone front end javascript framework you can use any html css javascript based user interface library such as jquery mobile framework7 or bootstrap external apis in order to be able to access real time data you will need to connect to external api to request weather currency exchange rate and name of the city country where you are located you could use the apis used in class or explorer different options specific requirements start with a template source code the following modifications should be made the application should have a welcome screen that displays the name of the city country based on the data collected by the gps sensor of the phone a section should exist in the application that allows the user to enter in a certain amount of usd to convert to the local currency and viseversa page 2 of 3 another section should exist in the application to check the weather of the current location of the user finally there must be button for the user to save automatically the current location and data collected from the different api s to be retrieved later this way the user can keep record of places where they have been this is an individual assignment deliverables upload a single zip file which contains o source code for your application o document containing screenshots of your application in action screenshots of the adobe cloud compiling process and justification for your design decisions graphic and logic o binary android package which can be downloaded from the adobe cloud website once compiled apk file extra marks if you would like to achieve a distinction consider to add some extra layers of functionality such as but not limited to saving a picture of the place with the record of the visit framework7 phonegap application framework7 http www idangero us framework7 is a mobile ui framework that can be used to build hybrid apps with phonegap this template allows you to get started using framework7 quickly for a more extensive framework7 sample see the one included in their github project https github com nolimits4web framework7 tree master dist or the demo apps on their website http www idangero us framework7 apps vpqcc5mrkjq also for an intro to framework7 check out this post on the phonegap blog http phonegap com blog 2015 11 30 framework7 usage phonegap cli phonegap create my app template phonegap template framework7 cordova cli cordova create my app template phonegap template framework7 desktop in your browser open the file www index html | front_end |
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HSE-NLP-Solution | natural language processing course solutions solutions to coursera course on natural language processing by higher school of economics hse please pull if you find error in code or missing file | ai |
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Food_Delivery_System_TeamGreen | food delivery system from team green in this project we are building a food delivery system our objective is to take specific use cases and develop the interactions between entities and databases we hope to use several different nosql databases including mongodb redis and neo4j use cases use cases include assigning the optimal driver to a particular order storing gps coordinates in a reliable and efficient manner dealing with customer reviews and complaints and making restaurant recommendations for the users we will be using python as the programming language to interact with the databases | server |
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rabbit-deterrence | computer vision based rabbit deterrence system project overview this is a computer vision based rabbit deterrence system that leverages the raspberry pi and roboflow to detect and scare away rabbits how it essentially works is the raspberry pi has an object detection model trained with roboflow train hosted on it and when it detects a rabbit the bluetooth speaker will play a sound such as a baby crying or a car honking which is meant to scare off the rabbit special features of the system this system implements a couple of different features including trained model using roboflow train which is based on yolov5 a state of the art object detection model uses the makerhawk external power supply allows for ease of portability leverages active learning via the roboflow upload api includes a flask web server allowing the user to view the detections remotely integrates a bluetooth speaker to scare off the rabbits materials software used hardware a raspberry pi external power supply https www amazon com makerhawk raspberry uninterruptible management expansion dp b082cvwh3r ref sr 1 6 crid 3ljgha055o4vl dchild 1 keywords battery for raspberry pi qid 1623698007 sprefix battery for raspbe 2caps 2c184 sr 8 6 camera for the pi https www amazon com arducam megapixels sensor ov5647 raspberry dp b012v1hep4 ref sr 1 6 dchild 1 keywords raspberry pi camera qid 1624689746 sr 8 6 bluetooth speaker https www amazon com audiovox sp881bl portable bluetooth rechargeable dp b07f8n6kj9 ref sr 1 4 crid 2363n4jzd3b18 dchild 1 keywords canz bluetooth speaker qid 1626056945 sprefix canz bluetoot 2caps 2c173 sr 8 4 software public dataset https public roboflow com object detection eastern cottontail rabbits roboflow annotate https docs roboflow com annotate roboflow train https docs roboflow com train roboflow inference api https docs roboflow com inference further reading blog https blog roboflow com rabbit deterrence system https blog roboflow com rabbit deterrence system video documentation https www youtube com watch v opvqkgq3ppc https www youtube com watch v opvqkgq3ppc | ai |
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web-dev-w-go | apress source code this repository accompanies web development with go http www apress com 9781484210536 by shiju varghese apress 2015 cover image 9781484210536 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | front_end |
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secure-mobile-development | secure mobile development at nowsecure we spend a lot of time attacking mobile apps hacking breaking encryption finding flaws penetration testing and looking for sensitive data stored insecurely we do it for the right reasons to help developers make their apps more secure this document represents some of the knowledge we share with our clients and partners we are driven to advance mobile app security worldwide using this guide this guide gives specific recommendations to use during your development process the descriptions of attacks and security recommendations in this report are not exhaustive or perfect but you will get practical advice that you can use to make your apps more secure we revise our best practices periodically and invite contributions https github com nowsecure secure mobile development pulls and the updated guide is published here https books nowsecure com secure mobile development as changes are accepted into the main repository to learn about all the vectors that attackers might use on your app read our mobile security primer en primer mobile security md categories we categorize our secure mobile development best practices under eight topics you can find a complete table of contents here en summary md coding practices en coding practices readme md handling sensitive data en sensitive data readme md caching and logging en caching logging readme md webviews en webviews readme md ios en ios readme md android en android readme md servers en servers readme md technology stack the book is written with gitbook contributing we revise our best practices periodically and invite contributions https github com nowsecure secure mobile development pulls and the updated guide is published here https books nowsecure com secure mobile development as changes are accepted into the main repository we welcome contributions from knowledgeable developers and security professionals all contributors must read our contributing contributing md page and accept the terms in their pull requests please follow the template and format provided if you do contribute we publish this guide under a creative commons attribution noncommercial sharealike 4 0 international license license md we will review contributions and periodically publish updated recommendations if you have questions or feedback please let us know https www nowsecure com contact instructions first fork this repository make your changes and submit them back to this repository as a pull request if you are unfamiliar with this process please read the github user documentation https help github com articles creating a pull request adding a best practice tbd | secure-development mobile-security ios android best-practices gitbook nowsecure apple | front_end |
Simplilearn_Capstone_Project_MyMoviePlan | mymovieplan description create a dynamic and responsive web application for booking movie tickets online for different genres and languages background of the problem statement nms cinemas is a chain of single screen theatres that screen movie shows of different genres and languages at very genuine prices it was established in 2004 in pune india recently the business analysts noticed a decline in sales since 2010 they found out that the online booking of movie tickets from apps such as bookmyshow and paytm were gaining more profit by eliminating middlemen from the equation as a result the team decided to hire a full stack developer to develop an online movie ticket booking web application with a rich and user friendly interface you are hired as the full stack java developer and are asked to develop the web application the management team has provided you with the requirements and their business model so that you can easily arrange different components of the application features of the application registration login payment gateway searching filtering sorting dynamic data responsive and compatible with different devices recommended technologies database management mysql and oracle backend logic java programming nodejs frontend development jsp angular bootstrap html css and javascript automation and testing technologies selenium jasmine and testng devops and production technologies git github jenkins docker kubernetes and aws project development guidelines the project will be delivered within four sprints with every sprint delivering a minimal viable product it is mandatory to perform proper sprint planning with user stories to develop all the components of the project the learner can use any technology from the above mentioned technologies for different layers of the project the web application should be responsive and should fetch or send data dynamically without hardcoded values the learner must maintain the version of the application over github and every new change should be sent to the repository the learner must implement a ci cd pipeline using jenkins the learner should also deploy and host the application on an aws ec2 instance the learner should also implement automation testing before the application enters the ci cd pipeline the learner should use git branching to do basic automation testing of the application in it separately the learner should make a rich frontend of the application which is user friendly and easy for the user to navigate through the application there will be two portals in the application namely admin and user portal admin portal it deals with all the backend data generation and product information the admin user should be able to add or remove different genres to or from the application to build a rich product line edit movie details like name ticket price language description and show timings to keep it aligned to the current prices enable or disable the movie shows from the application user portal it deals with the user activities the end user should be able to sign in to the application to maintain a record of activities search for movie tickets based on the search keyword apply filters and sort results based on different genres add all the selected movie tickets to a cart and customize the purchase at the end experience a seamless payment process receive a booking summary page once the payment is complete | server |
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ZooManager | zoo manager project for simple zoo managment current components consist of client app zoomanager communication dll zoocom server zooservice 1 client app wpf application connecting through dll module with server to obtain data about zoo 2 communication dll dll module responsible for transfering data between client app and server protocols of how client app will receive data from database are described here it is possible to define another method of communication other than tcp connection by replacing zoocom dll in client app directory also data types are defined here used both by server and client app to send and receive database data 3 server tcp server responsible for communicating with client app and local database database is based on sql server schema and initial data in zooschema sql to access database entity framework is used and standard sqlconnection current versions of used programs ide visual studio 2017 database sql server 2017 express orm entity framework 6 | server |
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Sound-Orientation-System | sound orientation system code for embedded system design course written by xc group seedclass 2015 code is here sound orientation system https github com wustardust sound orientation system git sound loction x presentation mp4 | os |
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FoodApp-Backend | foodapp backend backend of food app under development | server |
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F2E.im | about f2e im f2e is a community for front end developer how to contribute fork and send pull request how to run f2e im on your own machine 1 install all required modules shell pip install r requirements txt 2 create database and then execute sql file in dbstructure shell mysql u yourusername p mysql create database f2e mysql exit shell mysql u yourusername p database f2e dbstructure f2e sql 3 set your mysql user password and smtp server config in application py and lib sendmail py 4 check above using python application py to start server shell python application py port 9001 mysql database f2e mysql host localhost mysql password yourpassword mysql user yourusername how to set up a production enironment you need to know a little of supervisor and nginx all config files are available in conf | front_end |
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Design-Of-Android-10.0-Graphic-System | design of android 10 0 graphic system learn and reuse the design of android 10 0 graphic system | os |
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datasets-for-start | datasets for start datasets for start with machine learning binray classification breast cancer wisconsin 2d circles 2d simple 3d spheres spect credit adult sonar ionosphere spam diabetes employee attrition multiclass classification digits wine mnist glass iris regression housing house prices regression 1 regression 2 | ai |
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vlfeat | vlfeat vision lab features library version 0 9 21 the vlfeat open source library implements popular computer vision algorithms specialising in image understanding and local featurexs extraction and matching algorithms incldue fisher vector vlad sift mser k means hierarchical k means agglomerative information bottleneck slic superpixes quick shift superpixels large scale svm training and many others it is written in c for efficiency and compatibility with interfaces in matlab for ease of use and detailed documentation throughout it supports windows mac os x and linux vlfeat is distributed under the bsd license see the copying file the documentation is available online http www vlfeat org index html and shipped with the library as doc index html see also using with matlab http www vlfeat org install matlab html using the command line utilities http www vlfeat org install shell html using the c api http www vlfeat org install c html compiling from source http www vlfeat org compiling html quick start with matlab to start using vlfeat as a matlab toolbox download the latest vlfeat binary package http www vlfeat org download note that the pre compiled binaries require matlab 2009b and later unpack it for example by using winzip windows by double clicking on the archive mac or by using the command line linux and mac tar xzf vlfeat x y z bin tar gz here x y z denotes the latest version start matlab and run the vlfeat setup command run vlfeatroot toolbox vl setup here vlfeatroot should be replaced with the path to the vlfeat directory created by unpacking the archive all vlfeat demos can now be run in a row by the command vl demo check out the individual demos by editing this file edit vl demo octave support the toolbox should be laregly compatible with gnu octave an open source matlab equivalent however the binary distribution does not ship with pre built gnu octave mex files to compile them use cd vlfeat directory make mkoctfile path to the mkoctfile program changes 0 9 21 maintenance release bugfixes 0 9 20 maintenance release bugfixes 0 9 19 maintenance release minor bugfixes and fixes compilation with matlab 2014a 0 9 18 several bugfixes improved documentation particularly of the covariant detectors minor enhancements of the fisher vectors 0 9 17 rewritten svm implementation adding support for sgd and sdca optimisers and various loss functions hinge squared hinge logistic etc and improving the interface added infrastructure to support multi core computations using openmp matlab 2009b or later required added openmp support to kd trees and kmeans added new gaussian mixture models vlad encoding and fisher vector encodings also with openmp support added liop feature descriptors added new object category recognition example code supporting several standard benchmarks off the shelf 0 9 16 added vl covdet this function implements the following detectors dog hessian harris laplace hessian laplace multiscale hessian multiscale harris it also implements affine adaptation estiamtion of feature orientation computation of descriptors on the affine patches including raw patches and sourcing of custom feature frame 0 9 15 added vl hog hog features added vl svmpegasos and a vastly improved svm implementation added vl ihashsum hashed counting improved inthist integral histogram added vl cummax improved the implementation of vl roc and vl pr added vl det detection error trade off det curves improved the verbosity control to aib added support for xcode 4 3 improved support for past and future xcode versions completed the migration of the old test code in toolbox test moving the functionality to the new unit tests toolbox xtest 0 9 14 added slic superpixels added vl alphanum improved windows binary package and added support for visual studio 2010 improved the documentation layout and added a proper bibliography bugfixes and other minor improvements moved from the gpl to the less restrictive bsd license 0 9 13 fixed windows binary package 0 9 12 fixes vl compile and the architecture string on linux 32 bit 0 9 11 fixes a compatibility problem on older mac os x versions a few bugfixes are included too 0 9 10 improves the homogeneous kernel map plenty of small tweaks and improvements make maci64 the default architecture on the mac 0 9 9 added sift matching example extended caltech 101 classification example to use kd trees 0 9 8 added image distance transform pegasos floating point k means homogeneous kernel maps a caltech 101 classification example improved documentation 0 9 7 changed the mac os x binary distribution to require a less recent version of mac os x 10 5 0 9 6 changed the gnu linux binary distribution to require a less recent version of the c library 0 9 5 added kd tree and new sse accelerated vector histogram comparison code improved dense sift dsift implementation added snow leopard and matlab r2009b support 0 9 4 added quick shift renamed dhog to dsift and improved implementation and documentation improved tutorials added 64 bit windows binaries many other small changes 0 9 3 namespace change everything begins with a vl prefix now many other changes to provide compilation support on windows with matlab 7 beta 3 completes to the ikmeans code beta 2 many additions beta 1 initial public release | ai |
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intelligent-app-workshop | intelligent app workshop open in visual studio code https img shields io static v1 logo visualstudiocode label message open 20in 20vs 20code labelcolor 2c2c32 color 007acc logocolor 007acc https github dev azure intelligent app workshop license cc by sa 4 0 https img shields io badge license cc 20by sa 222 svg https creativecommons org licenses by sa 4 0 this is an envisioning workshop based on microsoft s copilot stack https learn microsoft com en us semantic kernel overview semantic kernel is at the center of the copilot stack to rethink user experience architecture and app development by leveraging the intelligence of foundation models this comprehensive workshop utilizes the miyagi https github com azure samples miyagi codebase and semantic kernel sk along with sk s design thinking material to guide you through the entire lifecycle of app development from identifying user needs to deploying a fully functional production grade app on azure note work in progress meanwhile signup at intelligentapp dev https intelligentapp dev for updates and checkout our related repo that showcases generative ai capabilities for cloud native event driven microservices azure reddog solutions https github com azure reddog solutions readme as a mandatory pre requisite please signup for azure openai aoai https customervoice microsoft com pages responsepage aspx id v4j5cvggr0grqy180bhbr7en2ais5pxktso pz4b1 xuofa5qk1uwdrbmjg0wfhpmkiztzhkq1dwnyqlqcn0pwcu and complete getting started with aoai module https learn microsoft com en us training modules get started openai to benefit from this workshop for a preview catch the recording on cosmos db live tv https www youtube com watch v v8dlevxdgem t 144s p align left a href http www youtube com watch feature player embedded v v8dlevxdgem t 144s target blank img src docs assets images video recording png alt miyagi walkthrough width 40 height 40 border 10 a p key takeaways 1 uncover innovative ways to transform user experience and app design by integrating foundation models into interactions 2 learn how to effectively harness large language models llms with the miyagi https github com azure samples miyagi codebase for streamlined development 3 rethink how to serve your end user needs with semantic kernel s design thinking workshop 4 gain valuable insights into the production and deployment of apps powered by large language models llms p align center img src docs assets images basic arch png width 50 p wip azure docs assets images wip azure png ui docs assets images ui annotations png plugins docs assets images plugin png disclaimer this software is provided for demonstration purposes only it is not intended to be relied upon for any purpose the creators of this software make no representations or warranties of any kind express or implied about the completeness accuracy reliability suitability or availability with respect to the software or the information products services or related graphics contained in the software for any purpose any reliance you place on such information is therefore strictly at your own risk license this software is provided for demonstration purposes only it is not intended to be relied upon for any purpose the software is provided as is and without any warranties express or implied the software is not intended to be used for any commercial purpose the software is provided solely for demonstration purposes and should not be used for any other purpose the software is provided without any warranty of any kind either express or implied including but not limited to the implied warranties of merchantability fitness for a particular purpose or non infringement the software is provided as is and without any warranty of any kind the user assumes all risk and responsibility for the use of the software | ai gpt-35-turbo intelligent-agents ml mlops prompt-engineering semantic-kernel foundation-models llm intelligent-app | server |
