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PaperWeeklyAI | div id outputfigdisplay class fig pre id taag output text style left class flag contenteditable true code code pre div paperweeklyai ai csdn follow p align center a href https charmve blog csdn net target blank img src https img shields io badge blog charmve blue alt blog a a href https charmve blog csdn net target blank img src https www geekxh com trending svg label html alt trending a a href https github com charmve img src https img shields io github stars geekxh hello algorithm svg alt star a p p align center p div align center img src maiweiai com png height 330 width 330 div div align center size 2 b b div div align center size 3 b pdf b div br nlp ai div align center img src menu maiweiai jpg raw true div p align center b b p br table thead tr th style text align left a href https github com charmve paperweeklyai a th th style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese ai a th th style text align left a href https github com charmve hello algorithm tree master e6 b8 85 e6 99 b0 e7 89 88 e7 94 b5 e5 ad 90 e4 b9 a61000 e6 9c ac 10000 a th th style text align left a href https github com charmve hello algorithm tree master e4 b8 93 e6 a0 8f it a th th style text align left colspan 2 a href https github com charmve hello algorithm tree master e5 a4 a7 e5 8e 82 e9 9d a2 e7 bb 8f e6 b1 87 e6 80 bb100 e7 af 87 a th tr thead tbody tr td style text align left a href https github com charmve paperweeklyai tree master 03 maiwei 20ai 20paperweekly 01 nlp e8 ae ba e6 96 87 e7 a0 94 e8 af bb nlp a td td style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese a td td style text align left a href https github com charmve hello algorithm tree master e8 b6 85 e6 b8 85 e6 80 9d e7 bb b4 e5 af bc e5 9b be100 e5 bc a0 a td td style text align left a href https github com charmve hello algorithm tree master e4 b8 93 e6 a0 8f e6 93 8d e4 bd 9c e7 b3 bb e7 bb 9f os a td td style text align left a href https github com charmve hello algorithm tree master e5 a4 a7 e5 8e 82 e9 9d a2 e7 bb 8f e6 b1 87 e6 80 bb100 e7 af 87 c 26c 2b 2b c amp c a td td a href https github com charmve hello algorithm tree master e5 a4 a7 e5 8e 82 e9 9d a2 e7 bb 8f e6 b1 87 e6 80 bb100 e7 af 87 java java a td tr tr td style text align left a href https github com charmve paperweeklyai tree master 03 maiwei 20ai 20paperweekly 02 e8 ae a1 e7 ae 97 e6 9c ba e8 a7 86 e8 a7 89 e8 ae ba e6 96 87 cv a td td style text align left a href https github com charmve paperweeklyai tree master 04 appliedml a td td style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese amp a td td style text align left a href https github com charmve hello algorithm tree master e4 b8 93 e6 a0 8f e8 ae a1 e7 ae 97 e6 9c ba e7 bd 91 e7 bb 9c a td td style text align left a href https github com charmve hello algorithm tree master e5 a4 a7 e5 8e 82 e9 9d a2 e7 bb 8f e6 b1 87 e6 80 bb100 e7 af 87 e6 9e b6 e6 9e 84 e5 b8 88 a td td a href https github com charmve hello algorithm tree master e5 a4 a7 e5 8e 82 e9 9d a2 e7 bb 8f e6 b1 87 e6 80 bb100 e7 af 87 e5 89 8d e7 ab af a td tr tr td style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese 05 ai e8 ae ba e6 96 87 e5 bf 85 e8 af bb e7 af 87 e7 9b ae10 e7 af 87 ai 10 a td td style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese linux a td td style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese a td td style text align left a href https github com charmve hello algorithm tree master e4 b8 93 e6 a0 8f linux linux a td td style text align left a href https github com charmve hello algorithm tree master e5 a4 a7 e5 8e 82 e9 9d a2 e7 bb 8f e6 b1 87 e6 80 bb100 e7 af 87 python python a td td style text align left a href https github com charmve leetcode4flag leetcode4flag a td tr tr td style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese 06 cvpr2020 e8 ae ba e6 96 8730 e7 af 87 cvpr2020 30 a td td style text align left a href https github com charmve ml basics ml basics a td td style text align left a href https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese a td td style text align left a href https github com charmve hello algorithm tree master e4 b8 93 e6 a0 8f mysql mysql a td td style text align left a href https github com charmve hello algorithm tree master e5 a4 a7 e5 8e 82 e9 9d a2 e7 bb 8f e6 b1 87 e6 80 bb100 e7 af 87 mysql mysql a td td td tr tr td style text align left a href https github com charmve paperweeklyai tree master 04 appliedml appliedml a td td style text align left a href https github com charmve paperweeklyai tree master 06 ml surveys ml surveys a td td style text align left td td style text align left a href https github com charmve hello algorithm tree master e4 b8 93 e6 a0 8f e8 ae be e8 ae a1 e6 a8 a1 e5 bc 8f a td td style text align left a href https github com charmve hello algorithm tree master e4 b8 93 e6 a0 8f e5 89 91 e6 8c 87offer offer a td td td tr tbody table br table of content open source ebooks https github com charmve paperweeklyai tree master 00 guidebookspdf english 2bchinese classic paper top10 https github com charmve paperweeklyai tree master 01 ai e8 ae ba e6 96 87 e5 bf 85 e8 af bb e7 af 87 e7 9b ae10 e7 af 87 cvpr2020 paper top30 https github com charmve paperweeklyai tree master 02 cvpr2020 e8 ae ba e6 96 8730 e7 af 87 paperweeklyai maiweiai lab https github com charmve paperweeklyai tree master 03 maiwei 20ai 20paperweekly nlp natural language processing nlp cv computer vision algorithm study machine learning in action advanced machine learning ai explore the future ai path to machine learning opencs courses https github com charmve opencs courses ml surveys https github com charmve paperweeklyai tree master 06 ml surveys applied ml https github com charmve paperweeklyai tree master 04 appliedml image segmentation https github com charmve paperweeklyai tree master 05 image 20segmentation community community br p align right p nlp div class divcss5 color lightblue div br title 01 ambert clue glue bert https github com charmve paperweeklyai blob master 03 maiwei 20ai 20paperweekly 01 nlp e8 ae ba e6 96 87 e7 a0 94 e8 af bb 01 e6 9d 8e e8 88 aa e7 ad 89 e6 8f 90 e5 87 ba e5 a4 9a e7 b2 92 e5 ba a6ambert e6 a8 a1 e5 9e 8b ef bc 8cclue e3 80 81glue e4 b8 8a e4 bc 98 e4 ba 8ebert ef bc 8c e4 b8 ad e6 96 87 e6 8f 90 e5 8d 87 e6 98 be e8 91 97 md 02 bert https mp weixin qq com s qrsdz87mdrylz9eqldkkta 03 https mp weixin qq com s diaczy3thhid 34gioepsw 04 7 papers radios sigir 2020 lasso https github com charmve paperweeklyai tree master 04 appliedml 05 word2vec doc2vec https github com charmve paperweeklyai tree master 04 appliedml 06 ai https github com charmve paperweeklyai tree master 04 appliedml 07 https github com charmve paperweeklyai tree master 04 appliedml 08 https github com charmve paperweeklyai tree master 04 appliedml 09 qq https mp weixin qq com s qdmrcqije5mbkzynfdtx g 10 https mp weixin qq com s sh qf6r5y3sdj ri8g9pxg 11 ai neurips https mp weixin qq com s xwbekobl47mjoouwiyv ia 12 neurips 2020 https mp weixin qq com s oqvn6gqckya r8zkqdd1wq 13 https mp weixin qq com s 7wodtbi4emzl5m hlauqbw 14 dnn https mp weixin qq com s 300m1cbwehir3wepwlqmgw br strong back to contents strong div class divcss5 color lightblue div br title 01 imagenet eccv 2020 https github com charmve paperweeklyai blob master 03 maiwei 20ai 20paperweekly 01 nlp e8 ae ba e6 96 87 e7 a0 94 e8 af bb 01 e6 9d 8e e8 88 aa e7 ad 89 e6 8f 90 e5 87 ba e5 a4 9a e7 b2 92 e5 ba a6ambert e6 a8 a1 e5 9e 8b ef bc 8cclue e3 80 81glue e4 b8 8a e4 bc 98 e4 ba 8ebert ef bc 8c e4 b8 ad e6 96 87 e6 8f 90 e5 8d 87 e6 98 be e8 91 97 md 02 cvpr https github com charmve mirror glass detection 03 https github com charmve mirror glass detection 04 cvpr2020 https github com charmve mirror glass detection 05 https mp weixin qq com s nyvowfianm j9mica07mlw 06 sota https github com charmve mirror glass detection 07 gan https mp weixin qq com s dceijus2sov47dbwpxeaiq 08 3d eccv https github com charmve mirror glass detection 09 eccv 2020 https github com charmve paperweeklyai 10 sota https github com charmve mirror glass detection 11 https mp weixin qq com s xlq o6edfste mtsjq2eg 12 deepfake https github com charmve mirror glass detection 13 cvpr 2020 bsp 3d https github com charmve mirror glass detection 14 40 https github com charmve mirror glass detection 15 cvpr2020 spsr https mp weixin qq com s cfyd9clnr a89tsfuji q 16 kaggle x cvpr 2020 workshop https github com charmve paperweeklyai blob master 03 maiwei 20ai 20paperweekly 02 e8 ae a1 e7 ae 97 e6 9c ba e8 a7 86 e8 a7 89 e8 ae ba e6 96 87 e2 80 8bkaggle 20x e5 85 89 e8 82 ba e7 82 8e e6 a3 80 e6 b5 8b e6 af 94 e8 b5 9b e7 ac ac e4 ba 8c e5 90 8d e6 96 b9 e6 a1 88 e8 a7 a3 e6 9e 90 20 7c 20cvpr 202020 20workshop md 17 ai 3d github 4 4k https mp weixin qq com s ydw5k zvt3yzyzz gevj3q 18 https mp weixin qq com s iwmsbixnrb9fw7aukb79ma 19 sift https mp weixin qq com s zlsoy2gwajs4bygokzmyw 20 modnet https mp weixin qq com s z5fyt0mwnosdkp7cq0tqla br strong back to contents strong div class divcss5 color lightblue div br title 01 k k nearest neighbor knn https github com charmve paperweeklyai blob master 03 maiwei 20ai 20paperweekly 03 e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e6 b7 b1 e5 ba a6 e5 ad a6 e4 b9 a0 e7 90 86 e8 ae ba e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e7 ae 97 e6 b3 95 e4 b9 8b e2 80 94 e2 80 94k e6 9c 80 e8 bf 91 e9 82 bb k nearest 20neighbor ef bc 8cknn e5 88 86 e7 b1 bb e7 ae 97 e6 b3 95 e5 8e 9f e7 90 86 e8 ae b2 e8 a7 a3 md 02 hidden markov models hmm https github com charmve paperweeklyai 03 k k nearest neighbor knn python https github com charmve paperweeklyai blob master 03 maiwei 20ai 20paperweekly 03 e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 26 e6 b7 b1 e5 ba a6 e5 ad a6 e4 b9 a0 e7 90 86 e8 ae ba e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e7 ae 97 e6 b3 95 e4 b9 8b e2 80 94 e2 80 94k e6 9c 80 e8 bf 91 e9 82 bb k nearest 20neighbor ef bc 8cknn e5 88 86 e7 b1 bb e7 ae 97 e6 b3 95python e5 ae 9e e7 8e b0 md 04 cnn https github com charmve computer vision in action tree main docs 1 e7 90 86 e8 ae ba e7 af 87 chapter2 cnn chapter2 cnn md 05 logistic regression https github com charmve paperweeklyai 06 uc https github com charmve paperweeklyai 07 https blog csdn net charmve article details 106341911 08 gnn deepmind icml https blog csdn net charmve article details 113830377 09 https github com charmve paperweeklyai tree master 03 maiwei 20ai 20paperweekly 10 https github com charmve paperweeklyai tree master 03 maiwei 20ai 20paperweekly 11 https github com charmve paperweeklyai tree master 03 maiwei 20ai 20paperweekly 12 https github com charmve paperweeklyai tree master 03 maiwei 20ai 20paperweekly 13 yolov5 https mp weixin qq com s bxn3pv2 s1bddfliyd1unq br strong back to contents strong div class divcss5 color lightblue div br title 01 kaggle https colab research google com github charmve paperweeklyai blob master 03 maiwei 20ai 20paperweekly 04 e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e5 ae 9e e6 88 98 e7 af 87 house price predication ipynb 02 kaggle https github com charmve paperweeklyai blob master 03 maiwei 20ai 20paperweekly 04 e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e5 ae 9e e6 88 98 e7 af 87 e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e5 ae 9e e6 88 98 20 7c 20 e9 80 bb e8 be 91 e5 9b 9e e5 bd 92 e5 ba 94 e7 94 a8 e4 b9 8b e2 80 9ckaggle e6 b3 b0 e5 9d a6 e5 b0 bc e5 85 8b e4 b9 8b e7 81 be e2 80 9d md 03 pytorch cifar10 https github com charmve paperweeklyai 04 fer keras https github com charmve paperweeklyai 05 cvpr 2020 deepblueai https github com charmve paperweeklyai 06 gm kaggle https github com charmve paperweeklyai 07 https github com charmve paperweeklyai 08 https github com charmve 09 cvpr2020 tutorial https github com charmve 10 39 kaggle tips tricks https mp weixin qq com s bnhjsyoyskb6wgsuhc7gna br strong back to contents strong ai div class divcss5 color lightblue div br title 01 pytorch github 9 4k https github com charmve 02 github https github com charmve 03 numpy https github com charmve 04 github 23k pytorch https github com charmve 05 ai 10 pdf https github com charmve 06 https github com charmve 07 https github com charmve 08 https github com charmve 09 5 https github com charmve br strong back to contents strong ai div class divcss5 color lightblue div br title 01 hinton nature https github com charmve design of a 3d dynamic display system based on voice control 02 007 8 688 nature https github com charmve design of a 3d dynamic display system based on voice control 03 nature https github com charmve design of a 3d dynamic display system based on voice control 04 deepmind https github com charmve design of a 3d dynamic display system based on voice control 05 deepmind ai https github com charmve design of a 3d dynamic display system based on voice control 06 6 5g 6g https github com charmve design of a 3d dynamic display system based on voice control 07 science https github com charmve design of a 3d dynamic display system based on voice control 08 https github com charmve design of a 3d dynamic display system based on voice control 10 science ai https github com charmve design of a 3d dynamic display system based on voice control 11 python 6 science https github com charmve design of a 3d dynamic display system based on voice control 12 https github com charmve design of a 3d dynamic display system based on voice control 13 gpt 3 open ai api https mp weixin qq com s i8 05xhtokxrnukfssmjg 14 deepfake https mp weixin qq com s es0kkbwqclhfedfl5w8 pg strong back to contents strong community github a href https github com charmve paperweeklyai discussions target blank discussions a or a href https github com charmve paperweeklyai issues target blank issues a qq 1042054445 join us wellcome all you who interest in machine learning to join us contact e mail yidazhang gmail dot com with your letter cv or donating to help me continue working on this project a href https www buymeacoffee com charmve target blank img src https cdn buymeacoffee com buttons v2 default red png alt buy me a coffee width 150 a a href https www paypal com paypalme charmve img src https img shields io badge 20donate paypal blue alt donate with paypal a a href https charmve github io sponsor png img src https img shields io badge 20donate wechat green alt donate with wechat a br b ai b br br div align center img src https imgconvert csdnimg cn ahr0chm6ly9tbwjpei5xcgljlmnul21tyml6x3buzy9atmrov05pyjnjuknynmzrq0lcqzjtzwxvyjdrdunqzvjtcxjymmw5nhrote1id0pwuenhsvvur3pqmw5ncdvcmkrvrtvaudfgsw01dm9oeuiycxmwowcvnjqw x oss process image format png div div align center size 3 b paperweeklyai b div br div align center div | study-papers machine-learning-algorithms computer-vision nlp papers applied-machine-learning data-science machine-learning deep-learning data-mining paper-with-code advanced tutorials | ai |
meta-iot2050 | meta iot2050 this isar https github com ilbers isar layer contains recipes configuration and other artifacts that are specific to debian based iot2050 product build example image before building the system you will need to install docker on build host for example under debian linux shell sudo apt install docker io if you want to run docker as non root user then you need to add your user to the docker group shell sudo usermod ag docker user you may need to re login or issue newgrp then open the menu to select the desired image and options shell kas container menu after the build completed the final image is under text build tmp deploy images iot2050 iot2050 image example iot2050 debian iot2050 wic build user sdk note current sdk only supports linux x86 64 host machine shell kas container build kas iot2050 example yml kas opt sdk yml or select sdk in kas menu after the build completed the sdk tarball is located at text build tmp deploy images iot2050 sdk isar arm64 tar xz please follow the further instruction file readme sdk in the sdk tarball the sdk is also available as docker image to import it into a docker host run shell docker load i build tmp deploy images iot2050 sdk iot2050 debian arm64 docker archive tar xz build qemu image to boot iot2050 image from qemu you need a customized image for proper booting please use kas menu with the following command and select qemu image for target build with example image or example image with swupdate support shell kas container menu then below command can be used to boot qemu image on a platform that qemu system aarch64 is installed run qemu shell bin sh start qemu iot2050 sh clean build result shell kas container isar clean booting the image from sd card under linux insert an unused sd card assuming the sd card takes device dev mmcblk0 use dd to copy the image to it for example shell sudo dd if build tmp deploy images iot2050 iot2050 image example iot2050 debian iot2050 wic of dev mmcblk0 bs 4m oflag sync alternatively install the bmap tools package and run the following command which is generally faster and safer shell sudo bmaptool copy build tmp deploy images iot2050 iot2050 image example iot2050 debian iot2050 wic dev mmcblk0 the example image starts with the ip 192 168 200 1 preconfigured on the first ethernet interface and use dhcp at another you can use ssh to connect to the system the bsp image does not configure the network if you want to ssh into the system you can use the root terminal via uart to ifconfig the ip address and use that to ssh in note to login the default username and password is root and you are required to change the default password when first login installing the image on the emmc iot2050 advanced only during the very first boot of the image from an sd card or usb stick you can request the installation to the emmc for that press the user button while the status led is blinking orange during that first boot hold the button for at least 5 seconds to start the installation note all content of the emmc will be overwritten by this procedure the ongoing installation is signaled by a fast blinking status led wait for several minutes until the led stops blinking and the device reboots to the emmc you can safely remove the sd card or usb stick at that point the installation can also be triggered automatically by creating the file etc install on emmc on the vanilla image by mounting it under linux and executing e g touch mountpoint etc install on emmc selecting a boot device by default the boot loader will pick the first bootable device if that device may no longer fully start you can select an alternative boot device in the u boot shell attach a usb uart adapter to x14 connect it to a host pc and open a terminal program on that port reset the device and interrupt the boot when it counts down hit any key to stop autoboot then type shell setenv boot targets mmc0 run distro bootcmd to boot from the microsd card use usb0 for the first usb mass storage device note this selection is not persistent the boot loader will fall back to its default boot order after reset building with swupdate support it is possible to create an image with a swupdate based double copy root file system https sbabic github io swupdate overview html double copy with fall back for over the air updates by selecting the option example image with swupdate support during the image configuration with kas container menu you can also build the image by calling shell kas container build kas iot2050 example yml kas opt swupdate example yml you can find the final image under build tmp deploy images iot2050 iot2050 image swu example iot2050 debian iot2050 wic this image holds the necessary partition layout with two root file systems the image iot2050 image swu example iot2050 debian iot2050 wic can be flashed directly to a sd card as described in section booting the image from sd card booting the image from sd card note as the image contains 2 root file systems it has a size of 7 gigabytes it also will create a binary for updating the system at build tmp deploy images iot2050 iot2050 image swu example iot2050 debian iot2050 swu update an image with swupdate the following steps are necessary to update an image created with swupdate support 1 transfer the swupdate binary iot2050 image swu example iot2050 debian iot2050 swu to the target system 2 update the system with swupdate by executing shell swupdate i iot2050 image swu example iot2050 debian iot2050 swu you can find more details and options for the command swupdate in the swupdate documentation https sbabic github io swupdate swupdate html running swupdate note the used swupdate package does not contain a web app example swupdate will write the image to the unused root partition and updates the efi boot guard state efi boot guard is the bootloader that controls which of the two kernels and root file systems are booted 3 reboot the system into the new root file system the switch between the root file systems occurs automatically and requires no user interaction 4 confirm that the new root file system is correctly booted after a reboot the device boots into the new root file system if the boot is successful the update process needs to be completed by calling shell complete update sh the script sets the update state in the efi boot guard configuration to the initial state if the update is deemed failed resetting the device will select the previous root file system u boot environment the bootloader environment needs to be adapted to select the correct root file system during boot this adaptation occurs automatically during the first boot by executing the patch u boot env service this systemd service activates the hardware watchdog in the u boot environment setting it to 60 seconds by default revert to the default environment if it is necessary to revert to the default u boot environment the following command can be used shell fw setenv f etc u boot initial env | isar debian sdk iot-gateway | server |
Modern-JavaScript-Web-Development-Cookbook | modern javascript web development cookbook a href https www packtpub com web development modern javascript web development cookbook utm source github utm medium repository utm campaign 9781788992749 img src https d255esdrn735hr cloudfront net sites default files imagecache ppv4 main book cover b09958 png alt modern javascript web development cookbook height 256px align right a this is the code repository for mastering javascript functional programming https www packtpub com web development modern javascript web development cookbook utm source github utm medium repository utm campaign 9781788992749 published by packt easy solutions to common and everyday javascript development problems what is this book about javascript has evolved into a language that you can use on any platform modern javascript web development cookbook is a perfect blend of solutions for traditional javascript development and modern areas that developers have lately been exploring with javascript this comprehensive guide teaches you how to work with javascript on servers browsers mobile phones and desktops you will start by exploring the new features of es8 you will then move on to learning the use of es8 on servers with node js with the objective of producing services and microservices and dealing with authentication and cors once you get accustomed to es8 you will learn to apply it to browsers using frameworks such as react and redux which interact through ajax with services you will then understand the use of a modern framework to develop the ui in addition to this development for mobile devices with react native will walk you through the benefits of creating native apps both for android and ios finally you ll be able to apply your new found knowledge of server side and client side tools to develop applications with electron this book covers the following exciting features first 5 what you ll learn points use the latest features of es8 and learn new ways to code with javascript develop server side services and microservices with node js learn to do unit testing and to debug your code build client side web applications using react and redux create native mobile applications for android and ios with react native if you feel this book is for you get your copy https www amazon com dp 1788992741 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 source file src roundmath js flow use strict continues following is what you need for this book this book is for developers who want to explore the latest javascript features frameworks and tools for building complete mobile desktop and web apps including server and client side code you are expected to have working knowledge of javascript to get the most out of this book with the following software and hardware list you can run all code files present in the book chapter 1 13 software and hardware list chapter software required hardware required 1 git windows mac os x and linux any 1 13 node and npm windows mac os x and linux any 1 13 flow windows mac os x and linux any 1 13 prettier windows mac os x and linux any 1 13 eslint windows mac os x and linux any 4 5 mariadb mysql windows mac os x and linux any 5 winston morgan postman swagger windows mac os x and linux any 5 10 jest windows mac os x and linux any 6 10 react windows mac os x and linux any 6 10 storybook windows mac os x and linux any 8 10 redux windows mac os x and linux any 10 12 enzyme windows mac os x and linux any 11 12 react native windows mac os x and linux any 13 electron 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 https www packtpub com sites default files downloads 9781788992749 colorimages pdf related products other books you may enjoy building enterprise javascript applications packt https www packtpub com web development building enterprise javascript applications utm source github utm medium repository utm campaign 9781788477321 amazon https www amazon com dp 1788477324 learn blockchain programming with javascript packt https www packtpub com web development learn blockchain programming javascript utm source github utm medium repository utm campaign 9781789618822 amazon https www amazon com dp 1789618827 get to know the author s federico kereki federico kereki is a uruguayan systems engineer with a master s degree in education and over 30 years experience as a consultant system developer university professor and writer he is currently a subject matter expert at globant and he has taught cs courses at universidad de la rep blica universidad ort uruguay and universidad de la empresa he has written for the linux journal and the linuxpro magazine in the usa linux and mundo linux in europe and websites such as linux dot com and ibm developerworks he has also written booklets on computer security and two books essential gwt and mastering javascript functional programming other books by the authors mastering javascript functional programming https www packtpub com web development mastering javascript functional programming utm source github utm medium repository utm campaign 9781787287440 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781788992749 https packt link free ebook 9781788992749 a p | front_end |
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Quick-GPT | gui for large language models api this project is a graphical user interface gui developed using pyqt6 for interacting with the large language models api the gui allows users to upload documents and use a voice recorder to input text data for processing by the api donation bitcoin donate button https i stack imgur com mnq6v png https www blockonomics co pay url aac9917abdc443b4 features chromadb vectore store users can store their pdf locally without needing third party db upload documents users can select and upload documents to be processed by the large language models api the uploaded documents can be in pdf voice recorder the gui includes a voice recorder feature that allows users to record their voice and convert it into text this text data can then be sent to the large language models api for processing customization you can edit largelanguagemodel py to fit your needs make sure to keep pysignal prerequisites before running the gui make sure you have the following installed python 3 8 or above large language models api credentials installation 1 clone the repository git clone https github com haithemyoucefkhoudja quick gpt git 2 install the required dependencies pip install r requirements txt 3 set up the large language models api credentials obtain an api key from the large language models api provider replace the placeholder api key in settings panel to start use it alt text assets key tutorial gif usage to run the gui execute the following command python quickgpt py to create an executable app you can run the following commands pip install pyinstaller pyinstaller noconsole icon quick gpt logo ico hidden import tiktoken ext openai public hidden import tiktoken ex quickgpt py the gui window will open providing options for uploading documents and using the voice recorder follow the on screen instructions to interact with the gui and utilize the features contributing contributions are welcome if you have any suggestions or improvements please open an issue or submit a pull request license this project is licensed under the mit license license | ai |
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GCP-practice-project | gcp practice project practice project associate cloud engineering working on changes trying again to merge and pull | cloud |
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Question-Answer-System | nlp wiki system how to run ask testdata our team spanish language raw txt 6 answer testdata our team spanish language raw txt testdata our team spanish language question txt log 02 20 2016 wenyanh 1 i have uploaded four wiki raw files we once used for our questions asking answering in txt format 2 the wikipretreament py is used for pretreatment the wiki file into an operable txt file for your further steps it is in the command line format for your file input and output and i have produced 4 data files data1 4 for you to use contact me directly if there is any problem 3 i tried to fetch the main information from the website directly but haven t found a good way for the data cleaning now the code is in the txtfetch py it would be better if you could give me some advice or need we complete this function 02 22 2016 xc2 1 something have to do easy question answer no 2 proper noun | ai |
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eShopOnUWP | introduction note this repo is obsolete please visit the new project at vanarsdel inventory sample https github com microsoft inventorysample this repo contains a uwp sample application for windows 10 fall creators update it is focused on line of business scenarios showing how to use the latest windows capabilities in desktop applications some of the features showcased by this application include the mvvm design pattern use of fluent design system https fluent microsoft com ink capabilities windows hello cortana telerik controls ci build build status https ci appveyor com api projects status wqn7or9m95xjurjy svg true https ci appveyor com project rido min eshoponuwp cd build build status https rido visualstudio com apis public build definitions 989ddbdd c86a 4fa8 8d80 89eb785d8056 83 badge https aka ms eshopuwp what s new in version v 1 0 94 support for multiple providers new local provider new rest api provider added settings page manage and switch providers new project eshop server asp net core 2 0 emulates a web api server with crud operations prerequisites system requirements windows 10 fall creators update to run the application you should be running a windows version greater than 10 0 16299 you can get it as described here https blogs windows com windowsexperience 2017 10 17 get windows 10 fall creators update visual studio 15 4 download http visualstudio com including the windows 10 fcu sdk 10 0 16299 getting started you can install a working version of the app from https aka ms eshopuwp https aka ms eshopuwp features windows ink select and edit multiple products using the surface pen there are several signs with differents functions this application recognizes a hand gesture and translate it to text these are the supported forms circle o select multiple items tick v active or inactive the status cross x remove an item windows ink docs windowink gif windows hello there are two ways to log in either with user password or using windows hello the first time the application is run the user password log in option will appear by default you will be able to log in through windows hello once you enter the user name and the button enabling to log in is activated once you have logged in using windows hello your user will be saved and the next time you open the application windows hello authentication will appear as default depending on how your windows hello settings are settings accounts sign in options you will be able to authenticate by using pin face recognition fingerprint et al fluent design acrylic material acrylic material https docs microsoft com es es windows uwp style acrylic is a type of brush that creates a partially transparent texture acrylic material docs acrylicfluent png connected animations connected animations https docs microsoft com es es windows uwp style connected animation let you create a dynamic and compelling navigation experience by animating the transition of an element between two different views connected animations docs connectedanimation gif reveal reveal https docs microsoft com es es windows uwp style reveal is a lighting effect that helps bring depth and focus to your app s interactive elements connected animations docs revealfluent gif shyheader shyheader https github com microsoft windowsuidevlabs tree master samplegallery samples sdk 2014393 shyheader demonstrates how to use expressionanimations tookit with a scrollviewer to create a shinking header tied to scroll position shyheader docs shyheadertoolkit gif cortana you have to run cortana at least once because when done it installs the voice command definitions once it has been run you can close the application and start using cortana s search voice commands these are some supported voice commands cortana may take time to refresh its voice commands shop show me mug type products shop give me shirt type products shop show me cap type products shop show me sheet type products | uwp dotnet windows | os |
ros2_computer_vision | computer vision using ros2 this project contains code examples created in visual studio code for computer vision using c opencv point cloud library pcl using ros2 these examples are created for the computer vision subject of robotics software engineering degree at urjc this package allows running different gazebo worlds including the aws robomaker https github com aws robotics worlds using the tiago robot from pal robotics https github com pal robotics installation source your ros2 distro bash source opt ros ros2 distro setup bash execute installation script bash setup sh run gazebo tiago in ros2 bash export rmw implementation rmw cyclonedds cpp source install setup bash ros2 launch computer vision simulation launch py to change the gazebo world or the initial position rotation of the tiago robot you can modify the config params yaml file if you have a low performance close the gazebo s client check gzclient process and kill it bash kill 9 pgrep f gzclient run navigation in ros2 you can use nav2 using tiago in the selected world bash source install setup sh ros2 launch computer vision tiago navigation launch py also you can use keepout zones just create a new map including the excluded areas and use the same name adding keep now publish the map running bash source install setup sh ros2 launch computer vision keepzone launch py just some aws worlds are included you can navigate while mapping and create your own map using the slam toolbox provided in different terminals run the slam toolbox bash ros2 launch slam toolbox online async launch py params file install slam toolbox share slam toolbox config mapper params online async yaml use sim time true in slam toolbox config mapper params online async yaml change scan topic from scan to scan raw activate the map server bash ros2 launch nav2 map server map saver server launch py check the map in rviz bash rviz2 ros args p use sim time true save the map bash ros2 run nav2 map server map saver cli ros args p use sim time true run examples in ros2 opencv node bash source install setup sh ros2 run computer vision cv node pcl node bash source install setup sh ros2 run computer vision pcl node opencv pcl node bash source install setup sh ros2 run computer vision cv pcl node help you can check the commands file to run other nodes such as key teleop to move tiago using your keyboard about this is a project made by jos miguel guerrero associate professor at universidad rey juan carlos copyright copy 2022 twitter https img shields io badge follow jm guerrero green svg https twitter com jm guerrero license shield cc by sa 4 0 cc by sa shield cc by sa this work is licensed under a creative commons attribution sharealike 4 0 international license cc by sa cc by sa 4 0 cc by sa image cc by sa cc by sa http creativecommons org licenses by sa 4 0 cc by sa image https licensebuttons net l by sa 4 0 88x31 png cc by sa shield https img shields io badge license cc 20by sa 204 0 lightgrey svg universidad rey juan carlos https www urjc es jos miguel guerrero https sites google com view jmguerrero nav2 https navigation ros org keepout zones https navigation ros org tutorials docs navigation2 with keepout filter html highlight keep slam toolbox https vimeo com 378682207 navigate while mapping https navigation ros org tutorials docs navigation2 with slam html | computer-vision opencv ros2 pcl-library robotics urjc cpp humble | ai |
honeypot-iot | iot honeypot plateform https img shields io badge platform linux 2fmacos red svg python https img shields io badge python v 2 blue svg logo python isetsohoney to git https user images githubusercontent com 23563528 44610164 50430700 a7fb 11e8 9fc8 9d9934c0db25 gif this tool to simulate device iot router attacks in python which logs hackerip and all the tracing he does into a logfile then a database requirements python 2 7 or 3 0 apache2 mysql server httrack installation clone the repository git clone https github com anouarbensaad http honeypot git and switch into the directory cd http honeypot configuration database run mysql with root user sudo mysql u root create the database isetsohoney create database isetsohoney add the privileges to root grant all privileges on to root localhost identified by isetso create table log with this fields create table log id int not null primary key date datetime iphacker varchar 255 uri varchar 255 running run server with command python http honeypot server py starting server on 999 username root password toor run httrack for copy real websites to local directory and copy it in sys fake test scan the server banner with nmap nmap sv script banner 192 168 1 1 p999 open http 192 168 1 1 999 logs files p align center img src https user images githubusercontent com 23563528 43874380 d7dd5066 9b8b 11e8 8ce7 28903206cdeb png p license mit license | honeypot http-server honeyd iot-device socket network security-tools | server |
lambda-refarch-iotbackend | serverless reference architecture iot backend the serverless iot backend reference architecture is a general purpose event driven iot data processing architecture that uses aws lambda https aws amazon com lambda this architecture is ideal for workloads where iot data needs to be ingested custom rules applied and prepared for further analysis such as machine learning this architecture and sample implementation provides a framework for ingesting iot messages utilizing aws iot core https aws amazon com iot core and aws lambda in this demo humidity sensor application iot soil moisture sensors deliver messages to the iot backend iot rules check if humidity is under a set threshold and invokes a lambda function if that s the case the lambda function sends an alert email notification all of the iot data is stored in s3 for downstream processing architectural diagram reference architecture iot backend iot serverless backend architecture png application components note downstream data processing components in the architecture diagram aws iot analytics machine learning amazon kinesis data streams and amazon dynamodb are not included in this deployment iot device thing a soil moisture iot sensor simulates a message with timestamp and soil humidity and sends the message to an iot topic using the mqtt protocol iot core aws iot core is a managed cloud service that lets connected devices easily and securely interact with cloud applications and other devices all the messages from the sensor are delivered to an iot topic iot rules act on incoming data based on the condition defined in this example one iot rule is configured to save all messages to a s3 bucket another iot rule checks if humidity in the message is less than certain threshold set to 35 in this example and invokes an aws lambda function the aws lambda function parses the message to obtain device name and humidity and sends an alert using an amazon sns topic downstream processing as mentioned in the note downstream processing is not implemented as part of this deployment and is shown as art of possible the data ingested in aws iot core is stored in a s3 bucket this data can be analyzed using services like amazon sagemaker to train a machine learning model or can be analyzed using aws iot analytics running the example you can use the provided aws cloudformation template iot backend yaml to launch a stack that demonstrates the lambda file processing reference architecture details about the resources created by this template are provided in the cloudformation template resources section of this document deploying the template important you can deploy the template in the following regions us east 1 or eu west 1 change the snsemail address parameter to your own email and run the command on your terminal to deploy the cloudformation template aws cloudformation deploy template file iot backend yaml stack name iot backend capabilities capability named iam parameter overrides snsemail test amazon lu region eu west 1 or you can directly go to cloudformation console and upload the template there seeing the iot messages and alerts after you successfully deploy the stack you will observe the following 1 as part of the template an amazon ec2 instance will be provisioned and it will start sending messages to the iot core automatically to see the messages go to iot core console then click test and put without the quotes in subscription topic field which will start accepting messages from all topics keep every other field as is click subscribe to topic 2 you will see the messages flowing from the devices to the iot core in the console messages will look like below name soilsensor3 humidity 29 timestampepoch 1598536072677 timestampiso 2020 08 27t13 47 52 677721 3 you will get an email with subject line aws notification subscription confirmation to confirm that you want to get the notifications in case the humidity sensor devices record data under a certain threshold confirm the subscription from the email 4 you should start getting emails for messages where humidity is below the threshold 35 notification email will look like attention humidity under threshold humidity 32 under threshold for device name soilsensor2 5 to stop getting emails go to the email you received in step 3 and click sns opt out at the bottom of that email 6 to cleanup the resources after you are done please perform the following steps cleaning up to delete all the resources created in this example please follow the following steps 1 delete the s3 rulefilterbucket go to s3 and your bucket will be with name like iot backend rulefilterbucket xxxxxxxxxxx delete all the contents of the bucket and then delete the bucket itself 2 delete the iot policy go to the iot core secure policies select serverless iot backend policy and delete it 3 delete the iot thing go to the iot core manage things select serverless iot backend thing and delete it 4 finally you should go to cloudformation console and select the stack with name iot backend and click the delete button this will delete all the other resources created in this example cloudformation template resources resources the provided template iot backend yaml creates the following resources ecinstanceprofile we need devices that will monitor the soil humidity temperature in this example we are using an ec2 instance that will simulate the data generated by devices and send it to the mqtt queue we are using ssm systems manager to launch this instance aws systems manager is an aws service that you can use to view and control your infrastructure on aws using the systems manager console you can view operational data from multiple aws services and automate operational tasks across your aws resources systems manager helps you maintain security and compliance by scanning your managed instances and reporting on or taking corrective action on any policy violations it detects iotpolicy aws iot policies are json documents aws iot policies allow you to control access to the aws iot data plane the aws iot data plane consists of operations that allow you to connect to the aws iot message broker send and receive mqtt messages iotthing this is the representation of devices in iot a thing is a representation of a specific device or logical entity it can be a physical device or sensor for example a light bulb or a switch on a wall it can also be a logical entity like an instance of an application or physical entity that does not connect to aws iot but is related to other devices that do for example a car that has engine sensors or a control panel rulefilterbucket an s3 bucket that holds the data that comes from the devices soil sensors iottopicrule rules give your devices the ability to interact with aws services rules are analyzed and actions are performed based on the mqtt topic stream in this case the iot rule simply simply puts the data received from the devices to rulefilterbucket in the following format rulefilterbucket deviceid timestamp iotfunctionputimageevent this is an iot topic rule it triggers the lambda function to publish the message in sns topic if the humidity falls below a certain threshold iotfunction this is the lambda function that that publishes the message in the sns topic alertsnstopic amazon simple notification service sns is a highly available durable secure fully managed pub sub messaging service that enables you to decouple microservices distributed systems and serverless applications amazon sns provides topics for high throughput push based many to many messaging in our example we are using amazon sns topic to fan out notifications to end users using email in case soil humidity falls below certain threshold | server |
