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Freestyle | freestyle pixate freestyle is a toolkit for styling native mobile applications it consists of a comprehensive collection of css classes that work with the pixate framework http www pixate com to provide an easily customized base of styles for applications think of it as bootstrap for native apps currently it only supports ios 7 android support is coming soon my image https raw github com pixate freestyle gh pages freestyle typography png my image https raw github com pixate freestyle gh pages freestyle form styles png getting started getting started with freestyle is easy you need to install the pixate framework http www pixate com add the freestyle css or sass files to your project and apply the freestyle classes to views and controls in your app installing the pixate framework the pixate framework lets you style your native ios applications with stylesheets if you aren t already familiar with pixate check out the getting started http www pixate com docs framework ios latest getting started guide to familiarize yourself with how it works you can install it in three ways download directly from pixate http download pixate com freestyle is already packed in this download install using cocoapods http cocoapods org spec name is pixate clone the pixate framework repo git clone git github com pixate pixateframework git you can also clone the freestyle repo https github com pixate freestyle and use the showcase app as a starter app which already has the latest release of pixate installed note that the showcase uses the freestyle css file so you can try it out without installing sass if you want to play around with variables and such you ll need to follow the instructions below for compiling the sass files once you have the framework follow the getting started http www pixate com docs framework ios latest getting started guide to set up the framework in your application note that you will need pixate 2 0 1 or greater for freestyle to work properly also the pixate framework now includes freestyle so you won t need to download freestyle separately get freestyle you can grab freestyle two ways download the latest release https github com pixate freestyle archive master zip clone this repo git clone git github com pixate freestyle git note the freestyle currently only contains styles for apps targeting ios 7 you can certainly add your own styles for ios 6 add freestyle to your app once you ve downloaded http download pixate com and installed http www pixate com docs framework ios latest getting started pixate and downloaded freestyle you have two options to add freestyle to your project add the default css file from the freestyle repo to your project this contains all of the styles but will be harder to customize as it is not modular add the sass http sass lang com files to your project you will need to set up the sass precompiler see the compiling sass section below this option makes it a lot easier to customize the styles since it s completely modular once the css or scss files are added you can apply the classes to any relevant view or control by adding a styleclass runtime attribute in interface builder to the view or control or importing pixate pixate h in your view controller and setting the styleclass property e g mybutton styleclass button small the pixate getting started http www pixate com docs framework ios latest getting started using css guide has more details on setting classes what s included in the toolkit freestyle css default css scss default scss base scss buttons js collections js forms scss icons js tables js type scss variables scss pixatefreestyleshowcase pixatefreestyleshowcase pixatefreestyleshowcasetests pixatefreestyleshowcase xcodeproj compiling sass included in freestyle are several sass http sass lang com files that once compiled generate the freestyle css that will be used by the pixate framework sass allows freestyle to be modular you can modify variables to theme the default styles or edit any part of the sass to your heart s content you can simply grab the compiled css file from this repo and add it to your project see getting started http www pixate com docs framework ios latest getting started or you can follow these simple steps to compile the scss files once you ve installed pixate and added the freestyle sass files to your application do the following install sass http sass lang com install this requires ruby which you should already have if you re running os x if you already added the freestyle default css file to your project remove it it will be replaced by the compiled sass files add all of the files in freestyle scss to your project in xcode in xcode click on your project in the project navigator click on the build phases tab from the top menu under editor select add build phase then add build script run phase in the new run script field enter source users user rvm environments default gem home bin sass target build dir contents folder path default scss target build dir contents folder path default css do not put anything under input files or output files now when you build your project the sass files should be compiled into a default css file that will be used by the pixate framework documentation we will be building docs for freestyle once the beta stabilizes in the meantime please submit any questions on the issues page https github com pixate freestyle issues the showcase app is meant to serve as an example application using all of the styles as well as a reference app for starting a new project or creating themes we used storyboards in the app to make it easy to play with but we had to write a few lines of code to make certain things work to enable static scroll views used for the typography and form pages that are taller than the device resolution we added the following viewdidlayoutsubviews method to our view controllers void viewdidlayoutsubviews super viewdidlayoutsubviews enable scrolling in the scrollview self scrollview layoutifneeded self scrollview contentsize self contentview bounds size place the scrollview in the proper position cgrect screenbound uiscreen mainscreen bounds cgfloat width screenbound size width cgfloat tabbarheight self tabbarcontroller tabbar frame size height cgfloat navbarheight self navigationcontroller navigationbar frame size height cgfloat height screenbound size height tabbarheight navbarheight self scrollview frame cgrectmake 0 0 width height additionally to set the height of these scroll views we changed the size setting to freeform under simulated metrics in the attributes inspector for the view controller we also needed code in other places to generate our table view collection view and our icons feedback and bugs we are releasing freestyle in beta because we really want to hear feedback or feature requests what would an ideal mobile app toolkit look like for you please open a new issue https github com pixate freestyle issues new before opening a new issue please search existing issues contributing we currently are not accepting any pull requests as we want to get freestyle 1 0 officially out first in the future we ll welcome contributions with open arms including new themes and samples from the community | front_end |
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SwampCooler | swampcooler swamp cooler final project embedded systems design cpe301 spring 2022 built a model of a swamp cooler using an arduino with four working states that could be switched between by interacting with peripherals team members alexis elquist julianne maghirang samuel suk | os |
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Data-Pipelines-with-Apache-Airflow | data pipelines with apache airflow purpose the purpose of this project is to automate and monitor a cloud data warehouse etl pipeline for a fictitious music streaming startup called sparkify that is interested in understanding which songs users are listening to sparkify has song and event log datasets stored on s3 at s3 udacity dend song data and s3 udacity dend log data respectively this project uses apache airflow to automate quality check and monitor an etl pipeline that copies each dataset from s3 to staging tables in amazon redshift then inserts data into star schema tables from the staging tables datasets song dataset the files found in s3 udacity dend song data are a subset of real data from the million song dataset http millionsongdataset com each file is in json format and contains metadata about a song and the artist of that song the files are partitioned by the first three letters of each song s track id for example here are paths to two files in this dataset s3 udacity dend song data a b c trabcei128f424c983 json s3 udacity dend song data a a b traabjl12903cdcf1a json below is an example of what a single song file traabjl12903cdcf1a json looks like num songs 1 artist id arjie2y1187b994ab7 artist latitude null artist longitude null artist location artist name line renaud song id soupiru12a6d4fa1e1 title der kleine dompfaff duration 152 92036 year 0 log dataset the second dataset found in s3 udacity dend log data consists of log files in json format generated by this event simulator https github com interana eventsim based on the songs in the dataset above these simulate activity logs from a music streaming app based on specified configurations the log files in this dataset are partitioned by year and month for example here are filepaths to two files in this dataset s3 udacity dend log data 2018 11 2018 11 12 events json s3 udacity dend log data 2018 11 2018 11 13 events json below is an example of what the data in a log file 2018 11 12 events json looks like event log https video udacity data com topher 2019 february 5c6c15e9 log data log data png redshift data warehouse design the staging tables columns directly correspond to those of the files in the datasets the star schema is modeled as follows fact table 1 songplays records in log data associated with song plays i e records with page nextsong songplay id start time user id level song id artist id session id location user agent dimension tables 2 users users in the app user id first name ast name gender level 3 songs songs in music database song id title artist id year duration 4 artists artists in music database artist id name location latitude longitude 5 time timestamps of records in songplays broken down into specific units start time hour day week month year weekday for more details on how this data is modeled as it pertains to analytical queries see this project https github com spaydar data modeling with postgres airflow pipeline dag the data pipeline uses a single dag for each task which implements the following requirements in its default parameters the dag does not have dependencies on past runs on failure a task is retried 3 times task retries happen every 5 minutes catchup schedule backfill is turned off emails are not sent on task retry tasks tasks dependencies are configured in order to adhere to the following graph task dependency graph view https video udacity data com topher 2019 january 5c48ba31 example dag example dag png operators the data pipeline uses 4 modular custom operators that stage the data transform the data and run checks on data quality each operator runs sql statements against the redshift database which they connect to using airflow s postgreshook stage operator the stage operator can load any json formatted files from s3 to redshift the operator creates and runs a sql copy statement based on the parameters provided the operator s parameters specify where in s3 the file is loaded from and what is the targeted table here are examples of copy statements generated and executed by the stage operator copy staging events from s3 udacity dend log data access key id aws key secret access key aws secret json s3 udacity dend log json path json copy staging songs from s3 udacity dend song data access key id aws key secret access key aws secret json auto fact and dimension operators the fact and dimension operators utilitze the sql helper class to run data transformations and insert the results into the star schema tables the dimension operator has a parameter to allow for a truncate insert pattern in which the target table is emptied before the load data quality operator the data quality operator is used to run quality checks on the data itself the operator accepts at least one sql based test case with a function to evaluate if the result of the query matches the expected output if there is no match the operator raises an exception so the task can retry and fail eventually file and code structure most of the sql queries for the project are written in the dags create tables sql and plugins helpers sql queries py files for purposes of modularity and ease of maintenance these files contain commands to drop all tables if they exists create tables and select data from staging tables to insert into fact and dimension tables the definitions of the custom operators reside in plugins operators logic for connecting to redshift and executing queries is implemented directly in the operators along with minimal sql skeleton code that is filled in with parameters passed to the operators the data pipeline is implemented in the dags udac example dag py script in this script the dag and its default args are instantiated also operators are instantiated as tasks and their dependencies are defined to run the pipeline start an airflow server and turn on the dag the pipeline will run once at the time the dag is turned on and will continue to run at the top of every hour that the dag remains on the airflow ui can be used to monitor the status of each dag run and details about each task can be viewed in its logs code samples instantiate the dag and its default parameters default args owner udacity start date datetime 2019 1 12 depends on past false retries 3 retry delay timedelta minutes 5 email on retry false dag dag udac example dag default args default args description load and transform data in redshift with airflow schedule interval 0 catchup false instantiate a load dimension table task load user dimension table loaddimensionoperator task id load user dim table dag dag table users redshift conn id redshift sql statement sqlqueries user table insert truncate insert on true instantiate the data quality check task run quality checks dataqualityoperator task id run data quality checks dag dag table names songplays users songs artists time redshift conn id redshift sql queries dict select count from lambda x x 0 define task dependencies stage events to redshift load songplays table stage songs to redshift load songplays table | cloud |
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crypto-tracker | crypto tracker a cryptocurrency tracker using coin marketcap api for mobile application development subject | front_end |
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parallel-corpora-tools | corpora cleaning tools tools for filtering and cleaning parallel and monolingual corpora in order to train better neural machine translation systems inspired by the data filtering and data pre processing sections of tilde s http tilde com wmt17 paper http www statmt org wmt17 pdf wmt37 pdf this repository includes some of the more basic scripts that can help to get rid of the majority of junk from parallel corpora tools included parallel https github com m4t1ss parallel corpora tools blob master parallel tools for parallel corpora mono https github com m4t1ss parallel corpora tools blob master mono tools for monolingual corpora requirements python with langid py https github com saffsd langid py php moses scripts https github com moses smt mosesdecoder subword nmt https github com rsennrich subword nmt bash pip install subword nmt pip install langid publications if you use this tool please cite the following paper mat ss rikters 2018 impact of corpora quality on neural machine translation https arxiv org abs 1810 08392 in proceedings of the 8th conference human language technologies the baltic perspective baltic hlt 2018 2018 bibtex inproceedings rikters2018baltichlt author rikters mat ss booktitle in proceedings of the 8th conference human language technologies the baltic perspective baltic hlt 2018 title impact of corpora quality on neural machine translation address tartu estonia year 2018 | nmt neural machine translation machine-translation neural-machine-translation corpora corpus-tools nlp language language-processing natural-language-processing natural-language filtering cleaning data-science data-processing | ai |
hashview | hashview v0 8 1 hashview is a tool for security professionals to help organize and automate the repetitious tasks related to password cracking it is broken into two compoents the hashview server and hashview agent the hashview server is a web application that manages one or more agents deployed by you on dedicated hardware note you can run the server and agent on the same machine hashview strives to bring constiency in your hashcat tasks while delivering analytics with pretty pictures ready for ctrl c ctrl v into your reports note if you are running version v0 8 0 and want to upgrade all you need to do is git pull on main and start hashview py this should automatically upgrade your instance to the latest version server requirements 1 python 3 7 2 mysql db installed with known username password 3 access to a smtp email service used for password resets and notifications agent requirements 1 python 3 7 2 hashcat 6 2 x installation follow these instructions to install hashview server on ubuntu 20 04 3 lts server in theory hashview should be able to run on any nix system but the dev s only installed tested on debian ubuntu 1 setup mysql sudo apt update sudo apt install mysql server sudo service mysql start sudo mysql secure installation 2 configure mysql log into your mysql server and create a dedicated user for hashview hashview can run as root but doesnt need to and since we practice what we preach we should use a lower priv account for this if you re installing hashview on a different server than the system where the mysql db is running on adjust the account creation sudo mysql create user hashview localhost identified by donotusethispassword123 grant all privileges on hashview to hashview localhost flush privileges create database hashview exit 3 install hashview server the following are to install hashview after the mysql db has been setup sudo apt get install python3 python3 pip python3 flask git clone https github com hashview hashview cd hashview pip3 install r requirements txt setup py hashview py note you can add a debug if you are attempting to troubleshoot an issue 4 log into your hashview server navigate to your server default port is 8443 https ip 8443 note because hashview is installed with a self signed certificate you will be prompted about it being invalid you re welcome to use properly signed certs by replacing the files under hashview hashview control ssl once logged in before you can start cracking hashes you need to install a hashview agent installing hashview agent after you ve installed hashview you will need to install a hashview agent the agent can run on the same system as hashview but doesn t have to 1 log into hashview as an administrator 2 navigate to agents menu 3 click download agent to get a tgz package of the hashview agent 4 move agent to the system you d like to run it on 5 install agent you will need to decompress the package and run the hashview agent py script upon initial execution it will prompt you for information about your hashview server tar xzvf hashview agent version tgz cd install cp r hashview agent cd hashview agent pip3 install r requirements txt python3 hashview agent py 6 once running you or another admin will need to navigate back into hashview manage agents and approve the agent developing and contributing please see the contribution guide https github com hashview hashview wiki contributing for how to develop and contribute if you have any problems please consult issues https github com hashview hashview issues page first if you don t see a related issue feel free to add one and we ll help feature requests we accept pull requests but if you d like a feature without submitting code first check the issues section to see if someone has already requested it if so go ahead an upvote that request otherwise feel free to create your own new feature request no promises it ll get implemented but it cant hurt to ask authors contact us on twitter jarsnah12 | python hashcat web flask analytics distributed | front_end |
grazier | grazier easily call large language models from a unified api grazier is a python library for easily calling large language models from a unified api supported large models from openai gpt 4 base 32k chat and completion engines gpt 3 5 chatgpt chat and completion engines gpt 3 davinci v2 v3 ada babbage curie completion engine from anthropic claude 2 base chat and completion engines claude base 100k chat and completion engines claude instant base 100k chat and completion engines from google gcp palm chat and completion engines from huggingface gpt 2 base medium large xl completion engine gpt neo 125m 1 3b 2 7b completion engine gpt j 6b completion engine falcon 7b 40b rw 1b rw 7b completion engine dolly v1 6b v2 3b 7b 12b chat and completion engines mpt instruct 7b 30b chat and completion engines from facebook via huggingface llama 7b 13b 30b 65b completion engine llama 2 7b 13b 70b completion engine opt 125m 350m 1 3b 2 7b 6 7b 13b 30b 66b completion engine from stanford via huggingface alpaca 7b chat and completion engines from berkeley via huggingface koala 7b 13b v1 13b v2 chat and completion engines vicuna 7b 13b chat and completion engines from stabilityai via huggingface stablelm 7b 13b chat and completion engines from allenai via huggingface tulu 7b 13b 30b 65b chat and completion engines open instruct sharegpt 7b 13b 30b 65b chat and completion engines from ai21 jurassic 2 light mid ultra completion engines image language models blip2 2 7b 6 7b image text engine open flamingo 3b 4b 9b image text engine installation grazier can easily be installed using pip bash pip install grazier each of the llms may need additional setup which you can find in the engine setup section below usage for completion engines it s as simple as python import grazier grazier list models gptj 6b gpt2 gpt2 med gpt2 lg gpt2 xl distilgpt2 gptneo 125m gptneo 1 3b gptneo 2 7b stablelm 3b stablelm 7b opt 125m opt 350m opt 1 3b opt 2 7b opt 6 7b opt 13b opt 30b opt 66b llama 7b llama 13b llama 30b llama 65b gpt3 davinci3 gpt3 davinci2 gpt3 curie gpt3 babbage gpt3 ada palm gpt2 grazier get gpt2 completion gpt2 i enjoy walking with my cute dog but sometimes he gets scared and print completion for chat engines all you need to do is add the type chat parameter python from grazier import conversation speaker get list models conversation conversation conversation add turn you are a funny person speaker speaker system conversation add turn hi how are you speaker speaker user conversation add turn i am doing well how about you speaker speaker ai conversation add turn what are you planning to do today speaker speaker user list models type chat claude claude 100k claude instant claude instant 100k bard koala 7b koala 13b v1 koala 13b v2 vicuna 7b vicuna 13b alpaca 13b chat gpt gpt4 gpt4 32k stablelm 3b stablelm 7b palm gpt4 get gpt4 type chat next turn gpt4 conversation print next turn for vision augmented image engines use type image python import grazier from pil import image grazier list models type image blip2 opt 2 7b blip2 opt 6 7b blip2 opt 2 7b coco blip2 opt 6 7b coco blip2 flan t5 xl blip2 flan t5 xxl blip2 flan t5 xl coco openflamingo 3b vitl mpt1b openflamingo 3b vitl mpt1b dolly openflamingo 9b vitl mpt7b openflamingo 4b vitl rpj3b blip2 grazier get blip2 opt 2 7b type image image image open test data dog jpg completion blip2 image a photo of print completion individual engine setup each engine may require some specific details to be passed in for example openai engines require an api key these details are generally set up with environment variables openai engines for openai engines you will need to set the openai api key and openai api org environment variables you can find your api key and organization id on the openai dashboard https platform openai com you can set these environment variables in your shell or in a env file in the root of your project for example in a env file you would have bash openai api key your key openai api org your org id or on the command line bash export openai api key your key export openai api org your org id anthropic engines for anthropic engines you will need to set the anthropic api key environment variable you can find your api key at the anthropic dashboard https console anthropic com account keys you can set this environment variable in your shell or in a env file in the root of your project for example in a env file you would have bash anthropic api key your key or on the command line bash export anthropic api key your key vertex engines palm for google engines we use the vertex cloud api which requires a google cloud platform gcp project you can create a gcp project at the gcp console https console cloud google com you will also need to enable the vertex ai api for your project set up a services account and download the account json credentials you can find instructions for that following steps 1 to 6 of the tutorial here https cloud google com vertex ai docs tutorials image recognition automl finally you will need to set the google application credentials environment variable to the path of the json file you can set this environment variable in your shell or in a env file in the root of your project for example in a env file you would have bash google application credentials path to your json file or on the command line bash export google application credentials path to your json file bard for the bard engine you will need to get your bard secure 1psid and secure 1psidts tokens get the value of this variable by first going to https bard google com then log in press f12 for console and go to the application tab then cookies then copy the value of the secure 1psid and secure 1psidts cookies you can then set the environment variables bash bard secure 1psid your session id bard secure 1psidts your session id timestamp huggingface engines most of the huggingface engines require no additional setup however some of the larger models require a gpu to run with any kind of efficiency and some require multiple gpus with large amounts of memory you can find more details about the requirements for each model on the huggingface model hub https huggingface co models llama alpaca koala vicuna and allenai engines for these engines you will need to obtain and postprocess the weights yourself due to facebook s licensing you can find the instructions for doing so on each model page llama https huggingface co docs transformers main model doc llama alpaca https github com tatsu lab stanford alpaca recovering alpaca weights koala https github com young geng easylm blob main docs koala md vicuna https github com lm sys fastchat vicuna weights allenai https huggingface co allenai tulu 65b once the weights have been downloaded and processed you can set the following environment variables to the root directory containing a folder for each variant the format is root dir model prefix weights bin the root directory would be root dir and the model prefix is the name of the model e g tulu 65b bash llama weights root path to the llama weights alpaca weights root path to the alpaca weights koala weights root path to the koala weights vicuna weights root path to the vicuna weights allenai weights root path to the allenai weights ai21 models jurassic for ai21 models you will need to set the ai21 api key environment variable you can find your api key at the ai21 studio dashboard https studio ai21 com account api key you can set this environment variable in your shell or in a env file in the root of your project for example in a env file you would have bash ai21 api key your key or on the command line bash export ai21 api key your key citation if you use grazier in your work please cite misc grazier author david chan title grazier easily call large language models from a unified api year 2023 publisher github journal github repository howpublished url https github com davidmchan grazier license grazier is licensed under the terms of the mit license see license license for more information | ai |
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GPU-Benchmarks-on-LLM-Inference | gpu benchmarks on llm inference multiple nvidia gpus or apple silicon for large language model inference description use llama cpp https github com ggerganov llama cpp to test the llama https arxiv org abs 2302 13971 models inference speed of different gpus on runpod https www runpod io m1 max macbook pro and m2 ultra mac studio overview average eval time ms token by gpus less eval time is better gpu 7b q4 0 7b f16 65b q4 0 65b f16 a4500 20gb 11 33 26 96 oom oom 3090 24gb 8 83 19 55 oom oom 4090 24gb 7 24 16 83 oom oom a4500 20gb 2 19 55 28 08 oom oom a6000 48gb 9 61 23 23 70 48 oom a6000ada 48gb 51 04 166 29 736 06 oom 3090 24gb 2 17 95 22 69 66 94 oom 4090 24gb 2 16 24 21 61 62 54 oom 3090 24gb 3 21 51 25 62 72 7 oom 4090 24gb 3 17 16 21 05 59 71 oom a100 80gb 11 55 14 27 68 09 oom a6000 48gb 2 21 49 28 21 86 83 oom 3090 24gb 6 33 46 35 08 96 03 116 01 m1 max 24 core gpu 32gb 20 78 71 6 oom oom m2 ultra 76 core gpu 192gb 12 04 34 96 70 21 262 87 average prompt eval time ms token by gpus gpu 7b q4 0 7b f16 65b q4 0 65b f16 a4500 20gb 2 15 1 63 oom oom 3090 24gb 1 52 1 57 oom oom 4090 24gb 0 92 0 98 oom oom a4500 20gb 2 4 42 4 37 oom oom a6000 48gb 1 25 1 36 9 58 oom a6000ada 48gb 4 66 6 21 79 78 oom 3090 24gb 2 5 37 4 83 16 91 oom 4090 24gb 2 3 44 3 42 12 29 oom 3090 24gb 3 7 95 7 94 24 94 oom 4090 24gb 3 6 87 6 66 20 72 oom a100 80gb 1 04 1 01 6 5 oom a6000 48gb 2 4 96 4 86 18 66 oom 3090 24gb 6 18 17 18 16 49 24 49 43 m1 max 24 core gpu 32gb 5 02 4 68 oom oom m2 ultra 76 core gpu 192gb 1 65 1 57 11 91 11 07 model thanks to shawwn for llama model weights 7b 13b 30b 65b llama dl https github com shawwn llama dl access llama 2 from meta ai https ai meta com llama usage for nvidia gpus this provides blas acceleration using the cuda cores of your nvidia gpu multiple gpu works fine with no cpu bottleneck ngl 10000 to make sure all layers are offloaded to gpu thanks to https github com ggerganov llama cpp pull 1827 bash make clean llama cublas 1 make j test arguments switch to gguf models after 21 aug 2023 see https github com ggerganov llama cpp pull 2398 bash main color ngl 10000 t 1 temp 0 7 repeat penalty 1 1 n 512 ignore eos m models 7b ggml model q4 0 bin p i believe the meaning of life is for apple silicon using metal allows the computation to be executed on the gpu for apple devices bash make clean llama metal 1 make j test arguments bash main color no mmap ngl 1 temp 0 7 repeat penalty 1 1 n 512 ignore eos m models 7b v2 ggml model q4 0 gguf p i believe the meaning of life is check the recommendedmaxworkingsetsize in the result to see how much memory can be allocated on gpu and maintain its performance only 65 of unified memory can be allocated to the gpu on 32gb m1 max and we expect 75 of usable memory for the gpu on larger memory source https developer apple com videos play tech talks 10580 time 346 to utilize the whole memory use ngl 0 or delete it to only use the cpu for inference thanks to https github com ggerganov llama cpp pull 1826 bash main color no mmap temp 0 7 repeat penalty 1 1 n 512 ignore eos m models 13b v2 ggml model f16 gguf p i believe the meaning of life is total vram requirements nivida gpus snapshots in july 2023 model quantized size 4 bit original size f16 7b 4 8 gb 13 6 gb 13b 8 7 gb 25 9 gb 30b 20 5 gb 63 7 gb 65b 40 gb 127 gb apple silicon snapshots in aug 2023 model quantized size 4 bit original size f16 7b 4 1 gb 13 1 gb 13b 7 6 gb 25 gb 30b 18 3 gb 61 8 gb 65b 36 2 gb 123 6 gb 70b 38 gb 130 3 gb benchmarks the whole original data including the 13b and 30b models run three times for each model add llama 2 for apple silicon nvidia gpus cpu amd epyc os ubuntu 22 04 2 lts pytorch 2 0 1 py3 10 cuda11 8 0 on runpod snapshots in july 2023 llama gpu model eval time prompt eval time mean eval time mean prompt eval time a4500 20gb 7b q4 0 11 29 11 34 11 36 1 52 1 52 3 4 11 33 2 15 13b q4 0 19 65 19 67 19 7 2 89 2 88 2 9 19 67 2 89 30b q4 0 oom oom oom oom oom oom oom oom 65b q4 0 oom oom oom oom oom oom oom oom 7b f16 26 94 26 94 26 99 1 62 1 63 1 63 26 96 1 63 13b f16 oom oom oom oom oom oom oom oom 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom 3090 24gb 7b q4 0 8 83 8 81 8 84 1 56 1 5 1 5 8 83 1 52 13b q4 0 14 41 14 42 14 41 2 53 2 49 2 49 14 41 2 5 30b q4 0 33 67 33 75 33 8 5 78 5 78 5 78 33 74 5 78 65b q4 0 oom oom oom oom oom oom oom oom 7b f16 19 54 19 55 19 56 1 57 1 57 1 57 19 55 1 57 13b f16 oom oom oom oom oom oom oom oom 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom 4090 24gb 7b q4 0 7 23 7 23 7 26 0 92 0 92 0 93 7 24 0 92 13b q4 0 12 06 12 06 12 07 1 64 1 64 1 64 12 06 1 64 30b q4 0 27 4 27 44 27 46 3 88 3 88 3 89 27 43 3 88 65b q4 0 oom oom oom oom oom oom oom oom 7b f16 16 84 16 82 16 83 0 98 0 98 0 98 16 83 0 98 13b f16 oom oom oom oom oom oom oom oom 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom a4500 20gb 2 7b q4 0 19 5 19 63 19 51 4 44 4 39 4 42 19 55 4 42 13b q4 0 27 75 27 55 27 6 5 79 5 81 5 81 27 63 5 8 30b q4 0 50 95 50 73 51 08 10 07 10 02 10 06 50 92 10 05 65b q4 0 oom oom oom oom oom oom oom oom 7b f16 28 06 28 02 28 16 4 37 4 39 4 34 28 08 4 37 13b f16 43 15 43 18 43 11 5 71 5 74 5 74 43 15 5 73 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom a6000 48gb 7b q4 0 9 62 9 6 9 61 1 26 1 25 1 25 9 61 1 25 13b q4 0 15 96 16 16 2 1 2 11 2 11 15 99 2 11 30b q4 0 36 79 36 86 36 91 4 83 4 84 4 86 36 85 4 84 65b q4 0 70 37 70 47 70 59 9 55 9 58 9 6 70 48 9 58 7b f16 23 23 23 23 23 24 1 36 1 36 1 36 23 23 1 36 13b f16 41 38 41 4 41 4 2 31 2 31 2 3 41 39 2 31 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom a6000ada 48gb 7b q4 0 47 78 51 21 54 14 4 4 4 69 4 89 51 04 4 66 13b q4 0 116 38 125 55 127 52 12 46 13 44 13 9 123 15 13 27 30b q4 0 341 13 344 18 340 12 37 76 38 48 37 74 341 81 37 99 65b q4 0 723 65 743 35 741 19 77 63 80 68 81 04 736 06 79 78 7b f16 164 97 168 95 164 95 6 29 6 18 6 16 166 29 6 21 13b f16 303 48 303 54 303 5 15 06 15 52 15 62 303 51 15 4 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom 3090 24gb 2 7b q4 0 17 99 17 93 17 92 6 21 4 78 5 11 17 95 5 37 13b q4 0 24 2 24 23 24 28 6 82 6 91 6 82 24 24 6 85 30b q4 0 42 99 42 92 42 88 11 63 11 62 11 82 42 93 11 69 65b q4 0 66 82 66 84 67 16 17 56 16 23 16 95 66 94 16 91 7b f16 22 7 22 73 22 64 4 69 4 8 5 22 69 4 83 13b f16 33 93 33 83 33 97 6 82 6 8 6 74 33 91 6 79 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom 4090 24gb 2 7b q4 0 16 25 16 25 16 21 3 47 3 45 3 4 16 24 3 44 13b q4 0 22 23 22 2 22 43 4 77 4 82 4 79 22 29 4 79 30b q4 0 40 27 40 48 40 21 8 34 8 33 8 22 40 32 8 3 65b q4 0 62 71 62 56 62 35 12 41 12 18 12 27 62 54 12 29 7b f16 21 58 21 62 21 64 3 47 3 4 3 4 21 61 3 42 13b f16 32 67 32 64 32 64 4 79 4 83 4 77 32 65 4 8 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom 3090 24gb 3 7b q4 0 21 52 21 48 21 54 8 7 92 7 92 21 51 7 95 13b q4 0 28 42 28 42 28 45 11 04 10 69 10 96 28 43 10 9 30b q4 0 48 12 47 95 48 1 17 17 17 57 17 29 48 06 17 34 65b q4 0 72 68 72 59 72 84 24 91 24 87 25 03 72 7 24 94 7b f16 25 64 25 57 25 66 7 97 7 94 7 91 25 62 7 94 13b f16 35 67 35 79 35 77 10 76 10 66 10 73 35 74 10 72 30b f16 63 79 63 73 63 88 17 55 17 56 17 25 63 8 17 45 65b f16 oom oom oom oom oom oom oom oom 4090 24gb 3 7b q4 0 17 07 17 24 17 16 7 08 6 73 6 8 17 16 6 87 13b q4 0 22 95 22 93 22 73 8 4 8 78 8 96 22 87 8 71 30b q4 0 39 67 39 86 39 77 14 38 14 01 14 18 39 77 14 19 65b q4 0 59 65 59 78 59 7 20 77 20 22 21 16 59 71 20 72 7b f16 21 05 21 07 21 04 6 68 6 51 6 8 21 05 6 66 13b f16 30 28 30 36 30 22 8 59 9 8 34 30 29 8 64 30b f16 57 06 57 06 57 15 14 32 14 43 14 17 57 09 14 31 65b f16 oom oom oom oom oom oom oom oom a100 80gb 7b q4 0 11 61 11 54 11 49 1 11 1 01 1 11 55 1 04 13b q4 0 17 35 17 42 17 34 1 65 1 65 1 65 17 37 1 65 30b q4 0 35 29 35 16 35 26 3 62 3 56 3 58 35 24 3 59 65b q4 0 68 13 68 08 68 07 6 5 6 5 6 51 68 09 6 5 7b f16 14 26 14 28 14 28 1 01 1 01 1 01 14 27 1 01 13b f16 23 58 23 49 23 49 1 64 1 65 1 64 23 52 1 64 30b f16 52 09 52 3 52 36 3 55 3 62 3 57 52 25 3 58 65b f16 oom oom oom oom oom oom oom oom a6000 48gb 2 7b q4 0 21 67 21 45 21 35 5 05 5 11 4 73 21 49 4 96 13b q4 0 29 47 30 75 29 22 6 22 6 17 7 06 29 81 6 48 30b q4 0 55 22 56 35 56 44 11 42 11 58 11 02 56 11 34 65b q4 0 86 42 84 99 89 09 19 3 18 6 18 08 86 83 18 66 7b f16 28 42 28 67 27 55 4 97 5 08 4 52 28 21 4 86 13b f16 43 43 43 42 43 74 6 23 6 29 6 26 43 53 6 26 30b f16 84 32 87 28 87 41 12 03 12 45 11 97 86 34 12 15 65b f16 oom oom oom oom oom oom oom oom 3090 24gb 6 7b q4 0 33 56 33 4 33 42 18 06 18 38 18 07 33 46 18 17 13b q4 0 42 75 43 02 42 9 23 1 23 26 23 12 42 89 23 16 30b q4 0 67 82 67 4 67 35 35 61 35 7 35 66 67 52 35 66 65b q4 0 96 15 96 1 95 85 49 61 49 18 48 93 96 03 49 24 7b f16 35 12 35 05 35 06 18 26 18 08 18 15 35 08 18 16 13b f16 45 78 46 03 45 77 22 99 23 14 23 45 86 23 04 30b f16 75 76 75 84 75 98 35 63 35 81 35 62 75 86 35 69 65b f16 116 09 116 03 115 9 49 27 49 43 49 6 116 01 49 43 apple silicon snapshots in aug 2023 llama gpu model eval time prompt eval time mean eval time mean prompt eval time m1 max 24 core gpu 32gb 7b q4 0 20 46 20 45 21 44 5 02 5 02 5 01 20 78 5 02 13b q4 0 34 97 36 85 40 58 8 93 10 42 10 76 37 47 10 04 30b q4 0 75 88 76 15 76 21 20 29 20 27 20 29 76 08 20 28 65b q4 0 oom oom oom oom oom oom oom oom 7b f16 71 5 71 65 71 64 4 68 4 69 4 68 71 6 4 68 13b f16 cpu 222 71 230 77 222 93 37 66 35 52 53 63 225 47 42 27 30b f16 oom oom oom oom oom oom oom oom 65b f16 oom oom oom oom oom oom oom oom m2 ultra 76 core gpu 192gb 7b q4 0 12 04 12 03 12 04 1 64 1 65 1 65 12 04 1 65 13b q4 0 19 52 19 08 19 07 2 91 2 9 2 9 19 22 2 9 30b q4 0 40 66 39 54 40 75 6 49 6 47 6 47 40 32 6 48 65b q4 0 71 12 69 87 69 65 11 94 11 87 11 92 70 21 11 91 7b f16 34 86 34 99 35 02 1 57 1 57 1 56 34 96 1 57 13b f16 61 47 62 13 63 01 2 71 2 73 2 72 62 2 2 72 30b f16 138 91 137 71 139 48 6 6 03 6 01 138 7 6 01 65b f16 262 82 262 95 262 83 11 08 11 06 11 06 262 87 11 07 llama 2 gpu model eval time prompt eval time mean eval time mean prompt eval time m1 max 24 core gpu 32gb 7b q4 0 20 49 20 65 20 53 5 01 5 01 5 01 20 56 5 01 13b q4 0 38 31 37 36 38 01 8 95 9 9 10 97 37 89 9 94 70b q4 0 oom oom oom oom oom oom oom oom 7b f16 71 52 72 63 72 83 4 7 4 68 4 7 72 33 4 69 13b f16 cpu 227 05 225 6 228 64 36 28 37 9 40 11 227 1 38 1 70b f16 oom oom oom oom oom oom oom oom m2 ultra 76 core gpu 192gb 7b q4 0 12 07 12 05 12 1 65 1 64 1 66 12 04 1 65 13b q4 0 19 09 19 09 19 11 2 91 2 9 2 91 19 1 2 91 70b q4 0 73 55 72 95 73 06 13 76 13 75 13 74 73 19 13 75 7b f16 34 18 34 19 34 22 1 55 1 55 1 55 34 2 1 55 13b f16 61 48 61 53 61 54 2 71 2 71 2 71 61 52 2 71 70b f16 277 83 278 09 277 73 12 94 12 93 12 93 277 88 12 93 conclusion same performance on llama and llama 2 of the same size and quantization multiple nvidia gpus might affect the performance for llm inference buy 3090s to save money buy 4090s if you want to speed up buy a100s if you are rich buy mac studio if you want to put your computer on your desk save energy be quiet and don t wanna maintenance if you want to train llm choose nivida if you find this information helpful please give me a star feel free to contact me if you have any advice thank you appendix all the latest results are welcome thanks to michaeldays mac llama 2 gpu model eval time prompt eval time mean eval time mean prompt eval time mac pro 2019 intel 16 core 384gb cpu 7b q4 0 54 78 61 74 59 77 35 72 37 29 35 88 58 76 36 30 13b q4 0 102 20 101 97 100 26 63 28 62 77 62 93 101 48 62 99 70b q4 0 547 89 456 45 457 12 295 31 296 16 295 12 487 15 295 53 m2 mini pro 12 19 32gb cpu 7b q4 0 48 04 45 78 49 61 17 34 16 81 16 82 47 81 16 99 13b q4 0 75 93 72 56 71 66 29 39 28 50 28 46 73 38 28 78 70b q4 0 225956 54 oom oom 27220 19 oom oom oom oom 7b f16 118 10 116 46 118 95 17 11 16 62 17 84 117 84 17 19 m2 mini pro 12 19 32gb gpu 7b q4 0 28 92 28 54 29 27 5 89 5 95 5 86 28 91 5 90 13b q4 0 50 76 50 71 50 63 10 30 10 29 10 28 50 70 10 29 70b q4 0 oom oom oom oom oom oom oom oom 7b f16 92 43 91 91 92 11 5 56 5 53 5 56 92 15 5 55 | ai |
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design-system | hack club design system hack club has transitioned to using hackclub theme as its design system this project is only being patched when needed https design hackclub com sh yarn add hackclub design system this project is heavily inspired by priceline com s design system pcln contributing please be sure to read the contributing docs contributing md doc documentation docs getting started docs gettingstarted md contributing docs contributing md theme docs theme md themeprovider docs themeprovider md goals the core goals of this project are to speed up design and development velocity help create consistent beautiful and usable ui in our various websites promote best practices for accessibility and responsive web design we hope to accomplish these goals by reducing the number of decisions needed when iterating on ui reducing the amount of code duplication in our apps serving as the standard for hackclub com s visual language providing easy to use and extensible components for anyone to consume pcln https github com pricelinelabs design system site https design hackclub com mit license license md | design-system hackclub | os |
gin-x | gin x golang web framework base on gin https github com gin gonic gin more components to improve development efficiency sourcegraph https sourcegraph com github com hhxsv5 gin x badge svg https sourcegraph com github com hhxsv5 gin x badge components slim router cron config database enum helper log middleware orm model pager redis response skeleton skeleton https github com hhxsv5 gin x skeleton license mit https github com hhxsv5 gin x blob master license | gin router cron database helper | front_end |
videoplayR | important notice i have stopped developing videoplayr instead i have created two libraries to replace it 1 ropencvlite https github com swarm lab ropencvlite this library downloads compiles and installs opencv within the r library in a standardized location across mac windows and linux it doesn t provide any computer vision functions directly but allows developers to create packages using opencv without having to worry about the platform of the end user 2 rvision https github com swarm lab rvision this library depends on ropencvlite and provides computer vision functions somewhat similar to the discontinued videoplayr package besides making sure that opencv is installed in a standardized location across platforms this new organization also allows updating rvision frequently without having to recompile opencv every time like it was happening with videoplayr ropencvlite needs reinstalling only when a new release of opencv becomes available i recommend that you try out the two new packages instead they should both install fine on windows and report bugs feature requests and comments on their respective repository i don t have much time at the moment to push their development but it ll be helpful to know what people have a need for when i get back to it videoplayr videoplayr is an r package that provides a few functions for now to manipulate video and image files with r it requires the installation of opencv package vignette http rpubs com sjmgarnier videoplayr http rpubs com sjmgarnier videoplayr package overview this package uses the opencv c library http opencv org to provide r users with some basic functions to read and manipulate video and image files as of now videoplayr can load can access video and image files as well as camera streams grab frames from videos and camera streams and convert them to images convert images to r matrices and arrays convert r matrices and arrays to images display images in the default r graphics device perform basic image processing operations such as thresholding flattening blending perfom simple background reconstruction and blob detection general information videoplayr requires the installation of the opencv library to compile and run for the moment the package can only be installed on mac and linux computers installation instructions for opencv and videoplayr as well as a brief tutorial for the different functions of the package are available on rpubs at the following address http rpubs com sjmgarnier videoplayr http rpubs com sjmgarnier videoplayr issues and suggestions can be reported here https github com swarm lab videoplayr issues https github com swarm lab videoplayr issues pull requests can be submitted here https github com swarm lab videoplayr pulls ttps github com swarm lab videoplayr pulls author s simon garnier sjmgarnier https twitter com sjmgarnier garnier njit edu references this package uses the opencv library to read and process video files more information about opencv can be found at http opencv org http opencv org | ai |
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anlp19 | course materials for applied natural language processing spring 2019 syllabus http people ischool berkeley edu dbamman info256 html notebook description 1 words evaluatetokenizationforsentiment ipynb the impact of tokenization choices on sentiment classification 1 words exploretokenization ipynb different methods for tokenizing texts whitespace nltk spacy regex 1 words tokenizeprintedbooks ipynb design a better tokenizer for printed books 2 distinctive terms chisquare ipynb find distinctive terms using the chi square test 2 distinctive terms comparecorpora ipynb find distinctive terms using the mann whitney rank sums test 3 dictionaries dictionarytimeseries ipynb plot sentiment over time using human defined dictionaries 4 classification checkdata todo ipynb gather data for classification 4 classification featureexploration todo ipynb feature engineering for text classification 4 classification featureweights todo ipynb analyze feature weights for text classification 4 classification hyperparameters todo ipynb explore hyperparameter choices on classification accuracy 5 text regression regularization ipynb linear regression with l1 l2 regularization for box office prediction 6 tests bootstrapconfidenceintervals ipynb estimate confidence intervals with the bootstrap 6 tests parametrictest ipynb hypothesis testing with parametric normal tests 6 tests permutationtest ipynb hypothesis testing with non parametric permutation tests 7 embeddings distributionalsimilarity ipynb explore distributional hypothesis to build high dimensional sparse representations for words 7 embeddings tfidf ipynb explore distributional hypothesis to build high dimensional sparse representations for words with tf idf scaling 7 embeddings turneylittman2003 ipynb use word embeddings to implement the method of turney and littman 2003 for calculating the semantic orientation of a term defined by proximity to other terms in two polar dictionaries 7 embeddings wordembeddings ipynb explore word embeddings using gensim 8 neural mlp ipynb mlp for text classification keras 8 neural exploremlp ipynb explore mlp for your data keras 8 neural cnn ipynb cnn for text classification keras 8 neural lstm ipynb lstm for text classification keras 8 neural attention ipynb attention over word embeddings for document classification keras 8 neural attentionlstm ipynb attention over lstm output for text classification keras 9 annotation iaametrics ipynb calculate inter annotator agreement cohen s kappa krippendorff s alpha 10 wordnet explorewordnet ipynb explore wordnet synsets with a simple method for finding in a text all mentions of all hyponyms of a given node in the wordnet hierarchy e g finding all buildings in a text 10 wordnet lesk ipynb implement the lesk algorithm for wsd using word embeddings 10 wordnet retrofitting ipynb explore retrofit word vectors 11 pos keyphraseextraction ipynb keyphrase extraction with tf idf and pos filtering 11 pos pos tagging ipynb understand the penn treebank pos tags through tagged texts 12 ner extractingsocialnetworks ipynb extract social networks from literary texts 12 ner sequencelabelingbilstm ipynb bilstm sequence labeling for twitter ner 12 ner toponymresolution ipynb extract place names from text geolocate them and visualize on map 13 mwe justesonkatz95 ipynb implement justeson and katz 1995 for identifying mwes using pos tag patterns 14 syntax syntacticrelations ipynb explore dependency parsing by identifying the actions and objects that are characteristically associated with male and female characters 15 coref corefsetup ipynb install neuralcoref for coreference resolution 15 coref extracttimeline ipynb use coreference resolution for the task of timeline generation for a given biography on wikipedia can you extract all of the events associated with the people mentioned and create one timeline for each person 16 ie dependencypatterns ipynb measuring common dependency paths between two entities that hold a given relation to each other 16 ie entitylinking ipynb explore named entity disambiguation and entity linking to wikipedia pages 17 clustering topicmodeling todo ipynb explore topic modeling to discover broad themes in a collection of movie summaries | natural-language-processing | ai |
cuckooml | cuckooml machine learning for cuckoo sandbox join the chat at https gitter im honeynet cuckooml https badges gitter im honeynet cuckooml svg https gitter im honeynet cuckooml utm source badge utm medium badge utm campaign pr badge utm content badge read the blog at https honeynet github io cuckooml https img shields io badge blog read green svg https honeynet github io cuckooml cuckooml is a project that aims to deliver the possibility to find similarities between malware samples based on static and dynamic analysis features by using anomaly detection techniques such mechanism will be able to cluster and identify new types of malware and will constitute an invaluable tool for security researchers through the project state of the art data science and machine learning approaches will be implemented and integrated into the cuckoo sandbox and will be made accessible as a command line toolkit and as a web based interface cuckooml development is supported by the google summer of code 2016 https summerofcode withgoogle com and mentored by the honeynet project https www honeynet org | ai |
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video2scenario | video2scenario forms a tree like folder dataset with l parts at the lowest level then recursively writes descriptions of scenes with large language models and image captioning models the lowest level clips are captioned with blip2 https github com exponentialml video blip2 preprocessor support for multimodal models such as llava is planned the descriptions are gathered in a list and then the llm is asked to describe the overall scene then the process continutes until the top level any openai like text completion model can be used for this in my tests oobabooga s text generation webui https github com oobabooga text generation webui is used as the api endpoint user can also provide the master prompt to help the model and edit the resulting descriptions with a gradio demo interface there is also an option to store the resulting corrected output for better fine tuning the models for example using a lora the gradio interface has a dropdown to select each description clip pair on each level the goal of this subproject is to make a diffusionoverdiffusion dataset to train infinet https github com kabachuha infinet and the future complex script based text2video models with minimal human labeling efforts | gradio image-captioning large-language-models video-labeling | ai |
udagram-assignment | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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jurassicIT | jurassicit jurassic it jurassic information technology | xhtml | server |
maxmertkit | maxmertkit http maxmert com not supported anymore join the chat at https gitter im maxmert maxmertkit https badges gitter im join 20chat svg https gitter im maxmert maxmertkit utm source badge utm medium badge utm campaign pr badge utm content badge build status https travis ci org maxmert maxmertkit svg branch master https travis ci org maxmert maxmertkit maxmertkit powerful most customizable and easiest for usage mobile first front end framework for web development created by vetrenko maxim http twitter com vmaxmert and maintained by the core team https github com maxmert tab members with the support and involvement of the community to get started check out http maxmert com table of contents quick start quick start bugs and feature requests bugs and feature requests documentation documentation compiling css and javascript compiling css and javascript contributing contributing community community versioning versioning author author copyright and license copyright and license quick start three quick start options are available download the latest release https github com maxmert maxmertkit releases latest clone the repo git clone https github com maxmert maxmertkit git install with bower http bower io bower install maxmertkit read the start page http maxmert com start for information on the framework contents howto videos examples and more what s included within the download you ll find the following directories and files logically grouping common assets and providing both compiled and minified variations bugs errors and feature requests have a bug text error or a feature request please first read the issue guidelines https github com maxmert maxmertkit blob master contributing md using the issue tracker and search for existing and closed issues if your problem or idea is not addressed yet please open a new issue https github com maxmert maxmertkit issues new documentation maxmertkit s documentation included in this repo in the root directory is run with nodejs http nodejs org the docs may be run locally running documentation locally go to maxmert com start maxmert com start and watch howto video 1 if necessary install nodejs http nodejs org install npm http npmjs org install bower http bower io 2 from the root maxmertkit directory run npm install in the command line 3 run bower install in the command line 4 from the docs directory run npm install in the command line 5 from the docs directory run bower install in the command line 6 from the root maxmertkit directory run gulp in the command line 7 open http localhost 3333 in your browser documentation for previous releases documentation for v0 0 2 has been made available for the time being at http old maxmert com while folks transition to maxmertkit 1 0 0 compiling css and javascript maxmertkit uses gulp http gulpjs com with convenient methods for working with the framework it s how we compile our code run tests and more to use it install the required dependencies as directed and then run some gulp commands install gulp from the command line 1 install gulp globally with npm install g gulp maybe you ll need to run it with sudo sudo npm install g gulp 2 read about running documentation locally when completed you ll be able to run the various gulp commands provided from the command line unfamiliar with npm don t have node installed that s a okay npm stands for node packaged modules http npmjs org and is a way to manage development dependencies through node js download and install node js http nodejs org download before proceeding available gulp commands build and watch development gulp run gulp to run buld and run documentation locally it will compile coffeescript http coffeescript org and sass http sass lang com into docs and run nodemon https github com remy nodemon server at port 3333 only compile css and javascript production gulp build run gulp build to clear the build directory and recompile all coffeescript http coffeescript org and sass http sass lang com files with gzip and standart version tests gulp test troubleshooting dependencies should you encounter problems with installing dependencies or running gulp commands uninstall all previous dependency versions global and local then rerun npm install and bower install in the root and docs directory contributing please read through our contributing guidelines https github com maxmert maxmertkit blob master contributing md included are directions for opening issues coding standards and notes on development moreover if your pull request contains javascript patches or features please include relevant unit tests all html and css should conform to the code guide http github com mdo code guide maintained by mark otto http github com mdo community keep track of development and community news follow maxmertkit on twitter http twitter com maxmertkit follow vmaxmert on twitter http twitter com vmaxmert implementation help may be found at stack overflow tagged maxmertkit 1 http stackoverflow com questions tagged maxmertkit 1 versioning for transparency into our release cycle and in striving to maintain backward compatibility maxmertkit is maintained under the semantic versioning guidelines sometimes i screw up but i ll adhere to these rules whenever possible releases will be numbered with the following format major minor patch and constructed with the following guidelines breaking backward compatibility bumps the major while resetting minor and patch new additions without breaking backward compatibility bumps the minor while resetting the patch bug fixes and misc changes bumps only the patch for more information on semver please visit http semver org author vetrenko maxim http twitter com vmaxmert http github com maxmert http facebook com vetrenko maxim copyright and license code and documentation copyright 2012 2014 maxmert code released under the mit license license docs released under creative commons docs license bitdeli badge https d2weczhvl823v0 cloudfront net maxmert 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An-Efficient-DDoS-Attack-Detection-Mechanism-in-SDN-Environment | an efficient ddos attack detection mechanism in sdn environment abstract traditional intrusion detection systems are insufficient to identify recent and modern sophisticated attempts with unpredictable patterns the ability to reliably detect modern cyberattacks is vital current machine learning based intrusion detection methods in the field of information technology cannot keep up with the exponential growth of network data and features for the optimum and decreasing selection of high dimensional incursion characteristics deep convolutional neural networks dcnn can be an efficient approach traditional convolutional neural networks cnn are still limited to several parameters and are susceptible to local optimality in this paper we propose a dcnn model to detect attacks and test it on a software defined network sdn environment we use the insdn dataset specifically developed for the sdn environment additionally the model has been trained using the cic ids2017 and the cic ddos2019 datasets to demonstrate the applicability of the model our model outperforms most of the recent attack detection methods and could achieve a 99 99 accuracy rate with only a 0 0016 loss rate journal international journal of information technology publisher springer manuscript id bjit d 22 03013 first submission 19 december 2022 first revision 26 february 2023 accepted on 30 may 2023 doi 10 1007 s41870 023 01332 5 url https link springer com article 10 1007 s41870 023 01332 5 if you find this code and paper useful kindly consider to cite from your valuable work how to cite hnamte v hussain j an efficient ddos attack detection mechanism in sdn environment int j inf tecnol 2023 https doi org 10 1007 s41870 023 01332 5 | dcnn ids sdn cicddos2019 cicids-2017 insdn | server |