qpcpp | qp framework https www state machine com img qp banner jpg what s new github release latest by date https img shields io github v release quantumleaps qpcpp https github com quantumleaps qpcpp releases latest view qp c revision history at https www state machine com qpcpp history html note if you re interested in the latest qp c version from github it is highly recommened that you clone this repo like that git clone https github com quantumleaps qpcpp recurse submodules depth 1 alternatively you can also download the latest qp c release https github com quantumleaps qpcpp releases getting started with qp c the most recommended way of obtaining qp c is by downloading the qp bundle https www state machine com downloads which includes qp c as well as the qm modeling tool and the qtools collection the main advantage of obtaining qp c bundled together like that is that you get all components tools and examples ready to go getting started resources qp c tutorial tutorial describes a series of progressively advanced qp c example applications video getting started with qp real time embedded frameworks video provides instructions on how to download install and get started with qp appnote getting started with qp real time embedded frameworks an contains also a tutorial in which you build a simple blinky application about qp c qp c quantum platform in c is a lightweight open source real time embedded framework rtef rtef for building modern embedded software as systems of asynchronous event driven active objects active actors the qp c framework is a member of a qp family consisting of qp c and qp c frameworks which are strictly quality controlled thoroughly documented and commercially licensable lic safer model of concurrency the qp framework family is based on the active object active actor design pattern which inherently supports and automatically enforces the following best practices of concurrent programming keep data isolated and bound to active objects threads threads should hide encapsulate their private data and other resources and not share them with the rest of the system communicate among active object threads asynchronously via event objects using asynchronous events keeps the threads running truly independently without blocking on each other active object threads should spend their lifetime responding to incoming events so their mainline should consist of an event loop that handles events one at a time to completion thus avoiding any concurrency hazards within an active object thread itself this architecture is generally safer more responsive and easier to understand and maintain than the shared state concurrency of a conventional rtos it also provides higher level of abstraction and the correct abstractions to effectively apply modeling and code generation to deeply embedded real time systems hierarchical state machines the behavior of active objects is specified in qp c by means of hierarchical state machines hsm uml statecharts the framework supports manual coding of uml state machines in c as well as automatic code generation by means of the free qm modeling tool qm built in real time kernels the qp c framework can run on bare metal single chip microcontrollers completely replacing a traditional rtos the framework contains a selection of built in real time kernels such as the cooperative qv kernel the preemptive non blocking qk kernel and the preemptive blocking qxk kernel that provides all the features you might expect from a traditional rtos native qp ports and ready to use examples are provided for major cpus such as arm cortex m m0 m0 m3 m4 m7 traditional rtos os qp c can also work with a traditional rtos such as threadx freertos embos uc os ii and ti rtos as well as with embedded linux posix and windows popularity and maturity with 20 years of continuous development over 350 commercial licensees cust and many times more open source users worldwide the qp frameworks are the most popular such offering on the market they power countless electronic products ranging from implantable medical devices to complex weapon systems qp c licensing qp c is licensed under the sustainable dual licensing model lic in which both the open source software distribution mechanism and traditional closed source software distribution models are combined note if your company has a policy forbidding open source in your product all qp frameworks can be licensed commercially lic in which case you don t use any open source license and you do not violate your policy qp c documentation the online html documention for the latest version of qp c is located at https www state machine com qpcpp the offline html documentation for this particular version of qp c is located in the sub folder html html to view the offline documentation open the file html index html html index html in your web browser how to get help free support forum https sourceforge net p qpc discussion 668726 bug reports https sourceforge net p qpc bugs feature requests https sourceforge net p qpc feature requests quantum leaps website https www state machine com quantum leaps licensing https www state machine com licensing info state machine com mailto info state machine com how to help this project if you like this project please give it a star in the upper right corner of your browser window github star https www state machine com img github star jpg rtef https www state machine com rtef qp https www state machine com products qp qp c https www state machine com qpc qp c https www state machine com qpcpp qm https www state machine com products qm active https www state machine com active object hsm https www state machine com fsm hsm lic https www state machine com licensing cust https www state machine com customers an https www state machine com doc an getting started with qp pdf tutorial https www state machine com qpcpp gs tut html video https youtu be o7er6 vqih0 | active-object actor actor-model arm arm-cortex-m0 arm-cortex-m3 arm-cortex-m4f arm-cortex-m7 embedded-systems embedded-c event-driven framework qp rtos samek statechart state-machine uml-state-machine fsm hierarchical-state-machine | os |
IIT | iit introduction into information technology | server |
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GestoJS | gestojs gesture lib for javascript mobile developments basic usage js var gesto new gestojs gesto addgesture swipeleft gesto addgesture swiperight gesto addeventlistener ongesture function event var gesture event gestures 0 switch gesture name case swipeleft do something on swipe left break case swiperight do something on swipe right break version history new feature v0 2 live gesture capturing feature gesture managing refactor updated examples v0 1 alpha first release | front_end |
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FreeRTOS_Deadline_Driven_Scheduler | freertos deadline driven scheduler deadline driven scheduler using earliest deadline first edf implemented ontop of the freertos scheduler system validation completed using a toy application utilizing on board leds and and the user button to create aperiodic tasks on the stm32f407g discovery board image test1 gif task execution time relative deadline led colour periodic task 1 95 500 amber periodic task 2 150 500 green periodic task 3 250 750 blue aperiodic task 1 50 200 red all times are in ticks set at 1khz therefore 1000 ticks 1 second | os |
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deep_learning_for_computer_vision | deep learning for computer vision graphics open pose gif 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 lectures lecture notebook slides key topics additional reading viewing introduction to pytorch notebook https github com unccv deep learning for computer vision blob master notebooks 01 introduction to pytorch ipynb why pytorch pytorch as numpy with gpu support simple neural network in pytorch automatic differentiation nn module pytorch layers pytorch optim nn sequential great torch intro by jeremy howard https pytorch org tutorials beginner nn tutorial html get results fast with fastai open in colab https colab research google com assets colab badge svg https colab research google com github unccv deep learning for computer vision blob master notebooks 02 image classification with fastai ipynb open in colab https colab research google com assets colab badge svg https colab research google com github unccv deep learning for computer vision blob master notebooks 03 bounding box detection with fastai ipynb open in colab https colab research google com assets colab badge svg https colab research google com github unccv deep learning for computer vision blob master notebooks 04 semantic segmentation with fastai ipynb jeremy howard and the fastai philosophy databunches learners classifiction fastai course https github com fastai course v3 deep learning in depth part 1 notebook https github com unccv deep learning for computer vision blob master notebooks 05 deep learning in depth 1 ipynb stochastic gradient descent regression vs classification one hot encoding cost functions and maximum likelihood cross entropy ian goodfellow s deep learning chapter 1 section 6 2 and section 8 1 https www deeplearningbook org deep learning in depth part 2 notebook https github com unccv deep learning for computer vision blob master notebooks 06 deep learning in depth 2 ipynb cnns pooling and strides alexnet walkthrough imagenet transfer learning adaptive pooling dropout data augmentation a little historical perspective alexnet paper https papers nips cc paper 4824 imagenet classification with deep convolutional neural networks pdf gans notebook https github com unccv deep learning for computer vision blob master notebooks 07 generative adversarial networks ipynb ian goodfellow invents gans the world s simplest gan nash equilibria a dive into higher dimensions dcgan to the rescue visualizing gans gan grow up sortof stylegan insanity the unbelievably interesting world of gan variants goodfellow et al 2014 https arxiv org pdf 1406 2661 pdf stylegan https arxiv org pdf 1812 04948 pdf cyclegan https arxiv org pdf 1703 10593 pdf setting up your computing environment installing the software you need to train deep learning models can be difficult for the purposes of this workshop we re offering 3 recommended methods of setting up your computing environment your level of experience and access to machines should help you determine which appraoch is right for you option pros cons cost instructions 1 google colab virtually no setup required start coding right away gpus not always available limited session times limited ram free there s also a paid tier at 10 month https colab research google com signup colab setup https github com stephencwelch dsgo dl workshop summer 2020 21 setup google colab 2 your own linux gpu machine no recurring cost complete control over hardware high up front cost takes time to configure 1000 fixed up front cost linux setup https github com stephencwelch dsgo dl workshop summer 2020 22 setup on your own gpu machine running linux 3 virtual machine highly configurable flexible pay for the performance level you need can be difficult to configure only terminal based interface starts 1 hour vm setup https github com stephencwelch dsgo dl workshop summer 2020 23 setup a virtual machine 1 setup google colab google colab is delightfully easy to setup all you really need to is a google account clicking one of the open in colab links above should take you directly to that notebook in google colab ready to run the only configuration change you ll be required to make is changing your runtime type simply click the runtime menu dropdown at the top of your notebook select change runtime type and select gpu as your hardware accelerator you also can open any notebook available on github in colab 2 setup on your own gpu machine running linux after doing this for a while my preferred configuration is training models on my own linux gpu machine this can require some up front investment but if you re going to be training a lot of models having your own machine really makes your life easier 2 1 install anaconda python https www anaconda com products individual 2 2 clone this repository git clone https github com unccv deep learning for computer vision 2 3 optional create conda environment conda create n unccv dl python 3 7 conda activate unccv dl 2 4 install packages cd deep learning for computer vision pip install r requirements txt 2 5 depending on your existing setup you may need to install nvidia drivers and the nvidia cuda toolkit https docs nvidia com cuda cuda installation guide linux index html 2 6 launch jupyter jupyter notebook 2 3 setup a virtual machine virtual machines provide a nice highly configurable platform for data science tasks i recommnend following the fastai server setup https course fast ai start azure html guide for the cloud platform of your choice | ai |
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large-language-models | large language models book building this book uses jupyter book https jupyterbook org install the dependencies shell pip install r requirements txt clone this repository and run the build from the root directory shell jupyter book build this will generate the html in build html contribution execute the notebook s before committing as most of them would be too expensive to automatically execute in a pipeline shell jupyter nbconvert to notebook inplace execute notebook ipynb | deep-learning natural-language-processing nlp | ai |
HH_DEIS_2021 | hh deis 2021 these are the codes i used to do the tasks required for the design embedded and intelligent systems course there are 2 folders one for arduino codes and the other for ros2 code arduino files 1 black line follower test 1 ino this is the file used to do the line following task in tollgate 3 2 manual mode test ino this file was used to do the manual control task in tollgate 3 3 exit test 1 ino this file was used to do the roundabout exit task and the teamwork task in tollgate 4 ros2 and python files 1 manual control py this file was used to manually control the robot in the teamwork task 2 publisher member function py this file was used to do the teamwork task automatically 3 publisher member function 2 py this file was used in addition to manual control py file to do the teamwork task manually 4 subscriber member function2 py this file was used to do the manual control task in tollgate 3 5 subscriber member function4 py this file was used to do the roundabout exit task in tollgate 4 | os |
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platform-coding | my personal platform engineering coding list bash gnu project website https gnu org the bash reference manual http www gnu org software bash manual bash html man bash pure bash bible https github com dylanaraps pure bash bible sh posix shell the open group https www opengroup org the shell command language https pubs opengroup org onlinepubs 9699919799 utilities v3 chap02 html man sh pure sh bible https github com dylanaraps pure sh bible go go project website https go dev a tour of go https go dev tour go by example https gobyexample com effective go https go dev doc effective go go user manual https go dev doc standard library https pkg go dev std rwxrob s awesome go repository https github com rwxrob awesome go python python project website https python org the python tutorial https docs python org 3 tutorial index html the python standard library https docs python org 3 library index html the python language reference https docs python org 3 reference index html extending and embedding the python interpreter https docs python org 3 extending index html python c api https docs python org 3 c api index html using python https docs python org 3 using index html python howtos https docs python org 3 howto index html python glossary https docs python org 3 glossary html sql postgresql postgresql project website https postgresql org postgresql documentation https www postgresql org docs postgresql manual https www postgresql org docs current postgresql books https www postgresql org docs books postgresql tutorials other resources https www postgresql org docs online resources postgresql faq https www postgresql org docs faq postgresql wiki https wiki postgresql org mysql mysql project website https mysql com mysql documentation https dev mysql com doc mysql server reference manual https dev mysql com doc refman en mysql enterprise https dev mysql com doc index enterprise html innodb cluster documentation https dev mysql com doc mysql shell en mysql innodb cluster html mysql ndb cluster documentation https dev mysql com doc index cluster html mysql connectors documentation https dev mysql com doc index connectors html more mysql resources https dev mysql com doc index other html nosql redis redis project website https redis io redis documentation https redis io docs getting started https redis io docs getting started mongodb mongodb project website https mongodb com mongodb documentation https www mongodb com docs start with guides https www mongodb com docs guides git git project website https git scm com git documentation https git scm com doc git reference manual https git scm com docs github cheat sheet https github github com training kit visual git cheat sheet https ndpsoftware com git cheatsheet html pro git book https git scm com book curated git resources https git scm com doc ext man git | cloud |
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Build-Your-Own-RESTful-API | build your own restful api example documents id objectid 5c139771d79ac8eac11e754a title api content api stands for application programming interface it is a set of subroutine definitions communication protocols and tools for building software in general terms it is a set of clearly defined methods of communication among various components a good api makes it easier to develop a computer program by providing all the building blocks which are then put together by the programmer id objectid 5c1398aad79ac8eac11e7561 title bootstrap content this is a framework developed by twitter that contains pre made front end templates for web design id objectid 5c1398ecd79ac8eac11e7567 title dom content the document object model is like an api for interacting with our html server starting code jshint esversion 6 const express require express const bodyparser require body parser const ejs require ejs const mongoose require mongoose const app express app set view engine ejs app use bodyparser urlencoded extended true app use express static public todo app listen 3000 function console log server started on port 3000 re populate database id 5c18e1892998bdb3b3d355bf title rest content rest is short for representational state transfer iit s an architectural style for designing apis id objectid 5c139771d79ac8eac11e754a title api content api stands for application programming interface it is a set of subroutine definitions communication protocols and tools for building software in general terms it is a set of clearly defined methods of communication among various components a good api makes it easier to develop a computer program by providing all the building blocks which are then put together by the programmer id objectid 5c1398aad79ac8eac11e7561 title bootstrap content this is a framework developed by twitter that contains pre made front end templates for web design id objectid 5c1398ecd79ac8eac11e7567 title dom content the document object model is like an api for interacting with our html id 5c18f35cde40ab6cc551cd60 title jack bauer content jack bauer once stepped into quicksand the quicksand couldn t escape and nearly drowned v 0 | front_end |
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castor | castor storybook https raw githubusercontent com storybooks brand master badge badge storybook svg https castor vercel app castor is onfido s design system it has been designed to support different renderers and plain html css browser support img src https raw githubusercontent com alrra browser logos master src chrome chrome 48x48 png alt chrome width 24px height 24px br chrome img src https raw githubusercontent com alrra browser logos master src safari safari 48x48 png alt safari width 24px height 24px br safari img src https raw githubusercontent com alrra browser logos master src firefox firefox 48x48 png alt firefox width 24px height 24px br firefox img src https raw githubusercontent com alrra browser logos master src edge edge 48x48 png alt edge width 24px height 24px br edge last 2 versions last 2 versions last 2 versions last 2 versions install packages for html css only sh npm install onfido castor setup following core documentation packages core if you use react sh npm install onfido castor react also setup following react documentation packages react sponsors vercel https www datocms assets com 31049 1618983297 powered by vercel svg https vercel com utm source onfido oss utm campaign oss | design-system html-css react | os |