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JS_Mining | js mining js technology information mining | server |
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stanford-cs193p-swiftui | awards ranking dev global top 200 certificate https leetcode com sergeyleschev a href https leetcode com sergeyleschev img src https github com sergeyleschev sergeyleschev blob main leetcode ranking png raw true alt drawing width 410 a a href https leetcode com sergeyleschev img src https github com sergeyleschev sergeyleschev blob main leetcode medals png raw true alt drawing width 280 a languages swift stanford cs193p swiftui stanford cs193p swiftui xcode 12 ios 14 c s leschev additional stanford cs193p swift 5 uikit xcode 11 ios 12 https github com sergeyleschev stanford cs193p c s leschev | swift swiftui apple xcode stanford-university stanford cs193p appdevelopment sergeyleschev algorithm algorithms appstore ios ios-app ios-swift macos appstoreconnect appdeveloper package-manager pods | os |
Stardust | h1 align center img height 200 src logo png p align center stardust p h1 p align center a href https circleci com gh tillersystems stardust tree master img src https circleci com gh tillersystems stardust tree master svg style shield circle token b0f5e2b1a128b053d85347edb4e13cbb412bff13 alt circleci build status a a href https www npmjs com tillersystems stardust img src https badge fury io js 40tillersystems 2fstardust svg alt npm package a a href https www apache org licenses license 2 0 img src https img shields io badge license apache 202 blue svg alt apache 2 0 a p this repo contains all sources used to build a tiller design system library install you can install stardust using either of the methods below for yarn users yarn add tillersystems stardust for npm users npm install tillersystems stardust explore we have a few examples on storybook you can see them by running yarn storybook or npm run storybook contribute getting started to install the project you should follow the installation guide https github com tillersystems stardust wiki installation guide after that you should be able to start the application with bash yarn start directory structure todo coding style eslint to run eslint http eslint org you could run yarn lint use atom with this plugin https github com atomlinter linter eslint in settings make sure use global eslint installation is unchecked to be sure that you are using project eslint configuration plus don t hesitate to check fix errors on save you can override airbnb rules add plugins or settings by editting eslintrc file discuss with team to improve remove rules don t prefix your css rules webkit moz it will be done automatically testing unit tests the project uses jest https facebook github io jest to run unit tests to run the tests in the project just simply run bash yarn test dependencies each dependency should be installed with yarn add dependency name command each dev dependency should be installed with yarn add dependency name d command thanks | design-system react react-components storybook reactjs javascript javascript-library | os |
irremote | irremote for esp open rtos super simple irremote module for esp open rtos https github com superhouse esp open rtos usage 1 put this directory in the lib directory of your project then in your makefile extra components extras other module lib irremote 2 then in your code use it like this enable gpio pin and turn it off gpio enable gpio ir pin gpio output gpio write gpio ir pin 0 set the gpio pin ir set pin gpio ir pin send raw message ir send raw buffer buffer size 38 | os |
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ITPProject | itpproject information and technology project | server |
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ddev | ddev logo images ddev logo svg circleci https circleci com gh ddev ddev svg style shield https circleci com gh ddev ddev project is maintained https img shields io maintenance yes 2024 svg gitpod ready to code https img shields io badge gitpod ready to code blue logo gitpod https gitpod io https github com ddev ddev a href https github com codespaces new hide repo select true amp ref 20221220 codespaces amp repo 80669528 amp machine basiclinux32gb amp location westus2 img src https github com codespaces badge svg alt open in github codespaces style max width 100 height 20px a ddev is an open source tool for running local web development environments for php python and node js ready in minutes its powerful flexible per project environment configurations can be extended version controlled and shared ddev allows development teams to adopt a consistent docker workflow without the complexities of bespoke configuration get started 1 check system requirements https ddev readthedocs io macos intel and apple silicon windows 10 11 wsl2 linux gitpod https www gitpod io and github codespaces https github com codespaces 2 install docker colima and ddev https ddev readthedocs io en latest users install 3 try a cms quick start guide https ddev readthedocs io en latest users quickstart if you need help our friendly community provides great support https ddev readthedocs io en latest users support highlighted features quickly create local web development environments based on code repositories with minimal configuration import a database to any of your local environments import upload files to match the project e g drupal sites default files or wordpress wp content uploads customizable integration with hosting platforms like platform sh https platform sh pantheon https pantheon io acquia https www acquia com and others run commands within the docker environment using ddev exec view logs from the web and database containers use ddev ssh to explore the linux environment inside the container list running projects with ddev list snapshot databases with ddev snapshot temporarily share your development website with others using ddev share create custom commands as simple shell scripts enjoy effortless trusted https support extend and customize environments as much or as little as you need to run ddev to see all the commands https ddev readthedocs io en latest users usage cli contributing see how can i contribute to ddev in the faq https ddev readthedocs io en latest users usage faq and the contributing contributing md page overview of github contributions https repobeats axiom co api embed 941b040a17921e974655fc01d7735aa350a53603 svg repobeats analytics image wonderful sponsors ddev featured sponsor logos https ddev com resources featured sponsors svg | drupal wordpress development docker macos linux windows typo3 php mariadb local-development ddev magento magento2 laravel backdrop craftcms moodle nodejs | front_end |
M5P01_MuProkaron | h1 align center img width 300 src https raw githubusercontent com edi systems m5p1 muprokaron master documents demo logo png alt logo h1 one rtos rmp github release https img shields io github release edi systems m5p1 muprokaron svg https github com edi systems m5p1 muprokaron releases latest github commits https img shields io github commits since edi systems m5p1 muprokaron master 30day svg https github com edi systems m5p1 muprokaron compare master 30day master language https img shields io badge language c orange svg build https travis ci org edi systems m5p1 muprokaron svg branch master https travis ci org edi systems m5p1 muprokaron cii best practices https bestpractices coreinfrastructure org projects 1684 badge https bestpractices coreinfrastructure org projects 1684 codacy badge https api codacy com project badge grade be656c1e1f014a6abf038b4455b03bba https www codacy com app edi systems m5p1 muprokaron utm source github com amp utm medium referral amp utm content edi systems m5p1 muprokaron amp utm campaign badge grade join the chat at https gitter im m5p1 muprokaron lobby https badges gitter im m5p1 muprokaron lobby svg https gitter im m5p1 muprokaron lobby utm source badge utm medium badge utm campaign pr badge utm content badge readme cn md ensp ensp ensp ensp rmp is a small real time operating system which focuses on formal reliability and simplicity it achieves reliability by deployment of formal techniques not completed yet only whitebox testing with 100 branch coverage done the kernel can be regarded as pre certified iec 61508 sil2 or eal 4 all the basic functionalities that are necessary for rtoses are provided but nothing more this guarantees that the system is the minimum possible kernel and is also suitable to be used as a guest operating system when hosted on virtual machine monitors ensp ensp ensp ensp this operating system is much leaner than any other rtoses especially when compared to freertos or rt thread and understanding it should be simple enough yet it provides a complete set of functions that you may need during resource constrained microcontroller development such as efficient memory management anti aliasing graphics and various helper functions all these features come in a single c file and are without any extra ram consumption ensp ensp ensp ensp the manual of the operating system can be found here https github com edi systems m5p1 muprokaron blob master documents m5p1 light weight rtos user manual pdf ensp ensp ensp ensp read contributing contributing md and code of conduct code of conduct md if you want to contribute and pull request template pull request template md when you make pull requests this software is an official work of edi and thus belongs to the public domain all copyrights reserved by edi are granted to all entities under all applicable laws to the maximum extent ensp ensp ensp ensp for vendor supplied packages and hardware abstraction libraries please refer to the m0p0 library https github com edi systems m0p0 library repo to download and use them properly quick demo linux minimal runnable binary ensp ensp ensp ensp download the precompiled 32 bit linux binary here project eclipse gcc linux rmp debug rmp and watch benchmark results built in graphics widgets example and fxaa anti aliasing controls https raw githubusercontent com edi systems m5p1 muprokaron master documents demo controls png calculator https raw githubusercontent com edi systems m5p1 muprokaron master documents demo calc png fxaa https raw githubusercontent com edi systems m5p1 muprokaron master documents demo fxaa png basic thread operations create a thread c rmp thd crt thd 1 thread control block func 1 thread entry stack 1 238 stack address void 0x12345678 parameter 1 priority 5 timeslices delete a thread c rmp thd del thd 1 thread control block suspend a thread c rmp thd suspend thd 1 thread control block resume a thread c rmp thd resume thd 1 thread control block delaying a thread delay https raw githubusercontent com edi systems m5p1 muprokaron master documents demo delay gif c void func 1 void param rmp printk s parameter passed is rmp printk u ptr t param rmp printk s r n while 1 rmp thd delay 30000 rmp printk s delayed 30000 cycles r n r n void rmp init hook void rmp thd crt thd 1 func 1 stack 1 238 void 0x12345678 1 5 send from one thread to another send https raw githubusercontent com edi systems m5p1 muprokaron master documents demo send gif c void func 1 void param ptr t time 0 while 1 rmp thd delay 30000 rmp thd snd thd 2 time rmp max slices time void func 2 void param ptr t data while 1 rmp thd rcv data rmp max slices rmp printk s received rmp printk i data rmp printk s n void rmp init hook void rmp thd crt thd 1 func 1 stack 1 238 void 0x12345678 1 5 rmp thd crt thd 2 func 2 stack 2 238 void 0x87654321 1 5 counting semaphores semaphore https raw githubusercontent com edi systems m5p1 muprokaron master documents demo semaphore gif c void func 1 void param while 1 rmp thd delay 30000 rmp sem post sem 1 1 void func 2 void param ptr t data while 1 rmp sem pend sem 1 rmp max slices rmp printk s semaphore successfully acquired r n r n void rmp init hook void rmp sem crt sem 1 0 rmp thd crt thd 1 func 1 stack 1 238 void 0x12345678 1 5 rmp thd crt thd 2 func 2 stack 2 238 void 0x87654321 1 5 memory pool operations c initialize memory pool rmp mem init pool pool size allocate from the pool mem rmp malloc pool alloc size free allocated memory rmp free pool mem typical performance figures for all supported architectures ensp ensp ensp ensp flash and sram consumption is calculated in kb while the other figures are calculated in cpu clock cycles all values listed in the table below are typical useful system values not minimum values because minimum values on system size seldom make any real sense hal library are also included in the size numbers ensp ensp ensp ensp the absolute minimum value for rmp is about 1 6k rom and 432 byte ram which is reached on the hc32l136k8ta cortex m0 port and this number even included the 60 byte thread control block and 256 byte stack of the first thread and a 64 byte kernel interrupt response stack the os kernel and the stripped down hal only consumes 52 bytes of memory combined if you are willing to push this limit even further then the manufacturer hal is a rip off for you and you can roll your own machine toolchain flash sram yield mail sem mail int sem int mem dspic33e xc16 gcc 4 46 1 15 526 828 750 914 884 579 msp430 ti ccs7 2 90 0 64 495 906 786 830 736 1575 cortex m0 keil uvision 5 4 94 1 65 374 663 616 659 617 n a cortex m0 keil uvision 5 6 25 1 65 334 607 544 588 552 n a cortex m3 keil uvision 5 5 31 1 65 252 513 448 465 418 311 cortex m4 keil uvision 5 5 46 1 66 188 386 353 361 329 233 cortex m7 keil uvision 5 6 66 1 65 196 288 277 296 296 183 cortex m7 gcc 7 71 1 98 176 313 276 290 268 193 cortex m7 rvm keil uvision 5 2 09 2 29 1068 1256 1195 884 866 176 cortex m7 rvm gcc 2 15 2 10 1103 1277 1225 907 866 177 cortex r4 ti ccs7 15 1 1 42 281 458 406 424 368 274 cortex r5 ti ccs7 18 2 3 72 305 471 426 472 432 267 mips m14k xc32 gcc 17 2 2 46 263 378 358 430 420 211 rv32imac gcc 2 24 2 89 261 585 506 800 800 n a x86 linux gcc n a n a 33000 35000 33000 35000 33000 136 as a comparison rt linux 4 12 s best context switch time on cortex m7 is bigger than 25000 cycles this is measured with futex if other forms of ipc such as pipes are used this time is even longer this is for reference only the part used for evaluation relies on spi flash interface and can sometimes have a response time of 45000 cycles on a single instruction cache miss conventionally external spi flash based device need large internal memory to run their code from to make these measurements however this part simply does not have that much memory msp430 gcc rl78 gcc cortex m3 gcc cortex m4 gcc cortex m7 gcc cortex r4 gcc cortex r5 gcc dspic33e is evaluated with dspic33ep512mu810 msp430 is evaluated with msp430fr5994 cortex m0 is evaluated with stm32f030f4p6 cortex m0 is evaluated with stm32l053c8t6 cortex m3 is evaluated with stm32f103ret6 cortex m4 is evaluated with stm32f405rgt6 cortex m7 is evaluated with stm32f767igt6 cortex m7 rvm is evaluated with stm32f767igt6 and the rmp runs as a guest os in the rvm https github com edi systems m7m2 muammonite embedded hypervisor cortex r4 is evaluated with tms570ls0432 cortex r5 is evaluated with tms570lc4357 mips m14k is evaluated with pic32mz2048efm100 rv32imac is evaluated with fe310 g000 x86 linux is evaluated with ubuntu 16 04 on i7 4820k 3 7ghz ensp ensp ensp ensp all compiler options are the highest optimization usually o3 and optimized for time yield the time to yield between different threads mail the mailbox communication time between two threads sem the semaphore communication time between two threads mail int the time to send to a mailbox from interrupt sem int the time to post to a semaphore from interrupt mem the time to do an operation on memory e g allocation free possible new architecture supports architecture reason priority rl78 largely used 16 bit mcu star star star star star ti c2000 largely used dsp star star star star microblaze largely used soft core star star nios ii largely used soft core star architectures not supported architecture reason workaround pic18 hardware stack use rms state machine based os https github com edi systems m2a1 musimpron avr32 in decline use more popular cortex m and cortex rs armv5 new versions available use newer cortex m and cortex rs x86 64 advanced system use rme microkernel based os https github com edi systems m7m1 mueukaron cortex a advanced system use rme microkernel based os https github com edi systems m7m1 mueukaron coldfire in decline use more popular cortex m and cortex rs powerpc in decline use more popular cortex m and cortex rs rx100 600 600s rarely used use more popular cortex m and cortex rs tricore rarely used use more popular cortex m and cortex rs mb91460 rarely used use more popular cortex m and cortex rs getting started ensp ensp ensp ensp these instructions will get you a copy of the project up and running on your local machine for development and testing purposes see deployment for notes on how to deploy the project on a live system prerequisites ensp ensp ensp ensp you need cortex m or cortex r or mips or msp430 microcontroller development kits to run the tests this rtos focuses on value line mcus and do not concentrate on high end mcus or mpus do not use qemu simulator to test the projects because they do not behave correctly in many scenarios ensp ensp ensp ensp if you don t have a development board a x86 based linux port of rmp is also available however running rmp on top of linux uses the ptrace https en wikipedia org wiki ptrace system call and signal https en wikipedia org wiki signal ipc system thus it is not particularly fast just run the example and observe benchmark output ensp ensp ensp ensp other platform supports should be simple to implement however they are not scheduled yet for cortex a and other cpus with a memory management unit mmu https en wikipedia org wiki memory management unit go m7m1 mueukaron https github com edi systems m7m1 mueukaron real time multi core microkernel instead m7m1 supports some cortex ms and cortex rs as well compilation ensp ensp ensp ensp the vendor toolchain or eclipse projects for various microcontrollers are available in the project folder refer to the readme files in each folder for specific instructions about how to run them however keep in mind that some examples may need vendor specific libraries such as the stmicroelectronics hal some additional drivers may be required too these can be found in m0p0 library https github com edi systems m0p0 library repo running the tests ensp ensp ensp ensp to run the sample programs simply download them into the development board and start step by step debugging some examples will use one or two leds to indicate the system status in that case it is necessary to fill the led blinking wrapper functions ensp ensp ensp ensp to use the graphics library and other advanced features please refer to the user manual deployment ensp ensp ensp when deploying this into a production system it is recommended that you read the manual in the documents folder carefully to configure all macros correctly built with keil uvision 5 armcc code composer studio mplab x xc32 gcc clang llvm ensp ensp ensp ensp other toolchains are not recommended nor supported at this point though it might be possible to support them later on contributing ensp ensp ensp ensp please read contributing md contributing md for details on our code of conduct and the process for submitting pull requests to us edi project information ensp ensp ensp ensp mutate protero prokaron m5p1 r4t1 starring contributors ensp ensp ensp ensp leifeng song arm cortex m3 4 7 assembly port | keil-uvision microcontroller leds rtos formal thread cortex simplicity kernel | os |
CowboyShootOut | cowboyshootout embedded systems design final project the driver code for the project other not included files include timer c adc c dac c and st7735 c | os |
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php-opencv-examples | examples detect face by cascade classifier https github com php opencv php opencv examples blob master detect face by cascade classifier php detect face by cascade classifier jpg https raw githubusercontent com php opencv php opencv examples master results detect face by cascade classifier jpg detect face by pretrained caffe model res10 300x300 ssd by ddn module https github com php opencv php opencv examples blob master detect face by dnn ssd php detect face by dnn ssd jpg https raw githubusercontent com php opencv php opencv examples master results detect face by dnn ssd jpg detect facemarks by lbf algorithm https github com php opencv php opencv examples blob master detect facemarks by lbf php detect facemarks by lbf jpg https raw githubusercontent com php opencv php opencv examples master results detect facemarks by lbf jpg recognize face by lbph algorithm https github com php opencv php opencv examples blob master recognize face by lbph php recognize face by dnn openface https github com php opencv php opencv examples blob master recognize face by dnn openface php recognize face by dnn insightface https github com php opencv php opencv examples blob master recognize face by dnn insightface php recognize face by lbph jpg https raw githubusercontent com php opencv php opencv examples master results recognize face by lbph jpg upscale image x2 by waifu2x https github com php opencv php opencv examples blob master upscale image x2 by dnn waifu2x php upscale image x2 by dnn waifu2x png https raw githubusercontent com php opencv php opencv examples master images icon 64x64 png icon 64x64 png https raw githubusercontent com php opencv php opencv examples master results upscale image x2 by dnn waifu2x png classify image by pretrained caffe model mobilenet https github com php opencv php opencv examples blob master classify image by dnn mobilenet php cat jpg https raw githubusercontent com php opencv php opencv examples master images cat jpg results 87 egyptian cat 4 tabby tabby cat 2 tiger cat detect objects by pretrained tensorflow model mobilenet https github com php opencv php opencv examples blob master detect objects by dnn mobilenet php detect objects by dnn mobilenet png https raw githubusercontent com php opencv php opencv examples master results detect objects by dnn mobilenet png helper for autocomplete and highlighting in your ide phpdoc file https github com php opencv php opencv examples blob master phpdoc php php opencv ide helper https github com hihozhou php opencv ide helper installation 3 steps install opencv 4 5 5 https github com php opencv php opencv wiki installation opencv install php opencv for your php version https github com php opencv php opencv wiki installation php opencv install php opencv examples https github com php opencv php opencv wiki installation php opencv examples resolve problems https github com php opencv php opencv wiki installation troubleshooting if you need it requirements php 7 0 7 1 7 2 7 3 7 4 8 0 8 1 8 2 opencv 4 5 5 php opencv https github com php opencv php opencv thanks author of examples and contributor of library https github com morozovsk send your pr for library there https github com php opencv php opencv author of library https github com hihozhou | php opencv face detection recognition dnn caffe torch lbf lbph facemark facial-landmarks waifu2x docker mobilenet imagenet tensorflow ml onnx darknet | ai |
react-atomic-design | img width 100 alt atomic design src https user images githubusercontent com 4838076 33235048 d083dca6 d217 11e7 9aea 9a5ef5ae6fe7 png react atomic design this is a boilerplate with the methodology atomic design using a few cool things like storybook flow and css modules feel free to test change e adapt everything read full article https danilowoz com blog atomic design with react what is atomic design https github com danilowoz react atomic design what is atomic design atomic https github com danilowoz react atomic design atoms molecules https github com danilowoz react atomic design molecules organisms https github com danilowoz react atomic design organisms templates https github com danilowoz react atomic design templates pages https github com danilowoz react atomic design pages react atomic design https github com danilowoz react atomic design react atomic design scripts https github com danilowoz react atomic design scripts libraries https github com danilowoz react atomic design libraries contributors https github com danilowoz react atomic design contributors license https github com danilowoz react atomic design license what is atomic design popularly known within the design world atomic design helps to build consistent solid and reusable design systems plus in the world of react vue and frameworks that stimulate the componentization atomic design is used unconsciously but when used in the right way it becomes a powerful ally for developers the name atomic design comes from the idea of separating the components in atoms molecules organisms templates and pages like in the image above but what are the responsibilities of each separated part atoms img width 152 alt atoms src https user images githubusercontent com 4838076 33235102 b310bc56 d218 11e7 83e1 f5f819bab789 png atoms are the smallest possible components such as buttons titles inputs or event color pallets animations and fonts they can be applied on any context globally or within other components and templates besides having many states such as this example of button disabled hover different sizes etc molecules img width 654 alt molecules src https user images githubusercontent com 4838076 33235103 b352cc2c d218 11e7 84e1 17d1bc8e4517 png they are the composition of one or more components of atoms here we begin to compose complex components and reuse some of those components molecules can have their own properties and create functionalities by using atoms which don t have any function or action by themselves organisms img width 1356 alt organisms src https user images githubusercontent com 4838076 33235104 b3a247ca d218 11e7 9c0e 4b7f18a48627 png organisms are the combination of molecules that work together or even with atoms that compose more elaborate interfaces at this level the components begin to have the final shape but they are still ensured to be independent portable and reusable enough to be reusable in any content templates img width 585 alt templates src https user images githubusercontent com 4838076 33235105 b3f98184 d218 11e7 9490 1ff6c4046562 png in this state we stop composing components and begin to set their context moreover the templates create relationships between the organisms and others components through positions placements and patterns of the pages but it doesn t have any style color or component rendered that s why it looks like a wireframe pages img width 585 alt pages src https user images githubusercontent com 4838076 33663026 74d35e02 da75 11e7 8e1b 4cb9941dd61c png pages are the navigate parts of the application and it s where the components are distributed in one specific template the components get real content and they re connected with the whole application at this stage we can test the efficiency of the design system to analyse if all the components are independent enough or if we need to split them in smaller parts react atomic design when we started to use atomic design within react we had to adjust some rules of the methodology to ensure that components were reused as much as possible that they were stateless without styles of positions and very specific margins so to avoid any side effects in the pages of application so with each new component we asked ourselves are these components generic enough to avoid specificity and or repeated code in whatever context they are used so we were able to write a few rules the atomic design should have a file of variables and it must be imported by each component the atoms should be written without margins and positions only the molecules and organisms can set the positions of atoms but these stacks can t have any styles of margins and positions templates have only one function to set the grid of pages but never positions of specific components pages render the components with a template defined and it s here that the atomic design will be connected to the rest of the application scripts script desc yarn start start a simple webpack server yarn dev create a server to development at port 5000 yarn storybook start storybook with the stories imported yarn flow validate the flow types libraries webpack css loader svg sprite loader file loader flow types storybook storybook info css autoprefixer import nested variables inline svg babel loader preset es2015 project atomic design styles folder structure eslint prettier contributors danilowoz https github com danilowoz dleitee https github com dleitee license mit | css-architecture design-patterns react atomic-design atomic-css boilerplate storybook css-modules flowtype | os |
A2_Group_22 | simple b group assignment for introduction to information technology at rmit online br a2 group 22 br website hosted at a href https a2 simple b github io a2 group 22 simple b a | server |
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chat-app | p align center a href http nestjs com target blank img src https nestjs com img logo small svg width 200 alt nest logo a p circleci image https img shields io circleci build github nestjs nest master token abc123def456 circleci url https circleci com gh nestjs nest p align center a progressive a href http nodejs org target blank node js a framework for building efficient and scalable server side applications p p align center a href https www npmjs com nestjscore target blank img src https img shields io npm v nestjs core svg alt npm version a a href https www npmjs com nestjscore target blank img src https img shields io npm l nestjs core svg alt package license a a href https www npmjs com nestjscore target blank img src https img shields io npm dm nestjs common svg alt npm downloads a a href https circleci com gh nestjs nest target blank img src https img shields io circleci build github nestjs nest master alt circleci a a href https coveralls io github nestjs nest branch master target blank img src https coveralls io repos github nestjs nest badge svg branch master 9 alt coverage a a href https discord gg g7qnnhy target blank img src https img shields io badge discord online brightgreen svg alt discord a a href https opencollective com nest backer target blank img src https opencollective com nest backers badge svg alt backers on open collective a a href https opencollective com nest sponsor target blank img src https opencollective com nest sponsors badge svg alt sponsors on open collective a a href https paypal me kamilmysliwiec target blank img src https img shields io badge donate paypal ff3f59 svg a a href https opencollective com nest sponsor target blank img src https img shields io badge support 20us open 20collective 41b883 svg alt support us a a href https twitter com nestframework target blank img src https img shields io twitter follow nestframework svg style social label follow a p backers on open collective https opencollective com nest backers badge svg https opencollective com nest backer sponsors on open collective https opencollective com nest sponsors badge svg https opencollective com nest sponsor description nest https github com nestjs nest framework typescript starter repository installation bash npm install running the app bash development npm run start watch mode npm run start dev production mode npm run start prod test bash unit tests npm run test e2e tests npm run test e2e test coverage npm run test cov support nest is an mit licensed open source project it can grow thanks to the sponsors and support by the amazing backers if you d like to join them please read more here https docs nestjs com support stay in touch author kamil my liwiec https kamilmysliwiec com website https nestjs com https nestjs com twitter nestframework https twitter com nestframework license nest is mit licensed license | server |
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logparser | p align center a href https github com logpai img src https cdn jsdelivr net gh logpai logpai github io master img logpai logo jpg width 480 a p logparser div a href https pypi org project logparser3 img src https img shields io badge python 3 6 blue style max width 100 alt python version a a href https pypi org project logparser3 img src https img shields io pypi v logparser3 svg style max width 100 alt pypi version a a href https github com logpai logparser actions workflows ci yml img src https github com logpai logparser workflows ci badge svg event push style max width 100 alt pypi version a a href https pepy tech project logparser3 img src https static pepy tech badge logparser3 style max width 100 alt downloads a a href https github com logpai logparser blob main license md img src https img shields io badge license view brightgreen style max width 100 alt license a a href https github com logpai logparser discussion img src https img shields io badge chat wechat brightgreen style flat style max width 100 a div hr div a href https github com logpai logparser stargazers img src https reporoster com stars logpai logparser a div logparser provides a machine learning toolkit and benchmarks for automated log parsing which is a crucial step for structured log analytics by applying logparser users can automatically extract event templates from unstructured logs and convert raw log messages into a sequence of structured events the process of log parsing is also known as message template extraction log key extraction or log message clustering in the literature p align center img src https cdn jsdelivr net gh logpai logparser main docs img example jpg width 485 br an example of log parsing p new updates since the first release of logparser many prs and issues have been submitted due to incompatibility with python 3 finally we update logparser v1 0 0 with support for python 3 thanks for all the contributions pr86 https github com logpai logparser pull 86 pr85 https github com logpai logparser pull 85 pr83 https github com logpai logparser pull 83 pr80 https github com logpai logparser pull 80 pr65 https github com logpai logparser pull 65 pr57 https github com logpai logparser pull 57 pr53 https github com logpai logparser pull 53 pr52 https github com logpai logparser pull 52 pr51 https github com logpai logparser pull 51 pr49 https github com logpai logparser pull 49 pr18 https github com logpai logparser pull 18 pr22 https github com logpai logparser pull 22 we build the package wheel logparser3 and release it on pypi please install via pip install logparser3 we refactor the code structure and beautify the code via the python code formatter black log parsers available publication parser paper title benchmark ipom 03 slct https github com logpai logparser tree main logparser slct slct a data clustering algorithm for mining patterns from event logs https ristov github io publications slct ipom03 web pdf by risto vaarandi arrow upper right https github com logpai logparser tree main logparser slct benchmark qsic 08 ael https github com logpai logparser tree main logparser ael ael abstracting execution logs to execution events for enterprise applications https www researchgate net publication 4366728 abstracting execution logs to execution events for enterprise applications short paper by zhen ming jiang ahmed e hassan parminder flora gilbert hamann arrow upper right https github com logpai logparser tree main logparser ael benchmark kdd 09 iplom https github com logpai logparser tree main logparser iplom iplom clustering event logs using iterative partitioning https web cs dal ca makanju publications paper kdd09 pdf by adetokunbo makanju a nur zincir heywood evangelos e milios arrow upper right https github com logpai logparser tree main logparser iplom benchmark icdm 09 lke https github com logpai logparser tree main logparser lke lke execution anomaly detection in distributed systems through unstructured log analysis https www microsoft com en us research wp content uploads 2016 02 dm790 cr pdf by qiang fu jian guang lou yi wang jiang li microsoft arrow upper right https github com logpai logparser tree main logparser lke benchmark msr 10 lfa https github com logpai logparser tree main logparser lfa lfa abstracting log lines to log event types for mining software system logs http www se rit edu mei publications pdfs abstracting log lines to log event types for mining software system logs pdf by meiyappan nagappan mladen a vouk arrow upper right https github com logpai logparser tree main logparser lfa benchmark cikm 11 logsig https github com logpai logparser tree main logparser logsig logsig logsig generating system events from raw textual logs https users cs fiu edu taoli pub liang cikm2011 pdf by liang tang tao li chang shing perng arrow upper right https github com logpai logparser tree main logparser logsig benchmark scc 13 shiso https github com logpai logparser tree main logparser shiso shiso incremental mining of system log format http ieeexplore ieee org document 6649746 by masayoshi mizutani arrow upper right https github com logpai logparser tree main logparser shiso benchmark cnsm 15 logcluster https github com logpai logparser tree main logparser logcluster logcluster logcluster a data clustering and pattern mining algorithm for event logs http dl ifip org db conf cnsm cnsm2015 1570161213 pdf by risto vaarandi mauno pihelgas arrow upper right https github com logpai logparser tree main logparser logcluster benchmark cnsm 15 lenma https github com logpai logparser tree main logparser lenma lenma length matters clustering system log messages using length of words https arxiv org pdf 1611 03213 pdf by keiichi shima arrow upper right https github com logpai logparser tree main logparser lenma benchmark cikm 16 logmine https github com logpai logparser tree main logparser logmine logmine logmine fast pattern recognition for log analytics http www cs unm edu mueen papers logmine pdf by hossein hamooni biplob debnath jianwu xu hui zhang geoff jiang adbullah mueen nec arrow upper right https github com logpai logparser tree main logparser logmine benchmark icdm 16 spell https github com logpai logparser tree main logparser spell spell spell streaming parsing of system event logs https www cs utah edu lifeifei papers spell pdf by min du feifei li arrow upper right https github com logpai logparser tree main logparser spell benchmark icws 17 drain https github com logpai logparser tree main logparser drain drain drain an online log parsing approach with fixed depth tree https jiemingzhu github io pub pjhe icws2017 pdf by pinjia he jieming zhu zibin zheng and michael r lyu arrow upper right https github com logpai logparser tree main logparser drain benchmark icpc 18 molfi https github com logpai logparser tree main logparser molfi molfi a search based approach for accurate identification of log message formats http publications uni lu bitstream 10993 35286 1 icpc 2018 pdf by salma messaoudi annibale panichella domenico bianculli lionel briand raimondas sasnauskas arrow upper right https github com logpai logparser tree main logparser molfi benchmark tse 20 logram https github com logpai logparser tree main logparser logram logram logram efficient log parsing using n gram dictionaries https arxiv org pdf 2001 03038 pdf by hetong dai heng li che shao chen weiyi shang and tse hsun peter chen arrow upper right https github com logpai logparser tree main logparser logram benchmark ecml pkdd 20 nulog https github com logpai logparser tree main logparser nulog nulog self supervised log parsing https arxiv org abs 2003 07905 by sasho nedelkoski jasmin bogatinovski alexander acker jorge cardoso odej kao arrow upper right https github com logpai logparser tree main logparser nulog benchmark icsme 22 ulp https github com logpai logparser tree main logparser ulp ulp an effective approach for parsing large log files https users encs concordia ca abdelw papers icsme2022 ulp pdf by issam sedki abdelwahab hamou lhadj otmane ait mohamed mohammed a shehab arrow upper right https github com logpai logparser tree main logparser ulp benchmark tsc 23 brain https github com logpai logparser tree main logparser brain brain brain log parsing with bidirectional parallel tree https ieeexplore ieee org abstract document 10109145 by siyu yu pinjia he ningjiang chen yifan wu arrow upper right https github com logpai logparser tree main logparser brain benchmark bulb welcome to submit a pr to push your parser code to logparser and add your paper to the table installation we recommend installing the logparser package and requirements via pip install pip install logparser3 in particular the package depends on the following requirements note that regex matching in python is brittle so we recommend fixing the regex library to version 2022 3 2 python 3 6 regex 2022 3 2 numpy pandas scipy scikit learn conditional requirements if using molfi deap if using shiso nltk if using slct gcc if using logcluster perl if using nulog torch torchvision keras preprocessing get started 1 run the demo for each log parser we provide a demo to help you get started each demo shows the basic usage of a target log parser and the hyper parameters to configure for example the following command shows how to run the demo for drain cd logparser drain python demo py 2 run the benchmark for each log parser we provide a benchmark script to run log parsing on the loghub 2k datasets https github com logpai logparser tree main data loghub 2k for evaluating parsing accuarcy you can also use other benchmark datasets for log parsing https github com logpai logparser tree main data datasets cd logparser drain python benchmark py the benchmarking results can be found at the readme file of each parser e g https github com logpai logparser tree main logparser drain benchmark 3 parse your own logs it is easy to apply logparser to parsing your own log data to do so you need to install the logparser3 package first then you can develop your own script following the below code snippet to start log parsing see the full example code at example parse your own logs py https github com logpai logparser blob main example parse your own logs py python from logparser drain import logparser input dir path to logs the input directory of log file output dir result the output directory of parsing results log file unknow log the input log file name log format date time level content define log format to split message fields regular expression list for optional preprocessing default regex r 0 9 3 0 9 0 9 ip st 0 5 similarity threshold depth 4 depth of all leaf nodes parser logparser log format indir input dir outdir output dir depth depth st st rex regex parser parse log file after running logparser you can obtain extracted event templates and parsed structured logs in the output folder templates csv see example hdfs 2k log templates csv https github com logpai logparser blob main logparser drain demo result hdfs 2k log templates csv eventid eventtemplate occurrences dc2c74b7 packetresponder for block terminating 311 e3df2680 received block of size from 292 09a53393 receiving block src dest 292 structured csv see example hdfs 2k log structured csv https github com logpai logparser blob main logparser drain demo result hdfs 2k log structured csv level content eventid eventtemplate parameterlist info packetresponder 1 for block blk 38865049064139660 terminating dc2c74b7 packetresponder for block terminating 1 blk 38865049064139660 info received block blk 3587508140051953248 of size 67108864 from 10 251 42 84 e3df2680 received block of size from blk 3587508140051953248 67108864 10 251 42 84 info verification succeeded for blk 4980916519894289629 32777b38 verification succeeded for blk 4980916519894289629 production use the main goal of logparser is used for research and benchmark purpose researchers can use logparser as a code base to develop new log parsers while practitioners could assess the performance and scalability of current log parsing methods through our benchmarking we strongly recommend practitioners to try logparser in your production environment but be aware that the current implementation of logparser is far from ready for production use whereas we currently have no plan to do that we do have a few suggestions for developers who want to build an intelligent production level log parser please be aware of the licenses of third party libraries https github com logpai logparser blob main license md used in logparser we suggest to keep one parser and delete the others and then re build the package wheel this would not break the use of logparser please enhance logparser with efficiency and scalability with multi processing add failure recovery add persistence to disk or message queue kafka drain3 https github com logpai drain3 provides a good example for your reference that is built with practical enhancements https github com logpai drain3 new features for production scenarios citation if you use our logparser tools or benchmarking results in your publication please cite the following papers icse 19 jieming zhu shilin he jinyang liu pinjia he qi xie zibin zheng michael r lyu tools and benchmarks for automated log parsing https arxiv org pdf 1811 03509 pdf international conference on software engineering icse 2019 dsn 16 pinjia he jieming zhu shilin he jian li michael r lyu an evaluation study on log parsing and its use in log mining https jiemingzhu github io pub pjhe dsn2016 pdf ieee ifip international conference on dependable systems and networks dsn 2016 discussion welcome to join our wechat group for any question and discussion alternatively you can open an issue here https github com logpai logparser issues new scan qr code https cdn jsdelivr net gh logpai logpai github io master img wechat png | log log-mining log-analysis log-parser log-parsing anomaly-detection benchmark | ai |