Fall16_MAD | fall16 mad mobile app development class examples | front_end |
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portfolio | p align center img alt inventistudio portfolio src static logo trans white svg br br p inventistudio https inventi studio portfolio p a href https inventi studio target blank inventistudio a is a web development agency based in wroc aw poland we excel at building modern web applications providing vue js front end node js back end and ui ux design services for enterprises companies and startups a href mailto hello inventi studio get in touch with us a p https img shields io travis inventistudio portfolio svg https img shields io travis inventistudio portfolio svg table of contents setup setup development development production production setup requirements nodejs https nodejs org 8 0 0 yarn https yarnpkg com lang en 0 21 3 development bash clone repo and install deps git clone git github com inventistudio portfolio git cd portfolio yarn bash run dev server with hot reload at localhost 8080 yarn dev linters bash run sass linter yarn lint sass run javascript linter yarn lint es run all linters yarn lint tests bash run unit tests yarn test unit run e2e tests yarn test e2e production bash build for production with minification yarn build build for production and view the bundle analyzer report yarn build report run production server at localhost 8080 yarn start | vuejs vue-js website portfolio agency studio sass | front_end |
PatternDesign-In-C | pattern design for embedded systems in c patterns used in that chapter chapter 1 what is embedded programming chapter 1 project details summary patterns details summary that file contains client server pattern push pop pattern and observer pattern details chapter 2 embedded programming with the harmony tm for embedded realtime process chapter 1 project details summary patterns details summary that file contains client server pattern push pop pattern and observer pattern details chapter 3 design patterns for accessing hardware chapter 2 project details summary patterns details summary that file contains interrupt polling adapter proxy debouncing mediator and observer patterns in form of hal driver that kinda interact directly with the hardware details chapter 4 design patterns for embedding concurrency and resource management chapter 4 project details summary patterns details summary that file contains static priority pattern critical region pattern guarded call pattern queuing pattern rendezvous pattern simultaneous locking pattern and ordered locking pattern in form of hal driver that kinda interact directly with the hardware or interact with hardware through general hal layer details chapter 5 design patterns for state machines chapter 4 project chapter 6 safety and reliability pattern chapter 5 project some tested drivers to make sure that it works fine testing lcd testing sw layers testing drivers app testing lcd details summary project s details summary using i2c lcd serial interface module to control the lcd details testing i2c testing sw layers testing drivers app testing i2c details summary project s details summary using i2c lcd serial interface module as gpio expander and control leds with it details testing i2c 2 testing sw layers testing drivers app testing i2c 2 details summary project s details summary using i2c lcd serial interface module as gpio expander and control leds with it details testing ds1037 testing sw layers testing drivers app testing ds1037 details summary project s details summary using ds1037 i set the initial time and then read it every 3 second this project test all the operations in my i2c driver details testing adc testing sw layers testing drivers app testing adc details summary project s details summary using the potentiometer on the module i2c lcd serial interface to test my driver details testing dio testing sw layers testing drivers app testing dio isr details summary project s details summary trying on board buttons and leds to test my dio isr and normal functions in dio driver details steps you need to do if you are using ccs or another ide 1 https user images githubusercontent com 63866803 222526259 960e4c60 2f62 4480 9530 2af350e88dee png 2 https user images githubusercontent com 63866803 222526260 67a399bc ef22 4bd9 aed1 154a2a38027c png 3 https user images githubusercontent com 63866803 222526262 faab5933 2d10 4353 be9a 31c2206a61de png 4 https user images githubusercontent com 63866803 222526247 cfd451e3 874e 4ac8 9acc 5a64db1d3ebf png 5 https user images githubusercontent com 63866803 222526257 3f7cf3d4 8698 46eb add7 a1c8a523ed5b png 6 https user images githubusercontent com 63866803 232917522 eb1c80d2 d17e 4941 a9f7 dd9b4a5542c5 png skills c embedded c arm architecture | design-patterns i2c lcd-serial-interface-module-as-a-gpio-expander | os |
Intro_to_IT | intro to it introduction to information technology | server |
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UML | tongji univ system analysis and design course youmu online bookstore 1 overview 1overview 1 1 requirement 11 requirement 1 2 background 12 background 1 3 purpose amp definition 13 purpose definition 1 4 system overview 14 system overview 1 4 1 system structure 141 system structure 1 4 2 design 142 design 1 4 3 description of implementations 143 description of implementations 1 5 progress for each time 15 progress for each time 2 architecture 2architecture 2 1 global architecture 21 global architecture 2 1 1 architecture diagram 211 architecture diagram 2 1 2 architecture description 212 architecture description 2 1 3 deployment diagram 213 deployment diagram 2 2 subsystems and interfaces 22 subsystems and interfaces 3 use case modelling 3use case modelling 3 1 global system use case 31 global system use case 3 2 subsystem use case 32 subsystem use case 3 2 1 login amp register subsystem 321 login register subsystem 3 2 2 search amp visitbookpage subsystem 322 search visitbookpage subsystem 3 2 3 homepage subsystem 323 homepage subsystem 3 2 4 message subsystem 324 message subsystem 3 2 5 view data subsystem 325 view data subsystem 3 3 glossary of terms 33 glossary of terms 3 4 supplementary specification 34 supplementary specification 4 domain model 4domain model 4 1 class diagram 41 class diagram 4 2 relationships between classes 42 relationships between classes 5 analysis model 5analysis model 5 1 login 51 login 5 2 register 52 register 5 3 search 53 search 5 4 viewbookpage 54 viewbookpage 5 5 viewshoppingcart 55 viewshoppingcart 5 6 visitorderform 56 visitorderform 5 7 advertise 57 advertise 5 8 viewmessage 58 viewmessage 5 9 analysisdata 59 analysisdata 6 design mechanism 6design mechanism 6 1 overview 61 overview 6 2 persistence 62 persistence 6 3 information exchange 63 information exchange 7 realization 7realization 7 1 interfaces 71 interfaces 7 1 1 interfaces between our system and external systems 711 interfaces between our system and external systems 7 1 2 order query subsystem and its interfaces specification 712 order query subsystem and its interfaces specification 7 2 use case realization 72 use case realization 7 2 1 login 721 login 7 2 2 visit order form 722 visit order form 8 prototyping 8prototyping 8 1 description 81 description 8 2 register example 82 register example 8 2 1 interact with database 821 interact with database 8 2 2 front end 822 front end 8 2 3 click method 82 3 click method 8 3 initial snapshot 83 initial snapshot 8 3 1 mobile login 831 mobile login 8 3 2 mobile personal page 832 mobile personal page 8 3 3 moblie shopping cart 833 moblie shopping cart 8 3 4 moblie payment 834 moblie payment 8 3 5 pc main page 835 pc main page 8 3 6 background statistics 836 background statistics 8 3 7 background add book 837 background add book 9 quality 9quality 9 1 architecture style 91 architecture style 9 2 design patterns 92 design patterns 9 3 critical decisions 93 critical decisions 10 improvement 10improvement 10 1 limitation 101 limitation 10 1 1 user experience lack of special handling mechanism for ordinary users 1011 user experience lack of special handling mechanism for ordinary users 10 1 2 lower level design a rough design of database 1012 lower level design a rough design of database 10 2 potential improvements 102 potential improvements 10 2 1 design asynchronous version apis on the presentation layer 1021 design asynchronous version apis on the presentation layer 10 2 2 update the service layer by splitting it into two parts 1022 update the service layer by splitting it into two parts 10 2 3 add a waf at the outside boundary of the infrastructure layer 1023 add a waf at the outside boundary of the infrastructure layer 10 2 4 add a model layer to get data more efficiently 1024 add a model layer to get data more efficiently 10 3 open issues 103 open issues 10 3 1 robustness 1031 robustness 10 3 2 maintainability amp expandability 1032 maintainability expandability 10 3 3 form a book dispatching schedual system 1033 form a book dispatching schedual system 10 4 development plan 104 development plan 11 course reflection 11course reflection 11 1 teamwork 111 teamwork 11 2 uml developing tool 112 uml developing tool 11 3 presentation 113 presentation 11 4 materials 114 materials 11 5 self reflection 115 self reflection zhe zhang team leader zhe zhang team leader siteng ma siteng ma gefei wang gefei wang le yang le yang kaixin chen kaixin chen 12 contributions of team member 12contributions of team member 13 references 13references 1 overview 1 1 requirement a new business will be initiated a virtual bookstore the system to support it must manage the acquisition and selling processes of the company access for customers and management people must be accomplished through a web site the user can access it via pc or mobile device when a book is ordered it is delivered immediately if available in stock or else the book is ordered to a publisher and a compatible deadline is informed to the customer the system shall handle the return orders the system must calculate taxes and delivery fee as well as applying discounts to the sale when applicable the system should be able to monitor the inventory and issue the warning when certain products need to be replenished the system must allow a manager to generate reports on bestselling books and on most profitable customers as well as suggest books for buying based on past customer s interests furthermore the system must have the capability of predicting the sales in order to provide better decision inventory reordering products etc with the solid foundation constraints a customers would pay by credit card alipay or wechat b the bookstore will deal only with new books not used ones c access to the system will be available through a web site via pc mobile devices etc 1 2 background as the booming of network in recent years e commerce has been widely accepted by a variety of the internet users being a part of which online book store system is developing rapidly as well in a form of website or application online book store can be taken advantage to sell books online book store opens one after another with the broad adaptation of e commerce in the aspect of book selling for one thing a majority of people have formed the habit that shop online the number of which has been increasing continuously for another thing from the aspect of shopping items books are relatively cheap and standard so it is of few risks to buy a book and online purchasing can be accepted easily comparing to the traditional book store the online book store have a lot of advantages as for sellers they can not only avoid the limitation and blindness while ordering books but also surmount the obstacles of high expanses tough management and costly ordering as for customers they are capable of learning details looking through samples and ordering books swiftly it is difficult for offline transaction to have the above advantages all in all developing an online book store system is available as well as fashionable 1 3 purpose definition based on the fundamental functions of online book store and our brainstorming the participants of the online book store system involve users visitors registered user senior user administrators publishers and third party payment enterprises according to the advanced design the online book store system can maintain the following functions 1 as for users have access to check and alter part of the personal information be able to search add to shopping cart add to order form and ask for after sale service can share own reading schedule and comments 2 as for administrators update the details of each book control the basic settings of the system send messages to users in time and get touch with publishers in order to inform them to supply the stock 3 as for publishers receive the timely messages which come from administrators and feedback the stock information 4 as for third party payment enterprises give authority to the payments in the order forms 5 keep limits of authority distinct so that it is conducive to the protection of data and intimacy securities 6 find latent users in order to expand our customers to coordinate with the online book store s style we are expecting to provide a cozy environment for users to stroll in the book store at the same time of feeling the pleasure of reading besides make it possible that users are able to easily buy a book online by using our online book store system users can attain a positive shopping and reading experience moreover as an online book store we are determined to endow a sense of belonging to every user by forming a unique culture so we have designed an ui of chinoiserie and have entitled our store youmu book store 1 4 system overview 1 4 1 system structure the high level architecture is divided as four different layers which have their own certain functions named as presentation layer services layer controller layer and data layer 1 4 2 design the customers will send request from the browser on his her device then the browser accesses the targeted web pages through uniform resource locator url which colloquially termed a web address after retrieving the request the view invokes the model to retrieve information from the backend database the tuples in the database will be returned as an object then the view will process them select the appropriate templates padding with the appropriate parameters and finally send the html web page to the browser in fact our architecture is framework based views and models are implemented at different levels 1 4 3 description of implementations we choose to use the popular css and javascript frameworks such as vue2 0 and jquery to design our html web pages jquery is a cross platform javascript library designed to simplify the client side scripting of html it is a free open source software using the permissive mit license web analysis indicates that it is the most widely deployed javascript library by a large margin vue2 0 is a progressive framework for building user interfaces unlike other monolithic frameworks vue is designed from the ground up to be incrementally adoptable the core library is focused on the view layer only and is easy to pick up and integrate with other libraries or existing projects based on google s v8 engine node js is an event driven i o server side javascript environment simply node js runs in the backend written by javascript we take advantage of express as framework to develop server and what s more to apply to the web application attaching great functions to our online book store system as for the persistence we use the mysql as dbms to maintain the data 1 5 progress for each time a1 a2 for the use case diagram we integrated the original user and administrator use case diagrams together and deleted some redundant relationships increasing the types and generalization of users for activity diagrams in order to clarify the behavior of different participants in the activity we added swim lanes for each activity map and optimized the layout of the activities for the system snapshot in order to match the chinese style theme of yu mu shu wu we redesigned a corresponding ui and added a mobile prototype in order to clarify the system architecture reduce system redundancy and facilitate teamwork we layered the whole system and gave the system package diagram and analyzed and clarified the architecture of the whole system for the design of the domain model we designed the concept class based on the principle of focusing on the application side rather than the system design drawing the overall concept class diagram adding the relationship between the classes and finally sorting into tables a2 a3 since the last project a great progress has been made the english version is totally developed which means english is adapted to each of our diagrams as well as word descriptions as for the architecture according to the result of our brainstorming we have refined the architecture with considering the ways of implementation based on the original 4 layers each layer has specific dependent platform what s more a label of subsystems and interfaces is listed to demonstrate the operations and interfaces clearly and several samples are presented to show the details of our process as for the design mechanisms we have searched a lot of relative reports to learn about it and make use of some of them to our online book store system persistence mechanism and information exchange mechanism are exemplified specifically with their class diagram sequence diagram and description including the suitable implementation platform as for the use case realization the above design mechanism is combined with the architecture presented through the use case as for the prototyping we have already created a demo connecting the database and our login subsystem interface users are able to register and log in though the browse the coordinate data of him or her will be stored perpetually apart from the use case model we made in advance we analyzed the whole system and separated it to 9 subsystems suppling the class diagram the sequence diagram etc the class diagrams are updated from the previous analysis class diagrams being suppled with functions and boundary or control classes as for the design mechanism the architecture and interfaces are presented especially the detailed interfaces showing the major pages of our online book store system the interpretation is shown in these pages a3 a4 for the final solution and self reflection we first make some improvement of our primary architecture the basic principle we use in architectural design is layered architecture which is the most common architecture and has become almost a standard norm we introduce asychronus mechanism on the web front end by using asychronous design version apis such as ajax and jquery what s more we update the service layer by splitting it into two parts the web apis containing dialog controller and order info interface and the real time apis containing mapping system and payment system the updated layer is shown as below services layer png https upload images jianshu io upload images 12014150 c18b46665fc91f5e png imagemogr2 auto orient strip 7cimageview2 2 w 1240 we also add a web application firewall at the outside boundary of the infrastructure layer the way middleware controls all data access to our api then we add a model layer between controller layer and data layer in order to search data more efficiently by accessing the database more clearly and easily furthermore we make critical design for data storage and classes first we decided to use mysql mongodb and redis together to store our data considering the three kinds of data in our system which are data that is highly relational data that is less relational and data that is performance sensitive second we create a class called senior user derived from normal user in order to help reduce the data that needs to be stored and transfered for coupons also we find some open issues for our system the first is improving robustness which is very important for our system we need to improve the robustness in architecture design and program coding the main way to improve the robustness in architecture design is to exchange time and space for performance and to improve the robustness for coding we can utilize some program static analysis tools such as sonar pmd and so on the second open issue is for maintainability and expandability take the processing of online payment as an example our system class provides an interface and concrete class and whenever we want to change the way we pay we can only extend the functinality of this subsystem finally it comes to our self reflection we reflect our project in teamwork developing tools presentation and resources and we also reflect what each of our group members has achieved it is the group discussion from time to time that help us to progress and we find a good developing tool draw io most importantly we want to thank our professors for their directions 2 architecture 2 1 global architecture 2 1 1 architecture diagram global architecture png https upload images jianshu io upload images 12014150 933912158d3f6dbe png imagemogr2 auto orient strip 7cimageview2 2 w 1240 2 1 2 architecture description the high level architecture is divided as four different layers which have their own certain function and also the majority of them correlative with others they named as presentation layer services layer controller layer and data layer presentation layer the presentation layer is in charge of providing user interface and handle user interactions we renew it from the last assignment which we called it as ui layer and the packages were also renew from the mobile terminal ui and web terminal ui on the web frontend we choose the popular css and javascript frameworks such as vue js and jquery to design our html web pages why do we choose vue js vue is a progressive framework for building user interfaces unlike other large frames vue is designed to be applied from the bottom up vue s core library focuses only on the view layer making it easy to get started and easy to integrate with third party libraries or existing projects on the other hand vue is also fully capable of driving complex single page applications when used in conjunction with modern toolchains and various support libraries why do we choose jquery jquery is a javascript tool library class library that gets a whole set of defined methods by encapsulating native javascript functions integrates the power of javascript css dom and ajax we can write only a little code to gain a wonderful effect the template engine is a tool for parsing corresponding type template files and then dynamically generating view files consisting of data and static pages it responds to various parsing actions through tags and dynamically displays the corresponding data to a specified location by means of variable occupancy we have also imported the template engine of django in the realization of web frontend which serves as the templates of mtv architecture pattern since our customer app and delivery app are based on android frameworks like okhttp3 retrofit2 gson will be used in those modules okhttp mainstream web request framework the whole process is convert the constructed request to call through okhttpclient then perform asynchronous or synchronous tasks in realcall and finally send out the network request and get the returned response through some interceptor interceptor why do we choose okhttp 1 support http2 share all requests for one machine to share the same socket 2 built in connection pool support connection multiplexing reduce latency 3 support transparent gzip compression response body 4 avoid duplicate requests through caching 5 automatically retry the host s other ip when the request fails automatically redirect 6 easy to use api retrofit2 retrofit2 is simply a network request adapter that translates a basic java interface into an http request via a dynamic proxy and sends the request through okhttp it also has great scalability support for various format conversions and rxjava gson gson is a java class library provided by google to map between java objects and json data you can convert a json character into a java object or convert a java to a json string why do we choose gson 1 fast and efficient 2 the amount of code is small simple 3 object oriented 4 data transfer and analysis is convenient services layer it provides unified api interfaces of all functionalities for both web clients and mobile clients the web apis are provided in the form of json services based on http https in the dialogue controller subsystem we import django s url library django conf urls url the amap api is called in the mapping system and the sf api is called in the express system which is used for checking logistics information such as where the books now i bought how far is it from me etc the payment system supports three kinds of payment modes alipay wechatpay and unionpay we read the official document of the api and designed adapters for those to help us to use it conveniently pay png https upload images jianshu io upload images 12014150 65aa22c8ddc4e894 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 controller layer in this layer we build three main controllers user controller book controller order controller and we also hold a delivery schedule and django view in user controller it mainly contain the behavior that the users interact with the system the user can check and modify his or her information modify personal information releases notices this is only for administrator and get issue coupons in book controller it contain modify the book information search book order by sold amount stack check if close to the limit amount we should send a message to the publisher modify the evaluation and purchase alarm in order controller it contain check order information modify generate or delete order refund payment reminder statistics order and return we also create a express controller which is powered by amap api and sf api it is used for customer to check his or her express information and can get the location of the books he or she brought at the first time data layer the data access layer is in charge of all data accessing operations it provides unified data accessing interfaces for different data models the system introduces three different databases for storing data mysql for storing most part of the data which is highly relational and has relatively high demand of acid mongodb for storing mass data that is less relational and has less demand of acid such as gps location data from all gps sensors in each collecting period 30 seconds mongodb enables those kinds of data to be stored and accessed more efficiently finally the system requires redis which is an in memory however data can be persistent high performance key value database which is used to store cache and sessions that are highly performance sensitive notice that redis also supports a rich kind of data structures which can significantly boost the calculation of some kind of ranking related tasks for mongodb the system uses odm object document mapping frameworks to map objects to mongodb queries for mysql the system uses orm object relational mapping frameworks to map objects to sql queries orm and odm frameworks provides better readability while fundamentally prevents sql injections both odm and orm frameworks exposes interface under the caching layer in this kind of architecture the caching layer is transparent to the developers developers need not to care about whether to access the cache or database this can simplify the development process while keeping high performance the cache layer is dependent on redis and its drivers of course 2 1 3 deployment diagram deployment diagram jpg https upload images jianshu io upload images 12014150 d5ceef20304b0ecd jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 user client we hold two kinds of service mode for user client android app and browser they are provided services by different servers firewall there is a flow distributor on the firewall which distribute the assess from the client all operations on the server are filtered through the firewall web server the session server is designed for provide authentication services page routing and rendering are finished by django server database server the web server and app server share one database server where redis provides key value cache and improves authentication speed besides sql server and mongo server will assume relational and non relational data storage services respectively why do we choose mongo server mongodb atlas delivers the world s leading database for modern applications as a fully automated cloud service engineered and run by the same team that builds the database proven operational and security practices are built in automating time consuming administration tasks such as infrastructure provisioning database setup ensuring availability global distribution backups and more the easy to use ui and api let you spend more time building your applications and less time managing your database why do we choose redis server redis is an open source bsd licensed in memory data structure store used as a database cache and message broker it supports data structures such as strings hashes lists sets sorted sets with range queries bitmaps hyperloglogs geospatial indexes with radius queries and streams redis has built in replication lua scripting lru eviction transactions and different levels of on disk persistence and provides high availability via redis sentinel and automatic partitioning with redis cluster 2 2 subsystems and interfaces subsystem png https upload images jianshu io upload images 12014150 30b3c755f3a8ecaf png imagemogr2 auto orient strip 7cimageview2 2 w 1240 3 use case modelling 3 1 global system use case global use case diagram jpg https upload images jianshu io upload images 12014150 938a49d76bad530d jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 3 2 subsystem use case 3 2 1 login register subsystem use case diagram login register png https upload images jianshu io upload images 12014150 82dd0630c9ba54df png imagemogr2 auto orient strip 7cimageview2 2 w 1240 use case login register system actor customers administrator publisher basic flow br 1 customers administrator publisher select the button log in br 2 customers administrator publisher input their username and password into the system br 3 customers administrator publisher enter the system successively alternative flow br 1 if the customer is a new one who enter our system he or she are supposed to select the button register and then input required information and set password for their accounts br 2 customers enter the password wrongly the system will allow them to have tries for another 3 times br 3 customers forget the password then the system will offer a security code to customers asking customers to reset the password br 4 administrators forget the password he or she can use his or her id to reset the password again br 5 publishers forget the password he or she can send a message to administrators to send their password again post condition br customers have access to checking up the information of the order paying for the bill and signing the package 3 2 2 search visitbookpage subsystem use case diagram search visitbookpage png https upload images jianshu io upload images 12014150 d7a49ee06fcc1e86 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 use case search visitbookpage subsystem actor customer precondition connect to the internet and successfully access the page or app basic flow br a the user enters the content in the search box author name or book name or publisher name br b return search results br emsp a the book or author does not exist and returns the bookstore does not have this author book or publisher br emsp emsp 1 display a list of related books related to the book br emsp b the book or author exists br emsp emsp 1 display simple information about the book title author price publisher br emsp emsp 2 similar results appear at the bottom of the search results br emsp c search results can be adjusted by sorting sales descending ascending rating descending ascending br c the user clicks on the book to access the book homepage br d the book s home page displays the details of the book cover preview title author price publisher reader comments br e collecting books br emsp a the user is not logged in and jumps to the login screen br emsp b user has logged in add books to favorites br f show comments for this book br emsp a the user is not logged in and can only view comments br emsp b user login br emsp 1 the book has not been purchased only comments can be viewed br emsp 2 purchased the book to comment on the book or respond to comments from others activity diagram jpg https upload images jianshu io upload images 12014150 7ca1ef1a38f49289 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 3 2 3 homepage subsystem use case diagram homepage system jpg https upload images jianshu io upload images 12014150 7f9ae8e88749c2e2 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 use case view advertisement actor user administrator precondition user administrator login flow of events br 1 user administrator view our rank list and homepage recommentation br 2 user administrator click on the book he she want to further learn about br 3 user administrator enter the selected book s page successively post condition br customers can learn more about the book and have some actions on it 3 2 4 message subsystem use case diagram message system jpg https upload images jianshu io upload images 12014150 30655b9ae0379a18 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 use case send message actor user publisher administrator precondition for private message administrator publisher and user know the id of each other flow of events br 1 the administrator publisher or user send private message br emsp a the administrator publisher or user open the message window br emsp b the administrator publisher or user set the receiver s id br emsp c the administrator publisher or user enter content in the message box br emsp d the administrator publisher or user click on send button br emsp e the receiver receive message br 2 the administrator send system message br emsp a the administrator open the message window br emsp b the administrator set the type system message br emsp c the administrator enter content in the message box br emsp d the administrator click on send button use case receive message actor user publisher administrator precondition administrator publisher and user know the id of each other and send message flow of events br 1 the administrator publisher or user receive private message br emsp a the administrator publisher or user open the message window br emsp b the administrator publisher or user view the message content br 2 the publisher or user receive system message br emsp a the administrator view the homepage and see the system message activity diagram jpg https upload images jianshu io upload images 12014150 f886f1eb8ebff2d1 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 jpg https upload images jianshu io upload images 12014150 e90bbdefbb02fe3a jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 3 2 5 view data subsystem use case diagram view data system jpg https upload images jianshu io upload images 12014150 742fb460aa1f4c4b jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 use case view data actor administrator precondition administrator login and is verified flow of events br 1 administrator view data analysis window br 2 administrator choose user s data br emsp a administrator check if the user s data is right and tell the user to correct it br emsp b adminstrator modify the user s data like change some user into superior user br 3 administrator choose book s data br emsp a administrator check if the book s data is right and correct it br emsp b administrator update book s data like modifying its price post condition br customers can learn more about the data and have some actions on it activity diagram jpg https upload images jianshu io upload images 12014150 b4d75942fb22c54e jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 3 3 glossary of terms term explanation registration visitors fill in their personal information create exclusive account information and assign accounts to them after registration visitors become registered users of the system login registered users use personal account passwords to access personal accounts to obtain appropriate user rights and to use user functions such as purchases favorites comments and so on administrator that is the e bookstore system is backstage and the e bookstore system is used to interact with other participants of the system and some data of the system can be modified advanced users registered users can become advanced users after multiple purchases of books advanced users are marked as profitable and administrators can issue coupons or push book advertisements to advanced users tourists no system account is registered only information can be browsed and communicated with the administrator but there are no user functions such as comments favorites shopping carts purchases etc publisher the e book store s source of the book s goods the administrator can adjust the purchase volume replenish the source of goods etc by interacting with the publisher coupon the administrator issues it to a profitable senior user and can be used to exchange a discount book page the book page displays detailed information about the book including price author publisher comment etc users need to enter the book page to comment collect add to the shopping cart purchase etc shopping cart registered users can add books to their shopping carts for temporary storage and then they can purchase all the books in the shopping cart by billing order an order is generated when the user settles and the payment information the delivery address and the like are displayed homepage recommendation on the homepage of the e book store you can display the recent hot selling or discounted books users can access the corresponding book pages by clicking directly hot sales ranking the system statistics are automatically generated according to the purchase order of the books in the near future the user can view the hot selling behaviors and select the books to provide reference for them the administrator can view the hot selling rankings to know the sales of the books and adjust the purchase volume of the books collecting books users can collect favorite books and purchase them later in the book after sales service a return service that can be requested after a user purchases a book freight and tax rates when you purchase a book online you must deduct a certain fee based on the tax rate and the shipping cost when you mail it personal information basic information filled out by registered users including personal details shipping address etc 3 4 supplementary specification item explanation goal the purpose of this document is to determine the requirements for an online bookstore system this supplemental specification lists the requirements that are not easily readable in the use case model use case supplementary specifications and use case models work together to complete a complete set of requirements for the system area this specification applies to online bookstore systems which are intended for users who wish to purchase books at the bookstore br this specification defines the non functional requirements of the system such as reliability availability performance security and functional requirements that are common across multiple use cases function requirements are defined in the use case specification functionality no availability the user interface of the online bookstore system on the mobile phone side should be adapted to the mobile phone interface and the user interface of the online bookstore system on the pc end should be compatible with the operating system corresponding to the computer reliability the main system must be running 98 of the time the system must be up and running during the online bookstore run security the system should prevent users or administrators from changing any valid information other than permissions in addition both the clerk level and the store owner level administrator can change the bibliographic information but the store owner level takes precedence 4 domain model 4 1 class diagram this jpg https upload images jianshu io upload images 12014150 18754e4acfd97c2b jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 4 2 relationships between classes conceptual symbol attributes user browsinghistory book systemmessage systemmessage visitor browsinghistory book systemmessage systemmessage registeduser browsinghistory book systemmessage systemmessage id string name string order orderform address string phonenumber string privatemessage privatemessage ordinaryuser browsinghistory book systemmessage systemmessage id string name string order orderform address string phonenumber string privatemessage privatemessage senioruser browsinghistory book systemmessage systemmessage id string name string order orderform address string phonenumber string privatemessage privatemessage coupon coupon advertisement ranklist book book time string basedfeature string homepagerecommen dation book book coupon applicablebook book duetime string amound float book name string author string publisher string price float isbn string comment comment salesvolume int comment content string commentator registeduser time string orderform book book number string time string pricesum float purchaser string ispaid bool isreceived bool shoppingcart book book booksum int pricesum float data userdata bookdata administrator id string systemmessage systemmessage privatemessage privatemessage message content string time string sender string receiver string privatemessage content string time string sender string receiver string systemmessage content string time string sender string receiver string publisher id string systemmessage systemmessage privatemessage privatemessage 5 analysis model 5 1 login class diagram login jpg https upload images jianshu io upload images 12014150 866c7c4bc9b8e7a9 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq login jpg https upload images jianshu io upload images 12014150 d73e905a2c27bdd2 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 2 register class diagram register jpg https upload images jianshu io upload images 12014150 2f7a7df8641f1556 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq register jpg https upload images jianshu io upload images 12014150 91359e809c7efc73 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 3 search class diagram search jpg https upload images jianshu io upload images 12014150 7f86a5e386b86bf6 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq search jpg https upload images jianshu io upload images 12014150 4d5ef04b0697fe3f jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 4 viewbookpage class diagram bookpage jpg https upload images jianshu io upload images 12014150 4c5f4008059622ca jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq bookpage jpg https upload images jianshu io upload images 12014150 faa2fc434c8d3009 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 5 viewshoppingcart class diagram visitshoppingcart jpg https upload images jianshu io upload images 12014150 bf56a465b1dbfb5c jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq visitshoppingcart jpg https upload images jianshu io upload images 12014150 51b80c44ab8f8db2 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 6 visitorderform class diagram visitorderform jpg https upload images jianshu io upload images 12014150 dc6698db5ce2632d jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq visitorderform jpg https upload images jianshu io upload images 12014150 f279d0533c81608b jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 7 advertise class diagram advertisement jpg https upload images jianshu io upload images 12014150 ecdcd2fc0e6e2440 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq advertisement jpg https upload images jianshu io upload images 12014150 be63c912a1c345f2 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 8 viewmessage class diagram messagepage jpg https upload images jianshu io upload images 12014150 46719bc95c6bae9c jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq messagepage jpg https upload images jianshu io upload images 12014150 18adbed2d61216ec jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 5 9 analysisdata class diagram data jpg https upload images jianshu io upload images 12014150 7d3705dc567d14f1 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram seq data jpg https upload images jianshu io upload images 12014150 611734658ee3fb59 jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 6 design mechanism 6 1 overview for the critical design mechanisms we set login as interface specification in the login use case the communication between the registereduser class visiter class and database is showed as mechanism persistence customer can enter the relative information in the login page to make this to realize this use case request from customer app frontend in presentation layer will be sent to the logincontroller class of the user controller in controller layer then go into our persistence mechanism arriving at certain database finally return the results layer by layer database entirely being separated from frontend and controller which receives network request and builds or closes the connections makes our design model perfectly persistent another is the information exchange mechanism using visitorderform as interface specification when user wants to have communications with administrators request from app and web frontend will be distributed to the service layer through this communication to realize the use case when a customer using wants to visit order form after entered the address and chosen the third party payment method the frontend first call a function to send a request to the orderformcontroller getting to the order controller interface int the controller layer and if he would like to connect administrator our client send request through user access or administrator access to informationcontroller and connected to our server and okhttp to achieve this 6 2 persistence class diagram image png https upload images jianshu io upload images 12014150 80b29965fb5cb7dd png imagemogr2 auto orient strip 7cimageview2 2 w 1240 there are three parts of the above class diagram 1 commandset can handle the requests from front end 2 httprequestcontroller and connection can separate the database from the front end moreover they can make use of the functions named connect and close to keep the persistence of database 3 sqlcompiler and statement can get touch with real operation of database what the use of the classes are as follows 1 commandset sort the front end s requests and process to the operation of database 2 httprequestcontroller send http requests and receive response 3 connection build and close connection with database 4 sqlcompiler compile the given operation to sql and execute it in the database 5 statement record the connections with database and contain some other attributes sequence diagram image png https upload images jianshu io upload images 12014150 68e2c1dced82c4e0 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 after receiving the request from front end the commandset will send command to the httprequestcontroller the latter will then send request to the back end the connection with the database will be built at same time of the production and execution of sql when the result list is returned from sql compiler the connection close simultaneously and return the result straightforwardly to the front end 6 3 information exchange class diagram in the process of communication among user administrator publisher and web page we need the information exchange mechanism to handle this our view message system provides user access adminitrator access and publisher access for our clients and the web page is used for system message and it uses webfronted there classes can use the api to contact with informationcontroller and the informationcontroller is connected to okhttp and our server image png https upload images jianshu io upload images 12014150 00232c2d016410d1 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram firstly the messagecontroller of the web will build a client by calling the build method of the okhttpclient at the same time it will try to connect the server as well the messagecontroller will also get the connection and make it available to every end the userend administratorend and publisherend can be used by users administrators publishers in order to send messages to the message end that is the messagecontroller it will process the messages and show them on the corresponding ends image png https upload images jianshu io upload images 12014150 204e2a8134deedc3 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 7 realization 7 1 interfaces 7 1 1 interfaces between our system and external systems specify the 3rd party api as well as the map services in detail express check subsystem if the customer want to know whether the books are shipped where the books i brought yesterday how far is it from me and so on he or she can use this subsystem to know these information after order query the query page will save the order s receiver s tracking number and adress in the cookie so that the express check page can get it by reading the cookie and the query will get the express information by calling the sf api other information for address especially the estimate time by calling the amap api and interfaces in sequence get customer code and check code apply for the api interface https qiao sf express com index html we can get the url clientcode checkword url http bsp oisp sf express com bsp oisp sfexpressservice xml message description 1 request an xml message the service attribute defines the service name the head element defines the customer code xml request service server name head customer code head body message description xml body 2 response xml message the value of the head element is ok or err ok means the transaction is successful err means that the system or business is abnormal the transaction fails for the batch trading scenario it can only be success failure no partial success partial failure xml response service server name head ok err head body correct corresponding data xml body error code nnnn error details error json request example json shippercode sf ordercode sf201608081055208281 logisticcode 3100707578976 paytype 1 exptype 1 customername customerpwd monthcode isnotice 0 sender name 1255760 tel mobile 13700000000 provincename cityname expareaname address receiver name 1255760 tel mobile 13800000000 provincename cityname expareaname address 2 commodity goodsname book name json return example json ebusinessid 1151847 updatetime 2016 08 09 16 42 38 success true reason estimateddeliverytime 2016 8 12 api interface call java requestxml getorderservicerequestxml params methodname orderserivce requestxml getordersearchservicerequestxml params methodname ordersearchservice requestxml getrouteservicerequestxml params methodname routeservice post request sfexpressresponse response sfexpressresponse xmlutil converttoobject sfexpressresponse class resultstr sequence diagram expresscheck sequencediagram png https upload images jianshu io upload images 12014150 8e981ca6e371a9cf png imagemogr2 auto orient strip 7cimageview2 2 w 1240 communication diagram expresscheck communicationdiagram png https upload images jianshu io upload images 12014150 45a28f899731294b png imagemogr2 auto orient strip 7cimageview2 2 w 1240 7 1 2 order query subsystem and its interfaces specification specify the data and database in detail order query subsystem if the customer want to query his orders he can take the following steps 1 input the order id and press the query button in the order information page 2 with the order id attached the page will then send a request to the order controller 3 once the controller get the request it will got to the order database to get the information and then send it back to the page and the page will show it to the customer 4 if the order id doesn t exist the controller will return failure information the interfaces we designed are mainly showed as follows respondtorequest bool this interface is implemented in the order query boundary class it is invoked after the user press the query button then it will call function to execute the query getorder orderid string order this interface is used to query order by order id it will receive the order id and then send a http request with the order id to the back end api the api will integrate the order id into sql and execute the order query in the order database and return the query result as a json object to this function and the function will analyses it and return the result json json format of order orderid orderidnumber sender sendername receiver receivername signstatus true false deliverystatus true false paystatus true false receiveraddress address object currentlocation address object sendertel sendertelephone receivertel receivertelephone displayresult order order bool this interface is implemented in the order query boundary class this interface will receive the result of query and display it fitting the webpage design if the query result is empty it will display a message that the order id doesn t exist in the database sequence diagram orderquery sequencediagram jpg https upload images jianshu io upload images 12014150 84ec5763ef8d689c jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 communication diagram orderquery communicationdiagram jpg https upload images jianshu io upload images 12014150 6e2098d0607cf06d jpg imagemogr2 auto orient strip 7cimageview2 2 w 1240 7 2 use case realization 