awesome-deep-trading | awesome deep trading awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome list of code papers and resources for ai deep learning machine learning neural networks applied to algorithmic trading open access all rights granted for use and re use of any kind by anyone at no cost under your choice of either the free mit license or creative commons cc by international public license 2021 craig bailes cbailes https github com cbailes patreon https www patreon com craigbailes contact craigbailes com mailto contact craigbailes com contents papers papers meta analyses systematic reviews meta analyses systematic reviews convolutional neural networks cnns convolutional neural networks cnns long short term memory lstms long short term memory lstms generative adversarial networks gans generative adversarial networks gans high frequency high frequency portfolio portfolio reinforcement learning reinforcement learning vulnerabilities vulnerabilities cryptocurrency cryptocurrency social processing social processing behavioral analysis behavioral analysis sentiment analysis sentiment analysis repositories repositories generative adversarial networks gans generative adversarial networks gans 1 guides guides cryptocurrency cryptocurrency 1 datasets datasets simulation simulation resources resources presentations presentations courses courses further reading further reading papers classification based financial markets prediction using deep neural networks https arxiv org pdf 1603 08604 matthew dixon diego klabjan jin hoon bang 2016 deep learning for limit order books https arxiv org pdf 1601 01987 justin sirignano 2016 high frequency trading strategy based on deep neural networks https link springer com chapter 10 1007 2f978 3 319 42297 8 40 andr s ar valo jaime ni o german hern ndez javier sandoval 2016 a deep reinforcement learning framework for the financial portfolio management problem https arxiv org pdf 1706 10059 zhengyao jiang dixing xu jinjun liang 2017 agent inspired trading using recurrent reinforcement learning and lstm neural networks https arxiv org pdf 1707 07338 pdf david w lu 2017 deep hedging https arxiv org pdf 1802 03042 hans b hler lukas gonon josef teichmann ben wood 2018 stock trading bot using deep reinforcement learning https link springer com chapter 10 1007 978 981 10 8201 6 5 akhil raj azhikodan anvitha g k bhat mamatha v jadhav 2018 financial trading as a game a deep reinforcement learning approach https arxiv org pdf 1807 02787 chien yi huang 2018 practical deep reinforcement learning approach for stock trading https arxiv org pdf 1811 07522 zhuoran xiong xiao yang liu shan zhong hongyang yang anwar walid 2018 algorithmic trading and machine learning based on gpu http ceur ws org vol 2147 p09 pdf mantas vaitonis saulius masteika konstantinas korovkinas 2018 a quantitative trading method using deep convolution neural network https iopscience iop org article 10 1088 1757 899x 490 4 042018 pdf haibo chen daolei liang ll zhao 2019 deep learning in exchange markets https www sciencedirect com science article pii s0167624518300702 rui gon alves vitor miguel ribeiro fernando lobo pereira ana paula rocha 2019 financial trading model with stock bar chart image time series with deep convolutional neural networks https arxiv org abs 1903 04610 omer berat sezer ahmet murat ozbayoglu 2019 deep reinforcement learning for financial trading using price trailing https ieeexplore ieee org document 8683161 konstantinos saitas zarkias nikolaos passalis avraam tsantekidis anastasios tefas 2019 cooperative multi agent reinforcement learning framework for scalping trading https arxiv org abs 1904 00441 uk jo taehyun jo wanjun kim iljoo yoon dongseok lee seungho lee 2019 improving financial trading decisions using deep q learning predicting the number of shares action strategies and transfer learning https www sciencedirect com science article pii s0957417418306134 gyeeun jeong ha young kim 2019 deep execution value and policy based reinforcement learning for trading and beating market benchmarks https papers ssrn com sol3 papers cfm abstract id 3374766 kevin dab rius elvin granat patrik karlsson 2019 an empirical study of machine learning algorithms for stock daily trading strategy https www hindawi com journals mpe 2019 7816154 ref dongdong lv shuhan yuan meizi li yang xiang 2019 recipe for quantitative trading with machine learning http dx doi org 10 2139 ssrn 3232143 daniel alexandre bloch 2019 exploring possible improvements to momentum strategies with deep learning http hdl handle net 2105 49940 adam tak cs x xiao 2019 enhancing time series momentum strategies using deep neural networks https arxiv org abs 1904 04912 bryan lim stefan zohren stephen roberts 2019 multi agent deep reinforcement learning for liquidation strategy analysis https arxiv org abs 1906 11046 wenhang bao xiao yang liu 2019 deep learning based feature engineering for stock price movement prediction https www sciencedirect com science article abs pii s0950705118305264 wen long zhichen lu lingxiao cui 2019 review on stock market forecasting analysis https www researchgate net publication 340583328 review on stock market forecasting analysis lstm long short term memory holt s seasonal methodann artificial neural network arima auto regressive integrated minimum average pca mlp multi layers percep anirban bal debayan ganguly kingshuk chatterjee 2019 neural networks as a forecasting tool in the context of the russian financial market digitalization https www researchgate net publication 340474330 neural networks as a forecasting tool in the context of the russian financial market digitalization valery aleshin oleg sviridov inna nekrasova dmitry shevchenko 2020 deep hierarchical strategy model for multi source driven quantitative investment https ieeexplore ieee org abstract document 8743385 chunming tang wenyan zhu xiang yu 2019 finding efficient stocks in bse100 implementation of buffet approach introduction https www researchgate net publication 340501895 asian journal of management finding efficient stocks in bse100 implementation of buffet approach introduction sherin varghese sandeep thakur medha dhingra 2020 deep learning in asset pricing https arxiv org abs 1904 00745 luyang chen markus pelger jason zhu 2020 meta analyses systematic reviews application of machine learning in stock trading a review http dx doi org 10 14419 ijet v7i2 33 15479 kok sheng tan rajasvaran logeswaran 2018 evaluating the performance of machine learning algorithms in financial market forecasting a comprehensive survey https arxiv org abs 1906 07786 lukas ryll sebastian seidens 2019 reinforcement learning in financial markets https www mdpi com 2306 5729 4 3 110 pdf terry lingze meng matloob khushi 2019 financial time series forecasting with deep learning a systematic literature review 2005 2019 https arxiv org abs 1911 13288 omer berat sezer mehmet ugur gudelek ahmet murat ozbayoglu 2019 a systematic review of fundamental and technical analysis of stock market predictions https www researchgate net publication 335274959 a systematic review of fundamental and technical analysis of stock market predictions isaac kofi nti adebayo adekoya benjamin asubam weyori 2019 convolutional neural networks cnns a deep learning based stock trading model with 2 d cnn trend detection https www researchgate net publication 323131323 a deep learning based stock trading model with 2 d cnn trend detection ugur gudelek s arda boluk murat ozbayoglu murat ozbayoglu 2017 algorithmic financial trading with deep convolutional neural networks time series to image conversion approach https www researchgate net publication 324802031 algorithmic financial trading with deep convolutional neural networks time series to image conversion approach omer berat sezar murat ozbayoglu 2018 deeplob deep convolutional neural networks for limit order books https ieeexplore ieee org abstract document 8673598 zihao zhang stefan zohren stephen roberts 2019 long short term memory lstms application of deep learning to algorithmic trading stanford cs229 http cs229 stanford edu proj2017 final reports 5241098 pdf guanting chen yatong chen takahiro fushimi 2017 stock prices prediction using deep learning models https arxiv org abs 1909 12227 jialin liu fei chao yu chen lin chih min lin 2019 deep learning for stock market trading a superior trading strategy https doi org 10 14311 nnw 2019 29 011 d fister j c mun v jagri t jagri 2019 performance evaluation of recurrent neural networks for short term investment decision in stock market https www researchgate net publication 339751012 performance evaluation of recurrent neural networks for short term investment decision in stock market alexandre p da silva silas s l pereira m rio w l moreira joel j p c rodrigues ricardo a l rab lo kashif saleem 2020 research on financial assets transaction prediction model based on lstm neural network https doi org 10 1007 s00521 020 04992 7 xue yan wang weihan miao chang 2020 prediction of stock trend for swing trades using long short term memory neural network model https www researchgate net publication 340789607 prediction of stock trend for swing trades using long short term memory neural network model varun totakura v devasekhar madhu sake 2020 a novel deep learning framework prediction and analysis of financial time series using ceemd and lstm https www sciencedirect com science article abs pii s0957417420304334 yong an zhang binbin yan memon aasma 2020 deep stock predictions https arxiv org abs 2006 04992 akash doshi alexander issa puneet sachdeva sina rafati somnath rakshit 2020 generative adversarial networks gans generative adversarial networks for financial trading strategies fine tuning and combination https deepai org publication generative adversarial networks for financial trading strategies fine tuning and combination adriano koshiyama 2019 stock market prediction based on generative adversarial network https doi org 10 1016 j procs 2019 01 256 kang zhang guoqiang zhong junyu dong shengke wang yong wang 2019 generative adversarial network for stock market price prediction https cs230 stanford edu projects fall 2019 reports 26259829 pdf ricardo alberto carrillo romero 2019 generative adversarial network for market hourly discrimination https mpra ub uni muenchen de id eprint 99846 luca grilli domenico santoro 2020 high frequency algorithmic trading using deep neural networks on high frequency data https link springer com chapter 10 1007 978 3 319 66963 2 14 andr s ar valo jaime ni o german hernandez javier sandoval diego le n arbey arag n 2017 stock market prediction on high frequency data using generative adversarial nets https doi org 10 1155 2018 4907423 xingyu zhou zhisong pan guyu hu siqi tang cheng zhao 2018 deep neural networks in high frequency trading https arxiv org pdf 1809 01506 prakhar ganesh puneet rakheja 2018 application of machine learning in high frequency trading of stocks https www ijser org researchpaper application of machine learning in high frequency trading of stocks pdf obi bertrand obi 2019 portfolio multi scenario financial planning via deep reinforcement learning ai https papers ssrn com sol3 papers cfm abstract id 3516480 gordon irlam 2020 g learner and girl goal based wealth management with reinforcement learning https arxiv org abs 2002 10990 matthew dixon igor halperin 2020 reinforcement learning based portfolio management with augmented asset movement prediction states https www aaai org papers aaai 2020gb aaai yey 4483 pdf yunan ye hengzhi pei boxin wang pin yu chen yada zhu jun xiao bo li 2020 reinforcement learning reinforcement learning in financial markets a survey http hdl handle net 10419 183139 thomas g fischer 2018 alphastock a buying winners and selling losers investment strategy using interpretable deep reinforcement attention networks https arxiv org abs 1908 02646 jingyuan wang yang zhang ke tang junjie wu zhang xiong capturing financial markets to apply deep reinforcement learning https arxiv org abs 1907 04373 souradeep chakraborty 2019 reinforcement learning for fx trading http stanford edu class msande448 2019 final reports gr2 pdf yuqin dai chris wang iris wang yilun xu 2019 an application of deep reinforcement learning to algorithmic trading https arxiv org abs 2004 06627 thibaut th ate damien ernst 2020 single asset trading a recurrent reinforcement learning approach http urn kb se resolve urn urn nbn se mdh diva 47505 marko nikolic 2020 beat china s stock market by using deep reinforcement learning https www researchgate net profile huang gang9 publication 340438304 beat china s stock market by using deep reinforcement learning links 5e88e007299bf130797c7a68 beat chinas stock market by using deep reinforcement learning pdf gang huang xiaohua zhou qingyang song 2020 an adaptive financial trading system using deep reinforcement learning with candlestick decomposing features https doi org 10 1109 access 2020 2982662 ding fengqian luo chao 2020 application of deep q network in portfolio management https arxiv org abs 2003 06365 ziming gao yuan gao yi hu zhengyong jiang jionglong su 2020 deep reinforcement learning pairs trading with a double deep q network https ieeexplore ieee org abstract document 9031159 andrew brim 2020 a reinforcement learning model based on reward correction for quantitative stock selection https doi org 10 1088 1757 899x 768 7 072036 haibo chen chenyu zhang yunke li 2020 aamdrl augmented asset management with deep reinforcement learning https arxiv org abs 2010 08497 eric benhamou david saltiel sandrine ungari abhishek mukhopadhyay jamal atif 2020 guides stock price prediction and forecasting using stacked lstm deep learning https www youtube com watch v h6du pfuzne krish naik 2020 comparing arima model and lstm rnn model in time series forecasting https analyticsindiamag com comparing arima model and lstm rnn model in time series forecasting vaibhav kumar 2020 lstm to predict dow jones industrial average a time series forecasting model https medium com analytics vidhya lstm to predict dow jones industrial average time series 647b0115f28c sarit maitra 2020 vulnerabilities adversarial attacks on deep algorithmic trading policies https arxiv org abs 2010 11388 yaser faghan nancirose piazza vahid behzadan ali fathi 2020 cryptocurrency recommending cryptocurrency trading points with deep reinforcement learning approach https doi org 10 3390 app10041506 otabek sattarov azamjon muminov cheol won lee hyun kyu kang ryumduck oh junho ahn hyung jun oh heung seok jeon 2020 social processing behavioral analysis can deep learning predict risky retail investors a case study in financial risk behavior forecasting https www researchgate net publication 329734839 can deep learning predict risky retail investors a case study in financial risk behavior forecasting yaodong yang alisa kolesnikova stefan lessmann tiejun ma ming chien sung johnnie e v johnson 2019 investor behaviour monitoring based on deep learning https www tandfonline com doi full 10 1080 0144929x 2020 1717627 casa token heptguqeb3kaaaaa 3ab1d3l4udpw0l3nw0sjhspz9tvdjptw3hfdqa 3xrus 9owfarbhnurpsdtcy54kzr05atdntwhbnma song wang xiaoguang wang fanglin meng rongjun yang yuanjun zhao 2020 sentiment analysis improving decision analytics with deep learning the case of financial disclosures https arxiv org pdf 1508 01993 stefan feuerriegel ralph fehrer 2015 big data deep learning for financial sentiment analysis https journalofbigdata springeropen com articles 10 1186 s40537 017 0111 6 sahar sohangir dingding wang anna pomeranets taghi m khoshgoftaar 2018 using machine learning to predict stock prices https medium com analytics vidhya using machine learning to predict stock prices c4d0b23b029a vivek palaniappan 2018 stock prediction using twitter https towardsdatascience com stock prediction using twitter e432b35e14bd khan saad bin hasan 2019 sentiment and knowledge based algorithmic trading with deep reinforcement learning https arxiv org abs 2001 09403 abhishek nan anandh perumal osmar r zaiane 2020 repositories yvictor tradinggym https github com yvictor tradinggym trading and backtesting environment for training reinforcement learning agent or simple rule base algo rachnog deep trading https github com rachnog deep trading experimental time series forecasting jobvisser03 deep trading advisor https github com jobvisser03 deep trading advisor deep trading advisor uses mlp cnn and rnn lstm with keras zipline dash and plotly rosdyana cnn financial data https github com rosdyana cnn financial data deep trading using a convolutional neural network iamstone deep trader cnn kospi200futures https github com iamstone deep trader cnn kospi200futures kospi200 index futures prediction using cnn ha2emnomer deep trading https github com ha2emnomer deep trading keras based lstm rnn gujiuxiang deep trader pytorch https github com gujiuxiang deep trader pytorch this project uses reinforcement learning on stock market and agent tries to learn trading pytorch based zhengyaojiang pgportfolio https github com zhengyaojiang pgportfolio pgportfolio policy gradient portfolio the source code of a deep reinforcement learning framework for the financial portfolio management problem yuriak rlquant https github com yuriak rlquant applying reinforcement learning in quantitative trading policy gradient direct rl ucaiado qlearning trading https github com ucaiado qlearning trading trading using q learning laikasinjason deep q learning trading system on hk stocks market https github com laikasinjason deep q learning trading system on hk stocks market deep q learning implementation on the hong kong stock exchange golsun deep rl trading https github com golsun deep rl trading codebase for paper deep reinforcement learning for time series playing idealized trading games by xiang gao huseinzol05 stock prediction models https github com huseinzol05 stock prediction models gathers machine learning and deep learning models for stock forecasting included trading bots and simulations jiewwantan startrader https github com jiewwantan startrader trains an agent to trade like a human using a deep reinforcement learning algorithm deep deterministic policy gradient ddpg learning algorithm notadamking rltrader https github com notadamking rltrader a cryptocurrency trading environment using deep reinforcement learning and openai s gym generative adversarial networks gans borisbanushev stockpredictionai https github com borisbanushev stockpredictionai a notebook for stock price movement prediction using an lstm generator and cnn discriminator kah ve marketgan https github com kah ve marketgan implementing a generative adversarial network on the stock market cryptocurrency samre12 deep trading agent https github com samre12 deep trading agent deep reinforcement learning based trading agent for bitcoin using deepsense network for q function approximation thirstyscholar trading bitcoin with reinforcement learning https github com thirstyscholar trading bitcoin with reinforcement learning trading bitcoin with reinforcement learning lefnire tforce btc trader https github com lefnire tforce btc trader a tensorforce based bitcoin trading bot algo trader uses deep reinforcement learning to automatically buy sell hold btc based on price history datasets kaggle huge stock market dataset https www kaggle com borismarjanovic price volume data for all us stocks etfs historical daily prices and volumes of all u s stocks and etfs alpha vantage https www alphavantage co free apis in json and csv formats realtime and historical stock data fx and cryptocurrency feeds 50 technical indicators quandl https quandl com simulation generating realistic stock market order streams https openreview net pdf id rke41hc5km anonymous authors 2018 deep hedging learning to simulate equity option markets https arxiv org abs 1911 01700 magnus wiese lianjun bai ben wood hans buehler 2019 resources presentations bigdatafinance neural networks intro http bigdatafinance eu wp wp content uploads 2016 06 tefas bigdatafinanceneuralnetworks intro web pdf anastasios tefas assistant professor at aristotle university of thessaloniki 2016 trading using deep learning motivation challenges solutions http on demand gputechconf com gtc il 2017 presentation sil7121 yam peleg deep learning for high frequency trading 20 2 pdf yam peleg gpu technology conference 2017 fintech ai machine learning in finance https www slideshare net sanjivdas fintech ai machine learning in finance sanjiv das 2018 deep residual learning for portfolio optimization with attention and switching modules https engineering nyu edu sites default files 2019 03 nyu 20fre 20seminar jifei 20wang 20 28slides 29 pdf jeff wang ph d nyu courses artificial intelligence for trading nd880 nanodegree at udacity https www udacity com course ai for trading nd880 github code repo https github com udacity artificial intelligence for trading neural networks in trading course by dr ernest p chan at quantra https quantra quantinsti com course neural networks deep learning trading ernest chan machine learning and reinforcement learning in finance specialization by nyu at coursera https www coursera org specializations machine learning reinforcement finance meetups artificial intelligence in finance algorithmic trading on meetup https www meetup com artificial intelligence in finance algorithmic trading new york city further reading neural networks for algorithmic trading simple time series forecasting https medium com alexrachnog neural networks for algorithmic trading part one simple time series forecasting f992daa1045a alex rachnog 2016 predicting cryptocurrency prices with deep learning https dashee87 github io deep 20learning python predicting cryptocurrency prices with deep learning david sheehan 2017 introduction to learning to trade with reinforcement learning http www wildml com 2018 02 introduction to learning to trade with reinforcement learning denny britz 2018 webinar how to forecast stock prices using deep neural networks https www youtube com watch v rmh8autqwq8 erez katz lucena research 2018 creating bitcoin trading bots that don t lose money https towardsdatascience com creating bitcoin trading bots that dont lose money 2e7165fb0b29 adam king 2019 why deep reinforcement learning can help improve trading efficiency https medium com viktortachev why deep reinforcement learning can help improve trading efficiency 5af57e8faf9d viktor tachev 2019 optimizing deep learning trading bots using state of the art techniques https towardsdatascience com using reinforcement learning to trade bitcoin for massive profit b69d0e8f583b adam king 2019 using the latest advancements in deep learning to predict stock price movements https towardsdatascience com aifortrading 2edd6fac689d boris banushev 2019 rnn and lstm the neural networks with memory https levelup gitconnected com rnn and lstm the neural networks with memory 24e4cb152d1b nagesh singh chauhan 2020 introduction to deep learning trading in hedge funds https www toptal com deep learning deep learning trading hedge funds neven pi uljan | deep-trading deep-learning trading-algorithms trading-bot quantitative-trading fintech stock-trading trading-bots machine-learning frequency-trading deep-reinforcement-learning deep-neural-networks cryptocurrency | ai |
Shadows | shadows an android app made it using android development this app is about online shopping of books i have also used firebase for the backend | server |
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HSDES-LAB03-PULP_DSP | hsdes lab03 digital signal processing programming on pulp the goals of this lab session are 1 simd vectorized fir filters 2 loop unrolling techniques for matrix multiplications 3 simd vectorized matmul on your own 4 convolution on pulp a final assignment is described here https github com eeeslab hsdes lab03 pulp dsp blob master lab assignment readme md getting started open a shell and clone the repository with the sample code remember also to source the pulp sdk configuration script shell source pulp sourceme sh cd working dir git clone https github com eeeslab hsdes lab03 pulp dsp references pulp builtins https greenwaves technologies com manuals build pulp os html group groupbuiltinsapi html pmisis apis https greenwaves technologies com manuals build pmsis api html index html pmisis bsp https greenwaves technologies com manuals build pmsis bsp html index html | os |