SharpCV | sharpcv a image library combines opencv https github com opencv opencv and numsharp https github com scisharp numsharp lite together sharpcv returns mat object with ndarray supported which makes it easier to do data manipulation like slicing join the chat at https gitter im publiclab publiclab https badges gitter im join 20chat svg https gitter im sci sharp community nuget https img shields io nuget dt sharpcv svg https www nuget org packages sharpcv how to use install opencv prebuild binary powershell pm install package sharpcv pm install package opencvsharp4 runtime win import sharpcv and opencv library csharp using sharpcv using static sharpcv binding interact with ndarray csharp ndarray kernel new float 0 1 0 1 5 1 0 1 0 var mat new mat kernel assert areequal 3 3 mat shape assert areequal kernel 0 mat data 0 0 1 0 assert areequal kernel 1 mat data 1 1 5 1 assert areequal kernel 2 mat data 2 0 1 0 pixel level access csharp var img cv2 imread imgsolar imread color imread grayscale byte p img 8 8 assert areequal 18 p img cv2 imread imgsolar var b g r img 8 8 assert areequal 32 19 11 b g r convert to black and white image csharp var img cv2 imread solar jpg var gray cv2 cvtcolor img colorconversioncodes color rgb2gray var ret binary cv2 threshold gray 0 255 thresholdtypes thresh binary thresholdtypes thresh triangle cv2 imshow black and white binary cv2 waitkey 0 video capture from file or camera csharp var vid cv2 videocapture road mp4 var loaded frame vid read while loaded loaded frame vid read cv2 imshow video frame if you want to learn more about the api implementation please refer to the official documentation https docs opencv org | ai |
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web-2020 | web 2020 introduction to web and cloud programming subjects html5 css3 bootstrap responsive design javascript jquery cdn forms python django framework ajax json | cloud |
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Scarabei | scarabei collection of open source reusable java components java https jitpack io v scarabei scarabei svg https jitpack io scarabei scarabei installation see also https jitpack io scarabei scarabei apply plugin java apply plugin maven sourcecompatibility 1 8 compilejava compiletestjava options encoding utf 8 repositories maven url https jitpack io mavencentral dependencies format compile com github scarabei scarabei project name version compile com github scarabei scarabei scarabei api snapshot compile com github scarabei scarabei scarabei api desktop snapshot compile com github scarabei scarabei scarabei red snapshot compile com github scarabei scarabei scarabei red desktop snapshot compile com github scarabei scarabei scarabei gson snapshot compile com github scarabei scarabei scarabei apache cmns snapshot | java file io aws android ios api | front_end |
react-project | react project state of the art web development a dependency not a boilerplate to make your react project a delight to develop this is brand new not ready for production unless you are ready and willing to contribute to the project basically just building something we want here if it interests you please help also it has no tests also it s kind of awesome node npm versions i m running node v5 7 0 and npm v3 6 0 as i tinker there s no plan to support older versions at the moment contributing please see contributing md contributing md getting started the quickest way to get started is to use create react project sh npm install g create react project create react project the best app ever cd the best app ever npm install npm start now open http localhost 8080 http localhost 8080 go edit a file notice the app reloads you can enable hot module replacement by adding auto reload hot to env also npm test also node env production npm start minified gzipped long term hashed assets and server pre rendering and more you can use this as a dependency of an existing app for now the result of create react project is your best documentation on how to do that features react react react router router modern javascript with babel babel es2015 es2015 react preset react preset jsx stage 1 proposals preset stage1 gotta have that awesome stuff configurable babelrc with babelrc polyfills es5 shims es5 promise promise fetch fetch server and client express express server rendering server only routes serverroute what document titles documenttitle code splitting splitting vendor app initial bundles lazy route config loading lazy lazy route component loading lazy auto reload refresh entire page hot module replacement hmr just the changed components none let you refresh when you re ready webpack loaders loaders babel babel loader css modules cssmodules json jsonloader url urlloader fonts images optimized production build gzip compression minification uglify long term asset caching caching base64 urlloader inlined images fonts svg 10k test runner karma karma mocha mocha future features pull requests welcome please see contributing md contributing md and help out philosophy cut the crap and start building an app right away wrap up the stuff that is good for all apps keep the app in charge config lives in the app defaults provided by the framework are imported into the app the app is not imported into the framework escape hatches are important upgrading should be simple benefits should come w o changing app code usually semver versioning as soon as i ship a real app with this i ll ship 1 0 api npm scripts after running react project init your package json will have some new tasks npm start starts the server it s smart enough to know which node env you re in if node env production you ll get the full production build if you re shipping to heroku for instance deploying is just git push heroku master it ll create a production build up there npm test runs any files named modules test js with karma and mocha implementation needs work a name test runner a desired api is app doesn t need tests webpack js context junk app only has a karma config and a webpack tests config karma config configurable on package json react project like karma karma conf js blueprint default is export karmaconfig from react project test webpack test config one more export from webpack config js both configs should be babel d this way people can mess w the default configs both webpack and karma or take full control env vars react project will use environment variables to know how to build for production v development for local development you can edit the env file to change environment variables in production you ll want to set them on the box typical production environment variables node env production port 80 public path or a cdn you push your assets to server rendering on when npm start is called on a machine with those environment variables your app will be optimized with things like gzip compression development environment variables the host webpack assets are served from dev host localhost the port webpack assets are served from dev port 8081 reload the browser on app changes auto reload refresh hot reload changed modules only no page reload careful this thing can be finicky auto reload hot don t reload anything on code changes auto reload none react project import lazy serverroute from react project lazy convenience method to simplify lazy route configuration with bundle loader bundle loader js import lazy from react project bundle loader returns a function here that will load dashboard lazily it won t be in the initial bundle import loaddashboard from bundle lazy dashboard now wrap that load function with lazy and you re done you ve got super simple code splitting the dashboard code won t be downloaded until the user visits this route route getcomponent lazy loaddashboard just fyi lazy doesn t do anything other than wrap up the callback signatures of getcomponent and the bundle loader without lazy you would be doing this route getcomponent location cb loaddashboard dashboard cb dashboard default serverroute defines a route to only be available on the server add handlers functions to the different http methods note you have to restart the server after making changes to server routes but only until somebody implements hmr for the server you can nest routes to get path nesting but only the final matched route s handler is called maybe we could do something cool later with the handlers js import serverroute from react project server import listevents createevent getevent updateevent deleteevent from events export default route path api serverroute path events get listevents post createevent serverroute path id get getevent patch updateevent delete deleteevent serverroute route serverroutehandler req res params location route req an express request object res an express resonponse object params the url parameters location the matched location route the matched server route react project server createserver getapp import createserver from react project server createserver req res cb cb null renderdocument renderapp routes start creates and returns a new express express server with a new start method renderdocument props callback app supplied function to render the top level document callback with a document document component you ll probably want to just tweak the document component supplied by the blueprint callback err reactelement initialstate reactelement is the react element to be rendered initialstate is initial state from the server for data re hydration on the client renderapp props callback app supplied function to render the application content should call back with routercontext props or something that renders a routercontext at the end of the render tree callback err reactelement if you call back with an error object with a status key the server will respond with that status callback status 404 routes the app s routes react project cli it s not intended that you use this directly task should be done with npm scripts react project init initializes the app copies over a blueprint app updates package json with tasks etc react project build builds the assets called from npm start not normally called directly react project start starts the server called from npm start not normally called directly react project help not implemented ni react project version not implemented ni ni contributing md express http expressjs com document todo bundle loader https github com webpack bundle loader router https github com reactjs react router hmr server hmr server react https facebook github io react babel https babeljs io es2015 https babeljs io docs learn es2015 react preset https babeljs io docs plugins preset react stage1 https babeljs io docs plugins preset stage 1 babelrc https babeljs io docs usage babelrc es5 https github com es shims es5 shim shims promise https github com stefanpenner es6 promise fetch https github com github fetch express http expressjs com serverroute serverroute lazy lazy documenttitle https github com ryanflorence react title component splitting https webpack github io docs code splitting html hmr https webpack github io docs hot module replacement with webpack html loaders https webpack github io docs loaders html babel loader https github com babel babel loader cssmodules https github com css modules css modules jsonloader https github com webpack json loader urlloader https github com webpack url loader compression https github com expressjs compression uglify https github com mishoo uglifyjs2 caching http webpack github io docs long term caching html urlloader https github com webpack url loader karma https karma runner github io 0 13 index html mocha https mochajs org | front_end |
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feature-selection-for-machine-learning | pythonversion https img shields io badge python 3 6 20 3 7 20 203 8 20 203 9 success license https github com solegalli feature selection for machine learning blob master license https img shields io badge license bsd success svg https github com solegalli feature selection for machine learning blob master license sponsorship https www trainindata com https img shields io badge powered 20by trainindata orange svg https www trainindata com feature selection for machine learning code repository published february 2018 actively maintained img src feature selection png width 248 https www courses trainindata com p feature selection for machine learning links online course https www courses trainindata com p feature selection for machine learning table of contents 1 basic selection methods 1 removing constant features 2 removing quasi constant features 3 removing duplicated features 2 correlation feature selection 1 removing correlated features 2 basic selection methods correlation pipeline 3 filter methods univariate statistical methods 1 mutual information 2 chi square distribution 3 anova 4 basic selection methods statistical methods pipeline 4 filter methods other methods and metrics 1 univariate roc auc mse etc 2 method used in a kdd competition 2009 5 wrapper methods 1 step forward feature selection 2 step backward feature selection 3 exhaustive feature selection 6 embedded methods linear model coefficients 1 logistic regression coefficients 2 linear regression coefficients 3 effect of regularization on coefficients 4 basic selection methods correlation embedded pipeline 7 embedded methods lasso 1 lasso 2 basic selection methods correlation lasso pipeline 8 embedded methods tree importance 1 random forest derived feature importance 2 tree importance recursive feature elimination 3 basic selection methods correlation tree importance pipeline 9 hybrid feature selection methods 1 feature shuffling 2 recursive feature elimination 3 recursive feature addition | machine-learning data-science feature-selection python | ai |
Classification-of-Personality-based-on-Users-Twitter-Data | memo classifying personality of a person based on his twitter data hitcount http hits dwyl com priyansh19 https githubcom priyansh19 classification of personality based on users twitter data svg http hits dwyl com priyansh19 https githubcom priyansh19 classification of personality based on users twitter data eclipse marketplace https img shields io eclipse marketplace l notepad4e a natural language processing nlp machine learning and data mining project which will automate the screening process before hiring a professional or can be used in psychiatry to check effectivity of patient therapy br uses the twitter rest api to mine tweets for personality identification create n grams and word vectors for the hashtags emoticons and phrases using nlp techniques like tf idf train the machine to classify the personality types by using a naive bayes text classifier accurately predict the user s myers briggs personality type using 10 fold cross validation rainbow types of personalities in myer s briggs type indicator classification we have 16 types of personality which can be categorized as br types https user images githubusercontent com 33621094 74926796 4ddb5200 538b 11ea 944a 78ad55c3395f png rocket usage 1 first step is to run the requirement txt file to install all the libraries and dependencies 1 run pygen py first to generate your naive bayes classifier models for all 4 different classes it will generate few scores which will give the training data size and the features used while training the model 1 run pypredict use your own twitter keys and enter the username you want to predict palm tree contribution yet to be added | ai |
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Blockchain-Course-Geth | http www acloudfan com raj acloudfan com hands on blockchain for developers and arhitects part of a course on windows run bat files to launch geth on linux mac the shell scripts are under bash subfolder to run them you must prefix the file with the shell command ie sh geth testnet sh or simply copy and paste the commands from the files to the terminal to use the commands 1 create a local directory 2 open a terminal window geth testnet this would setup the data directories for chain data data 3 open a new terminal window geth testnet attach this would open a console to the running geth try out the other files to learn geth cli ps ensure terminal windows are open with the current directory as the one that you created | blockchain |
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Styler | cover assets cover png styler this is a plugin for figma that generates styles based on selected layers basically you can control your styles by changing layer properties and updating the styles features extract styles creates layers based on local styles this can be useful to transfer style from a project to another or for libraries that already have styles created but needs to make bulk changes extract styles will generate layers in the current page starting at position 0 you ll be zoomed at the created layers generate styles is a multi purpose action for creating renaming or updating styles based on layer properties create when there is no match between layer and existing styles rename when there is a style attached to the layer and no style with desired new name it is much faster to use bulk rename feature of figma to rename layers cmd r mac or ctrl r windows more info https help figma com hc en us articles 360039958934 rename layers update when there is a name match apply styles currently this action apply the styles based only on layer name and local styles found detach styles works on selected layers remove styles all types by fill type by stroke type by text type by effect type by grid type customize plugin notification timeout changes the duration of all notification alerts that appears while interacting with styler show last style in description appends the name of the latest style that was applied to the layer to the current style description this is working nice with update using local styles and remote styles that were applied before making the changes update using local styles this option will change the behaviour of generate styles action and as results local styles can be used as base to modify an existing style is working only with local styles and sometimes produces unexpected results for example if you ll try to rename layers from layer 01 layer 02 layer 03 to layer 00 layer 01 layer 02 instead of renaming it will only rename 1 of them and update the rest extend name match also changes the behaviour of generate styles by looking for a partial name match between layer name and style name as results will be many styles found use this with caution texts per column and frames per row controls the grid of the extracted layers reverse generation layers change the order of how styles are created when using generate styles known issues 1 some of the type details of the text layers are not saved into the style this is also a limitation of the api fixed 1 while trying to rename the styles using underscore or point prefixes the style will not change the publish status it will not become unpublish this is a limitation of the api wip after you create styles you cannot reorder them using figma api notes 1 any change can be undo 1 try to avoid same name for multiple layers it will create a single style but it will update its properties 1 only local styles are supported still you can use external styles to update local ones 1 there is no support for groups and i don t plan to support it 1 for text layers styler gets only the text properties by default but now is possible to get other properties by adding as prefix to the layer name pairing well with 1 themer https github com thomas lowry themer 1 match fills to local styles https www figma com community plugin 783240561193792353 match fills to local styles many thanks to cristi nica https github com cristi9512 for support inspired by sketch style generator https github com lucaorio sketch styles generator made by luca orio | figma figma-plugin figma-styles | os |
js-project-templates | js project templates this repository contains javascript project templates used by tra information technologies templates react vite https github com tra tech js project templates tree main react vite react https react dev project template with vite https vitejs dev | server |
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vitamin-web | important this current version of vitamin will no longer evolve and only accept bug fixes from now on more details here https github com decathlon vitamin design blob main important note md br p align center img width 300px src https user images githubusercontent com 9600228 102414461 e3b92b00 3ff6 11eb 9c96 5f37c4d5e02c png gh light mode only alt vitamin decathlon design system logo img width 300px src https user images githubusercontent com 9600228 147513091 66fcc204 279b 4140 9be5 c16744c0f637 png gh dark mode only alt vitamin decathlon design system logo p h1 align center vitamin web h1 p align center decathlon design system libraries for web applications p p align center a href https www decathlon design website a a href https decathlon github io vitamin web showcases a p p align center a aria label contributors graph href https github com decathlon vitamin web graphs contributors img src https img shields io github contributors decathlon vitamin web svg a a aria label last commit href https github com decathlon vitamin web commits img alt src https img shields io github last commit decathlon vitamin web svg a a aria label license href https github com decathlon vitamin web blob main license img src https img shields io github license decathlon vitamin web svg alt a a aria label github actions build main branch href https github com decathlon vitamin web actions img src https github com decathlon vitamin web workflows build 20main 20branch badge svg alt a a aria label slack href https join slack com t decathlon design shared invite zt 13kxb50ar ihzqv olsu4 nckepj5c4g img src https img shields io badge slack decathlon 20design 20system purple svg logo slack alt a p introduction decathlon design system https decathlon design is the framework that helps our ecosystem to design and develop consistent and quality experiences for its digital section https www decathlon design 726f8c765 p 6145b2 overview it is called vitamin this repository hosts libraries for web applications core packages in order to allow you to consume the elements of the design system according to your product constraints we give you the possibility of using the vitamin web libraries with different technologies levels of granularity with core packages developed and maintained by the vitamin core team global css styles this package provides you with a complete css with a huge set of utility classes as it is generated with tailwind css https tailwindcss com then it will be up to you to optimize for production https purgecss com by purging the css according to the classes used in your html table tr th vtmn css th td a href https github com decathlon vitamin web tree main packages sources css readme readme a td td a href https decathlon github io vitamin web vtmn showcase css img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css img src https img shields io npm v vtmn css style flat logo npm alt npm version a td td a href https sonarcloud io dashboard id decathlon vitamin web css img src https sonarcloud io api project badges measure project decathlon vitamin web css metric alert status alt quality gate status a td tr table if you already using tailwind css https tailwindcss com in your project or you want to take full advantage of all its features like functions directives https tailwindcss com docs functions and directives by building your own classes via apply https tailwindcss com docs functions and directives apply for example you can check our package vtmn css tailwind preset https github com decathlon vitamin web tree main packages sources css presets tailwind readme which will explain you how to use vitamin styles in a tailwind css project specific css styles these packages allow the consumption of elements with a higher level of granularity you get only the styles you need and what you consume is pure css without css custom properties https developer mozilla org en us docs web css therefore ie 11 compatible details summary for design tokens summary table tr th vtmn css design tokens th td a href https github com decathlon vitamin web tree main packages sources css src design tokens readme readme a td td a href https decathlon github io vitamin web vtmn showcase css index html path story guidelines colors page img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css design tokens img src https img shields io npm v vtmn css design tokens style flat logo npm alt npm version a td td a href https sonarcloud io dashboard id decathlon vitamin web css design tokens img src https sonarcloud io api project badges measure project decathlon vitamin web css design tokens metric alert status alt quality gate status a td tr table details details summary for each component summary h4 actions h4 table tr th vtmn css button th td a href https github com decathlon vitamin web tree main packages sources css src components actions button readme readme a td td a href https www decathlon design 726f8c765 p 8008f8 button img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components actions button overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css button img src https img shields io npm v vtmn css button style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components button img src https sonarcloud io api project badges measure project decathlon vitamin web css components button metric alert status alt quality gate status a td tr tr th vtmn css dropdown th td a href https github com decathlon vitamin web tree main packages sources css src components actions dropdown readme readme a td td a href https www decathlon design 726f8c765 v 0 p 42d824 dropdown img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components actions dropdown overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css dropdown img src https img shields io npm v vtmn css dropdown style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components dropdown img src https sonarcloud io api project badges measure project decathlon vitamin web css components dropdown metric alert status alt quality gate status a td tr tr th vtmn css link th td a href https github com decathlon vitamin web tree main packages sources css src components actions link readme readme a td td a href https www decathlon design 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github io vitamin web vtmn showcase css path docs components forms text input overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css text input img src https img shields io npm v vtmn css text input style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components text input img src https sonarcloud io api project badges measure project decathlon vitamin web css components text input metric alert status alt quality gate status a td tr table h4 indicators h4 table tr th vtmn css badge th td a href https github com decathlon vitamin web tree main packages sources css src components indicators badge readme readme a td td a href https www decathlon design 726f8c765 p 465f7c badge beta img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn 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https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css loader img src https img shields io npm v vtmn css loader style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components loader img src https sonarcloud io api project badges measure project decathlon vitamin web css components loader metric alert status alt quality gate status a td tr tr th vtmn css price th td a href https github com decathlon vitamin web tree main packages sources css src components indicators price readme readme a td td a href https www decathlon design 726f8c765 v 0 p 81e6be price beta img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components indicators price overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css price img src https img shields io npm v vtmn css price style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components price img src https sonarcloud io api project badges measure project decathlon vitamin web css components price metric alert status alt quality gate status a td tr tr th vtmn css progressbar th td a href https github com decathlon vitamin web tree main packages sources css src components indicators progressbar readme readme a td td a href https www decathlon design 726f8c765 p 2388f2 progressbar beta img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components indicators progressbar overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css progressbar img src https img shields io npm v vtmn css progressbar style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components progressbar img src https sonarcloud io api project badges measure project decathlon vitamin web css components progressbar metric alert status alt quality gate status a td tr tr th vtmn css rating th td a href https github com decathlon vitamin web tree main packages sources css src components indicators rating readme readme a td td a href https www decathlon design 726f8c765 p 19ec87 rating beta b 5496b9 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components indicators rating overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css rating img src https img shields io npm v vtmn css rating style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components rating img src https sonarcloud io api project badges measure project decathlon vitamin web css components rating metric alert status alt quality gate status a td tr tr th vtmn css tag th td a href https github com decathlon vitamin web tree main packages sources css src components indicators tags readme readme a td td a href https www decathlon design 726f8c765 v 0 p 129f57 tag beta img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components indicators tag overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css tag img src https img shields io npm v vtmn css tag style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components tag img src https sonarcloud io api project badges measure project decathlon vitamin web css components tag metric alert status alt quality gate status a td tr table h4 navigation h4 table tr th vtmn css breadcrumb th td a href https github com decathlon vitamin web tree main packages sources css src components navigation breadcrumb readme readme a td td a href https www decathlon design 726f8c765 v 0 p 95fc13 breadcrumb img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components navigation breadcrumb overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css breadcrumb img src https img shields io npm v vtmn css breadcrumb style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components breadcrumb img src https sonarcloud io api project badges measure project decathlon vitamin web css components breadcrumb metric alert status alt quality gate status a td tr tr th vtmn css navbar th td a href https github com decathlon vitamin web tree main packages sources css src components navigation navbar readme readme a td td a href https www decathlon design 726f8c765 p 9596dd navbar beta b 08a8f6 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components navigation navbar overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css navbar img src https img shields io npm v vtmn css navbar style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components navbar img src https sonarcloud io api project badges measure project decathlon vitamin web css components navbar metric alert status alt quality gate status a td tr tr th vtmn css search th td a href https github com decathlon vitamin web tree main packages sources css src components navigation search readme readme a td td a href https www decathlon design 726f8c765 v 0 p 666649 search beta b 711780 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components navigation search overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css search img src https img shields io npm v vtmn css search style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components search img src https sonarcloud io api project badges measure project decathlon vitamin web css components search metric alert status alt quality gate status a td tr tr th vtmn css tabs th td a href https github com decathlon vitamin web tree main packages sources css src components navigation tabs readme readme a td td a href https www decathlon design 726f8c765 p 705308 tabs beta b 4177c3 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components navigation tabs overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css tabs img src https img shields io npm v vtmn css tabs style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components tabs img src https sonarcloud io api project badges measure project decathlon vitamin web css components tabs metric alert status alt quality gate status a td tr table h4 overlays h4 table tr th vtmn css alert th td a href https github com decathlon vitamin web tree main packages sources css src components overlays alert readme readme a td td a href https www decathlon design 726f8c765 p 64b4b5 alert beta b 129609 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components overlays alert overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css alert img src https img shields io npm v vtmn css alert style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components alert img src https sonarcloud io api project badges measure project decathlon vitamin web css components alert metric alert status alt quality gate status a td tr tr th vtmn css modal th td a href https github com decathlon vitamin web tree main packages sources css src components overlays modal readme readme a td td a href https www decathlon design 726f8c765 p 9596dd modal beta b 08a8f6 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components overlays modal overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css modal img src https img shields io npm v vtmn css modal style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components modal img src https sonarcloud io api project badges measure project decathlon vitamin web css components modal metric alert status alt quality gate status a td tr tr th vtmn css popover th td a href https github com decathlon vitamin web tree main packages sources css src components overlays popover readme readme a td td a href https www decathlon design 726f8c765 p 3086dd popover beta b 129609 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components overlays popover overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css popover img src https img shields io npm v vtmn css popover style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components popover img src https sonarcloud io api project badges measure project decathlon vitamin web css components popover metric alert status alt quality gate status a td tr tr th vtmn css snackbar th td a href https github com decathlon vitamin web tree main packages sources css src components overlays snackbar readme readme a td td a href https www decathlon design 726f8c765 p 798580 snackbar beta b 129609 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components overlays snackbar overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css snackbar img src https img shields io npm v vtmn css snackbar style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components snackbar img src https sonarcloud io api project badges measure project decathlon vitamin web css components snackbar metric alert status alt quality gate status a td tr tr th vtmn css toast th td a href https github com decathlon vitamin web tree main packages sources css src components overlays toast readme readme a td td a href https www decathlon design 726f8c765 p 4450b2 toast beta b 129609 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components overlays toast overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css toast img src https img shields io npm v vtmn css toast style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components toast img src https sonarcloud io api project badges measure project decathlon vitamin web css components toast metric alert status alt quality gate status a td tr tr th vtmn css tooltip th td a href https github com decathlon vitamin web tree main packages sources css src components overlays tooltip readme readme a td td a href https www decathlon design 726f8c765 p 595266 tooltip beta b 97ce1d img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components overlays tooltip overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css tooltip img src https img shields io npm v vtmn css tooltip style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components tooltip img src https sonarcloud io api project badges measure project decathlon vitamin web css components tooltip metric alert status alt quality gate status a td tr table h4 selection controls h4 table tr th vtmn css checkbox th td a href https github com decathlon vitamin web tree main packages sources css src components selection controls checkbox readme readme a td td a href https www decathlon design 726f8c765 p 953c37 checkbox img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components selection controls checkbox overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css checkbox img src https img shields io npm v vtmn css checkbox style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components checkbox img src https sonarcloud io api project badges measure project decathlon vitamin web css components checkbox metric alert status alt quality gate status a td tr tr th vtmn css chip th td a href https github com decathlon vitamin web tree main packages sources css src components selection controls chip readme readme a td td a href https www decathlon design 726f8c765 p 953c37 chip img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components selection controls chip overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css chip img src https img shields io npm v vtmn css chip style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components chip img src https sonarcloud io api project badges measure project decathlon vitamin web css components chip metric alert status alt quality gate status a td tr tr th vtmn css quantity th td a href https github com decathlon vitamin web tree main packages sources css src components selection controls quantity readme readme a td td a href https www decathlon design 726f8c765 v 0 p 207abd quantity beta b 75bea6 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components selection controls quantity overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css quantity img src https img shields io npm v vtmn css quantity style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components quantity img src https sonarcloud io api project badges measure project decathlon vitamin web css components quantity metric alert status alt quality gate status a td tr tr th vtmn css radio button th td a href https github com decathlon vitamin web tree main packages sources css src components selection controls radio button readme readme a td td a href https www decathlon design 726f8c765 p 31e934 radio button img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components selection controls radio button overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css radio button img src https img shields io npm v vtmn css radio button style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components radio button img src https sonarcloud io api project badges measure project decathlon vitamin web css components radio button metric alert status alt quality gate status a td tr tr th vtmn css toggle th td a href https github com decathlon vitamin web tree main packages sources css src components selection controls toggle readme readme a td td a href https www decathlon design 726f8c765 p 99628d toggle img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components selection controls toggle overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css toggle img src https img shields io npm v vtmn css toggle style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components toggle img src https sonarcloud io api project badges measure project decathlon vitamin web css components toggle metric alert status alt quality gate status a td tr table h4 structure h4 table tr th vtmn css accordion th td a href https github com decathlon vitamin web tree main packages sources css src components structure accordion readme readme a td td a href https www decathlon design 726f8c765 v 0 p 04348b accordion beta img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components structure accordion overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css accordion img src https img shields io npm v vtmn css accordion style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components accordion img src https sonarcloud io api project badges measure project decathlon vitamin web css components accordion metric alert status alt quality gate status a td tr tr th vtmn css card th td a href https github com decathlon vitamin web tree main packages sources css src components structure card readme readme a td td a href https www decathlon design 726f8c765 p 88fc2b card beta b 51e109 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components structure card overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css card img src https img shields io npm v vtmn css card style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components card img src https sonarcloud io api project badges measure project decathlon vitamin web css components card metric alert status alt quality gate status a td tr tr th vtmn css divider th td a href https github com decathlon vitamin web tree main packages sources css src components structure divider readme readme a td td a href https www decathlon design 726f8c765 p 28ad9b divider beta b 75bea6 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components structure divider overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css divider img src https img shields io npm v vtmn css divider style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components divider img src https sonarcloud io api project badges measure project decathlon vitamin web css components divider metric alert status alt quality gate status a td tr tr th vtmn css list th td a href https github com decathlon vitamin web tree main packages sources css src components structure list readme readme a td td a href https www decathlon design 726f8c765 p 078570 list beta b 9115e7 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components structure list overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css list img src https img shields io npm v vtmn css list style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components list img src https sonarcloud io api project badges measure project decathlon vitamin web css components list metric alert status alt quality gate status a td tr tr th vtmn css skeleton th td a href https github com decathlon vitamin web tree main packages sources css src components structure skeleton readme readme a td td a href https www decathlon design 726f8c765 p 79685a skeleton beta b 122d98 img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase css path docs components structure skeleton overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css skeleton img src https img shields io npm v vtmn css skeleton style flat logo npm alt npm version a td td a href https sonarcloud io project overview id decathlon vitamin web css components skeleton img src https sonarcloud io api project badges measure project decathlon vitamin web css components skeleton metric alert status alt quality gate status a td tr table details details summary for utilities summary table tr th vtmn css utilities th td a href https github com decathlon vitamin web tree main packages sources css src utilities readme readme a td td a href https decathlon github io vitamin web vtmn showcase css index html path docs guidelines typography overview img src https img shields io badge storybook css d891bc style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn css utilities img src https img shields io npm v vtmn css utilities style flat logo npm alt npm version a td td a href https sonarcloud io dashboard id decathlon vitamin web css utilities img src https sonarcloud io api project badges measure project decathlon vitamin web css utilities metric alert status alt quality gate status a td tr table details icons this package provides you with a library of svg icons that you can include in a handful of ways svgs icon font sprite css embedded table tr th vtmn icons th td a href https github com decathlon vitamin web tree main packages sources icons readme readme a td td a href https www decathlon design 726f8c765 p 91dc94 iconography img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https decathlon github io vitamin web vtmn showcase icons img src https img shields io badge storybook icons 295573 style flat logo storybook alt storybook a td td a href https www npmjs com package vtmn icons img src https img shields io npm v vtmn icons style flat logo npm alt npm version a td tr table assets this package provides you with a library of svg assets that you can include in a handful of ways svgs asset font sprite css embedded table tr th vtmn assets th td a href https github com decathlon vitamin web tree main packages sources assets readme readme a td td a href https www decathlon design 726f8c765 p 895daa assets img src https img shields io badge decathlon design docs 007dbc alt documentation a td td a href https www npmjs com package vtmn assets img src https img shields io npm v vtmn assets style flat logo npm alt npm version a td tr table community packages in order to enhance your developer experience we give you the possibility to develop your components in react svelte and vue based on our core packages just above these libraries are developed and maintained by the community the vitamin core team will be there to review and ensure the quality of your propositions feel free to contribute react this package provides you with a library of react https reactjs org components table tr th vtmn react th td a href https github com decathlon vitamin web tree main packages sources react readme readme a td td a href https decathlon github io vitamin web vtmn showcase react img src https img shields io badge storybook react 61dafb style flat logo storybook alt storybook a a td td a href https www npmjs com package vtmn react img src https img shields io npm v vtmn react style flat logo npm alt npm version a td td a href https sonarcloud io dashboard id decathlon vitamin web react img src https sonarcloud io api project badges measure project decathlon vitamin web react metric alert status alt quality gate status a td tr table svelte this package provides you with a library of svelte https svelte dev components table tr th vtmn svelte th td a href https github com decathlon vitamin web tree main packages sources svelte readme readme a td td a href https decathlon github io vitamin web vtmn showcase svelte img src https img shields io badge storybook svelte f13c03 style flat logo storybook alt storybook a a td td a href https www npmjs com package vtmn svelte img src https img shields io npm v vtmn svelte style flat logo npm alt npm version a td td a href https sonarcloud io dashboard id decathlon vitamin web svelte img src https sonarcloud io api project badges measure project decathlon vitamin web svelte metric alert status alt quality gate status a td tr table vue this package provides you with a library of vue https vuejs org components table tr th vtmn vue th td a href https github com decathlon vitamin web tree main packages sources vue readme readme a td td a href https decathlon github io vitamin web vtmn showcase vue img src https img shields io badge storybook vue 41b883 style flat logo storybook alt storybook a a td td a href https www npmjs com package vtmn vue img src https img shields io npm v vtmn vue style flat logo npm alt npm version a td td a href https sonarcloud io dashboard id decathlon vitamin web vue img src https sonarcloud io api project badges measure project decathlon vitamin web vue metric alert status alt quality gate status a td tr table contributing one of the decathlon design system goals is to provide guidelines components to gain in consistency efficiency accessibility the best way to achieve this is together that s why we are on github we would love contributions from the community bug reports feature requests suggestions pull requests whatever you want yarn workspaces https yarnpkg com lang en docs workspaces and nx https nx dev are used to manage dependencies and build config across packages lerna https github com lerna lerna is used to manage versioning publishing run the following to setup your local dev environment sh install yarn alternatives at https yarnpkg com en docs install brew install yarn clone or fork vitamin web git clone git github com decathlon vitamin web git or your fork cd vitamin web install dependencies yarn build all packages yarn build start all showcases and build sources in watch mode hot reload yarn start or you can launch separately recommended yarn start css yarn start icons yarn start react yarn start svelte yarn start vue see the contributing docs contributing md for more information about how to contribute special thanks thank you to the contributors contributors md involved in these vitamin web libraries even before they were open source a href https github com decathlon vitamin web graphs contributors img src https contrib rocks image repo decathlon vitamin web a thank you also remix icon https remixicon com because vitamix icons is the official decathlon icon library based on their open source icon library https github com remix design remixicon remix design 2020 this original library is under the license apache 2 0 and has been modified by decathlon learn more https www decathlon design 726f8c765 p 58575f vitamix license license copyright 2021 decathlon licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | design-system libraries components design-tokens ui-components ui-kit styleguide yarn showcases vitamin ui-library monorepo lerna css decathlon storybook html nx accessibility tailwindcss | os |
khipu | khipu enterprise blockchain platform khipu is an enterprise blockchain platform based on ethereum it is built on earlier work on mantis https github com input output hk mantis the major researches of khipu so far try to execute transactions in a block as in parallel as possible average 80 transactions in one block could be executed in parallel currently kafka based storage engine carefully designed for blockchain with high performance both on random sequential reading and writing the fastest ethereum implemention status beta release 0 4 x this version of the code supports peer discovery fast sync download a recent state trie snapshot and all blocks this is the default behaviour regular sync download and execute every transaction in every block in the chain this will be enabled once fast sync finished json rpc api features to be done x reduce disk usage x reduce memory usage cpu mining execute transactions in parallel in mining morden testnet and private network unit tests notice this version s data storage format may be changed before productional release during fast sync sometimes the syncing looks like stopped with no more state nodes or blocks being downloaded a possible reason that may be the current left handshaked peers could not respond to state nodes or blocks request any more in case of this try to stop khipu and restart it again minimum requirements to run khipu 16g ram 500g disk space ssd is preferred although hdd is okay installation and running building the latest release can be downloaded from here https github com khipu io khipu releases running from command line unzip khipu eth x x x zip cd khipu eth x x x bin khipu eth or nohup khipu eth tail f nohup khipu data directory is home khipu eth ls khipu eth kesque logs keystore nodeid keys rocksdb remove kesque logs and rocksdb will level a installation with empty blockchain data but the keystore and nodeid keys will be kept prerequisites to build jdk 1 8 download from java com http www java com sbt download sbt http www scala sbt org download html build the client as an alternative to downloading the client build the client from source git clone https github com khipu io khipu git cd khipu sbt khipu eth dist or sbt clean khipu eth dist the packaged zip file could be found at khipu khipu eth target universal license khipu is licensed under the mit license found in the license file in the root directory | ethereum blockchain scala akka | blockchain |
cs733 | cs733 advanced distributed computing engineering a cloud iit bombay assignments project | cloud |
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Packman | packman build https github com tonnyl packman workflows build badge svg https github com tonnyl packman actions query workflow 3abuild a mobile application made for managing gitlab ci cd jobs demonstrating how to implement some latest technologies in mobile software development p align center img src https user images githubusercontent com 13329148 222216780 d851cc33 3eaf 4c57 90bd 603b6e521f32 jpg height 500 img src https user images githubusercontent com 13329148 222216998 9646f0d0 bac0 4e5f bd5d 885acb16c5d4 jpg height 500 p feature 100 kotlin the whole ui is written with compose multiplatform https github com jetbrains compose jb the business logic is shared between android and ios with kotlin multiplatform mobile https kotlinlang org docs multiplatform html build instructions prerequisites android studio electric eel 2022 1 1 patch 1 or higher xcode 14 2 or higher if you want to build ios app jdk 17 or higher kotlin 1 8 0 or higher kotlin multiplatform mobile plugin https plugins jetbrains com plugin 14936 kotlin multiplatform mobile installed in android studio optional api keys and configurations put them into local properties file java access token your access token trigger pipeline access token your project level token project path path of your project project id your project id graph ql server url https your gitlab server address api graphql rest server url https your gitlab server address api v4 store file path path to store file store password password key alias key alias key password key password build shell gradlew build license packman is under an mit license see the license license file for more information | android compose compose-jb gitlab gitlab-ci graphql ios jetpack jetpack-compose kmm kmp kotlin kotlin-multiplatform kotlin-multiplatform-mobile ktor multiplatform-compose spotless realm realm-kotlin | front_end |
TLDR-component | div align center h1 img src https lightningaidev wpengine com wp content uploads 2022 11 image 6 png br tl dr with lightning br h1 div align center p align center a href run run a a href https www lightning ai lightning ai a a href https lightning ai lightning docs docs a p readthedocs https readthedocs org projects pytorch lightning badge version stable https lightning ai lightning docs slack https img shields io badge slack chat green svg logo slack https www pytorchlightning ai community license https img shields io badge license apache 202 0 blue svg https github com lightning ai lightning blob master license div div use tldr to pre train or fine tune a large language model for text summarization with as many parameters as you want up to billions you can do this using multiple gpus across multiple machines on your own data all without any infrastructure hassle all handled easily with the lightning apps framework https lightning ai lightning docs run to run tldr paste the following code snippet in a file app py python pip install git https github com lightning ai lai tldr component git https github com lightning ai lightning llms curl https raw githubusercontent com shivanandroy t5 finetuning pytorch main data news summary csv create dirs o home data summary news csv c import lightning as l import os from transformers import t5forconditionalgeneration t5tokenizerfast as t5tokenizer from lit llms tensorboard import drivetensorboardlogger multinodelightningtrainerwithtensorboard from lai tldr import tldrdatamodule default callbacks predict tldrlightningmodule class tldr l lightningwork finetune on a text summarization task def init self tb drive kwargs super init kwargs self tensorboard drive tb drive def run self for huggingface transformers os environ tokenizers parallelism false configure your model model type t5 base t5 tokenizer t5tokenizer from pretrained model type t5 model t5forconditionalgeneration from pretrained model type return dict true lightning module tldrlightningmodule t5 model tokenizer t5 tokenizer configure your data data module tldrdatamodule os path expanduser data summary news csv t5 tokenizer run your training strategy deepspeed stage 3 offload if l app utilities cloud is running in cloud else ddp trainer l trainer max epochs 2 limit train batches 250 precision 16 strategy strategy callbacks default callbacks log every n steps 1 logger drivetensorboardlogger save dir drive self tensorboard drive trainer fit lightning module data module if trainer global rank 0 sample text summarize ml ops platforms come in many flavors from platforms that train models to platforms that label data and auto retrain models to build an ml ops platform requires dozens of engineers multiple years and 10 million in funding the majority of that work will go into infrastructure multi cloud user management consumption models billing and much more build your platform with lightning and launch in weeks not months focus on the workflow you want to enable label data then train models lightning will handle all the infrastructure billing user management and the other operational headaches predictions predict lightning module to trainer strategy root device sample text print input text n sample text print summarized text n predictions 0 app l lightningapp multinodelightningtrainerwithtensorboard tldr num nodes 2 cloud compute l cloudcompute gpu fast multi disk size 50 running locally bash lightning run app app py setup running on cloud bash lightning run app app py setup cloud don t want to use the public cloud contact us at product lightning ai for early access to run on your private cluster byoc | ai |
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The-Journal-of-Blockchain | the journal of blockchain p align center img src eos 2018 04 28 jpg height 130 p p align center a href https github com tonycai the journal of blockchain network img src https img shields io github forks tonycai the journal of blockchain svg alt forks a a href https github com tonycai the journal of blockchain stargazers img src https img shields io github stars tonycai the journal of blockchain svg alt stars a a href license md img src https img shields io badge license gnu blue svg alt license a p tcp ip 0 code is law ethereum white paper https github com ethereum wiki wiki white paper eos white paper https github com eosio documentation blob master technicalwhitepaper md cosmos white paper https github com cosmos cosmos blob master whitepaper md quorum white paper https github com jpmorganchase quorum br https www binance com ref 10113920 otcbtc https otcbtc com referrals otcking mytoken app http mytoken io https btc com https www huobi pro zh cn okex 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https www reddit com http www weilaicaijing com br https www feixiaohao com imtoken app https token im btc https btc com botvs https www botvs com aicoin sosobtc app https www aicoin net cn myetherwallet https www myetherwallet com eth https etherscan io http www btcjl com mytoken app https mytoken io blockchain https bitcoin org zh cn choose your wallet ltc https chainz cryptoid info ltc https www ricequant com coinmarketcap https coinmarketcap com https blockchain info zh cn wallet qtum https qtumexplorer io http www abuquant com btc123 https www btc123 com trades https electrum ltc org usdt http omnichest info lookupsp aspx sp 31 https guobinet com bitkan http bitkan com price http bitpie com neo http antcha in http www bilianghua com cryptowat https cryptowat ch bither https bither net bitinfocharts https bitinfocharts com zh samaritan http samaritan stockdb org investing https cn investing com bitbank https www bitbank com bitbank https block bitbank com http www bityixia com 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bnb https t xiaomiquan com 3rf2rbi https mp weixin qq com s u1aum4i5pjemybmkonmlna http liyuechun org blog https t xiaomiquan com vfeqzba https mp weixin qq com s wbf4reersath 6cunxpqzw https t xiaomiquan com 7aamea6 https t xiaomiquan com fi2vnji miao https mp weixin qq com s fcqri5d61se5vbpq2b9aig https t xiaomiquan com 3zburby ipfs ipfs https t xiaomiquan com znamfiq https mp weixin qq com s vey187 3thdgxdecohw9ug https t xiaomiquan com ieuniuv https mp weixin qq com s xl4qrqqcsthbb z4o7ib6a https t xiaomiquan com rrvvzvr https mp weixin qq com s n dx2ru5i7oyajlmlrrwpw 90 https mp weixin qq com s m1ncdcs f wmnttq35ywva https mp weixin qq com s 2btcjsaxcvfz63ir6kw3qg 45 https mp weixin qq com s c5cnthax6xv0m osbushra http mp weixin qq com s hd6 tgjo3ejquzubo0ezzg https mp weixin qq com s yt4yqfu9y0vx s thwvixa ethfans https github com ethfans wiki wiki br https www bitmain com http www bubi cn icoroot https www icoroot com antpool https antpool com home htm http www wxblockchain com crushcrypto https crushcrypto com btc https pool btc com https trustsql qq com 52icoifo http www 52ico com viabtc https pool viabtc com http www yunphant com topicolist http pieifo com f2pool https www f2pool com http www 33 cn icorating https topicolist com http www btc top https www jingtum com icoweb https icorating com https pool btcchina com https taiyiyun com icoalert http www icoweb co https www hashnest com https www anlink com icodrops https www icoalert com https pow8 com coingecko http icodrops com btc http bitcoin sipa be miningpool https miningpoolhub com etc http www 91pool com http pandaminer com https pool bixin com home https eth ethfans org br https e jd com 30161987 html http book 8btc com books 6 masterbitcoin2cn book eth http ethfans org wikis e4 bb a5 e5 a4 aa e5 9d 8a e7 99 bd e7 9a ae e4 b9 a6 https item jd com 12033804 html http book 8btc com books 1 gaosheng blockchain report book eos http chainx org paper index index id 20 html https item jd com 10401239824 html http book 8btc com books 6 cnfinance201617 book http chainx org paper index index id 13 html https item jd com 12014042 html solidity http book 8btc com books 6 solidity zh book polkadot http chainx org paper index index id 6 html https item jd com 11922237 html kyber http chainx org paper index index id 26 html https weidian com item html itemid 1878011740 https minerdl yunfan com miner lltoken uploadfile pdf llt white paper pdf https item jd com 11936509 html https e jd com 30373377 html https item jd com 12114952 html https weidian com item html itemid 2133865911 http item jd com 12007317 html https item jd com 13612791347 html https item jd com 12188306 html https item jd com 12159265 html br solidity https ethereum github io browser solidity version soljson v0 4 18 commit 9cf6e910 js ted https weibo com tv v fzcpgfu1y fid 1034 01ea26af661b0e4bf56a63f6628fba57 eth https www cryptokitties co https mp weixin qq com s biz mziynty0mdm5oq amp mid 2247483975 amp idx 1 amp sn 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mpshare 1 amp scene 1 amp srcid 1213niin9rn4yzdefzwdm14d rd btc http rainpool io https mp weixin qq com s biz mzawntu2odq2mw amp mid 2651979176 amp idx 1 amp sn 5126d668d07c6763fdc66709b69c68a7 amp chksm 80ffff08b788761ebae3abc498e0d16aefc5f48b94febbe0c660993d17123629c64479acb7b5 amp mpshare 1 amp scene 1 amp srcid 1213xrrxqkds3ub0odix0kgt rd btc http gamefaucet com https mp weixin qq com s biz mzawntu2odq2mw amp mid 2651979182 amp idx 1 amp sn b4e00c32bb508a3ae0580fa1d848ac7d amp chksm 80ffff0eb7887618409e70bd3f52466143df8c1d4ae72b50067aa39ed9b1daf6daa5d8c5ba9d amp mpshare 1 amp scene 1 amp srcid 1213mlekuna5dkuveweydqxl rd https forkgen tech https mp weixin qq com s biz mzaxmjmzmdg4oa amp mid 2650539156 amp idx 1 amp sn bda79443e43d17cffc4a2a2d0f14d608 amp chksm 83bbd673b4cc5f65b275ffbfeab5f15706aafb83331abaa20eed429cf880e265b4b4764ebf6c amp mpshare 1 amp scene 1 amp srcid 1214ffhnory7zgykqgukgcyx rd http bi 163 com https mp weixin qq com s biz mzaxmjmzmdg4oa amp mid 2650538875 amp idx 1 amp sn f42c21c8c4b49025b87c61026f40fd8d amp chksm 83bbd19cb4cc588ad0b67f588912d1566feb954e4aac137769de2bb35b7c20040b8be8b78fdf amp mpshare 1 amp scene 1 amp srcid 1214e4ggdtmks24cc2fmgrok rd gxs https wechat gxb io activity2 token e41e9105867a238c1206f20f03b63ac4e743539984df6830d72776c5029484db https mp weixin qq com s biz mzaxmjmzmdg4oa amp mid 2650538880 amp idx 1 amp sn ae74dea790873f975ff4bcadb9491e69 amp chksm 83bbd167b4cc587170b111d532f12cc6115ea84ea4e435e5bef462e7362bd1a41cd368e3ad4a amp mpshare 1 amp scene 1 amp srcid 1214phsisydyngn1it1br57v rd 2011 btc https blog codingnow com 2011 05 bitcoin html 2011 https www zhihu com question 19982269 2017 https mp weixin qq com s biz mziyndk1nzu4oa amp mid 2247484665 amp idx 1 amp sn c0e3a0a9b72315065920f9aeeb4c144d amp chksm e8064102df71c8148fc42b200e8b30276d0df8803bd1e3a18330bda3a13c46445aa8663b9d88 amp mpshare 1 amp scene 1 amp srcid 1214thpgs7rch0q3viqx4smr rd br ipfs filecoin filecoin fil protocol labs ipfs ipfs io filecoin filecoin io ipfs https discuss ipfs io im http webchat freenode net irc ipfs filecoin https github com ipfs filecoin blog https filecoin io blog ipfs twitter ipfsbot filecoin twitter minefilecoin twitter juanbenet https protocol ai ipfs filecoin filecoin ico ico https coinlist co ico https coinlist co filecoin http juan benet ai p align center img src images jpeg width 620 p 7x24 p align center img src images png width 620 p how to install bitcoin on ubuntu 16 4 https github com tonycai the journal of blockchain wiki how to install bitcoin on ubuntu 16 4 how to install ethereum on ubuntu 16 04 https github com tonycai the journal of blockchain wiki how to install ethereum on ubuntu 16 04 dapp skirmantas janu kas dappradar com dapp https dappradar com 1 2016 4 26 gem gem health 2 google ibm factom bithealth blockverify dna bits bitfury 2018 3 27 ai all in p align center img src images jpg width 620 p tips tonytasks gmail com donation btc 1frpjxuqdrteqrgfbf7ag1lsvjaufzjqva eth 0xb8198455c0f69dd7c7454cf68a6c36c569a913d5 | blockchain ipfs bitcoin ethereum neo dapp crypto cryptocurrency journal cosmos ethermint | blockchain |
intermediate-nlp | intermediate natural language processing there are three classes in this series 1 building text analytics pipelines using nlp text analytics 2 extracting insights from text data using nlp and word embeddings embeddings 3 leveraging nlp and word embeddings in machine learning projects ml projects | ai |
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LLM4Mol | can large language models empower molecular properties prediction requirements the following pakages are required to run the code python 3 8 0 pytorch 1 12 1 cudatoolkit 11 3 networkx 2 5 rdkit 2023 3 1 deepchem 2 7 1 training and evaluation you can directly run the program after modifying the basic hyperparameters in the script example sh bash sh example sh some code resources that we benefit from https github com cedricvincentcuaz tfgw https github com xiaoxinhe tape | ai |
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AirSENSE_ESP32-IDF_RTOS | airsense esp32 idf this is main development repo for esp32 device in 20221 semester a simple buildable esp idf example for more information please contact nhu hai long nguyen https www linkedin com in h i long nguy n nh 95b718207 or gmail long27032002 gmaill com rocket features feature rich br it has all the features which you need in a iot system read sensor data save data to micro sd card publish data to mqtt server to integrate airsense into your project all you need is an esp32 board a computer with visual studio code to run platformio usb capble for flashing free and portable support platformio on visual studio code can be compiled to esp32 for running freertos os distributed under the mit licence so you can easily use it in commercial projects too using esp idf in this project we use esp idf version 4 4 3 docs tools and services we are writing about documents heart sponsor airsense facebook br airsense https www facebook com airsenseairqualitymornitoringsystem airsense another version br another repositories https github com orgs air sense repositories how to use project we encourage the users to use the example as a template for the new projects a recommended way is to follow the instructions on a docs page https docs espressif com projects esp idf en latest api guides build system html start a new project project structure br below is short explanation of remaining files in the project folder component this is the folder store all component of project bme280 bme280 h bme280 c cmakelists txt component mk kconfig projbuild buttons datamanager ds3231 filemanager mhz14a ota pms7003 sntp sync main cmakelists txt component mk kconfig projbuild main c server certs this is the folder store file pem for update ota test this is the folder store test tool python file cmakelists txt license makefile readme md this is the file you are currently reading note kconfig projbuild is file to config number pin gpio parameters frequency these values can be changed run the command idf py menuconfig in the esp idf terminal to change it to suit your hardware in this project we used to use some libraries of github account unclerus star2 contributing airsense is an open project and contribution is very welcome there are many ways to contribute from simply speaking about your project through writing examples improving the documentation fixing bugs or even hosting your own project under the airsense organization alot of people already left their fingerprint in airsense be one them see your here slightly smiling face a href https github com air sense airsense esp32 idf rtos graphs contributors img src https contrib rocks image repo air sense airsense esp32 idf rtos max 48 a and many other | esp32 esp-idf airsense bme280 c cmake ds3231 freertos kconfig makefile sdcard pms7003 mqtt wifi iot-device | os |
ci-dockerfiles | content moved the content of this repository has moved to https github com zephyrproject rtos docker image | ci docker | os |
Master-or-Bachelor-thesis-Template-Eindhoven-University-of-Technology | master or bachelor thesis template eindhoven university of technology this is a latex template for bachelor and master theses at eindhoven university of technology the template provides a basic latex document structure and gives hints on how to structure your report and where to place which information pdf of the template https github com dfahland master or bachelor thesis template eindhoven university of technology releases download 2020 10 master or bachelor thesis template eindhoven university of technology pdf | server |
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Flowe | flowe cs 329e mobile development final project name of project flowe team members mehmet zenginerler michael walters hiran manoharan yixing ma dependencies xcode 13 1 swift 5 ios 13 special instructions use an iphone 12 simulator before running the app run pod install inside the flowe folder where the podfile is located create a new account or use this test account for logging in email test gmail com password test123 work distribution mehmet zenginerler login signup onboarding pages firebase pod functionality swiftui to uikit integration darkmode functionality animation integration lottie curvy background integration design svg gradient integration removed profile page ui overall bug fixes michael walters backend core data organization projects tab designed to keep track of project due dates and descriptions has archive functionality projects cell view project archive project archive cell view overall ui design color scheming some background images created by carla davis sound effect implementation animation hiran manoharan profile edit profile tabs designed to edit personalised profile for future functionality with projects tab will be able to collaborate with other users in projects settings tab change app settings such as sounds theme and avatar workflow tab designed to keep track of tasks throughout the day add task task row user defaults for settings and core data for profiles and workflow logo creation yixing ma journal designed to keep track of thoughts like a journal and adds thoughts to the calendar pomodoro designed to ensure user works for 25 mins and takes a break for 5 mins creates a workflow implementing animation calendar homepage required feature checklist x login register path with firebase x settings x dark mode x sleep mode x home screen on launch sound x change avatar x non default fonts and colors used two major elements used x core data user profile path using camera and photo library multithreading x swiftui minor elements used x two additional view types such as sliders segmented controllers etc the two we implemented are x segmented controllers x sliders one of the following x table view collection view x tab vc page vc two of the following x alerts popovers x stack views scroll views haptics x user defaults at least one of the following per team member x local notifications core graphics x gesture recognition x animation x calendar core motion core location mapkit x core audio preview https user images githubusercontent com 64453575 145943072 5c5e4730 b2cd 4aeb ad17 5fefecb64a06 mov | ios-app swift university-project | front_end |
cppserdes | cppserdes icon images cppserdes icon png cppserdes is a serialization deserialization library designed with embedded systems in mind build status https travis ci com darrenlevine cppserdes svg branch main https travis ci com darrenlevine cppserdes license https img shields io badge license mit informational version https img shields io badge version 1 0 blue features bitpacking support any bit alignment any bit padding portability uses bit shifting and masking with no dynamic memory allocation avoids most std containers serializes to from 8 bit arrays and 16 bit 32 bit and 64 bit arrays with no api changes to your code to work with hardware drivers directly in their native bit widths common embedded systems types floating point unsigned signed classes enums arrays custom types etc while avoiding pulling in atypically embedded std types such as std string std vector etc arrays fixed sized arrays dynamically sized arrays and delimited arrays memory safety bound checking for both serial buffers and array fields choose from both high abstraction level object oriented streams and low level memcpy like apis custom formatter types such as optional validation checks attached right to a field virtual fields and pure virtual fields allowing you to easily change formats at runtime header only and works with c 11 and greater constexpr support for c 14 or greater using bitcpy function depends on compiler support compiles with high warning levels pedantic wall etc documentation darrenlevine github io cppserdes https darrenlevine github io cppserdes quick start 1 check out the examples compile and run examples 01 simple example cpp read through the examples folder for various usage cases 2 use the library in your project manual inclusion make sure cppserdes include is added to your project s path and use as desired with cmake add the following lines to your cmakelists txt project file cmake add subdirectory cppserdes target link libraries your project name cppserdes object oriented example using serdes packet base cpp include serdes h struct my packet serdes packet base int8 t x 6 y 7 z 8 define a format used for both serialization and deserialization void format serdes packet serdes process serdes process x y serdes pad 1 serdes bitpack z 7 int main uint16 t serial data 0x0102 0x7b00 my packet obj1 obj1 load serial data loads serial data into obj1 x 1 y 2 z 5 my packet obj2 obj2 store serial data stores 0x0607 0x0800 into serial data from obj2 functional example using serdes bitcpy cpp include bitcpy h int main uint16 t serial data 0x0102 0xfb00 int8 t x y z deserialization serdes bitcpy x serial data 0 8 serdes bitcpy y serial data 8 8 serdes bitcpy z serial data 16 8 serialization serdes bitcpy serial data x 0 8 serdes bitcpy serial data y 8 8 serdes bitcpy serial data z 16 8 streams example using serdes packet cpp include serdes h int main uint16 t serial data 0x0102 0x0304 0x0506 int8 t x y z take some serial data and place it into variables serdes packet serial data x y z take same variables and place it into serial data serdes packet serial data x y z known limitations little endian serialization is not yet supported note little and big endian platforms are both supported constexpr bitcpy of enums and floating point types is not supported since the constexpr intepretation of their memory is undefined behaviour i e non constexpr memcpy must be used behind the scenes cannot bitcpy more than 0 25gb of data in a single function call due to a memory usage speed design decision the examples are written using c 17 syntax for succinctness and simplicity so if you re using an older compiler templated fields might need to have their templates explicitly specified for example cpp serdes bitpack 123 serdes bit length 2 works in c 17 serdes bitpack int int 123 serdes bit length 2 needed in c 17 planned features little endian serialization support compile time only is planned the ability to avoid serialization deserialization by setting pointers to point to the appropriate spot within the serial buffer instead of making a copy into a new buffer thus saving time and memory if the serial buffer s lifetime persists longer than the reference and the memory is aligned appropriately to allow this optimization similar to how capnproto and flatbuffers work requirements c 11 or greater | os |
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blockchain-spv | blockchain spv npm version https img shields io npm v axerunners blockchain spv svg https www npmjs com package axerunners blockchain spv build status https travis ci com axerunners blockchain spv svg branch master https travis ci com axerunners blockchain spv dependency status https david dm org axerunners blockchain spv svg https david dm org axerunners blockchain spv stores blockchain headers and verifies transactions with spv usage npm install axerunners blockchain spv js import blockchain parameters for axe var params require webcoin axe blockchain create a levelup database where the block data should be stored var db levelup axe chain db require memdown create blockchain var blockchain require axerunners blockchain spv var chain new blockchain params db wait for the blockchain to be ready chain on ready function use the blockchain blockchain stores and verifies block headers and does spv lite client verification it is compatible with axe and bitcoin derived blockchains new blockchain params db creates an spv blockchain which stores and verifies block headers params should be the blockchain parameters for the blockchain you wish to use parameters for axe are available at require webcoin axe blockchain for more info about params you can use see the parameters parameters section db should be a levelup https github com level levelup instance where block data will be stored the db should not be shared with another blockchain if you need to use level sublevel https github com dominictarr level sublevel to create a sub section of your db make sure to wait for the ready event before using the blockchain chain addheaders headers callback adds block headers to the chain headers should be an array of contiguous ascending block headers the headers will be verified checked to make sure the expected amount of work was done the difficulty was correct etc the callback will be called with cb err header where header is an invalid header if there was a validation error chain createwritestream returns a writable stream that takes in arrays of block headers and adds them to the chain this is essentially just a stream wrapper for chain addheaders making it easier to get headers from a headerstream from the bitcoin net https github com mappum bitcoin net module chain createreadstream opts returns a readable stream that outputs blocks from the blockchain in order the stream will stay open even when it reaches the chain tip and will output new blocks as they are received this means you don t have to think about whether the chain is done syncing or not if a reorg happens blocks that have been emitted are now on an invalid fork the stream will emit the now invalid blocks again in descending order so that each can be un processed each block has a boolean add property which is false if it is being removed from the chain note it is important to always check the value of block add and un process blocks when they are invalidated opts may contain the following options from buffer default null start the stream at the block with this hash exclusive if from is null the stream will start at the genesis block chain getblock hash callback gets a block in the chain with hash hash hash must be a buffer the callback is called with cb err block chain getblockatheight height callback gets a block in the chain with height height the callback is called with cb err block note that this requires the blockchain to be traversed from the tip or genesis block whichever is closest so it runs in o n 2 time chain getblockattime time callback gets the highest block with a time that comes before or on time time should be in unix time https en wikipedia org wiki unix time measured in seconds not milliseconds as returned by date now the callback is called with cb err block note that this requires the blockchain to be traversed from the tip or genesis block whichever is closest so it runs in o n time chain gettip returns the highest block added to the chain chain getpath from to callback gets the path of blocks between from and to this is useful to know which blocks to process or unprocess when getting from one part of a chain to another including going across forks calls the callback with cb err path where path is the following js add array an array of blocks which should be processed remove array an array of blocks which should be unprocessed fork block the first block of the fork if any examples a b c d getpath a d results in add b c d remove fork undefined getpath d a results in add remove d c b fork undefined a b c d e f getpath f d results in remove f e add b c d fork e chain getpathtotip from callback a convenience method for chain getpath from chain gettip cb parameters parameters specify blockchain rules and constants for different cryptocurrencies and blockchains parameters should contain the following js required the data used in the header of the genesis block for this blockchain genesisheader version number prevhash buffer merkleroot buffer time number bits number nonce number called to check if we should recalculate the difficulty this block should call the callback with cb err retarget where retarget is a boolean shouldretarget function block callback called to calculate the expected difficulty for block should call the callback with cb err target where target is a buffer containing the target hash calculatetarget function block blockchain callback called to compute the hash of the header used to verify mining should call the callback with cb err hash where hash is a buffer mininghash function header callback optional an array of blocks to use to speed up initial blockchain sync or as an extra source of data for verifying headers received from peers any number of blocks can be provided and they should be sorted ascending by height checkpoints height number header version number prevhash buffer merkleroot buffer time number bits number nonce number for some examples see these parameter repos webcoin bitcoin https github com mappum webcoin bitcoin blob master src blockchain js webcoin bitcoin testnet https github com mappum webcoin bitcoin testnet blob master src blockchain js webcoin zcash alpha https github com mappum webcoin zcash alpha blob master src blockchain js | spv | blockchain |