7 2 1 login login realization image png https upload images jianshu io upload images 12014150 fb8a56634c10506a png imagemogr2 auto orient strip 7cimageview2 2 w 1240 class diagram image png https upload images jianshu io upload images 12014150 e1d23d148d7da9b8 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram image png https upload images jianshu io upload images 12014150 3d36e8895580d88e png imagemogr2 auto orient strip 7cimageview2 2 w 1240 in the login use case the communiaction between the registereduser class visitor class and database is showed as mechanism persistence if the visitor wants to log in he should first request to log in and then put his id and password in the loginwindow and then the window will send these information to the logincontroller the logincontroller will check the database of users and verify the information is true or not if it is true the visitor can then change his logstatus and turn into a registed user but if the information is false the loginwindow will send a failure message 7 2 2 visit order form visitorderform realization image png https upload images jianshu io upload images 12014150 23d3301a8d2187f5 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 class diagram image png https upload images jianshu io upload images 12014150 ad5c79d0ba8bd35c png imagemogr2 auto orient strip 7cimageview2 2 w 1240 sequence diagram image png https upload images jianshu io upload images 12014150 22cc54433cd190d7 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 in the createorderform use case the communication between the orderform class and database is shown as mechanism persistence and that between registereduser class and administrator class is designed as mechanism information exchange if the registereduser wants to confirm the order form he or she will have to enter the address and choose third party payment method which along with the order form fundamental information are stored perpetually after this the window will show the details of order form the registereduser can also choose after sale service to communicate with administrator which requests the message system 8 prototyping 8 1 description in the prototyping part we implement three functions register login and search mainly via vue and node js in localhost webserver both of the two javascript frameworks are very popular recently as for vue it is a front end javascript framework which is highly efficient and easy to maintain by using declarative rendering component system and vue router it allows developer to focus on the data and logic instead of dom operation ad it is responsive it can also help releasing brower s resources as for node js it is a javascript runtime built on chrome s v8 engine it uses an event driven non blocking i o model that makes it lightweight and efficient as it is a demo that contains few data we choose mysql as our database management system 8 2 register example 8 2 1 interact with database reg png https upload images jianshu io upload images 12014150 3ee320f2f6c287ab png imagemogr2 auto orient strip 7cimageview2 2 w 1240 this snippets define how the front end interact with data base via reg fisrt it will recieve two parameters from json named regname and regpasswd second in order to secure the information while transfering we will use md5 method to encrypt regpasswd next it will insert it into database and find if the user is successfully registered 8 2 2 front end frontend png https upload images jianshu io upload images 12014150 7e0d74bdbca4dbe1 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 this part define the out look of the page together with vue template 8 2 3 click method clickmethod png https upload images jianshu io upload images 12014150 de2ff0486aa45944 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 this part shows how to handle click method as a whole it will judge if the input is valid them send the callback to view layer through the functions and frameworks showed below we can allow a user to register in a mobile web page send the response and data to different layers and show the feedback to user 8 3 initial snapshot 8 3 1 mobile login m login png https upload images jianshu io upload images 12014150 8db413c7af391a63 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 8 3 2 mobile personal page personal png https upload images jianshu io upload images 12014150 910c9c83afd7d234 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 8 3 3 moblie shopping cart cart png https upload images jianshu io upload images 12014150 c2d3c6944cc4bbe7 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 8 3 4 moblie payment pay png https upload images jianshu io upload images 12014150 a5ebcab0942c1669 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 8 3 5 pc main page web main png https upload images jianshu io upload images 12014150 8f4d25ad1e404666 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 8 3 6 background statistics statistics png https upload images jianshu io upload images 12014150 e16827198d17a7e4 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 8 3 7 background add book addbook png https upload images jianshu io upload images 12014150 7b3b9db0bfbeda5a png imagemogr2 auto orient strip 7cimageview2 2 w 1240 9 quality 9 1 architecture style the basic principle we use in architectural design is layered architecture a layered architecture is the most common architecture and is also known as an n tier architecture many companies and companies have used this architecture in their projects for many years and it has become almost a standard norm so in our architecture there are four layers presentation layer services layer controller layer and data layer layering does not need to know how other layers do it for example the controller layer does not need to know how the data layer queries the database on the contrary when the controller layer calls the specific method of the data layer it only needs to pay attention to whether part of the data or all the data is needed this is what we call the separation of concerns which is a very powerful feature to let each layer is responsible for its responsibilities 9 2 design patterns to achieve our web platform we mainly use django and vue js so in this part we will mainly talk about the design patterns of this two frameworks as for django it follows the model view template mvt pattern which can be treated as a special form of mvc it consists if an object relational mapper orm that mediates between data models and the workflow if mvt pattern is as follows 1 web server middleware receives a http request 2 url dispatcher dispatches the request to specific views 3 view functions call corresponding models to access data and invoke the corresponding template to present the page to user 4 view functions finishes its task and return the http response to middleware 5 web server middleware sends the response back to client as for vue js it is a progressive framework for building user interfaces unlike other large frameworks vue is designed from the bottom up the core library of vue focuses only on the view layer making it easy to get started and easy to integrate with third party libraries or existing projects image png https upload images jianshu io upload images 12014150 74ef1f8fbcfee53a png imagemogr2 auto orient strip 7cimageview2 2 w 1240 9 3 critical decisions 6 1 data storage to choose a suitable storage method for our project we first analyze the data types that we may encounter data that is highly relational and has great demand of acid data that is less relational and data that is highly performance sensitive like cache and sessions after discussion we decide to use mysql mongodb and redis together to store the three kinds of data above for mongodb the system uses odm object document mapping frameworks to map objects to mongodb queries and for mysql it uses orm object relational mapping frameworks to map objects to sql queries both the two methods and database management systems are indispensable and can make the queries and update more efficient image png https upload images jianshu io upload images 12014150 cfabb16f121814c5 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 6 2 senior user when trying to define all the classes that contained in our project we create a class called senior user derived from normal user this class has the privilege to possess coupons and can interactive with the manager as we are a private bookstore we do not need to create various kinds of coupons and create a special kind of user can help reduce the data that needs to be stored and transferred this png https upload images jianshu io upload images 12014150 df7a0fdc063db01b png imagemogr2 auto orient strip 7cimageview2 2 w 1240 10 improvement 10 1 limitation 10 1 1 user experience lack of special handling mechanism for ordinary users in our system we distinguish between ordinary users and senior users by issuing coupons or not which means ordinary users can never get coupons unless they become senior users by some ways this mechanism is bound to be conducive to senior users while that keeps the senior users being absorbed in our online book store but for the ordinary users the book store is not that attractive the lack of special handling mechanism for ordinary users may lead to the loss of new users who can not buy books firstly in a lower price 10 1 2 lower level design a rough design of database during our analysis and design work we did not design a relational database systematically but only made a rough design of database in several times this mirrors the lack of design in data layer and model layer without a design of the whole database structure we only considered the functions of each entity class which led to some repetition and some omissions of methods among entity classes this rough design really reduced our ef ciency because we uni ed the functions and operations among different data models more than once and we have learned from this limitation that the systematical design of the whole structure should always come rst 10 2 potential improvements in the third assignment we designed a primary architecture and we made some improvement of it in this assignment 10 2 1 design asynchronous version apis on the presentation layer introduce asynchronous mechanism on the web frontend the updated web frontend uses the asynchronous design version apis such as ajax and jquery for example the working flow for a specific procedure 1 at first the customer wants to get contract list from the customer frontend 2 then the customerfrontend will send a get or post request to the contractcontroller 3 the controller will get the contract list from the database of contract 4 the controller returns the serialize of the contract list to the customerfrontend 5 the customerfrontend updates only the part which needs to update 10 2 2 update the service layer by splitting it into two parts we know that the service layer provides unified api interfaces of all functionalities for both web clients and mobile clients the updated api is split into two parts the web apis containing mapping system and payment system and the real time apis containing dialog controller and order info interface the updated layer is shown as below services layer png https upload images jianshu io upload images 12014150 c18b46665fc91f5e png imagemogr2 auto orient strip 7cimageview2 2 w 1240 the web apis are provided in the form of json services based on http https web api interface serve functionalities that fit the request and response model most of the functionalities are provided by this kind of api the real time apis are provided in the form of json services based on websocket protocol which allows modern web browsers such as ie10 chrome and firefox to access it using html5 websocket feature functionalities that requires server side push are provided by this kind of api one typical example is the package real time tracking functionality 10 2 3 add a waf at the outside boundary of the infrastructure layer considering the safety issues waf web application firewall can be added at the most outside boundary of the infrastructure layer the waf middleware controls all data access to api the waf should at least provide limiting rating mechanism and application layer access control for example ip blocking functionality aims to reduce security ricks from large scale brute force dictionary based enumerations captchas and access blockings are both kinds of rate limiting mechanism 10 2 4 add a model layer to get data more efficiently in order to search data more efficiently we add a model layer between controller layer and data layer models are divided according to classification in subsystems and are in correspondence with classes in database we design this layer to let upper layer get access to the database more clearly and easily the model layer is shown as below model layer png https upload images jianshu io upload images 12014150 be85ab0876f1c0f1 png imagemogr2 auto orient strip 7cimageview2 2 w 1240 10 3 open issues 10 3 1 robustness improve robustness of our design model includes architecture design program coding architecture design is a very large range generally we are not particularly real time business to exchange time for performance space for performance and other programs for example we can store some original data about books first in the database until the middle of the night when the pressure is small and then we deal with them further for inquiries with the aim to read more and write less we generally take cqrs command query responsibility segregation program as reading may also use the search engine technology and we do not use distributed transactions but take the compensation mechanism in order to coordinate multiple system changes as for program codes which is the last remaining thing to be seen it is necessary for programmers to write robust codes it can be partly measured by the judgement of the null value of string moreover timely de allocating the objects avoiding writing dead loops to implement sleep avoiding writing a possible death cycle when writing code programs can fully utilize some program static analysis tools to find bugs and improve quality of codes such as sonar pmd and so on 10 3 2 maintainability expandability take the processing of online payment as an example our system provides an interface and concrete class for various kinds of payment platforms the system encapsulates the payment subsystem and whenever we want to change the way we pay we can only extend the functionality of this subsystem thus we can extend the other functions according to the original subsystem just in the similiar way 10 3 3 form a book dispatching schedual system as the bookstore grows bigger it may need more and more methods to fetch tht books and send them if there are certain somebody to handle the dispatching job then it is really unnecessary and a waste of resources form a dispatching schedual system can not only save the cost and resources but provide the 24 hours analysis for the current stock and orders by which bookstore can know whether it need to buy books from the publishers or send books to customers in time we may also add some machine learning algorithms to make the system smarter and more efficient 10 4 development plan because it need hard work to build the front end and the user interface we plan to have two of our group members to learn some knowledge about the implement of the front end and the user interface and after that they will implement corresponding subsystems through calling required and provided interfaces the learning and implement time will take one and a half months for the design of database and back end it is done by three of our group members two of them may spend one week to design er diagrams and turn them into tables we are going to dispatch the left person to build and manage database of mysql redis and mongodb as we think it will not be too hard so we plan to finish this in two weeks then for the sever connection and the connection between back end and front end as it is very important this part is distributed to the back end team and front end team together this part of work is to deal with requests and return information so the back end need to coordinate with the front end as we may encounter some problems two months is necessarily needed finally after the above work has been done we need to test our system debug and complete deficiencies that have been found during the test process this part may take up one month or more because the whole group has to do that together and be responsibility for every subsystem 11 course reflection 11 1 teamwork for the teamwork actually it shouldn t be neglected because it is so important for our efficiency at first we were completely in the dark about project and so worried about it but it is the group discussions from time to time that help us to make process we first have a brainstorm about the project and it truly help us to find the direction then we made plans and divided our team into small groups to finish the job finally as the professor told us we do this big project step by step in an iterative way therefore just as the old saying goes where there s a will there s a way we learned new things and make process step by step and now we are proud of our final project we can say that our efforts are worth the time 11 2 uml developing tool for the uml developing tool our group members are using different tools at first three members use staruml and two members use processon these two tools have their advantages and disadvantages staruml is more convenient and processon is more beautiful for our third assignment we find a new tool named draw io we use it to draw our architecture system diagram and class diagram because its style is good and it is easy to use but our use case diagram are drawed with staruml because we can simply drag diagrams into the diagram and it can generate global diagram automatically by using staruml 11 3 presentation there are totally 4 presentations for all the groups in this team we can always make full use of our presentation time because we are group 3 because we do not to be normal in addition to the our assignments we present what makes us outstanding every time most importantly we try our best to make our work conciseness and correctness what s more we organize our slides logically and pleasant to watch at first our presentations are in chinese but then we use english to present our idea because our class is in english 11 4 materials our professors direct us in our class but we still need to searching for lots of materials for reference in addition to our textbook we use google and our library s site on the internet to look for materials which is truly helpful now though we are still not very professional we have learned a lot things about object oriented analysis and design 11 5 self reflection zhe zhang team leader in this semester as the team leader of group 3 i have witness the whole system analysis design for our online bookstore youmu um bookstore at the beginning of our design we don t know how to start the whole things which means you have got an assignment to build a system like dangdang or other online bookstore you don t even know where to start interfaces function architecture or other thing at the first class this semester our professor cao told us what is an ideal design and what s the development mode of our common design and also we get a global view of what is uml and what is object oriented analysis and design and when we do our assignment 1 we start with with a brainstorm everyone shared his standpoints and we list all the thing there then we discuss all the items step by step in detail and we confirm the users use cases main function of our bookstore and more things nonetheless on the first presentation we found ourselves forgot something more personal which mean we regard the origin task as a project and homework we must finish not a real system we build our self so we polish the first document into fresh version which add our own idea for our online bookstore we name the bookstore as youmu bookstore which the letter in chinese is um it represent the content we take in class uml and since the second presentation we create our own ppt template which reflect our idea of the bookstore chinoriserie we have tried so many tools to draw the uml chart more efficient and beautiful and meanwhile we learned a lot in use these software and website as for the references we searched quite a lot in the website and library because at the beginning of the design we cannot hold a clear mind so we read some article which introduce how to design analysis and design a system and to be honest we also take example by some blogs and article but i swear we done all the thing by ourselves and we just used them as references as the team leader i have learnt something more this semester teamwork you should master the whole thing we are supposed to do prior to your team members and you should organize the short meeting nearly three times a weak i tried to allocate the task in equal and i do think what i done well was that i allocated different work for each one in each assignment so i think all of my team member have learned many concept in uml not only one part and we changed 4 individuals for each presentations this will also help them grasp more skill in the end of this course i want to say that both the professor cao and professor liu did an extraordinary job which made me enjoy this course one thing is special in my mind in the first presentation professor liu waited until 10 30pm to listen all the group and she even stop during the 3 hours and the essential thing was that she gave an evaluation for each group so we can find our deficiency and we can complete ourselves i must say thanks to our teachers you are our role model i hope we will take our analysis and design into real work and we can finish our own youmu online bookstore one day thanks professor cao and professor liu again and i want to say to my team members that you have worked hard thanks everyone siteng ma from my perspective uml is useful but not easy to be taken advantage the online bookstore conception gets mature by the discussion and cooperation of us five except for the knowledge taught during the class there are a variety of principles for us to learn by doing and searching in the internet by designing the subsystem and drawing plenty of diagrams i realize the importance of uml after implementing the whole system the vary steps to refine the whole project are miserable but essential we have overturned the models several times and replaced with a better one so that we have finally established a uml model which is not that bad we five boys consolidate firmly and learn much by doing these four assignments all in all i benefit a lot through the team work in this semester gefei wang during the whole project although i mainly focus on prototyping presentation and some activity diagrams i also learned a lot while communicating and discussing with all my team members choosing which architecture and framework to use requires us to have a clear understanding about the advantages and disadvantages of all the methods as a sophomore that dose not have so many experiences i searched a lot information on the internet and referred many books that recommended by our professor after getting my own part done we also shared our knowledge with other team members to make sure that we are always keeping in touch as for the limitations i think we still lack of real experiences which means we did not have many choices when implementation actually as the user s requirements will also change it is acceptable that we will have to make changes to our previous architecture and that is exactly the way a good product is made le yang during the process of developing our project i was mainly responsible for design the use case model analysis model at first i was totally in the dark about what to do but with the direction of our professor and help from my team members i improved myself a lot and finish my job the main shortcoming of me is that i was too hurried and sometimes neglected the feelings of other team members so i need to overcome this in the future because the feelings of other team members is very important for good team work here i want to thank my team members and my director for they made me do better than i could do kaixin chen participating in a group to complete the uml project is a very rare experience through this period of experience i learned how to get along with others how to hand over and complete tasks how to be a team member to make the team s progress more efficient at the same time i also learned a lot of basic techniques and methods of system analysis and design know the system builds function points use cases and the various tools and methods used to achieve these goals during development for example i tried several uml developing tools during this semester among them staruml is a powerful and free uml developing tool and can clearly show the structure of our project and the relationship between classes however taking the whole group work into consideration it s more convenient to develop uml via processon really apprciate for this experience 12 contributions of team member zhe zhang team leader first presentation ppt global architecture architecture diagram description deployment diagram subsystems and interfaces in architecture realization two detailed interfaces potential inprovements update the diagram organize documents second third final gefei wang second presentation ppt prototyping description register example 3 initial snapshot 7 implement the login system codes design patterns critical decisions kaixin chen third presentation ppt glossary of terms supplementary specification architecture style open issuses 3 potential inprovements organize documents first third le yang forth presentation global system use case subsystem use case login register subsystem search visitbookpage subsystem domain modal class diagram part relationships between classes part analysis model part design mechanism persistence use case realization login development plan siteng ma forth ppt subsystem use case message subsystem view data subsystem domain modal class diagram part relationships between classes part analysis model part design mechanism information exchange use case realization visit order form limitation 13 references reference description uml rational rose 2003 this book introduces the basics of uml language in detail as well as the application of uml in object oriented software system analysis and design and explains the object oriented analysis and design process through rich examples inspiring readers how to learn in uml language applying uml and patterns from this book we further understand and identify analysis and design and concepts and understanding object oriented analysis emphasizes the discovery and description of objects in the problem domain creating domain descriptions from the perspective of objects therefore when doing analysis we choose to start with the big problems embodied in the use case diagram such as how users shop correctly and gradually split the basic objects and relationships and analyze related domain models and relationships uml2 we focus on the parts of demand analysis design principles and patterns architecture design and component design further understanding the methods and considerations of requirements analysis learning the drawing methods of related expressions such as domain models and concept class diagrams and understanding the form of the representation structure of the package diagram https blog csdn net fanxiaobin577328725 article details 51700528 from the above article we further learn the meaning and detailed introduction of the package diagram including but not limited to name element visibility import and export and the relationship between package and package and generalization this has played an important role in building our own architectural analysis https blog csdn net gstrong298 article details 22623509 the article introduces the knowledge of concept class diagrams we have learned the methods of finding conceptual classes the differences between conceptual class diagrams and design class diagrams the drawing methods of class diagrams the meaning of associations and properties etc which provide a great reference for us to draw conceptual class diagrams http plantuml com zh sequence diagram the article tells us how to draw various timing diagrams and the various syntaxes and functions in the sequence diagram it has important reference value for our uml mapping https www tutorialspoint com uml uml architecture htm any real world system is used by different users the users can be developers testers business people analysts and many more hence before designing a system the architecture is made with different perspectives in mind the most important part is to visualize the system from the perspective of different viewers the better we understand the better we can build the system this page help us learn the architecture of ump development https blog csdn net fanxiaobin577328725 article details 51700528 this article describes the drawing methods and considerations of the package diagram and the details it is very helpful for the drawing and attention of our system architecture logic package diagram | uml online-book-store architecture use-case domain-model analysis-model design-mechanism prototype tongji-university | os |
yn.rs | ci badge ci docs badge docs yn want to check if a value is yes no maybe both maybe you want to check if a value is somewhat yes somewhat no we ve got you covered this is state of the art ehem natural language processing with yn you will be the one who can safely say no examples if you want to use this very advanced natural language processing technology you simply need to do the following to check if something is yes rust extern crate yn assert yn yes yes this is yes you can be 100 sure if you don t have time to write yes rust extern crate yn assert yn yes y this is also yes if you re a bleepity blooping machine rust extern crate yn assert yn yes 1 of course this is not yes rust extern crate yn assert yn yes no you can also check if something is somewhat yes rust extern crate yn assert yn is somewhat yes this has a y so it is the word or if you want to check if something just contains y rust extern crate yn assert yn is kinda yes very much so same goes for no the options are endless your imagination is the limit license licensed under mit ci https travis ci org theunitedstatesofamerica yn rs ci badge https travis ci org theunitedstatesofamerica yn rs svg branch master docs https docs rs yn docs badge https img shields io badge docs online 5023dd svg | natural-language-processing rust | ai |
MCTE_4342_Embedded_System_Design | mcte 4342 embedded system design semester 1 2020 2021 aiman bin dasuqi cheong 1719467 this repository is dedicated weekly assessment and updates on mcte 4342 embedded system design course go to weekly assessments and exercise for weekly assesment based on lecture slides project for updates regarding project | os |
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TIVA_CAN_Bus_Project | tiva can bus project for our term project in ece 473 embedded system design at the university of michigan dearborn we built a can transmitter receiver system using the ucos ii real time operating system and several tiva tm4c123g launchpad microcontrollers project presentation and demo can be seen here https youtu be jyfgp3oomso | os |
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user-plugin | front end user plugin front end user management for october cms requirements october cms 3 0 or above the ajax framework https octobercms com docs cms ajax to be included in your layout page installation instructions run the following to install this plugin bash php artisan plugin install rainlab user to uninstall this plugin bash php artisan plugin remove rainlab user if you are using october cms v1 or v2 install v1 7 with the following commands bash composer require rainlab user plugin 1 7 managing users users are managed on the users tab found in the back end each user provides minimal data fields name surname email and password the name can represent either the person s first name or their full name making the surname field optional depending on the complexity of your site below the email field is an checkbox to block all outgoing mail sent to the user this is a useful feature for accounts with an email address that is bouncing mail or has reported spam when checked no mail will ever be sent to this address except for the mail template used for resetting the password plugin settings this plugin creates a settings menu item found by navigating to settings users user settings this page allows the setting of common features described in more detail below registration registration to the site is allowed by default if you are running a closed site or need to temporarily disable registration you may disable this feature by switching allow user registration to the off setting activation activation is a process of vetting a user who joins the site by default users are activated automatically when they register and an activated account is required to sign in the activation mode specifies the activation workflow automatic this mode will automatically activate a user when they first register this is the same as disabling activation entirely and is the default setting user the user can activate their account by responding to a confirmation message sent to their nominated email address administrator the user can only be activated by an administrator via the back end area you can allow users to sign in without activating by switching sign in requires activation to the off setting this is useful for minimising friction when registering however with this approach it is often a good idea to disable any identity sensitive features until the user has been activated such as posting content alternatively you could implement a grace period that deletes users with sufficient warning who have not activated within a given period of time users have the ability to resend the activation email by clicking send the verification email again found in the account component sign in by default a user will sign in to the site using their email address as a unique identifier you may use a unique login name instead by changing the login attribute value to username this will introduce a new field called username for each user allowing them to specify their own short name or alias for identification both the email address and username must be unique to the user if a user experiences too many failed sign in attempts their account will be temporarily suspended for a period of time this feature is enabled by default and will suspend an account for 15 minutes after 5 failed sign in attempts for a given ip address you may disable this feature by switching throttle attempts to the off setting as a security precaution you may restrict users from having sessions across multiple devices at the same time enable the prevent concurrent sessions setting to use this feature when a user signs in to their account it will automatically sign out the user for all other sessions notifications this feature is implemented by the notify plugin how to use this feature install the rainlab notify plugin navigate to settings notification rules click new notification rule select user activated click add action select compose a mail message select user email address for the send to field here you may select the mail template previously defined in the user settings click save extended features for extra functionality consider also installing the user plus plugin http octobercms com plugin rainlab userplus rainlab userplus session component the session component should be added to a layout that has registered users it has no default markup user variable you can check the logged in user by accessing the user twig variable twig if user p hello user name p else p nobody is logged in p endif signing out the session component allows a user to sign out of their session html a data request onlogout data request data redirect good bye sign out a page restriction the session component allows the restriction of a page or layout by allowing only signed in users only guests or no restriction this example shows how to restrict a page to users only ini title restricted page url users only session security user redirect home the security property can be user guest or all the redirect property refers to a page name to redirect to when access is restricted route restriction access to routes can be restricted by applying the authmiddleware php route group middleware rainlab user classes authmiddleware class function all routes here will require authentication token variable the token twig variable can be used for generating a new bearer token for the signed in user twig do response ajaxhandler onsignin withvars token session token the checktoken property of the component is used to verify a supplied token in the request headers authorization bearer token ini session checktoken 1 account component the account component provides a user sign in form registration form activation form and update form to display the form ini title account url account code account redirect home paramcode code component account if the user is logged out this will display a sign in and registration form otherwise it will display an update form the redirect property is the page name to redirect to after the submit process is complete the paramcode is the url routing code used for activating the user only used if the feature is enabled reset password component the reset password component allows a user to reset their password if they have forgotten it ini title forgotten your password url forgot password code resetpassword paramcode code component resetpassword this will display the initial restoration request form and also the password reset form used after the verification email has been received by the user the paramcode is the url routing code used for resetting the password using a login name by default the user plugin will use the email address as the login name to switch to using a user defined login name navigate to the backend under system users user settings and change the login attribute under the sign in tab to be username then simply ask for a username upon registration by adding the username field twig form data request onregister label full name label input name name type text placeholder enter your full name label email label input name email type email placeholder enter your email label username label input name username placeholder pick a login name label password label input name password type password placeholder choose a password button type submit register button form we can add any other additional fields here too such as phone company etc password requirements password length by default the user plugin requires a minimum password length of 8 characters for all users when registering or changing their password you can change this length requirement by going to backend and navigating to system users user settings inside the registration tab a minimum password length field is provided allowing you to increase or decrease this limit to your preferred length password confirmation the password confirmation field can be used to prompt the user to enter their password a second time this input name is optional if it is found in the postback data then it will be validated the following is an example twig input name password type password placeholder choose a password input name password confirmation type password placeholder repeat password error handling flash messages this plugin makes use of october s flash api http octobercms com docs markup tag flash in order to display the error messages you need to place the following snippet in your layout or page twig flash div class alert alert type error danger type message div endflash ajax errors the user plugin displays ajax error messages in a simple alert box by default however this might scare non technical users you can change the default behavior of an ajax error from displaying an alert message like this js script window on ajaxerrormessage function event message this can be any custom javascript you want alert something bad happened mate here it is message this will stop the default alert message event preventdefault script checking email username availability here is a simple example of how you can quickly check if an email address username is available in your registration forms first inside the page code define the following ajax handler to check the login name here we are using the email address php public function oncheckemail return istaken auth finduserbylogin post email 1 0 for the email input we use the data request and data track input attributes to call the oncheckemail handler any time the field is updated the data request success attribute will call some jquery code to toggle the alert box html div class form group label email address label input name email type email class form control data request oncheckemail data request success logintaken toggle data istaken data track input div div id logintaken class alert alert danger style display none sorry that login name is already taken div overriding functionality here is how you would override the onsignin handler to log any error messages inside the page code define this method php function onsignin try return this account onsignin catch exception ex log error ex here the local handler method will take priority over the account component s event handler then we simply inherit the logic by calling the parent handler manually via the component object this account auth facade there is an auth facade you may use for common tasks it primarily inherits the october rain auth manager class for functionality you may use auth register to register an account php user auth register name some user email some website tld password changeme password confirmation changeme the second argument can specify if the account should be automatically activated php auto activate this user user auth register true the auth check method is a quick way to check if the user is signed in php returns true if signed in loggedin auth check to return the user model that is signed in use auth getuser instead php returns the signed in user user auth getuser you may authenticate a user by providing their login and password with auth authenticate php authenticate user by credentials user auth authenticate login post login password post password the second argument is used to store a non expire cookie for the user php user auth authenticate true you can also authenticate as a user simply by passing the user model along with auth login php sign in as a specific user auth login user the second argument is the same php sign in and remember the user auth login user true you may look up a user by their login name using the auth finduserbylogin method php user auth finduserbylogin some email tld when working with authentication via bearer tokens the auth getbearertoken method can be used to obtain a bearer token jwt for the current user it expires after 1 hour by default php token auth getbearertoken the auth checkbearertoken method is used to verify a supplied token and authenticate the user the method returns true if the verification was successful php if jwttoken request bearertoken auth checkbearertoken jwttoken guest users creating a guest user allows the registration process to be deferred for example making a purchase without needing to register first guest users are not able to sign in and will be added to the user group with the code guest use the auth registerguest method to create a guest user it will return a user object and can be called multiple times the unique identifier is the email address which is a required field php user auth registerguest email person acme tld when a user registers with the same email address using the auth register method they will inherit the existing guest user account php this will not throw an email already taken error user auth register email person acme tld password changeme password confirmation changeme important if you are using guest accounts it is important to disable sensitive functionality for user accounts that are not verified since it may be possible for anyone to inherit a guest account you may also convert a guest to a registered user with the converttoregistered method this will generate a random password and sends an invitation using the rainlab user mail invite template php user converttoregistered to disable the notification and password reset pass the first argument as false php user converttoregistered false working with apis when building api endpoints using cms pages https docs octobercms com 3 x cms resources building apis html it can be useful to use a page for handling the authentication logic the following is a simple example that includes various api endpoints twig title user api page url api user action resetpassword account session checktoken 1 if this param action signin do response ajaxhandler onsignin withvars token session token endif if this param action register do response ajaxhandler onregister endif if this param action logout do response ajaxhandler onlogout endif if this param action refresh do response data token session token endif an api layout to verify the user can be used for other api endpoints twig description auth api layout is priority 1 session checktoken 1 if session user page else do abort 403 access denied endif events this plugin will fire some global events that can be useful for interacting with other plugins rainlab user beforeregister before the user s registration is processed passed the data variable by reference to enable direct modifications to the data provided to the auth register method rainlab user register the user has successfully registered passed the user object and the submitted data variable rainlab user beforeauthenticate before the user is attempting to authenticate using the account component rainlab user login the user has successfully signed in rainlab user logout the user has successfully signed out rainlab user deactivate the user has opted out of the site by deactivating their account this should be used to disable any content the user may want removed rainlab user reactivate the user has reactivated their own account by signing back in this should revive the users content on the site rainlab user getnotificationvars fires when sending a user notification to enable passing more variables to the email templates passes the user model the template will be for rainlab user view extendlisttoolbar fires when the user listing page s toolbar is rendered rainlab user view extendpreviewtoolbar fires when the user preview page s toolbar is rendered here is an example of hooking an event php event listen rainlab user deactivate function user hide all posts by the user a common requirement is to adapt another to a legacy authentication system in the example below the wordpresslogin check method would check the user password using an alternative hashing method and if successful update to the new one used by october php event listen rainlab user beforeauthenticate function component credentials login array get credentials login password array get credentials password no such user exists if user auth finduserbylogin login return the user is logging in with their old wordpress account for the first time rehash their password using the new october system if wordpresslogin check user password password user password user password confirmation password user forcesave license this plugin is an official extension of the october cms platform and is free to use if you have a platform license see eula license license md for more details | front_end |
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Matador | matador a node based web interface for the a href https github com optimalbits bull bull job manager a update note version 0 1 0 1 0 0 in the latest update matador went from relying on kraken to relying on just express dust which is why i incremented by a major version number this occurred because kraken has changed quite a bit since matador was made with quite a bit of the previous code becoming deprecated i also re worked some of the code to try to make it quicker and resolve some of the issues feature requests why we needed a job manager and we wanted to stick to one that only really relied on node and redis so we looked and looked until we found bull bull looked really nice but we also wanted to be able to monitor the jobs without having to actually log into the aws server to access the redis database thus matador was born why not just use kue kue has some a href https github com learnboost kue issues 53 really old a a href https github com learnboost kue issues 130 outstanding bugs a that we encounted just while testing it we tested bull quite a bit and couldn t reproduce these bugs we thought it d be easier to build an interface for bull than to use kue and deal with those bugs notice that the first one while closed was never actually fixed getting started installing easy if you re using bull already then all you need to do is clone this repo and run npm install running standalone from npm you can run the app standalone with node index js or npm start this standalone method will require you to modify the config development json and config production json files so that it has the right values for your host and port for your redis server also any additional redis options such as passwords or you can simply npm install bull ui js var app require bull ui app options app listen 1337 function console log bull ui started listening on port this address port http localhost 1337 where options is completely optional if not specified it will default to the development settings in config you can also pass in your own redis configuration thusly js var matador require bull ui app redis host your host name port your port number password optional auth password or use a url js var matador require bull ui app redis url redis u password hostname if you are including matador inside of another express app declare the basepath when you mount it to the other app js var app someexpressapp var matador require bull ui app options app use matador function req res next req basepath matador res locals basepath matador next matador app listen 9000 http localhost 9000 matador if you re not using bull and you think you want to use matador for some reason then you should go check out bull over a href https github com optimalbits bull here a after that if you decide you like it come back and check out matador what is it built on matador requires node obviously and is built on experess other npm packages utilized include but are probably not limited to a href http lodash com lodash a a href https github com mranney node redis redis a a href https github com kriskowal q q a on top of that matador also utilizes several open source javascript css libraries and tools including but not limited to a href http jquery com jquery a a href http knockoutjs com knockout a a href http malsup com jquery block blockui a a href http ned im noty noty a a href http www chartjs org chart js a a href http getbootstrap com bootstrap a a href http fortawesome github io font awesome font awesome a screenshot img src https cloud githubusercontent com assets 561207 5333224 66e5334a 7e36 11e4 8394 1728f1662569 png width 600 alt overview q a what is a stuck job a stuck job is a job that has no state it s possible that jobs listed as stuck are just in between states but if there s ever a job with no state for a long time the overview page gives you a place where you can delete or revert such jobs back the pending queue because stuck jobs are any job without a state and while jobs are processing they may become stateless there is no way to mass manage stuck jobs this is because it would be really easy to accidentally modify jobs you didn t mean to if a job was being processed while you were mass managing stuck jobs wrap up have an issue feel free to open an issue in the a href https github com shanek matador issues issues page a you can also send me a a href https twitter com shanetheking tweet a and i d be glad to respond if you wanna help feel free to fork and make pull requests the general layout and feel of matador is pretty basic and i tried to keep my code relatively clean though some of it is pretty awful looking right now sorry about that license the mit license mit copyright c 2014 shane king permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | front_end |
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data-science-club | data science club data science club dsc is a platform created in 2017 by exponea in collaboration with faculty of informatics and information technology fiit in bratislava we organise regular meetups in the building of fiit dsc supports transfer of ideas between academic and industry research related to data science machine learning and ai place of meetups fiit building map link below https www google sk maps place faculty of informatics and information technologies of slovak university of technology 48 1538693 17 0696816 17z room sw studio time 5pm 8pm facebook page with events https www facebook com exponeasociety past events summer semester 2019 comming soon winter semester 2018 date theme speakers 01 october 2018 data driven politics julia language https www facebook com events 176436476583875 dominik csiba renn donner 08 october 2018 data exporation in r weather forecasting https www facebook com events 298996494230486 martin bago rudolf jak a 22 october 2018 databases apache spark https www facebook com events 293387614601506 peter kr tky ondrej brichta 05 november 2018 automatic models ml in gaming https www facebook com events 296759247842024 j n dolinsk r bert magyar 12 november 2018 biometrics ab tests https www facebook com events 319306738866813 dominik csiba viktor gregor summer semester 2018 date theme speakers 19 february 2018 data integration https www facebook com events 410643472689754 jakub mot l mat cimerman 05 march 2018 data visualisation https www facebook com events 1762567290460618 matej novotn martin bago 22 march 2018 feature engineering https www facebook com events 760290230827677 ondrej brichta filip v tek 09 april deep learning https www facebook com events 219925512087317 boris vavr k michal mal 23 april deploying ai solutions https www facebook com events 201868260600759 peter carsky jakub ma ina winter semester 2017 date theme speakers 16 october 2017 introduction to data science https www facebook com events 119400508754924 jo o kov r bert la ok 30 october 2017 data storage exploratory data analysis https www facebook com events 171223513461825 juraj sottnik jakub ev ech 13 november 2017 game analytics dwh and sql analytics https www facebook com events 1629047520491079 ivan tran k tom synek mat nagy 27 november 2017 feature extraction natural language processing https www facebook com events 1918853005098858 wanda benesova jakub ev ech jakub ma ina past lectures and presentations winter semester 2018 1 data driven politics julia language 1 from zero to a data science project corobiapolitici sk https drive google com open id 1hhtc wwjg27nuebculsiw0zwtbepcqgrkddkkb8efgk by dominik csiba 2 julia a language for fast numerical code as well as general programming https drive google com open id 1tg5nugkpqkfp5gkrktj9gw c5lajsqls by renn donner 2 data exporation in r weather forecasting 1 how can we quickly find out which kind of data we have https drive google com file d 1k62xawmgnafnnqh vknphzuyyvn6rvkt by martin bago 2 case study weather predictions aggregation system http meteo mdj sk by rudolf jak a 3 databases apache spark 1 ke u rela n datab za nesta https docs google com presentation d 1chpiwge pffe dpac3uz6h nsxnlwfuomxbp083equk by peter kr tky 2 spracovanie d t z eventov pomocou sparku https docs google com presentation d 1rnn4q s9fxt8d21muz6cpyttnaf2t4420ekrl0ckuri by ondrej brichta 4 automatic models ml in gaming 1 automatic model building for time series with application in energy industry https drive google com file d 1jshzi4ej9s6 zodqzm0z7elozxu1siob by j n dolinsk 2 machine learning in action how we doubled revenue on a game with over a billion players https docs google com presentation d 1nobg ptywdj6ot4m01h2ktvif ii1mupgtaeemoitvg by r bert magyar 5 biometrics ab tests 1 fingerprint recognition from standard methods to small area matchers https docs google com presentation d 1kowo9y8togj mifklulapqvvkdf9k1gekssy76 hua by dominik csiba 2 faster and better a b tests with bayesian inference https docs google com presentation d 1 llf gwclh2 xmfyxwf8s fueo71odteqhhje5qtxlg by viktor gregor past lectures and presentations summer semester 2018 1 data integration 1 a big query frog in a small pond https www slideshare net data science club a big query frog in a small pond jakub motyl buffpanel by jakub motyl 2 batch spark and streaming kafka data preprocessing https www slideshare net data science club batch spark and streaming kafka datapreprocessing by matus cimerman 2 data visualization 1 principles of big data analytics visualization https www slideshare net data science club principles of big data analytics visualization by matej novotny 2 how to present campaign results to your boss https www slideshare net data science club how to present campaign results to your boss by martin bago 3 feature engineering 1 feature engineering on schemaless data https docs google com presentation d 1e e1zqnxux4ahs3sxu04yokb00tqo8z8 kafw8jusn4 edit slide id p by ondrej brichta 2 feature engineering your trump card in ai http mocnedata sk fiit features by filip vitek 4 deep learning 1 building deep learning assistant for health care specialist presentation not available by boris vavrik 2 robust processing of neural network output presentation not available by michal maly 5 deploying ai solutions 1 quantile regression and its application in the insurance business presentation not available peter carsky 2 real time personalized recommendations using embeddings presentation not available by jakub macina past lectures and presentations winter semester 2017 1 introduction to data science presentations 1 welcome and introduction to data science club https www slideshare net secret djspj1tgnv7dwt by jozo kovac 2 data scientists the new rock stars https docs google com presentation d 1lenej02xtprdk1j5hcctdx3ivnvcsjanzz7qsyoxfl0 edit usp sharing by robert lacok workshop full stack introduction to data science application development https github com exponea data science club tree master 16 10 2017 20 20full stack 20introduction 20to 20data 20science 20application 20development by robert lacok 2 data storage exploratory data analysis 1 how to store data it depends https docs google com presentation d 1i9g1npkzlubdhtusbusumac71c8nlcqzi4jb e8leeo edit slide id g29325b1889 0 0 by juraj sottnik 2 exploratory data analysis https github com exponea data science club blob master 30 10 2017 20 20data 20storage 20and 20exploratory 20analysis explorativna analyza ipynb by jakub sevcech 3 game analytics dwh and sql analytics presentations 1 why successful games need analytics https www slideshare net data science club why successful games need analytics by ivan tran k 2 dwh and sql analytics presentation not available by tomas synek and matus nagy workshop sql analytics https github com exponea data science club tree master 13 11 2017 20 20data 20warehousing 20and 20sql by matus cimerman 4 feature extraction natural language processing 1 feature extraction https github com exponea data science club tree master 27 11 2017 20 20feature 20extraction by jakub sevcech 2 introduction to natural language processing https www slideshare net dmacjam introduction to natural language processing 85818192 by jakub macina | data-science data-science-clubs bratislava machine-learning | server |