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ELIDEK-Database | elidek database simulation of a database for elidek project for the 6th semester course databases of the school of electrical and computer engineering of the national technical university of athens members anastasios stefanos anagnostou 03119051 spyridon papadopoylos 03119058 image https user images githubusercontent com 62098670 172059410 343cf6ef f8ba 43ca 9e2e 95878dcd0fbd png in order to execute the web app one must have an installation of apache http server mariadb database and an interpreter that executes the php scripts all of these can be found in an xampp installation once those requirements are met the steps are the following 1 open xampp 2 activate the apache and mysql modules 3 connect to the server with root privileges using a dbms 4 execute the elidek creation sql script 5 wait 6 locate your htdocs folder 7 move the elidek website folder in the htdocs folder 8 open a browser 9 type localhost elidek website in the search bar | server |
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nlposs.github.io | workshop for natural language processing open source software nlp oss this is the codebase for the nlp oss webpage the actual proposal is located at https github com nlposs nlp oss requirements 1 gem install bundler 1 bundle install development run local server make serve | nlp-oss oss linguistics nlp | ai |
database_engineering_2022_spring | database engineering 2022 mysql data aware ado net nhibernate | server |
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IREC | irec documentation and code for the intercollegiate rocketry engineering challenge seds and arc collaboration payload drone | cloud |
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AutoTiering | autotiering this repo contains the kernel code in the following paper exploring the design space of page management for multi tiered memory systems atc 21 https www usenix org conference atc21 presentation kim jonghyeon also there is user space experiment scripts in autotiering userspace https github com csl ajou autotiering userspace preliminary at least two numa node system are needed to define upper tier and lower tier node those different tier memory nodes should have performance gap e g lower tier node is non volatile memory or remote socket memory kernel configuration to build autotiering you should add options related to autotiering to your kernel configuration file config and load the file by using make menuconfig bash in config file config page balancing y config page balancing debug y optional config page fault profile y optioanl bash make menuconfig if you want to trace page access tracking across numa nodes enable page balancing debug if you want to profile page fault latency including page promotion and demotion enable page fault profile kernel build bash make j make modules install install description there are three options for autotiering which are cpm opm and exchange autotiering opm includes autotiering cpm in short enabling autotiering opm allow conservative promotion and migration automatically 000 default automatic numa balancing 001 enable conservative promotion or migration autotiering cpm 010 enable opportunistic promotion or migration autotiering opm 100 enable page exchange usage it is necessary to enable the original autonuma balancing option for adopting autotiering bash default autonuma balancing sysctl kernel numa balancing 1 cpm sysctl kernel numa balancing extended 1 001 cpmx sysctl kernel numa balancing extended 5 101 opm sysctl kernel numa balancing extended 2 010 opmx sysctl kernel numa balancing extended 6 110 opm background demotion sysctl kernel numa balancing extended 2 010 echo 1 sys kernel mm page balancing background demotion it is also need to define the promotion migration demotion path of the numa node bash if the path is not available ex upper tier node can t have the promotion path that path should be defiend to 1 the migration path between same tier memory nodes sys devices system node noden migration path the promotion path to upper tier memory node sys devices system node noden promotion path the demotion path to lower tier memory node sys devices system node noden demotion path proc lapinfo show current lap pages tracked by the system and counts of demotion by lap level in sys kernel mm page balancing directory nr reserved pages show current reserved page pool that can be promoted background demotion demote pages using background kernel thread batch demotion demote pages in batches skip lower tier skip access tracking lower tier memory node thp mt copy thp copy using multi thread publication inproceedings 273808 author jonghyeon kim and wonkyo choe and jeongseob ahn title exploring the design space of page management for multi tiered memory systems booktitle 2021 usenix annual technical conference usenix atc 21 year 2021 isbn 978 1 939133 23 6 pages 715 728 url https www usenix org conference atc21 presentation kim jonghyeon publisher usenix association month jul | linux-kernel | os |
miniRTO | mini rto alt header https cloud githubusercontent com assets 7499644 23590695 eaac510e 020a 11e7 9ec1 831b8cf358b8 png mini rto lets you find the details of an indian vehicle with its registration number details include vehicle name fuel type displacement cc engine no chassis no owner name registered location registered date mini rto also contains the details of most rt offices across india you can easily share details as text or image a href https play google com store apps details id com chandruscm minirto hl en img alt get it on google play src https play google com intl en us badges images generic en badge web generic png height 80px a why mini rto has been discontinued the updated terms and conditions on the source govt website prohibits 3rd parties from using their data in any form features maintaining fragment states across viewpager expandable cardview with custom layout feeding recyclerview using database cursor web scraping using jsoup validating indian vehicle registration number using regex using pre populated database libraries jsoup java html parser https github com jhy jsoup android sqliteassethelper https github com jgilfelt android sqlite asset helper card library https github com gabrielemariotti cardslib recyclerview fastscroll https github com timusus recyclerview fastscroll material dialogs https github com afollestad material dialogs material tap target prompt https github com sjwall materialtaptargetprompt android tooltip https github com sephiroth74 android target tooltip material icons https github com google material design icons commons lang https github com apache commons lang license copyright 2017 chandramohan sudar licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | os |
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Iot-Telemetry-Simulator | page type sample languages csharp powershell dockerfile products azure iot hub azure event hubs azure container instances azure container registry name azure iot device telemetry simulator urlfragment azure iot device telemetry simulator description the iot telemetry simulator allows you to test azure iot hub event hub or kafka ingestion at scale master test and push https github com azure samples iot telemetry simulator workflows master 20test 20and 20push badge svg azure iot device telemetry simulator the iot telemetry simulator allows you to test azure iot hub event hub or kafka ingestion at scale the implementation is communicating with azure iot hub using multiplexed amqp connections an automation library allows you to run it as load test as part of a ci cd pipeline a single amqp connection can handle approximately 995 devices quick start the quickest way to generate telemetry is using docker with the following command bash docker run it e iothubconnectionstring hostname your iothub name azure devices net sharedaccesskeyname device sharedaccesskey your iothub key mcr microsoft com oss azure samples azureiot telemetrysimulator the simulator expects the devices to already exist in azure iot hub if you need help creating simulation devices in an azure iot hub use the included project iotsimulatordeviceprovisioning or the docker image bash docker run it e iothubconnectionstring hostname your iothub name azure devices net sharedaccesskeyname registryreadwrite sharedaccesskey your iothub key e devicecount 1000 mcr microsoft com oss azure samples azureiot simulatordeviceprovisioning simulator input parameters the amount of devices their names and telemetry generated can be customized using parameter the list below contains the supported configuration parameters name description iothubconnectionstring iot hub connection string device our iot hub owner scopes are good example hostname your iothub name azure devices net sharedaccesskeyname device sharedaccesskey your iothub key eventhubconnectionstring event hub connection string sas policy send is required for eventhub no device registration is required example endpoint sb your eventhub namespace servicebus windows net sharedaccesskeyname send sharedaccesskey your send sas primary key entitypath your eventhub name kafkaconnectionproperties kafka connection properties as a json string example bootstrap servers kafka kafkatopic kafka topic name devicelist comma separated list of device identifiers default use it to generate telemetry for specific devices instead of numeric generated identifiers if the parameter has a value the following parameters are ignored deviceprefix deviceindex and devicecount are ignored deviceprefix device identifier prefix default sim deviceindex starting device number default 1 devicecount amount of simulated devices default 1 messagecount amount of messages to send by device default 10 set to zero if you wish to send messages until cancelled interval interval between each message in milliseconds default 1000 template telemetry payload template see telemetry template fixpayload fix telemetry payload in base64 format use this setting if the content of the message does not need to change fixpayloadsize fix telemetry payload size in bytes use this setting if the content of the message does not need to change will be an array filled with zeros payloaddistribution allows the generation of payloads based on a distribution br example fixsize 10 12 template 25 default fix 65 aaaabbbbbccc generates 10 a fix payload of 10 bytes 25 a template generated payload and 65 of the time a fix payload from values aaaabbbbbccc header telemetry header template see telemetry template partitionkey optional partition key https docs microsoft com en us azure event hubs event hubs features partitions template for event hubs see telemetry template and advanced options advanced options variables telemetry variables see telemetry template duplicateeverynevents if 0 send duplicates of the given fraction of messages see advanced options advanced options default 0 file defines a json file where templates variables and device based intervals can be defined file and environment variable configuration can be used in conjunction intervals allows customizing the intervals between messages per device if an array is given its elements are iterated one after another so the interval can vary for the same device the array is cycled when there s more values to send than elements in it telemetry template the simulator is able to create user customizable telemetry with dynamic variables random counter time unique identifier value range to generate a custom piece of telemetry it is required to set the template and optionally variables the template defines how the telemetry looks like having placeholders for variables variables are declared in the telemetry as variablename the optional header defines properties that will be transmitted as message properties variables are declared defining how values in the template will be resolved built in variables the following variables are provided out of the box name description deviceid outputs the device identifier guid a unique identifier value time outputs the utc time in which the telemetry was generated in iso 8601 format localtime outputs the local time in which the telemetry was generated in iso 8601 format ticks outputs the ticks in which the telemetry was generated epoch outputs the time in which the telemetry was generated in epoch format seconds machinename outputs the machine name where the generator is running pod name if running in kubernetes customizable variables customizable variables can be created with the following properties name description name name of the property defines what will be replaced in the template telemetry name random make the value random limited by min and max step if the value is not random will be incremented each time by the value of step randomdouble make the value random and double limited by min and max min for random integer or double values defines it s minimum otherwise will be the starting value max the maximum value generated values defines an array of possible values example on off customlengthstring creates a random string of n bytes provide n as parameter sequence create a sequence of values as defined in values property producing one after the other values can reference other non sequence variables example 1 telemetry with temperature between 23 and 25 and a counter starting from 100 template json deviceid deviceid rand int temp rand double doublevalue ticks ticks counter counter time time variables json name temp random true max 25 min 23 name counter min 100 max 102 name doublevalue randomdouble true min 0 22 max 1 25 output json deviceid sim000001 rand int 23 rand double 0 207759137669466 ticks 637097550115091350 counter 100 time 2019 11 19t10 10 11 5091350z deviceid sim000001 rand int 23 rand double 1 207232427664231 ticks 637097550115952079 counter 101 time 2019 11 19t10 10 11 5952079z deviceid sim000001 rand int 24 rand double 0 992871827638167 ticks 637097550116627320 counter 102 time 2019 11 19t10 10 11 6627320z deviceid sim000001 rand int 24 rand double 0 779288272162327 ticks 637097550117027320 counter 100 time 2019 11 19t10 10 11 7027320z running with docker powershell docker run it e iothubconnectionstring hostname your iothub name azure devices net sharedaccesskeyname device sharedaccesskey your iothub key e template deviceid deviceid rand int temp rand double doublevalue ticks ticks counter counter time time e variables name temp random true max 25 min 23 name counter min 100 max 102 mcr microsoft com oss azure samples azureiot telemetrysimulator calling from powershell powershell docker run it e iothubconnectionstring hostname your iothub name azure devices net sharedaccesskeyname iothubowner sharedaccesskey your iothub key e template deviceid deviceid rand int temp rand double randomdouble ticks ticks counter counter time time engine engine e variables name temp random true max 25 min 23 name counter min 100 max 102 name engine values on off e devicecount 1 e messagecount 3 mcr microsoft com oss azure samples azureiot telemetrysimulator example 2 adding the engine status on or off to the telemetry template json deviceid deviceid rand int temp ticks ticks counter counter time time engine engine variables json name temp random true max 25 min 23 name counter min 100 name engine values on off output json deviceid sim000001 rand int 23 ticks 637097644549666920 counter 100 time 2019 11 19t12 47 34 9666920z engine off deviceid sim000001 rand int 24 ticks 637097644550326096 counter 101 time 2019 11 19t12 47 35 0326096z engine on running with docker bash docker run it e iothubconnectionstring hostname your iothub name azure devices net sharedaccesskeyname device sharedaccesskey your iothub key e template deviceid deviceid rand int temp ticks ticks counter counter time time engine engine e variables name temp random true max 25 min 23 name counter min 100 name engine values on off mcr microsoft com oss azure samples azureiot telemetrysimulator example 3 using a configuration file to customize simulation bash docker run it e iothubconnectionstring hostname your iothub name azure devices net sharedaccesskeyname device sharedaccesskey your iothub key e file config files test4 config multiple internals per device json e devicecount 3 mount type bind source pwd test iottelemetrysimulator test test files target config files readonly mcr microsoft com oss azure samples azureiot telemetrysimulator where the file content is plain variables name devicesequencevalue1 sequence true values counter counter counter counter counter true false counter name device1tags sequence true values producedpartcount producedpartcount producedpartcount producedpartcount producedpartcount downtime downtime producedpartcount name device1downtime values true true true true false name counter intervals sim000001 10000 sim000002 100 200 payloads type template deviceid sim000001 template device deviceid value devicesequencevalue1 tags device1tags type fix deviceid sim000002 value value myfixvalue type template deviceid sim000003 template device deviceid a b value devicesequencevalue1 generating high volume of telemetry in order to generate a constant high volume of messages a single computer might not be enough this section describes two way to run the simulator to ingest a high volume of messages azure container instance azure has container instances which allow the execution of containers with micro billing this repository has a powershell script that creates azure container instances in your subscription requirements are having az cli installed https docs microsoft com en us cli azure install azure cli view azure cli latest to start the simulator in a single container instance powershell simulatorcloudrunner ps1 you will be asked to enter the azure iot hub connection string after that a resource group and one or more container instances will be created the cloud runner can be customized with the following parameters as parametername parametervalue name description location location of the resource group being created default westeurope for a list of locations try az account list locations o table resourcegroup resource group will be created if it does not exist where container instances will be created default iothubsimulator devicecount total amout of devices default 100 containercount total amount of container instances to create the total devicecount will be divided among all instances default 1 messagecount total amount of messages to send per device 0 means no limit causing the container to never end it is your job to stop and delete it default 100 intervals interval in which each device will send messages in milliseconds default 1000 provide a comma separated list in case intervals change after each message template telemetry payload template to be used br default deviceid deviceid temp temp ticks ticks counter counter time time engine engine source machinename payloaddistribution allows the generation of payloads based on a distribution br example fixsize 10 12 template 25 default fix 65 aaaabbbbbccc generates 10 a fix payload of 10 bytes 25 a template generated payload and 65 of the time a fix payload from values aaaabbbbbccc header header properties template to be used br default variables variables used to create the telemetry br default name temp random true max 25 min 23 name counter min 100 name engine values on off cpu amount of cpus allocated to each container instance default 1 0 iothubconnectionstring azure iot hub connection string kubernetes this repository also contains a helm chart to deploy the simulator to a kubernetes cluster an example release with helm for 5000 devices in 5 pods bash helm install sims iot telemetry simulator namespace iotsimulator set iothubconnectionstring hostname xxxx azure devices net sharedaccesskeyname iothubowner sharedaccesskey xxxxxxx set replicacount 5 set devicecount 5000 automation the iottelemtrysimulator automation net core 2 1 library allows you to run the iot telemetry simulator as part of a pipeline or any other automation framework see the automation guide automation md advanced options partitionkey option for event hubs when processing events downstream with a distributed engine it is often more efficient to have related messages in the same partition for example if computing values over windows of events per device in spark having all the events for a given device in the same partition ensures they are received by a single compute node and ordered on the other hand the default round robin behavior has advantages too such as ensuring all partitions have equal load and that the system remains available even if a partition fails therefore it is good to give the user control over the partition key duplicateevery option to duplicate events upstream systems generating events usually offer at least once delivery guarantees given that there is no mechanism in iot hub event hubs for distributed transactions it is always possible for the client not to receive the response from the server after sending a message or crashing before persisting the response and therefore to retry sending an event this results in message duplicates when testing event processing solution it s important to inject such behavior so as to test if how the system reacts to it e g by deduplication the option duplicateevery allows randomly duplicating a fraction of messages e g setting duplicateevery to 1000 will duplicate messages with a probability of 1 1000 for each i e will duplicate on average every 1000th message note that messages may be duplicated more than once a message will be duplicated n times with a probability of 1 duplicateevery n | azure-iot csharp iot-telemetry-simulator | server |