azure | microsofrt azure data engineering tools microsoft auzre cloud documentation of data engineering tools | cloud |
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INB-Principles | inblockchain ico investing principles by li xiaolai founder of inb inblockchain com http inblockchain com chinese md 1 what is an ico ico i nitial c oin o ffering is a means for blockchain startups to raise capital from the public that is somewhat equivalent to an ipo on the stock markets 2 what is a blockchain blockchain refers to a public ledger system maintained by a decentralized network that serves as an open and immutable database 3 what is bitcoin the term bitcoin can be understood in several different ways which is why people always get confused and often disagree with each other when it comes to discussion of bitcoin related issues first of all bitcoin is the first most successful blockchain application moreover bitcoin is a world bank that is not run by any authority but instead by a decentralized network in addition this decentralized world bank bitcoin issued a currency that happens to have the exact same name bitcoin some are more meticulous so as to refer the currency to btc rather than bitcoin even seven years after the birth of bitcoin 2017 few people have realized that btc could also be thought of as the stock of the decentralized world bank bitcoin 4 what is an altcoin since the birth of bitcoin thousands of other cryptocurrencies have been issued though most of them are already extinct some of them are still alive and some even prosperous litecoin https litecoin com for example is the most famous example of an altcoin and dogecoin http dogecoin com is another at first many people thought of these cryptocurrencies as copycats with no intrinsic value at all later on after witnessing that what they thought to have no value persisted or even flourished they started using another term altcoin perhaps the world needs more than one world bank who knows and who cares here is what i think in the markets people are buying two kinds of value the intrinsic and the superficial an undeniable fact is for those who are buying the superficial it looks exactly the same as the intrinsic 5 what are mbacoins by mbacoins i mean coins issued by m eaningful b lockchain a pplications mba by meaningful i mean the applications are solving real problems not problems that have already been solved by bitcoin honestly i don t think altcoins have solved any problem that bitcoin didn t and i hope not believe that at the end of the day one world bank would be enough the truth is the world needs more than one blockchain application perhaps the second mbacoin is namecoin https namecoin org a blockchain application trying to offer a decentralized domain service ethereum https www ethereum org is another successful mbacoin that is built upon blockchain technology to offer new functions including smart contracts at the time of publishing june 2017 many mbas have been running for more than 6 months and are performing pretty well for example sia http sia tech provides a decentralized storage service just like dropbox steem https steemit com is another young mba that creates discovers and distributes content with a public ledger built upon blockchain technology 6 what is the trend in the past few years bitcoin dominated the blockchain world with its single largest market cap in april 2017 however bitcoin s dominance dropped to 52 although its price had been surging along with other blockchain assets this is an important signal that the blockchain world is transforming into a forest not a piece of land with a single tree anymore if this is the case then this prediction might well be true in the future bitcoin dominance will continuously drop and even drop to less than 5 7 what have we done in the past i m a large bitcoin holder since i think of btc as the stock of a world bank it s quite natural for me to spend as little btc as possible before 2014 we invested in bitshares https bitshares org and yunbi com https yunbi com and i have been personally maintaining bitcoinsand com http bitcoinsand com a bitcoin bank for years since the last quarter of 2015 my team and i started to invest in mbas in earnest but with a rigorous set of principles we have invested in sia http sia tech steem https steemit com zcash https z cash qtum https qtum org en eos https bitcointalk org index php topic 1904415 0 and numerous others often we invest both in startup equity and also in the blockchain assets the startups issue we are actively participating icos around the world 8 why open source my investing principles investment is by far the most risky activity because every skill that it requires cannot be genetically inherited the birth of every true innovation is followed by many copycats and frauds this is especially true when the innovation comes with inherent financial functionality investing in blockchain startups and their assets is extremely risky in that people are often attracted by the tremendous profits they have heard about rather than by any thorough understanding of the value that the application creates by publicizing our investing principles we are trying protect ourselves ideally this altruism serves selfish motives the less fraud happens the safer we are 9 principles sometimes asking questions is much more effective than describing principles by struggling to get an absolute answer to appropriate questions principles are naturally adhered to here are major questions we have asked ourselves again and again before we decide to invest any blockchain startup or blockchain asset is this truly needed by the world what otherwise unsolvable problem does it solve completely is decentralization truly needed is an open ledger truly necessary can an open ledger truly boost the efficiency of the business model to what extent is it a dac https www youtube com watch v v26zjond cs decentralized autonomous corporation if we decide to invest what percentage of our fund should we use exactly that s all the simplest questions tend to need the toughest thinking to get to an accurate and concrete answer 10 two analogies when one looks into the mirror every thing looks exactly same but in fact everything is reversed not only the objects but also their order investment might well be thought of as happening in the mirror every detail in the mirror looks familiar and simple but naive investors don t know they are in the mirror world they don t know what they see in the mirror doesn t operate the way it does in the outside world more often than not opportunities look like danger and bait smells like cheese another good analogy is to driving a car if you are an australian and trying to drive in the united states you will get uncomfortable and often confused because you are accustomed to driving on the left you might even get yourself killed if you don t prepare yourself for the new rules so be careful driving on the other side | blockchain |
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IoTSeeker | iotseeker this scanner will scan a network for specific types of iot devices to detect if they are using the default factory set credentials the recent internet outage has been attributed to use the iot devices cctv cameras dvrs and others with default credentials it s the intention of this tool to help organizations scan their networks to detect these types of iot devices and to identify whether credentials have been changed or if the device is still using the factory setting note that mirai malware suspected to have been used to launch the massive internet outage on oct 21 2016 mainly focuses on telnet services iotseeker focuses on http https services in order to accommodate large ip ranges and make it capable of finding a large number of different types of iot devices this tool was designed with high parallelism so that it can scan thousands of iot s at the same time extensibility making it easy to support new types of devices without needing to change or write lots of code the software has two parts one is the device configuration file which is in json format the other is the scanner coded in perl that does scanning device identification and logging under the control the device configuration file this software uses the perl module anyevent for high parallelism and as a result it only runs on linux or mac os here are the steps to install and run it make sure perl and cpan are installed install perl packages by cpan anyevent http data dumper json perl iotscanner pl ipranges example perl iotscanner pl 1 1 1 1 1 1 4 254 2 1 1 1 2 2 3 254 | server |
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Blockchain-PHP | blockchain build status https travis ci org ohmybrew blockchain php svg branch master http travis ci org ohmybrew blockchain php coverage status https coveralls io repos github ohmybrew blockchain php badge svg branch master https coveralls io github ohmybrew blockchain php branch master styleci https styleci io repos 122662663 shield branch master https styleci io repos 122662663 license https poser pugx org ohmybrew blockchain php license https packagist org packages ohmybrew blockchain php a simple object oriented blockchain implementation screenshot https github com ohmybrew blockchain php raw master example example gif usage coming soon example code see example generating a blockchain php example create basic chain php num blocks where number of num blocks is the number of blocks you wish to generate loading an existing blockchain into the code php example load chain php path where path is the json file created from create basic chain php license this project is released under the mit license https github com ohmybrew blockchain php blob master license | blockchain example | blockchain |
Teaching-HEIGVD-CM_APPMOB | comem appmob course on mobile application development slides see the slides directory slides links repo to develop the citizen engagement mobile application https github com softeng heigvd teaching heigvd cm appmob 2016 citizenengagement repo with the completed skeleton application for the citizen engagement mobile application https github com softeng heigvd teaching heigvd cm appmob 2016 skeletonapp if you want to use the reference implementation of the citizen engagement api rather than your own documentation for the reference implementation of the citizen engagement api https polar brook 7624 herokuapp com repo of the express reference implementation https github com softeng heigvd teaching heigvd cm webs 2015 labo express impl repo of the api documentation for the reference implementation https github com softeng heigvd teaching heigvd cm webs 2015 labo doc impl resources ionic installation http ionicframework com getting started ionic documentation http ionicframework com docs angularjs https angularjs org angularjs developer guide https docs angularjs org guide angularjs api reference https docs angularjs org api angular ui router https github com angular ui ui router angular ui router guide https github com angular ui ui router wiki angular ui router api reference http angular ui github io ui router site api ui router mapbox https www mapbox com angular leaflet directive for mapbox https github com tombatossals angular leaflet directive mapbox javascript library https www mapbox com mapbox js api v2 4 0 cordova camera plugin https github com apache cordova plugin camera qimg https github com softeng heigvd qimg qimg api reference http softeng heigvd github io qimg lab delivery delivery date april 18th at noon you are asked to provide the citizen engagement mobile application with the following requirements it must use geolocation it must use mapbox https www mapbox com it must use the camera works only on physical devices the appropriate configuration to run your application in both development and production mode a user guide or presentation page for the application you can choose from the following options one is enough you can present the application in the readme of the github repository for the application if you forked the starting repository https github com softeng heigvd teaching heigvd cm appmob 2016 citizenengagement get rid of the original setup instructions and replace them with your presentation you can upload your application to a store e g google play and write the presentation page as you would for a real application you can use any other presentation tool but your presentation page must be available online optional a webcast demonstrating usage of the mobile application this is not mandatory but may improve your grade if you did not fulfill all the other requirements how to deliver each group must send an e mail to simon oulevay at heig vd ch with the following information who is in your group the link to the github repository of your citizen engagement mobile application the link to the user guide or presentation page for the application if it s not in the readme of the repository the link to the webcast for the application if you made one the configuration files e g config development json and config production json used to develop and deploy your application known issues ionic view app issue 10 upload doesn t seem to be updating js or css files https github com driftyco ionic view issues issues 10 if you are using the ionic view app to develop your mobile application on ios be aware that sometimes uploading is not enough to refresh your code on the device you might need to stop your app swipe down with three fingers clear app data and restart the ionic view app double tap on home button and close ionic view app then you can re download your app and it should be the latest version sass compilation issues if gulp sass fails to install with compilation issues when you run npm install and you do not plan on writing css with sass you can remove this dependency by commenting the following line in gulpfile js var sass require gulp sass also remove the gulp sass dependency from your package json file you cannot use comments in json gulp sass 2 0 4 then re run npm install | front_end |
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Pewlett-Hackard-Analysis | pewlett hackard analysis build an employee database with sql by applying data modeling engineering and analysis skills to generate a list of employees who are eligible for retirement packages overview pewlett hackard has been around for quite some time and it has thousands of employees who will be retiring soon the objective of this analysis is to determine how many of those employees there are and what kind of positions or roles they will be leaving ph also wants to develop a mentorship program for those who are starting to reach retirement age these employees will start mentoring new hires this will enable the company to better prepare for the future results from the tables created for this analysis the following are the findings out of the 300k employees at pewlet hackard there are 90k who have the age to to retire that is roughly 30 of the total number of employees most of the positions that could potentially be vacated are higher level individual contributor roles the two roles that would have the most vacancies are in the senior engineer and senior staff roles the tables are only based on the people who were born from 1952 t0 1955 the tables do not take into consideration when they were hired or if they are still working at the company there are about 1550 employees who are not quite at retirement age that can start mentoring new hires this allows experienced employees to pass down their knowledge before reaching retirement summary i would re create the 3 tables retirement titles unique titles and retiring titles to take in consideration not just birth dates but also when the employees were hired if they are still employed at the company the queries and tables are named retirement titles2 unique titles2 and retiring titles2 after running these queries there are more than 33 000 roles that could potentially be vacated this means expanding the criteria of the mentorship program would be recommended a query and table that shows people who are eligible for retirement per department will also be helpful for each team to be prepared this is seen in the query table named retirees with department from here we can query how many positions will possibly be vacated for each department as shown below alt text https github com abonuan pewlett hackard analysis blob main numberofretireesperdept png raw true | server |
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Online-Bus-Booking-Tracking-System | online bus booking tracking system https youtu be sjeg5smqja executive below sql query after import database update system user set password 1e6bdb9d266d9c4073b34cdfa174b635 where username admin update system user set password 1e6bdb9d266d9c4073b34cdfa174b635 where username kasun update system user set password 1e6bdb9d266d9c4073b34cdfa174b635 where username nevil update system user set password 1e6bdb9d266d9c4073b34cdfa174b635 where username sumith admin user name admin password 12345 operator user name nevil password 12345 booker user name kasun password 12345 conduct user name sumith password 12345 | server |
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nlp | natural language processing introduction natural language processing nlp can be used to answer a variety of questions about unstructured text as well as facilitating open ended exploration it can be applied to datasets such as emails online articles and comments tweets and novels although the source is text transformations are applied to convert this data to vectors dictionaries and symbols which can be handled very effectively by q many operations such as searching clustering and keyword extraction can all be done using very simple data structures such as feature vectors features the nlp allows users to parse dataset using the spacy model from python in which it runs tokenisation sentence detection part of speech tagging and lemmatization in addition to parsing users can cluster text documents together using different clustering algorithms like mcl k means and radix you can also run sentiment analysis which indicates whether a word has a positive or negative sentiment for full documentation go to nlp https code kx com q ml nlp installation clone the nlp repo to qhome and load using q l nlp init q requirements the following python packages are required 1 numpy 2 beautifulsoup4 3 spacy to install these packages run pip install r requirements txt download the english model using python m spacy download en status the nlp library is still in development and is available here as a beta release if you have any issues questions or suggestions please write to ai kx com | ai |
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HealthKeeper | healthkeeper mobile development technology based on java language creating healthy diet and calorie management app comp5216 mobile computing project university of sydney semester 2 2020 description an android app that makes record people s calorie intake and provides daily health reports including diet proportion nutrient intake and predict user weight description healthkeeper keeps track of the user s diet and analyses the user s health status the main functions are to record food information by taking photos or adding meals manually and to provide health reports of the user s daily intake and predict his weight installation 1 download the app source code or clone it to your directory or get from version control https github com henry 929 healthkeeper git 2 open android studio open file from your directory 3 after gradle successfully built the app should be able to run in an android virtual device compatible with the testing device or on a compatible android device testing environment device1 oneplus 7 pro display resolution 3120x 1440pixels 19 5 9 ratio 516 ppi density android os hydrogen os 10 0 7 gm21 device2 huawei p30 display resolution 2340 x 1080 pixels 19 5 9 ratio 398 ppi density android os version 10 0 0 316 device3 xiaomi cc 9 display resolution 2340 x 1080 pixels 19 5 9 ratio 403 ppi density android os version 9 miui v11 3 4 pfccnxm device4 razer phone 2 0 display resolution 2560 x 1440 pixels 16 9 ratio android os version 9 api android under api 29 java java 8 ide android studio 4 0 1 android studio modify the project level build gradle file to use gradle s google service plugin add classpath com google gms google services 4 3 4 import the firebase bom through implementation platform com google firebase firebase bom 25 11 0 locate build gradle module app to be able to preview the layout through implementation com android support design 25 0 1 implementation com android support support v4 25 0 1 implementation group com google android material name material version 1 1 0 alpha05 layout preview uses pixel 2 as a default device for proper operation of the application camera functions the emulator api should be api 29 or less libraries aip java sdk 4 15 1 json 20160810 slf4j api 1 7 25 slf4j simple 1 7 25 food stored in database we currently have stored 35 types of food in our database including apple avocado banana beetroot blueberry brinjal broccoli cake capsicum carrot cheese chips cucumber curry fish garlic hamburger honey kiwifruit milk onion orange pancake pea pizza potato pumpkin rice sandwich sausage shrimp steak strawberry sweet potato tomato most of them can be image recognized and all of them can be recorded manually | front_end |
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unfetter-insight | unfetter insight babelfish logo http mjmobbs com wp content uploads 2010 08 babel jpg unfetter insight performs natural language processing and analysis to detect possible att ck patterns unfetter insight currently accepts files with the following extensions txt pdf and html starting in order to run babelfish you will need docker https docs docker com once you have finished setting up your docker environment clone the unfetter insight project from github and proceed to build the docker images sh git clone https github com unfetter discover unfetter insight git cd unfetter insight docker compose up now your unfetter insight instance is ready and running at http localhost 8080 using unfetter insight in order to use unfetter insight you place the files to be processed in the directory unfetter insight a sub directory can be created to manage test files the sub directory test files can be referenced to set up these test files can also be run to test insight as shown below to use unfetter insight underneath the file processing header on the left select the file to be processed after making your selection click on the green submit button directly to the right after the file finishes processing you will be taken to a report page that will display the results of the analysis the line graph details the occurences of cooresponding threats detected based on the confidence of the specified threat existing in the given range of text it is important to note that the x axis of the graph represents the entirety of the document and there may be multple occurances of the same threat being detected below the graph there is a details link that will display the marked report generated by babelfish this report is the bare text of the document with highlights indicating the threats it detected below the link there is a bar graph for a simplified visual on the threats detected in the document and its cooresponding confidence note that as with the line graph the bar graph may depict depict multiple threats of the same type usage babelfish package to use babelfish package for report classification simply do sh python import babelfish babelfish classify report sample txt 100 278 278 00 00 00 00 663 89it s bypass user account control | ai |
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crm-mobilesdk-library-for-android | microsoft dynamics crm mobile sdk for android java this document describes the java version of the dynamics crm mobile sdk that is being released under an open source license on github com http github com dynamicscrm this sdk provides service proxy classes and message classes that enable you to develop android mobile applications that can connect to the crm organization web service and perform various operations the supported messages in this sdk that are defined later in this document represents a usable subset of what is available in the net version of the crm sdk in general it is best to be familiar with the net version of the crm sdk https msdn microsoft com en us library gg309408 aspx as the programming api in this mobile sdk was based on it the following documentation describes the key features and api available in this sdk oauth http oauth net 2 authentication with the dynamics crm web service is not managed within this sdk that functionality is handled in app level code using the azure active directory authentication library https github com azuread azure activedirectory library for java adal for an example of using this sdk in a real world android application complete with oauth web service authentication refer to the sample android app that is provided as a separate download requirements this sdk supports and has been tested with the following development environment android api level 18 through 23 android studio version 1 0 and later at build time the following open source or 3rd party libraries are downloaded and installed which require and active internet connection on the development computer azure active directory authentication library https github com azuread azure activedirectory library for android square s retrofit http square github io retrofit apache commons http commons apache org this sdk is known to work with dynamics crm 2013 and 2015 for both on premises and online deployments on premises identity authentication is provided by active directory federation services 3 0 or greater while online identity authentication is provided by microsoft azure active directory getting started prior to using the methods provided by this mobile sdk you must build and run the net mobilesdkgen command line tool to generate early bound classes for each entity in the crm organization that your app needs to access the tool plus related documentation is provided as a separate download this tool is similar to the crmsvcutil tool provided in the net version of the crm sdk after running the mobilesdkgen tool the result is a folder filled with java class files one for each entity type including custom entity types and option sets that you have specified for more information about using early bound entity types refer to the related crm sdk topic use the early bound entity classes in code https msdn microsoft com en us library gg328210 aspx for more information about the crmsvcutil tool see create early bound entity classes with the code generation tool https goo gl qhikbz to include this library into your project you should just take a built aar file from the releases section or from the build folder of this project you should place that file into your lib folder for you application and include the following into your application s gradle build file groovy compile name crmsdk2015 ext aar transitive true also keep in mind that if you are using proguard you should add this to your proguard rules pro file proguard keep class extends com microsoft xrm sdk entity web service proxies there are two web service proxy classes provided in this mobile sdk organizationserviceproxy and restorganizationserviceproxy it is through the methods of these proxies that you send message requests to the organization web service and receive responses java public organizationserviceproxy string uri string sessiontoken java public restorganizationserviceproxy string uri requestinterceptor authheader creating either of these proxy objects requires you to manage user logon using active directory and pass into the constructor either the authentication access token or a pre built header requestinterceptor is a header system that you can create using the retrofit library the uri value in these constructors is the ssl https address of your crm organization s web service organization svc this address can be found in the crm web application by navigating to settings customizations developer resources the reason for two proxies is that organizationserviceproxy supports sending soap messages while restorganizationserviceproxy supports sending odata messages most messages supported by crm are implemented as soap messages a small subset of messages are supported by the odata v2 endpoint for more information on the odata v2 endpoint see use web service data in web resources odata and modern app soap endpoint https msdn microsoft com en us library gg490659 aspx restorganizationserviceproxy methods the following sections describe the available methods of the odata based service proxy each proxy method takes a callback class instance as a parameter the callback class defines two methods named success and failure one of these methods is called after the web service attempts to perform the intended operation it is through the success callback that you can obtain the response create creates an entity record java void create entity entity callback uuid callback void create entity relatedto entity entity string relationshipname callback uuid callback for the entity instance provided to the method to be serialized correctly it must be an early bound subclass of the entity class provided in the mobile sdk you can generate these classes using the mobilesdkgen tool in the example below the contact class was generated using this tool however if you are using create to make a related entity it does let you pass in the second entity even if it isn t a subclass of entity it will of course throw a network error if you don t use proper schema names for the attributes java contact contact contact build setfirstname john setlastname doe setnumberofchildren 2 try restservice create contact new callback uuid override public void success uuid uuid response response id of the new contact override public void failure retrofiterror error handle network error catch invalidclassexception ex didn t pass in a subclass of entity update updates an existing entity record java void update entity entity nullable callback callback for the entity instance provided to the method to be serialized correctly it must be an early bound subclass of the entity class provided in the mobile sdk you can generate these classes using the mobilesdkgen tool in the example below the contact class was generated using this tool for an update the id of the existing record must be supplied the callback is nullable so that you can either ignore it or if you like create one to handle any network failures coming back java contact contact contact build setcontactid uuid fromstring ab4db725 0aab 45c9 a23d d7a865635974 setnumberofchildren 3 try restservice update contact new callback object override public void success object object response response this will never come back override public void failure retrofiterror error handle network error catch invalidclassexception ex didn t pass in a subclass of entity delete deletes an existing entity record java void delete string entityschemaname uuid id nullable callback callback java restservice delete contact uuid fromstring ab4db725 0aab 45c9 a23d d7a865635974 null retrieve two methods are provided to retrieve records one to retrieve a single record and another to retrieve all records of the given type to which the user has read access or get all related items to a specific entity filtering is possible with the odata endpoint using the expand filter orderby select skip and top parameters from queryoptions these calls return generic entities and should be cast to early bound entities using the toentity class t method java void retrievemultiple string entityschemaname uuid id string relationshipname nonnull queryoptions queryoptions final callback entitycollection callback void retrievemultiple string entityschemaname queryoptions query callback entitycollection callback java retrieve the top ten activities that have an end date and are related to the contact queryoptions queryoptions queryoptions build puttop 10 putselect constants activity select putfilter actualend ne null putorderby actualend desc restservice retrievemultiple contact class getsimplename mcontact getid contact activitypointers queryoptions new callback entitycollection override public void success entitycollection entitycollection response response handle response override public void failure retrofiterror error handle network error organizationserviceproxy methods the following sections describe the available methods of the service proxy that sends soap based messages to the organization web service each proxy method takes a callback class instance as a parameter the callback class defines two methods named success and failure one of these methods is called after the web service attempts to perform the intended operation it is through the success callback that you can obtain the response execute the execute method sends a message request to the organization web service and receives a response back the message request and response classes listed later in this document are derived from organizationrequest and organizationresponse there are many more messages available to execute than there are methods on the proxy class where there is overlap for example a create message and a create method you can choose to use either there is not a significant performance difference between the two techniques java void execute organizationrequest request callback organizationresponse callback the mobile sdk provides a wide range of message request and response classes for you to use with the execute proxy method for example java setstaterequest setstaterequest setstaterequest build setentitymoniker new entityreference task activityid setstate new optionsetvalue 1 setstatus new optionsetvalue 5 morgservice execute setstaterequest new callback organizationresponse override public void success organizationresponse organizationresponse response response handle response override public void failure retrofiterror error handle error create creates an entity record this is equivalent to using a createrequest with the execute method java void create entity entity callback uuid callback update updates an existing entity record this is equivalent to using an updaterequest with the execute method java void update entity entity nullable callback callback for this method you have the option to use the callback the success method of the callback class will never get called however you will get any network failures back through the callback s failure method delete deletes an existing entity record this is equivalent to using an deleterequest with the execute method java void delete string entityname uuid id nullable callback callback for this request you have the option to use the callback the success method of the callback class will never get called however you will get any network failures back through the callback s failure method retrieve retrieves an existing entity record this is equivalent to using a retrieverequest with the execute method java void retrieve string entityschemaname uuid id nonnull columnset columnset callback entity callback the column set defines the attributes that you want returned in the entity record retrievemultiple retrieves multiple entity records this method is equivalent to using a retrievemultiplerequest with the execute method one thing that is unique about the soap version of retrievemultiple compared to the odata version is that both the method and the message request support fetchxml for the query java void retrievemultiple querybase query callback entitycollection callback java fetchexpression fetchexpression new fetchexpression string format fetch mapping logical count 25 entity name contact attribute name contactid attribute name fullname attribute name jobtitle link entity name account from accountid to parentcustomerid link type outer attribute name name alias accountname link entity filter type and condition attribute fullname operator like value s filter entity fetch searchterm morgservice retrievemultiple fetchexpression new callback entitycollection override public void success entitycollection entitycollection response response handle response override public void failure retrofiterror error handle network error for more information about fetchxml see build queries with fetchxml https msdn microsoft com en us library gg328332 aspx associate creates a link between records that participate in a relationship this is equivalent to using an associaterequest with the execute method java void associate string entityname uuid entityid relationship relationship entityreferencecollection relatedentities nullable callback callback disassociate removes the link between records this is equivalent to using an disassociaterequest with the execute method java void disassociate string entityname uuid entityid relationship relationship entityreferencecollection relatedentities nullable callback callback supported organization web service soap messages for each of the messages listed below there exists a request and response class for example the create message includes createrequest and createresponse classes these classes can be found in the folder mobilesdk android crmsdk2015 src main java com microsoft xrm sdk messages for more information about each of these messages see crm messages in the organization service https msdn microsoft com en us library gg309482 aspx and xrm messages in the organization service https msdn microsoft com en us library gg334698 aspx a additemcampaign addlistmemberslist addmemberlist addprincipaltoqueue addproducttokit addrecurrence addtoqueue assign associateentities associate b backgroundsendemail book c canbereferenced canbereferencing cancelcontract cancelsalesorder canmanytomany checkincomingemail checkpromoteemail clonecontract closeincident closequote copydynamiclisttostatic createentity createexception createinstance createoptionset create d deleteattribute deleteentity deleteopeninstances deleteoptionset deleteoptionvalue deleterelationship delete deliverincomingemail deliverpromoteemail disassociateentities disassociate g getvalidmanytomany getvalidreferencedentities getvalidreferencingentities i insertoptionvalue insertstatusvalue isdataencryptionactive l lockinvoicepricing locksalesorderpricing loseopportunity m merge o orderoption p pickfromqueue q qualifylead r recalculate releasetoqueue removefromqueue removeitemcampaign removememberlist removeprivilegerole removerelated replaceprivilegesrole reschedule retrieveallentities retrieveallmanagedproperties retrievealloptionsets retrieveattribute retrievedataencryptionkey retrieveentity retrievemanagedproperty retrievemetadatachanges retrievemultiple retrieveoptionset retrieverelationship retrieve retrievetimestamp retrieveuserqueues routeto s sendemail sendfax setdataencryptionkey setrelated setstate u updateattribute updateoptionset updateoptionvalue updaterelationship update updatestatevalue v validaterecurrencerule w winopportunity winquote | front_end |
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nlp-search-poc | nlp elasticsearch powered product search sample app intended to illustrate how natural language processing nlp specifically named entity recognition ner can be used to improve the accuracy of elasticsearch queries and the overall user experience there are two key benefits to using nlp alongside elasticsearch or any other full text search engine pre selecting search filters given the query black jacket costing less than 200 we can infer the color and max price and apply these search filters for the user this concept can be extended to other fields e g brand and also support conjugations e g black or dark green barbour jacket distinguishing between essential and desirable query terms imagine you work for an outdoor clothing and equipment store you re building a catalog search feature given the query packable jacket how should the database choose between a packable mosquito net and a lightweight jacket both products partially match tf idf will most likely select the mosquito net as there will be fewer instances of packable than jacket in the corpus however when looking at the query it s clear that the lightweight jacket would be the better match we typically solve this problem by boosting certain document fields e g by attaching more weight to the title or product type fields than the description this sort of works but the logic is wrong we re essentially telling the shopper based on what we sell this is what we think is important to you humans understand that given the query packable jacket the shopper wants a jacket first and foremost that s because we understand that jacket is a product type and packable is an attribute of the product natural language processing nlp allows us to apply this same reasoning programmatically in simple terms we can perform an elasticsearch bool query in which we must have a match for jacket and should have a match for packable caveats firstly and most importantly this is not a production implementation the nlp model used for this example is really basic for production use we d build something far more robust trained with historic search data we d also employ part of speech tagging along with dependency parsing to get a better understanding of the sentences and fragments of text secondly the elasticsearch code is very basic for production use we d want to use custom tokenizers analysers synonyms of course we d have many more fields and lots more documents finally there s no error handling so please treat this in the spirit in which it was created a proof of concept getting started 1 setup your environment 2 fire up an elasticsearch instance 3 create the index and mapping 4 import some test data 5 fire up a simple webserver to handle search queries 6 cleanup setup your environment the python code needs a 3 9 7 environment i recommend running this in a virtualenv using either venv https docs python org 3 library venv html or pyenv virtualenv https github com pyenv pyenv virtualenv shell pyenv install 3 9 7 pyenv virtualenv 3 9 7 nlp search poc pyenv local nlp search poc pip install u pip pip install r requirements txt run elasticsearch i ve provided a docker compose yml file so you can fire up a simple elasticsearch instance shell docker compose up d elasticsearch 7 test the setup python dependencies and paths can be tricky so i provided a simple utility to check everything is working as expected note elasticsearch can take a few seconds to come online shell python m src tools ping elasticsearch alive true create the index import test data shell python m src tools create productrepository info creating products index productrepository info products created python m src tools ingest productrepository info ingesting lightweight black jacket productrepository info ingesting midweight black jacket run the server i created a wrapper shell script to fire up uvicorn fastapi shell bin server sh uvicorn error info uvicorn running on http 127 0 0 1 8000 press ctrl c to quit perform the search make a get request to http localhost 8000 passing a json body json query lightweight black jacket less than 100 postman is probably the best tool for this but i ve also included a simple client shell python m src client lightweight black jacket less than 100 json ner prediction text lightweight black jacket less than 100 product jacket price from null price to 100 colors black attrs lightweight results title lightweight black jacket product type jacket price 100 colors black attrs lightweight important if you choose to use this script you should enclose your search query in single quotes to avoid variable expansion cleanup kill the running server hit kbd ctrl kbd kbd c kbd don t worry about the asyncio exceptions cancellederror it s caused by the hot reload feature of the uvicorn server drop the index shell python m src tools drop productrepository info dropping products index productrepository info products dropped take down elasticsearch shell docker compose down stopping elasticsearch 7 done removing elasticsearch 7 done removing network nlp search poc default docker optional i ve provided a dockerfile in case you want to run everything inside docker shell docker build t nlp search poc then run elasticsearch and the server shell docker compose up d ingesting test data if you also want to use docker to ingest the test data into elasticsearch you can do so shell docker run it rm network nlp search poc default e elastic search host elasticsearch 7 nlp search poc python m src tools reset note the network name is determined by docker s networking rules https docs docker com compose networking querying docker compose yml exposes the server s port 8000 so you can query as before shell python m src client packable jacket | elasticsearch spacy-nlp ner full-text-search spacy | ai |