A-Practical-Guide-To-Web-Development | a practical guide web development this guide is a compilation of resources for full stack web development the layout of this guide is borrowed from traversy media s web development guide https www youtube com watch v untqvlqmdq0 with additional links to resources everything is awesome https img youtube com vi untqvlqmdq0 0 jpg https www youtube com watch v untqvlqmdq0 web development in 2019 a practical guide table of contents general resources general resources getting started getting started basic software tools basic software tools the building blocks the building blocks html css html css responsive layout responsive layout make it live make it live basic deployment basic deployment better css better css sass pre processor sass pre processor get dynamic get dynamic vanilla javascript vanilla javascript you re skill set youre skill set basic front end web developer basic front end web developer now what now what what to learn next what to learn next easier ui s prototyping easier uis prototyping html css framework choose one htmlcss framework choose one your workflow your workflow git tooling git tooling javascript javascript front end framework front end framework state management state management you re skill set youre skill set 1 full fledged front end web developer full fledged front end web developer backend programming backend programming server side language choose one server side language choose one server side framework choose one server side framework choose one database choose one or two database choose one or two full stack programming full stack programming server rendered pages server rendered pages content management system cms choose one content management system cms choose one devops deployment more devops deployment more you re skill set youre skill set 2 full stack badass full stack badass beyond the browser beyond the browser mobile development choose one mobile development choose one desktop apps with electron desktop apps with electron 2019 trends 2019 trends graphql apollo graphql apollo typescript typescript serverless architecture serverless architecture ai machine learning ai machine learning blockchain technology blockchain technology progrssive web apps pwa progressive web apps pwa contributing contributing general resources youtube channels traversy media https www youtube com user techguyweb learncode academy https www youtube com user learncodeacademy featured learn webcode https www youtube com user learnwebcode videos leveluptuts https www youtube com user leveluptuts devtips https www youtube com user devtipsfordesigners articles medium https medium com curriculum like learning free resources github student developer https education github com pack freecodecamp https www freecodecamp org reddit r learnprogramming https reddit com r learnprogramming r webdev https reddit com r webdev practice hackerrank https www hackerrank com codewars https www codewars com paid resources udemy https www udemy com lynda https www lynda com udacity https www udacity com pluralsight https www pluralsight com getting started basic software tools basic tools for web development text editor or ide vscode https code visualstudio com atom https atom io sublime text https www sublimetext com web browser google chrome https www google com chrome firefox https www mozilla org en us firefox new design mockup optional adobe xd https www adobe com products xd html adobe photoshop https www adobe com products photoshop html sketch https www sketchapp com figma https www figma com 3rd party terminal optional git bash https git scm com downloads wsl https docs microsoft com en us windows wsl about iterm2 https www iterm2 com hyper https hyper is the building blocks html css always the very first thing to learn what to be proficient in semantic html5 elements basic css positioning box model etc flexbox css grid css variables custom properties browser dev tools resources youtube videos html crash course for beginners https www youtube com watch v ub1o30fr ee css crash course for beginners https www youtube com watch v yfoy53qxeni css grid crash course https www youtube com watch v jv8b24rsn5o flexbox in 20 minutes https www youtube com watch v jjsoeo8jsnc udemy courses build responsive real world websites with html5 and css3 https www udemy com design and develop a killer website with html5 and css3 articles learn html in 5 minutes https medium freecodecamp org learn html in 5 minutes ccd378d2ab72 learn css flexbox in 5 minutes https medium freecodecamp org learn css flexbox in 5 minutes b941f0affc34 books html css design and build web sites https www barnesandnoble com p html css jon duckett 1110769868 2684039025388 responsive layout responsive layouts are no longer a luxury they are a necessity what to be proficient in set viewport fluid widths media queries rem over px mobile first stacked columns resources youtube videos build an html5 website with a responsive layout https www youtube com watch v wm6cukswsnw responsive landing page using html css https www youtube com watch v gjxxf3 dcng build a responsive mobile first website html5 css3 https www youtube com watch v xsenj 1hg2o pluralsight login page clone html css https www youtube com watch v wix1o5y5eb4 articles everything you need to know about responsive web design https medium com swlh everything you need to know about responsive web design 54c2059a7e99 best practices of responsive web design https medium com level up web best practices of responsive web design 6da8578f65c4 using media queries for responsive design https www smashingmagazine com 2018 02 media queries responsive design 2018 how to make your html responsive by adding a single line of css https medium freecodecamp org how to make your html responsive by adding a single line of css 2a62de81e431 make it live basic deployment learn to deploy a static website register a domain name namecheap https www namecheap com google domains https domains google managed shared hosting or vps inmotion https www inmotionhosting com hostgator https www hostgator com bluehost https www bluehost com digital ocean https www digitalocean com ftp or sftp file upload filezilla https filezilla project org cyberduck https cyberduck io static hosting need to know git netlify https www netlify com github pages https pages github com resources youtube videos web hosting cpanel guide https www youtube com watch v un7s4zd8h k deploy websites in seconds with netlify https www youtube com watch v bjvuqvccnxm github pages deploy domain https www youtube com watch v skxkc4sqtrk articles how to deploy a website to netlify https medium com the codelog how to deploy a website to netlify 35274f478144 how to deploy a static website to heroku https medium com adityaniloi how to deploy a static website to heroku 49d55e07cb94 how to host static website on firebase hosting for free https medium com aleemuddin13 how to host static website on firebase hosting for free 9de8917bebf2 better css sass pre processor not mandatory but recommended it is easy enough to learn the basics what to be proficient in structured css variables nested css mixins functions inheritance resources youtube videos responsive portfolio website series https www youtube com watch v gyzhs n2gqu list plillgf rfqbyogocjkomokvznv6asxkzu fancy form ui from scratch https www youtube com watch v aitdhlc8jco sass workflow with gulp https www youtube com watch v rmxvmfx3rno articles getting started with sass https medium com thejasonfile getting started with sass dedb271bdf5a intro to sass https codeburst io intro to sass 386eb8f2e005 learn sass in 15 minutes https tutorialzine com 2016 01 learn sass in 15 minutes get dynamic vanilla javascript start learning javascript without any library or framework what to be proficient in data types functions conditionals loops operators dom manipulation events json fetch api es6 arrow functions promises async await destructuring resources youtube videos javascript fundamentals for beginners https www youtube com watch v verou2xtpr8 javascript dom crash course https www youtube com watch v 0ik6x4djkcc list plillgf rfqbye6ik euxa2izfce082b3s vanilla javascript projects playlist https www youtube com watch v verou2xtpr8 list plillgf rfqbbnegy3roilwk7jmcusyqtx udemy courses modern javascript from the beginning https www udemy com modern javascript from the beginning javascript understanding the weird parts https www udemy com understand javascript javascript basics for beginners https www udemy com javascript basics for beginners articles you don t know js https github com getify you dont know js javascript info https javascript info learn vanilla javascript from tutorials https medium mybridge co learn plain javascript from top tutorials for the past year v 2018 d964bc597a45 books javascript for kids a playful introduction to programming https www amazon com javascript kids playful introduction programming dp 1593274084 eloquent javascript a modern introduction to programming https www amazon com eloquent javascript modern introduction programming dp 1593275846 javascript the good parts https www amazon com javascript good parts douglas crockford dp 0596517742 javascript the definitive guide https www amazon com javascript definitive guide activate guides dp 0596805527 practice javascript30 https javascript30 com you re skill set basic front end web developer at this point if successfully learnt and practiced the above technologies it is possible to find an entry level job but you are most likely going to need to know more freelancing is a possibility you should be able to build static websites small business informational build ui layouts take a design and create the html css add dynamic functionality modals slideshows etc deploy and mantain wesbites now what what to learn next what to do next depends on what you want to do you have a few choices html css framework git tooling front end js framework server side language database easier ui s prototyping html css framework choose one html css frameworks are becoming a bit less relevant but i will suggest learning one they can make your life much easier and great for prototyping bootstrap https getbootstrap com materialize https materializecss com bulma https bulma io resources youtube videos bootstrap crash course https www youtube com watch v 5gcqtldgxy8 materialize crash course https www youtube com watch v nqt8c5ofjeq bulma crash course https www youtube com watch v iipqyqt2 wg articles learn the bootstrap grid in 15 minutes https tutorialzine com 2015 10 learn the bootstrap grid in 15 minutes materialize tutorial https www tutorialspoint com materialize let s code our portfolio a practical approach to materializecss https codeburst io lets code our portfolio a practical approach to materializecss 61adfb17e308 learn bulma in 5 minutes https medium freecodecamp org learn bulma in 5 minutes ec5188c53e83 scrimba learn bootstrap 4 for free https scrimba com g gbootstrap4 learn bulma css for free https scrimba com g gbulma udemy bootsrap 4 from scratch https www udemy com bootstrap 4 from scratch with 5 projects materialize css from scratch https www udemy com materialize css from scratch with 5 projects your workflow git tooling git is absolutely necessary for all web developers here are some other tooling suggestions basic command line basic software tools used for so much including these tools git https git scm com npm https www npmjs com or yarn https yarnpkg com en installing packages webpack https webpack js org or parcel https parceljs org module bundling gulp https gulpjs com or grunt https gruntjs com task runners editor extensions eslint prettier live server etc resources youtube videos linux command line basics https www youtube com watch v cbokz0ltizk git crash course https www youtube com watch v swyqp7iy tc npm crash course https www youtube com watch v jhdhasskmb0 yarn crash course https www youtube com watch v g9 6kmibisk webpack crash course https www youtube com watch v lziunmk 8eq gulp crash course https www youtube com watch v 1rw9mfileeg vscode setup https www youtube com watch v fnphjhn0jte javascript front end framework it is becoming a necessity to learn a js front end framework very popular in the industry more interactive intersting uis components modular front end code good for teams most common front end frameworks react https reactjs org most popular in the industry vue https vuejs org easy to use and really gaining traction angular https angular io fading a bit used in enterprise resources youtube videos react crash course https www youtube com watch v sbws8msxn7a vue js in 60 minutes https www youtube com watch v z6hqqgvgi4y angular in 60 minutes https www youtube com watch v khzgshnhnbi articles how to learn react a roadmap from beginner to advanced https medium freecodecamp org learning react roadmap from scratch to advanced bff7735531b6 scrimba learn vue js for free https scrimba com g glearnvuev udemy courses react front to back https www udemy com react front to back angular front to back https www udemy com angular 4 front to back state management for larger apps with a framework you may need to learn methods to manage app level state redux https redux js org context api https hackernoon com how do i use react context 3eeb879169a2 apollo https www apollographql com graphql client vuex https vuex vuejs org ngrx https ngrx io what to be proficient in immutable state stores reducers mutations getters actions observables resources youtube videos redux crash course https www youtube com watch v 93p3lxr9xfm react context api project https www youtube com watch v ndet0kddbhk list plillgf rfqbaxgxkkgkk1xljavcx31xri udemy courses modern react with redux https www udemy com react redux mern stack front to back https www udemy com mern stack front to back you re skill set full fledged front end web developer at this point you should be able to get a front end job or work for yourseld and do pretty well you should be able to build incredible front end applications smoot steady front end workflow work well with teams familiar with git connect to backend api s work with data backend programming server side language choose one to be a full stack or software engineer you will need to learn a server side language technology node js https nodejs org python https www python org php http www php net c https docs microsoft com en us dotnet csharp go https golang org what to be proficient in fundamental syntax structure workflow package management http routing resources youtube videos node js for beginners https www youtube com watch v u8xf6afgqlc python for beginners https www youtube com watch v jjmcl1n2kqs php front to back https www youtube com watch v ojbfyzaa2qa list plillgf rfqbap2ib6zs4bbbcypagajpjn c application in 60 minutes https www youtube com watch v gcfjjpmfjvi go crash course https www youtube com watch v sqrbiluwr0u udemy courses node js dev to deployment https www udemy com nodejs express mongodb dev to deployment php mvc object oriented https www udemy com object oriented php mvc python django dev to deployment https www udemy com python django dev to deployment server side framework choose one do not reinvent the wheel learn a framework to build better and faster node js express https expressjs com koa https koajs com adonis https adonisjs com python django https www djangoproject com flask http flask pocoo org php laravel https laravel com symfony https symfony com c asp net https www asp net resources youtube videos express crash course https www youtube com watch v gnso8 xj8rs koa crash course https www youtube com watch v z84utk5zmak getting started with adonis https www youtube com watch v 2oxpbk2inqu list plillgf rfqby6wy9yzjcpsmzvhn icug6 django crash course https www youtube com watch v d6estdolxh4 flask from scratch https www youtube com watch v zrwy8gtgj1a list plillgf rfqbbbpz6gsem9hlqobuqjnoj laravel from scratch https www youtube com watch v eu7prmcpx 0 list plillgf rfqbyhqsn5wmxy6vsdmkgadrj up running with symfony https www youtube com watch v t5zedknwx9e list plillgf rfqba pqbbef14 fi6lafvvdys database choose one or two most applications will use some kind of database there are different types here are some options relational database mysql https www mysql com postgresql https www postgresql org ms sql https www microsoft com en us sql server default aspx nosql mongodb https www mongodb com couchbase https www couchbase com cloud firebase https firebase google com amazon web services aws https aws amazon com azure documentdb https azure microsoft com en us services cosmos db lightweight sqlite https www sqlite org nedb https github com louischatriot nedb redis https redis io resources youtube videos sql crash course https www youtube com watch v nwew3scmd2k mongodb in 30 minutes https www youtube com watch v pwbmrx5rvbe redis crash course https www youtube com watch v hbt56gfj998 postgresql for beginners https www youtube com watch v aufpf cllls couchbase training https www youtube com playlist list pljud88mugnonbfx9ajdza7kcuittavv9f firebase beginner s guide https www youtube com watch v 9krgvxulbag aws for beginners https www youtube com watch v it1x42d1kea getting started with azure documentdb https www youtube com watch v py4pufhw0u4 sqlite complete overview https www youtube com watch v pd 0g0migua full stack programming server rendered pages frameworks like react vue angular can also be rendered on the server which can actually make things relatively easier next js https nextjs org react nuxt js https nuxtjs org vue angular universal https angular io guide universal angular resources youtube videos next js crash course https www youtube com watch v ikove40sy0u nuxt js introduction https www youtube com watch v ntedxuqbfn0 angular universal course https www youtube com watch v v6cklv7kgvm list ploa5yiicjj wnope ze6 bsp mbgd vyy content management system cms choose one content management systems allow for quick development and give your clients the ability to update their content may not be a bad idea to pick one up great for freelancers php based wordpress https wordpress com drupal https www drupal org js based ghost https ghost org keystone https keystonejs com python based mezzazine http mezzanine jupo org net piranha http piranhacms org orchard cms https orchardproject net resources youtube videos keystone js tutorial https www youtube com watch v dpxdfeuek3g wordpress for beginners https www youtube com watch v exkwak0j3k8 drupal beginner tutorials https www youtube com watch v dysucv1 9w list pltaxux0nezk9mky clwcpkgtoegyltyco ghost tutorials https www youtube com playlist list pleayhfkbozusdofrd f5jcb jpvbnvotg intro to mezzanine cms https www youtube com watch v nsdnh btwgo list plz0trsmt5kvmlanrzetcvoqkyinvijyrd devops deployment more learning languages and frameworks is one thing setting up environments testing deploying is another deployment linux ssh git https gist github com noelboss 3fe13927025b89757f8fb12e9066f2fa server software nginx https www nginx com apache https httpd apache org platforms digital ocean https www digitalocean com amazon web services aws https aws amazon com heroku https www heroku com azure https azure microsoft com en us virtualization docker https www docker com vagrant https www vagrantup com testing unit integration functional system resources youtube videos getting started with docker https www youtube com watch v kyx2psuwome vagrant crash course https www youtube com watch v vbrexjkizgo jest crash course unit testing https www youtube com watch v 7r4xvdi2vho deploy node app to server https www youtube com watch v re2plyfqcze you re skill set full stack badass you are a frigging rockstar you should be able to setup full stack dev environments workflows build back end apis micro services work with databases construct full stack apps front end framework server deploy to the cloud ssh git servers etc beyond the browser mobile development choose one there are some frameworks that allow us to create native apps with web technologies react native https facebook github io react native build native apps with react nativescript https www nativescript org angular typescript javascript ionic https ionicframework com hybrid apps with html css js flutter https flutter io mobile sdk for android ios uses dart xamarin https visualstudio microsoft com xamarin mobile apps with c resources youtube videos react native crash course https www youtube com watch v mkualzprzcs ionic mobile framework series https www youtube com watch v ghpl 2iszli list plillgf rfqbbtvhn2l1soszejz6lpclci nativescript step by step https www youtube com watch v 2jdxndutlgs list plikwvuuoqtpbaendddngegqybm0g4 0gd flutter for beginners https www youtube com watch v glsg wh ywc xamarin for beginners https www youtube com watch v iio1udehpni list plsrzv8shpwjn z6f44p4gxo2l5cobvfba desktop apps with electron electron is used to build powerful cross platform desktop applications using javascript uses chromium node js compatible with windows mac linux crash reporting debugging profiling resources youtube videos intro to electron https www youtube com watch v mr9mtm trpw build an electron app in 60 minutes https www youtube com watch v kn1czs0m1su 2019 trends graphql apollo graphql is a revolutionary new way to thing about apis query language that is much less rigid than standard rest ask for only what you want front back end can collaborate more smoothly writing queries are very easy and similar to json apollo is a client to make requests to a graphql server used with the gatsby static site generator resources youtube videos build a graphql server https www youtube com watch v pecjxkylcrm list plillgf rfqbyzty73 phbqkrdnv7ikh68 graphql with react apollo https www youtube com watch v semtj8w04z8 list plillgf rfqbzrjw48exldm4dsohurclzx typescript typescript is a superset of js with additional features including static typing what to be proficient in types for variables functions etc classes other es6 like features used in angular but can be implements in react vue resources youtube videos typescript crash course https www youtube com watch v ray 3siqt e serverless architecture eliminate the need for creating and managing your own server use 3rd party services to execture serverless functions faas https docs openfaas com examples are aws https aws amazon com netlify https www netlify com firebase https firebase google com popular with gatsby https www gatsbyjs org static sites serverless framework toolkit for building serverless apps resources youtube videos serverless lambda functions https www youtube com watch v drjwmld9mjo gatsby crash course https www youtube com watch v 6yhqq2zw1sc ai machine learning ai machine learning have been huge in almost every area of programming technology including web development machine learning can allow web apps to adapt over time ai has a long way to go but i suspect we will see more of it in web development used heavily in python but we also have js libraries like tenserflow js https js tensorflow org and brain js https github com brainjs brain js resources youtube videos machine learning with brainjs https www youtube com watch v rvmhhttquxc blockchain technology companies are using blockchain for digital transactions in order to make them more efficient and secure solidity https solidity readthedocs io language for implementing contracts mist https github com ethereum mist used for strong ethereum sending transactions contracts coinbase api https developers coinbase com blochain devs can easily build apps and integrate bitcoin resources youtube videos learning solidity https www youtube com watch v v hu0jptlto list pl16wqdaj66scodl6xifbke xqg2gw avg coinbase api tutorial https www youtube com watch v sq neuum2u4 list plvhpbus6m16aguxgdk0ifamhn5sqnxg2d progrssive web apps pwa progressive web apps are regular web apps but give the user a native app experience in terms of layout and functionality responsive to fit any form factor service workers for offline availability app like interactions https reliable fast engaging resources youtube videos intro to service workers https www youtube com watch v ksxwawhcw6k pwa from scratch https www youtube com watch v gcx 3qi7t7c web assembly assembly like binary format for code that can be executed by web browsers can be generated from higher level languages like c c rust faster than javascript secure enforces same origin security policies in the browser open debuggable resources docs developer s guide to web assembly https webassembly org getting started developers guide contributing if you would like to add quality resources to this guide open a pull request all i ask you is to follow the way i ve been linking resources in this guide | hacktoberfest hacktoberfest2021 | front_end |
angular-iot | build status https travis ci org urish angular iot svg branch master https travis ci org urish angular iot angular iot iot support for angular angular iot is an experimental technology that allows you to program physical hardware buttons leds etc using angular it provides a set of directives such as iot button and iot led that let you interface with the hardware it is even possible to create an application that renders both a web interface and an iot hardware interface with a single code base see ng2 simon https github com urish ng2 simon for an example behind the scenes it uses a combination of angular universal https github com angular universal for running the application inside node js and the johnny five https github com rwaldron johnny five library for interfacing with the hardware building running the blink example arduino the blink example https github com urish angular iot blob master examples blink ts will blink the built in led on an adruino board that is connected to your pc you will need to upload the standardfirmata firmware https github com firmata arduino to your arduino board first git clone https github com urish angular iot cd angular iot npm install npm run example build npm run example run note the example program will try to detect the serial port that the arduino is connected to automatically you can manually specify the port name by setting the serial port environment variable prior to running the example c h i p 9 computer chip https www getchip com is a tiny 9 computer that has enough computing power to run angular it also has wifi and bluetooth built in which makes it an ideal iot platform there is a version of the blink example examples blink chip ts that runs on chip in order to run it make sure you have node 6 x installed on your chip you can install it via nvm https github com creationix nvm and the run the following commands git clone https github com urish angular iot cd angular iot npm install npm install chip io npm run example build npm run example chip the on board status led should start blinking presentation april 2016 angular iot talk presentation png https skillsmatter com skillscasts 7934 javascript from the world wide web to the world of iot video check out ng2 simon https github com urish ng2 simon for a complete example of using angular iot for powering a game that can be run both inside the web browser and on real hardware license mit license https img shields io badge license mit blue svg style flat license | server |
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XAM110 | xam110 introduction to cross platform mobile development this repository contains exercises for the xamarin university xam110 introduction to cross platform mobile development training course this course is retired and not being updated for the latest free xamarin training head over to microsoft learn https aka ms learn xamarin course materials course video https youtu be 0l3flnbwj3g exercise instructions https xamarinuniversity github io xam110 | front_end |
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portfolio | h1 align center portfolio website h1 h2 align center dina blaszczak front end developer h2 p align center live preview a href https themalni github io portfolio portfolio website a p br p align center img src https user images githubusercontent com 12295765 43483406 dde55e8a 950b 11e8 8841 d616ee0c0551 jpg width 550 alt portfolio website p | portfolio-website project front-end front-end-development web-design css html javascript | front_end |
fabric-iot | fabric iot hyperledger fabric english readme en md what we have 1 abac golang chaincode 2 fabric iot 3 0 0 0 github hyperledger fabric https github com hyperledger fabric doc 1 4 https hyperledger fabric readthedocs io en release 1 4 node sdk 1 4 https hyperledger github io fabric sdk node release 1 4 module fabric network html 0 1 os mac os 10 13 ubuntu 16 04 0 2 git 2 0 0 docker 19 docker compose 1 24 node 12 golang 1 10 hyperledger fabric 1 4 golang node bin path fabric 1 4 3 fabric 1 4 3 bootstrap sh version 1 4 3 ca version 1 4 3 0 3 go go get github com newham fabric iot 0 4 hyperledger fabric bin bash bootstrap sh export path bash export path path pwd fabric samples bin export path hyperledger fabric bin path 1 chaincode go golang chaincode client nodejs nodejs network fabric iot channel artifacts channel compose files docker compose conn conf docker crypto config scripts cli ccp generate sh ccp template json json ccp template yaml yaml clear docker sh docker configtx yaml configtxgen crypto config yaml down sh shell env sh shell import generate sh shell install cc sh shell rm cc sh shell up sh shell 2 https github com newham fabric iot tree distributed 2 1 shell cd network shell generate sh crypto config 2 2 shell up sh 2 3 shell cc install sh 2 4 chain code new version shell cc upgrade sh new version chaincode chaincode go golang 2 5 shell down sh docker 3 3 1 shell shell bash cd network cc test sh bash invoke shell action cc name cc version cc src fname args add policy cc sh invoke pc 1 0 go pc addpolicy as userid 13800010001 role u1 group g1 ao deviceid d100010001 mac 00 11 22 33 44 55 query policy cc sh invoke pc 1 0 go pc querypolicy 40db810e4ccb4cc1f3d5bc5803fb61e863cf05ea7fc2f63165599ef53adf5623 3 2 node js 3 2 1 nodejs shell cd client nodejs shell npm install 3 2 2 shell node enrolladmin js shell node registeruser js 3 2 3 chaincode shell node invoke js chaincode name function name args br br 2019 a href mailto liuhanshmtu 163 com liuhanshmtu 163 com a all rights reserved | iot hyperledger-fabric chaincode | server |
CvStudio | cv studio img alt src logo png width 200px img cv studio is a graphical annotation tool to address different computer vision tasks cv studio is developed in python qt sqlite and uses pytorch s resources to train deep learning models interface images image png cvstudio supports datasets create and manage your datasets for images manually annotate images using a label system for classification problems using a bounding box for localization and object detection problems using a polygon tool or freehand selection for segmentation tasks auto annotate images with a pretrained model to continue tagging the images by your own watch a demo video https www youtube com watch v xtnhwr083lm roadmap datasets annotations for videos platforms macos support installation note cv studio only have been developed and tested on windows and linux future platforms are in the roadmap 1 install cvstudio pip install cvstudio 2 install pytorch using gpu br console pip install pre torch torchvision f https download pytorch org whl nightly cu101 torch nightly html using cpu br console pip install pre torch torchvision f https download pytorch org whl nightly cpu torch nightly html 3 download pre trained models this command must be executed from the cvstudio folder windows powershell console invoke webrequest outfile models ms deeplab resnet trained voc pth https data vision ee ethz ch csergi share dextr ms deeplab resnet trained voc pth invoke webrequest outfile models dextr pascal sbd pth https data vision ee ethz ch csergi share dextr dextr pascal sbd pth linux console wget https data vision ee ethz ch csergi share dextr ms deeplab resnet trained voc pth p models wget https data vision ee ethz ch csergi share dextr dextr pascal sbd pth p models 4 run cvstudio console cvstudio documentation check out the wiki https github com haruiz cvstudio wiki how to contribute send a pull request license free software mit license https github com haruiz cvstudio git blob master license citation haruiz cv studio git code 2019 https github com haruiz cvstudio credits images and icons div icons made by a href https www flaticon com authors dave gandy title dave gandy dave gandy a from a href https www flaticon com title flaticon www flaticon com a div div icons made by a href https www flaticon com authors pixelmeetup title pixelmeetup pixelmeetup a from a href https www flaticon com title flaticon www flaticon com a div div icons made by a href https www flaticon com authors smashicons title smashicons smashicons a from a href https www flaticon com title flaticon www flaticon com a div div icons made by a href https www flaticon com authors eucalyp title eucalyp eucalyp a from a href https www flaticon com title flaticon www flaticon com a div div icons made by a href https www flaticon com authors becris title becris becris a from a href https www flaticon com title flaticon www flaticon com a div div icons made by a href https www flaticon com authors smashicons title smashicons smashicons a from a href https www flaticon com title flaticon www flaticon com a div div icons made by a href https www flaticon com authors freepik title freepik freepik a from a href https www flaticon com title flaticon www flaticon com a div div icons made by a href https www flaticon com authors those icons title those icons those icons a from a href https www flaticon com title flaticon www flaticon com a div models deep extreme cut from extreme points to object segmentation https github com scaelles dextr pytorch img alt src images dextr png img | ai |
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transformers | understanding large language models decoder models decoder https raw githubusercontent com abacaj transformers main decoder formatted png encoder models encoder https raw githubusercontent com abacaj transformers main encoder formatted png encoder decoder models encoder decodder https raw githubusercontent com abacaj transformers main enc dec formatted png | ai |
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Backend_App | backend app this is the backend development of an app | server |
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IT | it information technology | server |
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tpf-mlapps-ITBA | tpf mlapps itba tp final del m dulo de machine learning applications de la diplomatura en cloud data engineering de itba contexto acaba de ser contratado como el primer ingeniero de datos de una peque a empresa de viajes su primera tarea para usted fue demostrar el valor y los conocimientos que se pueden generar a partir de las canalizaciones de datos su plan es que una vez que demuestre lo valiosos que pueden ser los datos comenzar n a invertir en el uso de un proveedor de instancias en la nube por ahora su propia computadora tendr que hacerlo objetivo crear un dag de airflow que act e de etl para extraer datos est ticos de s3 y los cargue en una base de datos de postgres para llevar a cabo el desarrollo se utilizar el dataset de demoras y cancelaciones de viajes a reos https www kaggle com yuanyuwendymu airline delay and cancellation data 2009 2018 select 2009 csv de kaggle se deben cumplir los siguientes pasos 1 configurar airflow para que corra en aws 2 crear una instancia rds de postgres 3 desarrollar un dag de airflow con schedule anual que realice detecci n de anomal as para identificar por cada aeropuerto si hubo alg n d a con demoras fuera de lo normal por cada aeropuerto produzca un gr fico desde python en el cual se pueda ver la cantidad de vuelos de cada d a con alguna indicaci n en los d as que fueron considerados an malos y almac nelo en s3 en un path f cilmente identificable por a o y aeropuerto analizado luego se debe cargar la data sumarizada junto con un indicador de dato an malo 4 desarrollar una visualizaci n de los datos cargados para m s detalle sobre las consignas ver el archivo consignas md consignas md arquitectura del trabajo desarrollado el diagrama de aquitectura se puede observar en el archivo architecture png architecture png la arquitectura de este proyecto se compone de los siguientes elementos terraform se opt por utilizar terraform para aprovisionar y desaprovisionar la totalidad de la arquitectura para poder automatizar esta instancia y evitar de esta manera costos extras por recursos sin eliminar o creados de forma incompleta o erronea bucket de s3 en este bucket se descargar la data desde el sitio de kaggle adem s de contener los archivos necesarios para configurara airflow dags creados y arquivos de requerimiento de librer as de python vpc y 3 capas de subnets en 2 azs diferentes una capa de subnets p blicas fue creada para colocar nat gateways y el load balancer necesario la primera capa de subnets privadas representa la capa de aplicaci n con el servicio de superset la tercera capa de subnets privadas incluye la base de datos y el server de airflow instancia de rds postgres la base de datos se encuentra ubicada en una capa de subnets privadas idealmente en multi az para aumentar la disponibilidad auqne en el proyecto no se habilit esta opci n por un tema de costos mwaa se opt por utilizar managed workflows for apache airflow en lugar de montar manualmente apache airflow en una capa de c mputo de cualquier tipo para poder experimentar c mo es el funcionamiento de esta herramienta open source siendo casi totalmente administrada por amazon secrets manager se utiliz este recurso como secret backend para airflow consiguiendo de esta forma generar autom ticamente las credenciales de conecci n a la instacia de rds cluster de ecs en este cluster se encuentra corriendo el servicio de superset montado desde una imagen de docker p blica se opt por esta herramienta de visualizaci n ya que utilizar quicksight para conectarse a la base de datos que se encuentra montada en subnets privadas era necesario habilitar una licencia bastante costosa se seleccion una imagen de superset reducida a un solo container ya que para este proyecto no era necesario tanto poder de c mputo pero si fuera necesario se podr a reemplazar ecs por eks y sobre kubernetes montar todos los componentes individuales de esta herramienta cache db workers database etc application load balancer para poder acceder al servicio de superset se mont un lb apuntando al servicio de ecs iam roles y policies fue necesario crear un rol y policies espec ficas para mwaa adem s de agregar algunas policies a roles existentes data flow en el diagrama del archivo dataflow png dataflow png se puede observar el flujo de los datos siguiendo el siguiente esquema se descargan los datos en formato csv de internet al bucket mencionado airflow toma los datos de dicho bucket los procesa y genera dos salidas imagenes que guarda en el mismo bucket info estructurada que guarda en rds desde el servicio de superset se puede acceder a los datos almacenados en la bd para realizar visualizaciones instrucciones para correr el proyecto se pueden seguir las instrucciones detalladas en el archivo instrucciones md instrucciones md sobre el desarrollo del proyecto el proyecto fue desarrollado en etapas para poder organizar mejor el trabajo y poder realizar las pruebas necesarias de forrma efectiva las etapas fueron pensadas en 3 estad os generales dev exploraci n de los datos y desarrollo de la l gica del dag para procesamiento de los mismos la arquitectura esta montada en containers de docker de modo provisiorio para avanzar con el desarrollo se opt por un algoritmo simple basado en la distribuci n normal del tiempo de delay diario y por aeropuerto marcando como an malos aquellos datos que se encuentren por fuera de los 3 desv os valor que cubre aprox el 98 de los datos test una vez finalizado el desarrollo de la l gica se comenzaron a hacer pruebas con la arquitectura espec fica para ello se utiliz un container espec fico llamado local runner brindado por aws info en este link https docs aws amazon com mwaa latest userguide tutorials docker html prod finalmente se crearon las plantillas de terraform y se realizaron las pruebas y ajustes sobre la arquitectura final para poder ir avanzando en los distintos estad os se utiliz ron 3 branches de git que pueden encontrarse en este proyecto a excepcion de la branch de prod que ser mergeada en main para conocer un detalle mayor sobre los pasos efectuados y las problematicas resueltas en las instancias de dev y prod pueden encontrarse los archivos steps dev md https github com jezabeld tpf mlapps itba blob dev steps dev md y steps test md https github com jezabeld tpf mlapps itba blob test steps test md en las respectivas ramas a modo de resumen algunas cuestiones que tuvieron que irse sorteando hasta conseguir la arquitectura final exploraci n de los datos y dise o del algoritmo de detecci n de outliers outlier detection py dags delay tasks outlier detection py dise o del modelo de datos a persistir en la bd models py dags utils models py dise o y codificaci n de los gr ficos por a o y aeropuerto plot py dags delay tasks plot py conexi n a la base de datos desde el dag de airflow db connections py dags utils db connections py funciones varias para conectar el dag de airflow con el bucket recuperar y guardar objetos functions py dags utils functions py referencias aws example code https docs aws amazon com mwaa latest userguide samples dag run info to csv html pandas r w from s3 https towardsdatascience com reading and writing files from to amazon s3 with pandas ccaf90bfe86c upload plot to s3 https stackoverflow com questions 31485660 python uploading a plot from memory to s3 using matplotlib and boto opciones de configuraci n de airflow en mwaa referencias configuring env variables https docs aws amazon com mwaa latest userguide configuring env variables html configuraci n del secret backend para airflow referencias move your apache airflow connections and variables to aws secrets manager https awscloudfeed com whats new open source move your apache airflow connections and variables to aws secrets manager connections secrets manager https docs aws amazon com mwaa latest userguide connections secrets manager html airflow connections https docs aws amazon com mwaa latest userguide samples secrets manager html aprender a utilizar aws cli2 para aprovisionar infraestructura referencias install awscli2 https docs aws amazon com cli latest userguide getting started install html documentaci n aws cli2 https awscli amazonaws com v2 documentation api latest reference index html create bucket https docs aws amazon com cli latest reference s3api create bucket html aprender a utilizar terraform con aws the infrastructure developer s guide to terraform aws edition https cloudacademy com learning paths terraform on aws 1 2377 build infrastructure terraform aws example https learn hashicorp com tutorials terraform aws build in terraform aws get started module composition https www terraform io language modules develop composition provisioners https www terraform io language resources provisioners syntax creacion del environment de mwaa con terraform referencias mwaa environment https registry terraform io providers hashicorp aws latest docs resources mwaa environment terraform aws mwaa https github com idealo terraform aws mwaa intento de utilizar quicksight para acceder a la base de datos en subnets privadas verificando que la opcion solo se encuentra disponible con qs enterprice edition referencias connecting to a vpc with amazon quicksight https docs aws amazon com quicksight latest user working with aws vpc html busqueda de una versi n de superset para docker referencias installing superset using docker compose https superset apache org docs installation installing superset using docker compose apache superset https hub docker com r apache superset tylerfowler docker superset https github com tylerfowler docker superset para ver capturas de la arquitectura creada y corriendo se puede acceder a esta carpeta compartida https drive google com drive folders 1jsrqnkkmqg7hfuwiperdctmozqusqpik usp sharing | airflow aws python superset terraform | cloud |
frontend-bootcamp-online | front end bootcamp online em desenvolvimento muito em breve tutorial mean png https i postimg cc 9mghhfs3 tutorial mean png https postimg cc wfzhmsts reposit rio respons vel pelo front end bootcamp online objetivo do bootcamp o front end bootcamp tem como objetivo reunir o conhecimento dos as principais desenvolvedores as da comunidade t cnica brasileira com o objetivo de ensinar de uma maneira pr tica as principais stacks e frameworks do mercado com o objetivo principal em ajudar as futuras pessoas desenvolvedoras a conseguirem criar um portf lio para conseguir um trabalho como front end developer o front end bootcamp online um projeto gratuito e seguimos com o seguinte slogan desenvolvido pela comunidade para a comunidade pois a nossa inten o e principal objetivo compartilhar conhecimento e claro ajudar a todos as na comunidade t cnica brasileira a conseguirem adquirir o conhecimento necess rio para conseguir um trabalho o projeto aberto para todas as pessoas podendo ser realizado de maneira pessoal por meetups ou workshops locais quero ajudar a montar o bootcamp como fa o caso se sinta desejoso a em fazer parte desse incr vel projeto e deseja colaborar com os conte dos conosco basta entrar em contato comigo pelo e mail ao lado aqui mailto glaucia lemos86 hotmail com pessoas da comunidade t cnica brasileira que j est o contribuindo com o projeto glaucia lemos https twitter com glaucia lemos86 william oliveira https twitter com w oliveiras marco bruno https twitter com marcobrunobr tulio faria https twitter com tuliofaria pr requisitos todas as pessoas s o muito bem vindos as a participarem do front end bootcamp online pois como dito acima o nosso objetivo principal justamente ajudar a todos as a aprenderem as stacks necess rias para criar um portf lio para darem os seus primeiros passos como front end developer o que eu vou aprender ensinaremos a todos as aqui as seguintes stacks html css javascript git github node js front end vue js react js angular typescript mongodb deploy da aplica o na nuvem desenvolvimento de um projeto final agenda a agenda do bootcamp consiste de 3 fases s o elas fase 1 iniciante na fase 1 do front end bootcamp online todos as aprender o os principais conceitos sobre html css javascript git github no final da fase 1 as pessoas ter o que criar um projeto com as stacks aprendidas fase 2 intermedi rio na fase 2 do front end bootcamp online a pessoa poder optar em aprender 1 framework a sua escolha node js para front end obrigat rio mongodb obrigat rio vue js escolha do desenvolvedor a angular typescript escolha do desenvolvedor a react escolha do desenvolvedor a no final da fase 2 as pessoas ter o que criar um projeto com as stacks aprendidas ou mevn mean ou mern fase 3 avan ado na fase 3 do front end bootcamp online todos as aprender o como realizar o deploy e hospedar as suas aplica es na nuvem visando criar um portf lio no github e aprender o as seguintes stacks conceitos de nuvem azure azure storage account azure web app service azure devops apresenta o do projeto final capstone project nessa fase as pessoas ter o que pegar o projeto criado na fase 2 e fazer os aprimoramentos necess rios depois precisam realizar o deploy hospedagem da aplica o na nuvem ao final do desenvolvimento do projeto a pessoa desenvolvedora ter que realizar um v deo de 5 minutos explicando o que desenvolveu e postar no youtube e abrir uma issue aqui https github com glaucia86 frontend bootcamp online issues com as seguintes informa es nome link do v deo link do projeto github recursos utilizados no front end bootcamp visual studio code https aka ms aa5k0dt node js https nodejs org en vue js https vuejs org react js https pt br reactjs org angular https angular io typescript https www typescriptlang org mongodb community server https www mongodb com download center community mongodb compass gui https www mongodb com download center compass postman https www getpostman com azure app service https aka ms aa5jszp azure storage account https aka ms aa5jszq azure devops https aka ms aa5k880 conta azure https aka ms aa5k87y conta azure for students caso voc seja um a estudante de alguma institui o de ensino de faculdade ou universidade poder criar sua conta no azure for students https aka ms aa5k886 essa conta te dar o benef cio em possuir cr dito de usd 100 00 para usar os servi os de maneira gratuita sem necessidade de possuir um cart o de cr dito para ativar essa conta bastam acessar o link ao lado aqui https aka ms aa5k886 tenho d vidas o que fa o caso tenham d vidas aos c digos dos projetos relacionados as fases citadas acima sintam se a vontade em abrir uma issue aqui https github com glaucia86 frontend bootcamp online issues assim que poss vel estaremos respondendo todas as d vidas que tiverem documenta o em desenvolvimento | javascript vue angular react git github html css azure webapp bootcamp | front_end |