awesome-design-language-system | awesome design language system awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome a curated list of awesome resources about digital design system keywords design system design language framework design styleguide visual language vertical rhythm design grid system contents how to create own how to design system design system design system frontend design system frontend tools tools blog post blog post grid grid other other how to create own ui components handbook https www uiguideline com components the definitive guide to standardize the design code of the ui components based on the 39 most popular reference systems proportio https proportio app tool for creating proportional scales for typography iconography spacing and components in design systems meta design system atomic design http atomicdesign bradfrost com ebook http atomicdesign bradfrost com table of contents blog post https bradfrost com blog post atomic web design design system atlassian design language https atlassian design bbc gel http www bbc co uk gel blackboard http design blackboard com carbon by ibm http carbondesignsystem com code for america http style codeforamerica org cloudscape https cloudscape design by amazon an open source design system for the cloud github https github com cloudscape design components disqus https disqus com pages style guide dropbox https www dropbox com branding facebook brand resource center https en facebookbrand com futurelearn https www futurelearn com pattern library google material design https material google com harmony http harmony intuit com healthcare gov https design cms gov ibm http www ibm com design language ios human interface guidelines apple https developer apple com ios human interface guidelines mailchimp email design guide http mailchimp com resources email design guide mailchimp content style guide http styleguide mailchimp com microsoft design https www microsoft com en us design mixpanel design http mixpanel github io mixpanel common examples style guide new nordnet https www nordnet se brand opentable http brand opentable com photon design system firefox http design firefox com photon welcome html shopify polaris https polaris shopify com uber https brand uber com ubuntu http design ubuntu com apps get started overview universal windows platform guidelines microsoft https developer microsoft com en us windows apps design us gov https standards usa gov wonderbly design system http design system lostmy name gov design http gov design github https github com govdesign https design alfabank ru github https github com alfa laboratory arui feather demo https design alfabank ru components amount https design ivi ru https guides kontur ru consta https consta design by github https github com consta design system figma https www figma com consta vc consta https vc ru gazpromneft 676527 kak dizayn sistema consta pomogaet sozdavat novye interfeysy iz gotovyh komponentov design system frontend ark ui https ark ui com is a headless library for building reusable scalable design systems that works for a wide range of js frameworks github https github com chakra ui ark react vue agnosticui https www agnosticui com accessible react component primitives that also work with vue 3 svelte and angular github https github com agnosticui agnosticui demo https developtodesign com agnosticui examples alta ui http www oracle com webfolder ux middleware alta index html by oracle ant design a ui design language https ant design atlas kit atlassian react https atlaskit atlassian com backpack ui https lonelyplanet github io backpack ui by lonely planet github https github com lonelyplanet backpack ui demo https lonelyplanet github io backpack ui bootstrap http getbootstrap com brainly style guide css http styleguide brainly com carbon by ibm react http react carbondesignsystem com clarity design system https vmware github io clarity co op design manual https coop design manual herokuapp com coursera ui react https webedx spark github io coursera ui element vue 2 http element eleme io ellucian https styleguide elluciancloud com evergreen https evergreen segment com by segment github https github com segmentio evergreen demo https evergreen segment com fabric http dev office com fabric by microsoft for ms office github https github com officedev office ui fabric react demo https developer microsoft com en us fabric components grommet http grommet io bu hewlett packard github https github com grommet grommet demo http grommet io docs components lightning design system https www lightningdesignsystem com by salesforce demo https www lightningdesignsystem com components overview github https github com salesforce ux design system mailchimp pattern library https ux mailchimp com by mailchimp marvel https marvelapp com styleguide by marvel material ui react http www material ui com mantine https mantine dev react components library with native dark theme support demo https ui mantine dev github https github com mantinedev mantine modulz design system https www modulz co an open source design system of styles components templates tools and documentation demo https www modulz co showcase github https github com modulz modulz medium how to construct a design system https medium freecodecamp org how to construct a design system 864adbf2a117 pajamas https design gitlab com by gitlab source https gitlab com gitlab org gitlab services design gitlab com sketch https gitlab com gitlab org gitlab design blob master doc sketch ui kit md files pivotal https styleguide pivotal io plasma design system http plasma guide bu weconnect github https github com weconnect plasma demo https www predix ui com gallery medium plasma design system creating and documenting a product design system https medium com p predix ui https www predix ui com github https github com predixdev demo http primercss io storybook primer http primercss io by github github https github com primer primer demo http primercss io storybook ring ui jetbrains react http www jetbrains org ring ui rizzo https rizzo lonelyplanet com by lonely planet github https github com lonelyplanet rizzo rsuite https rsuitejs com en github https github com rsuite rsuite demo https rsuitejs com en components overview semantic ui https semantic ui com solid http solid buzzfeed com by buzzfeed github https github com buzzfeed solid medium introducing solid https medium com buzzfeed design introducing solid 1c16b1bf4868 storybook design system https storybook design system netlify com by storybook demo https storybook design system netlify com github https github com storybookjs design system ui kit https getuikit com yelp https www yelp com styleguide vkui https vkcom github io vkui styleguide by vkontakte github https github com vkcom vkui android ios ui kit tools fabricator https fbrctr github io fractal http fractal build pattern lab http patternlab io storybook https storybook js org blog post airbnb article http airbnb design building a visual language how we re using component based design https medium com lewisplushumphreys how were using component based design 5f9e3176babb creating a design system language https medium com globoforce design creating a design system 158a2d832551 plasma design system https medium com andrewcouldwell plasma design system 4d63fb6c1afc building a visual language behind the scenes of our airbnb design system https medium com airbnb design building a visual language behind the scenes of our airbnb design system 224748775e4e building a visual language http airbnb design building a visual language a list of style guides brand guidelines and front end frameworks https medium com theearlcarlson a list of style guides brand guidelines and front end frameworks e5bb62db91e5 4 sketch https habrahabr ru post 320990 deep dive css font metrics line height and vertical align http iamvdo me en blog css font metrics line height and vertical align align svg icons to text https blog prototypr io align svg icons to text and say goodbye to font icons d44b3d7b26b4 design systems sprint 0 the silver bullet of product development https medium com marcintreder design systems sprint 0 the silver bullet of product development 8c0ed83bf00d design systems sprint 1 the interface inventory https medium com marcintreder design systems sprint 1 the interface inventory 1f78d376e49a design system sprint 2 one color palette to rule them all https medium com marcintreder design system sprint 2 one color palette to rule them all d0114ed1f659 design system sprint 3 managing the basics https medium com marcintreder design system sprint 3 managing the basics 50ff588cbac8 design system sprint 4 design principles https medium com marcintreder design system sprint 4 design principles 8efb22d8a208 design system sprint 5 managing typography https medium com marcintreder design system sprint 4 managing typography 303e335894ee design system sprint 6 the fastest icons on earth https medium com marcintreder design system sprint 6 the fastest icons on earth bf91c0a47ef9 building a design system for healthcare gov https blog navapbc com building a design system for healthcare gov 20dc1a833ab3 create design system guid https www uxpin com create design system guide grid specifics 001 the 8 point grid https spec fm specifics 8 pt grid sketch workflow 8 point soft grids https medium com sketch app sources 8 point soft grids in sketch e8f1d5ca2cd4 https habrahabr ru company everydaytools blog 319700 intro to the 8 point grid system https medium com built to adapt intro to the 8 point grid system d2573cde8632 google material design metrics keylines article https material io guidelines layout metrics keylines html metrics keylines touch target size intuit s harmony design system http harmony intuit com grid other style guide guide a boilerplate for creating superb style guides https bradfrost github io style guide guide | design-system design-language design-language-framework styleguide design-styleguide visual-language vertical-rhythm grid-system | os |
Algorithmer | uni inje database lab 2019 10 9 | server |
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ospreyh7-rtos-core | ospreyh7 rtos core for ahrs ospreyh7 flight controller rtos core for ahrs https raw githubusercontent com ibrahimcahit aldebaran rtos core main stm32cubeide 20files 20220109 235230 jpg https raw githubusercontent com ibrahimcahit aldebaran rtos core main stm32cubeide 20files uart debug png ospreyh7 flight control board has been designed purely for hobby and experimental purposes it has no commercial purpose or objective the card itself its designs or software is not available for sale ospreyh7 board https raw githubusercontent com ibrahimcahit ospreyh7 rtos core main core 20libraries aldebaran pcb png | os |
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Natural-Language-Processing-Stanford-Coursera | natural language processing assignment from stanford nlp on coursera by yichen gong br br current progress hw status task hw1 complete implemented regular expression to filter all phone number and email address hw2 complete implemented laplace unigram bigram stupid backoff kneser ney smoothing language model to complete the autocorrect task reached expected 25 accuracy hw3 complete implemented naive bayes algorithm to predict the sentiment of movie reviews reached expected 80 accuracy hw4 partially complete implemented boolean naive bayes and sentiment lexicon to predict movie review sentiments hw5 complete extracted the features from names implemented the word shape generalization reached the expected f1 score 0 85 br hw6 complete implemented the propabilitic context free grammar features expected perplexity lower than 400 achieved 368 br hw7 lose points implemented the cky algorithm and implemented the vertical markovization br hw8 complete implemented the inverted index boolean retrieval system tf idf scoring and cosine similarity algorithm pass all tests br contact me yg1053 nyu edu | ai |
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DenseNet-NLP | very deep convolutional networks for natural language processing in tensorflow this is the densenet implementation of the paper do convolutional networks need to be deep for text classification in tensorflow we study in the paper the importance of depth in convolutional models for text classification either when character or word inputs are considered we show on 5 standard text classification and sentiment analysis tasks that deep models indeed give better performances than shallow networks when the text input is represented as a sequence of characters however a simple shallow and wide network outperforms deep models such as densenet with word inputs our shallow word model further establishes new state of the art performances on two datasets yelp binary 95 9 and yelp full 64 9 paper hoa t le christophe cerisara alexandre denis do convolutional networks need to be deep for text classification association for the advancement of artificial intelligence 2018 aaai 18 workshop on affective content analysis https arxiv org abs 1707 04108 article dblp journals corr lecd17 author hoa t le and christophe cerisara and alexandre denis title do convolutional networks need to be deep for text classification journal corr year 2017 p align center img src https github com lethienhoa very deep convolutional networks for natural language processing blob master selection 134 png raw true p results p align center img src https github com lethienhoa very deep convolutional networks for natural language processing blob master selection 135 png raw true p p align center img src https github com lethienhoa very deep convolutional networks for natural language processing blob master selection 136 png raw true p reference source codes https github com dennybritz cnn text classification tf | ai |
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thesis | unit tests https github com nlindenau thesis actions workflows run pytest yml badge svg https github com nlindenau thesis actions workflows run pytest yml about the project rvs bot returns the nutritional value of your meal based on the ingredients this project is part of my master s thesis at centria uas | cloud |
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ndarray-vision | ndarray vision build status https travis ci org xd009642 ndarray vision svg branch master https travis ci org xd009642 ndarray vision license mit https img shields io badge license mit yellow svg https opensource org licenses mit coverage status https coveralls io repos github xd009642 ndarray vision badge svg branch master https coveralls io github xd009642 ndarray vision branch master this project is a computer vision library built on top of ndarray this project is a work in progress basic image encoding decoding and processing are currently implemented see the examples and tests for basic usage features conversions between grayscale rgb hsv and ciexyz image convolutions and common kernels box linear gaussian laplace median filtering sobel operator canny edge detection histogram equalisation thresholding basic mean otsu encoding and decoding ppm binary or plaintext performance not a lot of work has been put towards performance yet but a rudimentary benchmarking project exists here https github com rust cv ndarray vision benchmarking for comparative benchmarks against other image processing libraries in rust | ndarray computer-vision image-processing hacktoberfest | ai |
Andreas-Wahl | andreas wahl welzheim de information technology communication technology electronics innovative competent efficient welcome to my github profile i m here to collaborate with other developers and friends on exciting and interesting hardware and software projects in addition i will occasionally publish some of my own projects here private website you can find more information about me my professional activity and career as well as my hobbies on my private website https www andreas wahl de social media you can also find and reach me at the following social media linkedin https www linkedin com in andreas wahl welzheim xing https www xing com profile andreas wahl14 facebook https www facebook com andywhm twitter https twitter com andywhm andreas wahl andreas wahl is a special repository because its readme md this file appears on your github profile here are some ideas to get you started i m currently working on i m currently learning i m looking to collaborate on i m looking for help with ask me about how to reach me pronouns fun fact | server |
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sql-challenge | sql challenge employee database a mystery in two parts background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data engineering 3 data analysis note you may hear the term data modeling in place of data engineering but they are the same terms data engineering is the more modern wording instead of data modeling before you begin 1 create a new repository for this project called sql challenge do not add this homework to an existing repository 2 clone the new repository to your computer 3 inside your local git repository create a directory for the sql challenge use a folder name to correspond to the challenge employeesql 4 add your files to this folder 5 push the above changes to github instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes to 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 note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name sex and salary 2 list first name last name and hire date for employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name 4 list the department of each employee with the following information employee number last name first name and department name 5 list first name last name and sex for employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name epilogue evidence in hand you march into your boss s office and present the visualization with a sly grin your boss thanks you for your work on your way out of the office you hear the words search your id number you look down at your badge to see that your employee id number is 499942 | employee-number data-engineering csvs data-analysis foreign-keys sql | server |
Top-Rentals-Cineplex | top rentals cineplex best movies 1614634680 https user images githubusercontent com 70767722 140617292 35f799d5 434d 4438 a6b9 d4449b092a5f jpeg motivation emotions help us feel human again and connect us to each other by watching the lives of different characters and feel everything they feel through 24 frames per seconds of the movies in this way movies stir up your emotions and is one of the great things about them i personally a movie lover and sometimes i call myself as a cinephile who is passionate about movies and want to know a lot about them moreover a cinephile should be an educated film consumer with the tool kit to distinguish average films from outstanding ones depending on some particular metrics with that motivation i create my data engineering capstone which we can quickly see the top 36 movie rental from cineplex website along with the imdb rating tomatometer and audience score from rotten tomatoes in addition dedicating to the nerdy movie lover we can look up the cast in detail with the curated data sets from imdb idea of the project image https user images githubusercontent com 70767722 173257293 12300058 23d5 419e a4c0 454f4434d036 png when we click into the top rentals in cineplex website we might be confused and indecisive before choosing the right movie that you like this is when the project top rentals cineplex comes in handy it can quickly show you the essential information about the movie you want to rent imdb rating tomato meter audience score synopsis top critics from rotten tomato for my more demo visualizations please click this link below toprentalcineplex my canva site https toprentalcineplex my canva site project etl diagram image https user images githubusercontent com 70767722 173257308 936bbe43 1d55 4c6f afa9 f879006c4a90 png events the project data is collected scraped from multiple sources by using selenium webdriver and api 1 top rentals cineplex is the source to be scraped top 36 movie rentals title on cineplex website 2 imdb com official subsets of imdb data that are available for personal and commercial use imdb data sets contain imdb id which is used as primary foreign key to link tables following downloaded list br title basics tsv gz contains the essential information for the movie titles title crew tsv gz contains the director and writer information for tall the titles title principals tsv gz contains the principal cast crew for titles title ratings tsv gz contains the imdb rating and votes information for titles name basics tsv gz contains the following information for names actors actresses directors writers etc 3 themoviedb org is a community built movie and tv database which has api available for everyone to use i personally use their api to cumulate the movies synopsis along with the imdb id for the top movie rentals from cineplex 4 rottentomatoes com is used to achieve data for corresponding top 36 movie rentals title the top critics from credible reviewers or audience s review for titles that don t have many credible reviewers tomatometer and audience score cloud computing solution i use azure databricks which is a fast easy and collaborative apache spark based big data analytics service designed for data science and data engineering i created 4 notebooks in databricks and incorporated some customized libraries br imdb datasets downloader br automates the official data sets downloading process from imdb website using selenium br extracts gz files and convert tsv files to parquet files using pyspark br saves files to azure blob storage abs as data warehouse br top rentals cineplex scrapper br scrapes top 36 movie rentals titles on cineplex website save as parquet file to abs using pyspark br applies slowly changing dimension type 2 for table structure that stores and manages the current and historical data over time in terms of the top titles orders e g top 1 2 3 and the data is current or not current with date time br themoviedb and rottentomatoes data scraper br scrapes synopsis from themoviedb org using api and top critics from rottentomatoes com using selenium br saves table as parquet file to abs using pyspark br top rental rating from imdb and rotten tomatoes data br extracts imdb rating from imdb data set and merge with tomatometer and audience score from rottentomatoes com into 1 table with corresponding imdb id of the top 36 movie rentals using pyspark br saves as parquet file to abs br top rentals cineplex s er diagram image https user images githubusercontent com 70767722 173257470 7fcd39c8 7a76 4519 97d4 b4bf0e0967f6 png data management azure databricks workflows is used to manage the scheduling jobs which automate the data scraping tasks and saving data into data warehouse azure blob storage screen shot 2022 06 12 at 7 13 30 pm https user images githubusercontent com 70767722 173257507 4bd188dc f4a4 419c a87a c99c2ef51478 png demo data output using pyspark to run the query for the fact table python top rental cineplex join top rental rating top rental cineplex imdb id top rental rating imdb id how inner join synopsis table synopsis table imdb id top rental cineplex imdb id how inner filter top rental cineplex is current 1 select top rental cineplex title top rental cineplex ordering synopsis table synopsis top rental rating imdb rating top rental rating tomato meter top rental rating audience score orderby top rental cineplex ordering show screen shot 2022 06 12 at 7 15 10 pm https user images githubusercontent com 70767722 173257560 c2a06e45 327d 4abf 87fd 4202e4da3b86 png | api-client azure-databricks azure-storage data-engineering selenium-webdriver slowly-changing-dimensions | cloud |