autodev | autodev llm based coding assistance functions this repository contains two projects the autodev python project autodev readme md providing the core functionality autodev including auto completion models that can suggest completions based on the current editing context fine tuning of completion models to teach them new languages or to teach them about your libraries your code style etc quantitative qualitative evaluation optimization of models for inference including quantization code based assistance functions where an instruction following model is given a task based on an existing code snippet e g reviewing code adding comments or input checks explaining code etc an inference service which provides access to the above functions question answering on document databases including source code documents a java project implementing the autodev intellij idea plugin idea plugin readme md which provides access to the coding assistance functions within jetbrains ides such as intellij idea pycharm and others idea plugin please refer to the projects individual readme files for further information linked above autodev in action fine tuned auto completion generating completions for the ruby programming language based on a fine tuned version of bigcode santacoder which originally knew only python java and javascript example auto completion images auto completion ruby gif auto completion in intellij idea assistance functions built on instruction following models adding input checks to a function example adding input checks images add input checks gif adding input checks in intellij idea identifying potential problems in a piece of code example identifying potential problems images potential problems gif identifying potential problems of a piece of code in intellij idea structural overview here s a structural overview showing the main components and their interactions example auto completion images arch png structural overview for auto completion the model is served directly by the autodev inference service i e the model is always locally provided and is either an unmodified open source model from the hugging face hub https huggingface co docs hub index or a fine tuned version thereof fine tuning may use community data or our own data for other assistance functions built on instruction following models you have the option of using either a fine tuned open source model as in the previous case or a proprietary model such as chatgpt | coding-assistant intellij intellij-plugin large-language-models llm | ai |
ray | ray wework design system circleci https circleci com gh wework ray svg style svg https circleci com gh wework ray commitizen friendly https img shields io badge commitizen friendly brightgreen svg http commitizen github io cz cli coverage status https coveralls io repos github wework ray badge svg branch master https coveralls io github wework ray branch master npm version https img shields io npm v wework ray core svg https www npmjs com package wework ray core contributors https img shields io github contributors wework ray svg contributors page intro resources for building interfaces with wework s design system npm package https www npmjs com package wework ray core page intro getting started see the getting started docs https ray wework com getting started development see https ray wework com contributing for documentation release https github com wework ray releases wework staff see releasing md releasing md for documentation ownership this repository is maintained by the consumer web team of product engineering at wework reach out to the team at ray wework com mailto ray wework com for questions or concerns | design-system css js ui | os |
django-llm | python 3 11 https img shields io badge python 3 11 blue svg https www python org downloads release python 3112 code style black https img shields io badge code 20style black 000000 svg https github com psf black django llm an app for django to aid development of large language model llm workflows have you ever wanted to store chatgpt queries in your database now you can powered by langchain https github com hwchase17 langchain wiki https github com mikrl django llm wiki sample project https github com mikrl django llm sample information configure your llm workflow from django build a business layer for your llm application install latest binary bash pip install django llm build and install from source bash build sh pip install dist whl tests stabilizing bash pip install r static txt static sh pytest tests features run chatgpt queries through django shell and model code bash docker build t django llm docker run it django llm from llm models prompts import prompt prompt prompt template give a bombastic and raucous hello name to the user prompt save prompt objects all queryset prompt prompt object 1 prompt template give a bombastic and raucous hello name to the user prompt variables name from llm models import modelproviderapi openai modelproviderapi service openai api key your openai api key openai save openai service openai from llm models queries import openaichatquery query openaichatquery prompt prompt api openai query do query name world hello world welcome to the mighty realm of technology and innovation prepare to be astounded and dazzled by the power of code and the endless possibilities of the digital age let s rock and roll more model to configure and execute chatgpt query model to hold prompt and determine prompt variables model to store api keys for various services | ai |
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biocollect | biocollect build status master branch travis build https travis ci org atlasoflivingaustralia biocollect svg branch master https travis ci org atlasoflivingaustralia biocollect develop branch travis build https travis ci org atlasoflivingaustralia biocollect svg branch develop https travis ci org atlasoflivingaustralia biocollect about this repo is a fork of the fieldcapture hubs repo https github com atlasoflivingaustralia fieldcapture hubs where the plugin has been promoted to become the host app from the moment of creation of this repo biocollect and merit will go separate ways the resulting project has been significantly refactored all the merit inherited server side code base is now under the package name au org ala biocollect merit it would be convenient to organically remove all the code that we won t be using in biocollect new server side classes that are custom to biocollect should be under the package name au org ala biocollect general information technologies grails framework 5 1 2 java 8 knockout js jquery 3 4 1 gradle development setup this project requires you to run the ecodata project https github com atlasoflivingaustralia ecodata on port 8080 use this guide to setup biocollect in intellij setup md running grails4 version run biocollect gradlew bootrun to run biocollect with support for hot reloading of changes to the ecodata client plugin ala map plugin clone the ecodata client plugin ala map plugin repositories into the same parent folder as the biocollect project run biocollect with additional parameters gradlew bootrun dgrails run active true pinplace true note the leading colon before the bootrun task this is required as when inplace true gradle is configured in a multi project build configuration running javascript automatic tests executing the tests requires node js it is recommended to install the intellij node js and karma plugins to install the test dependencies run the following command in the repo root folder npm install after that you can run the test directly from intellij by right clicking on the karma conf js file | ala-product-biocollect | front_end |
BatchProcessor | batchprocessor a lightweight cross platform cloud vendor independent library for batch processing in net core initial commit working prototype part of a final project to achieve msc in advanced software engineering distinction university of leicester uk initial version of nuget packages can be found here https www nuget org profiles mssawan | batch-processing batch net-core net-core-2 net-core2 library azure aws aws-sqs aws-s3 aws-dynamodb simpledb azure-service-bus queue azure-queue | cloud |
exp-pn-sequence-generator-iitr | introduction b discipline b fill your discipline name here b lab b fill your lab name here b experiment b fill your experiment name and number here about the experiment fill a brief description of this experiment here b name of developer b fill the name of experiment owner here b institute b b email id b b department contributors list srno name faculty or student department institute email id 1 2 | ext-ph3 iitr | os |
cv | cv computer vision description manifold learning images are high dimensional objects that live on manifolds the figure below shows t sne embedding of 64 dimensional digits dataset in 2 d a kd tree is used to find nearest neighbors to an image query on the manifold p align center img src https github com vsmolyakov cv blob master manifold figures manifold merged png p references l van der maaten visualizing data using t sne jmlr 2008 r szeliski computer vision algorithms and applications 2010 visual words image search can be formulated as topic similarity for a topic model build from visual words visual words are cluster centers of image features extracted at image keypoints in this example dense sift features are extracted from a collection of 10 face images of 40 different people the olivetti faces dataset the 128 dimensional sift features are clustered using mini batch k means into a dictionary of k visual words an online variational bayes algorithm is used to learn the lda topic model and extract topic proportions for training image data test image data is converted into topic space and training images are retrieved based on cosine similarity between topic proportions of the train and test images p align center img src https github com vsmolyakov cv blob master visual words figures visual words merged png p the figure above shows the olivetti dataset mean training image left followed by the pca visualization of visual words middle the learned visual topics are shown at the bottom a random sample of test images along with the retrieved nearest neighbors are shown on the right note that there is no overlap between training and test images i e the individuals are different based on the retrieved nearest neighbors we can see that certain faces explain the test images best which suggests that ranking or post processing of the retrieved results can improve performance references r szeliski computer vision algorithms and applications 2010 segmentation image segmentation is a very common problem in computer vision we often like to identify objects in an image before further processing p align center img src https github com vsmolyakov cv blob master segmentation figures seg merged png p the figure above compares two non parametric algorithms dp gmm and dbscan applied to image segmentation from the images we can see that different parameter settings resulted in different discovery of clusters or segments in an image the on line vb algorithm configured with alpha 10 produced a binary segmentation separating the road from the rest of the image in contrast dbscan based on kd tree algorithm produced 15 clusters both algorithms do not assume that the number of segments in an image is known ahead of time references d blei and m jordan variational inference for dirichlet process mixtures bayesian analysis 2006 ester m h p kriegel j sander and x xu a density based algorithm for discovering clusters in large spatial databases with noise aaai 1996 conv nets convolutional neural networks cnn revolutionized the field of computer vision trained on millions of images cnns such as alexnet perform very well on a variety of vision tasks p align center img src https github com vsmolyakov cv blob master caffe figures alexnet merged png p the figure above depicts alexnet classifier trained on imagenet used to classify a super pixelized image of an anemone fish using the fastscsp algorithm interestingly enough with k 286 superpixels the alexnet correctly predicts the two ground truth categories anemone fish and sea anemone as indicated by the two spikes in the right most figure references o friefeld y li and j w fisher iii a fast method for inferring high quality simply connected superpixels icip 2015 image search a vgg 16 convolutional neural net is used to construct an image representation in latent space based on a vector of activations images from caltech101 dataset are mapped into activation vectors and a query image is used to find k nearest distance neighbors p align center img src https github com vsmolyakov cv blob master image search figures image search merged png p the figure above shows the query image of a chair left and a retrieved nearest neighbor images right the retrieved images closely resemble the query image references k simonyan and a zisserman very deep convolutional networks for large scale image recognition iclr 2015 kalman filter tracking the kalman filter is an algorithm for exact bayesian filtering of linear dynamic system lds models the kalman update step mu n amu n 1 k n x n camu n 1 consists of the previous value and a correction term multiplied by the kalman gain k n the kalman filter outputs a marginal distribution p z n x 1 x n while the kalman smoother takes all observations into account p align center img src https github com vsmolyakov cv blob master kalman figures kalman merged png p the figure above shows the predictions of kalman filter left and kalman smoother right we can see that the kalman smoother produces a smooth trajectory due both forward and backward update steps references c bishop pattern recognition and machine learning 2006 particle filter particle filter is a sequential monte carlo method of estimating the internal states of a switching linear dynamic system slds a set of particles is used to represent the posterior distribution of the states of the markov process at each iteration the particles get re sampled and the ones that explain the observations best propagate to the next iteration p align center img src https github com vsmolyakov cv blob master particles figures particle filter merged png p the figure above shows the generated slds states left and the inferred states center by particle filter pf and the rao blackwellized version rbpf we can see that the inferred states closely correspond to the ground truth also shown is a particle re sampling step right where only a fraction of particles survive to the next iteration references nando de freitas rao blackwellized particle filter for fault diagnosis 2002 siamese neural network siamese neural network consists of two identical networks with shared weights tied together by a contrastive loss function the network is trained to find similarities between inputs that can take on different modalities such as images cnns or sentences rnns p align center img src https github com vsmolyakov cv blob master siamese figures siamese merged png p the figure above shows a siamese network applied to pairs of mnist digits the training set alternates between positive matching and negative different pairs of digits the network is able to achieve 99 5 accuracy after 20 training epochs besides image recognition siamese architectures have been used in signature matching and sentence similarity references g koch et al siamese neural networks for one shot image recognition icml 2015 generative adversarial network generative adversarial network gan consists of two networks trained simultaneously a generative model g that generates output example given random noise as input and a discrimantive model d that distinguishes between real and generated examples p align center img src https github com vsmolyakov cv blob master gan figures dcgan cifar10 png p as a result of training the generative model learns to produce output examples that look very realistic see cifar10 example above recovering distribution of training data while the discriminative network outputs equal class probability for real vs generated data references i goodfellow et al generative adversarial networks 2014 f chollet deep learning with python manning publications 2017 neural style transfer neural style transfer takes as input a content image and a style image and produces an artistic rendering of the content image it does that by minimizing the sum of content loss and style loss the content of the image is captured by the higher layers of a cnn while the style of the image refers to its texture and simple geometric shapes represented in the lower layers of a cnn p align center img src https github com vsmolyakov cv blob master style transfer figures style transfer merged png p the figure above shows the content image left the style image middle and the output image right the output image is found by minimizing the content and style loss for a vgg19 cnn using l bfgs solver the output image is the result of 20 iterations of l bfgs references l gatys a ecker and m bethge a neural algorithm of artistic style arxiv 2015 f chollet deep learning with python manning publications 2017 captions generator an image caption generator is a multi input neural network that consists of an image branch resnet50 cnn and a language branch lstm the two branches are merged into a common rnn that predicts the next word given the image and the previous caption words p align center img src https github com vsmolyakov cv blob master captions figures captions merged png p the neural captions generator is trained end to end on flicker8k dataset the training and validation loss and accuracy are shown in the figure above along with the generated captions using beam search the neural caption generator achieves a bleu score of 0 61 on the test dataset references o vinyals et al show and tell a neural image caption generator cvpr 2015 object detection an image almost always consists of multiple objects thus we lose information by encoding an image with a single vector if instead we learn to detect objects in the image and classify them we can use this information for visual search and many other applications here we focus on the ssd architecture with inception v2 convolutional base pretrained on coco dataset and made available as part of tensorflow object detection api the api features multiple detectors faster r cnn yolo ssd with different speed vs accuracy tradeoffs p align center img src https github com vsmolyakov cv blob master object detection figures ssd results png p the figure below shows the results of ssd object detector trained on open images dataset filtered for fashion for 200k global steps on 2 tesla m60 aws gpus we can see that with just 4 days of training we are able to get correct localization of bounding boxes and class labels we can use mean average precision map iou 0 5 for a given intersection over union iou threshold to evaluate performance of our object detector references tensorflow object detection api https github com tensorflow models tree master research object detection open images dataset https storage googleapis com openimages web index html dependencies matlab 2014a python 2 7 tensorflow 1 3 0 opencv | computer-vision deep-learning tracking image-segmentation manifold topic-modeling image-processing | ai |
intel-iot-gateway | discontinuation of project this project will no longer be maintained by intel intel has ceased development and contributions including but not limited to maintenance bug fixes new releases or updates to this project intel no longer accepts patches to this project intel iot gateway technology here you will find recipes tutorials and examples for connecting sensors and cloud solutions to iot gateways that use intel iot gateway technology changing the default ip address changing 20the 20default 20ip 20address readme md clearing secure boot keys clearing 20secure 20boot 20keys readme md changing the iot gateway to a wifi client changing 20the 20iot 20gateway 20to 20a 20wifi 20client readme md getting started with node red and aws iot getting 20started 20with 20node red 20and 20aws 20iot readme md getting started with cloud commander getting 20started 20with 20cloud 20commander readme md getting started with node red and arduino 101 with the grove shield getting 20started 20with 20node red 20and 20arduino 20101 20with 20the 20grove 20shield readme md getting started with node red and bluemix getting 20started 20with 20node red 20and 20bluemix readme md getting started with node red and comet t0310 getting 20started 20with 20node red 20and 20comet 20t0310 readme md getting started with node red and lightblue bean getting 20started 20with 20node red 20and 20lightblue 20bean readme md getting started with node red and microsoft azure iot hub getting 20started 20with 20node red 20and 20microsoft 20azure 20iot 20hub readme md getting started with node red and omega rh usb getting 20started 20with 20node red 20and 20omega 20rh usb readme md getting started with node red and philips hue getting 20started 20with 20node red 20and 20philips 20hue readme md getting started with node red and rfid getting 20started 20with 20node red 20and 20rfid readme md getting started with node red and ti sensortag getting 20started 20with 20node red 20and 20ti 20sensortag readme md getting started with node red and z wave getting 20started 20with 20node red 20and 20z wave readme md getting started with node red and nest getting 20started 20with 20node red 20and 20nest readme md getting started with remote debug node js getting 20started 20with 20remote 20debug readme md updating the intel iot gateway developer packages updating 20the 20intel 20iot 20gateway 20developer 20packages readme md | server |
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HumanPrompt | humanprompt p align middle a href https img shields io badge version v0 0 1 blue img src https img shields io badge version v0 0 1 blue a a href https img shields io badge prs welcome red img src https img shields io badge prs welcome red a a href https img shields io github last commit hkunlp humanprompt color green img src https img shields io github last commit hkunlp humanprompt color green a a a href https join slack com t slack cwl8878 shared invite zt 1q0ai1g6b t9hul209jtnxiy2e3twpa img src https img shields io badge slack join blueviolet logo slack amp a br p humanprompt is a framework for easier human in the loop design manage sharing and usage of prompt and prompt methods it is specially designed for researchers it is still in progress we highly welcome new contributions on methods and modules check out our proposal here https docs google com document d 1xj1upjangog miaoefhukskzlqkwa2xrylrcnsv ppo edit usp sharing content to start to start to accelerate your research to accelerate your research config config run experiment run experiment architecture architecture contributing contributing pre commit pre commit used by used by citation citation to start firstly clone this repo then run bash pip install e this will install humanprompt package and add soft link hub to humanprompt artifacts hub then you need to set some environmental variables like openai api key bash export openai api key your openai api key then it depends on how you will use this repo for now this repo s mission is to help researchers on verifying their ideas therefore we make it really flexible to extend and use a minimal example to run a method is as follows our usage is quite simple it is almost similar if you have used huggingface transformers https huggingface co docs transformers index before for example use the chain of thought on commonsenseqa python from humanprompt methods auto method auto import automethod from humanprompt tasks dataset loader import datasetloader get one built in method method automethod from config method name cot get one dataset select one example for demo data datasetloader load dataset dataset name commonsense qa dataset split test data item data 0 adapt the raw data to the method s input format we will improve this part later data item context answer choices format join format label lower text lower for label text in zip data item choices label data item choices text run the method result method run data item print result print data item zero shot text2sql python import os from humanprompt methods auto method auto import automethod from humanprompt tasks dataset loader import datasetloader method automethod from config db text2sql data datasetloader load dataset dataset name spider dataset split validation data item data 0 data item db os path join data item db path data item db id data item db id sqlite result method run data item print result print data item to accelerate your research config we adopt one config one experiment paradigm to facilitate research especially when benchmarking different prompting methods in each experiment s config file yaml under examples configs you can config the dataset prompting method and metrics following is a config file example for chain of thought method on gsm8k yaml dataset dataset name gsm8k dataset name aligned with huggingface dataset if loaded from it dataset split test dataset split dataset subset name main dataset subset name null if not used dataset key map mapping original dataset keys to humanprompt task keys to unify the interface question question answer answer method method name cot method name to initialize the prompting method class method config file path null method config file path null if not used will be overriden by method args method args client name openai llm api client name adopted from github com hazyresearch manifest transform cot gsm8k transform cot gsm8k cotgsm8ktransform user defined transform class to build the prompts extract cot gsm8k extract cot gsm8k cotgsm8kextract user defined extract class to extract the answers from output extraction regex the answer is n user defined regex to extract the answer from output prompt file path cot gsm8k prompt txt prompt file path max tokens 512 max generated tokens temperature 0 temperature for generated tokens engine code davinci 002 llm engine stop sequence n n stop sequence for generation metrics exact match metrics to evaluate the results users can create the transform and extract classes to customize the prompt generation and answer extraction process prompt file can be replaced or specified according to the user s need run experiment to run experiments you can specify the experiment name and other meta configs in command line under examples directory for example run the following command to run chain of thought on gsm8k bash python run experiment py exp name cot gsm8k num test samples 300 for new combination of methods and tasks you can simply add a new config file under examples configs and run the command architecture text examples configs config files for experiments main py one sample demo script run experiment py experiment script hub hub contains static files for methods and tasks cot method chain of thought gsm8k task gsm8k containing prompt file and transform extract classes etc ama prompting method ask me anything binder method binder db text2sql method text2sql react method react standard method standard prompting zero shot cot method zero shot chain of thought humanprompt humanprompt package containing building blocks for the complete prompting pipeline artifacts artifact py hub components key components for the prompting pipeline aggregate aggregate classes to aggregate the answers extract extract classes to extract the answers from output post hoc py post hoc processing prompt py prompt classes to build the prompts retrieve retrieve classes to retrieve in context examples transform transform classes to transform the raw data to the method s input format evaluators evaluators evaluator py evaluator class to evaluate the dataset results methods prompting methods usually one method is related to one paper ama prompting ask me anything https arxiv org pdf 2210 02441 pdf binder binder https arxiv org pdf 2210 02875 pdf tasks dataset loading and preprocessing add sub py addsub dataset wikitq py wikitablequestions dataset third party third party packages utils utils config utils py integrations py tests test scripts conftest py test datasetloader py test method py contributing this repository is designed for researchers to give a quick usages and easy manipulation of different prompt methods we spent a lot of time on making it easy to extend and use thus we hope you can contribute to this repo if you are interested in contributing your method into this framework you can 1 bring up an issue about your required method and we will add it into our todo list and implement as soon as possible 2 add your method into humanprompt methods folder yourself to do that you should follow the following steps 1 clone the repo 2 create a branch from main branch named you methods 3 commit your code into your branch you need to 1 add code in humanprompt methods and add your method into humanprompt methods your method name folder 2 create a hub of your method in hub your method name 3 make sure to have an examples folder in hub your method name to config the basic usage this method 4 a minimal demo in examples for running and testing your method 4 create a demo of usage in examples folder 5 require a pr to merge your branch into main branch 6 we will handle the last few steps for you to make sure your method is well integrated into this framework pre commit we use pre commit to control the quality of code before you commit make sure to run the code below to go over your code and fix the issues pip install pre commit pre commit install install all hooks pre commit run all files trigger all hooks you can use git commit no verify to skip and allow us to handle that later on used by batch prompting https github com hkunlp batch prompting citation if you find this repo useful please cite our project and manifest https github com hazyresearch manifest bibtex software humanprompt author tianbao xie and zhoujun cheng and yiheng xu and peng shi and tao yu title a framework for human readable prompt based method with large language models howpublished url https github com hkunlp humanprompt year 2022 month october bibtex misc orr2022manifest author orr laurel title manifest year 2022 publisher github howpublished url https github com hazyresearch manifest | large-language-models natural-language-processing prompt-engineering | ai |
NLP | natural language processing projects overview this is where i play with nlp aka natural language processing the art of teaching machine to understand and mimic human s natural language semantically and or syntactically projects in this repository are still in progress and gradually updated readme s content will be updated accordingly maybe i ll write some blog posts if necessary content at present this repository contains the projects below 1 embeddings https github com chunml nlp tree master embeddings done cbow skip gram todo rewrite in tensorflow 2 0 add pytorch implementation write blog posts add more types of embeddings such as glove etc 2 text generator https github com chunml nlp tree master text generation done tensorflow implementation both in 1 x and 2 0 blog post for tensorflow implementation link https machinetalk org 2019 02 08 text generation with tensorflow pytorch implementation blog post for pytorch implementation link https machinetalk org 2019 02 08 text generation with pytorch 3 machine translation https github com chunml nlp tree master machine translation done tensorflow implementation no attention bahdanau luong blog posts for tensorflow implementation data preparation link https machinetalk org 2019 02 20 neural machine translation with tensorflow data preparation model creation link https machinetalk org 2019 02 27 neural machine translation with tensorflow model creation training link https machinetalk org 2019 03 05 neural machine translation with tensorflow training todo add pytorch implementation blog post for pytorch implementation rewrite in tensorflow 2 0 blog post for tensorflow 2 0 implementation 4 chat bot https github com chunml nlp tree master chatbot done tensorflow implementation todo rewrite in tensorflow 2 0 add pytorch implementation write blog posts 5 sentiment analysis https github com chunml nlp tree master sentiment analysis done tensorflow 2 0 implementation for imdb movie reviews classification quora insincere questions classification blog post for imdb english version link https machinetalk org 2019 03 18 introduction to tensorflow datasets japanese version link https machinetalk org 2019 03 13 introduction to tensorflow datasets jp todo add pytorch implementation write blog post for quora data 6 pos tagging https github com chunml nlp tree master pos tagging done pytorch implementation for toy example todo update pytorch implementation for real dataset add tensorflow 2 0 implementation write blog posts | nlp natural-language-processing python tensorflow tensorflow-experiments tensorflow-tutorials | ai |
Image-Hoster | image hoster 1 month by using the knowledge of software engineering and server side development build a image hoster like imgur com implemented the in mvc architecture and database orms skills utilized git and github design pattern unit testing refactoring client server architecture spring mvc springboot | server |
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Data-Science-Course | data science fall 2018 this repository contains lectures homeworks labs quizes and exams for the course data science https github com faizsaeed data science course blob master img data science jpg data science is an interdisciplinary field that uses scientific methods processes algorithms and systems to extract knowledge and insights from data in various forms both structured and unstructured 1 this course introduces methods for four key aspects of data science data acquiring data preprocessing exploratory data analysis data modelling is this course for me this course is designed for graduates from various fields br so if you are a student in for example computer science electrical engineering mechatronics telecom engineering social sciences business etc and want to learn some computational and quantitative skills to be successful in your field as it transforms in an age increasingly dominated by data and computation this course might be for you this is an advanced course we will introduce both programming concepts and the necessary mathematics br we expect you to have background in python programming and math their is no formal pre requisite for the course because we are expecting a class of graduate engineers that are already acquainted with the right skills for the course how do i enroll in this class itu students can enroll for the course by contacting the academics office br instructors faisal kamiran https itu edu pk faculty itu dr faisal kamiran br director data science lab itu br e mail faisal kamiran itu edu pk teaching assistants faizan saeed umair majeed logistics the class meets tuesday 05 30 pm 07 00 pm in lt 5 and thursday 07 15 pm 08 45 pm in lab lectures are used to introduce theoretical concepts br labs are used to teach coding skills and revisit the concepts introduced in lectures in practical terms we will typically run coding exercises in the lab please bring your laptop computer office hours br ta office hours tuesday and thursday 05 30 08 30 pm data science lab br dr faisal office hours wednesday 4 00 6 00 pm data science lab br online discussion submissions grades etc we use google classroom for discussions and questions 1 https en wikipedia org wiki data science | server |
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fluent-svelte | h1 align center img src https raw githubusercontent com tropix126 fluent svelte e78982fb9fa48a6ea0b7cc61f4ff8fae9df88db3 static logo svg width 72 height 128 br fluent svelte h1 warning this project is still in alpha stages i would not consider it production ready yet assume any undocumented component to be in the 0 x phase of development and therefore unstable what is this fluent svelte is an experimental svelte http svelte dev component ui library that emulates the look and feel of microsoft s windows ui controls https github com microsoft microsoft ui xaml which conform to the fluent design system features sveltekit https kit svelte dev and ssr compatible typescript https typescriptlang org and type definitions are supported but optional full rtl support with no additional configuration all components are accessible according to wai aria https www w3 org wai standards guidelines aria standards theming support using css custom properties minimal third party dependency usage reduced motion support easy setup just install the library add some base styles and you re ready to go minimal css overhead styles are included and scoped alongside their respective components only bundling the css you need undocumented components the documentation site https fluent svelte vercel app is still not entirely finished many components exported in the library are not yet documented that progress can be tracked here https github com tropix126 fluent svelte issues 13 for now i ve setup a testing page with every component included in the library that is viewable here https fluent svelte vercel app test it s source can be viewed here https github com tropix126 fluent svelte blob main src routes test index svelte please keep in mind that any undocumented component is considered to be in the 0 x phase of development this means that they could potentially recieve breaking api changes or be heavily updated before being finalized changelog see changelog md https github com tropix126 fluent svelte blob main changelog md | svelte sveltejs sveltekit ui ui-components microsoft windows-ui fluent-design javascript css scss | os |
iotplatform | iot platform an open source iot platform that enables rapid development management and scaling of iot projects with this iot platform you are able to 1 provision and control devices 2 collect and visualize data from devices 3 analyze device data and trigger alarms 4 deliver device data to other systems 5 enable use case specific features using customizable rules and plugins archicteture https osswangxining github io images newiotpdocs architecture png iot device sdk this iot platform provides an sdk to help you easily and quickly connect your hardware device or your mobile application this iot device sdk enables your devices to connect authenticate and exchange messages with this iot platform using the mqtt http or websockets protocols the device sdk supports java javascript and includes the client libraries the developer guide and the porting guide for manufacturers you can also use an open source alternative or write your own sdk device gateway iot device gateway enables devices to securely and efficiently communicate with this iot platofrm the device gateway can exchange messages using a publication subscription model which enables one to one and one to many communications with this one to many communication pattern iot platform makes it possible for a connected device to broadcast data to multiple subscribers for a given topic the device gateway supports mqtt websockets and http 1 1 protocols the device gateway scales automatically to support over a billion devices without provisioning infrastructure authentication and authorization this provides mutual authentication and encryption at all points of connection so that data is never exchanged between devices and iot platform without proven identity x 509 certificate based authentication is supported device registry the device registry establishes an identity for devices and tracks metadata such as the devices attributes and capabilities the registry assigns a unique identity to each device that is consistently formatted regardless of the type of device or how it connects it also supports metadata that describes the capabilities of a device for example whether a sensor reports temperature and if the data are fahrenheit or celsius device shadow through this device shadow you can create a persistent virtual version or shadow of each device that includes the device s latest state so that applications or other devices can read messages and interact with the device the device shadows persist the last reported state and desired future state of each device even when the device is offline you can retrieve the last reported state of a device or set a desired future state through the api or using the rules engine device shadows make it easier to build applications that interact with your devices by providing always available rest apis in addition applications can set the desired future state of a device without accounting for the devices current state then it will compare the difference between the desired and last reported state and command the device to make up the difference the iot device sdk makes it easy for your device to synchronize its state with its shadow and to respond to desired future states set via the shadow iot analytics rule engine and advanced analytics the rules engine makes it possible to build iot applications that gather process analyze and act on data generated by connected devices at global scale without having to manage any infrastructure the rules engine evaluates inbound messages published into iot platform and transforms and delivers them to another device or a cloud service based on business rules you define a rule can apply to data from one or many devices and it can take one or many actions in parallel the rules engine can also route messages to external 3rd party cloud services such as amazon aws cloud services ibm or azure cloud services aliyun cloud services etc besides these rule based analytics this iot platform also provides the advanced analytics including complex and compound computation spark streaming based timewindow rules spark machine learning enabled analytics etc frequency oftentimes an application wants to control the frequency that continuously generated analytic results are made available to other parts of the application or published to other applications or an event hub high performance actor model enables high performant concurrent processing of messages from devices as long as server side api calls akka is used as an actor system implementation with following actor hierarchies brief description of each actors functionality is listed below app actor responsible for management of tenant system rules plugins actors instance of this actor is always present in memory tenant actor responsible for management of tenant device rules plugins actors instance of this actor is always present in memory device actor maintain state of the device active sessions subscriptions pending rpc commands etc caches current device attributes in memory for performance reasons actor is created when first message from device is processed actor is stopped when there is no messages from devices for a certain time rule actor filter and process incoming messages converts them to actions and forward this actions to plugin actors instance of this actor is always present in memory plugin actor process incoming messages and report results back to rule actor also handles server side requests instance of this actor is always present in memory device session manager actor responsible for management of device session actors creates session actors on first message with corresponding session id closes session actors when corresponding session is closed session actor represents communication session between device and thingsboard server session may be synchronous http coap and asynchronous mqtt coap with observe option rpc session manager actor responsible for management of cluster rpc session actors creates session actor when new server is up closes session actor when server is down rpc session actor represents communication session between two thingsboard servers in the cluster mode communication is done using http 2 based on grpc high availability service discovery this iot platform uses zookeeper for service discovery where all the nodes are identical and registered as ephemeral in zookeeper apache curator path cache receipt is used to keep track of all available sibling nodes consistent hashing this iot platform adopts consistent hashing to ensure scalability and availability message from device a that is received on particular node may be forwarded to the other node based on the hash of the device id although this introduce certain networking overhead it allows to process all messages from particular device using corresponding device actor on a determined server which introduce following advantages improve cache hit rate device attributes and other device related data are fetched by device actor on a specific server avoid race conditions all messages for particular device are processed on a determined server allows to target server side api calls based on device id setup maven clean maven install info reactor summary info info whole iot platform success 0 650 s info iot information management success 0 203 s info iot information management data success 0 730 s info iot information management dao success 0 734 s info rule based iot analytics success 0 006 s info rule engine common messages success 0 125 s info rule engine api success 0 272 s info rule engine core extensions success 0 342 s info action plugins success 0 006 s info kafka plugin success 2 126 s info mqtt plugin success 0 132 s info webhook plugin success 0 476 s info iot hub success 0 553 s info iot platform ui success 02 14 min info iot platform server success 0 730 s info info build success info info total time 02 22 min info finished at 2017 10 22t21 16 41 08 00 info final memory 42m 625m info keep kafka and cassandra running xis macbook pro xiningwang docker ps container id image command created status ports names 8b0def0e1594 cassandra docker entrypoin 3 months ago up 7 seconds 0 0 0 0 7000 7001 7000 7001 tcp 0 0 0 0 7199 7199 tcp 0 0 0 0 9042 9042 tcp 0 0 0 0 9160 9160 tcp mycassandra a6975c6bf0c2 spotify kafka supervisord n 3 months ago up 14 seconds 0 0 0 0 2181 2181 tcp 0 0 0 0 9092 9092 tcp licenses this project is released under apache 2 0 license this open source project is based on several other open source projects or libraries and free for experiments no any commercial support with apache license version 2 0 notice thingsboard project https github com thingsboard thingsboard is the original idea for this project | iot iot-platform device-gateway device-registry sdk rule-engine analytics iot-framework iot-gateway iothub | server |