Spiralflow | spiralflow a framework for creating guided spirals for large language models this project is designed to help with creating formatting and extracting data from text based conversations with openai language models to allow for more complicated ideas such as flows and spirals it includes the following components message a base class representing a message with content role and variables inputmessage a class to create input messages with pre defined content formats outputmessage a class to create output messages and extract variables from them inputjsonmessage a class to create input messages with pre defined content formats that are able to access dict variables outputjsonmessage a class to create output messages and extract json formated dict variables from them chatllm a class to interface with openai s chat models chathistory a class to store a sequence of messages in a conversation chathistorymanager a class to manage many different chat histories chatflow a class to represent a conversation flow combining input and output messages funcchatflow a class to represent a function in a conversation flow chatflowwrapper a class to wrap chatflows to allow for more complicated ideas such as nohistory nohistory a class to block the flow of specific histories whether in or out of the flow history a class that allows for more complicated flows of histories utilizing the chathistorymanager memorychatflow a class that allows a chat flow to query external memory conditonalchatflow a class to represent a conversation flow with multiple branches that depend on an output of a flow sequentialchatflows a class to represent multiple conversation flows flowing sequentially concurrentchatflows a class to represent multiple conversation flows flowing separately and concurrently chatspiral a class to represent a spiral of conversation flows textloader a class to load text files optionally splitting them into chunks using a specified chunker pdfloader a class to load pdf files utilizing the fitz pymupdf library to extract text content htmlloader a class to load html files extracting text content using the beautifulsoup library directoryloader a class to load files from a directory using a specified loader instance for each file with options for recursion and filtering by file names directorymultiloader a class to load files from a directory using different loader instances based on file names with options for recursion chunker a class to split text into chunks of a specified size with overlap smartchunker a class that splits text into chunks while respecting delimiters tolerances and overlap example projects data chat https github com tiger767 spiralflow data chat a constructued chatflow for responding to prompts using your data google search results and general llm knowledge installation to use this project you will need to install the required dependencies 1 install python 3 9 or higher 2 install faiss package conda install c conda forge pytorch faiss cpu 3 install the this package git clone https github com tiger767 spiralflow git and then pip install inside the repo 4 make sure openai api key is set as an environment variable with your openai api key usage here is a quick example to demonstrate how to use the project python from spiralflow message import role inputmessage outputmessage inputjsonmessage outputjsonmessage from spiralflow chat llm import chatllm from spiralflow chat history import chathistory from spiralflow flow import chatflow create input and output messages chat flow chatflow from dicts role system all your outputs follow the format the capital of country is capital country role user what is the capital of country1 role assistant the capital of country1 is capital1 type output optionally specify the type of message for user assistant role user what is the capital of country2 role assistant the capital of country2 is capital2 role system you are a historian who writes very brief comparison analyses role user compare capital1 and capital2 ensure the response can be parsed by python json loads exactly as analyses a details b details role assistant comparison type output json role user is the following you said true for capital1 country1 n comparison analyses a type input json expected input varnames comparison analyses a role assistant reflect response verbose false initialize the chatllm with the desired openai model chat llm chatllm gpt model gpt 3 5 turbo temperature 0 3 define input variables for the chatflow input variables country1 france country2 spain starting conversation input chat history chathistory inputmessage what is the capital of mexico outputmessage the capital of mexico is mexico city inputmessage what is the capital of united states outputmessage the capital of united states is washington d c execute the chatflow and obtain the extracted variables and chat history variables history chat flow input variables chat llm chat llm input chat history input chat history print extracted variables variables print chat history for message in history messages print f message role title message content this will output extracted variables reflect response yes that statement is true paris is the capital of france while madrid is the capital of spain chat history user what is the capital of mexico assistant the capital of mexico is mexico city user what is the capital of united states assistant the capital of united states is washington d c system all your outputs follow the format the capital of country is capital country user what is the capital of france assistant the capital of france is paris user what is the capital of spain assistant the capital of spain is madrid system you are a historian who writes very brief comparison analyses user compare paris and madrid ensure the response can be parsed by python json loads exactly as analyses a details b details assistant analyses a paris is the capital of france while madrid is the capital of spain b paris is known for its art fashion and cuisine while madrid is known for its museums parks and nightlife user is the following you said true for paris france paris is the capital of france while madrid is the capital of spain assistant yes that statement is true paris is the capital of france while madrid is the capital of spain here is an example using a spiral and some of the more advanced features python from spiralflow message import role from spiralflow chat llm import chatllm from spiralflow flow import chatflow conditonalchatflow funcchatflow nohistory chatspiral decision flow chatflow from dicts role user options n n 1 keep asking and answering questions n n 2 choose this when you have provided 2 prompts in the total conversation history n n only respond by typing the number of the option you choose say nothing else other than 1 or 2 role assistant decision verbose true branch1 flow chatflow from dicts role user prompt prompt n n role assistant answer role user give me a new prompt role assistant prompt verbose true def quit variables chat llm input chat history raise chatspiral exit branch2 flow funcchatflow func quit input varnames set output varnames set answer prompt verbose true cond flow conditonalchatflow nohistory decision flow allow input history true allow rtn input history true 1 branch1 flow 2 branch2 flow verbose true a spiral chatspiral cond flow verbose false chat llm chatllm gpt model gpt 3 5 turbo temperature 0 3 variables history a spiral spiral prompt what is math chat llm chat llm max iterations 10 reset history false print extracted variables for key value in variables items print f key value n print chat history for message in history messages print f message role title message content this will output something like extracted variables prompt sure here s your new prompt explain the greenhouse effect and its impact on climate change decision 1 answer artificial intelligence ai is a branch of computer science that deals with the creation of intelligent machines that can perform tasks that typically require human intelligence ai involves the development of algorithms and computer programs that can learn from data recognize patterns and make decisions based on that information there are two main types of ai narrow or weak ai and general or strong ai narrow ai is designed to perform a specific task such as playing chess or driving a car it is programmed to follow a set of rules and make decisions based on those rules general ai on the other hand is designed to be more flexible and adaptable it can learn and reason like a human and can perform a wide range of tasks across different domains ai is used in a variety of applications from virtual assistants like siri and alexa to self driving cars and medical diagnosis it has the potential to revolutionize many industries and improve our lives in countless ways however there are also concerns about the ethical implications of ai such as the potential for job displacement and the risk of bias in decision making algorithms as ai continues to evolve and become more advanced it will be important to carefully consider these issues and ensure that the technology is used in a responsible and beneficial way chat history user prompt what is math assistant math short for mathematics is a subject that deals with the study of numbers quantities and shapes it involves the use of logical reasoning and critical thinking to solve problems and make sense of the world around us math is a fundamental tool in many fields including science engineering finance and technology it encompasses various branches such as algebra geometry calculus statistics and more math is not just about memorizing formulas and equations but also about understanding concepts and applying them to real world situations it plays a crucial role in our daily lives from calculating the cost of groceries to designing complex structures overall math is a universal language that helps us make sense of the world and solve problems in a logical and systematic way user give me a new prompt assistant sure here s your new prompt explain the concept of artificial intelligence user prompt sure here s your new prompt explain the concept of artificial intelligence assistant artificial intelligence ai is a branch of computer science that deals with the creation of intelligent machines that can perform tasks that typically require human intelligence ai involves the development of algorithms and computer programs that can learn from data recognize patterns and make decisions based on that information there are two main types of ai narrow or weak ai and general or strong ai narrow ai is designed to perform a specific task such as playing chess or driving a car it is programmed to follow a set of rules and make decisions based on those rules general ai on the other hand is designed to be more flexible and adaptable it can learn and reason like a human and can perform a wide range of tasks across different domains ai is used in a variety of applications from virtual assistants like siri and alexa to self driving cars and medical diagnosis it has the potential to revolutionize many industries and improve our lives in countless ways however there are also concerns about the ethical implications of ai such as the potential for job displacement and the risk of bias in decision making algorithms as ai continues to evolve and become more advanced it will be important to carefully consider these issues and ensure that the technology is used in a responsible and beneficial way user give me a new prompt assistant sure here s your new prompt explain the greenhouse effect and its impact on climate change license this project is released under the mit license | autogpt chatbots chatgpt llms openai | ai |
llm-cooperation | llm cooperation github workflow status https github com phelps sg llm cooperation actions workflows tests yaml badge svg https github com phelps sg llm cooperation actions workflows tests yaml this repo contains code explanations and results of experiments to ascertain the propensity of large language models to cooperate in social dilemmas the experiments are described in the following papers s phelps and y i russell investigating emergent goal like behaviour in large language models using experimental economics working paper may 2023 arxiv 2305 07970 https arxiv org abs 2305 07970 s phelps and r rannson of models and tin men a behavioural economics study of principal agent problems in ai alignment using large language models working paper july 2023 arxiv 2307 11137 https arxiv org abs 2307 11137 getting started 1 install mambaforge https github com conda forge miniforge mambaforge 2 in a shell bash export openai api key my key make install make run configuration to run specific experiments and parameter combinations follow instructions below 1 in a shell bash mkdir llm cooperation cat llm cooperation llm config py eof grid temperature 0 1 0 6 model gpt 3 5 turbo gpt 4 max tokens 300 sample size 3 experiments dictator dilemma eof 2 edit home llm cooperation llm config py with required values 3 in a shell bash export openai api key key make run contributing if you have a new experiment then please submit a pull request all code should have corresponding tests and all experiments should be replicable | economics experimental-economics gametheory gpt-3 llm prisoners-dilemma ai-safety ai-alignment behavioral-economics gpt-4 social-dilemmas principal-agent-problem | ai |
haxe-linguistics | scratching my own itch here needed some basic natural language processing tools for simple projects i turned to nltk and naturalnode which both are just for a single programming language sure you can work around that or wrap it but that doesn t give me the fun of working with haxe and picking my own layout pattern i have no formal training in linguistics but have contributed to multiple open source projects kinda making things up as i need them need something contribute or open a ticket usage installing you can git clone the repository directly or use the latest version in haxelib using haxelib haxelib install haxe linguistics using haxelib git haxelib git haxe linguistics https github com sexybiggetje haxe linguistics running your application example applications have been included in the examples folder haxe main my namespace application cp src interp supported languages since the beginning english as main language dutch and german have been supported as first class citizens i added basic support for the frisian language as a second class citizen want to contribute take a peek at the dutch nl implementation and send a pull request tokenizing basic tokenizers are present for all current supported languages haxe linguistics getinstance setlanguage dutch var tokenizer itokenizer linguistics getinstance getbasictokenizer trace tokenizer tokenize nederlanders drinken s morgens gemiddeld 2 koppen koffie removing a set of tokens using a token filter haxe linguistics getinstance setlanguage dutch var tokenizer itokenizer linguistics getinstance getbasictokenizer var tokenset array itoken tokenizer tokenize nederlanders drinken s morgens gemiddeld 2 koppen koffie trace tokenizer applyfilter stopwordtokenfilter dictionary a dictionary indexes tokenized words and keeps track of word count by default it uses the raw token but you can specify to use the normalized token if no tokenizer is specified it defaults to the basictokenizer for your given language haxe var dict dictionary new dictionary dict adddocument to be or not to be that is the question trace dict getdictionarywords or by adding tokens directly for example after filtering them haxe linguistics getinstance setlanguage dutch var dict dictionary new dictionary var tokenizer itokenizer linguistics getinstance getbasictokenizer var tokenset array itoken tokenizer tokenize nederlanders drinken s morgens gemiddeld 2 koppen koffie dict addtokens tokenizer applyfilter tokenset stopwordtokenfilter trace dict getdictionarywords string distance using levenshtein distance calculation haxe trace levenshteindistance getdistance kitten sitting classification currently there is an implementation for a naive bayes classificator the classificator uses normalized tokens and if no tokenizer is specified when calling the train method it will default to the basictokenizer specified for your language since there is no stemming support yet in this library tokens are unstemmed and unfiltered example is shamelessly copied from naturalnode documentation haxe var classifier iclassifier new naivebayesclassifier classifier adddocument i am the long qqqq buy classifier adddocument buy the q s buy classifier adddocument short gold sell classifier adddocument sell gold sell classifier train trace classifier classify i am short silver stemming currently stemming is being implemented stemmers follow a simple implementation they use istemmer stem winning the basic stemmers will be used optionally during tokenization the snippet below returns the porter stemmer implementation for english haxe linguistics getinstance setlanguage english var stemmer istemmer linguistics getinstance getbasicstemmer trace stemmer stem consigned tests some tests might be mising or incomplete due to the premature state of the project but i try to keep them up to date at the moment the tests run only on hx source i try to support every output format of haxe but haven t compiled the tests for everything just yet running tests quickest way is to use haxe in interpreter mode haxe cp src main tests testcaserunner interp building the testrunner for all targets install jdk for hxjava see oracle com install mono mdk for monocs see mono project com download setting up dependencies haxelib install hxcpp haxelib install hxjava haxelib install hxcs in your root below src haxe build hxml roadmap in progress concordance analysis support for n grams language detection in progress nested tokenization allowing tokens to have a parent and children opening doors for a sentence or quotation token in progress applying tags to tokens and being able to filter them based on a token this differs from classification but a classification could be a tag in progress stemming of languages porter stemmer would be sufficient parts of speech tagging this would require stemming and pos dictionaries wordnet support and for other languages alternatives to it | haxe natural-language-processing nlp library linguistics | ai |
k-means | k means project for the cloud computing course of the master of science in computer engineering university of pisa the project consists in the design and implementation of the k means algorithm using the hadoop and spark frameworks overview the repository is organized in the following way hadoop contains the hadoop code spark contains the spark code points gen contains a script for generating dataset of points plotter contains a script for visualizing the results useful for validating the implementations test contains a script to launch batch performance tests on both the implementations requirements the requirements are needed for executing both the spark code and the utility scripts they can be installed by bash pip install r requirements txt contributors francesco barbarulo https github com francescobarbarulo diego casu https github com diegocasu bruk tekalgne gurmesa https github com brukt giovan battista rolandi https github com giomba | cloud |
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Computational-Linguistics | computational linguistics materials for researchers and technologists in the area of natural language processing deep learning etc courses stanford cs224d deep learning for natural language processing http cs224d stanford edu cs224d stanford cs231n convolutional neural networks for visual recognition http cs231n stanford edu cs231n stanford cs330 deep multi task and meta learning http cs330 stanford edu uc berkeley cs294 deep reinforcement learning http rll berkeley edu deeprlcourse cs294 video https www youtube com watch v 8jqikgtzqd4 list plkfd6 40kjiwtmsbcv9ovjb3yao4sfwkx cs294 video oxford deep learning for natural language processing http www cs ox ac uk teaching courses 2016 2017 dl video https www youtube com watch v rp3tzfcc2e8 list pl613dyigmxozbtzhbyibqb0qtgk6ojbpm deeplearning ai in coursera deep learning specification https www coursera org learn neural networks deep learning lecture stanford cs20 tensorflow for deep learning research https web stanford edu class cs20si syllabus html ntu in cousera machine learning foundations https www coursera org learn ntumlone mathematicalfoundations conferences journals conferences ccf a acl nips icml ijcai aaai ccf b emnlp coling ecai ccf c conll naacl ictai icann ksem iconip icpr ijcnn pricai aistats acml others eacl sem ijcnlp icsc nldb ldk pacling ltc icon icsc cicling journals ccf a ai tpami jmlr ccf b tslp computational linguistics taslp jslhr tap dke cybernetics tfs tnnls jair machine learning neural computation neural networks pattern recognition ccf c talip computer speech and language nle applied intelligence artificial life computational intelligence eaai expert system eswa fuzzy sets and systems ijcia ijis ijns ijprai nca npl neurocomputing paa prl others tacl paper meta learning concept jurgen schmidhuber learning to learn concept http people idsia ch juergen diploma html yoshua bengio samy bengio learning a synaptic learning rule http bengio abracadoudou com publications pdf bengio 1991 ijcnn pdf metic based gregory koch richard zemel ruslan salakhutdinov siamese neural networks for one shot image recognition http www cs toronto edu gkoch files msc thesis pdf icml 2015 code https github com goldesel23 siamese networks for one shot learning oriol vinyals etc matching networks for one shot learning https arxiv org pdf 1606 04080 pdf arxiv 1606 04080 2016 code https github com antreasantoniou matchingnetworks jake snell kevin swersky richard zemel prototypical networks for few shot learning http papers nips cc paper 6996 prototypical networks for few shot learning nips 2017 code https github com jakesnell prototypical networks mengye ren etc meta learning for semi supervised few shot classification https arxiv org pdf 1803 00676 pdf iclr 2018 code https github com renmengye few shot ssl public flood sung et al learning to compare relation network for few shot learning http openaccess thecvf com content cvpr 2018 html sung learning to compare cvpr 2018 paper html cvpr 2018 code https github com floodsung learningtocompare fsl optimization based sachin ravi hugo larochelle optimization as a aodel for few shot learning https openreview net pdf id rjy0 kcll iclr 2017 code https github com twitter meta learning lstm chelsea finn pieter abbeel sergey levine model agnostic meta learning for fast adaption of deep networks https arxiv org pdf 1703 03400 pdf icml 2017 code https github com cbfinn maml zhenguo li fengwei zhou fei chen hang li meta sgd learning to learn quickly for few shot learning https arxiv org pdf 1707 09835 pdf arxiv 1707 09835 2017 code https github com foolyc meta sgd alex nichol joshua achiam john schulman on first order meta learning algorithms https arxiv org abs 1803 02999 arxiv 2018 10 code https github com openai supervised reptile antreas antoniou harrison amos storkey how to train your maml https openreview net pdf id hjgven05y7 iclr 2019 code https github com antreasantoniou howtotrainyourmamlpytorch model based adam santoro et al meta learning with memory augmented neural networks http proceedings mlr press v48 santoro16 pdf icml 2016 code https github com vineetjain96 one shot mann tsendsuren munkhdalat hong yu meta networks https arxiv org abs 1703 00837 icml 2017 code https bitbucket org tsendeemts metanet orthogonal fengwei zhou bin wu zhenguo li deep meta learning learning to learn in the concept space https arxiv org pdf 1802 03596 pdf arxiv 1802 03596 2018 ruixiang zhang tong che zoubin ghahramani yoshua bengio yanqiu song metagan an approach to few shot learining https papers nips cc paper 7504 metagan an adversarial approach to few shot learning pdf nips 2018 code https github com sodabeta7 meta learning for few shot learning in nlp semi supervised learning and unsupervised learning domain adaptation machine translation ashish vaswani et al attention is all you need https arxiv org pdf 1706 03762 pdf arxiv 1706 03762 2017 code1 https github com tensorflow tensor2tensor code2 https github com kyubyong transformer text summarization sentence compression alexander m rush sumit chopra jason wetson a neural attention model for sentence summarization https arxiv org pdf 1509 00685 pdf emnlp 2015 sumit chopra michael auli alexander m rush abstractive sentence summarization with attentive recurrent neural networks http nlp seas harvard edu papers naacl16 summary pdf naacl 2016 code http github com facebook namas konstantin lopyrev generating news headlines with recurrent neural networks https nlp stanford edu courses cs224n 2015 reports 1 pdf arxiv 1512 01712 2015 ramesh nallapati bowen zhou cicero nogueira dos santos caglar gulcehre bing xiang abstractive text summarization using sequene to sequence rnns and beyond https arxiv org pdf 1602 06023 pdf arxiv 1602 06023 2016 gulcehre caglar sungjin ahn ramesh nallapati bowen zhou yoshua bengio pointing the unknown words https arxiv org pdf 1603 08148 pdf arxiv 1603 08148 2016 jiatao gu zhengdong lu hang li victor o k li incorporating copying mechanism in sequence to sequence learning https arxiv org pdf 1603 06393 pdf acl 2016 code https github com multipath copynet yuta kikuchi graham neubig ryohei sasano hiroya takamura manabu okumura controlling output length in neural encoder decoders https arxiv org pdf 1609 09552 pdf arxiv 1609 09552 2016 yishu miao phil blunsom language as a latent variable discrete generative models for sentence compression https arxiv org pdf 1609 07317 pdf emnlp 2016 wenyuan zeng wenjie luo sanja fidler raquel urtasun efficient summarization with read again and copy mechanism https openreview net pdf id hjpmdp9le arxiv 1611 03382 2016 single document summarization jingge yao xiaojun wan jianguo xiao recent advances in document summarization http www icst pku edu cn lcwm wanxj files summ survey draft pdf knowledge and information systems 2017 jianpeng cheng mirella lapata neural summarization by extracting sentences and words https arxiv org pdf 1603 07252 pdf arxiv 1603 07252 2016 ramesh nallapati feifei zhai bowen zhou summarunner a recurrent neural network based sequence model for extractive summarization of documents https arxiv org pdf 1611 04230 pdf aaai 2017 abigail see peter j liu christopher d manning get to the point summarization with pointer generator networks https nlp stanford edu pubs see2017get pdf acl 2017 code https github com abisee pointer generator romain paulus caiming xiong richard socher a deep reinforced model for abstractive summarization https arxiv org pdf 1705 04304 pdf arxiv 1705 04304 2017 jiwei tan xiaojun wan jianguo xiao abstractive document summarization with a graph based attentional neural model http aclweb org anthology p p17 p17 1108 pdf acl 2017 masaru isonuma etc extractive summarization using multi task learning with document classification http aclweb org anthology d17 1222 emnlp 2017 angela fan david grangier michael auli controllable abstractive summarization https arxiv org pdf 1711 05217 pdf arxiv 1711 05217 2017 multi document summarization piji li etc cascaded attention based unsupervised information distillation for compressive summarization http aclweb org anthology d17 1220 emnlp 2017 tobias falke iryna gurevych bringing structure into summaries crowdsourcing a benchmark corpus of concept maps https aclweb org anthology d d17 d17 1320 pdf emnlp 2017 query based document summarization machine reading dialogue system memory network alex graves greg wayne ivo danihelka neural turing machines https arxiv org pdf 1410 5401 pdf arxiv 1410 5401 2014 jason weston sumit chopra antoine bordes memory networks https arxiv org pdf 1410 3916 pdf iclr 2014 sainbayar sukhbaatar jason weston rob fergus end to end memory networks https arxiv org pdf 1503 08895 pdf nips 2015 ankit kumar ozan irsoy jonathan su james bradbury robert english brian pierce peter ondruska ishaan gulrajani richard socher ask me anything dynamic memory networks for natural language processing https arxiv org pdf 1506 07285 pdf arxiv 1506 07285 2015 gan vae datasets text summarization duc 2001 2002 2003 2004 2005 2006 2007 http www nlpir nist gov projects duc data html duc 2008 2009 2010 2011 2012 2013 2014 2015 http tac nist gov data gigawords https catalog ldc upenn edu ldc2012t21 lcsts chinese http icrc hitsz edu cn article show 139 html cmapsum https github com ukplab emnlp2017 cmapsum corpus opinosis http kavita ganesan com opinosis opinion dataset wlig67xwb0w machine reading q a cnn dailymail http cs nyu edu kcho dmqa cnn http datasets maluuba com newsqa marco microsoft http www msmarco org newsqa https github com maluuba newsqa squad https www aclweb org anthology d16 1264 pd cft chinese http www hfl tek com chinese rc babi facebook https research fb com downloads babi graphquestions https github com ysu1989 graphquestions story cloze http cs rochester edu nlp rocstories simplequestions http suo im 2eix0o wikiqa http suo im 3ajvyp dialogues ubuntu dialogue corpus http suo im 2pbkcc frames http datasets maluuba com frames pos tagger parser penn treebank http www cis upenn edu treebank home html wsj corpus https catalog ldc upenn edu ldc2000t43 negra german corpus http www coli uni saarland de projects sfb378 negra corpus tiger corpus http www ims uni stuttgart de projekte tiger tigercorpus alpino treebank http odur let rug nl vannoord trees bultreebank http www bultreebank org turin university treebank http www di unito it tutreeb prague dependency treebank http ufal mff cuni cz pdt2 0 semantic relation annotated corpus propbank nombank http nlp cs nyu edu meyers nombank html framenet http framenet icsi berkeley edu salsa http www coli uni saarland de projects salsa page php id index others trec semeval http alt qcri org semeval2017 index php id tasks coco microsoft http mscoco org yelp dataset challenge https www yelp com dataset challenge ai stars blogs kyunghyun cho http www kyunghyuncho me home andrej karpathy http karpathy github io colah s blog http colah github io posts 2015 08 understanding lstms richard socher http www socher org ian goodfellow http www iangoodfellow com chiyuan zhang http pluskid org peter norvig http norvig com jason wetson http www thespermwhale com jaseweston noah a smith http homes cs washington edu nasmith yoav goldberg https www cs bgu ac il yoavg uni sebastian ruder http ruder io open oriol vinyals https research google com pubs oriolvinyals html ehud reiter https ehudreiter com books deep learning book http www deeplearningbook org information theory inference and learning algorithms the elements of statistical learning the study of language http ocw up edu ps eeng 2314 the study of language pdf speech and language processing https web stanford edu jurafsky slp3 open tools opennmt http opennmt net models opennmt seq2seq google https google github io seq2seq seq2seq corenlp stanford https stanfordnlp github io corenlp corenlp thulac https github com thunlp thulac python stanfordnlp https github com stanfordnlp stanfordnlp torchtext https torchtext readthedocs io en latest | ai |
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acronis-cyber-cloud-whmcs | acronis cyber cloud whmcs provisioning module the acronis cyber cloud whmcs provisioning module is a high quality fully functional whmcs integration module developed by the acronis engineering team the module is the same as the one available at whmcs marketplace on the one hand it s a valuable example of acronis cyber platform api usage from php on the other hand it s a great quick start for those companies who want to build their own whmcs module for acronis cyber cloud a developer can fully use the code and business logic and change only what is needed as we publish it under the mit license acronis cyber platform client and authorization the implementation of the acronis cyber platform client provides a wide range of functionality you can find and use advanced workloads for working with tenants users reports and more in src includes acroniscloud vendor acronis ci lib php cloud client lib api all of these advanced workloads use the base implementation of low level api interactions located at src includes acroniscloud lib cloudapi basic procedures for api calls are implemented in the apiclient class and authorization routines are implemented in the authorizedapi class the client implementation is quite straightforward it wraps http calls into the callapi function that is used in the advanced workloads to call specific rest api let s pay attention to the way the authorization flow is implemented the main way to start is by using the obtainaccesstoken function in the authorizedapi class it supports two grant types password and client credentials for the password grant type we directly request the idp api to receive an authorization token for the client credentials grant type we need some additional preparation php param bool force return string protected function obtainaccesstoken force false return localapi decryptpassword this fromcache function if this getgranttype static grant type client credentials idptoken this fetchclientaccesstoken else var token idptoken idptoken this unauthorizedcall function return this getidpapi postidptoken password null this getlogin this getpassword return localapi encryptpassword idptoken getaccesstoken this getaccesstokenttl true this getcredentialshash force and then we call idp api as well php private function fetchclientaccesstoken clientid this getlogin clientsecret this getpassword authcredentials base64 encode clientid clientsecret this getapiclient getconfig adddefaultheader authorization basic authcredentials idptoken this unauthorizedcall function return this getidpapi postidptoken client credentials this getapiclient getconfig deletedefaultheader authorization return idptoken let s check what happens inside postidptoken you can see that it s just an http post call to the specific endpoint with all of the required parameters set php public function postidptoken grant type client id null username null password null refresh token null code null scope null assertion null device code null totp code null parameters check is removed here for code clearness parse inputs resourcepath idp token httpbody queryparams headerparams formparams header accept this apiclient selectheaderaccept application json if is null header accept headerparams accept header accept headerparams content type this apiclient selectheadercontenttype application x www form urlencoded params handling is removed here for code clearness for model json xml if isset tempbody httpbody tempbody tempbody is the method argument if present elseif count formparams 0 httpbody formparams for http post form make the api call try list response statuscode httpheader this apiclient callapi resourcepath post queryparams httpbody headerparams acronis cloud client model idp token idp token return new httpresponse statuscode httpheader this apiclient getserializer deserialize response acronis cloud client model idp token httpheader catch httpexception e exceptopn handling is removed here for code clearness throw e now we understand what class is used for api calls and how to review an access token let s look into another advanced api usage example working with api all api can be found in the api class located in src acroniscloud lib cloudapi api php it wraps most of the advanced api uses from src includes acroniscloud vendor acronis ci lib php cloud client lib api for example for a new tenant creation posttenants functions are used from the tenantsapi class php public function createtenant tenantpost body return this authorizedcall function use body return this gettenantsapi posttenants body and as it is for tenant api calls it wraps the post call to the tenants endpoint and uses the required parameters php operation posttenants tenants param acronis cloud client model tenants tenantpost body required param string issues optional throws acronis cloud client apiexception on non 2xx response return httpresponse public function posttenants body issues null parameters check is removed here for code clearness parse inputs resourcepath tenants httpbody queryparams headerparams formparams header accept this apiclient selectheaderaccept application json if is null header accept headerparams accept header accept headerparams content type this apiclient selectheadercontenttype application json params handling is removed here for code clearness for model json xml if isset tempbody httpbody tempbody tempbody is the method argument if present elseif count formparams 0 httpbody formparams for http post form this endpoint requires oauth access token if strlen this apiclient getconfig getaccesstoken 0 headerparams authorization bearer this apiclient getconfig getaccesstoken make the api call try list response statuscode httpheader this apiclient callapi resourcepath post queryparams httpbody headerparams acronis cloud client model tenants tenant tenants return new httpresponse statuscode httpheader this apiclient getserializer deserialize response acronis cloud client model tenants tenant httpheader catch httpexception e exceptopn handling is removed here for code clearness throw e finally now we understand acronis cyber platform code base structure and can find and use valuable code pieces if you have any questions regarding the acronis cyber platform api samples or usage see refer to acronis developer network portal https developer acronis com or use acronis cyber platform forum https forum acronis com forum acronis cyber platform forum apis and sdks acronis cyber platform forum apis and sdks acronis cyber cloud whmcs integration and hooks acronis cyber cloud whmcs provisioning module uses whmcs hooks model to integrate you can find registered hooks in src includes hooks acroniscloud php php hooks serveradd serverdelete serveredit clientedit productedit servicedelete adminareaheaderoutput clientareaheaderoutput orderproductupgradeoverride outputhooks adminareaheaderoutput clientareaheaderoutput foreach hooks as hook add hook hook 1 function parameters use hook outputhooks var dispatcher dispatcher response try dispatcher locator getinstance get dispatcherfactory name response dispatcher dispatch file acronis cloud service name requestinterface action name delimiter hook parameters catch exception e always suppress exceptions for hooks not to affect other whmcs modules return in array hook outputhooks response parameters and in includes acroniscloud controllers php you can find hooks mapping php hooks new moduleactionrouter serveradd server class updateserverinfo serveredit server class updateserverinfo serverdelete server class deleteinternaltag clientedit contactinfo class updatetenants productedit product class setupcustomfields servicedelete subscription class terminate orderproductupgradeoverride product class beforeupgrade adminareaheaderoutput server class adminoutput clientareaheaderoutput customheaderoutput class clientoutput for more information about whmcs integrations development refer to whmcs portal for developers https www whmcs com developer friendly building the module for whmcs to build the module you need to have the make utility and docker installed on your linux machine just type make in the root repository folder press enter and that it s bash make under the hood a docker image is built based on dockerfile in the root dir repository this image contains all of the utilities and components necessary to build the module successfully and finally the image is used to start a container and to build the module as described in the build sh file in the root folder of the repository 2003 2019 acronis international gmbh this source code is distributed under mit software license | cloud |
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sql-challenge | employee database a mystery in two parts 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 includes technical report of data modeling data engineering data analysis lists the following details of each employee employee number last name first name gender and salary lists employees who were hired in 1986 lists 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 lists the department of each employee with the following information employee number last name first name and department name lists all employees whose first name is hercules and last names begin with b lists all employees in the sales department including their employee number last name first name and department name lists all employees in the sales and development departments including their employee number last name first name and department name in descending order lists the frequency count of employee last names i e how many employees share each last name imports the sql database into pandas with sqlalchemy and creates a bar chart of average salary by title | server |
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Taxi-Ride-Project | taxi ride project this is an end to end data engineering project using the services available on google cloud platform | cloud |
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clean-architecture-front-end | hello here is a repository to send you my vision of clean architecture firstly on the front then later on back in different versions i wrote an article to support the examples of this project https www dimitri dumont fr blog hexagonal architecture front end https www dimitri dumont fr blog hexagonal architecture front end to understand the clean architecture you can also read my article on dependency inversion in front end https www dimitri dumont fr blog dependency inversion front end https www dimitri dumont fr blog dependency inversion front end for these examples i chose to use next js https nextjs org based on react https reactjs org but the whole point of this architecture is to be able to use it independently of the frameworks and libraries used external dependencies in order to have a simple example i chose a simple subject a todo list to see the tasks that still need to be done on the project go to the docs todo md file if you have any questions suggestions or anything else don t hesitate to contact me and if this repository has helped you consider sharing it with your acquaintances summary 1 getting started getting started 2 clean architecture clean architecture 1 use case use case 2 primary port primary port 3 primary adapter primary adapter 4 secondary port secondary port 5 secondary adapter secondary adapter 3 resources resources getting started first install the dependencies bash npm install or yarn install then run the development server bash npm run dev or yarn dev open http localhost 3000 http localhost 3000 with your browser to see the result to start unit tests bash jest to test the app online https front end clean architecture netlify app https front end clean architecture netlify app clean architecture the hexagonal architecture or architecture based on ports and adapters is an architectural pattern used in the field of software design it aims to create systems based on application components which are loosely coupled and which can be easily connected to their software environment by means of ports and adapters these components are modular and interchangeable which reinforces the consistency of processing and facilitates the automation of tests there are three parts in the clean architecture the application part the primary ports and adapters the domain the use cases the domain models etc and the infrastructure part the secondary ports and adapters this architecture is based on the port adapter pattern and the dependency inversion principle by documenting you on clean architecture or hexagonal architecture you will find different names for these parts the names chosen here are personal the goal being that they are understandable use case the uses cases define the actions of your users the goal is not to use any framework or libraries in these elements in order to keep a logic not coupled to these tools on the front they can be represented by function by class written in js or ts with react it is possible to use redux for this part in case redux is used the actions are the use cases the state is one of the models and the selectors are used to map primary port the primary port is used to establish a contract between the primary adapter and the use cases for this an interface can be created in practice the use case is also considered a primary port primary adapter then the implementation of these interfaces are used to dialogue with the domain the first is what we call the primary adapters their goal is to trigger the execution of use cases for example on the front these adapters can be the react components that perform triggers an action redux or not secondary port the secondary port is used to establish a contract between the secondary adapter and the use cases for this we usually create an interface this interface is used directly in the use case tips you can use dependency injection for that some state management libraries allow you to do that for example with redux thunk https github com reduxjs redux thunk injecting a custom argument and redux observable https redux observable js org docs recipes injectingdependenciesintoepics html it is possible to pass extraarguments which will be directly available in the redux actions in vanilla there is also inversifyjs https github com inversify inversifyjs secondary adapter the second implementation of interfaces ports is called secondary adapters they are called by the use cases for example in front these adapters can be the http requests the access to the data present in the local storage etc resources in english hexagonal architecture in front end https www dimitri dumont fr blog en hexagonal architecture front end dependency inversion in front end https www dimitri dumont fr blog dependency inversion front end hexagonal architecture by alistair cockburn https alistair cockburn us hexagonal architecture in french architecture hexagonale en front end https www dimitri dumont fr blog fr hexagonal architecture front end inversion de d pendances en front end https www dimitri dumont fr blog dependency inversion front end slack de wealcome https wealcome slack com la clean architecture catalyseur de productivit https medium com mickalwegerich la clean architecture catalyseur de productivit c3 a9 68ff61aa38ff architecture hexagonale trois principes et un exemple d impl mentation https blog octo com architecture hexagonale trois principes et un exemple dimplementation architecture hexagonale le guide pratique pour une clean architecture https beyondxscratch com fr 2018 09 11 architecture hexagonale le guide pratique pour une clean architecture | clean-architecture hexagonal-architecture frontend front-end nextjs | front_end |
elevator-system | elevator system designing a single will be extending to multiple server and multiple buildings elevator system to serve request like to server request of floor and calling and waiting in queue i am using state and singleton pattern in the system and java as coding language the data will be in main memory | java object-oriented-programming object-oriented-design design-pattern system-design singleton elevator elevator-problem | os |
fswd-lab-1 | purpose to run node and npm on the command line learn about git and tdd write javascript code and write tests for that code goal by the end of the lab you will have downloaded the initial project from github with the git command run npm multiple different ways written javascript functions and tests using multiple features of the javascript language var function if else for return arrays and more and have at least basic working versions of the fizzbuzz and fibonacci functions full stack web development lab 1 testing fizzbuzz and fibonacci these are classic programming interview questions we will start by cloning the repository cloning to your own machine huh cloning what s that cloning is the process where the git tool makes a copy of the code from github on your laptop including the code s history of changes how do i do that to start with open the terminal application on your laptop once you have the terminal window open you will need to change directories folders to where you will create the copy of the code on your machine where i like to set up a special directory on my machine to hold all my programming projects code and is short for your home directory which is where the terminal usually starts run mkdir code at the prompt mkdir make directory after you have done that run cd code to change the working directory of your shell the starting location for any command you run you can verify that it has been changed by running the pwd print working directory command then in your browser while looking at the page for the lab repository http github com betamore fswd lab 1 click the green clone or download button on the right side make sure clone with https is displayed in the title of the popup or click use https in the upper right if it is not and click the button to copy the repository url to your clipboard back in your terminal window where you changed the working directory type git and then paste that repository url in and hit the return key you should see something similar to this shell git clone https github com betamore fswd lab 1 git cloning into fswd lab 1 remote counting objects 136 done remote compressing objects 100 12 12 done remote total 136 delta 5 reused 15 delta 5 pack reused 118 receiving objects 100 136 136 52 11 kib 1 18 mib s done resolving deltas 100 53 53 done note that the url for the git repository is simply url of github page git now what the git tool created the fswd lab 1 directory inside the working directory of your terminal use the cd command again to change the working directory to fswd lab 1 hint cd fswd lab 1 once inside the directory if you installed the nvm tool run nvm use actually working with the code 1 run npm install to install the node modules for the project then npm test to run the tests 2 everything passes grab yourself a cookie you re all done 3 yeah i was kidding you re not done at all 4 open up visual studio code or your preferred text editor and look at the files in the code tests are in the test directory and the function skeletons are in the lib directory 5 the first cases are implemented and tested fizzbuzz 0 and fib 0 start by writing tests for the next case 1 and expand the function to handle it correctly 6 then 2 then 3 cloning https git scm com book en v2 git basics getting a git repository git cloning | javascript fizzbuzz fibonacci | front_end |
byggern | byggern gr5 ttk4155 industrial and embedded computer systems design project simen rogn aune br vebj rn eklo br gisle halvorsen | os |
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notes_app_fullstack | notes app fullstack | server |
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front-end-project | node krischans95 https fish pond 1253945200 cos ap guangzhou myqcloud com img base qrcode all1 png https fish pond 1253945200 cos ap guangzhou myqcloud com img base qrcode all1 png cs krisin2023 konwledge map png bff krisin2023 konwledge map bff png krisin2023 konwledge map png krisin2023 konwledge map png knowledge krisin2023 konwledge map knowledge png krisin2023 konwledge map png krisin2023 konwledge map png krisin2023 konwledge map png krisin2023 konwledge map png krisin2023 konwledge map png krisin2023 konwledge map png krisin2023 konwledge map png | front_end |
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ESP32-IR-FreeRTOS | esp32 ir freertos esp32 arduino ide freertos tasks queues isr this is a demo test of esp32 infrared receiver decoding using freertos tools in arduino build there is an edge triggered isr which times the ir device pulses in microseconds it pushes these timings to the ir task queue the ir task waits for a stream of timings on it s input queue it converts these timing to bit mapped words file ir bits jpg shows a snippet of the serial monitor output it was done with phillips remote set to sony the timings are 2400 start 1200 high 600 low 600 space the actual measurements vary because of isr response and or ir noise hardware huzzah32 tsop28328 vs1838b | os |
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altschool-cloud-exercises | altschool cloud exercises documenting my cloud engineering exercises progress | cloud |
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awesome-learning-resources | awesome learning resources awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com lauragift21 awesome learning resources license https img shields io github license mashape apistatus svg preview on the web https nicedoc io lauragift21 awesome learning resources table of contents awesome learning resources awesome learning resources table of contents table of contents agile agile android android angular angular apex apex bash bash bootstrap bootstrap c c career career css css computer vision computer vision data science data science deep learning deep learning developer blog developer blog developer stories developer stories django django elixir elixir flutter flutter flask flask git and github git and github golang golang graphql graphql hugo hugo hacking hacking infosec infosec ionic ionic javascript javascript java java kotlin kotlin laravel laravel machine learning machine learning malware analysis malware analysis markdown markdown nodejs nodejs php php plsql plsql podcasts podcasts pwa pwa python python reactjs reactjs react native react native ruby ruby ruby on rails ruby on rails rust rust serverless serverless sick picks sick picks startups startups svelte svelte swift swift tensorflow tensorflow twilio twilio typescript typescript voice voice vue vue women in tech women in tech xml xml agile manifesto for agile software development https agilemanifesto org martin fowler s website https martinfowler com understanding the agile methodology https linchpinseo com the agile method agile ceremonies https www atlassian com agile scrum ceremonies agile user stories https zepel io agile user stories the agile idea https www manning com books the agile idea android what is android https www android com what is android android 10 https www android com android 10 android open source project https source android com getting started and be an android developer https developer android com guide slices getting started introduction https developer android com guide build your first app https developer android com training basics firstapp whats new androidx https developer android com jetpack androidx migrate to androidx https developer android com jetpack androidx migrate learn android https www tutlane com tutorial android angular official angular quickstart https angular io guide quickstart angular s source code https github com angular angular maintained by google angular console https angularconsole com a nifty ui for the angular cli angular material https material angular io angular implementation of google s material design sample angular starter app https github com duluca lemon mart john papa s blog https johnpapa net angular2 version upgrade guide https update angular io angularjs to angular2 migration guide https angular io guide upgrade angular in depth https indepth dev angular ngrx https ngrx io devprojects free real world angular 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python_tutorial | learn python 3 for deep learning a repository of codes and tutorials for learning basics of python 3 natural language processing nlp and machine vision using deep learning with keras tensorflow and opencv this repository is used for developer training it is developed by rahul remanan remananr com with the help of full stack artificial intelligence company moad computer https www moad computer below we are detailing the scripts in this repo https about gitlab com handbook product technical writing markdown guide keep this handy nlp table of contents 1 example 1 one hot encoding https github com rahulremanan python tutorial tree master nlp 01 one hot encoding 2 example 2 twitter bot using a lstm model https github com rahulremanan python tutorial tree master nlp 02 twitter bot 3 example 3 alice nlp generator https github com rahulremanan python tutorial tree master nlp 03 natural language generation 4 example 4 character embedding https github com rahulremanan python tutorial tree master nlp 04 character embedding 5 example 5 bidirectional lstm classifier https github com rahulremanan python tutorial tree master nlp 05 bidirectional lstm 6 example 6 qa demo https github com rahulremanan python tutorial tree master nlp 06 conversational nlp machine vision | ai |
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restaurant-management-application | restaurant management application | java apache-poi javax-swing java-awt desktop-application | server |
bootcamp_python | p align center img src assets 42ai logo png width 200 alt 42 ai logo p h1 align center bootcamp python h1 h3 align center one week to learn python for machine learning rocket h3 table of contents download download curriculum curriculum module00 basics 1 eleven commandments module00 basics 1 eleven commandments module01 basics 2 module01 basics 2 module02 basics 3 module02 basics 3 module03 numpy module03 numpy module04 pandas module04 pandas acknowledgements acknowledgements contributors contributors beta testers beta testers thanks to ilyes and k vin for the pr thanks to ilyes and k vin for the pr this project is a python programming and machine learning bootcamp created by 42 ai http www 42ai fr no prior python programming or machine learning experience is required your mission should you choose to accept it is to come and learn some of the essential knowledge for machine learning data science and statistics in a single week you will start with the basics of the python language and then get acquainted with some libraries that are invaluable to any programmer interested in the field of ai or data science 42 artificial intelligence is a student organization of the paris campus of the school 42 our purpose is to foster discussion learning and interest in the field of artificial intelligence by organizing various activities such as lectures and workshops download the pdf files of each module can be downloaded from our realease page https github com 42 ai bootcamp python releases https github com 42 ai bootcamp python releases curriculum module00 basics 1 eleven commandments let s get started with the python language snake basic setup variables types functions module01 basics 2 get acquainted with object oriented programming and much more objects cast inheritance built in functions generator construtors iterator module02 basics 3 continue practicing with more advanced python programming exercises decorators multiprocessing lambda build package module03 numpy learn how to use the numpy library manipulate multidimensional arrays and perform complex mathematical operations on matrices numpy array slicing stacking dimensions broadcasting normalization etc module04 pandas time to use a python library that will allow you to manipulate dataframes pandas and bamboos panda face acknowledgements contributors amric trudel amric 42ai fr maxime choulika maxime 42ai fr baptiste lefeuvre blefeuvr student 42 fr mathilde boivin mboivin student 42 fr tristan duquesne tduquesn student 42 fr pierre peign ppeigne student 42 fr quentin feuillade montixi qfeuilla student 42 fr beta testers barth l my leveque bleveque student 42 fr remy oster roster student 42 fr quentin bragard qbragard student 42 fr marie dufourq madufour student 42 fr adrien vardon advardon student 42 fr thanks to ilyes and k vin for the pr ilyes bouchlaghem ibouchla student 42 fr k vin azoulay dessus kevazoul student 42lyon fr | learning machine-learning python-programming numpy pandas python-language | ai |