avalon | avalon get a node running dependencies mongodb https mongodb com nodejs https nodejs org en download v14 16 lts ntpd https linux die net man 8 ntpd or any ntp alternative for your system ntpd comes pre installed on most linux distributions install and run an avalon node for linux npm install to install nodejs dependencies chmod x scripts start sh scripts start sh environment variables the start sh shows the list of available environment variables you can set to make avalon behave slightly differently from the default install install and run an avalon node for windows npm install to install nodejs dependencies get your own keys with node src cli js keypair save your keys add your keys to scripts start bat define the path to your directory in scripts start bat run scripts start bat note to restore a genesis zip file you may need to download the mongo databasetools https www mongodb com try download database tools tck docs databasetools and put the mongorestore exe binary into your main directory syncing your node doc syncing your node md become a leader and produce blocks doc leader 101 md debian 10 quick install procedure doc debian 10 md get helped we have a discord channel dedicated to node owners aka leaders where you can get support to get set up join discorg gg dtube https discord gg dtube and go to dtube chain leader candidates using avalon once you have a node running there are many ways to interact with avalon with the cli tool you can use the cli tool to transact with avalon simply try node src cli help or node src cli command help for a full help using javalon javalon https www npmjs com package javalon is the javascript wrapper for avalon s api working on both browser and nodejs http api avalon s api uses 100 json the get calls will allow you to fetch the public information which is already available through the d tube ui examples account data account username i e https avalon d tube account rt international video data content username link i e https avalon d tube content rongibsonchannel qmdjvmdetttey1cjtdbtjuairkmp6h364dv4n7fswgpnph full list of api endpoints https docs google com spreadsheets d 1orohjrdq5v5oktchijtuoeyrztujvxtzcynyt ysvhw edit usp drive web ouid 109732502499946497195 https docs google com spreadsheets d 1orohjrdq5v5oktchijtuoeyrztujvxtzcynyt ysvhw edit usp drive web ouid 109732502499946497195 this lists all the available api endpoints for avalon we also have recommended security practises if you want to open your node s api to the world you can do it easily with nginx and avalon nginx config https github com dtube avalon nginx config sending transactions to the network post transact once you have an account and balance if you don t you can create one on https signup d tube https signup d tube your account will start generating bandwidth and voting power respectively the bw and vt fields in your account data you can consume those resources by transacting every transaction will have a bandwidth cost calculated based on the number of bytes required for the storage of your transaction inside a block certain transaction types will require you to spend voting power such as publishing a content voting or tagging a content to transact you need to use the transact post call of the avalon api all the examples here are for the cli tool but the same can be achieved with javalon https npmjs org javalon in javascript necessary for all transactions key your private key use k mykeyhere to use a plain text key or use f file json to use a key inside a file to sign this will prevent your key from showing on the screen too much user your username vote for a leader target the node owner to approve node src cli js vote leader k key m user target alice votes for bob as a leader node src cli js vote leader k 5dpwdjqtvmuykhimmzxthfkttpsnlzjjpbntkgnnjpaf m alice bob unvote a leader target the node owner to approve node src cli js vote leader k key m user target charlie does not want to vote for daniel as a leader anymore node src cli js unvote leader f charlie key txt m charlie daniel transfer tokens receiver username of the receiver of the transfer amount number of tokens to transfer to the receiver warning 1 dtc in ui 100 tokens memo arbitrary short text content node src cli js transfer k bob key m user receiver amount memo bob sends 50 dtc to charles node src cli js transfer k hkubq5ypejwvspt8qgz8pkpgwkdrmn3xecd4asn3anb3 m bob charles 50 thank you add a post commenting link a short string to be used as the index of the content parent author the username of the author of the parent post parent link the link of the parent post json arbitrary json input example string aye array 1 2 3 tag arbitrary short text content weight the number of vote tokens to spend on this vote node src cli js comment k key m user link parent author parent link json vote a post link the link of the post to vote on author the username of the author to vote on weight the number of vote tokens to spend on this vote tag arbitrary short text content node src cli js vote k key m user link author weight tag edit your user json object json arbitrary json input example string aye array 1 2 3 node src cli js profile k key m user json follow a user target the user to follow node src cli js follow k key m user target unfollow a user target the user to unfollow node src cli js unfollow k key m user target signing a raw transaction to create a transaction and export it to a file you can use the sign cli tool node src cli js sign priv key user tx tmptx json for example to approve a node owner and publishing it only 5 seconds later node src cli js sign k 4l1c3553kretk3y m alice type 1 data target miner1 tmptx json sleep 5 curl h content type application json data tmptx json http localhost 3001 transact other post calls mine block will force the node to try to produce a block even if it s unscheduled useful for block 1 and working on development curl http localhost 3001 mineblock add peer manually force connection to a peer without having to restart the node curl h content type application json data peer ws localhost 6001 http localhost 3001 addpeer data storage mongodb avalon saves the state of the chain into mongodb after each block you can easily query mongodb directly to get any data you want that wouldn t be provided by the api itself mongo db name db accounts findone name master db blocks findone id 0 however be sure not to write to any collection used by avalon in this database namely the accounts blocks and contents if you do your node will irremediably fork sooner or later elastic search avalon can also copy the accounts and contents into an elastic search database with monstache https github com rwynn monstache a configuration file for monstache is provided in the root of this repository once running you can do text queries on accounts or contents like so search contents curl http localhost 9200 avalon contents search q football search accounts curl http localhost 9200 avalon accounts search q satoshi please refer to elastic search documentation for more complex queries d tube runs a public elastic search mirror of the current testnet on https search d tube | blockchain dpos social-network | blockchain |
Xamarin-Cross-Platform-Mobile-Application-Development | xamarin cross platform mobile application development this is the code repository for xamarin cross platform mobile application development published by packt it contains all the necessary code files what you will learn share c code across platforms and call native objective c or java libraries from c submit your app to the apple app store and google play use the out of the box services to support third party libraries find out how to get feedback while your application is used by your users create shared data access using a local sqlite database and a rest service test and monitor your applications gain memory management skills to avoid memory leaks and premature code cycles while decreasing the memory print of your applications integrate network resources with cross platform applications design and implement eye catching and reusable ui components without compromising nativity in mobile applications 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 9781787120129 https packt link free ebook 9781787120129 a p | front_end |
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67442-app | virtual caddie 67 442 ios engineering this goal of this app is to help golfers improve their play by giving them acces to data driven insights into their game as a golfer enters a new round they are optionally prompted to describe several key pieces of information about each stroke using geolocation to track the location of each shot as well as these pieces of information that the user enters virtual caddie is able to obtain metrics about the golfer s performance currently only things such as distance straight flight percentage and perfect contact percentage are displayed about each club this enables the user to know exactly what to work on next time they head to the driving range virtual caddie aims to function as a professional caddie that fits in your pocket moving forward the plan is for virtual caddie to leverage machine learning to use information about the user in addition to crowdsourced data about other users with similar skill levels to give club and strategy suggestions on the course | os |
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llm4pddl | llm4pddl code for pddl planning with pretrained large language models https openreview net pdf id 1qmmub4zfl silver et al 2022 neurips foundation models for decision making workshop requirements python 3 8 tested on macos catalina and ubuntu 18 04 installation first time highly recommended make a virtual environment virtualenv venv source venv bin activate clone this repository with submodules git clone recursive https github com learning and intelligent systems llm4pddl git run pip install e develop to install the main dependencies for development if you encounter issues with the transformers dependency on macos we recommend doing the following 1 run curl proto https tlsv1 2 ssf https sh rustup rs sh in terminal 2 restart the terminal 3 run pip install transformers if problems persist see https github com huggingface transformers issues 2831 run pip install e llm4pddl third party pyperplan to install our fork of pyperplan example command python llm4pddl main py approach llm standard env pyperplan gripper num train tasks 2 num eval tasks 10 seed 0 debug see llm4pddl flags py for additional flags instructions for contributing after pulling the latest changes also run git submodule update init recursive to make sure that you have any changes to the pyperplan submodule you can t push directly to master make a new branch in this repository don t use a fork since that will not properly trigger the checks when you make a pr when your code is ready for review make a pr and request reviews from the appropriate people to merge a pr you need at least one approval and you have to pass the 4 checks defined in github workflows llm4pddl yml which you can run locally as follows pytest s tests cov config coveragerc cov llm4pddl cov tests cov fail under 100 cov report term missing skip covered durations 0 mypy pytest pylint m pylint pylint rcfile llm4pddl pylintrc run autoformat sh the first one is the unit testing check which verifies that unit tests pass and that code is adequately covered the 100 means that all lines in every file must be covered the second one is the static typing check which uses mypy to verify type annotations the third one is the linter check which runs pylint with the custom config file llm4pddl pylintrc in the root of this repository feel free to edit this file as necessary the fourth one is the autoformatting check which uses the custom config files style yapf and isort cfg in the root of this repository citation inproceedings silver2022pddl title pddl planning with pretrained large language models author silver tom and hariprasad varun and shuttleworth reece s and kumar nishanth and lozano p e rez tom a s and kaelbling leslie pack booktitle neurips 2022 foundation models for decision making workshop year 2022 | ai |
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Swiftjective-C | p align center img src assets images logo png raw true alt logo p swiftjective c this repository houses all the source code for swiftjective c https www swiftjectivec com it s been made available for educational and learning purposes in addition issues and pull requests for any of the articles are welcome downloading and running locally swiftjective c is built using jekyll download a copy of this repository locally and then use jekyll to get things going download jekyll gem install bundler jekyll grab this repo git clone git github com dreaminginbinary swiftjective c git cd swiftjective c spin up local web server bundle exec jekyll serve then open up your browser and swiftjective c should be available at http localhost 4000 get in touch please reach out on twitter https www twitter com jordanmorgan10 | os |
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Natural-Language-Processing-Zero-to-Hero | natural language processing zero to hero step by step road map to learn and understand natural language processing | nlp nltk-python python3 chatbot natural-language-processing bert | ai |
FreeRTOS-Community-Supported-Demos | freertos community supported demos this repository contains demos for freertos ports supported by freertos community members follow these steps https github com freertos freertos blob main freertos demo thirdparty template readme md to contribute a demo to this repository license this repository contains multiple directories each individually licensed please see the license file in each directory | os |
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Kodluyoruz-MGD-Bootcamp-Homework2 | kodluyoruz mgd bootcamp homework2 kodluyoruz mobile game development bootcamp 2nd homework br a simple shooting game the game consists of only one scene it uses the state system and works by toggling objects in the same scene the game contains 2 states pregamestate and playgamestate the player and spawner are disabled on the waiting screen pregamestate conversely the waiting screen is disabled on the gameplay screen playgamestate the player is expected to press the space key to start the game and the game state is switched from pregamestate to playgamestate the player can move with wasd keys or the arrow keys and shoot with the left mouse button the player can also shoots continuously by holding down the left mouse button there are spawners in 4 corners of the game and random enemies are instantiated from these spawners when the game starts and enemies move towards the player as soon as they are spawned the player has 100 health and takes 20 damage if enemies hit him which means the player dies in 5 hits enemies have 100 health and the player s bullets deals 25 damage each which means enemies die in 4 shoots the player earns 3 score points for each enemy killed when the player dies game state switched from playgamestate to pregamestate and the game resets the health and score | csharp unity unity3d unity-3d unity3d-games | front_end |
Achain | what is achain achain is a decentralized blockchain based on bts1 0 which integrates lua virtual machine based smart contracts it is also a software platform designed to help coordinate voluntary free market operations amongst a set of social actors achain is making efforts to build up a high performance decentralized enterprise blockchain platform to develop business blockchain applications to eliminate cognitive fear of enterprises facing blockchain technology or corresponding applications and to make the blockchain visualized and configurable replicas of the state machine are kept consistent using the result delegated proof of stake distributed consensus protocol for more information about achain please read the whitepaper achain white paper https www achain com achain 20whitepaper 202 0 en pdf supported operating systems achain currently supports the following operating systems 1 windows 7 and higher 2 centos rhel oracle linux 7 2 1511 7 3 1611 7 4 3 ubuntu 16 04 17 10 4 macos darwin 10 13 5 fedora 27 25 building achain different platforms have different build instructions windows https github com achain dev achain blob master build win32 md linux https github com achain dev achain blob master linux readme md how to deploy a private chain deploy private chain https github com achain dev achain blob master deploy private chain md integration instructions for exchanges achain exchange https github com achain dev achain exchange how to use the rpc api rpc api https www achain com help en rpc html contributing the source code can always be found at the achain github repository https github com achain dev achain master official achain releases are tagged from here this should only change for a new release storage optimization testing which version is better for smart contracts technical documentation is available at the achain github wiki https github com achain dev achain wiki support bugs can be reported directly to the achain issue tracker https github com achain dev achain issues technical support can be obtained via the achaintalk technical support forum https forum achain com license the achain project is licensed under the mit license license | achain blockchain exchange windows linux achain-github | blockchain |
Notes-App | notes app since you will be learning a pletheora of information during capital one s software engineering summit let s build an ios app for taking notes while building this app we ll cover the basics of ios and xcode content in this repo you ll find the end result of the notes app please see stepbystepinstructions md stepbystepinstructions md for detailed instructions on building this app during the workshop you can use the instructions to follow along bonus functionality if you re feeling up to it here is some additionaly functionality to work on add persistence as you ve probably noticed by now when you close the app and re open it all of your data is gone in order to save all your data you ll need to add persistence there are many options available to accomplish this here are some options nsuserdefaults https developer apple com documentation foundation nsuserdefaults core data https developer apple com library archive documentation cocoa conceptual coredata index html realm https realm io docs swift latest firebase https firebase google com docs ios setup add ui constraints currently the app only works for certain screen sizes make it so the app looks great on any iphone or ipad in any orientation getting started with auto layout https www raywenderlich com 443 auto layout tutorial in ios 11 getting started understanding auto layout https developer apple com library archive documentation userexperience conceptual autolayoutpg index html requirements this project has been updated to work with ios 12 xcode 10 1 and swift 4 2 | swift xcode ios | os |
aurora-design-system | aurora design system introduction we created aurora design system to standardize the visual language and user experience of the digital enablement s gctools online applications and tools aurora was built by a multi disciplinary team of developers designers ux researchers writers and data scientists combining the expertise of all of these roles allowed us to create a design system with a wide range of elements work for aurora was done entirely in the open we wanted to keep the spirit of government while providing the quality and fun seen in the private sector using aurora is complementary to the web experience toolkit wet canada ca style guide the federal identity policy fip and wcag 2 1 aurora is available for anyone who wants to use it how to use every element in aurora design system has a design component code and documentation by downloading our ui kit for adobe illustrator you have access to all of the components sketches in addition to the code provided on the site we recommend that while building a new product you begin by following the system as closely as possible then you can adapt certain elements i e colours language style icons to suit your own product or brand packages aurora css a npm component for aurora design system css installation npm install gctools components aurora css to use in your project enter the following import gctools components aurora css css aurora min css aurora ds a npm component for aurora design system css and js files installation npm install gctools components aurora ds to use in your project enter the following import gctools components aurora ds css aurora min css design the design files folder includes adobe illustrator ai files for the ui kit the aurora logo and the aurora banner contributing this repository is for all issues and comments related to the aurora development packages individual components and design files you can submit contributions related to aurora components via issues or pull requests in this repository if you wish to contribute new components or designs please contact sierra duffey tbs sct gc ca mailto sierra duffey tbs sct gc ca for issues or feedback related to the aurora documentation website please refer to the aurora website repository https github com gctools outilsgc aurora website syst me de conception aurora introduction nous avons cr le syst me de conception aurora pour normaliser le langage visuel et l exp rience utilisateur des applications et des outils en ligne de l habilitation num rique outilsgc aurora a t con u par une quipe multidisciplinaire de d veloppeurs de concepteurs de chercheurs sur l exp rience utilisateur de r dacteurs et de scientifiques des donn es la combinaison de l expertise de toutes ces personnes nous a permis de cr er un syst me de conception avec un vaste ventail d l ments le travail pour aurora s est fait enti rement dans l environnement ouvert nous voulions garder l esprit du gouvernement tout en offrant la qualit et la convivialit caract ristiques du secteur priv l utilisation d aurora est compl mentaire la bo te outils de l exp rience web boew au guide de r daction canada ca la politique sur l image de marque et aux wcag 2 1 aurora est disponible pour tous ceux qui veulent l utiliser comment l utiliser chaque l ment du syst me de conception aurora a une composante de conception un code et de la documentation en t l chargeant notre trousse de l iu pour adobe illustrator vous avez acc s toutes les esquisses des composants en plus du code fourni sur le site lors de l laboration d un nouveau produit nous vous conseillons de commencer par suivre le syst me la lettre dans la mesure du possible puis d adapter certains l ments p ex couleurs niveau de langue ic nes votre produit ou votre marque progiciels aurora css une composante npm pour le syst me de conception aurora css installation npm install gctools components aurora css pour l utiliser dans le cadre de votre projet saisissez ce qui suit import gctools components aurora css css aurora min css aurora ds une composante npm pour les fichiers css et js du syst me de conception aurora installation npm install gctools components aurora ds pour l utiliser dans le cadre de votre projet saisissez ce qui suit import gctools components aurora ds css aurora min css conception le dossier des fichiers de conception comprend les fichiers adobe illustrator ai pour la trousse d iu le logo aurora et la banni re aurora contribution ce r f rentiel est destin toutes les questions et tous les commentaires li s aux progiciels de d veloppement aurora aux l ments individuels et aux fichiers de conception vous pouvez pr senter des contributions li es aux composantes d aurora au moyen de probl mes ou demande de fusion de branches dans ce r pertoire si vous souhaitez ajouter de nouvelles composantes ou de nouvelles conceptions veuillez communiquer avec sierra duffey tbs sct gc ca mailto sierra duffey tbs sct gc ca pour des questions ou des commentaires concernant le site web de documentation aurora veuillez consulter le r f rentiel du site web aurora https github com gctools outilsgc aurora website | os |