sea-b26-foundations | welcome to foundations of cs web development bootcamp this simple repo will hold some basic class information | front_end |
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svg a href https raw githubusercontent com prplx svg logos master svg vercel svg img src svg vercel svg alt vercel width 100px a vercel https raw githubusercontent com prplx svg logos master svg vercel svg a href https raw githubusercontent com prplx svg logos master svg vue svg img src svg vue svg alt vue width 100px a vue https raw githubusercontent com prplx svg logos master svg vue svg w a href https raw githubusercontent com prplx svg logos master svg webpack svg img src svg webpack svg alt webpack width 100px a webpack https raw githubusercontent com prplx svg logos master svg webpack svg y a href https raw githubusercontent com prplx svg logos master svg yarn svg img src svg yarn svg alt yarn width 100px a yarn https raw githubusercontent com prplx svg logos master svg yarn svg a href https raw githubusercontent com prplx svg logos master svg yeoman svg img src svg yeoman svg alt yeoman width 100px a yeoman https raw githubusercontent com prplx svg logos master svg yeoman svg z a href https raw githubusercontent com prplx svg logos master svg zod svg img src svg zod svg alt zod width 100px a zod https raw githubusercontent com prplx svg logos master svg zod svg a href https raw githubusercontent com prplx svg logos master svg zurb svg img src svg zurb svg alt zurb width 100px a zurb https raw githubusercontent com prplx svg logos master svg zurb svg | svg svg-logo design | front_end |
Codebot | codebot https raw githubusercontent com dovyski depository master codebot logo text png codebot codebot is a free and open source mit license web based ide focused on game development screenshot of codebot in use https cloud githubusercontent com assets 512405 6548137 e344ebea c5cd 11e4 800e adfe8472884c png notice if you would like to try codebot check out the free and online demo https codebot cc available at https codebot cc https codebot cc simply put codebot is a code editing program equipped with built in tools for gamedev all running on the cloud it aims to help you make your game no matter where you are or the equipment you have read full thoughts here http www as3gamegears com blog codebot an ide focused on gamedev developing a game is much more than just coding you have to tweak art build levels convert files find assets extensions read docs about building and publishing and so on ides should help with those tasks or do them automatically imagine you are working on a ludum dare http www ludumdare com 1gam http onegameamonth com game and you need an 8 bit sfx you click a button a panel slides you type in a few keywords select what you want and done you have the sfx ready for use that s the idea behind codebot table of content features features getting started getting started prerequisites prerequisites installing installing configuring configuring usage usage contribute contribute license license changelog changelog similar projects similar projects features full featured code editor support for javascript projects haxe https haxe org coming soon files panel drag rename move files around built in 8 bit sfxs panel built in assets finder panel currently works with a curated list of opengameart org https opengameart org view different file types images sounds text projects manager support for plugins built using web technologies ready to run on the cloud getting started 1 prerequisites you need a webserver with php http php net support and mysql https www mysql com to run codebot 2 installing go to the documentroot folder of your webserver e g var www on ubuntu or c wamp www on windows cd var www clone codebot s repository https github com dovyski codebot including submodules git clone recursive https github com dovyski codebot git codebot from this point on you can continue the installation and configuration using codebot web based installer just access http localhost codebot and follow the instructions it will take less then 5 min codebot web based installer plugins ide web install img web installer png 3 configuring codebot has several files that are used for configuration the one responsible for the back end configuration is plugins ide web config php the best practice is to create a local configuration file named config local php i e plugins ide web config local php that overrides only the directives you want to change if you used codebot web based installer you already have the plugins ide web config local php file created for you you can edit the config local php file anytime to meet your needs e g to adjust oauth credentials debug mode etc by default the directives codebot debug mode and codebot dev mode will be set to true which makes codebot run in development mode when in such mode local authentication without username or password is allowed warning if codebot dev mode directive is set to true in production your server users might be at risk 4 usage after installing and configuring codebot visit http localhost codebot and start enjoying a cloud based gamedev environment if codebot dev mode is set to true default you will have the option to login without providing a username or a password contribute the development process is a little bit rough right now it s basically me coding and changing things a lot documenting things very superficially along the way at some point the apis will stabilize and it will be a lot easier to join the party if you want to help now you can take codebot for a ride and send me your feedback or suggest a feature or send a cool insane motivational message just ping me at as3gamegears http twitter com as3gamegears or open an issue https github com dovyski codebot issues new license codebot is licensed under the terms of the mit https choosealicense com licenses mit open source license and is available for free changelog see all changes in the changelog changelog md file similar projects codebot is by no means a novel idea below are a few projects that are similar to codebot i e web based ide code editor theia https github com theia ide theia codesandbox https codesandbox io stackblitz https stackblitz com codebox https github com codeboxide codebox cloud9 https c9 io ace https ace c9 io monaco https microsoft github io monaco editor | javascript game-development ide cloud-ide php web-development gamedev web-ide cloud | front_end |
Salon_App_Backend | salon app backend | server |
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MusicPlatform | music ranking comment and streaming platform author jiatong li lijiaton usc edu introduction in this project i built a platform for music lovers to rank comment and listen to the music they love users can search for artist album or track through its name on the music profile page users can see the detailed information share their own ratings about lyric melody singer or mv as well as share their reviews links to my application no longer available stable version can be found at http ee599musics env eba xmjfgjfi us west 1 elasticbeanstalk com more features version can be found at http ee599music env eba xycuv9fu us east 2 elasticbeanstalk com video demo on youtube video demo https img youtube com vi yb kukexqoo maxresdefault jpg https www youtube com watch v yb kukexqoo t 40s details there are four pages in this web app home artist album and track the search bar in home page takes you to any one of the album artist or track details page cover images of albums takes you to the corresponding album page cover images of tracks takes you to the corresponding track page p align center img src img musicplatform routinglogics png alt music platform routing width 600 p p align center music platform routing logics p the artist details page looks like the followings sharing the same layout as the album details page p align center img src img musicplatform artistpage1 png alt artist page width 600 p p align center img src img musicplatform artistpage2 png alt artist page width 600 p the track details page looks like the followings p align center img src img musicplatform trackpage1 png alt track page width 600 p p align center img src img musicplatform trackpage2 png alt track page width 600 p deployment please pre install node js in both backend server and frontend server to deploy our product create your own database and import the schema sql under backend jiatong folder to your database log in your backend instance and move the backend jiatong folder to the backend instance change the information in mysql json under backend jiatong folder according to your setting and do cd backend jiatong npm install chmod start server sh start server sh then you backend server should work to deploy the frontend program log in to your frontend instance do the following cd deploybeanstalk npm install node server js or you can simply upload the deploybeanstalk zip to aws beanstalk platform then you backend server should work to change the data source to your own backend your need to change the url prefix in the frontend jiatong s angular project s services and rebuild the pages p align center img src img musicplatform clouddeployment png alt all parts on cloud width 600 p p align center this project was completely deployed on aws p jiatong li s development instructions to run the backend part on local host use the following cd backend jiatong npm install node server js then the url prefix for the apis is http localhost 8080 to romotely access the backend already deployed to aws the url prefix for the apis is http ec2 18 144 45 158 us west 1 compute amazonaws com 8080 for backend apis api artist search artist name artist name api album search artist name artist name album name album name api track search artist name artist name track name track name api artist details id artist id api album artist id tadb artist id api track album id tadb album id api trackvideo track track id tadb track id artist id tadb artist id api post score post http please include track id lyric score melody score singer score mv score in the body api post review post http please include track id review in the body some advices for team everyone creates his her own folder and codes from there for easier updating main branch do not submit node modules to the version control platform to do so add node modules to the gitignore file if do not exist create one backend branch will keep changing and merging into the main branch pull the repository before developing every time to keep things up to date | cloud |
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react-native-ui-kitten | ui kitten img src https i imgur com omcxwz0 png alt eva design system height 20px link eva npm badge license build status badge github actions link github actions coverage status badge coveralls link coveralls documentation link doc homepage ui kitten is a react native ui library that allows you creating stunning multi brand cross platform mobile applications the library is based on eva design system which brings consistency and scalability in the design and development process it contains a set of general purpose ui components styled in a similar way and the most awesome thing the themes can be changed in the runtime with no need to reload the application 100 free and open source kitten material https camo githubusercontent com f0487d92194f3c685213539c53e9784113cd8a4b 68747470733a2f2f692e696d6775722e636f6d2f58384f344748622e706e67 link doc homepage https user images githubusercontent com 1452064 115417233 212a0100 a201 11eb 8bcf 0a60cca2e081 png what s included 25 general purpose components designed and tested to save your time comprehensive clear documentation with the tons of examples theming system use light and modern dark themes and create your own svg eva icons support 480 general purpose icons eva design system support construct an interface using basic components following eva specifications and it will always have a stunning design starter app kitten tricks react native starter kit link kitten tricks allows you to boost the development of a mobile app there is a huge variety of customizable layouts use as is or add new blocks over 40 screens in dark and light themes give you the possibility to create a bright and exclusive app while saving your time on compiling numerous details also you can download the source code and use it for your own benefit quick start start a new app with ui kitten template from a scratch bash npx react native init myapp template ui kitten template js or if you want to init with typescript bash npx react native init myapp template ui kitten template ts this will setup a new react native application configured with ui kitten refer to the documentation link doc where start for more options to start mobile backend bundles easy way to integrate ui kitten with backend java link ui kitten bundle java net core link ui kitten bundle dotnet core img src https i imgur com hvnzqgr jpg link ui kitten bundles how can i support the developers star our github repo star create pull requests submit bugs suggest new features or documentation updates wrench read us on medium link akveo medium follow us on twitter link akveo twitter like our page on facebook link akveo facebook license mit license txt license more from akveo eva icons link eva icons 480 beautiful open source icons from developers made with heart by akveo team link akveo homepage follow us on twitter link akveo twitter to get the latest news first we re always happy to receive your feedback badge license https img shields io npm l react native ui kitten svg badge github actions https github com akveo react native ui kitten workflows build badge svg badge coveralls https coveralls io repos github akveo react native ui kitten badge svg branch master link eva https eva design utm campaign eva design 20 20home 20 20ui kitten 20github utm source ui kitten utm medium referral utm content kitten github readme link github actions https github com akveo react native ui kitten actions link coveralls https coveralls io github akveo react native ui kitten branch master link doc homepage https akveo github io react native ui kitten utm campaign ui kitten 20 20home 20 20ui kitten 20github 20readme utm source ui kitten utm medium referral utm content homepage link link doc where start https akveo github io react native ui kitten docs getting started where to start utm campaign ui kitten 20 20home 20 20ui kitten 20github 20readme utm source ui kitten utm medium referral utm content where to start docs link link kitten tricks https github com akveo kittentricks link eva icons https github com akveo eva icons link akveo homepage https www akveo com utm campaign services 20 20homepage 20 20ui kitten 20github 20readme utm source ui kitten utm medium referral utm content ui kitten readme link akveo medium https medium com akveo engineering link akveo twitter https twitter com akveo link akveo facebook https www facebook com akveo link ui kitten bundles https store akveo com collections mobile bundles utm campaign akveo store 20 20mobile 20bundles 20 20ui kitten 20github 20readme utm source ui kitten utm medium banner utm content mobile bundles banner link ui kitten bundle java https store akveo com products java mobile starter bundle utm campaign akveo store 20 20mobile 20bundles 20 20ui kitten 20github 20readme utm source ui kitten utm medium referral utm content java bundle link link ui kitten bundle dotnet core https store akveo com products net core mobile starter bundle utm campaign akveo store 20 20mobile 20bundles 20 20ui kitten 20github 20readme utm source ui kitten utm medium referral utm content dotnet bundle link | react react-native ui-kit | os |
vue-admin-vuetify | vue admin vuetify h2 align center vuetify v2 0 is coming soon h2 h2 align center vue admin vuetify h2 p h4 align center code vue admin vuetify code is a front end component project h4 h4 align center component demo based on a href https github com vuejs vue vue a and a href https github com vuetifyjs vuetify vuetify a h4 p p align center a href https github com vasttian vue admin vuetify tags img src https img shields io github tag date vasttian vue admin vuetify svg alt tags a a href https github com vasttian vue admin vuetify releases img src https img shields io github release vasttian vue admin vuetify all svg alt release a a href https github com vasttian vue admin vuetify blob master license img src https img shields io github license mashape apistatus svg alt license a p github tag https img shields io github tag date vasttian vue admin vuetify svg https github com vasttian vue admin vuetify tags page with curl index warning heads up warning heads up art live demo art live demo rocket getting started rocket getting started camera screenshots camera screenshots white check mark roadmap white check mark roadmap exclamation issues exclamation issues copyright license copyright license warning heads up currently this is just a beta version art live demo need vpn proxy to view https vasttian github io vue admin vuetify https vasttian github io vue admin vuetify rocket getting started project setup bash npm install compiles and hot reloads for development bash npm run serve tada open http localhost 8090 to see the demo if hot reload https vue loader vuejs org guide hot reload html state preservation rules fails modify your vue config js javascript module exports chainwebpack config config resolve symlinks true or replace cnpm with npm npm config set registry https registry npm taobao org compiles and minifies for production bash npm run build lints and fixes files bash npm run lint camera screenshots vue admin vuetify png screenshots vue admin vuetify png charts line png screenshots charts line png components maps screenshots components maps png white check mark roadmap x add dashboard x add icons x add editor components x add jsonlint x mod sidebar x add charts components add ui components add render functions functional components x add demo site exclamation issues if you run into any issues you can contact me at issues https github com vasttian vue admin vuetify issues memo changelog detailed changes for each release are documented in the release notes https github com vasttian vue admin vuetify releases copyright license under the mit license see license http opensource org licenses mit file for more details | vuetify vue admin demo vue-admin-vuetify | front_end |
-Cruise-Control-Forward-Distance-Maintenance-Systems | cruise control forward distance maintenance systems in this project we designed our vehicle on fusion 360 we then implemented two embedded systems on it the first was a cruise control system while the other was a forward distance maintenance system both systems controllers were designed on matlab the parameters of the vehicle were experimentally obtained | os |
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pujangga | pujangga indonesian natural language processing rest api an interface for inanlp and deeplearning4j s word2vec for indonesian bahasa indonesia in the form of rest api below is the screenshot of pujangga s request and response using paw rest client screenshot media pujangga jpg screenshot of pujangga request and response using paw rest client credits dr eng ayu purwarianti st mt et al http ieeexplore ieee org document 7803103 yudi wibisono mt https yudiwbs wordpress com 2016 11 17 word2vec untuk bahasa indonesia inanlp related resources copied from teaolivia https github com teaolivia nospam tree master nospam lib local setup 1 install scala 2 12 2 and lightbend activator 2 clone the project git clone git github com panggi pujangga git 3 download the dependencies cd pujangga activator 4 pretrained word2vec model can be downloaded here https drive google com uc id 0b5ytktu2dokknuy1owjorlztcuu export download https drive google com uc id 0b5ytktu2dokknuy1owjorlztcuu export download 5 run application export word2vec file path to word2vec wiki id activator run 6 access on http localhost 9000 api endpoints stemmer request post stemmer string prof habibie akan melakukan kunjungan resmi ke pt pindad di bandung response status success data prof habibie akan laku kunjung resmi ke pt pindad di bandung phrase chunker request post phrasechunker string prof habibie akan melakukan kunjungan resmi ke pt pindad di bandung response status success data map pindad np prof habibie np di bandung pp akan melakukan kunjungan resmi ke pt vp list np vp np pp part of speech tagger request post postagger string prof habibie akan melakukan kunjungan resmi ke pt pindad di bandung response status success data map resmi jj akan md ke in di in bandung nnp pindad nnp pt nn prof nnp kunjungan nn habibie nnp melakukan vbt list nnp nnp md vbt nn jj in nn nnp in nnp named entity tagger request post netagger string prof habibie akan melakukan kunjungan resmi ke pt pindad di bandung response status success data other person b other other other other other location b other person b other location b formalizer request post formalizer string sis lu bisa nggak pesenin gw sepatu newbalance tipe 960 gpl ya hati2 sama penipuan anak 4l4y response status success data sis kamu bisa tidak pesankan saya sepatu newbalance tipe 960 tidak pakai lama iya hati hati sama penipuan anak norak stopwords removal request post stopwords string prof habibie akan melakukan kunjungan resmi ke pt pindad di bandung response status success data prof habibie kunjungan resmi pt pindad bandung sentence tokenizer request post sentence tokenizer string saya pergi ke bagian kanan rumah sakit prof dr soerojo response status success data saya pergi ke bagian kanan rumah sakit prof dr soerojo sentence tokenizer with composite words request post sentence tokenizer composite string saya pergi ke bagian kanan rumah sakit prof dr soerojo response status success data saya pergi ke bagian kanan rumah sakit prof dr soerojo sentence splitter request post sentence splitter string michael jeffrey jordan dilahirkan di brooklyn new york amerika serikat pada 17 februari 1963 adalah pemain bola basket profesional asal amerika michael jordan merupakan pemain terkenal di dunia dalam cabang olahraga itu setidaknya ia enam kali merebut kejuaraan nba bersama kelompok chicago bulls 1991 1993 1996 1998 ia memiliki tinggi badan 198 cm dan merebut gelar pemain terbaik response status success data michael jeffrey jordan dilahirkan di brooklyn new york amerika serikat pada 17 februari 1963 adalah pemain bola basket profesional asal amerika michael jordan merupakan pemain terkenal di dunia dalam cabang olahraga itu setidaknya ia enam kali merebut kejuaraan nba bersama kelompok chicago bulls 1991 1993 1996 1998 ia memiliki tinggi badan 198 cm dan merebut gelar pemain terbaik word2vec nearest words request post word2vec nearestwords string mobil n 10 response status success data motor dikendarai sepeda truk motornya mengemudikan mobil mobil mobilnya mengendarai pengemudi word2vec arithmetic request post word2vec arithmetic first string serang second string malang third string surabaya n 10 response status success data serang lebak puloampel keserangan bogor waringinkurung jawilan cianjur garut padarincang word2vec similarity request post word2vec similarity first string sore second string petang response status success data 0 7748607993125916 license all files in libs and resource directories are the property of dr eng ayu purwarianti st mt et al http ieeexplore ieee org document 7803103 and not part of the license below apache license version 2 0 all other custom codes made by panggi libersa jasri akadol are licensed under the apache license version 2 0 the license you may not use this project except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | natural-language-processing deep-learning word2vec deeplearning4j scala play-framework bahasa-indonesia indonesian-language | ai |
Graphics-And-Vision | graphics and vision student project spring 2015 it university of copenhagen http www itu dk en eye tracking system basic image analysis and image processing pipeline for eye tracking projective geometry projective geometry with homography and augmented reality stereo vision system basic stereo vision system and epipolar geometry note this project explored the different techniques used to implement experimental computer graphics and computer vision systems while the implementation is satisfying in an academic context it is nevertheless not suitable for deployment in a real life context because of performance issues algorithmic optimization was not part of the scope of this project dependencies implemented for and tested with python 2 7 9 img alt eye tracking src eye tracking system eye tracking jpg img alt projection src projective geometry projection jpg img alt epipolar lines src stereo vision system epipolar lines jpg | ai |
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Predicting-the-closing-stock-price-of-APPLE-using-LSTM | time series analysis a hands on guide using pandas and matplotlib with yahoo finance data time series data is a series of data points indexed in time order time series data is everywhere so manipulating them is important for any data analyst or data scientist in this project we will discover and explore data from the stock market particularly some technology stocks apple amazon google and microsoft we will learn how to use yfinance to get stock information and visualize different aspects of it using seaborn and matplotlib and finally we will look at a few ways of analyzing the risk of a stock based on its previous performance history getting the data the first step is to get the data and load it to memory we will get our stock data from the yahoo finance website yahoo finance is a rich resource of financial market data and tools to find compelling investments to get the data from yahoo finance we will be using yfinance library which offers a threaded and pythonic way to download market data from yahoo check this article to learn more about yfinance reliably download historical market data from with python open in colab https colab research google com assets colab badge svg https colab research google com drive 1bkegct3xljzao3kdlnm7cd8pg0rocxx1 | data-science deep-learning lstm lstm-neural-networks machine-learning python stock stock-market time-series time-series-analysis forcasting yahoo-finance yahoo-finance-api | server |
starter-cors-front-end | starter cors front end application installation 1 fork clone this repository 1 run npm install use npm start to run the application | front_end |
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grpc-web-devtools | grpc web dev tools prs welcome https img shields io badge prs welcome brightgreen svg http makeapullrequest com grpc web dev tools screenshots store light dark png now supports dark mode installation chrome via the chrome web store https chrome google com webstore detail grpc web developer tools kanmilmfkjnoladbbamlclhccicldjaj recommended or 1 build it with make build 1 open the extension management page by navigating to chrome extensions 1 enable developer mode by clicking the toggle switch next to developer mode 1 click the load unpacked button and select the extension build directory firefox via firefox browser add ons https addons mozilla org en us firefox addon grpc web developer tools recommended or 1 build and package with make package 1 enter about debugging in the url bar of firefox 1 click this firefox load temporary add on 1 select the grpc web devtools zip extention file usage javascript const enabledevtools window grpcweb devtools const client new echoserviceclient http myapi com enabledevtools client note requires that your generated client s use protoc gen grpc web 1 0 4 example the example uses docker compose to start a simple grpc server javascript client and the envoy proxy for grpc web bash make example up example will be running on http localhost 8080 http localhost 8080 to stop the example bash make example down connect web grpc web devtools now also supports connect web https github com bufbuild connect web ts connect web devtools is loaded in as a script so it is not guaranteed to be loaded before your code const interceptors interceptor window connect web devtools undefined window connect web devtools to get around the fact that connect web devtools might not be loaded we can listen for a custom event and then push the interceptor to our array once loaded window addeventlistener connect web dev tools ready if typeof window connect web devtools undefined interceptors push window connect web devtools now we can use the interceptors in our transport const transport transport creategrpcwebtransport baseurl getapihostname interceptors this will also work for the connect protocol ts const transport transport connecttransportoptions baseurl getapihostname interceptors | chrome-extension grpc-web chrome platform | front_end |
Web-Development-with-Bootstrap-4-and-Angular-2-Second-Edition | web development with bootstrap 4 and angular 2 second edition by packt overview this repository contains source code referenced in web development with bootstrap 4 and angular 2 second edition https www packtpub com web development learning web development bootstrap and angular second edition utm source github utm medium repository utm campaign 9781785880810 book about the book two of the most popular front end frameworks angular and bootstrap have undergone a major overhaul to embrace emerging web technologies so that developers can build cutting edge web applications you can build high quality single page apps with elegant user interfaces by combining the power of both these tools inside this title you ll dive fingers first into the basics of both the tools and once you re familiar with them you ll get your hands dirty by working with bootstrap s new grid system and angular s built in directives you ll then learn how to format output using angular s pipes and how to make use of the built in router to set up routes for all your components webpack will be your buddy to wrap up your project then after throwing in some sass to make things pretty you ll learn to validate the forms you ve built and debug your application finally you ll go on to learn how to obtain smooth transitioning from bootstrap to angular and then how to hook up with a server and use firebase as the persistence layer once you re done with this book you ll not only have a lovely little e commerce application running but you ll also take with you the confidence to innovate and build your own applications with ease who this book is for whether you know a little about bootstrap or angular or you re a complete beginner this book will enhance your capabilities in both frameworks and help you build a fully functional web app a working knowledge of html css and javascript is required to fully get to grips with bootstrap and angular organization the source code is organized by individual chapters code corresponding to each chapter is found in the chapterx folder where x represents the chapter number each chapterx folder further contains several sub folders each of them referenced from the chapter questions please send your questions and comments to author sergey akopkokhyants email mailto akserg gmail com related products bootstrap site blueprints https www packtpub com web development bootstrap site blueprints utm source github utm medium repository utm campaign 9781782164524 angularjs web application development cookbook https www packtpub com web development angularjs web application development cookbook utm source github utm medium repository utm campaign 9781783283354 building a responsive website with bootstrap video https www packtpub com web development building responsive website bootstrap video utm source github utm medium repository utm campaign 9781782164982 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions | front_end |
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ml-coursera-python-assignments | coursera machine learning mooc by andrew ng https www coursera org learn machine learning python programming assignments machinelearning jpg this repositry contains the python versions of the programming assignments for the machine learning online class https www coursera org learn machine learning taught by professor andrew ng this is perhaps the most popular introductory online machine learning class in addition to being popular it is also one of the best machine learning classes any interested student can take to get started with machine learning an unfortunate aspect of this class is that the programming assignments are in matlab or octave probably because this class was made before python became the go to language in machine learning the python machine learning ecosystem has grown exponentially in the past few years and is still gaining momentum i suspect that many students who want to get started with their machine learning journey would like to start it with python also it is for those reasons i have decided to re write all the programming assignments in python so students can get acquainted with its ecosystem from the start of their learning journey these assignments work seamlessly with the class and do not require any of the materials published in the matlab assignments here are some new and useful features for these sets of assignments the assignments use jupyter notebook http jupyter notebook beginner guide readthedocs io en latest what is jupyter html which provides an intuitive flow easier than the original matlab octave assignments the original assignment instructions have been completely re written and the parts which used to reference matlab octave functionality have been changed to reference its python counterpart the re written instructions are now embedded within the jupyter notebook along with the python starter code for each assignment all work is done solely within the notebook the python assignments can be submitted for grading they were tested to work perfectly well with the original coursera grader that is currently used to grade the matlab octave versions of the assignments after each part of a given assignment the jupyter notebook contains a cell which prompts the user for submitting the current part of the assignment for grading online workspace you can work on the assignments in an online workspace called deepnote https www deepnote com this allows you to play around with the code and access the assignments from your browser img height 22 src https beta deepnote com buttons launch in deepnote svg https beta deepnote com launch template data science url https 3a 2f 2fgithub com 2fdibgerge 2fml coursera python assignments downloading the assignments to get started you can start by either downloading a zip file of these assignments by clicking on the clone or download button if you have git installed on your system you can clone this repository using clone https github com dibgerge ml coursera python assignments git each assignment is contained in a separate folder for example assignment 1 is contained within the folder exercise1 each folder contains two files the assignment jupyter notebook which has a ipynb extension all the code which you need to write will be written within this notebook a python module utils py which contains some helper functions needed for the assignment functions within the utils module are called from the python notebook you do not need to modify or add any code to this file requirements these assignments has been tested and developed using the following libraries python 3 6 4 numpy 1 13 3 scipy 1 0 0 matplotlib 2 1 2 jupyter 1 0 0 jupyter client 5 0 1 we recommend using at least these versions of the required libraries or later python 2 is not supported python installation we highly recommend using anaconda for installing python click here https www anaconda com download to go to anaconda s download page make sure to download python 3 6 version if you are on a windows machine open the executable after download is complete and follow instructions once installation is complete open anaconda prompt from the start menu this will open a terminal with python enabled if you are on a linux machine open a terminal and navigate to the directory where anaconda was downloaded change the permission to the downloaded file so that it can be executed so if the downloaded file name is anaconda3 5 1 0 linux x86 64 sh then use the following command chmod a x anaconda3 5 1 0 linux x86 64 sh now run the installation script using anaconda3 5 1 0 linux x86 64 sh and follow installation instructions in the terminal once you have installed python create a new python environment will all the requirements using the following command conda env create f environment yml after the new environment is setup activate it using windows activate machine learning or if you are on a linux machine source activate machine learning now we have our python environment all set up we can start working on the assignments to do so navigate to the directory where the assignments were installed and launch the jupyter notebook from the terminal using the command jupyter notebook this should automatically open a tab in the default browser to start with assignment 1 open the notebook exercise1 exercise1 ipynb python tutorials if you are new to python and to jupyter notebooks no worries there is a plethora of tutorials and documentation to get you started here are a few links which might be of help python programming https pythonprogramming net introduction to python programming a turorial with videos about the basics of python numpy and matplotlib tutorial http cs231n github io python numpy tutorial we will be using numpy extensively for matrix and vector operations this is great tutorial to get you started with using numpy and matplotlib for plotting jupyter notebook https medium com codingthesmartway com blog getting started with jupyter notebook for python 4e7082bd5d46 getting started with the jupyter notebook python introduction based on the class s matlab tutorial https github com mstampfer coursera stanford ml python blob master coursera 20stanford 20ml 20python 20wiki ipynb this is the equivalent of class s matlab tutorial in python caveats and tips in many of the exercises the regularization parameter lambda is denoted as the variable name lambda notice the underscore at the end of the name this is because lambda is a reserved python keyword and should never be used as a variable name in numpy the function dot is used to perform matrix multiplication the operation only does element by element multiplication unlike matlab if you are using python version 3 5 the operator is the new matrix multiplication and it is equivalent to the dot function acknowledgements i would like to thank professor andrew ng and the crew of the stanford machine learning class on coursera for such an awesome class some of the material used especially the code for submitting assignments for grading is based on mstampfer s https github com mstampfer coursera stanford ml python python implementation of the assignments | ai |
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project-starter | project starter setup this repo is intended for having a fork starting point to create a web project it includes a few common setup files and folders has editorconfig with a base setup that should be recognized by vscode gitignore with most common ignore files just in case empty index html linked with an empty main css css and js folder for file dependencies assets folder for any project related files created for the web development course https www itschool ro cursuri curs web development online on itschool https www itschool ro | front_end |
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defold | ci main https github com defold defold workflows ci 20 20main badge svg ci editor only https github com defold defold workflows ci 20 20editor 20only badge svg ci engine nightly https github com defold defold workflows ci 20 20engine 20nightly badge svg join the chat at https discord gg chbde7j https img shields io discord 250018174974689280 color 237289da label defold logo discord logocolor white https discord gg chbde7j defold repository for the defold engine editor and command line tools supported by https defold com images logo others melsoft black png https melsoft games com https defold com images spacer32 png https defold com images logo others rive black png https www rive app https defold com images spacer32 png https defold com images logo others op games color png https www opgames org https defold com images spacer32 png https defold com images logo others heroiclabs blue png https www heroiclabs com https defold com images spacer32 png https defold com images logo others king color png https king com folder structure build tools build configuration and build tools used by build scripts ci continuous integration files for github ci more info readme ci md com dynamo cr bob engine engine editor editor packages external packages scripts build and utility scripts share misc shared stuff used by other tools waf build scripts valgrind suppression files etc setup and build setup engine follow the setup guide readme setup md to install all of the tools needed to build the defold engine build engine follow the build instructions readme build md to build the engine and command line tools setup build and run editor follow the instructions editor readme md in the editor folder engine overview an overview of the engine architecture and additional engine information can be viewed here readme engine md platform specifics ios readme ios md android readme android md html5 emscripten readme emscripten md releasing a new version the release process is documented here release md complying with licenses a full list of third party software licenses along with information on how to give attribution and include the licenses in your game can be found in the complying with licenses complying with licenses md document in the defold repository on github | defold game-engine c-plus-plus clojure game-development multi-platform gamedev hacktoberfest | front_end |