LLM-Gen | llm gen llm gen is used to construct dataset mixinstruct https huggingface co datasets llm blender mix instruct it s also part of project llm blender https github com yuchenlin llm blender updates 2023 6 19 add supports for guanaco nous hermes wizardlm and airoboros what llm gen for currently there are various open source llms however different llms are trained with different prompting templates to function properly otherwise you might get funny responses with problems like halluciations repetitions etc damaging the quality of generation severely while most llms along with their docs are available on hugging face it s really annoying to refer all those docs and get the corresponding prompting templates let alone the adaptions of special tokens eos token configuration during generation thanks to fastchat https github com lm sys fastchat who is consistently working on build a unified framework for most current llms we could easily get the prompt format through their conversation py https github com lm sys fastchat blob main fastchat conversation py however while fastchat supports easy to build command line and web inferface they did not provide a script for large scale generation in our local machine therefore here we implement this script with fastchat toolkits to facilitate the llm community usage installation bash pip install r requirements txt data formats please refer the example data for data self instruct test data json data self instruct test data json json id user oriented task 0 instance0 instruction the sentence you are given might be too wordy complicated or unclear rewrite the sentence and make your writing clearer by keeping it concise whenever possible break complex sentences into multiple sentences and eliminate unnecessary words input if you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project please let me know output if you have any questions about my rate or find it necessary to increase or decrease this project s scope please let me know candidates we provide a script data format data py data format data py to help the transforming the self instruct format data into our generation data format above for example with following bash command you can finish the formating bash cd data python format data py input file user oriented instructions jsonl output file self instruct test data json typically we treat each data with a dataset name and a set name train val test in the above example self instruct is the dataset name and test is the corresponding set name each data file is expected to located at data dataset name set name data json for the sake of generation script generation of candidates to generate candidates for the data at data dir data name set name data json you can simply change the data dir in generate candidates sh generate candidates sh and dataset name set name in generate candidates sh generate candidates sh by selecting the hugging face model by model you are ready to generate by running the shell bash dataset self instruct set test prompt max length 256 output max length 256 cmd bash model chavinlo alpaca 13b cmd generate candidates sh dataset set model prompt max length output max length currently tested models 1 chavinlo alpaca 13b 2 eachadea vicuna 13b 1 1 3 databricks dolly v2 12b 4 stabilityai stablelm tuned alpha 7b 5 openassistant oasst sft 4 pythia 12b epoch 3 5 6 thebloke koala 13b hf 7 project baize baize v2 13b 8 google flan t5 xxl 9 thudm chatglm 6b 10 fnlp moss moon 003 sft 11 mosaicml mpt 7b chat 12 thebloke guanaco 7b hf 13 nousresearch nous hermes 13b 14 ehartford wizardlm 13b uncensored 15 jondurbin airoboros 7b evaluating candidates to evaluate candidates with auto metrics you can refer to eval candidates sh eval candidates sh bash data dir data dataset self instruct set test num workers 1 overwrite false metrics rouge1 rouge2 rougel rougelsum bleu bertscore bleurt bartscore echo dataset dataset echo set set python eval candidates py data dir data dir dataset dataset set set num workers num workers metrics metrics overwrite overwrite save prepared true by specifying save prepared as true the script will finally aggrerate all the candidate from all llms along with their evaluated scores into a single file data dataset name set name data prepared json whose format is like followings json id unified chip2 69962 instruction input i ve always wondered what the difference is between a skeptic and a denier output a skeptic is someone who questions the validity of something while a denier is someone who outright rejects something without evidence or reason candidates decoding method top p sampling model oasst sft 4 pythia 12b epoch 3 5 text a skeptic is someone who doubts or expresses scores logprobs 0 02404022216796875 bleu 5 656152750894142 bertscore 0 7549101114273071 rouge1 0 2857142857142857 rouge2 0 1272727272727273 rougel 0 23214285714285715 rougelsum 0 23214285714285715 | ai |
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DataBasics | databases 2020 databasics platform mysql https img shields io badge mysql 8 0 24 9cf svg https www mysql com java https img shields io badge java 11 yellow svg https www oracle com java technologies javase jdk11 downloads html angular https img shields io badge angularcli 9 1 15 red svg https angular io typescript https img shields io badge typescript 3 8 3 blue svg https www typescriptlang org charts https img shields io badge charts ng2charts blueviolet svg https www typescriptlang org br mit license https img shields io badge license mit blue svg https github com milwaukee bugs ntua databasics blob master license open source love svg2 https badges frapsoft com os v2 open source svg v 103 https github com ellerbrock open source badges school of electrical computer engineering ntua br project for the course databases https www ece ntua gr en undergraduate courses flow 2 spring semester 2020 this repo was created for version control of the project implemented during database course in the department of electrical computer engireering school ntua the main goal was the construction of a data management platform of a virtual store further information can be found in the related pdf file https github com milwaukee bugs ntua databasics blob master project 2020 pdf in greek directory structure for the directories of this project we choose the structure mentioned above the source code for the back end is located in the folder back end the source code for the data model is located in the folder data model files for the er model are located in the folder er the folder sql database dumps containes files for generating the database and its test data the source code for the front end is locate in the folder front end as far as the dump files are concerned we do not recommend you to include them in your project repos however we did just for presentation purposes to the teachers of this class installation process a detailed guide for the installation and testing of our platfom can be found inside the installation md file technologies used in this project we implemented several different techonologies and frameworks the build tool that we used was gradle to implement the database we specifically used mysql to achieve communication between the database and the back end we used the spring jdbc for the back end server we used jetty server and java servlets from the restlet framework the implementation of the back end and data model was written in java 11 the implementation of the front end was written in typescript html5 css using the angular framework v 9 relying mainly on angular material our package manager was npm the diagrams used were based on ng2 charts directives | supermarket databases-course ntua ntua-ece | server |
3D-LLM | br p align center h1 align center 3d llm injecting the 3d world into large language models neurips 2023 spotlight h1 p align center a href https evelinehong github io yining hong a a href https haoyuzhen com haoyu zhen a a href https peihaochen github io peihao chen a a href https zsh2000 github io shuhong zheng a a href https yilundu github io yilun du a a href https zfchenunique github io zhenfang chen a a href https people csail mit edu ganchuang chuang gan a p p align center a href https arxiv org abs 2307 12981 img src https img shields io badge paper pdf red style flat logo arxiv logocolor red alt paper pdf a a href https vis www cs umass edu 3dllm style padding left 0 5rem img src https img shields io badge project page blue style flat logo google 20chrome logocolor blue alt project page a p p align center img src figs pipeline png alt logo width 80 p p 3d llm is the first large language model that could take 3d representations as inputs it is able to handle both object e g objaverse and scene data e g scannet hm3d installation install salesforce lavis https github com salesforce lavis shell conda create n lavis python 3 8 conda activate lavis git clone https github com salesforce lavis git salesforce lavis cd salesforce lavis pip install e pip install positional encodings checkpoints pretraining checkpoints pretrained checkpoints https drive google com file d 1tiis8modzgbzmr7vgztre4ni 2fe4ntr view usp drive link are released please use v2 finetuning checkpoints finetuning checkpoints https drive google com drive folders 1rkp1cz6r6h8yziec4f3mhw9dccxqchba usp drive link for scanqa https drive google com file d 1spynao8pi rpr4pwwtrx8wedtdmpsqtw view usp drive link sqa3d https drive google com file d 1ka9twv6cs6h ppaaqg1auiiqma2xbnfk view usp drive link and 3dmv vqa https drive google com file d 1 h2wppgo64hy5luca1bd8dlzx3wcsy8b view usp drive link are released the results are better than preprint version paper we will update the camera ready paper to the arxiv soon quick start inference download the objaverse subset features here https drive google com file d 1mjzonfwrefiuapyxp65d65us2eoplafr view usp drive link download the pretrained checkpoints https drive google com file d 1tiis8modzgbzmr7vgztre4ni 2fe4ntr view usp drive link for more details please refer to 3dllm blip2 base demo md cd 3dllm blip2 base conda activate lavis python inference py for objects python inference py mode room for scenes todo huggingface auto load checkpoint finetuning finetuning config yaml files that need to be changed are in this directory https github com umass foundation model 3d llm tree main 3dllm blip2 base lavis projects blip2 train 1 download the pretrained checkpoints https drive google com file d 1tiis8modzgbzmr7vgztre4ni 2fe4ntr view usp drive link modify the resume checkpoint path path in the yaml files 2 download the questions https drive google com drive folders 14mdidl6cch b27q0azgdelhaeobbpn2o usp drive link modify the annotations path in the yaml files 3 download the scannet features https drive google com drive folders 1cset48jj5ucyelgcxxjbkgh86qyece8d usp drive link or 3dmv vqa features https drive google com drive folders 1ndfkkn izxgezi6fxa60rf1uxtomhoet usp drive link modify the path both train and val in lavis datasets datasets threedvqa datasets py 4 cd 3dllm blip2 base conda activate lavis python m torch distributed run nproc per node 8 train py cfg path lavis projects blip2 train finetune yaml file you can also load the finetuning checkpoints here https drive google com drive folders 1rkp1cz6r6h8yziec4f3mhw9dccxqchba usp drive link 5 calculating scores cd calculate scores python calculate score task py folder your result dir epoch your epoch please also modify the feature and question path in the scripts todo huggingface auto load checkpoint data all data will be gradually released in google drive https drive google com drive folders 188yd7tmiufyct dvmpkq8q tnqkb 4bo usp sharing and huggingface https huggingface co datasets shuhongzheng 3d llm all files are released in google drive first and then huggingface please refer to the google drive for file structure pretraining data we are still cleaning the grounding navigation part all other pre training data are released object data emsp language annotations of object data released here https drive google com file d 17k1zgb1hpvpj36a84gefgqk7dokrp 32 view usp sharing emsp for downloading objaverse data please refer to objaverse website https objaverse allenai org emsp to get 3d features and point clouds of the objaverse data please refer to step1 https github com umass foundation model 3d llm blob main readme md step1 render images from different views of a scene and step3 https github com umass foundation model 3d llm blob main readme md step3 3d feature construction from rendered images of 3dlanguage data generation chatcaptioner based emsp a small set of objaverse features is released here https drive google com file d 1mjzonfwrefiuapyxp65d65us2eoplafr view usp drive link emsp todo we will probably release the whole set of objaverse 3d features scene data emsp language data released here https drive google com file d 18avyawwmwqp8nyzqftpemw1wfhghjw0b view usp drive link emsp 3d features and point clouds 250g are released here https drive google com drive folders 1bjpk00ufrzgh7tdx3paqdrrtfhnp6b2k usp drive link however if you want to explore generating the features yourself please refer to the three step 3d feature extraction part here https github com umass foundation model 3d llm three step 3d feature extraction please use v2 to be consistent with the checkpoints and also result in better performances finetuning data 3d features and point clouds of scannet used for finetuning scanqa and sqa3d are released in here https drive google com drive folders 1cset48jj5ucyelgcxxjbkgh86qyece8d usp drive link 3d features and point clouds of 3dmv vqa are released here https drive google com drive folders 1ndfkkn izxgezi6fxa60rf1uxtomhoet usp drive link 3dmv vqa data will be further updated for a clearer structure all questions can be found here https drive google com drive folders 14mdidl6cch b27q0azgdelhaeobbpn2o usp drive link 3dlanguage data generation chatcaptioner based three step 3d feature extraction objaverse step1 render images from different views of a scene follow the instruction in 3dlanguage data chatcaptioner based objaverse render readme md for installation the following code will render images of a objaverse scene e g f6e9ec5953854dff94176c36b877c519 the rendered images will be saved at 3dlanguage data chatcaptioner based objaverse render output please refer to 3dlanguage data chatcaptioner based objaverse render readme md for more details about the command shell cd 3dlanguage data chatcaptioner based objaverse render path to blender b p render py noaudio disable crash handler uid f6e9ec5953854dff94176c36b877c519 step2 generate caption for this objaverse scene installation please follow chatcaptioner https github com vision cair chatcaptioner to install the environment the following code will read the rended images of an objaverse scene e g f6e9ec5953854dff94176c36b877c519 and generate scene caption at 3dlanguage data chatcaptioner based output shell cd 3dlanguage data chatcaptioner based python chatcaption py specific scene f6e9ec5953854dff94176c36b877c519 step3 3d feature construction from rendered images follow the instruction in 3dlanguage data chatcaptioner based gen features readme md for extracting 3d features from rendered images shell cd 3dlanguage data chatcaptioner based gen features box demonstration instruction based todo revision based todo three step 3d feature extraction scene this section is for constructing 3d features for scene data if you already downloaded our released scene data please skip this section first step installation please follow mask2former https github com facebookresearch mask2former to install the environment and download the pretrained weight https dl fbaipublicfiles com maskformer mask2former coco instance maskformer2 swin large in21k 384 bs16 100ep model final e5f453 pkl to the current directory if extracting the masks with mask2former https openaccess thecvf com content cvpr2022 papers cheng masked attention mask transformer for universal image segmentation cvpr 2022 paper pdf please follow segment anything https github com facebookresearch segment anything to install the environment and download the pretrained weight https dl fbaipublicfiles com segment anything sam vit h 4b8939 pth to the current directory if extracting the masks with sam https arxiv org abs 2304 02643 extract masks with mask2former shell cd three steps 3d feature first step python maskformer mask py scene dir path data dir with rgb images save dir path dir you want to save the masks extract masks with segment anything shell cd three steps 3d feature first step python sam mask py scene dir path data dir with rgb images save dir path dir you want to save the masks after the first step we are expected to obtain a directory of masks specified by save dir path that contains extracted masks for multi view images of the scenes second step note blip features are for lavis blip2 clip features are for open flamingo installation the same as the following 3d llm blip2 based section to install salesforce lavis https github com salesforce lavis there are four options 1 extract clip feature with mask2former masks 2 extract clip feature with sam masks 3 extract blip feature with mask2former masks 4 extract blip feature with sam masks extract 2d clip features with mask2former masks shell cd three steps 3d feature second step python clip maskformer py scene dir path data dir with rgb images mask dir path mask dir from 1st step save dir path dir you want to save the feat for the other options the scripts are in similar format after the second step we are expected to obtain a directory of features specified by save dir path that contains 2d features for multi view images of the scenes third step direct reconstruction installation please install the habitat environment https github com facebookresearch habitat lab tree challenge 2022 reconstruct 3d feature from multi view 2d features shell cd three steps 3d feature third step python sam mask py data dir path data dir with rgb images depth dir path data dir with depth images feat dir path feature dir from 2nd step after the third step we are expected to obtain two files pcd pos pt and pcd feat pt for each room inside the corresponding rgb directory pcd pos pt contains the point positions of the 3d point cloud shape n 3 pcd feat pt contains the point features of the 3d point cloud shape n n dim n is the number of sampled points in the point cloud default 300000 and n dim is the feature dimension 1024 for clip feature 1408 for blip feature gradslam feature fusion refer to concept fusion https github com concept fusion concept fusion we will also release our reproduced version of concept fusion for our feature generation we reproduced the paper before their official release neural field please refer to 3d clr https github com evelinehong 3d clr official repository pre training shell cd 3dllm blip2 base conda activate lavis use facebook opt 2 7b todo use flant5 python m torch distributed run nproc per node 8 train py cfg path lavis projects blip2 train pretrain yaml 3d llm flamingo based todo citation if you find our work useful please consider citing article 3dllm author hong yining and zhen haoyu and chen peihao and zheng shuhong and du yilun and chen zhenfang and gan chuang title 3d llm injecting the 3d world into large language models journal neurips year 2023 acknowledgements https github com salesforce lavis https github com facebookresearch mask2former https github com facebookresearch segment anything https github com mlfoundations open flamingo https github com concept fusion concept fusion https github com evelinehong 3d clr official | ai |
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NuttX-apps | application folder contents general directory location built in applications nuttshell nsh built in commands synchronous built in commands application configuration file example built in application building nuttx with board specific pieces outside the source tree general this folder provides various applications found in sub directories these applications are not inherently a part of nuttx but are provided to help you develop your own applications the apps directory is a break away part of the configuration that you may choose to use or not directory location the default application directory used by the nuttx build should be named apps or apps x y z where x y z is the nuttx version number this apps directory should appear in the directory tree at the same level as the nuttx directory like nuttx apps if all of the above conditions are true then nuttx will be able to find the application directory if your application directory has a different name or is location at a different position then you will have to inform the nuttx build system of that location there are several ways to do that 1 you can define config apps dir to be the full path to your application directory in the nuttx configuration file 2 you can provide the path to the application directory on the command line like make appdir path or make config apps dir path 3 when you configure nuttx using tools configure sh you can provide that path to the application directory on the configuration command line like configure sh a app dir board name config name built in applications nuttx also supports applications that can be started using a name string in this case application entry points with their requirements are gathered together in two files builtin builtin proto h entry points prototype function builtin builtin list h application specific information and requirements the build occurs in several phases as different build targets are executed 1 context 2 depend and 3 default all application information is collected during the make context build phase to execute an application function exec builtin is defined in the apps include builtin builtin h nuttshell nsh built in commands one use of builtin applications is to provide a way of invoking your custom application through the nuttshell nsh command line nsh will support a seamless method invoking the applications when the following option is enabled in the nuttx configuration file conf config nsh builtin apps y applications registered in the apps builtin builtin list h file will then be accessible from the nsh command line if you type help at the nsh prompt you will see a list of the registered commands synchronous built in commands by default built in commands started from the nsh command line will run asynchronously with nsh if you want to force nsh to execute commands then wait for the command to execute you can enable that feature by adding the following to the nuttx configuration file conf config sched waitpid y the configuration option enables support for the waitpid rtos interface when that interface is enabled nsh will use it to wait sleeping until the built in command executes to completion of course even with config sched waitpid y defined specific commands can still be forced to run asynchronously by adding the ampersand after the nsh command application configuration file the nuttx configuration uses kconfig frontends tools and the nuttx configuration file config file for example the nuttx config may have conf config examples hello y this will select the apps examples hello in the following way the top level make will include apps examples make defs apps examples make defs will set configured apps appdir examples hello like this makefile ifneq config examples hello configured apps appdir examples hello endif example built in application an example application skeleton can be found under the examples hello sub directory this example shows how a builtin application can be added to the project one must 1 create sub directory as progname 2 in this directory there should be a make defs file that would be included by the apps makefile a kconfig file that would be used by the configuration tool see the file kconfig language txt in the nuttx tools repository this kconfig file should be included by the apps kconfig file a makefile and the application source code 3 the application source code should provide the entry point c main 4 set the requirements in the file makefile specially the lines makefile progname progname priority sched priority default stacksize 768 asrcs asm source file list as a asm b asm csrcs c source file list as foo1 c foo2 c 5 the make defs file should include a line like makefile ifneq config progname configured apps progname endif building nuttx with board specific pieces outside the source tree q has anyone come up with a tidy way to build nuttx with board specific pieces outside the source tree a here are three 1 there is a make target called make export it will build nuttx then bundle all of the header files libraries startup objects and other build components into a zip file you can move that zip file into any build environment you want you can even build nuttx under a dos cmd window this make target is documented in the top level nuttx readme txt 2 you can replace the entire apps directory if there is nothing in the apps directory that you need you can define config apps dir in your config file so that it points to a different custom application directory you can copy any pieces that you like from the old apps directory to your custom apps directory as necessary this is documented in nuttx boards readme txt and nuttx documentation nuttxportingguide html online at https bitbucket org nuttx nuttx src master documentation nuttxportingguide html apndxconfigs under build options and in the apps readme txt file 3 if you like the random collection of stuff in the apps directory but just want to expand the existing components with your own external sub directory then there is an easy way to that too you just create a symbolic link in the apps directory that redirects to your application sub directory in order to be incorporated into the build the directory that you link under the apps directory should contain 1 a makefile that supports the clean and distclean targets see other makefile s for examples and 2 a tiny make defs file that simply adds the custom build directories to the variable configured apps like makefile configured apps my directory1 my directory2 the apps makefile will always automatically check for the existence of subdirectories containing a makefile and a make defs file the makefile will be used only to support cleaning operations the make defs file provides the set of directories to be built these directories must also contain a makefile that makefile must be able to build the sources and add the objects to the apps libapps a archive see other makefile s for examples it should support the all install context and depend targets apps makefile does not depend on any hardcoded lists of directories instead it does a wildcard search to find all appropriate directories this means that to install a new application you simply have to copy the directory or link it into the apps directory if the new directory includes a makefile and make defs file then it will automatically be included in the build if the directory that you add also includes a kconfig file then it will automatically be included in the nuttx configuration system as well apps makefile uses a tool at apps tools mkkconfig sh that dynamically builds the apps kconfig file at pre configuration time you could for example create a script called install sh that installs a custom application configuration and board specific directory a copy myboard directory to boards myboard b add a symbolic link to myapplication at apps external c configure nuttx usually by bash tools configure sh myboard myconfiguration use of the name apps external is suggested because that name is included in the gitignore file and will save you some nuisance when working with git export restrictions this distribution includes cryptographic software the country in which you currently reside may have restrictions on the import possession use and or re export to another country of encryption software before using any encryption software please check your country s laws regulations and policies concerning the import possession or use and re export of encryption software to see if this is permitted see http www wassenaar org for more information the u s government department of commerce bureau of industry and security bis has classified this software as export commodity control number eccn 5d002 c 1 which includes information security software using or performing cryptographic functions with asymmetric algorithms the form and manner of this apache software foundation distribution makes it eligible for export under the license exception enc technology software unrestricted tsu exception see the bis export administration regulations section 740 13 for both object code and source code the following provides more details on the included cryptographic software https tls mbed org supported ssl ciphersuites | nuttx rtos px4 pixhawk | os |
311 | stefan bund s client side web development fall 2019 term instructional media 1 aug 29 gatsby get started guide https www gatsbyjs org docs quick start or alternatively use https www gatsbyjs org tutorial part zero to get started 2 sept 10 styling components using css modules in react https www gatsbyjs org tutorial part two style a component using css modules 3 css fundamentals to employ inside your new react components https developer mozilla org en us docs learn css then go to https css tricks com to investigate we will utilize examples posted at https developer mozilla org en us docs learn css first steps getting started 4 on div styling https developer mozilla org en us docs learn css first steps how css is structured functions 5 css grids https developer mozilla org en us docs web css css grid layout 6 guide to creating your own linux instance on ec2 aws https www loom com share c7e11169e2d14140ad526615962c44f7 7 project guide what to turn in on october 1 3 for first project https github com stefanbund 311 blob master october 201 20project 20guidelines pdf 8 oct 1 introducting bootstrap https www loom com share 05ecb40fed8c4c7d8d470356425392e1 9 oct 3 the boostrap rubric container row column input and buttons radios and checkbox ui elements https www loom com share 1779feb958f64f1889efa0220ceb1675 10 ui elements in css3 bootstrap text inputs radios checkboxes tooltips praefills and classic buttons https getbootstrap com docs 4 3 examples checkout 11 cards containers card decks in bootstrap and ecommerce layouts https getbootstrap com docs 4 3 examples pricing javascript elements 12 integrating javascript into html pages getelementbyid manipulating the dom adding html elements augmenting properties of html elements in the dom interactivity basic ui elements buttons text inputs links programming with links and the window object https github com stefanbund 311 tree master prior 20terms phase 201 movie version of lectures 12 1 set up amazon hosting via s3 https www useloom com share 7d8f2f2638b046c6aba7c57b2b2e123b 12 2 core html js css https www useloom com share 4ae64d7648344ab485dd82833f218962 12 3 javascript generating dom nodes via create element https www useloom com share 49b0c06e8a9b4d6ea176b76c7007ed0f 12 4 assignment one notes on drop down menus https www useloom com share 45160ba03085448ea70d8b5d2120bc23 13 javascript phase 2 13 1 url parameters https www useloom com share 6ef704b26d5a4b178e935fc3c06649a0 13 2 responsive interactivity data iteration and divs integrating custom css into interactive elements via appendchild https www useloom com share 9b273f6cd03a45d686393f6de726ee67 prior terms spring 19 phase 0 team build syllabus 1 go to https goo gl forms z0xxx9yt6kevyi713 to construct teams 2 go to https www useloom com share 836b7217ed9546ffab1f97ccccbd1c6a to view our motivational starter talk 3 please download the industry affinity spreadsheet from blackboard then view this tutorial on how to create teams with the data here https www useloom com share 7935a031f49a4c2ba423c016c1cd45bc phase 1 lesssons 1 set up amazon hosting via s3 https www useloom com share 7d8f2f2638b046c6aba7c57b2b2e123b 2 core html js css https www useloom com share 4ae64d7648344ab485dd82833f218962 3 javascript generating dom nodes via create element https www useloom com share 49b0c06e8a9b4d6ea176b76c7007ed0f 4 assignment one notes on drop down menus https www useloom com share 45160ba03085448ea70d8b5d2120bc23 phase 2 1 understand url parameters and make them happen https www useloom com share 6ef704b26d5a4b178e935fc3c06649a0 2 integrating url parameters for loop iteration into a responsive image gallery https www useloom com share 9b273f6cd03a45d686393f6de726ee67 phase 3 1 extensive tutorial on jquery programming cookies css3 grids and diy responsive design https www useloom com share abed46908fd5467ab7857a7866762619 phase 4 1 creating complete responsive websites using bootstrap https www useloom com share 81970de247124320a5585eda4d455533 phase 5 1 creating a github organization repository and adding members to a team https github com collab uniba socialcde4eclipse wiki how to setup a github organization project and team 2 basic collaboration steps https www youtube com watch v 61wbzs9xmwk 3 github s own guides to collaboration outstanding reference material 1 mdn beginner s reference https developer mozilla org en us docs learn getting started with the web html basics | javascript learning html css programming web development cal | front_end |
x-cube-azrtos-f4 | x cube azrtos f4 azure rtos software expansion for stm32cube latest tag https img shields io github v tag stmicroelectronics x cube azrtos f4 svg color brightgreen with azure rtos complementing the extensive stm32cube ecosystem providing free development tools software bricks and software expansion packages stm32 users can also leverage the rich services of azure rtos which meet the needs of tiny smart connected devices x cube azrtos f4 azure rtos software expansion for stm32cube provides a full integration of microsoft azure rtos in the stm32cube environment for the stm32f4 series of microcontrollers ready to run applicative examples are also provided for the nucleo f429zi and stm32469i disco evaluation boards thus reducing the learning curve and ensuring a smooth application development experience with azure rtos and stm32f4 mcus the scope of this package covers the following azure rtos middleware rtos threadx usb device and host usbx file system including nor nand memories support filex and levelx and networking including ethernet and wifi media netxduo htmresc overviewazurertos in stm32cube png azure rtos is a professional grade highly reliable and market proven middleware suite integrated and full featured rtos learn more threadx https aka ms threadx industrial grade networking stack optimized for performance coming with many iot protocols learn more netx duo https aka ms netxduo advanced fs ftl fully featured to support nand nor flash memories learn more filex https aka ms filex and levelx https docs microsoft com en us azure rtos levelx usb host and device stacks coming with multiple classes learn more usbx https aka ms usbx safety pre certifications from microsoft iec 61508 sil4 iec 62304 class c and iso 26262 asil d security pre certifications from microsoft eal4 for tls dtls fips 140 2 for sw crypto lib stm32 granted production license here https github com azure rtos threadx blob master licensed hardware txt prerequisites usbx filex and netxduo building require threadx as they are based on rtos model usbx host msc requires filex middleware usage usbx device ecm rndis classes require netxduo usage one of the following toolchains iar embedded workbench for arm ewarm toolchain 8 50 9 st linkv3 stm32cubeide v1 9 0 https www st com en development tools stm32cubeide html st linkv3 realview microcontroller development kit mdk arm toolchain v5 32 st linkv3 supported devices and boards nucleo f429zi https www st com en evaluation tools nucleo f429zi html mb1137 b01 stm32469i disco https www st com en evaluation tools 32f469idiscovery html mb1189 b03 repository structure the stmicroelectronics github x cube azrtos repository consists of the following repositories drivers contains stm32f4 cmsis hal and bsp drivers middlewares contains threadx netx duo filex levelx and usbx stacks including porting on stm32 hardware projects provides ready to run applicative examples for azure rtos on the supported boards projects are structured as follows htmresc azurertos projects structure png documentation more comprehensive documentation is available on stm32 mcu wiki https wiki st com stm32mcu wiki stm32coremw overview license azure rtos is distributed by microsoft under github https github com azure rtos and under microsoft license stmicroelectronics include azure rtos in selected stm32cube software packages whether delivered by microsoft under github or by stmicroelectronics through stm32 cubefw packages on www st com or github proprietary license terms are the same between microsoft and user source code is available user is granted modification user is granted redistribution of source modified or not and or binary provided he develops on an mcu mpu that is listed officially by microsoft user is granted production provided he develops on an mcu that is listed officially by microsoft licensed hardware https github com azure rtos threadx blob master licensed hardware txt user is forbidden to reuse provided software to create a competing offer whenever a new stm32 series includes support for azure rtos it is added by microsoft in the official list by using stm32f4 series you inherit the distribution and production use rights in section 2 of the microsoft software license terms for azure rtos for more details about license information relative to each component in this package please refer to license license md release note details about the content of this release are available in the release note here https htmlpreview github io https github com stmicroelectronics x cube azrtos f4 blob main release notes html applications middleware applications short description threadx tx thread creation it demonstrates how to create and destroy multiple threads using azure rtos threadx apis in addition it shows how to use preemption threshold between threads and change priorities on fly readme projects stm32f429zi nucleo applications threadx tx thread creation readme md threadx tx thread sync it demonstrates how to synchronize between threads using binary semaphores or mutexes readme projects stm32469i discovery applications threadx tx thread sync readme md threadx tx thread msgqueue it demonstrates how to exchange message between threads using message queue api and how to handle message from different queues using event queue chaining feature readme projects stm32f429zi nucleo applications threadx tx thread msgqueue readme md threadx tx freertos wrapper it demonstrates how to create threads using the freertos wrapper apis readme projects stm32f429zi nucleo applications threadx tx freertos wrapper readme md threadx tx cmsis wrapper it demonstrates how cmsis rtos adaptation layer for azure rtos threadx it shows how to develop an application using the cmsis rtos 2 apis readme projects stm32469i discovery applications threadx tx cmsis wrapper readme md threadx tx lowpower it demonstrates how to configure the lowpower feature of azure rtos threadx stack in addition it shows how to use threadx macro related to the lowpower feature readme projects stm32f429zi nucleo applications threadx tx lowpower readme md threadx tx mpu it demonstrates how to load start and unload modules in addition it shows how threadx memory protection on modules using the memory protection unit mpu readme projects stm32f429zi nucleo applications threadx tx mpu readme md usbx ux host msc it demonstrates how to develop usb host mass storage msc able to enumerate and communicates with a removable usb flash disk the application is designed to behave as an usb msc host able to operate with an usb flash disk using the bulk only transfer bot and small computer system interface scsi transparent commands combined with a file system azurertos filex readme projects stm32f429zi nucleo applications usbx ux host msc readme md usbx ux host hid it demonstrates how to develop usb host human interface hid able to enumerate and communicates with a mouse or a keyboard the application is designed to behave as an usb hid host the code provides required requests to properly enumerate hid devices hid class apis to decode hid reports received from a mouse or a keyboard and display data on uart hyperterminal readme projects stm32f429zi nucleo applications usbx ux host hid readme md usbx ux host cdc acm it demonstrates how to develop usb host cdc acm device able to properly enumerate cdc devices then send and receive data that can be displayed on hyperterminal or alike readme projects stm32f429zi nucleo applications usbx ux host cdc acm readme md usbx ux device msc it demonstrates how to develop usb device mass storage class based application the application is designed to emulate an usb msc device the code provides all required device descriptors framework and the associated class descriptor report to build a compliant usb msc device readme projects stm32469i discovery applications usbx ux device msc readme md usbx ux device cdc acm it demonstrates how to develop usb device communication class cdc acm based application the application is designed to emulate an usb to uart bridge following the virtual com port vcp implementation the code provides all required device descriptors framework and associated class descriptor report to build a compliant usb cdc acm device readme projects stm32469i discovery applications usbx ux device cdc acm readme md usbx ux device hid it demonstrates how to develop usb device human interface hid mouse based application the application is designed to emulate an usb hid mouse device the code provides all required device descriptors framework and associated class descriptor report to build a compliant usb hid mouse device readme projects stm32f429zi nucleo applications usbx ux device hid readme md usbx ux device cdc ecm it demonstrates how to develop usb device cdc ecm communication it shows how to run web http server based application stack over usb interface using usb cdc ecm protocol readme projects stm32469i discovery applications usbx ux device cdc ecm readme md usbx ux host dualclass it demonstrates how to develop usb host supporting two device classes usb hid mouse or keyboard and usb msc removable flash disk readme projects stm32469i discovery applications usbx ux host dualclass readme md usbx ux host hid cdc acm it demonstrates how to develop a composite usb hid cdc acm host that is able to enumerate and communicate with composite usb hid cdc acm and simple hid cdc acm devices readme projects stm32469i discovery applications usbx ux host hid cdc acm readme md usbx ux device hid cdc acm it demonstrates how to develop a composite usb device application the application is designed to emulate a usb hid mouse combined with an usb to uart bridge following the virtual com port vcp implementation the code provides all required device descriptors framework and associated class descriptor report to build a compliant composite usb hid and cdc acm device readme projects stm32469i discovery applications usbx ux device hid cdc acm readme md usbx ux device dfu it demonstrates how to develop usb device firmware upgrade dfu based application the application is designed to emulate an usb dfu device the code provides all required device descriptors framework and associated class descriptor report to build a compliant usb dfu device readme projects stm32f429zi nucleo applications usbx ux device dfu readme md filex fx usd file edit it demonstrates how to develop a basic sd card file operations application the application is designed to handle sd card insertion removal events and depending on that state it starts and stops file operations from and into the sd card readme projects stm32469i discovery applications filex fx usd file edit readme md filex fx multiaccess it demonstrates the filex s concurrent file access capabilities the application is designed to execute file operations on the sd card device the code provides all required software code for handling sd card i o operations readme projects stm32469i discovery applications filex fx multiaccess readme md filex fx nor write read file it demonstrates how to create a fat file system on the nor flash using filex alongside levelx the application is designed to execute file operations on the mx25lm51245g nor flash device the code provides all required software code for properly managing it readme projects stm32469i discovery applications filex fx nor write read file readme md filex fx dualinstance it demonstrates the coexistence capability of two filex levelx stacks running independently on each core readme projects stm32469i discovery applications filex fx dual instance readme md filex fx iap it demonstrates how to implement an in application programming iap using filex s sd file access capabilities the application is designed to erase and write to on chip flash memory it provides all required software code for handling sd card and flash memory i o operations this is a typical application on how to use the sd card peripheral for firmware upgrade application or iap allowing user to erase and write to on chip flash memory readme projects stm32469i discovery applications filex fx iap iap main readme md filex fx sram file edit standalone it demonstrates how to create a fat file system on the internal sram memory using filex running in standalone mode without threadx readme projects stm32f429zi nucleo applications filex fx sram file edit standalone readme md netxduo nx tcp echo server it demonstrates how to develop a netx tcp server to communicate with a remote client using the netx tcp socket api readme projects stm32f429zi nucleo applications netxduo nx tcp echo server readme md netxduo nx tcp echo client it demonstrates how to develop a netx tcp client to communicate with a remote sever using the netx tcp socket api readme projects stm32f429zi nucleo applications netxduo nx tcp echo client readme md netxduo nx udp echo server it demonstrates how to develop a netx udp server to communicate with a remote client using the netx udp socket api readme projects stm32f429zi nucleo applications netxduo nx udp echo server readme md netxduo nx udp echo client it demonstrates how to develop a netx udp client to communicate with a remote sever using the netx udp socket api readme projects stm32f429zi nucleo applications netxduo nx udp echo client readme md netxduo nx webserver it demonstrates how to develop web http server based application it is designed to load files and static web pages stored in sd card using a web http server the code provides all required features to build a compliant web http server readme projects stm32f429zi nucleo applications netxduo nx webserver readme md netxduo nx mqtt client it demonstrates how to exchange data between client and server using mqtt protocol in an encrypted mode supporting tls v1 2 readme projects stm32f429zi nucleo applications netxduo nx mqtt client readme md netxduo nx sntp client it demonstrates how to develop a netx sntp client and connect with an stnp server to get a time update readme projects stm32f429zi nucleo applications netxduo nx sntp client readme md troubleshooting caution the issues are strictly limited to submit problems or suggestions related to the software delivered in this repository for any other question related to the product the hardware performance or characteristics the tools the environment you can submit it to the st community on the stm32 mcus related page https community st com s topic 0to0x000000bsqswaw stm32 mcus | threadx stm32f4 filex azure-rtos x-cube netxduo usbx levelx | os |
sql-ERD-employee-data | sql employee database 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 this assignment utilises erd skills through quickdatabasediagrams csv file importing to sql platform pgadmin4 with postgress csv files found in data folder schema to create tables found in schema sql querying the csv data in pgadmin4 to get sepcific results query listed in query sql along with corresponding code juypter notebook for testing if data provided was ficticious material using sqlalchemy matplotlib pandas and numpy erd used to connect tables erd employeesql images quickdbd export png sample of queries in pgadmin4 sql code employeesql images sql code png histogram of salary range for company histogram employeesql images histogram png bar graph of average salary by title avg salary by title employeesql images avg salary png outcome upon reviewing the data the data is found to be false br in so much that it was fabricated by the company as a test and not actual company records br the employee id we were given returned april foolsday my id employeesql images my id png | server |
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aat3020-2023 | aat3020 2023 this is repository for natural language processing course of dept of art amp technology sogang university recommended materials 1 stanford cs224n http web stanford edu class cs224n 2021 lecture videos https youtube com playlist list ploromvodv4rosh4v6133s9lfprhjembmj 2 3blue1brown essence of linear algebra https youtube com playlist list plzhqobowtqdpd3mizzm2xvfitgf8he ab neural networks https youtube com playlist list plzhqobowtqdnu6r1 67000dx zcjb 3pi 3 word2vec the illustrated word2vec https jalammar github io illustrated word2vec | ai |
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TorchBlocks | torchblocks work in progress a pytorch based toolkit for natural language processing python https img shields io badge python 3 7 7c 3 8 7c 3 9 7c 3 10 blue logo python logocolor white pytorch https img shields io badge pytorch 1 10 ee4c2c logo pytorch logocolor white black https img shields io badge code 20style black black svg labelcolor gray license https img shields io badge license mit green svg labelcolor gray requirements torch 1 10 0 tokenizers 0 7 0 transformers 4 10 0 torchmetrics 0 11 3 torchblocks requires python 3 7 we recommend installing torchblocks in a linux or osx environment installation recommended because of active development shell git clone https github com lonepatient torchblocks git cd torchblocks python setup py install note this project is still in the development stage and some of the interfaces are subject to change tutorials tutorial 1 text classification task text classification cola py https github com lonepatient torchblocks blob master examples task text classification cola py tutorial 2 siamese similarity task siamese similarity afqmc py https github com lonepatient torchblocks blob master examples task siamese similarity afqmc py tutorial 3 sequence labeling task sequence labeling ner crf py https github com lonepatient torchblocks blob master examples task sequence labeling ner crf py tutorial 4 sentence similarity task sentence similarity lcqmc py https github com lonepatient torchblocks blob master examples task sentence similarity lcqmc py tutorial 5 triple similarity task triple similarity epidemic py https github com lonepatient torchblocks blob master examples task triple similarity epidemic py tutorial 6 sequence labeling task sequence labeling resume beam search softmax py https github com lonepatient torchblocks blob master examples task sequence labeling resume beam search softmax py tutorual 7 sequence labeling task sequence labeling resume global pointer py https github com lonepatient torchblocks blob master examples task sequence labeling resume global pointer py example scripts for each task torchblocks examples https github com lonepatient torchblocks tree master examples | pytorch nlp text-classification triplet-loss siamese-network text-similarity multilabel-classification advertising bert transformers relation-classification named-entity-recognition | ai |
front-end-example | front end example | front_end |
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ICE3015-project | ice3015 project inha univ embedded system capstone design final project arduino https img shields io badge c arduino 00979d logo arduino logocolor white style for the badge 1 homework 1 4 pdf 2 homework 2 4 pdf 3 homework 3 4 pdf 4 homework 4 4 pdf np01s 220 2 3 960 66 2 1 760 dual dc szh mdbl 010 1 1 980 mpu 6050 szh ek007 2 7 700 1 hc sr04 szh0usbc 004 2 3 960 50 50 smeaps55 1 1 870 97 90 1 3 300 awg24 50mm 1 3 410 aa 4 1 1 210 3 8 100 3 37 250 mcu atmega4808 resource datasheet atmega4808 pdf mcu mx1508 resource datasheet mx1508 pdf dc mpu6050 resource datasheet mpu6050 pdf hcsr04 resource datasheet hcsr04 pdf circuit resource orcad circuit jpg orcad circuit pdf esource orcad circuit pdf test arduino ino src test arduino ino test wire mpu6050 ino src test wire mpu6050 ino wire h mpu6050 test twi0 mpu6050 ino src test twi0 mpu6050 ino twi0 mpu6050 test tca0 timer ino src test tca0 timer ino tca0 test tca0 hcsr04 ino src test tca0 hcsr04 ino tca0 hc sr04 project mp4 resource video project gif mysegway ino src mysegway ino project mp4 resource video project gif resource project proposal pdf resource project interim pdf resource project final pdf resource project note 0512 pdf i2c resource project note 0518 pdf pwm resource project note 0524 pdf pdf 1 resource project note 0527 pdf pdf tca0 millis resource project note 0604 pdf resource project note 0609 pdf resource project note 0612 pdf resource project note 0613 pdf how is the life setup loop https int i github io cpp 2023 03 17 arudino setup loop mpu6050 https int i github io cpp 2023 06 24 mpu6050 complementary filter isr with tca https int i github io cpp 2023 07 04 arduino interrupt | arduino atmega4809 cpp hcsr04 i2c mpu6050 mx1508 pwm orcad | os |
CS623team8 | cs623team8 welcome to cs623 team 8 java project objectives of the project 1 reverse engineering the schema to an entity relationship diagram erd 2 developing a java application using jdbc that connects to the postgresql database 3 coding a transaction in java that implements the acid properties the depot d1 is deleted from depot and stock erd of product stock and depot relations alt text https github com nishanthulwan47 cs623team8 blob main erd png raw true schema of product stock and depot relations alt text https github com nishanthulwan47 cs623team8 blob main schema png raw true deliverables link to code https github com nishanthulwan47 cs623team8 blob main src javasql sqlacid java link to video https www youtube com watch v qkr1brswzza | server |
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angular-nlp-compromise | angular nlp compromise natural language processing in your angularjs application based on nlp compromise https github com spencermountain nlp compromise library example small angular example here http rawgit com kroid angular nlp compromise master example index html nlp compromise examples long text demo http rawgit com spencermountain nlp compromise master client side long demo index html specific methods http rawgit com spencermountain nlp compromise master client side basic demo index html realtime demo http rawgit com spencermountain nlp compromise master client side cute demo index html video http i vimeocdn com video 493948602 640 jpg https vimeo com 109880250 getting started get the code install via bower http bower io bower install save angular nlp compromise or download the release http rawgit com kroid angular nlp compromise master dist angular nlp compromise js minified http rawgit com kroid angular nlp compromise master dist angular nlp compromise min js add module to your application index html html html ng app app head head body ng controller appcontroller script src https ajax googleapis com ajax libs angularjs 1 3 13 angular min js script script src angular nlp compromise min js script script src app js script body html app js javascript function angular module app nlpcompromise controller appcontroller controller controller inject nlp function controller nlp console log nlp documentation see docs in nlp compromise https github com spencermountain nlp compromise repository | ai |
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Secure-Cloud-Storage | p align center img src https cdn icons png flaticon com 512 3812 3812209 png alt scs logo height 64px p secure cloud storage university project for foundations of cybersecurity course msc computer engineering university of pisa implementation of a secure cloud storage using c 17 and openssl 1 1 1 library overview the project consists of a secure cloud storage each client connects to a centralized server that allows to perform operations on its private dedicated storage in order to access to his storage a user must login after that he can execute the following operations list shows all files currently on the storage download retrieves a file from the storage to the local file system upload loads a file from the local file system to the storage rename changes the name of a file on the storage delete removes a file from the storage logout closes the connection to the server the login operation creates a secure connection between the server and a client through the negotiation of a set of session keys the cloud storage application has to guarantee the following requirements the key negotiation has to provide the perfect forward secrecy the client and server communication has to be encrypted and authenticated the client and server communication has to be protected against reply attacks client and server authenticate each other using their own public keys the server already knows all the registered users keys and the client retrieves the server s key through the certificate released by a certification authority getting started in order to compile the project openssl 1 1 1 is needed with the development library bash sudo apt install libssl dev to compile the project bash mkdir bin make to run the project bash bin server bin client project architecture secure cloud storage data biagio gianluca matteo docs resources certificates config h encrypted keys private keys public keys script src client packet security server utility authors biagio cornacchia b cornacchia studenti unipi it gianluca gemini g gemini studenti unipi it matteo abaterusso m abaterusso studenti unipi it | cybersecurity openssl cloud security storage | cloud |
desgin_patterns_c | desgin patterns c design patters for embedded system in c | os |