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csd-340 | web 340 web development with html and css bellevue university http bellevue edu bellevue university is a private non profit university located in bellevue nebraska united states address 1000 galvin rd s bellevue nebraska 68005 directions https www google com maps dir bellevue university 41 1509562 95 9896355 12z data 4m8 4m7 1m0 1m5 1m1 1s0x8793886a86ca807f 0x838e857240d175eb 2m2 1d 95 9195956 2d41 1509774 google maps bachelor of science in software development degree https www bellevue edu degrees bachelor software development bs designed by developers for developers course description this course examines the fundamentals specific to web development topics will include web standards accessibility usability and the markup languages which serve as the foundation for web development hypertext markup language 5 html5 cascading style sheets css and extensible markup language xml students work with these languages at a basic level learning the essential structures coding conventions and best practices associated with the effective use of html5 and css in modern web development environments prerequisite none repository overview carefully read the assigned chapters and narrative i ve included under each assignment most assignments have runnable sample code so you can visually see the concept in action approach every week from top to bottom and do not move to the next assignment without fully understanding the previous one clone bash git clone https github com buwebdev csd 340 git cd csd 340 | front_end |
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app-elections-backend | app elections v0 1 2 backend license mit https img shields io badge license mit blue svg https opensource org licenses mit warning this project is in alpha stage some breaking change may appear support this project your contribution can keep this project alive setup requirements node js v12 npm v6 how to build npm run build for yarn users yarn run build run production mode npm run start for yarn users yarn run start run development mode npm run dev for yarn users yarn run dev | elections polling | server |
det_public | det public database engineering technology public repo | server |
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devui | devui a complete front end solution for learners | front_end |
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physio-rom | physio rom computer vision ai range of motion reports what is physio rom physio rom stands for automated range of motion rom calculation for physiotherapists and is an application that helps physiotherapists and their patient to understand the range of motion of their limbs around a joint this is important for people that have hurt their joints or for some other reason and cannot use their limbs to the full extent such as after a fall a stroke or an accident the project includes a fully featured cloud based api which wraps state of the art real time keypoint dectection methods into an accessible and open source platform a href http www youtube com watch feature player embedded v 6p6oaiiskdm target blank img src http img youtube com vi 6p6oaiiskdm 0 jpg alt it waves back width 480 height 360 border 10 a installation the system consists of 1 a web front end that captures the video 2 a python based backend system to where the video is uploaded and pre processed 3 a c based computer vision system that calculates the limbs and the angles on the limbs from the pre processed video 4 a web service that serves a json file with the results 5 a web page that serves the video with the results rendered and overlayed on top of the video the web front end through which the video is captured can be found in the templates directory the python based backend system is run via the main py python program auto setup is set up via the python virtualenv install python virtualenv and run the following make env env bin activate make getreqs the c based computer vision system requires the installation and compilation of the openpose software suite this is detailed in the openpose documentation https github com cmu perceptual computing lab openpose tree master doc the dependencies for this package are listed in requirements txt usage server usage start the flask server by running python main py the server side implementation has thus far only been tested on unix systems we cannot guarantee certain linux distros may require running the code in sudo because in browser web camera streaming only functions in web ports apparently security reasons api workflow index page hosts a simple video capture framework which includes a graphical interface to the api host uploader dedicated video update page gui and interface to cli post get upload of video host upload host uploader the uploader returns a unique string called runid in json object this is the your key to your data in the api the uploaded videos are provcessed in ffmpeg and can be in any standard format process video passes uploaded video to openpose and returns the features of intrest in json format host process runid runid view video shows a player which loops the uploaded video along with identified pose host airom playvideo runid runid fps view framerate annotate video annotates the previous video with the angle of the specified joint where the number corresponds to a particular joint host airom getoverlay runid runid joint joint label joints labelled by index in the following list right elbow left elbow right shoulder left shoulder right knee left knee right hip left hip watch annotated video shows a player which loopes the uploaded video with both identified pose and annotated angle host airom playoverlay runid runid fps view framerate generate report generate a report for a given joint host airom getreport runid runid report report template futher api tools host airom getframe runid runid framenum frame index gets frame at index host airom getname runid runid framenum frame index gets frame by name further detailed usage information is included in the doc folder third party dependencies third party dependencies that have been used or modified for this project that have their own software licence include software copyright licence link use openpose see repo pose detection caffe bsd ml framework numpy bsd faster searching flask bsd web serving copyright and licence copyright c 2017 aqeel akber and collaborators all rights reserved you should have received a copy of the licence licence with this file if not please write to aqeel akber aqeel akber gmail com this licence applies to all files held under the copyright of this project only and not third party dependencies if you believe that anything in this project infringes your copyright please contact us with your information and a detailed description of your intellectual property including the specific place in this project where you believe your intellectual property is being infringed contributing those interested see contributing contributing org for guidelines | computer-vision openpose physiotherapy healthcare video-processing | ai |
blog | blog navigation https github com carloscn blog blob main readme md linux kernel 0x01 linuxkernel https github com carloscn blog issues 64 2022 7 5 0x02 linuxkernel smp https github com carloscn blog issues 66 todo 0x21 linuxkernel https github com carloscn blog issues 69 2022 8 9 0x22 linuxkernel https github com carloscn blog issues 68 2022 7 27 0x23 linuxkernel https github com carloscn blog issues 70 2022 7 29 0x24 linuxkernel https github com carloscn blog issues 8 2022 8 7 0x25 linuxkernel https github com carloscn blog issues 71 todo 0x26 linuxkernel https github com carloscn blog issues 72 2022 9 16 0x27 linuxkernel https github com carloscn blog issues 73 2022 9 20 0x28 linuxkernel https github com carloscn blog issues 74 todo 0x29 linuxkernel https github com carloscn blog issues 75 todo 0x30 linuxkernel https github com carloscn blog issues 76 todo 0x31 linuxkernel slab https github com carloscn blog issues 77 2022 9 1 0x32 linuxkernel https github com carloscn blog 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initramfs https github com carloscn blog issues 173 2023 01 15 arm bin utils 01 elf https github com carloscn blog issues 5 gcc https github com carloscn blog issues q is 3aissue is 3aopen label 3agcc linux https github com carloscn blog issues q is 3aissue is 3aopen label 3alinux 02 elf https github com carloscn blog issues 6 gcc https github com carloscn blog issues q is 3aissue is 3aopen label 3agcc linux https github com carloscn blog issues q is 3aissue is 3aopen label 3alinux 03 elf https github com carloscn blog issues 11 compiler https github com carloscn blog issues q is 3aissue is 3aopen label 3acompiler gcc https github com carloscn blog issues q is 3aissue is 3aopen label 3agcc linux https github com carloscn blog issues q is 3aissue is 3aopen label 3alinux 04 elf https github com carloscn blog issues 18 2022 4 3 2022 4 4 05 elf https github com carloscn blog issues 21 2022 4 8 2022 4 9 06 linux https github com carloscn blog issues 48 2022 4 15 07 elf armv8 https github 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gist github com carloscn 7774739514033fa7a9b5407f6fb880d8 starting with jlink debugger or qemu https github com carloscn blog issues 4 embedded https github com carloscn blog issues q is 3aissue is 3aopen label 3aembedded gdb https github com carloscn blog issues q is 3aissue is 3aopen label 3agdb using gdb and gdb multi command note https gist github com carloscn f628bb08453cdda3a33de58caa06ba1f linux 2451 https github com carloscn blog issues 27 2017 12 7 omapl138 sd linux https github com carloscn blog issues 30 2018 1 6 omapl linux3 3 https github com carloscn blog issues 31 2018 6 7 dsp arm omapl138 https github com carloscn blog issues 33 2018 6 8 omapl ad9833 notify demo https github com carloscn blog issues 34 2018 6 8 zynq linux linaro sd https github com carloscn blog issues 39 2018 8 27 embedded nxp imx6 initialization https github com carloscn blog issues 172 2023 01 14 optee 01 optee os https github com carloscn blog issues 91 2022 10 1 02 optee os trustzone atf https 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https github com carloscn blog issues 137 2022 10 23 3 1 security aes https github com carloscn blog issues 138 2022 10 25 3 2 security mac cmac hmac https github com carloscn blog issues 144 2022 10 27 3 3 security aead https github com carloscn blog issues 145 2022 11 27 8 0 security pkcs7 cms embedded https github com carloscn blog issues 186 2023 05 17 9 0 security pkcs11 hsm embedded https github com carloscn blog issues 181 2023 04 13 openssl evp to implement rsa and sm2 en dec sign verify https github com carloscn blog issues 44 2020 9 2 mac silicon m1 openssl https github com carloscn blog issues 45 2021 2 25 how to compile mbedtls library on linux mac windows https github com carloscn blog issues 46 rust system programming 02 sys rust io https github com carloscn blog issues 190 2023 05 24 dsp dsp f2812 cmd https github com carloscn blog issues 157 2014 04 22 dsp f2812 https github com carloscn blog issues 158 2014 04 23 dsp f2812 gpio https github com carloscn blog issues 159 2014 04 24 dsp f2812 cpu https github com carloscn blog issues 160 2014 04 25 dsp f2812 https github com carloscn blog issues 161 2014 05 01 dsp f2812 ev https github com carloscn blog issues 162 2014 05 02 dsp f2812 adc https github com carloscn blog issues 163 2014 05 03 dsp f2812 sci https github com carloscn blog issues 164 2014 05 04 nxp imx6 https gist github com carloscn a533af3bc5d769fc07a2c301a61f5802 2022 9 17 compiler optimization and the volatile keyword https gist github com carloscn 354c7b91e49fa44110dafa1b8b2776c3 2022 04 13 div align center img src https raw githubusercontent com carloscn blog main scope svg width 60 div design arm design private arm secure boot unit design cortex a and cortex m https github com carloscn design issues 1 | blog linux tech optee rtos kernel arm | os |
OpenBikeComputerRTOS_ESP32 | open bike computer rtos summary this repo is an open source bike computer based on freertos on esp8266 esp32 esp32 is much newer hardware no reason to stick with esp8266 right now this is an attempt of learning c and freertos env i ve first made bike computer using arduino nano so this might be refered as version 2 i ve also tackled a little of 3d design in blender to create a case for this bike computer creating community around this project for all programmers riding their bikes would be awesome features core current speed distance time average max speed total distance memory average max speed and total distance are stored on the device not synchronized for now only local display shows main and detailed ride data bottom half of the screen changes automaticly 8s big distance 3s details synchronization using wifi synchronize ride data with obc server see https github com random90 obc server wifi settings are hardcoded at compile time data is sent the the obc server on ride stop as a new ride after 30 seconds of inactivity configurable at compile time ride is restarted automaticly after ride synchonization succeded sntp time synchronization sets timestamp for your rides current supported hardware esp32 reed switch pcd8544 nokia 5110 lcd 3d printable case see 3dcase folder for files print both obc case bottom and obc case top stl files additional funcionality todos and ideas better sleep mode for battery performance local ride storage for later sync on new ride started by button press spiffs full ride logs with file rotation wifi access point setup multiple wifi access point settings timezone settings for time display modularization of components start with screen selection at compile time different hardware support e g different type of screens like oled e ink color lcd buttons automatic screen brightness live reed impulse online sync share your ride data realtime sync with strava api wifi geolocation even later in future gps module gprs module for live tracking hardware prototype version pritable case for version with no soldering required everything connected with dupont cables uses esp devkit with pins nokia 5110 screen with pins soldered powered up by micro usb cable prorotype https user images githubusercontent com 13310754 221653899 1e14fe55 5d9b 4f5c bfb3 7e1312a5d1ee jpg required components esp wroom 32 devkit v1 nokia 5110 lcd hxe reed switch default wiring obc uses vspi can be hspi but i use it for debug to connect to lcd esp32 lcd d4 rst d5 ce d25 dc d19 din d18 clk 3v3 5v vin rx2 g16 bl gnd gnd reedswitch esp d21 pin gnd jtag cjmcu 2232hl note to myself esp32 jtag d13 ad0 d12 ad1 d15 ad2 d14 ad3 gnd gnd micro fork version with battery work in progress standard version soon standard version with battery soon | os |
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GatorMiner | gatorminer build status https travis ci com allegheny ethical cs gatorminer svg branch master https travis ci com allegheny ethical cs gatorminer codecov https codecov io gh allegheny ethical cs gatorminer branch master graph badge svg https codecov io gh allegheny ethical cs gatorminer built with spacy https img shields io badge built 20with spacy 09a3d5 svg https spacy io built with streamlit https img shields io badge built 20with streamlit 09a3d5 svg https www streamlit io an automated text mining tool written in python to measure the technical responsibility of students in computer science courses being used to analyze students markdown reflection documents and five questions survey based on natural language processing in the department of computer science at allegheny college installation you can clone the repository by running the following command bash git clone git github com allegheny ethical cs gatorminer git cd into the project root folder bash cd gatorminer this program uses pipenv https github com pypa pipenv for dependency management if needed install and upgrade the pipenv with pip bash pip install pipenv u to create a default virtual environment and use the program bash pipenv install gatorminer relies on en core web sm and or en core web md english models trained on written web text blogs news comments that includes vocabulary vectors syntax and entities to install the pre trained model you can run one of the following commands bash pipenv run python m spacy download en core web sm pipenv run python m spacy download en core web md web interface gatorminer is mainly developed on its web interface with streamlit https www streamlit io in order to provide fast text analysis and visualizations in order to run the streamlit interface type and execute the following command in your terminal bash pipenv run streamlit run streamlit web py you then will see something like this in your terminal window bash you can now view your streamlit app in your browser local url http localhost 8501 network url http xxx xxx x x 8501 the web interface will be automatically opened in your browser img src resources images landing page png alt browser style width 100 data retreiving there are currently two ways to import text data for analysis through local file system or aws dynamodb local file system you can type in the path s to the directorie s that hold reflection markdown documents you are welcome to try the tool with the sample documents we provided in resources for example shell resources sample md reflections lab1 resources sample md reflections lab2 resources sample md reflections lab3 aws retrieving reflection documents from aws is a feature integrated with the use of gatorgrader https github com gatoreducator gatorgrader where students markdown reflection documents are being collected and stored inside the a pre configured dynamodb database in order to use this feature you will need to have some credential tokens listed below stored as environment variables bash export gator endpoint your endpoint export gator api key your api key export aws access key id your access key id export aws secret access key your secret access key it is likely that you already have these prepared when using gatorminer in conjunction with gatorgrader since these would already be exported when setting up the aws services you can read more about setting up an aws service with gatorgrader here https github com enpuyou script api lambda dynamodb once the documents are successfully imported you can then navigate through the select box in the sidebar to view the text analysis img src resources images select box png alt select box style width 100 reflection documents we are using markdown format for the student reflection documents its organized structure allows us to parse and perform text analysis easily with that said there are few requirements for the reflection document before it could be seamlessly processed and analyzed with gatorminer a template resources reflection template md is provided within the repo note that the headers with the assignment s and student s id name are required gatorminer is set in default to take the first header as assignment name and the second header as student name you can also check out the sample json report resources sample json report report 201 json to see the format of json reports gatorminer gathers from aws analysis img src resources images frequency png alt frequency style width 100 img src resources images sentiment png alt sentiment style width 100 img src resources images similarity png alt similarity style width 100 img src resources images topic png alt topic style width 100 contribution we are excited that you would take the time to contribute to gatorminer we have provided a contributing guideline contributing md that will help you effectively get started and make contributions to the project | textmining nlp streamlit spacy | ai |
data-engineering-capstone | data engineering nanodegree data engineering capstone project table of contents scope of works the purpose of this project is to demonstrate various skills associated with data engineering projects i will be developing highly scalable data ingestion architecture using airflow and spark constructing cloud data warehouses through redshift databases and s3 data storage as well as defining efficient star schema data model as an example i will perform a deep dive into i94 us immigration airport port of entry city and city weather primarily focusing on the type of visas being issued and the profiles associated the scope of this project is limited to the data sources listed below with data being aggregated across numerous dimensions such as visa type gender port of entry nationality and month etc by which air lines immigrants are coming to us which city they are landing what is the weather of that city further details and analysis please refer the data exploration part data sources description i94 immigration data this data comes from the us national tourism and trade office found here https travel trade gov research reports i94 historical 2016 html each report contains international visitor arrival statistics by world regions and select countries including top 20 type of visa mode of transportation age groups states visited first intended address only and the top ports of entry for select countries u s city demographic data this dataset contains information about the demographics of all us cities and census designated places with a population greater or equal to 65 000 dataset comes from opensoft found here https public opendatasoft com explore dataset us cities demographics export airport code table this is a simple table of airport codes and corresponding cities the airport codes may refer to either iata airport code a three letter code which is used in passenger reservation ticketing and baggage handling systems or the icao airport code which is a four letter code used by atc systems and for airports that do not have an iata airport code it comes from here https datahub io core airport codes data world temperature data this dataset came from kaggle found here https www kaggle com berkeleyearth climate change earth surface temperature data design goals at the project inception stage i have defined a set of design goals to help guide the architecture and development work for data lineage to deliver a complete accurate reliable and scalable lineage system mapping immigration diverse data landscape let s review a few of these principles 1 ensure data integrity accurately capture the relationship in data from disparate data sources to establish trust with users because without absolute trust lineage data may do more harm than good 1 enable seamless integration design the system to integrate with a growing list of data tools and platforms including the ones that do not have the built in meta data instrumentation to derive data lineage from 1 design a flexible data model represent a wide range of data artifacts and relationships among them using a generic data model to enable a wide variety of business use cases data exploration further details and analysis can be found capstone project final notebook this notebook is having all final notes here capstone project final notebook ipynb immgration data exploration this note book is having immigration related data exploration here immgration data exploration ipynb portofentry data exploration this note book is having port of entry related data exploration here portofentry data exploration ipynb world temperature data exploration this note book is having world temperature related