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LogicLLaMA | logicllama and malls logicllama a language model that translates natural language nl statements into first order logic fol rules it is trained by fine tuning the llama 7b model on the malls https huggingface co datasets yuan yang malls v0 dataset malls large language m odel gener a ted natural l anguage to first order l ogic pair s a dataset consists of 34k pairs of real world natural language nl statements and the corresponding first order logic fol rules annotations all pairs are generated by prompting gpt 4 and processed to ensure the validity of the fol rules harnessing the power of large language models for natural language to first order logic translation yuan yang https gblackout github io siheng xiong https dblp org pid 277 4221 html ali payani https scholar google com citations user 9rhwd8waaaaj hl en ehsan shareghi https eehsan github io and faramarz fekri https fekri ece gatech edu paper https arxiv org abs 2305 15541 data https huggingface co datasets yuan yang malls v0 model https huggingface co yuan yang release x 7 16 2023 we release the malls dataset large language m odel gener a ted natural l anguage to first order l ogic pair s which consists of 34k pairs of real world natural language nl statements and the corresponding first order logic fol rules annotations x 7 16 2023 we released the lora delta weights for direct translation and naive correction logicllama x 7 16 2023 support int8 loading colab demo pipeline for generating synthetically perturbed fol rules release weights for rlhf cot correction logicllama and the corresponding pipeline dataset and weights datasets malls v0 https huggingface co datasets yuan yang malls v0 blob main malls v0 json the file containing the 34k pairs of the malls dataset folio parsed https huggingface co datasets yuan yang malls v0 blob main folio parsed json the file containing 2k pairs collected and processed from the folio https github com yale lily folio datset lora delta weights direct translation https huggingface co yuan yang logicllama 7b direct translate delta v0 naive correction https huggingface co yuan yang logicllama 7b naive correction delta v0 download the dataset by running commandline sh data download sh install 1 clone this repo commandline git clone https github com gblackout logicllama git cd logicllama 2 prepare environment conda create n logicllama python 3 7 conda activate logicllama pip install r requirements txt notebook demo checkout out demo ipynb for a quick demonstration of logicllama inference and the fol rule parsing inference direct translation copy the template from scripts to the project root commandline cp scripts eval translation sh modify base model to the folder link of the base llama 7b model instructions https huggingface co docs transformers main model doc llama for getting the base model also modify data path to the path to the dataset that you want to infer or evaluate set load in 8bit false if your gpu does not support 8 bit quantization it can be the folio or the malls dataset you downloaded with data download sh or it can be your own dataset as long as it follows the following format commandline nl your nl statement fol optional fol rule the fol field is optional if provided we will evaluate the bleu and the logical equivalence le score of the predicted fol rule with respect to this field otherwise only inference is performed finally run sh eval translation sh naive correction in this mode logicllama serves as a downstream model that corrects the predicted fol rule by chatgpt which generally leads to better performance than chatgpt alone or the direct translation mode that said you need to first collect the response from chatgpt and then correct it 1 collect chatgpt predictions by copying the template from scripts to the project root commandline cp scripts gpt translation sh modify the following field api key your openai api key or the path to the key file dataset the path to the dataset save path the path to save the dataset set load in 8bit false if your gpu does not support 8 bit quantization finally run sh gpt translation sh you can also use your own methods to get the response as long as the final dataset has the following format commandline nl your nl statement pred fol gpt predicted fol rule fol optional fol rule 2 correct the output with logicllama copy the template from scripts to the project root commandline cp scripts eval correction sh modify the base model to the llama 7b base model and data path to the output dataset from step 1 finally run sh eval correction sh training you can also train logicllama from scratch on malls or your own dataset direct translation copy the template from scripts to the project root commandline cp scripts sft translation sh modify the following field base model path link to the base model data path the path to the dataset with both nl and fol fields output dir the path for saving the peft model use wandb whether to use wandb for logging set load in 8bit false if your gpu does not support 8 bit quantization finally run sh sft translation sh naive correction copy the template from scripts to the project root commandline cp scripts sft correction sh modify the same fields as above note that the dataset here needs to have nl fol ground truth fol and pred fol gpt predicted fol available you can obtain pred fol by running gpt translation sh following the instruction in the inference section finally run sh sft correction sh license the data code and weights are released under apache 2 0 license and are intended for research use only additionally the usage of the logicllama model should follow the license agreement of llama and alpaca the dataset is cc by nc 4 0 and should follow the policy of openai https openai com policies terms of use as it is collected from gpt 4 acknowledgement this project is developed based on the following repos alpaca lora https github com tloen alpaca lora the sft and inference modules are mainly built upon this repo trl https github com lvwerra trl we refer to the rlhf scripts in this repo when implementing the rlhf cot correction citation commandline article yang2023harnessing title harnessing the power of large language models for natural language to first order logic translation author yuan yang and siheng xiong and ali payani and ehsan shareghi and faramarz fekri journal arxiv preprint arxiv 2305 15541 year 2023 | ai |
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us-forms-system | u s forms system the us forms system is an open source library and set of guiding principles for building complex web based forms using react https reactjs org the json schema standard http json schema org and the us web design system https designsystem digital gov this project is not being actively maintained based on the experience and interviews with prospective users we re considering a redesign that incorporates the feedback we have gained from building this library if you re interested in helping us design and build the next usfs please open an issue https github com usds us forms system issues about this project inspired by mozilla s react jsonschema form https github com mozilla services react jsonschema form library the us forms system is specifically intended for consistently styled governmental web based forms this library enables you to describe form fields in a json schema configuration file which then renders the backing react components necessary to build your form you ll build forms significantly faster than existing methods and benefit from the us web design system s best practices in user experience and data collection validation and transmission using the library for information about setting up and using the us forms system see the us forms system documentation docs readme md additionally the us forms system starter app https github com usds us forms system starter app provides the basic files and configuration needed to get started building a form using the us forms system contributing to this project please read our code of conduct https github com usds us forms system blob master code of conduct md and contributing md https github com usds us forms system blob master contributing md for more details join the contributor mailing list to receive regular updates about this project please join our mailing list by sending an email to forms subscribe request listserv gsa gov contact the project team if you want to directly contact the project team you can send your questions to forms lists usds gov more project artifacts roadmap research and process us forms system wiki https github com usds us forms system wiki project progress us forms system project board https github com orgs usds projects 4 powerpoint about the us forms system https github com ju liem product things blob master gif v1 pptx | os |
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golang-united-school-homework-10 | web requests handling in this task we want to add some handlefunctions using of github com gorilla mux is advised but not mandatory func start host string port int is required to run tests it should start web listener all tests must pass endpoints expected by tests method request response get name param body hello param get bad status 500 post data body param body i got message nparam post headers headers a 2 b 3 header a b 5 if not defined in table request will be no body set no headers set response expected to have status 200 ok empty body | front_end |
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multichain-web-demo | multichain web demo multichain web demo is a simple web interface for multichain http www multichain com blockchains written in php https github com multichain multichain web demo copyright c coin sciences ltd license gnu affero general public license see the file license txt welcome to multichain web demo this software uses php to provide a web front end for a multichain http www multichain com blockchain node it currently supports the following features viewing the node s overall status creating addresses and giving them real names names are visible to all nodes changing global permissions for addresses issuing assets including custom fields and uploading a file updating assets including issuing more units and updating custom fields and file viewing issued assets including the full history of fields and files sending assets from one address to another creating decoding and accepting offers for exchanges of assets creating streams publishing items to streams as json or text or an uploaded file viewing stream items including listing by key or publisher and downloading files writing testing and approving smart filters both transaction and stream filters the web demo does not yet support the following important functionality in the multichain api managing per asset and per stream permissions multisignature addresses and transactions adding metadata or stream items to permissions or asset transactions viewing an addresses transactions subscribing to assets and viewing their transactions viewing a list of keys or publishers in a stream peer to peer node management message signing and verification blockchain upgrading working with the binary cache the multichain web demo is still under development so please contact us http www multichain com contact us if any of these things are crucial for your needs system requirements a computer running web server software such as apache php 5 x or later with the curl and json extensions multichain 1 0 alpha 26 or later including multichain 2 x create and launch a multichain blockchain if you do not yet have a chain to work with download multichain http www multichain com download install to install multichain and create a chain named chain1 as follows multichain util create chain1 multichaind chain1 daemon if your web server is running on the same computer as multichaind you can skip the rest of this section otherwise multichain cli chain1 stop then add this to multichain chain1 multichain conf rpcallowip ip address of your web server then start multichain again multichaind chain1 daemon configure the web demo this section assumes your blockchain is named chain1 and you are running the node and web server on a unix variant such as linux if not please substitute accordingly make your life easy for the next step by running these on the node s server cat multichain chain1 multichain conf grep rpc port multichain chain1 params dat in the web demo directory copy the config example txt file to config txt cp config example txt config txt in the demo website directory enter chain details in config txt e g default name default name to display in the web interface default rpchost 127 0 0 1 ip address or domain of multichain node default rpcsecure 0 set to 1 to access rpc via https e g for multichain on azure default rpcport 12345 usually default rpc port from params dat default rpcuser multichainrpc username for rpc from multichain conf default rpcpassword mnbh8ahp4mun password for rpc from multichain conf multiple chains are supported by the web demo by copying the same section again but with different prefixes before the period for example another name another rpchost note that the config txt file is readable by users of your web demo installation and contains your multichain api password so you should never use this basic setup for a production system launch the web demo no additional configuration or setup is required based on where you installed the web demo open the appropriate address in your web browser and you are ready to go | blockchain |
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rust-i2c-nuttx | rust talks i2c to bosch bme280 sensor on apache nuttx rtos tested on pine64 pinecone bl602 read the articles rust talks i2c on apache nuttx rtos https lupyuen github io articles rusti2c rust on apache nuttx os https lupyuen github io articles rust2 this repo depends on lupyuen rust nuttx https github com lupyuen rust nuttx install app to add this repo to your nuttx project bash todo change this to the path of our incubator nuttx apps folder pushd nuttx apps examples git submodule add https github com lupyuen rust i2c nuttx rust i2c popd then update the nuttx build config bash todo change this to the path of our incubator nuttx folder cd nuttx nuttx preserve the build config cp config config erase the build config make distclean for bl602 configure the build for bl602 tools configure sh bl602evb nsh for esp32 configure the build for esp32 todo change esp32 devkitc to our esp32 board tools configure sh esp32 devkitc nsh restore the build config cp config config edit the build config make menuconfig in menuconfig enable the rust i2c app under application configuration examples to build the nuttx rust project bash cd nuttx apps examples rust i2c run sh in nuttx shell enter this to run the app bash rust i2c here s the output text nuttshell nsh nuttx 10 2 0 rc0 nsh rust i2c hello from rust test i2c test i2c register 0xd0 is 0x60 test hal read test hal read register 0xd0 is 0x60 test hal write test hal write write 0xa0 to register test hal write register value is 0xa0 test hal write write 0x00 to register test hal write register value is 0x00 read bme280 relative humidity 89 284164 temperature 29 942907 deg c pressure 100483 04 pascals done nsh rust embedded hal for nuttx the rust embedded hal for nuttx has been published at crates io https crates io crates nuttx embedded hal to use it in your project add this to your cargo toml rust cargo toml text external rust libraries used by this module see crates io dependencies nuttx embedded hal 1 0 6 rust embedded hal for nuttx https crates io crates nuttx embedded hal always use the latest version of nuttx embedded hal https crates io crates nuttx embedded hal from c to rust this is how we read an i2c register in c c static int bme280 reg read const struct device priv uint8 t start uint8 t buf int size debugassert priv null debugassert buf null struct i2c msg s msg 2 int ret msg 0 frequency priv freq msg 0 addr priv addr ifdef config bl602 i2c0 for bl602 register id must be passed as i2c sub address msg 0 flags i2c m nostop else otherwise pass register id as i2c data msg 0 flags 0 endif config bl602 i2c0 msg 0 buffer start msg 0 length 1 msg 1 frequency priv freq msg 1 addr priv addr msg 1 flags i2c m read msg 1 buffer buf msg 1 length size ret i2c transfer priv i2c msg 2 source https github com lupyuen bme280 nuttx blob main driver c l155 l183 how do we call i2c transfer from a nuttx app thanks to the i2c demo app we have the answer c int i2ctool get far struct i2ctool s i2ctool int fd uint8 t regaddr far uint16 t result struct i2c msg s msg 2 int ret i2cdev transfer fd msg 2 source https github com lupyuen incubator nuttx apps blob rusti2c system i2c i2c get c l158 l206 i2cdev transfer is defined as c int i2cdev transfer int fd far struct i2c msg s msgv int msgc struct i2c transfer s xfer set up the ioctl argument xfer msgv msgv xfer msgc msgc perform the ioctl return ioctl fd i2cioc transfer unsigned long uintptr t xfer source https github com lupyuen incubator nuttx apps blob rusti2c system i2c i2c devif c l117 l129 let s port this to rust c types and constants earlier we ve seen i2c msg s and i2c transfer s they are defined as c struct i2c msg s uint32 t frequency i2c frequency uint16 t addr slave address 7 or 10 bit uint16 t flags see i2c m definitions far uint8 t buffer buffer to be transferred ssize t length length of the buffer in bytes source https github com lupyuen incubator nuttx blob rusti2c include nuttx i2c i2c master h l208 l215 c struct i2c transfer s far struct i2c msg s msgv array of i2c messages for the transfer size t msgc number of messages in the array source https github com lupyuen incubator nuttx blob rusti2c include nuttx i2c i2c master h l231 l235 i2cioc transfer is defined as c define i2cioc transfer i2cioc 0x0001 source https github com lupyuen incubator nuttx blob rusti2c include nuttx i2c i2c master h l105 l129 i2cioc is defined as c define i2cioc nr ioc i2cbase nr source https github com lupyuen incubator nuttx blob rusti2c include nuttx fs ioctl h l467 l468 ioc and i2cbase are defined as c define ioc type nr type nr source https github com lupyuen incubator nuttx blob rusti2c include nuttx fs ioctl h l107 c define i2cbase 0x2100 i2c driver commands source https github com lupyuen incubator nuttx blob rusti2c include nuttx fs ioctl h l73 we ll port these c types and constants to rust as well read i2c register in rust here s how we read an i2c register in rust ported from the above c code rust test the i2c port by reading an i2c register pub fn test i2c println test i2c open i2c port let i2c unsafe open b dev i2c0 0 as ptr o rdwr assert i2c 0 read one i2c register starting at device id let mut start bme280 reg id 1 let mut buf 0u8 1 compose i2c transfer let msg i2c msg s 2 first i2c message send register id i2c msg s frequency bme280 freq i2c frequency addr bme280 addr i2c address buffer start as mut ptr buffer to be sent length start len as ssize t length of the buffer in bytes for bl602 register id must be passed as i2c sub address cfg target arch riscv32 if architecture is risc v 32 bit flags i2c m nostop i2c flags send i2c sub address otherwise pass register id as i2c data cfg not target arch riscv32 if architecture is not risc v 32 bit flags 0 i2c flags none todo check for bl602 specifically not just risc v 32 bit second i2c message receive register value i2c msg s frequency bme280 freq i2c frequency addr bme280 addr i2c address buffer buf as mut ptr buffer to be received length buf len as ssize t length of the buffer in bytes flags i2c m read i2c flags read from i2c device compose ioctl argument let xfer i2c transfer s msgv msg as ptr array of i2c messages for the transfer msgc msg len as size t number of messages in the array execute i2c transfer let ret unsafe ioctl i2c i2cioc transfer xfer assert ret 0 show the received register value println test i2c register 0x 02x is 0x 02x bme280 reg id register id 0xd0 buf 0 register value 0x60 register value must be bme280 device id 0x60 assert buf 0 bme280 chip id close the i2c port unsafe close i2c sleep 5 seconds unsafe sleep 5 source rust src test rs the nuttx types are ported from c to rust like so rust i2c message struct i2c transaction segment beginning with a start a number of these can be transferred together to form an arbitrary sequence of write read transfer to an i2c device todo import with bindgen from https github com lupyuen incubator nuttx blob rusti2c include nuttx i2c i2c master h l208 l215 repr c pub struct i2c msg s i2c frequency pub frequency u32 i2c address pub addr u16 i2c flags i2c m pub flags u16 buffer to be transferred pub buffer mut u8 length of the buffer in bytes pub length ssize t i2c transfer struct this structure is used to communicate with the i2c character driver in order to perform ioctl transfers todo import with bindgen from https github com lupyuen incubator nuttx blob rusti2c include nuttx i2c i2c master h l231 l235 repr c pub struct i2c transfer s array of i2c messages for the transfer pub msgv const i2c msg s number of messages in the array pub msgc size t source https github com lupyuen nuttx embedded hal blob main src lib rs l125 l153 test i2c port to build the nuttx rust project bash cd nuttx apps examples rust i2c run sh in nuttx shell enter this to run our rust app bash rust i2c our rust app reads bme280 register 0xd0 device id which should contain 0x60 text nuttshell nsh nuttx 10 2 0 rc0 nsh rust i2c hello from rust test i2c i2cdrvr ioctl cmd 2101 arg 4201c378 bl602 i2c transfer subflag 1 subaddr 0xd0 sublen 1 bl602 i2c recvdata count 1 temp 0x60 bl602 i2c transfer i2c transfer success test i2c register 0xd0 is 0x60 done nsh yep our rust app reads the bme280 i2c register correctly rust embedded hal rust embedded hal defines a standard api for i2c operations let s wrap the nuttx i2c ioctl commands and expose as rust embedded hal interfaces rust nuttx implementation of i2c read impl i2c read for i2c todo read i2c data fn read mut self addr u8 buf mut u8 result self error nuttx implementation of i2c write impl i2c write for i2c todo write i2c data fn write mut self addr u8 buf u8 result self error nuttx implementation of i2c writeread impl i2c writeread for i2c todo write and read i2c data fn write read mut self addr u8 wbuf u8 rbuf mut u8 result self error source https github com lupyuen nuttx embedded hal blob main src hal rs l20 l160 read i2c register here s how we implement the rust embedded hal to read an i2c register rust nuttx implementation of i2c writeread impl i2c writeread for i2c error type type error i32 write wbuf to i2c port and read rbuf from i2c port we assume this is a read i2c register operation with register id at wbuf 0 todo handle other kinds of i2c operations fn write read mut self addr u8 wbuf u8 rbuf mut u8 result self error we assume this is a read i2c register operation with register id at wbuf 0 assert eq wbuf len 1 let reg id wbuf 0 read i2c registers starting at register id let mut start reg id 1 compose i2c transfer let msg first i2c message send register id i2c msg s frequency self frequency i2c frequency addr addr as u16 i2c address buffer start as mut ptr buffer to be sent length start len as ssize t number of bytes to send for bl602 register id must be passed as i2c sub address cfg target arch riscv32 if architecture is risc v 32 bit flags crate i2c m nostop i2c flags send i2c sub address otherwise pass register id as i2c data cfg not target arch riscv32 if architecture is not risc v 32 bit flags 0 i2c flags none todo check for bl602 specifically by target abi not just risc v 32 bit second i2c message receive register values i2c msg s frequency self frequency i2c frequency addr addr as u16 i2c address buffer rbuf as mut ptr buffer to be received length rbuf len as ssize t number of bytes to receive flags i2c m read i2c flags read i2c data compose ioctl argument let xfer i2c transfer s msgv msg as ptr array of i2c messages for the transfer msgc msg len as size t number of messages in the array execute i2c transfer let ret unsafe ioctl self fd i2cioc transfer xfer assert ret 0 ok source https github com lupyuen nuttx embedded hal blob main src hal rs l98 l160 to read an i2c register we call the rust embedded hal like so rust test the i2c hal by reading an i2c register pub fn test hal read open i2c port let mut i2c nuttx embedded hal i2c new dev i2c0 i2c port bme280 freq i2c frequency expect open failed buffer for received i2c data let mut buf 0 1 read one i2c register starting at device id i2c write read bme280 addr as u8 i2c address bme280 reg id register id 0x60 mut buf buffer to be received expect read register failed show the received register value println test hal read register 0x 02x is 0x 02x bme280 reg id register id 0xd0 buf 0 register value 0x60 register value must be bme280 device id 0x60 assert eq buf 0 bme280 chip id source rust src test rs test i2c hal rust embedded hal works ok for reading an i2c register text nuttshell nsh nuttx 10 2 0 rc0 nsh rust i2c hello from rust test hal read i2cdrvr ioctl cmd 2101 arg 4201c360 bl602 i2c transfer subflag 1 subaddr 0xd0 sublen 1 bl602 i2c recvdata count 1 temp 0x60 bl602 i2c transfer i2c transfer success test hal read register 0xd0 is 0x60 done nsh write i2c register this code calls the rust embedded hal to write the value 0xa0 to the i2c register 0xf5 rust test the i2c hal by writing an i2c register pub fn test hal write open i2c port let mut i2c nuttx embedded hal i2c new dev i2c0 i2c port bme280 freq i2c frequency expect open failed write 0xa0 to register 0xf5 i2c write bme280 addr as u8 i2c address bme280 reg config 0xa0 register id and value expect write register failed source rust src test rs but the logic analyser shows that bl602 is writing to i2c the value 0x00 instead of 0xa0 text setup write to 0xee ack 0xf5 ack 0x00 ack bl602 is writing to i2c the value 0x00 instead of 0xa0 https lupyuen github io images rusti2c logic1 png let s fix this here s the log for the i2c write text nsh rust i2c hello from rust test hal write i2cdrvr ioctl cmd 2101 arg 4201c370 bl602 i2c transfer subflag 1 subaddr 0xf5 sublen 1 bl602 i2c send data count 1 temp 0xa0 bl602 i2c transfer i2c transfer success test hal write write 0xa0 to register fix i2c write bl602 has a peculiar i2c port that uses i2c sub addresses let s make it work with rust embedded hal quirks in bl602 i2c driver https lupyuen github io articles bme280 appendix quirks in bl602 nuttx i2c driver we tried all sequences of i2c read write sub address only this strange sequence works for writing to i2c registers 1 write i2c register id and i2c data together as i2c sub address 1 followed by read i2c data rust nuttx implementation of i2c write impl i2c write for i2c error type type error i32 write buf to i2c port we assume this is a write i2c register operation with register id at buf 0 todo handle other kinds of i2c operations fn write mut self addr u8 buf u8 result self error copy to local buffer because we need a mutable reference let mut buf2 0 64 assert buf len buf2 len buf2 buf len copy from slice buf buffer for received i2c data let mut rbuf 0 1 compose i2c transfer let msg first i2c message send register id and i2c data as i2c sub address i2c msg s frequency self frequency i2c frequency addr addr as u16 i2c address buffer buf2 as mut ptr buffer to be sent length buf len as ssize t number of bytes to send for bl602 register id must be passed as i2c sub address cfg target arch riscv32 if architecture is risc v 32 bit flags crate i2c m nostop i2c flags send i2c sub address otherwise pass register id as i2c data cfg not target arch riscv32 if architecture is not risc v 32 bit flags 0 i2c flags none todo check for bl602 specifically by target abi not just risc v 32 bit second i2c message read i2c data because this forces bl602 to send the first message correctly i2c msg s frequency self frequency i2c frequency addr addr as u16 i2c address buffer rbuf as mut ptr buffer to be received length rbuf len as ssize t number of bytes to receive flags i2c m read i2c flags read i2c data compose ioctl argument to write i2c registers let xfer i2c transfer s msgv msg as ptr array of i2c messages for the transfer msgc msg len as size t number of messages in the array execute i2c transfer to write i2c registers let ret unsafe ioctl self fd i2c port i2cioc transfer i2c transfer xfer i2c messages for the transfer assert ret 0 ok source https github com lupyuen nuttx embedded hal blob main src hal rs l33 l96 after fixing the logic analyser shows that bl602 writes correctly to the i2c register with a harmless i2c read at the end text setup write to 0xee ack 0xf5 ack 0xa0 ack setup read to 0xef ack 0xa0 nak bl602 writes correctly to the i2c register with a harmless i2c read at the end https lupyuen github io images rusti2c logic3a png here s the log text nsh rust i2c hello from rust test hal write i2cdrvr ioctl cmd 2101 arg 4201c358 bl602 i2c transfer subflag 1 subaddr 0xa0f5 sublen 2 bl602 i2c recvdata count 1 temp 0xa0 bl602 i2c transfer i2c transfer success test hal write write 0xa0 to register i2cdrvr ioctl cmd 2101 arg 4201c370 bl602 i2c transfer subflag 1 subaddr 0xf5 sublen 1 bl602 i2c recvdata count 1 temp 0xa0 bl602 i2c transfer i2c transfer success test hal write register value is 0xa0 i2cdrvr ioctl cmd 2101 arg 4201c358 bl602 i2c transfer subflag 1 subaddr 0xf5 sublen 2 bl602 i2c recvdata count 1 temp 0x0 bl602 i2c transfer i2c transfer success test hal write write 0x00 to register i2cdrvr ioctl cmd 2101 arg 4201c370 bl602 i2c transfer subflag 1 subaddr 0xf5 sublen 1 bl602 i2c recvdata count 1 temp 0x0 bl602 i2c transfer i2c transfer success test hal write register value is 0x00 done nsh what if we write to the i2c register without reading the i2c address is sent incorrectly 0x02 and the i2c write gets truncated text setup write to 0x02 nak write to i2c register without reading https lupyuen github io images rusti2c noread png what if we send the register id and register value as i2c data flags 0 instead of i2c sub address the register id and value are sent incorrectly as 0x00 0x00 text setup write to 0xee ack 0x00 ack 0x00 ack 600 microseconds later setup read to 0xef ack 0x00 nak send the register id and register value as i2c data instead of i2c sub address https lupyuen github io images rusti2c nosubaddr png rust embedded driver for bme280 now that our rust embedded hal is working on nuttx let s test the rust embedded driver for bme280 https crates io crates bme280 we add the bme280 driver to cargo toml rust cargo toml text external rust libraries used by this module see crates io dependencies bme280 0 2 1 bme280 driver https crates io crates bme280 here s how we read the temperature humidity and pressure from the bme280 driver rust read temperature pressure and humidity from bme280 sensor over i2c pub fn read bme280 println read bme280 open i2c port let i2c nuttx embedded hal i2c new dev i2c0 i2c port 400000 i2c frequency 400 khz expect open failed init the bme280 driver let mut bme280 bme280 bme280 new i2c i2c port 0x77 i2c address of bme280 nuttx embedded hal delay delay interface init the bme280 senor bme280 init expect init failed measure temperature pressure and humidity let measurements bme280 measure expect measure failed print the measurements println relative humidity measurements humidity println temperature deg c measurements temperature println pressure pascals measurements pressure source rust src bme280 rs test rust driver for bme280 rust driver for bme280 works ok on nuttx text nsh rust i2c hello from rust read bme280 i2cdrvr ioctl cmd 2101 arg 4201c340 bl602 i2c transfer subflag 1 subaddr 0xd0 sublen 1 bl602 i2c recvdata count 1 temp 0x60 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c328 bl602 i2c transfer subflag 1 subaddr 0xb6e0 sublen 2 bl602 i2c recvdata count 1 temp 0x0 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c350 bl602 i2c transfer subflag 1 subaddr 0x88 sublen 1 bl602 i2c recvdata count 26 temp 0x65e66e97 bl602 i2c recvdata count 22 temp 0x8f990032 bl602 i2c recvdata count 18 temp 0xbd0d581 bl602 i2c recvdata count 14 temp 0xffdb1e71 bl602 i2c recvdata count 10 temp 0x26acfff9 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c350 bl602 i2c transfer subflag 1 subaddr 0xe1 sublen 1 bl602 i2c recvdata count 7 temp 0x14000165 bl602 i2c recvdata count 3 temp 0x141e000b bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c340 bl602 i2c transfer subflag 1 subaddr 0xf4 sublen 1 bl602 i2c recvdata count 1 temp 0x141e0000 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c338 bl602 i2c transfer subflag 1 subaddr 0x1f2 sublen 2 bl602 i2c recvdata count 1 temp 0x141e0001 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c340 bl602 i2c transfer subflag 1 subaddr 0xf4 sublen 1 bl602 i2c recvdata count 1 temp 0x141e0000 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c338 bl602 i2c transfer subflag 1 subaddr 0xf4 sublen 2 bl602 i2c recvdata count 1 temp 0x141e0000 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c340 bl602 i2c transfer subflag 1 subaddr 0xf4 sublen 1 bl602 i2c recvdata count 1 temp 0x141e0000 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c338 bl602 i2c transfer subflag 1 subaddr 0x54f4 sublen 2 bl602 i2c recvdata count 1 temp 0x141e0054 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c340 bl602 i2c transfer subflag 1 subaddr 0xf5 sublen 1 bl602 i2c recvdata count 1 temp 0x141e0000 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c338 bl602 i2c transfer subflag 1 subaddr 0x10f5 sublen 2 bl602 i2c recvdata count 1 temp 0x141e0010 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c370 bl602 i2c transfer subflag 1 subaddr 0xf4 sublen 1 bl602 i2c recvdata count 1 temp 0x141e0054 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c370 bl602 i2c transfer subflag 1 subaddr 0xf4 sublen 1 bl602 i2c recvdata count 1 temp 0x141e0054 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c368 bl602 i2c transfer subflag 1 subaddr 0x55f4 sublen 2 bl602 i2c recvdata count 1 temp 0x141e0055 bl602 i2c transfer i2c transfer success i2cdrvr ioctl cmd 2101 arg 4201c380 bl602 i2c transfer subflag 1 subaddr 0xf7 sublen 1 bl602 i2c recvdata count 8 temp 0x86f0b752 bl602 i2c recvdata count 4 temp 0x7b8f806b bl602 i2c transfer i2c transfer success relative humidity 87 667625 temperature 30 358515 deg c pressure 100967 46 pascals done | rust nuttx i2c bl602 pinecone bme280 rust-embedded | os |
awesome-design-systems-jobs | hi job seeker due to our limited time for side projects we ve decided to not maintain this repository anymore thanks for understanding awesome design systems jobs awesome https camo githubusercontent com 13c4e50d88df7178ae1882a203ed57b641674f94 68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f643733303566333864323966656437386661383536353265336136336531353464643865383832392f6d656469612f62616467652e737667 a curated list of design systems jobs across the universe we curate only jobs which promise to work on design systems contributions welcome https camo githubusercontent com 9c8a9bb0456c3bff0d34d8ea66071420f1ab2c44 68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6e747269627574696f6e732d77656c636f6d652d626c75652e737667 contributions welcome do you know about some job position that isn t listed below is any listed position already inactive br feel free to create a new pull request https github com darkwindcz awesome design systems jobs pulls or open an issue https github com darkwindcz awesome design systems jobs issues new br br please read the contribution guidelines contributing md before contributing engineering awesome opportunities for ui engineers front end engineers and other engineering roles added in past week or so company location user experience ux engineer https nuskinhr csod com ux ats careersite 4 home requisition 73 nu skin provo ut usa ux developer https boards greenhouse io outdoorsy jobs 4252218002 outdoorsy austin tx usa br remote front end developer ii component library https jobs lever co loblawdigital cfc9ba14 ede5 4dbe 9d6a 4186f91eb427 loblaw digital toronto canada senior front end developer component library https jobs lever co loblawdigital 1464ab09 dac1 4154 a66f f1159e1580e4 loblaw digital toronto canada senior frontend developer for design systems https www takeaway com jobs vacancy vacancy it frontend developer for design systems m f d berlin takeaway com berlin germany senior front end engineer design systems and tooling https www researchgate net careers senior front end engineer design systems and tooling m f d 5c2bfe6d 8950 4d2b b55d 8b3a10ff3a2a researchgate berlin germany senior ux engineer design system https www compass com careers gh jid 1546972 compass new york ny usa ui developer intern https career4 successfactors com career career ns job listing company ineight e en us career job req id 7205 ineight omaha ne usa ui developer https boards greenhouse io bottomlinetechnologies jobs 4284914002 bottomline technologies remote sr ui engineer https boards greenhouse io joybird jobs 4058558002 joybird commerce ca usa br remote usa platform ui engineer https jobs smartrecruiters com pluralsight 743999688551967 platform ui engineer pluralsight remote ut usa digital sr design technologist https jobs jpmorganchase com showjob id 225852 digital 20 e2 80 93 20sr 20design 20technologist jpmorgan chase co palo alto ca usa senior design technologist https www bold com jobs description gh jid 1676701 bold san francisco ca usa front end engineer https recruiting ultipro com usg1006 jobboard dfc53730 57d1 3460 336f ddafabd108f3 opportunitydetail opportunityid eb1f90bb d4d9 4536 bacb 5987ee4ee999 ultimate software toronto canada br ft lauderdale fl usa software engineer iii front end full stack dev https www linkedin com jobs view sr ux design system developer at f5 networks 1118352858 f5 networks seattle wa us react developer global design https clear score workable com jobs 572476 clearscore london uk principal developer design systems team https jobs dell com job cambridge principal developer design systems team 375 11298204 dell cambridge ma usa sr ui engineer https cbre referrals selectminds com jobs sr ui engineer 45552 cbre new york ny usa senior frontend software engineer design systems https www etsy com careers job 139be11f de4c 4bf5 82ee d1de4a630d92 etsy remote frontend engineer design systems https www flexport com careers job 541800 flexport san francisco ca usa software engineer design system for ios https jobs lever co ring 4df29132 e9ba 4f79 9353 91514f77ddce ring inc malvern pa usa software engineer design system for android https jobs lever co ring 6c1e4fd4 8e69 4da6 8ae5 72aeb24c352b ring inc malvern pa usa design technologist android https proteus wd5 myworkdayjobs com en us proteus job redwood city ca design technologist r3368716 proteus redwood city ca usa front end developer https www bisnode se om bisnode om oss karriar front end developer bisnode stockholm sweden design system engineer front end developer https angel co eximchain jobs 404593 design system engineer front end developer eximchain singapore br remote 2020 design tooling graduate developer https www atlassian com company careers detail 46c5d582 ea5f 466a 86d6 642ce6715d61 atlassian sydney australia developer carbon design system https careers ibm com showjob id 576741 developer carbon design system ibm austin tx usa design system engineer https workday wd5 myworkdayjobs com en us workday job usa ca pleasanton senior design system engineer jr 30692 workday pleasanton ca usa br san francisco ca usa ui platform engineer https boards greenhouse io cbinsights jobs 1115711 cb insights new york ny usa sr lead ux engineer design systems framework https salesforce wd1 myworkdayjobs com en us external career site job california san francisco sr lead ux engineer design systems framework jr34812 salesforce us remote br san francisco ca usa design technologist https recruiting ultipro com nat1011natpr jobboard af823b19 a43b 4cda b6c2 c06508d84cf6 opportunitydetail opportunityid 1ec3b745 6702 422f a474 7d0c87ef0b71 npr washington dc usa frontend engineer dashboard design systems https jobs lever co auth0 0aaa432d 2ec2 4326 b5c2 7a728ee3d5cf auth0 remote design systems engineer web https www spotifyjobs com job design systems engineer web oron9fwv spotify stockholm sweden design systems engineer mobile ios https www spotifyjobs com job design systems engineer mobile ios oqiu7fwt spotify stockholm sweden ux engineer https www signalsciences com careers ux engineer signal sciences culver city ca usa design awesome job opportunities for ui designers design system leads and other design added in past week or so company location senior visual designer https h2oai applytojob com apply 91noxnspci senior visual designer mountain viewremote h2o ai mountain view ca usa br remote user experience lead design operations https careers tripadvisor com job trinus1722095 user experience lead design operations tripadvisor needham ma usa sr visual systems designer https carmax wd1 myworkdayjobs com en us external job corporate richmond sr visual systems designer jr 011547 carmax richmond va usa lead product designer design systems https jobsearch paypal corp com en us job lead product designer design systems j3m0rc64t11670v5nvr paypal san jose ca usa specialist design system https jobs rogers com job montreal specialist 2c design system qc h5a 1k3 558206100 fido montreal canada designer design systems threads https jobs lever co plaid bfd9d747 dbd3 4dc9 ae87 2d905f5ab11a plaid san francisco ca usa user experience designer design systems https jobs zs com americas job evanston user experience designer 28evanston 2c il 29 il 60201 522554500 zs associates evanston il usa ui designer design system https britecore bamboohr com jobs view php id 28 britecore remote product designer design system https jobs lever co klarna 55e0184a 27cc 4355 a931 96099fd91c3f klarna stockholm sweden digital ui designer https ifyoucouldjobs com job hfxodiv0v the economist london uk product designer design systems https www hubspot com jobs apply gh jid 1681851 hubspot cambridge ma usa interaction designer design systems https www mongodb com careers jobs 1674427 mongodb new york ny usa senior product designer design systems http jobs jobvite com sofi job ocin8fw9 sofi san francisco ca usa ui designer design system https www bill com about us careers jobs p job 2foxv19fwm bill com palo alto ca usa ui designer esri design system https www esri com en us about careers job detail req 10809 esri redlands ca usa br portland or usa sr product designer ui and design systems https www smartrecruiters com ancestry 743999686933198 ancestry san francisco ca usa design lead ui and design systems https jobs lever co grandrounds feebde25 1196 4469 85e1 0346dbc8b493 grand rounds san francisco ca usa ux designer design systems https jobs adp com job 9478068 ux designer design systems new york ny adp new york ny usa senior ui ux designer https jobs lever co twitch 598ab72d 4130 45cf b614 25dba19ee170 twitch san francisco ca usa br irvine ca usa design system designer https www etradecareers com job 9468539 design system designer jersey city nj e trade jersey city nj usa lead principal visual designer design systems https boards greenhouse io fitbit92 jobs 1432238 fitbit san francisco ca usa principal designer ui and design systems https www smartrecruiters com ancestry 743999686704737 ancestry san francisco ca usa visual designer design systems https boards greenhouse io chanzuckerberginitiative jobs 1188519 chan zuckerberg initiative redwood city ca usa product designer design systems https www flexport com careers job 1631138 flexport san francisco ca usa senior systems designer https www framer com careers senior systems designer framer amsterdam netherlands senior designer design systems https careers twitter com en work for twitter 201904 senior designer design systems html twitter san francisco ca usa sr ui designer common ui design systems amp https jobs apple com en us details 200047764 sr ui designer common ui design systems amp apple cupertino ca usa senior product designer design systems https turo com jobs 1622856 turo san francisco ca usa systems designer https jobs apple com en us details 114352513 systems designer apple cupertino ca usa product designer design systems https asana com jobs apply 1589228 product designerdesign systems asana san francisco ca usa product designer design systems https www gotinder com jobs apply gh jid 1542703 tinder los angeles ca usa senior product designer design systems https www compass com careers gh jid 1434105 compass new york ny usa sr interactive designer design systems https herodigital com current openings gh jid 1596045 hero digital cupertino ca usa product design leadership design systems https boards greenhouse io gusto jobs 978162 gusto san francisco ca usa product designer design systems https www squarespace com careers jobs 1550877 squarespace new york ny usa product designer design systems jemstep http careers invesco com showjob id 227833 invesco los altos ca usa visual designer design systems https grnh se 44b756c41 surveymonkey san mateo ca usa senior product designer platform fabric https www atlassian com company careers detail 83107944 a570 4d77 9594 2e35d2574c3c fbclid iwar3uyquwjmepr1x wsilz5qelitijz 8t2sjreuuw0vcdrjovagnorsvs o atlassian sydney australia senior ux designer design systems https rei jobs careers jobdetail senior user experience designer design systems 12700 rei co op kent wa usa senior product designer design systems https www zendesk com jobs view job 2274399a 699a 43e9 9e3f fa1f6d168ae2 zendesk san francisco ca usa other awesome opportunities for all content writers qa testers product managers added in past week or so company location director design systems https careers walmart com us jobs 1316378br director design systems san bruno ca walmart san bruno ca usa staff quality engineer https jobs jobvite com careers servicenow job otd69fw5 servicenow pleasanton ca usa product manager for dsm product https boards greenhouse io invision jobs 1682392 invision remote senior product design manager https www nerdwallet com careers job 1692697 nerdwallet san francisco ca usa design system s content writer https jobs kiwi com engineering design systems content writer kiwi com prague czech republic br brno czech republic br remote europe ux design systems lead https ffive wd5 myworkdayjobs com en us f5jobs job seattle ux product manager rp1012457 f5 networks seattle wa usa ui director https jobs lever co instructure 885c20c4 eceb 4827 a88d 6e14f93f940f instructure salt lake city ut usa director intuit design system https careers intuit com job category 2 design and user experience job 2019 9740 director intuit design system intuit mountain view ca usa director design systems https www verizon com about work jobs 3918338 director design systems verizon new york ny usa product manager design language system https www spotifyjobs com job product manager design language systems o73g9fwj spotify stockholm sweden product design manager design systems and operations https jobs disneycareers com job new york product design manager design systems and operations 391 11250720 disney new york ny usa ux design system director https workday wd5 myworkdayjobs com en us workday job usa ca pleasanton ux design system director jr 34610 workday pleasanton ca usa br san francisco ca usa ux design manager design system https workday wd5 myworkdayjobs com en us workday job usa ca san francisco ux design manager design system jr 33409 workday san francisco ca usa design director design systems https www compass com careers gh jid 1406356 compass new york ny usa principal quality engineer design systems team https jobs dell com job 375 11298203 dell cambridge ma usa follow new jobs on twitter we also publish all new jobs on twitter https twitter com designsysjobs to be more inclusive to other roles who are not super familiar with github follow us on twitter https twitter com designsysjobs | design-systems jobs engineering design | os |
FullStack_Info_Lab09 | fullstack info lab09 full stack web development lab 09 nosql | front_end |
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module-challenge-intro-to-git | git for web development project welcome to day 1 at bloomtech today we are going to spend some time setting up your computer and learning the tools that you will be using to complete this program just like your first day at a dev job you will need to get your environment set up to build and run your code before you are able to complete any of these steps you will need to install all required programs https www notion so bloomtech 4da087ea26dd441bbc8030198811150d v 5c67662450d14d64b0b2b6d32b841fee then set up your ssh key and attach it to your github account for the ssh key please watch the video on the bloomtech git workflow https bloomtech notion site bloomtech bloomtech git flow step by step 269f68ae3bf64eb689a8328715a179f9 to see how to do that complete the set up tasks and then get started on the research questions found inside the repository if you are using a windows machine please complete the pc md file if you are using mac or linux please complete mac md task 1 mvp you will need to follow the bloomtech git workflow https bloomtech notion site bloomtech bloomtech git flow step by step 269f68ae3bf64eb689a8328715a179f9 to add a file to this project follow the steps below create your own version of this repo using the fork button at the top right of the repository clone this repo by clicking the green code button then copy pasting the ssh url git clone git github com your github username git for web development project git create a branch git checkout b firstname lastname answer the questions in the mac md or pc md file depending on which operating system you are using follow the steps for adding committing and pushing your work from your local computer to github 1 git add file name 2 git commit m write a message to describe your changes here 3 git push u origin firstname lastname create a pull request to submit your work reference the bloomtech git workflow https bloomtech notion site bloomtech bloomtech git flow step by step 269f68ae3bf64eb689a8328715a179f9 for help with this make sure that your repository s main branch is listed on the left side of the screen and your repository s firstname lastname branch is listed on the right side this is telling github that you want to compare the work you did in your firstname lastname branch to the work in your main branch make sure that you don t click the merge button submission instructions please submit your project via canvas by following these instructions https bloomtech notion site bloomtech git flow step by step 269f68ae3bf64eb689a8328715a179f9 see part 1 submitting an assignment without codegrade you will need to submit your pull request url in canvas to turn in today s assignment | front_end |
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Beginning-symfony-2-web-development | tuts course getting started with symfony 2 instructor andrew perkins symfony 2 is one of the most popular modern php frameworks it has the advantages of being modular extensible and feature complete beginning symfony 2 web development is designed for first time users of the framework walking you through all its basic coding features we ll cover all the topics required to get started such as bundle templating with layouts routing building forms with validation as well as how to build a full crud app with database interaction by the end of the course you ll have the fundamental skills necessary to use the symfony 2 framework to build basic database driven web applications source files for the tuts course getting started with symfony 2 https courses tutsplus com courses getting started with symfony 2 symfony standard edition welcome to the symfony standard edition a fully functional symfony2 application that you can use as the skeleton for your new applications this document contains information on how to download install and start using symfony for a more detailed explanation see the installation 1 chapter of the symfony documentation 1 installing the standard edition when it comes to installing the symfony standard edition you have the following options use composer recommended as symfony uses composer 2 to manage its dependencies the recommended way to create a new project is to use it if you don t have composer yet download it following the instructions on http getcomposer org or just run the following command curl s http getcomposer org installer php then use the create project command to generate a new symfony application php composer phar create project symfony framework standard edition path to install composer will install symfony and all its dependencies under the path to install directory download an archive file to quickly test symfony you can also download an archive 3 of the standard edition and unpack it somewhere under your web server root directory if you downloaded an archive without vendors you also need to install all the necessary dependencies download composer see above and run the following command php composer phar install 2 checking your system configuration before starting coding make sure that your local system is properly configured for symfony execute the check php script from the command line php app check php the script returns a status code of 0 if all mandatory requirements are met 1 otherwise access the config php script from a browser http localhost path to project web config php if you get any warnings or recommendations fix them before moving on 3 browsing the demo application congratulations you re now ready to use symfony from the config php page click the bypass configuration and go to the welcome page link to load up your first symfony page you can also use a web based configurator by clicking on the configure your symfony application online link of the config php page to see a real live symfony page in action access the following page web app dev php demo hello fabien 4 getting started with symfony this distribution is meant to be the starting point for your symfony applications but it also contains some sample code that you can learn from and play with a great way to start learning symfony is via the quick tour 4 which will take you through all the basic features of symfony2 once you re feeling good you can move onto reading the official symfony2 book 5 a default bundle acmedemobundle shows you symfony2 in action after playing with it you can remove it by following these steps delete the src acme directory remove the routing entry referencing acmedemobundle in app config routing dev yml remove the acmedemobundle from the registered bundles in app appkernel php remove the web bundles acmedemo directory empty the security yml file or tweak the security configuration to fit your needs what s inside the symfony standard edition is configured with the following defaults twig is the only configured template engine doctrine orm dbal is configured swiftmailer is configured annotations for everything are enabled it comes pre configured with the following bundles frameworkbundle the core symfony framework bundle sensioframeworkextrabundle 6 adds several enhancements including template and routing annotation capability doctrinebundle 7 adds support for the doctrine orm twigbundle 8 adds support for the twig templating engine securitybundle 9 adds security by integrating symfony s security component swiftmailerbundle 10 adds support for swiftmailer a library for sending emails monologbundle 11 adds support for monolog a logging library asseticbundle 12 adds support for assetic an asset processing library webprofilerbundle in dev test env adds profiling functionality and the web debug toolbar sensiodistributionbundle in dev test env adds functionality for configuring and working with symfony distributions sensiogeneratorbundle 13 in dev test env adds code generation capabilities acmedemobundle in dev test env a demo bundle with some example code all libraries and bundles included in the symfony standard edition are released under the mit or bsd license enjoy 1 http symfony com doc 2 4 book installation html 2 http getcomposer org 3 http symfony com download 4 http symfony com doc 2 4 quick tour the big picture html 5 http symfony com doc 2 4 index html 6 http symfony com doc 2 4 bundles sensioframeworkextrabundle index html 7 http symfony com doc 2 4 book doctrine html 8 http symfony com doc 2 4 book templating html 9 http symfony com doc 2 4 book security html 10 http symfony com doc 2 4 cookbook email html 11 http symfony com doc 2 4 cookbook logging monolog html 12 http symfony com doc 2 4 cookbook assetic asset management html 13 http symfony com doc 2 4 bundles sensiogeneratorbundle index html | front_end |
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Micrium-uCOS | micrium ucos ii v290 c os ii kernel trial https www micrium com download ucos ii kernel trial c os ii user manual getting started with c os ii https doc micrium com pages viewpage action pageid 16879192 c os ii user manual porting c os ii https doc micrium com pages viewpage action pageid 16879501 c os ii user manual c os ii api reference https doc micrium com pages viewpage action pageid 16879606 c os ii user manual c os ii configuration manual https doc micrium com pages viewpage action pageid 16879637 c gui graphical user interface documentation https doc micrium com display guidoc c os iii user manual getting started with uc os iii https doc micrium com display osiiidoc getting started with uc os iii uc os iii reference manual uc os iii configuration manual https doc micrium com display osiiidoc uc os iii configuration manual uc os iii api reference api event flags https doc micrium com display osiiidoc api event flags c os iii ports and drivers https doc micrium com pages viewpage action pageid 12853428 | micrium ucos-ii ucosii ucgui rtos | os |
Machine-Learning-Projects | machine learning projects this repository contains mini projects in machine learning with jupyter notebook files go to the projects folder and see the readme for detailed instructions about the projects complete video tutorial for the projects http bit ly mlprojectsplaylist | machine-learning python jupyter-notebook classfication regression notebook-files | ai |
fluentui-react-native | fluentui react native npm version https badge fury io js 40fluentui 2freact native svg https badge fury io js 40fluentui 2freact native build status https dev azure com ms ui fabric react native apis build status pr branchname main https dev azure com ms ui fabric react native build latest definitionid 226 branchname main build status https dev azure com ms ui fabric react native apis build status publish branchname main https dev azure com ms ui fabric react native build latest definitionid 229 branchname main fluentui react native is a javascript component library that provides developers with controls that are part of the fluent design system https www microsoft com design fluent these controls are built on react native https reactnative dev and fully customizable fluentui react native is still in the alpha stages of development for both the components and the repo we encourage anyone who is interested in getting an early glimpse of our plans to download and use our components but please note that you may hit bumps along the way please leave us feedback or file issues if you run into bumps and we will continue to improve the quality of the repo development status on each platform windows macos ios android alpha in progress alpha in progress alpha in progress alpha in progress getting started if you have an existing react native project it s easy to begin using fluentui react native if you need to setup a new react native project please see the react native windows getting started documentation https microsoft github io react native windows docs getting started prerequisites standard react native dependencies https microsoft github io react native windows docs rnw dependencies manual setup node js https nodejs org en download setting up your react native development environment https reactnative dev docs environment setup create new react native project if needed 1 follow the instructions on the react native windows getting started documentation https microsoft github io react native windows docs getting started to create a react native project 2 navigate to the root folder of your project and use npm to install the package npm i fluentui react native 3 after successful installation you can test the package by importing components at the top of your app s entry file e g app js jsx import checkbox from fluentui react native 4 after importing the fluentui react native package you can use components such as text and checkbox in your jsx jsx in app js in a new project import react from react import view text from react native import checkbox from fluentui react native function helloworldapp return view style flex 1 justifycontent center alignitems center text hello world text checkbox label hello world checkbox view export default helloworldapp if you run into an error that says pragma and pragmafrag cannot be set when runtime is automatic you can try switching to classic runtime https babeljs io docs en babel preset react both runtimes documentation components and controls our component documentation can be found in spec md files for each component the current list can be found in our wiki s sidebar https github com microsoft fluentui react native wiki they will be uploaded to a website at a future time expanding component documentation our spec md files should reflect the current state of controls that have established the v1 set of properties on any one platform since the fluentui react native controls are cross platform but represented by a single page it s important to distinguish platform differences and limitations examples include if the component is not available on all supported platforms if the component has properties not available on all supported platforms if the component has limited support for a given property on any supported platforms if the component has distinguishable behavior on a supported platform that must be minded while used theming framework documentation for theming can be found in our docs file or for more in depth documentation along side the implementation theming overview docs pages theming basics md stylesheets docs pages theming themedstylesheet md customizing theme settings docs pages theming customtheme md theme reference packages framework theme readme md tokens packages framework use tokens readme md slots packages framework use slots readme md customize and compose packages framework composition readme md developing in the repo yarn lage this repo is set up as a monorepo using yarn workspaces to install yarn please follow instructions in the yarn documentation https classic yarnpkg com en docs install for running tasks the repo has switched to using lage https github com microsoft lage for task running the primary tasks that can be executed at the root are yarn build does the typescript build for all packages in the repository yarn test will build lint and run any applicable tests on all packages in the repo yarn bundle will bundle all packages in the repo yarn buildci will build lint run tests and bundle everything in the repo note that lage uses caching to avoid redundant steps and has very minimal output to avoid caching add no cache as a command line argument similarly adding verbose will give more detailed output setup your development environment to start developing in the repository you can 1 git clone https github com microsoft fluentui react native git 1 cd fluentui react native 1 yarn 1 yarn build after a successful yarn build you can explore fluentui tester our demo application to play with each of the controls to run fluentui tester please follow instructions in the fluentui tester readme apps fluent tester readme md note if your repo is located on either your desktop or documents folder you may encounter the error watchman error operation not permitted clone it in a different directory to avoid watchman permission issues prettier this repo is set up to run prettier https prettier io to run prettier in fix mode on the repo run yarn prettier fix at the root of the repo if you are using vscode https code visualstudio com as your editor you can configure it to run prettier on save prettier is a recommended extension for the repo you can configure it to run by 1 installing the prettier extension for vscode 2 going to settings text editor formatting check format on save contributing please visit our contribution guide contributing md for more information on contributing to this repo this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | os |
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Haptic-feedback-vest | haptic feedback vest for obstacle detection visit our instructable at http www instructables com id haptic feedback vest for obstacle detection to view a description of the system breakdown this design utilizes a stereo camera and software python and simplecv that parses the user s surroundings into a depth map using a raspberry pi platform this depth map is then used to vibrate the vest at a particular location to signify an obstacle in that location for example a vibration in the upper left region of the vest would indicate a hazard in the upper body leftward side of the surroundings we also developed a pcb and integrated the physical piezoelectric hardware into a vest to create a functional and wearable prototype final project for ee285 embedded systems workshop course at stanford university contributors shalini ranmuthu vikram prasad jacqueline speiser steven lin meredith burkle | os |
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3db | 3db a framework for analyzing computer vision models with simulated data paper https arxiv org abs 2106 03805 quickstart guide https 3db github io 3db usage quickstart html blog post http gradientscience org 3db installation follow instructions on https github com 3db installers complete demo for detailed step by step demonstration of the usage of the framework please visit https github com 3db demo documentation you can find in depth documentation for the package here https 3db github io 3db including a quickstart guide an explanation on the internal layout of the package as well as guides for extending 3db to new modalities tasks and output formats primer data we offer data for users to quickly get started 3db and reproduce the results of the paper mentioned below it is available on dropbox at this link https www dropbox com s 2gdprhp8jvku4zf threedb starting kit tar gz dl 0 one can use wget to download it it contains 3d models environments our studio and hdri backgrounds replacement textures to use with threedb controls blender material licensing credits for the data citation if you find 3db helpful please cite it as inproceedings leclerc2021three title 3db a framework for debugging computer vision models author guillaume leclerc hadi salman andrew ilyas sai vemprala logan engstrom vibhav vineet kai xiao pengchuan zhang shibani santurkar greg yang ashish kapoor aleksander madry year 2021 booktitle arxiv preprint arxiv 2106 03805 | ai |
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node-express-API | node express api my first attempt at rest backend development i m using nodejs to create a crud app | server |
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dl-nlp-rg | deep learning natural language processing reading group dl nlp university of british columbia ubc if you are interested in receiving the meeting information and attending please subscribe to the mail ubc nlp googlegroups com winter 2022 time thursday 10 00 11 30 vancouver time place iccs 104 zoom time topic paper presenter slides nov 17 diffusion lm improves controllable text generation https arxiv org pdf 2205 14217 pdf fastspeech 2 fast and high quality end to end text to speech https arxiv org pdf 2006 04558 pdf chiyu zhang abdellah el mekki slides https github com ubc nlp dl nlp rg blob master slides 20221110 diffusion lm pdf slides https github com ubc nlp dl nlp rg blob master slides 20221117 fastspeech2 pdf nov 10 diffusion lm improves controllable text generation https arxiv org pdf 2205 14217 pdf chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20221110 diffusion lm pdf oct 13 robust speech recognition via large scale weak supervision https cdn openai com papers whisper pdf peter sullivan slides https github com ubc nlp dl nlp rg blob master slides 20221013 whisper pdf oct 6 cross lingual retrieval for iterative self supervised training https arxiv org abs 2006 09526 bilingual lexicon induction via unsupervised bitext construction and word alignment https arxiv org abs 2101 00148 abdellah el mekki slides https github com ubc nlp dl nlp rg blob master slides 20221006 bilingual lexicon induction pdf sep 29 research discussion all members na sep 22 research discussion all members na sep 15 seqzero few shot compositional semantic parsing with sequential prompts and zero shot models https aclanthology org 2022 findings naacl 5 the power of scale for parameter efficient prompt tuning https aclanthology org 2021 emnlp main 243 wei rui chen slides https github com ubc nlp dl nlp rg blob master slides 20220915 language model prompting3 pdf summer 2022 time monday wednesday 10 30am 12 00pm vancouver time place iccs 104 zoom time topic paper presenter slides aug 3 research discussion all members na jul 18 research discussion all members na jul 6 a contrastive framework for neural text generation tawkat islam khondaker slides https github com ubc nlp dl nlp rg blob master slides 20220706 a contrastive framework for neural text generation pdf jul 4 hands on tutorial for speecht5 https aclanthology org 2022 acl long 393 pdf gagan bhatia notebook jun 29 speecht5 https aclanthology org 2022 acl long 393 pdf bashar talafha pending jun 27 research discussion all members na jun 15 engineering tutorial for contrastive learning chiyu zhang notebook https github com ubc nlp dl nlp rg blob master tutorial 20220613 contrastive learning ipynb jun 13 language model prompting recent research work https aclanthology org 2022 acl long 197 wei rui chen slides https github com ubc nlp dl nlp rg blob master slides 20220613 language model prompting pdf jun 8 introduction to language model prompting https aclanthology org 2022 acl long 197 wei rui chen slides https github com ubc nlp dl nlp rg blob master slides 20220608 language 20model 20prompting pdf jun 3 discussion of acl 2022 papers chiyu zhang tawkat islam khondaker na jun 1 discussion of acl 2022 papers ganesh jawahar ife adebara peter sullivan na may 16 contrastive learning with adversarial perturbations for conditional text generation https arxiv org pdf 2202 13469 pdf tawkat islam khondaker slides https github com ubc nlp dl nlp rg blob master slides 20220516 claps pdf may 11 research discussion all members na may 09 uctopic unsupervised contrastive learning for phrase representations and topic mining https arxiv org pdf 2202 13469 pdf tawkat islam khondaker slides https github com ubc nlp dl nlp rg blob master slides 20220509 uctopic pdf may 04 autobert zero evolving bert backbone from scratch https arxiv org abs 2107 07445 ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20220504 autobertzero lite pdf winter ii 2022 time monday wednesday 1 00pm 2 30pm vancouver time place iccs 104 zoom time topic paper presenter slides apr 25 diacritization https arxiv org pdf 2109 14150 pdf wei rui chen slides https github com ubc nlp dl nlp rg blob master slides 20220425 diacritization pdf apr 11 gradient matching for domain generalization https arxiv org abs 2104 09937 tawkat islam khondaker slides https github com ubc nlp dl nlp rg blob master slides 20220411 gradient matching pdf mar 14 greaselm graph reasoning enhanced language models for question answering https arxiv org abs 2201 08860 tawkat islam khondaker slides https github com ubc nlp dl nlp rg blob master slides 20220314 greaselm pdf mar 9 data2vec a general framework for self supervised learning in speech vision and language https arxiv org abs 2202 03555 gagan bhatia slides mar 7 diverse decoding methods for nlp cont d farhan samir ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20220228 diverse decoding methods for nlp pdf mar 2 w2v bert https arxiv org pdf 2108 06209 pdf and conformer https arxiv org pdf 2005 08100 pdf cont d peter sullivan bashar talafha slides1 https github com ubc nlp speech processing blob main presentations w2v bert wav2vec2gumbelvectorquantizer ipynb slides2 https github com ubc nlp speech processing blob main presentations w2v bert conformer 1 pdf feb 28 diverse decoding methods for nlp farhan samir ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20220228 diverse decoding methods for nlp pdf feb 23 w2v bert https arxiv org pdf 2108 06209 pdf and conformer https arxiv org pdf 2005 08100 pdf peter sullivan bashar talafha slides1 https github com ubc nlp speech processing blob main presentations w2v bert wav2vec2gumbelvectorquantizer ipynb slides2 https github com ubc nlp speech processing blob main presentations w2v bert conformer 1 pdf feb 14 contrastive learning in nlp chiyu zhang slides1 slides 20211209 contrastive learning pdf slides2 slides 20211209 contrastive learning ganesh pdf feb 2 speech features fundamental representations of spoken audio peter sullivan bashar talafha gagan bhatia slides slides 20220126 speech features pdf jan 26 speech features fundamental representations of spoken audio peter sullivan bashar talafha gagan bhatia slides slides 20220126 speech features pdf jan 31 graph representation learning https www cs mcgill ca wlh grl book files grl book pdf ganesh jawahar weirui chen tawkat islam khondaker slides slides 20220124 graph representation learning pdf jan 24 graph representation learning https www cs mcgill ca wlh grl book files grl book pdf ganesh jawahar weirui chen tawkat islam khondaker slides slides 20220124 graph representation learning pdf winter i 2021 time thursday 4 00pm 5 30pm vancouver time place totem fields studios 121 zoom time topic paper presenter slides sep 22 simcse simple contrastive learning of sentence embeddings https arxiv org pdf 2104 08821 pdf chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20210922 simcse pdf oct 7 machine translation into low resource language varieties https arxiv org pdf 2106 06797 pdf farhan samir slides https github com ubc nlp dl nlp rg blob master slides 20211007 machinetranslation pdf oct 14 micro services and docker images bashar talafha slides oct 21 graph attention networks https journals colorado edu index php computel article view 949 tawkat islam khondaker slides slides 20211021 graph attention network pdf oct 28 machine translation on low resource indigenous languages https journals colorado edu index php computel article view 949 garrett nicolai nov 4 graph neural networks for natural language processing https github com svjan5 gnns for nlp ganesh jawahar slides slides 20211104 gnn emnlp tutorial pdf nov 18 textless nlp generating expressive speech from raw audio https ai facebook com blog textless nlp generating expressive speech from raw audio peter sullivan slides slides 20211118 textless nlp pdf nov 15 graph neural networks for natural language processing https github com svjan5 gnns for nlp ganesh jawahar slides slides 20211115 emnlp 2021 pdf dec 2 towards trusted ai for healthcare from theory to practice xiaoxiao li slides dec 9 contrastive learning in nlp chiyu zhang ganesh jawahar slides1 slides 20211209 contrastive learning pdf slides2 slides 20211209 contrastive learning ganesh pdf summer i ii 2021 time wenesday 4 15pm 5 30pm vancouver time place zoom time topic paper presenter slides may 19 arabic speech processing ahmed ali june 2 arabic speech processing ahmed ali june 16 naacl 2021 follow up chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20210628 naacl 2021 followup pdf june 23 naacl 2021 follow up ganesh jawahar slides june 28 multi style transfer with discriminative feedback on disjoint corpus https arxiv org pdf 2010 11578 pdf chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20210628 multi style 20transfer 20with 20discriminative 20feedback 20on 20disjoint 20corpus pdf june 30 unsupervised speech recognition https arxiv org pdf 2105 11084 pdf peter sulliva slides https github com ubc nlp dl nlp rg blob master slides 20210630 unsupervised speech recognition pdf july 7 adapter modules parameter efficient transfer learning for nlp http proceedings mlr press v97 houlsby19a houlsby19a pdf parameter efficient multi task fine tuning for transformers via shared hypernetworks https arxiv org pdf 2106 04489 pdf chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20210707 adapter module pdf july 14 dexperts decoding time controlled text generation with experts and anti experts https arxiv org abs 2105 03023 alisa liu slides july 21 re visit adapter modules parameter efficient transfer learning for nlp http proceedings mlr press v97 houlsby19a houlsby19a pdf parameter efficient multi task fine tuning for transformers via shared hypernetworks https arxiv org pdf 2106 04489 pdf chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20210707 adapter module pdf aug 11 hubert how much can a bad teacher benefit asr pre training https ieeexplore ieee org document 9414460 peter sullivan slides https github com ubc nlp dl nlp rg blob master slides 20210811 hubert pdf aug 18 charformer fast character transformers via gradient based subword tokenization https arxiv org pdf 2106 12672 pdf ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20210818 charformers pdf winter ii 2021 time topic paper presenter slides jan 13 k bert enabling language representation with knowledge graph https arxiv org pdf 1909 07606 pdf dall e https openai com blog dall e chiyu zhang muhammad abdul mageed slides https github com ubc nlp dl nlp rg blob master slides 20210113 kbert pdf jan 20 visualizing linguistic diversity in vancouver https www cs ubc ca tmm courses 547 20 projects anika namratha roger final report pdf roger yu hsiang lo slides https www cs ubc ca tmm courses 547 20 projects anika namratha roger final slides pdf feb 10 train no evil selective masking for task guided pre training https www aclweb org anthology 2020 emnlp main 566 pdf ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20210210 selective masking pdf feb 17 as good as new how to successfully recycle english gpt 2 to make models for other languages https arxiv org pdf 2012 05628 pdf ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20210217 recycle gpt2 dapt tapt pdf feb 24 csp code switching pre training for neural machine translation https www aclweb org anthology 2020 emnlp main 208 pdf ife adebara slides https github com ubc nlp dl nlp rg blob master slides 20210224 code switching pre training pdf mar 10 language model prior for low resource neural machine translation https arxiv org pdf 2004 14928 pdf ife adebara slides slides 20210310 language model prior pdf mar 17 chrf character n gram f score for automatic mt evaluation https www aclweb org anthology w15 3049 chrf words helping character n grams https www statmt org wmt17 pdf wmt70 pdf weirui chen slides slides 20210317 chrf chrf pdf mar 24 controllable text simplification with explicit paraphrasing https arxiv org abs 2010 11004 ganesh jawahar slides slides 20210324 controllable text simplification pdf mar 31 reformulating unsupervised style transfer as paraphrase generation https www aclweb org anthology 2020 emnlp main 55 chiyu zhang slides slides 20210331 reformulating unsupervised style transfer as paraphrase generation pdf apr 7 if beam search is the answer what was the question https arxiv org pdf 2010 02650 pdf analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned https arxiv org pdf 1905 09418 pdf dujian ding slides slides 20210407 analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned pdf apr 14 challenges of language technologies for the indigenous languages of the americas https www aclweb org anthology c18 1006 indigenous language technologies in canada assessment challenges and successes https www aclweb org anthology c18 1222 weirui chen slides slides 20210414 low resource mt pdf apr 21 reusing a pretrained language model on languages with limited corpora for unsupervised nmt https www aclweb org anthology 2020 emnlp main 214 pdf improving the lexical ability of pretrained language models for unsupervised neural machine translation https arxiv org pdf 2103 10531 pdf ife adebara slides apr 28 eacl 2021 paper review acl anthology https www aclweb org anthology events eacl 2021 may 5 multilingual speech translation with efficient finetuning of pretrained models https arxiv org pdf 2010 12829 pdf peter sulliva slides https github com ubc nlp dl nlp rg blob master slides 20210505 multilingual 20speech 20translation 20with 20efficient 20finetuning 20of 20pretrained 20models pdf winter i 2020 time topic paper presenter slides sep 16 iarpa babel corpora and implications for multilingual grapheme to phoneme and phone detection models https www cair org za sites default files 2020 04 davel 2015 exploring minimal pronunciation pdf peter sullivan slides https github com ubc nlp dl nlp rg blob master slides 20200916 babel overview and discussion pdf sep 23 exploring the limits of transfer learning with a unified text to text transformer https arxiv org pdf 1910 10683 pdf muhammad abdul mageed slides https github com ubc nlp dl nlp rg blob master slides 20200923 t5 pdf sep 30 exploring the limits of transfer learning with a unified text to text transformer https arxiv org pdf 1910 10683 pdf muhammad abdul mageed slides https github com ubc nlp dl nlp rg blob master slides 20200923 t5 pdf oct 7 leveraging multi parallelism in the bible for cross lingual learning https www aclweb org anthology 2020 lrec 1 352 garrett nicolai video https youtu be rakwvkf 0oc oct 14 uncertainty aware self training for few shot text classification https www microsoft com en us research uploads prod 2020 06 uncertainty self training neurips 2020 pdf ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20201014 uncertainity sampling oct14 pdf oct 21 electra pre training text encoders as discriminators rather than generators https openreview net pdf id r1xmh1btvb chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20201025 electra pdf oct 28 context aware neural machine translation decoding https www aclweb org anthology d19 6502 pdf improving context aware neural machine translation with target side context https www aclweb org anthology d19 6502 pdf ife adebara slides https github com ubc nlp dl nlp rg blob master slides 20201104 context aware nmt presentation pdf nov 4 context aware monolingual repair for neural machine translation https arxiv org pdf 1909 01383 pdf ife adebara slides https github com ubc nlp dl nlp rg blob master slides 20201104 context aware nmt presentation pdf nov 4 huggingface datasets tutorial https github com huggingface datasets ganesh jawahar notebook https github com ubc nlp dl nlp rg blob master tutorial 20201104 huggingface datasets tutorial ipynb nov 11 remembrance day nov 18 self training for end to end speech translation https isca speech org archive interspeech 2020 pdfs 2938 pdf self training and pre training are complementary for speech recognition https arxiv org pdf 2010 11430 pdf peter sullivan slides https github com ubc nlp dl nlp rg blob master slides 20201118 self training for speech recognition pdf nov 25 caida bc ai showcase event https caida ubc ca event emerging technologies bcs ai showcase ganesh abdul moatez and chiyu posters https github com ubc nlp presentation collection dec 2 emnlp 2020 paper review ganesh jawahar slides dec 9 emnlp 2020 paper review ganesh jawahar chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20201209 emnlp 202020 20follow up pdf dec 16 bilingual acoustic voice variation is similarly structured across languages https www isca speech org archive interspeech 2020 abstracts 3095 html khia johnson slides https github com ubc nlp dl nlp rg blob master slides 20201216 voice variability for ubc dl nlp lab pdf dec 23 twitter user ideology detection via bert and social network chiyu zhang slides dec 30 automatic detection of manipulated text using factual knowledge ganesh jawahar slides summer i ii 2020 time topic paper presenter slides may 6 normalizing flows an introduction and review of current methods https arxiv org abs 1908 09257 sylvester li slides https github com ubc nlp dl nlp rg blob master slides 20200506 introduction to normalizing flow pdf may 13 neural text generation with unlikelihood training https arxiv org pdf 1908 04319 pdf ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20200513 unlikelihood training pdf june 17 language models are few shot learners https arxiv org pdf 2005 14165 pdf muhammad abdul mageed slides july 15 introduction to automatic speech recognition and speech translation peter sullivan slides https github com ubc nlp dl nlp rg blob master slides 20200715 speech presentation pdf july 22 acl 2020 paper review peter sullivan ganesh jawahar peter s slides https github com ubc nlp dl nlp rg blob master slides 20200722 peter acl recap pdf ganesh s slides https github com ubc nlp dl nlp rg blob master slides 20200722 ganesh acl 2020 summary pdf july 29 acl 2020 paper review ganesh jawahar ganesh s slides https github com ubc nlp dl nlp rg blob master slides 20200722 ganesh acl 2020 summary pdf aug 5 acl 2020 paper review chiyu zhang chiyu s slides https github com ubc nlp dl nlp rg blob master slides 20200805 acl2020 pdf aug 26 introducing the speech processing toolkit espnet https arxiv org pdf 1804 00015 pdf peter sullivan espnet documentation https espnet github io espnet index html spring winter ii 2020 time topic paper presenter slides jan 9 programming tutorial of pytorch https arxiv org pdf 1904 05862v4 pdf ganesh jawahar slides https github com ubc nlp dlnlp2019 resources blob master sl2 mod1 jan20 lab1 pytorch main ipynb jan 22 wav2vec unsupervised pre training for speech recognition https arxiv org pdf 1904 05862v4 pdf muhammad m abdul mageed slides jan 29 massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond https arxiv org pdf 1812 10464 pdf ife adebara slides https github com ubc nlp dl nlp rg blob master slides 20200129 zero shot cross lingual transfer pdf feb 5 controllable unsupervised text attribute transfer via editing entangled latent representation comprehension https papers nips cc paper 9284 controllable unsupervised text attribute transfer via editing entangled latent representation pdf chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20200205style 20transfer pdf feb 12 intro to speech processing https drive google com open id 1c7pgcl5xxu8ds1p3hn7ayo02zzgs9lv2 arian shamei notebook https github com ubc nlp dl nlp rg blob master tutorial 20200212introtospeech ipynb feb 19 reading break feb 12 intro to speech processing https drive google com open id 1c7pgcl5xxu8ds1p3hn7ayo02zzgs9lv2 arian shamei slides https github com ubc nlp dl nlp rg blob master slides 20200226 intro 20to 20speech 20processing pdf mar 4 effectiveness of self supervised pre training for speech recognition https arxiv org pdf 1911 03912 pdf muhammad m abdul mageed slides https github com ubc nlp dl nlp rg blob master slides 20200304vq wav2vec pdf mar 11 compressive transformers for long range sequence modelling https arxiv org pdf 1911 05507 pdf arun rajendran slides https arxiv org pdf 1911 05507 pdf mar 18 canceled mar 15 analysis methods in neural language processing a survey https www mitpressjournals org doi pdf 10 1162 tacl a 00254 ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20200325 analysis methods survey pdf apr 8 dialogue act recognition rudra saha ife adebara and chiyu zhang slides apr 15 gltr statistical detection and visualization of generated text https www aclweb org anthology p19 3019 pdf human and automatic detection of generated text https arxiv org pdf 1911 00650 pdf ganesh jawahar slides apr 22 developing yor b english machine translation models and assessing what they learn about yor b bare nouns ife adebara slides fall winter i 2019 time topic paper presenter slides sept 12 defending against neural fake news https arxiv org abs 1905 12616 ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20190912 neural fake news pdf sept 19 distilling task specific knowledge from bert into simple neural networks https arxiv org pdf 1903 12136 pdf chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20190919 distilling bert pdf sept 26 progressive self supervised attention learning for aspect level sentiment analysis https www aclweb org anthology p19 1053 azadeh hashemi slides https github com ubc nlp dl nlp rg blob master slides 20190926 progressive self supervised pdf oct 10 code switched language models using neural based synthetic data from parallel sentences https arxiv org pdf 1909 08582 pdf kaili vesik slides https github com ubc nlp dl nlp rg blob master slides 20191010 code switched 20language 20models pdf oct 17 learning transferable feature representations using neural networks https www aclweb org anthology p19 1404 ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20191024 dann acl19 pdf oct 24 learning transferable feature representations using neural networks https www aclweb org anthology p19 1404 ganesh jawahar slides https github com ubc nlp dl nlp rg blob master slides 20191024 dann acl19 pdf oct 31 albert https arxiv org abs 1909 11942 roberta https arxiv org abs 1907 11692 rudra saha slides https github com ubc nlp dl nlp rg blob master slides 20191031 albert 20 26 20roberta pdf nov 7 topic guided variational autoencoders for text generation https arxiv org abs 1909 11942 raymond li slides https github com ubc nlp dl nlp rg blob master slides 20191107 tgvae presentation pdf nov 14 bart denoising sequence to sequence pre training for natural language generation translation and comprehension https arxiv org pdf 1910 13461 pdf arun rajendran slides https github com ubc nlp dl nlp rg blob master slides 20191114 bart pdf spring winter ii 2018 time topic paper presenter slides jan 9 generative adversarial nets https papers nips cc paper 5423 generative adversarial nets pdf pramit saha slides https github com ubc nlp dlr blob master slides 20190109 gan pdf jan 16 recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images https arxiv org pdf 1810 06553 pdf borna ghotbi slides https github com ubc nlp dlr blob master slides 20190117 recipe1m pdf jan 18 bert pre training of deep bidirectional transformers for language understanding https arxiv org pdf 1810 04805 pdf arun rajendran slides https github com ubc nlp dlr blob master slides 20190118 bert pdf jan 23 bert pre training of deep bidirectional transformers for language understanding https arxiv org pdf 1810 04805 pdf arun rajendran slides https github com ubc nlp dlr blob master slides 20190118 bert pdf jan 25 multiple attribute text style transfer https arxiv org pdf 1811 00552 pdf ife adebara slides https github com ubc nlp dlr blob master slides 20190125 style transfer pdf feb 6 unsupervised machine translation using monolingual corpora only https research fb com wp content uploads 2018 03 unsupervised machine translation using monolingual corpora only pdf michael przystupa slides https github com ubc nlp dlr blob master slides 20190206 unsuperved machine translation pdf feb 8 openmt code tutorial http opennmt net michael przystupa slides https github com ubc nlp dlr blob master slides 20190208 opennmt pdf feb 13 graph convolutional networks for text classification https arxiv org pdf 1809 05679 pdf rudra saha slides https github com ubc nlp dlr blob master slides 20190213 graph 20convolutional 20networks 20for 20text 20classification pdf feb 15 auto encoding variational bayes https arxiv org pdf 1312 6114 pdf mohit bajaj slides https github com ubc nlp dlr blob master slides 20190215 variartional pdf feb 27 unsupervised image captioning https arxiv org pdf 1811 10787 pdf borna ghotbi slides https github com ubc nlp dlr blob master slides 20190227 unsupervised 20image 20captioning 20 pdf march 5 when convolutions meet reality structured neural networks in vision and graphics https www cs ubc ca event 2019 03 when convolutions meet reality structured neural networks vision and graphics andreas andreas lehrmann facebook ai research 3 00pm 4 00pm x836 https www cs ubc ca event 2019 03 when convolutions meet reality structured neural networks vision and graphics andreas march 7 artificial intelligence from analog to digital and back https caida ubc ca event artificial intelligence analog digital and back george dyson george dyson 3 30pm 4 30pm forest sciences centre room 1221 2424 main mall zone 4 https caida ubc ca event artificial intelligence analog digital and back george dyson march 8 transformer xl attentive language models beyond a fixed length context https arxiv org pdf 1901 02860 pdf chiyu zhang slides https github com ubc nlp dlr blob master slides 20190308 transformer xl pdf march 13 variational neural machine translation https arxiv org pdf 1605 07869 pdf michael przystupa slides https github com ubc nlp dlr blob master slides 20190313 variational 20neural 20machine 20translation pdf march 15 language models are unsupervised multitask learners https d4mucfpksywv cloudfront net better language models language models are unsupervised multitask learners pdf fbclid iwar0f3k6mlqwt2xholgkndwsf53biuo3f9jihsafncnplylr stvwahrmwqm arun rajendran slides march 20 video and language https www cs ubc ca event 2019 03 video and language prof jiebo luo university rochester 0 jiebo luo university of rochester 2 00pm 3 30pm iccs146 https www cs ubc ca event 2019 03 video and language prof jiebo luo university rochester 0 march 22 graph attention networks https arxiv org pdf 1710 10903 pdf rudra saha slides https github com ubc nlp dlr blob master slides 20190322 gat presentation pdf april 14 adversarial autoencoders https arxiv org pdf 1511 05644 pdf mohit bajaj slides https github com ubc nlp dlr blob master slides 20190414adversarial 20autoencoders pdf june 26 normalizing flows https blog evjang com 2018 01 nf1 html michael przystupa slides https github com ubc nlp dlr blob master slides 20190626 normalizing flows tutorial pdf july 4 xlnet generalized autoregressive pretraining for language understanding https arxiv org pdf 1906 08237 pdf arun rajendran slides https github com ubc nlp dlr blob master slides 20190704 xlnet pdf july 11 linguistic variation ife adebara slides https github com ubc nlp dlr blob master slides 20190419 language pdf july 25 analyzing polarization in social media method and application to tweets on 21 mass shootings https arxiv org pdf 1904 01596 pdf peter sullivan slides https github com ubc nlp dlr blob master slides 20190725analyzing polarization pdf august 1 analysis and detection of information types of open source software issue discussions https arxiv org pdf 1902 07093 pdf arthur marques slides https github com ubc nlp dlr blob master slides 20190801analysis and detection software issue pdf august 8 pytorch seq2seq https github com bentrevett pytorch seq2seq rudra saha slides https github com bentrevett pytorch seq2seq fall winter i 2018 time topic paper presenter slides oct 3 a large annotated corpus for learning natural language inference https nlp stanford edu pubs snli paper pdf mohamed elaraby slides https github com ubc nlp dl nlp rg blob master slides 20181003 a large annotated 20corpu pdf oct 10 glue a multi task benchmark and analysis platformfor natural language understanding https arxiv org pdf 1804 07461 pdf ife adebara slides https github com ubc nlp dl nlp rg blob master slides 20181010 glue pdf oct 17 towards automatic speech identification from vocal tract shape dynamics in real time mri https arxiv org pdf 1807 11089 pdf pramit saha slides https github com ubc nlp dl nlp rg blob master slides 20181017 towards 20automatic 20speech 20identification pdf oct 24 evaluating content selection in summarization the pyramid method http www cs columbia edu ani papers pyramid pdf arthur marques slides https github com ubc nlp dl nlp rg blob master slides 20181024 pyramid method pdf oct 31 a stylometric inquiry into hyperpartisan and fake news http aclweb org anthology p18 1022 chiyu zhang slides https github com ubc nlp dl nlp rg blob master slides 20181031 stylometric hyperpartisan pdf nov 7 attention is all you need https arxiv org pdf 1706 03762 pdf arun rajendran slides https github com ubc nlp dl nlp rg blob master slides 20181107 attention pdf nov 14 parti phrase based neural unsupervised machine translation https arxiv org pdf 1804 07755 pdf michael przystupa slides https github com ubc nlp dl nlp rg blob master slides 20181114 phrase machinetranslation pdf nov 21 part ii phrase based neural unsupervised machine translation https arxiv org pdf 1804 07755 pdf michael przystupa slides https github com ubc nlp dl nlp rg blob master slides 20181114 phrase machinetranslation pdf nov 28 xnli evaluating cross lingual sentence representations http aclweb org anthology d18 1269 ife adebara slides https github com ubc nlp dl nlp rg blob master slides 20181128 xnli pdf dec 12 word translation without parallel data https arxiv org pdf 1710 04087 pdf rudra saha slides https github com ubc nlp dl nlp rg blob master slides 20181212 word 20translation 20without 20parallel 20data pdf | deeplearning natural-language-processing nlp machine-learning informatics ubc dl-nlp | ai |
HalluQA | evaluating hallucinations in chinese large language models this repository contains data and evaluation scripts of halluqa chinese hallucination question answering benchmark the full data of halluqa is in halluqa json the paper introducing halluqa and detailed experimental results of many chinese large language models is here https arxiv org pdf 2310 03368 pdf data collection pipeline imgs pipeline png halluqa contains 450 meticulously designed adversarial questions spanning multiple domains and takes into account chinese historical culture customs and social phenomena the pipeline of data collection is shown above at step 1 we write questions which we think may induce model hallucinations at step 2 we use chatgpt3 5 puyu glm 130b to generate answers and collect adversarial questions at step 3 we write multiple correct and wrong answers for each adversarial question and add support evidence at step 4 we check all annotated question answer pairs and remove low quality samples data examples we show some data examples of halluqa here imgs examples png metric evaluation method we use non hallucination rate as the metric of halluqa which represents the percentage of answers that do not exhibit hallucinations out of all model generated answers for automated evaluation we use gpt 4 as the evaluator gpt 4 will judge whether a generated answer exhibit hallucinations based on the given criterias and reference correct answers the prompt for gpt 4 based evaluation is in calculate metrics py run evaluation for your models 1 install requirements pip install openai 2 run evaluation using our script python python calculate metrics py response file name gpt 4 0613 responses json replace with your own responses api key your openai api key organization organization of your openai account 3 the results and metric will be saved in results json and non hallucination rate txt respectively results leaderboard non hallucination rate of each model for different types of questions model misleading misleading hard knowledge total retrieval augmented chat model ernie bot 70 86 46 38 75 73 69 33 baichuan2 53b 59 43 43 48 83 98 68 22 chatglm pro 64 00 34 78 67 96 61 33 sparkdesk 59 43 27 54 71 36 60 00 chat model abab5 5 chat 60 57 39 13 57 77 56 00 gpt 4 0613 76 00 57 97 32 04 53 11 qwen 14b chat 75 43 23 19 30 58 46 89 baichuan2 13b chat 61 71 24 64 32 04 42 44 baichuan2 7b chat 54 86 28 99 32 52 40 67 gpt 3 5 turbo 0613 66 29 30 43 19 42 39 33 xverse 13b chat 65 14 23 19 22 33 39 11 xverse 7b chat 64 00 13 04 21 84 36 89 chatglm2 6b 55 43 23 19 21 36 34 89 qwen 7b chat 55 43 14 49 17 48 31 78 baichuan 13b chat 49 71 8 70 23 30 31 33 chatglm 6b 52 57 20 29 15 05 30 44 pre trained model qwen 14b 54 86 23 19 24 76 36 22 baichuan2 13b base 23 43 24 64 45 63 33 78 qwen 7b 48 57 20 29 16 99 29 78 xverse 13b 18 86 24 64 32 52 27 33 baichuan 13b base 9 71 18 84 40 78 25 33 baichuan2 7b base 8 00 21 74 41 26 25 33 baichuan 7b base 6 86 15 94 37 38 22 22 xverse 7b 12 00 13 04 29 61 20 22 detailed results each model s generated answers and the corresponding judgement of gpt 4 are in chinese llms outputs acknowledgements we sincerely thank annotators and staffs from shanghai ai lab who involved in this work i especially thank tianxiang sun xiangyang liu and wenwei zhang for their guidance and help i am also grateful to xinyang pu for her help and patience citation bibtex article dblp journals corr abs 2310 03368 author qinyuan cheng and tianxiang sun and wenwei zhang and siyin wang and xiangyang liu and mozhi zhang and junliang he and mianqiu huang and zhangyue yin and kai chen and xipeng qiu title evaluating hallucinations in chinese large language models journal corr volume abs 2310 03368 year 2023 url https doi org 10 48550 arxiv 2310 03368 doi 10 48550 arxiv 2310 03368 eprinttype arxiv eprint 2310 03368 timestamp thu 19 oct 2023 13 12 52 0200 biburl https dblp org rec journals corr abs 2310 03368 bib bibsource dblp computer science bibliography https dblp org | ai |
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cvtools | cvtools documentation status https readthedocs org projects cvtools badge version latest https cvtools readthedocs io zh latest badge latest travis ci build https travis ci com gfjiangly cvtools svg branch master https travis ci com gfjiangly cvtools codecov https codecov io gh gfjiangly cvtools branch master graph badge svg https codecov io gh gfjiangly cvtools pypi python version https img shields io pypi v cvtoolss https pypi org project cvtoolss github license https img shields io github license open mmlab mmcv svg https github com gfjiangly cvtools blob master license computer vision tool library introduction cvtools is a helpful python library for computer vision it provides the following functionalities dataset conversion voc to coco bdd to coco data augmentation random mirror random sample crop dataset analysis visualization cluster analysis image processing crop resize model web deployment command line usage useful utilities iou timer universal io apis see the documentation https cvtools readthedocs io zh latest for more features and usage installation try and start with bash pip install cvtoolss note there are two s at the end or install from source bash git clone https github com gfjiangly cvtools git cd cvtools pip install add e if you want to develop or modify the codes todolist x add web deployment models license this project is released under the apache 2 0 license license | ai |
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Natural-Language-Processing-with-Java-Second-Edition | natural language processing with java second edition a href https www packtpub com big data and business intelligence natural language processing java second edition utm source github utm medium repository utm campaign 9781789347999 img src https dz13w8afd47il cloudfront net sites default files imagecache ppv4 main book cover b10786 png alt book name height 256px align right a this is the code repository for natural language processing with java second edition https www packtpub com big data and business intelligence natural language processing java second edition utm source github utm medium repository utm campaign 9781789347999 published by packt techniques for building machine learning and neural network models for nlp what is this book about natural language processing nlp allows you to take any sentence and identify patterns special names company names and more the second edition of natural language processing with java teaches you how to perform language analysis with the help of java libraries while constantly gaining insights from the outcomes this book covers the following exciting features understand basic nlp tasks and how they relate to one another discover and use the available tokenization engines apply search techniques to find people as well as things within a document construct solutions to identify parts of speech within sentences use parsers to extract relationships between elements of a document if you feel this book is for you get your copy https www amazon com dp 1788993497 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following system out println tagger tagstring afaik she h8 cth system out println tagger tagstring btw had a gr8 tym at the party bbiam following is what you need for this book natural language processing with java is for you if you are a data analyst data scientist or machine learning engineer who wants to extract information from a language using java knowledge of java programming is needed while a basic understanding of statistics will be useful but not mandatory with the following software and hardware list you can run all code files present in the book chapter 1 12 software and hardware list chapter software required os required 1 opennlp windows mac os x and linux any stanford corenlp lingpipe standford tagger 2 opennlp models windows mac os x and linux any 3 lingpipe models windows mac os x and linux any 4 opennlpmodels windows mac os x and linux any 5 gate twitter model lingpipe pos models windows mac os x and linux any 6 stanford classifier windows mac os x and linux any 8 12 boilerpipe poi pdfbox windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it http www packtpub com sites default files downloads naturallanguageprocessingwithjavasecondedition colorimages pdf related products paste books from the other books you may enjoy section java deep learning projects packt https www packtpub com big data and business intelligence java deep learning projects utm source github utm medium repository utm campaign 9781788997454 amazon https www amazon com dp 178899745x hands on natural language processing with python packt https www packtpub com big data and business intelligence hands natural language processing python utm source github utm medium repository utm campaign 9781789139495 amazon https www amazon com dp 178913949x get to know the authors richard m reese has worked in both industry and academia for 17 years he worked in the telephone and aerospace industries serving in several capacities including research and development software development supervision and training he currently teaches at tarleton state university richard has written several java books and a c pointer book he uses a concise and easy to follow approach to teaching about topics his java books have addressed ejb 3 1 updates to java 7 and 8 certification functional programming jmonkeyengine and natural language processing ashishsingh bhatia is a learner reader seeker and developer at core he has over 10 years of it experience in different domains including banking erp and education he is persistently passionate about python java r and web and mobile development he is always ready to explore new technologies other books by the authors java for data science https www packtpub com big data and business intelligence java data science utm source github utm medium repository utm campaign 9781785280115 machine learning with r cookbook second edition https www packtpub com big data and business intelligence machine learning r cookbook second edition utm source github utm medium repository utm campaign 9781785280115 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions | ai |
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blockchain-security-contacts | blockchain security contacts this directory is a community curated resource for contacting security teams it identifies the best way to contact an organization s security team so that hackers can report vulnerabilities directly to the organizations that can resolve them this document is a work in progress we re happy to accept feedback questions or ideas for improvements file an issue https github com trailofbits blockchain security contacts issues new or join us on slack https slack empirehacking nyc to talk further recommendations refer to disclose io https disclose io for vulnerability disclosure program best practices don t make researchers agree to terms https twitter com matthew d green status 1025365194330066945 to report security issues to you create a security email address that delivers directly to your engineering team blockchains name contact more info aptos security aptoslabs com arweave team arweave org auroracoin m hannes auroracoin is bitcoin security bitcoincore org security page https bitcoincore org en contact bitcoin cash bitcoin gold admin bitcoingold org disclosure policy https github com btcgpu dev blob master responsible disclosure md bitshares contactbitshares bitshares org bytecoin contact bytecoin org cloakcoin anorak cloakcoin com dash bug bounty https bugcrowd com dashdigitalcash decred contact decred org dogecoin edgeware security commonwealth im eos bug bounty https hackerone com eosio ethereum bounty ethereum org bug bounty https bounty ethereum org ethereum classic security etcdevteam com horizen security horizen global bug bounty https horizenofficial atlassian net wiki spaces zen pages 136871957 bug bounty submission policy and scope hush hushteam protonmail com security page https github com myhush hush blob master doc security md icon hello icon foundation iov security iov one komodo security komodoplatform com litecoin contact litecoin org nem contact nem io neo contact neo org monero multiple 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NLP-Beginner | nlp beginner exercises of natural language process acknowledgements https nndl github io task 1 text categorization based on softmax regression language python tool numpy feature extraction bag of word n gram data classify the sentiment of sentences from the rotten tomatoes dataset https www kaggle com c sentiment analysis on movie reviews goal implement softmax regression to categorize different text into different sentiment all code has been uploaded please see my csdn blog https blog csdn net qq 42365109 article details 114844020 in chinese for further illustration task 2 text categorization based on rnn cnn language python tool pytorch https pytorch org get started locally numpy feature extraction random word embedding glove https nlp stanford edu projects glove word embedding data classify the sentiment of sentences from the rotten tomatoes dataset https www kaggle com c sentiment analysis on movie reviews goal implement recursive neural network rnn and convolution neural network cnn to categorize different text into different sentiment all code has been uploaded please see my csdn blog https blog csdn net qq 42365109 article details 115140450 in chinese for further illustration task 3 text matching based on attention language python tool pytorch https pytorch org get started locally numpy feature extraction random word embedding glove https nlp stanford edu projects glove word embedding data the stanford natural language inference snli https nlp stanford edu projects snli reference enhanced lstm for natural language inference https arxiv org pdf 1509 06664v1 pdf goal implement esim mentioned in the reference above all code has been uploaded please see my csdn blog https blog csdn net qq 42365109 article details 115704688 in chinese for further illustration task 4 sequence labeling based on lstm crf language python tool pytorch https pytorch org get started locally numpy feature extraction random word embedding glove https nlp stanford edu projects glove word embedding data conll 2003 https github com 0otedo0 nlp beginner blob main project 204 ef bc 9asequence 20labeling 20lstm 2bcrf conll 2003 rar data introduction conll 2003 https www clips uantwerpen be conll2003 ner reference neural architectures for named entity recognition https arxiv org pdf 1603 01360 pdf end to end sequence labeling via bi directional lstm cnns crf https arxiv org pdf 1603 01354 pdf goal sequence labeling of conll 2003 https www clips uantwerpen be conll2003 ner all code has been uploaded please see my csdn blog https blog csdn net qq 42365109 article details 119246515 in chinese for further illustration task 5 language model based on neural network language python tool pytorch https pytorch org get started locally numpy feature extraction random word embedding data poetryfromtang txt https github com 0otedo0 nlp beginner blob main project 205 20 20language 20model 20based 20on 20neural 20network poetryfromtang txt reference chapter 6 15 https nndl github io goal generating poems all code has been uploaded please see my csdn blog https blog csdn net qq 42365109 article details 121921018 in chinese for further illustration | nlp python | ai |
cc2538_FreeRTOS_port | freertos for cc2538 overview this project is compiled with latest freertos v10 on eclipse ide cpp 2019 using gcc arm none eabi version 9 we used segger j link debug probe as jtag to program and debug our application the test applications include few examples of freertos using gpio uart and rf radio rx and tx feel free to build these test application individually any feedbacks are very much appreciated basic references are given in documentation for now we will be updating the documentation real soon thank you for your contribution in advance | os |
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main | datasci w266 natural language processing welcome to nlp class this is the main repository we ll be using to distribute course materials week 1 introduction week1 week 2 language modeling i week2 week 3 clusters and distributions week3 week 4 language modeling ii week4 week 5 units of meaning week5 week 6 information retrieval week6 week 7 part of speech tagging i week7 week 8 part of speech tagging ii week8 | ai |
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Assignment-1---Intro-to-IT | assignment 1 intro to it intro to information technology | server |
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