data exploration here world temperature data exploration ipynb airport data exploration this note book is having airport related data exploration here airport data exploration ipynb notes data comes from different sources like log files data warehouses and third party apis etc it is important to explore the structure volume granularity and frequency of data data quality data relationship etc data modelling after doing the data exploration from various data sources i e i94 immigration data immigration codes file i94 sas labels descriptions sas u s city demographic data airport code and world tempeture data i was able to define a star schema by extracting the immigration fact table and various dimension tables as shown below img src images schema png etl pipeline framework etl design principles below are the design principles has been taken care during the pipeline deevlopment 1 variation in data size and cadency p align center img src images variation in data size and cadency png style height 100 width 100 max width 90 p 1 variation in access to raw data img src images variation in access to raw data png 1 variation in file formats img src images variation in file formats png 1 cover several aspects of i94 system img src images cover several aspects of i94 system png 1 security and privacy img src images security and privacy png 1 airflow schedule workflows img src images airflow schedule workflows png 1 spark distributed processing img src images spark distributed processing png 1 data quality img src images data quality png defining the data model and creating the star schema involves various steps made significantly easier through the use of airflow the process of extracting files from s3 buckets transforming the data using spark and saving into parquet files then writing parquet files to redshift is accomplished through various tasks highlighted below in the etl dag graph these steps include extracting data from sas documents and writing as csv files to s3 immigration bucket extracting remaining csv and parquet files from s3 immigration bucket processing parquet files store into star shchema dimenions format into s3 transformed bucket writing parquet files from s3 to redshift performing data quality checks on the newly created tables final etl pipeline looks like below img src images finaldatapipeline png data storage data was stored in s3 buckets in a collection of parquet files the immigration dataset extends to several million rows and thus this dataset was converted to parquet files to allow for easy data manipulation and processing through dask and the ability to write to redshift star schema with proper distribution key conclusion overall this project was a small undertaking to demonstrate the steps involved in developing a data warehouse that is easily scalable skills include understand the problem statement and data dig into data exploration for data structure volume granulity data quality relationship and frequency of data creating a redshift cluster iam roles security groups developing an etl pipeline that copies data from s3 buckets into staging tables to be processed into a star schema developing a star schema with optimization to specific queries required by the data analytics team using airflow to automate etl pipelines using airflow python spark and amazon redshift writing custom operators to perform tasks such as staging data filling the data warehouse and validation through data quality checks transforming data from various sources into a star schema optimized for the analytics team s use cases | server |
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AI-TensorFlow-Blockchain-Certificate | ai tensorflow blockchain certificate my notes for coursera course for ai tensorflow blockchain and the certificates i earned 1 ai tensorflow specialization ai tensorflow in practice specialization by deeplearning ai this is a very good introduction to ai ml level course and i think it is friendly to everyone including those who don t have any background the workload is not high you can finish this quickly 7 days and obtain basic understanding of ai ml and how to code them it consists of 4 courses 1 introduction to tensorflow for artificial intelligence machine learning and deep learning 2 convolutional neural networks in tensorflow 3 natural language processing in tensorflow 4 sequences time series and prediction my certificate to this speciallization ai tensorflow in practice specialization certificate fsj2tyxegazk pdf img src ai tensorflow in practice specialization certificate tensorflow png width 200 2 blockchain foundations and use cases use ethereum blockchain foundations and use cases by consensys academy this is a good introduction course to blockchain but it s worth to mention that it focus more on public blockchain permissionless blockchain it uses bitcoin and ethereum as examples in most cases permissionless public blockchain permissioned consortium blockchain private blockchain the first two modules talk about public blockchain module 3 mentions the public private consortium blockchains module 4 explain more detials mainly focus on detials of etereum it consists of 5 parts module 1 blockchain foundations blockchain foundations and use cases module1 blockchain foundation module1 md lesson 1 introduction lesson 2 the brief brief history of blockchain lesson 3 what is decentralization lesson 4 ledgers distributed ledgers and consensus lesson 5 the paper blockchain module 2 the technical side blockchain foundations and use cases module2 tech side module2 md lesson 1 public key cryptography lesson 2 cryptographic hash functions lesson 3 public key cryptography signing lesson 4 anatomy of a block lesson 5 the chain of blocks lesson 6 nodes and networks module 3 blockchain in use blockchain foundations and use cases module3 blockchain in use module3 md lesson 1 consensus mechanisms and trust frameworks lesson 2 public private consortium blockchains lesson 3 when to use a blockchain lesson 4 implications of blockchain on business module 4 further topics blockchain foundations and use cases module4 further topics module4 md lesson 1 cryptocurrency tokens lesson 2 wallets exchanges transactions lesson 3 bitcoin and ethereum lesson 4 smart contracts the evm lesson 5 decentralized apps lesson 6 blockchain platforms extensions lesson 7 blockchain solution architecture module 5 use cases ethereum blockchain foundations and use cases module5 use cases module5 md use case 1 uport self sovereign identity and reputation use case 2 meridio ownership and governance use case 3 viant supply chain and asset tracking use case 4 ujo royalties in the music industry my certificate blockchain foundations and use cases certificate coursera6t224tv58mav pdf img src blockchain foundations and use cases certificate certificate png width 200 3 blockchain and business applications and implications blockchain and business applications and implications by insead in this course you will learn how blockchain technology will penetrate into the structures of organizations how blockchain will transform the roles of the c suite and how a blockchain can be used to manage and protect intellectual property you will be able to identify the different layers of the blockchain technology stack and explain how these affect the governance of blockchain systems identify seven qualities that a region in the world needs in order to attract technology startups and to build a vibrant blockchain ecosystem module 1 re achitecting the firm blockchain and business applications and implications week1 re achitecting the firm module1 md decentralizing the enterprise re design the corporation decentralizing the enterprise transaction costs and the structure of the firm opportunities for blockchain search contracting coordination building trust corporate boundary decisions determining corporate boundaries module 2 distributed business entities blockchain and business applications and implications week2 distributed business entites module2 md distributed business entities new business models dapps strategic approaches to intellectual property module 3 blockchain and c suite blockchain and business applications and implications week3 blockchain and c suite module3 md intro to the c suite ceo coo clo cfo cmo cio cto chro module 4 leadership for next era blockchain and business applications and implications week4 leadership for next era module4 md leadership for next era blockchain regulation fundamental questions regulatory principles blockchain governance regulation vs governance the blockchain stack multiple layers of blockchain governance a new framework for blockchain governance seven conditions for success profile of a blockchain hotbed module 5 blueprint for a new social contract blockchain and business applications and implications week5 blueprint for a new social contract module5 md the current social contract is breaking drivers for change four pillars of society blueprint for a new social contract intro to a new social contract rethinking work the pre distribution of wealth distributed power collaborative institutions course wrap up my certificate blockchain and business applications and implications coursera 20s4cv95etcvg6 pdf img src blockchain and business applications and implications certificate png width 200 4 blockchain understanding its uses and implications blockchain understanding its uses and implications readme md by the linux foundation a excellent introduction to blockchain with a little technology details this one is my no 1 recommedation for blockchain introduction courses chapter 1 introduction to blockchain blockchain understanding its uses and implications chapter1 introduction to blockchain readme md internet history blockchain features blockchain usecases chapter 2 blockchain mechanics blockchain understanding its uses and implications chapter2 blockchain mechanics readme md ledgers cryptograhpy transparency and immutability chapter 3 blockchain functions blockchain understanding its uses and implications chapter3 blockchain functions readme md smart contracts blockchain security public and permissioned blockchains the blockchain transaction consensus chapter 4 blockchains and governance blockchain understanding its uses and implications chapter4 blockchains and governance readme md open source code governance identity and anonymity on blockchain chapter 5 blockchain problem solving and future trends blockchain understanding its uses and implications chapter5 blockchain problem solving and future trends readme md problems blockchain solves digital currencies future trends chapter 6 blockchain use cases blockchain understanding its uses and implications chapter6 blockchain use cases readme md blockchain in practice enterprise solutions public sector solutions social impact solutions platform developer s solutions end user solutions future of blockchain my certificate blockchain understanding its uses and implications linuxfoundationx 20lfs170x 20certificate 20 20edx pdf img src blockchain understanding its uses and implications certificate png width 200 5 introduction to hyperledger blockchain technologies introduction to hyperledger blockchain technologies readme md by the linux foundation chapter 1 discovering blockchain technologies introduction and learning objectives distributed ledger technology dlt bitcoin and ethereum blockchains exploring permissionless blockchains consensus algorithms hyperledger other open source permissioned distributed ledgers challenges in the adoption deployment of distributed ledger technologies knowledge check verified certificate track only chapter 2 introduction to hyperledger foundation introduction and learning objectives hyperledger foundation q a with brian behlendorf former executive director of hyperledger knowledge check verified certificate track only chapter 3 hyperledger foundation hosted projects introduction and learning objectives graduated hyperledger projects incubating hyperledger projects dormant hyperledger projects knowledge check verified certificate track only chapter 4 the promise of business blockchain technologies introduction and learning objectives existing blockchain use cases when to use or not to use blockchain technologies knowledge check verified certificate track only 6 power bi my certificate 1 powerbi prepare clean transform and load data using powerbi coursera 20mhfclz6cnl6g pdf img src powerbi prepare clean transform and load data using powerbi cert png width 200 my certificate 2 powerbi build dashboards in power bi coursera 20dpgrltpascww pdf img src powerbi build dashboards in power bi cert png width 200 7 microsoft azure ai fundamentals ai 900 specialization microsoft azure1 ai fundamentals ai 900 exam prep specialization readmd md by microsoft learners will acquire foundational knowledge of the core concepts related to artificial intelligence ai and the services in microsoft azure that can be used to create ai solutions this program is an opportunity to demonstrate knowledge of common ml and ai workloads and how to implement them on azure it consists of 4 courses course 1 artificial intelligence on microsoft azure img src microsoft azure1 ai fundamentals ai 900 exam prep specialization 1 artificial intelligence on microsoft azure ai900 1 png width 60 course 2 microsoft azure machine learning img src microsoft azure1 ai fundamentals ai 900 exam prep specialization 2 microsoft azure machine learning ai900 2 png width 60 course 3 computer vision in microsoft azure img src microsoft azure1 ai fundamentals ai 900 exam prep specialization 3 computer vision in microsoft azure ai900 3 png width 60 course 4 natural language processing in microsoft azure img src microsoft azure1 ai fundamentals ai 900 exam prep specialization 4 natural language processing in microsoft azure ai900 4 png width 60 my certificate to this speciallization microsoft azure1 ai fundamentals ai 900 exam prep specialization coursera 5wbz6rpnq2dt pdf img src microsoft azure1 ai fundamentals ai 900 exam prep specialization coursera 5wbz6rpnq2dt png width 200 microsoft certified azure ai fundamentals ai 900 microsoft azure1 ai fundamentals ai 900 exam prep specialization certifications dt 7380 microsoftlearn pdf img src microsoft azure1 ai fundamentals ai 900 exam prep specialization certifications dt 7380 microsoftlearn png width 200 cirtl center for the integration of research teaching and learning cirtl official website https www cirtl net events cornellx cirtl 1x fall 2022 an introduction to evidence based undergraduate stem teaching cirtl an introduction to evidence based undergraduate stem teaching cornell edx | coursera ai machine-learning neural-network convolutional-neural-networks tensorflow | blockchain |
nldl | nldl n atural l anguage for clojure s d ata l og flavor as present in datomic datascript datahike etc inspired by nl datalog https github com harc nl datalog and its demo http alexwarth com projects nl datalog usage clojure ns examples basic require nldl core as nldl datascript core as d nldl nl dl find movie where movie title is avatar movie rating is 8 find movie where movie title avatar movie rating 8 def db d empty db d db with db id 1 title titanic rating 3 db id 2 title avatar rating 8 db id 8 title avatar 2 rating 5 db id 3 title holy motors rating 8 db id 4 title greener grass rating 9 d q nldl nl dl find movie where movie title is avatar movie rating is 8 db 2 d q nldl nl dl find movie where movie title is avatar movie rating is 8 find fn var find list pull var db db id 2 rating 8 title avatar status alpha experimental i m toying with this for easier entry of datalog queries on mobile for my project zeal https github com den1k zeal where clojure datalog s beloved parentheses brackets question marks and colons are always at least two taps away thanks to its simple text processing nldl also allows for accessibility enhancements through speech to text queries license copyright 2020 fixme distributed under the eclipse public license either version 1 0 or at your option any later version | ai |
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sense_old | sense is now part of marvel the development of sense has moved into elasticsearch marvel you can find it here http www elasticsearch org overview marvel if you have any question or find an issue feel free to post on the elasticsearch user group http groups google com forum forum elasticsearch | front_end |
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WAPhotoRecon | wwww what is this an android application read details you will also need to have the other program running on your beaglebone black wireless for the system to be the complete product or project the bbw beagleboneblack wireless needs to be able and have running python3 5 this app was made for android version nougat for api 24 of 7 0 coded by who the application was programmed by royce aquino also i thank those who created the libraries that are also used in helping me make this application 3 3 3 | embeddedsystems java firebase android freebsd debian | os |
ag-blockchain | https ag mos ru parity ui windows 1 parity https github com moscow technologies ag blockchain raw master install agclientinstaller exe 64 1 parity ui imported 3461 d75f 27e3 0 txs 0 00 mgas 0 86 ms 0 57 kib 1 parity ui https github com moscow technologies ag blockchain d0 b8 d0 bd d1 81 d1 82 d1 80 d1 83 d0 ba d1 86 d0 b8 d1 8f d0 bf d0 be d0 bf d1 80 d0 be d1 81 d0 bc d0 be d1 82 d1 80 d1 83 d1 80 d0 b5 d0 b7 d1 83 d0 bb d1 8c d1 82 d0 b0 d1 82 d0 be d0 b2 d0 be d0 bf d1 80 d0 be d1 81 d0 be d0 b2 windows macos sh wget o https raw githubusercontent com moscow technologies ag blockchain master install run macosx sh bash 1 1 imported 3461 d75f 27e3 0 txs 0 00 mgas 0 86 ms 0 57 kib 1 parity ui or use the generated token https github com moscow technologies ag blockchain d0 b8 d0 bd d1 81 d1 82 d1 80 d1 83 d0 ba d1 86 d0 b8 d1 8f d0 bf d0 be d0 bf d1 80 d0 be d1 81 d0 bc d0 be d1 82 d1 80 d1 83 d1 80 d0 b5 d0 b7 d1 83 d0 bb d1 8c d1 82 d0 b0 d1 82 d0 be d0 b2 d0 be d0 bf d1 80 d0 be d1 81 d0 be d0 b2 docker 1 docker https www docker com 1 git git for windows https git for windows github io git for linux https git scm com book ru v1 d0 92 d0 b2 d0 b5 d0 b4 d0 b5 d0 bd d0 b8 d0 b5 d0 a3 d1 81 d1 82 d0 b0 d0 bd d0 be d0 b2 d0 ba d0 b0 git bash linux macos powershell windows 1 git clone https github com moscow technologies ag blockchain git 1 cd ag blockchain install 1 parity docker compose up d 1 docker compose logs grep token token q7j9 ofgq eeju 9nvt parity ui 1 1 parity ui parity ui uid 1 parity ui 1 1 your node is still syncing the values you see might be outdated wait until it s fully synced 1 parity wallet 1 settings contracts 1 contracts 1 1 watch next 1 network address 0xfdb76daaf371bf5c7122f6f1104458440454fbb1 contract name contract abi conracts root abi https github com moscow technologies ag blockchain blob master contracts root abi add contract 1 contracts getaddress https ag mos ru poll index query 1 contracts watch next network address contract name 3196 contract abi contracts poll abi https github com moscow technologies ag blockchain blob master contracts poll abi addcontract 1 contracts 3196 questionids questionsaddress contracts watch 3195 1 contract abi contracts pollquestion abi https github com moscow technologies ag blockchain blob master contracts pollquestion abi addcontract 1 questionid currentversiontitle votercount allexistingversions currentversion currentversionresults versions c json answeridsbyversion c answersbyversion c json resultsbyversion c voteofuser uid result value1 value2 | blockchain |
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IntelCup2022 | intelcup2022 dependencies python3 m pip install numpy sounddevice librosa keras tensorflow pandas torch pyttsx3 python vlc torchaudio | os |
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IT | welcome to github pages you can use the editor on github https github com s3824193 it edit main readme md to maintain and preview the content for your website in markdown files whenever you commit to this repository github pages will run jekyll https jekyllrb com to rebuild the pages in your site from the content in your markdown files markdown markdown is a lightweight and easy to use syntax for styling your writing it includes conventions for markdown syntax highlighted code block header 1 header 2 header 3 bulleted list 1 numbered 2 list bold and italic and code text link url and image src for more details see github flavored markdown https guides github com features mastering markdown jekyll themes your pages site will use the layout and styles from the jekyll theme you have selected in your repository settings https github com s3824193 it settings the name of this theme is saved in the jekyll config yml configuration file support or contact having trouble with pages check out our documentation https docs github com categories github pages basics or contact support https github com contact and we ll help you sort it out | server |
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php-blockchain | blockchain implementation in php minimum php version https img shields io badge php 3e 3d 208 1 8892bf svg https php net build https github com akondas php blockchain actions workflows build yaml badge svg https github com akondas php blockchain actions workflows build yaml license https poser pugx org akondas php blockchain license svg https packagist org packages akondas php blockchain clean code approach to blockchain technology learn blockchain by reading source code roadmap x block structure and hashing x genesis block x storing and validate blockchain x proof of work with difficulty missing consensus on the difficulty x communicating with other nodes controlling the node based on reactphp simple persistence layer going serverless with aws lambda experiment start working on kondascoin akondas coin https github com akondas coin rocket transactions wallet transaction relaying maybe some ui node to start the node bin node default web server port is 8080 but you can change it with http port param bin node http port 9090 default p2p server port is 3030 but you can change it with p2p port param bin node p2p port 2020 api to control node you can use simple pseudo rest api get blocks response list of all blocks index 0 hash 8b31c9ec8c2df21968aca3edd2bda8fc77ed45b0b3bc8bc39fa27d5c795bc829 previoushash createdat 2018 02 23 23 59 59 data php is awesome difficulty 0 nonce 0 post mine request raw data to mine any string response mined block index 1 hash a6eba6325a677802536337dc83268e524ffae5dc7db0950c98ff970846118f80 previoushash 8b31c9ec8c2df21968aca3edd2bda8fc77ed45b0b3bc8bc39fa27d5c795bc829 createdat 2018 03 13 22 37 07 data something goof difficulty 0 nonce 0 get peers response list of all connected peers host 127 0 0 1 port 3131 post peers add request json with peer host 127 0 0 1 port 3131 response 204 empty tests to run test suite composer tests coding standards checkers and fixers are in coding standard neon to run composer fix cs license php blockchain is released under the mit licence see the bundled license file for details author arkadiusz kondas arkadiuszkondas | blockchain php php-blockchain | blockchain |
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