resume-parser | resume parser extracting name email phonenumber skills code to parse information such as name email phone number skillset and the technology associated with it requirements following is the list of python libraries required cstringio re csv pdfminer beautifulsoup urllib2 spacy spacy is an industrial strength natural language processing tool which is used here to detect indian names in the resume after pip installing spacy make sure that you install xx model which supports foreign languages python m spacy download xx code set the directory to the one which contains the extracted files and save your resume with the file name resume pdf in the same directory python resumeparser py sample output page https i imgur com 4u6ifvx png | server |
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RAPmobile | rapmobile mobile development | front_end |
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car-and-line-detection | nku github https zhuanlan zhihu com p 31623107 https zhuanlan zhihu com p 29113411 https zhuanlan zhihu com p 35134563 https zhuanlan zhihu com p 35607432 https blog csdn net lhbbzh article details 42345931 https towardsdatascience com vehicles tracking with hog and linear svm c9f27eaf521a http download csdn net detail zhuangxiaobin 7326197 http download csdn net detail zhuangxiaobin 7326205 https link zhihu com target https 3a pan baidu com s 13ncryrdek7tydsuidiuhna https github com udacity self driving car tree master annotations sdn udacity self driving car kitti kitti http www cvlibs net datasets kitti opencv3 0 opencv contrib cmake clion opencv python opencv contrib ubuntu16 04 python python 2 7 clion main cpp clion cmake cmake cmake cmake dcmake build type release g codeblocks unix makefiles project cmake cmake cmake build project cmake build release target cv j 2 cmake cmake exe project clion c python pipline 1 hog svm hog svm 2 haar cascade haar cascade 3 nms pipline 1 2 x sobel 3 hls 4 2 3 5 6 7 figure img1 gif figure img2 gif hog haar haar model adaboost hog csdn hog svm https download csdn net download weixin 37762749 10489185 hog python func buildimglist py path vechile path non vechile path non vechile2 python train txt test txt txt img main cpp hogsvmtrainauto hogsvmtest model svm hog classifier xml hard example groung truth box bug hog xml python func validatebbox2 py data path annotation xml python python func buildretrainimglist py python func gethardex py retrain txt retrain pos txt txt img main cpp findhardexampleandretrain haar opencv main cpp finaldetect c void finaldetect string filename string model cascade haar string model hog hog int dataset bool isline dataset 1 dataset 2 python func madevedio py detection cpp c void adaboosttest string model path string img name void hogdetect string filename void multiscaledetect string model hog string model cascade string filename void linedetect string imgname adaboosttest haar hogdetect hog multiscaledetect haar hog linedetect main cpp linedetect | ai |
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EdgeNets | efficient networks for computer vision this repo contains source code of our work on designing efficient networks for different computer vision tasks span style color blue 1 image classification 2 object detection and 3 semantic segmentation span table tr td colspan 2 align center b real time semantic segmentation using espnetv2 on iphone7 see a href https github com sacmehta espnetv2 coreml target blank here a for ios application source code using coreml b td tr tr td img src images espnetv2 iphone7 video 1 gif alt seg demo on iphone7 img td td img src images espnetv2 iphone7 video 2 gif alt seg demo on iphone7 img td tr table table tr td colspan 2 align center b real time object detection using espnetv2 b td tr tr td colspan 2 align center img src images espnetv2 detection 2 gif alt demo 1 img td tr tr td img src images espnetv2 detection 1 gif alt demo 2 img td td img src images espnetv2 detection 3 gif alt demo 3 img td tr table table of contents 1 key highlihgts key highlights 2 supported networks supported networks 3 relevant papers relevant papers 4 blogs blogs 5 performance comparison performance comparison 6 training receipe training receipe 7 instructions for segmentation and detection demos instructions for segmentation and detection demos 8 citation citation 9 license license 10 acknowledgements acknowledgements 11 contributions want to help out 12 notes notes key highlights object classification on the imagenet and ms coco multi label semantic segmentation on the pascal voc and the cityscapes object detection on the pascal voc and the ms coco supports pytorch 1 0 integrated with tensorboard for easy visualization of training logs scripts for downloading different datasets semantic segmentation application using espnetv2 on iphone can be found here https github com sacmehta espnetv2 coreml supported networks this repo supports following networks espnetv2 classification segmentation detection dicenet classification segmentation detection shufflenetv2 classification relevant papers espnet eccv 18 https arxiv org abs 1803 06815 espnetv2 cvpr 19 https arxiv org abs 1811 11431 dicenet arxiv https arxiv org pdf 1906 03516 pdf blogs faster training for efficient networks https medium com p faster training of efficient cnns 657953aa080 source email dc17ff22fa63 writer postdistributed sk f60110289b6157de4c9e0c00c77f51e9 semantic segmentation using espnetv2 https medium com sachinmehta ngb espnetv2 for semantic segmentation 9e80f155d522 source friends link sk 91bca9326b088a972c170d1f7f5063e8 performance comparison imagenet below figure compares the performance of dicenet with other efficient networks on the imagenet dataset dicenet outperforms all existing efficient networks including mobilenetv2 and shufflenetv2 more details here model classification model zoo readme md dicenet performance on the imagenet images dicenet imagenet png object detection below table compares the performance of our architecture with other detection networks on the ms coco dataset our network is fast and accurate more details here model detection model zoo readme md table tr td td td colspan 3 align center b mscoco b td tr tr td td td align center b image size b td td align center b flops b td td align center b miou b td td align center b fps b td tr tr td ssd vgg td td align center 512x512 td td align center 100 b td td align center 26 8 td td align center 19 td tr tr td yolov2 td td align center 544x544 td td align center 17 5 b td td align center 21 6 td td align center 40 td tr tr td espnetv2 ssd ours td td align center 512x512 td td align center 3 2 b td td align center 24 54 td td align center 35 td tr table semantic segmentation below figure compares the performance of espnet and espnetv2 on two different datasets note that espnets are one of the first efficient networks that delivers competitive performance to existing networks on the pascal voc dataset even with low resolution images say 256x256 see here model segmentation model zoo readme md for more details table tr td td td colspan 3 align center b cityscapes b td td colspan 3 align center b pascal voc 2012 b td tr tr td td td align center b image size b td td align center b flops b td td align center b miou b td td align center b image b size td td align center b flops b td td align center b miou b td tr tr td espnet td td align center 1024x512 td td align center 4 5 b td td align center 60 3 td td align center 512x512 td td align center 2 2 b td td align center 63 td tr tr td espnetv2 td td align center 1024x512 td td align center 2 7 b td td align center b 66 2 b td td align center 384x384 td td align center 0 76 b td td align center b 68 b td tr table training receipe image classification details about training and testing are provided here readme classification md details about performance of different models are provided here model classification model zoo readme md semantic segmentation details about training and testing are provided here readme segmentation md details about performance of different models are provided here model segmentation model zoo readme md object detection details about training and testing are provided here readme detection md details about performance of different models are provided here model detection model zoo readme md instructions for segmentation and detection demos to run the segmentation demo just type python segmentation demo py to run the detection demo run the following command python detection demo py or python detection demo py live for other supported arguments please see the corresponding files citation if you find this repository helpful please feel free to cite our work article mehta2019dicenet author sachin mehta and hannaneh hajishirzi and mohammad rastegari title dicenet dimension wise convolutions for efficient networks year 2020 journal ieee transactions on pattern analysis and machine intelligence inproceedings mehta2018espnetv2 title espnetv2 a light weight power efficient and general purpose convolutional neural network author mehta sachin and rastegari mohammad and shapiro linda and hajishirzi hannaneh booktitle proceedings of the ieee conference on computer vision and pattern recognition year 2019 inproceedings mehta2018espnet title espnet efficient spatial pyramid of dilated convolutions for semantic segmentation author mehta sachin and rastegari mohammad and caspi anat and shapiro linda and hajishirzi hannaneh booktitle proceedings of the european conference on computer vision eccv pages 552 568 year 2018 license by downloading this software you acknowledge that you agree to the terms and conditions given here license acknowledgements most of our object detection code is adapted from ssd in pytorch https github com amdegroot ssd pytorch we thank authors for such an amazing work want to help out thanks for your interest in our work open tasks that are interesting tensorflow implementation i kind of wanna do this but not getting enough time if you are interested drop a message and we can talk about it optimizing the eesp and the dicenet block at cuda level optimize and port pretrained models across multiple mobile platforms including android other thoughts are also welcome notes notes about dicenet paper this repository contains dicenet s source code in pytorch only and you should be able to reproduce the results of v1 v2 of our arxiv paper to reproduce the results of our t pami paper you need to incorporate mobilenet tricks in section 5 3 https arxiv org pdf 1906 03516 pdf which are currently not a part of this repository | cnn cnn-classification object-detection semantic-segmentation mscoco cityscapes pascal-voc imagenet-dataset imagenet-classifier shufflenetv2 espnetv2 dicenet pytorch | ai |
rube | rube a pure dataflow aka frp aka reactive library for building application models that run by themselves on clojure or clojurescript thanks to cljc learn more on the rube wiki https github com kennytilton rube wiki | front_end |
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Billing-System | billing system a project was done for the course software engineering it is a billing system for a shop the language used is javafx and the database is mysql | server |
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Machine-Learning-Books-With-Python | master machine learning with python this repository contains notes and exercises solutions for the following books introduction to statistical learning if you are looking to become an expert check out my books master the fundamentals of python 0 master data analysis with python 1 master machine learning with python 2 they are all extremely comprehensive and offer lots of exercises with detailed solutions 0 https www dunderdata com master the fundamentals of python 1 https dunderdata com master data analysis with python 2 https www dunderdata com master machine learning with python | ai |
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machine-learning-experiments | interactive machine learning experiments ukraine is being attacked https war ukraine ua by russian army civilians are getting killed residential areas are getting bombed help ukraine via serhiy prytula charity foundation https prytulafoundation org en come back alive charity foundation https savelife in ua en donate en national bank of ukraine https bank gov ua en news all natsionalniy bank vidkriv spetsrahunok dlya zboru koshtiv na potrebi armiyi more info on war ukraine ua https war ukraine ua and mfa of ukraine https twitter com mfa ukraine hr this is a collection of interactive machine learning experiments each experiment consists of jupyter colab notebook to see how a model was trained and demo page to see a model in action right in your browser launch ml experiments demo http trekhleb github io machine learning experiments launch ml experiments jupyter notebooks https nbviewer jupyter org github trekhleb machine learning experiments tree master experiments this repository contains machine learning experiments and not a production ready reusable optimised and fine tuned code and models this is rather a sandbox or a playground for learning and trying different machine learning approaches algorithms and data sets models might not perform well and there is a place for overfitting underfitting experiments most of the models in these experiments were trained using tensorflow 2 https www tensorflow org with keras https www tensorflow org guide keras overview support supervised machine learning supervised learning https en wikipedia org wiki supervised learning is when you have input variables x and an output variable y and you use an algorithm to learn the mapping function from the input to the output y f x the goal is to approximate the mapping function so well that when you have new input data x that you can predict the output variables y for that data it is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process multilayer perceptron mlp or simple neural network nn a multilayer perceptron https en wikipedia org wiki multilayer perceptron mlp is a class of feedforward artificial neural network ann multilayer perceptrons are sometimes referred to as vanilla neural networks composed of multiple layers of perceptrons especially when they have a single hidden layer it can distinguish data that is not linearly separable table thead tr th align left width 150 style width 150px important th th align left width 200 style width 200px important experiment th th align left width 140 style width 140px important model demo training th th align left tags th th align left width 140 style width 140px important dataset th tr thead tbody experiment tr td img src demos src images digits recognition mlp png alt handwritten digits recognition mlp width 150 td td a href experiments digits recognition mlp digits recognition mlp ipynb b handwritten digits recognition mlp b a td td a href https trekhleb github io machine learning experiments experiments digitsrecognitionmlp img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments digits recognition mlp digits recognition mlp ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments digits recognition mlp digits recognition mlp ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code mlp code td td a href https www tensorflow org datasets catalog mnist mnist a td tr experiment tr td img src demos src images sketch recognition mlp png alt handwritten sketch recognition mlp width 150 td td a href experiments sketch recognition mlp sketch recognition mlp ipynb b handwritten sketch recognition mlp b a td td a href https trekhleb github io machine learning experiments experiments sketchrecognitionmlp img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments sketch recognition mlp sketch recognition mlp ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments sketch recognition mlp sketch recognition mlp ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code mlp code td td a href https github com googlecreativelab quickdraw dataset quickdraw a td tr tbody table convolutional neural networks cnn a convolutional neural network https en wikipedia org wiki convolutional neural network cnn or convnet is a class of deep neural networks most commonly applied to analyzing visual imagery photos videos they are used for detecting and classifying objects on photos and videos style transfer face recognition pose estimation etc table thead tr th align left width 150 style width 150px important th th align left width 200 style width 200px important experiment th th align left width 140 style width 140px important model demo training th th align left tags th th align left width 140 style width 140px important dataset th tr thead tbody experiment tr td img src demos src images digits recognition cnn png alt handwritten digits recognition cnn td td a href experiments digits recognition cnn digits recognition cnn ipynb b handwritten digits recognition cnn b a td td a href https trekhleb github io machine learning experiments experiments digitsrecognitioncnn img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments digits recognition cnn digits recognition cnn ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments digits recognition cnn digits recognition cnn ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code cnn code td td a href https www tensorflow org datasets catalog mnist mnist a td tr experiment tr td img src demos src images sketch recognition cnn png alt handwritten sketch recognition cnn width 150 td td a href experiments sketch recognition cnn sketch recognition cnn ipynb b handwritten sketch recognition cnn b a td td a href https trekhleb github io machine learning experiments experiments sketchrecognitioncnn img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments sketch recognition cnn sketch recognition cnn ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments sketch recognition cnn sketch recognition cnn ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code cnn code td td a href https github com googlecreativelab quickdraw dataset quickdraw a td tr experiment tr td img src demos src images rock paper scissors cnn jpg alt rock paper scissors width 150 td td a href experiments rock paper scissors cnn rock paper scissors cnn ipynb b rock paper scissors cnn b a td td a href https trekhleb github io machine learning experiments experiments rockpaperscissorscnn img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments rock paper scissors cnn rock paper scissors cnn ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments rock paper scissors cnn rock paper scissors cnn ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code cnn code td td a href http www laurencemoroney com rock paper scissors dataset rps a td tr experiment tr td img src demos src images rock paper scissors mobilenet v2 jpg alt rock paper scissors width 150 td td a href experiments rock paper scissors mobilenet v2 rock paper scissors mobilenet v2 ipynb b rock paper scissors mobilenetv2 b a td td a href https trekhleb github io machine learning experiments experiments rockpaperscissorsmobilenetv2 img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments rock paper scissors mobilenet v2 rock paper scissors mobilenet v2 ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments rock paper scissors mobilenet v2 rock paper scissors mobilenet v2 ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code mobilenetv2 code code transfer learning code code cnn code td td a href http www laurencemoroney com rock paper scissors dataset rps a a href http image net org explore imagenet a td tr experiment tr td img src demos src images objects detection ssdlite mobilenet v2 jpg alt objects detection width 150 td td a href experiments objects detection ssdlite mobilenet v2 objects detection ssdlite mobilenet v2 ipynb b objects detection mobilenetv2 b a td td a href https trekhleb github io machine learning experiments experiments objectsdetectionssdlitemobilenetv2 img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments objects detection ssdlite mobilenet v2 objects detection ssdlite mobilenet v2 ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments objects detection ssdlite mobilenet v2 objects detection ssdlite mobilenet v2 ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code mobilenetv2 code code ssdlite code code cnn code td td a href http cocodataset org home coco a td tr experiment tr td img src demos src images image classification mobilenet v2 jpg alt objects detection width 150 td td a href experiments image classification mobilenet v2 image classification mobilenet v2 ipynb b image classification mobilenetv2 b a td td a href https trekhleb github io machine learning experiments experiments imageclassificationmobilenetv2 img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments image classification mobilenet v2 image classification mobilenet v2 ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments image classification mobilenet v2 image classification mobilenet v2 ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code mobilenetv2 code code cnn code td td a href http image net org explore imagenet a td tr tbody table recurrent neural networks rnn a recurrent neural network https en wikipedia org wiki recurrent neural network rnn is a class of deep neural networks most commonly applied to sequence based data like speech voice text or music they are used for machine translation speech recognition voice synthesis etc table thead tr th align left width 150 style width 150px important th th align left width 200 style width 200px important experiment th th align left width 140 style width 140px important model demo training th th align left tags th th align left width 140 style width 140px important dataset th tr thead tbody experiment tr td img src demos src images numbers summation rnn png alt numbers summation rnn width 150 td td a href experiments numbers summation rnn numbers summation rnn ipynb b numbers summation rnn b a td td a href https trekhleb github io machine learning experiments experiments numberssummationrnn img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments numbers summation rnn numbers summation rnn ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments numbers summation rnn numbers summation rnn ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code lstm code code sequence to sequence code td td auto generated td tr experiment tr td img src demos src images text generation shakespeare rnn jpg alt shakespeare text generation rnn width 150 td td a href experiments text generation shakespeare rnn text generation shakespeare rnn ipynb b shakespeare text generation rnn b a td td a href https trekhleb github io machine learning experiments experiments textgenerationshakespearernn img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments text generation shakespeare rnn text generation shakespeare rnn ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments text generation shakespeare rnn text generation shakespeare rnn ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code lstm code code character based rnn code td td a href https storage googleapis com download tensorflow org data shakespeare txt shakespeare a td tr experiment tr td img src demos src images text generation wikipedia rnn png alt wikipedia text generation rnn width 150 td td a href experiments text generation wikipedia rnn text generation wikipedia rnn ipynb b wikipedia text generation rnn b a td td a href https trekhleb github io machine learning experiments experiments textgenerationwikipediarnn img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments text generation wikipedia rnn text generation wikipedia rnn ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments text generation wikipedia rnn text generation wikipedia rnn ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code lstm code code character based rnn code td td a href https www tensorflow org datasets catalog wikipedia wikipedia a td tr experiment tr td img src demos src images recipe generation rnn jpg alt recipe generation rnn width 150 td td a href experiments recipe generation rnn recipe generation rnn ipynb b recipe generation rnn b a td td a href https trekhleb github io machine learning experiments experiments recipegenerationrnn img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments recipe generation rnn recipe generation rnn ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments recipe generation rnn recipe generation rnn ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code lstm code code character based rnn code td td a href https eightportions com datasets recipes recipe box a td tr tbody table unsupervised machine learning unsupervised learning https en wikipedia org wiki unsupervised learning is when you only have input data x and no corresponding output variables the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data these are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher algorithms are left to their own to discover and present the interesting structure in the data generative adversarial networks gans a generative adversarial network https en wikipedia org wiki generative adversarial network gan is a class of machine learning frameworks where two neural networks contest with each other in a game two models are trained simultaneously by an adversarial process for example a generator the artist learns to create images that look real while a discriminator the art critic learns to tell real images apart from fakes table thead tr th align left width 150 style width 150px important th th align left width 200 style width 200px important experiment th th align left width 140 style width 140px important model demo training th th align left tags th th align left width 140 style width 140px important dataset th tr thead tbody experiment tr td img src demos src images clothes generation dcgan jpg alt clothes generation dcgan width 150 td td a href experiments clothes generation dcgan clothes generation dcgan ipynb b clothes generation dcgan b a td td a href https trekhleb github io machine learning experiments experiments clothesgenerationdcgan img src https img shields io static v1 label f0 9f 8e a8 20launch message demo color green alt launch demo a a href https nbviewer jupyter org github trekhleb machine learning experiments blob master experiments clothes generation dcgan clothes generation dcgan ipynb img src https mybinder org badge logo svg alt open in binder a a href https colab research google com github trekhleb machine learning experiments blob master experiments clothes generation dcgan clothes generation dcgan ipynb img src https colab research google com assets colab badge svg alt open in colab a td td code dcgan code td td a href https www tensorflow org datasets catalog fashion mnist fashion mnist a td tr tbody table how to use this repository locally setup virtual environment for experiments bash create experiments environment from the project root folder python3 m venv virtualenvs experiments activate environment source virtualenvs experiments bin activate or if you use fish source virtualenvs experiments bin activate fish to quit an environment run deactivate install dependencies bash upgrade pip and setuptools to the latest versions pip install upgrade pip setuptools install packages pip install r requirements txt to install new packages run pip install package name to add new packages to the requirements run pip freeze requirements txt launch jupyter locally in order to play around with jupyter notebooks and see how models were trained you need to launch a jupyter notebook https jupyter org server bash launch jupyter server jupyter notebook jupyter will be available locally at http localhost 8888 notebooks with experiments may be found in experiments folder launch demos locally demo application is made on react by means of create react app https github com facebook create react app bash switch to demos folder from project root cd demos install all dependencies yarn install start demo server on http yarn start or start demo server on https for camera access in browser to work on localhost yarn start https demos will be available locally at http localhost 3000 or at https localhost 3000 convert models the converter environment is used to convert the models that were trained during the experiments from h5 keras format to javascript understandable formats tfjs layers model or tfjs graph model formats with json and bin files for further usage with tensorflow js https www tensorflow org js in demo application http trekhleb github io machine learning experiments bash create converter environment from the project root folder python3 m venv virtualenvs converter activate converter environment source virtualenvs converter bin activate or if you use fish source virtualenvs converter bin activate fish install converter requirements pip install r requirements converter txt the conversion of keras models to tfjs layers model tfjs graph model formats is done by tfjs converter https github com tensorflow tfjs tree master tfjs converter for example bash tensorflowjs converter input format keras experiments digits recognition mlp digits recognition mlp h5 demos public models digits recognition mlp converting the models to js understandable formats and loading them to the browser directly might not be a good practice since in this case the user might need to load tens or hundreds of megabytes of data to the browser which is not efficient normally the model is being served from the back end i e tensorflow extended https www tensorflow org tfx and instead of loading it all to the browser the user will do a lightweight http request to do a prediction but since the demo app http trekhleb github io machine learning experiments is just an experiment and not a production ready app and for the sake of simplicity to avoid having an up and running back end we re converting the models to js understandable formats and loading them directly into the browser requirements recommended versions python 3 7 3 node 12 4 0 yarn 1 13 0 in case if you have python version 3 7 3 you might experience runtimeerror dictionary changed size during iteration error when trying to import tensorflow see the issue https github com tensorflow tensorflow issues 33183 you might also be interested in homemade machine learning https github com trekhleb homemade machine learning python examples of popular machine learning algorithms with interactive jupyter demos and math being explained nanoneuron https github com trekhleb nano neuron 7 simple javascript functions that will give you a feeling of how machines can actually learn playground and cheatsheet for learning python https github com trekhleb learn python collection of python scripts that are split by topics and contain code examples with explanations articles story behind the project https github com trekhleb machine learning experiments blob master assets story en md generating cooking recipes using tensorflow and lstm recurrent neural network https github com trekhleb machine learning experiments blob master assets recipes generation en md a step by step guide author trekhleb https trekhleb dev | machine-learning javascript python tensorflow jupyter-notebooks colab-notebook colab ai artificial-intelligence keras numpy | ai |
Panoptes-Front-End | panoptes front end build status https github com zooniverse panoptes front end actions workflows ci tests yml badge svg branch master coverage status https coveralls io repos github zooniverse panoptes front end badge svg https coveralls io github zooniverse panoptes front end getting started we are no longer actively developing features for this app prs will be accepted for bug fixes translations and content updates active feature development is happening at https github com zooniverse front end monorepo https github com zooniverse front end monorepo with docker to avoid having to install node js or any other dependencies you can run everything with docker and docker compose docker compose build will build a local docker image and run npm ci run this whenever you change dependencies in package json docker compose up starts a development web server that listens on port 3735 docker compose down stops the development server docker compose run rm shell starts a container running a shell eg for running tests with node js make sure you have node 8 and npm 5 or greater it s recommended you manage your node installations with nvm npm ci installs dependencies npm start builds and runs the site locally note 1 npm ci https docs npmjs com cli v8 commands npm ci clean install is preferred over npm install as it doesn t modify the package lock note 2 as of node 16 15 running npm ci results in errors such as npm err eresolve could not resolve and conflicting peer dependency foobar x y z you can bypass this problem by instead running npm ci legacy peer deps please see issue 6155 https github com zooniverse panoptes front end issues 6155 for more details viewing the website open your web browser of choice and go to https localhost 3735 if you want to login via the panoptes api and view authenticated pages then you ll need to set up and use https local zooniverse org 3735 instead of using localhost 3735 otherwise you ll run into cors errors you need to add the hostname to your hosts file pointing to local instructions are on our stackoverflow https stackoverflow com c zooniverse questions 109 troubleshooting web browser blocks local website the problem when attempting to view localhost 3735 or local zooniverse org 3735 my web browser stops me and shows a warning screen example errors your connection is not private net err cert authority invalid on chrome 104 warning potential security risk ahead on firefox 103 this connection is not private on safari 15 4 the cause the local web server is running https and it s using a self signed certificate modern web browsers consider these certificates very untrustworthy and a possible indicator of a man in the middle attack the solution s for firefox or safari open the advanced options on the warning page and then click whatever s the equivalent of accept risk and continue for chrome type in the interstitial bypass keyword thisisunsafe anywhere on the window to temporarily bypass the warning or manually add the ssl cert to your computer s list of trusted certificates see stackoverflow for additional details https stackoverflow com questions 7580508 getting chrome to accept self signed localhost certificate warning please be careful if you do change your web browser s or computer s security settings configuration the app can be configured using the following environment variables node env sets the environment of the code and determines whether to apply any production optimizations to the bundled code and which set of defaults to apply for e g api host url talk host url etc panoptes api application sets the application id to use when making authentication requests to the panoptes api defaults to that set by node env panoptes api host sets the url of the instance of the panoptes api defaults to that set by node env stat host sets the url of the instance of the stats api defaults to that set by node env sugar host sets the url of the instance of the sugar api defaults to that set by node env talk host sets the url of the instance of the talk api defaults to that set by node env configuration notes some of these environment variables are set by commands in the package json scripts block in order to override them you ll need to modify package json to see the defaults set by the node env environment variable see config js https github com zooniverse panoptes javascript client blob master lib config js in panoptes javascript client https github com zooniverse panoptes javascript client development new github prs from within the zooniverse organisation will be staged by jenkins as part of the ci process once ci finishes your changes should be staged at https pr pr number pfe preview zooniverse org jenkins sometimes times out before finishing the build if a pr build fails use the link to jenkins from your pr to log in and try restarting the build for testing with production data you can add env production to your development url e g localhost 3735 projects env production note that it is removed on every page refresh all the good stuff is in app start at app main cjsx we lint our javascript code against a modified version of the airbnb style guide https github com airbnb javascript please lint your changes with eslint using the eslintrc file at the root of this repo if you have any questions do feel free to ask us on github while editing do your best to follow style and architecture conventions already used by the project the codebase is large and styles have evolved during its development take a look at zooniverse front end monorepo https github com zooniverse front end monorepo to get an idea of our conventions for organising components what to do if it doesn t run try npm ci to freshen up your dependencies and read the warnings they should tell you if you re using the wrong version of node or npm or if you re missing any dependencies if you use docker compose to build and test the site you shouldn t run into any problems with the node version but docker compose build will build a new image with a fresh npm ci testing if you write a new component write a test each component should have its own spec js file the test runner is mocha https mochajs org and enzyme http airbnb io enzyme is available for testing react components mocha throws an error illegal import declaration when compiling coffeescript files that contain es6 import statements with template strings convert these imports to require statements you can run the tests with npm test deployment deployment is handled by github action staging on opening of pull requests a github action is triggered to deploy to a branch staging location the blob storage location depends on the pull request number e g https pr 5926 pfe preview zooniverse org on push to master a github action is triggered to deploy to master staging found at https master pfe preview zooniverse org production production deployments are triggered by an update to which commit the production release tag is pointed to this tag should be updated via chat ops and then a github action will run that builds and uploads the files to our cloud provider found at https www zooniverse org the production deployment can be run ad hoc in the actions tab as needed if you have the appropriate permissions on the repository but only do this in an emergency directory structure app classifier all things classifier related app collections collections related components app components misc generic reusable components app layout app level layout stuff goes here if it affects the main site header the main site footer or the layout of the main site content this is where it lives app lib individual functions and data that are reused across components app pages this is where the bulk of the app lives ideally each route points to a page component responsible for fetching data and handling any actions the user can perform on that data that page component uses that data to render the ui with dumb components passing actions down as necessary app partials originally intended to hold isolated components that wouldn t actually be reused anywhere these probably belong closer to where they re actually used app subjects subject views todoc how s this related to talk collections app talk talk related components public files here will get copied to the output directory during build classifier tasks each task component class should have a couple static components summary shows the post classification summary of the tasks s annotation editor the component used to edit the workflow task in the project builder there are also a few hooks into the rest of the classification interface available if the task needs to render outside the task area beforesubject html content to appear before the subject image during the task insidesubject svg content to appear over the subject image during the task aftersubject html content to appear after the subject image during the task these hooks can be prefixed with persist which will cause them to appear with the task and persist even after the user has moved on to the next task persist before after task work the same way but for the task area non persistent hooks are unnecessary for the task area each component also needs a few static methods getdefaulttask returns the task description to be used as the default when a user adds the task to a workflow in the project builder gettasktext given a task this returns a basic text description of the task e g the question in a question task the instruction in a drawing task etc getdefaultannotation the annotation to be generated when the classifier begins the task isannotationcomplete given a task and an annotation this determines whether or not the classifier will allow the user to move on to the next task testannotationquality given the user s annotation and a known good gold standard annotation for the same task this returns a number between 0 totally wrong and 1 totally correct indicating how close the user s annotation is to the standard task editors make sure you call this props onchange with the updated task when it changes drawing task tools some static methods called from the markinitializer component which controls the mark s values during the user s first mark creating action defaultvalues just some defaults for the mark initstart for every mousedown touchstart until iscomplete returns true return the values for the mark initmove for every mousemove touchmove return new values for the mark initrelease for every mouseup touchend return new values for the mark iscomplete is the mark complete some marks require multiple interactions before the initializer gives up control initvalid if a mark is invalid e g a rectangle with zero width or height it ll be destroyed automatically a couple helper components are the drawingtoolroot which handles selected disabled states and renders sub task popups and the deletebutton and draghandle which render consistent controls for drawing tools there s also a deleteifoutofbounds function that should be called after any whole mark drags conventions react requires each component in an array to have a sibling unique key when rendering arrays of things that do not have ids annotations answers provide a random key property if it doesn t exist ensure underscore prefixed properties aren t persisted that s automatic with the jsonapiclient model class cjsx ul for item in things item key math random li key item key item label li ul async helper components there are some nice unfortunate in hindsight components to help with async values they take a function as props children which looks a little horsey but works rather nicely most requested data is cached locally so these are usually safe but if you notice the same request being made multiple times in a row these are a good place to start looking for the redundant calls here s an example of re rendering when a project changes which results in checking the projects owners cjsx changelistener target props project promiserenderer promise props project get owners owner if owner is props user p this project is yours p else p this project belongs to owner display name p promiserenderer changelistener do not write new code using changelistener or promiserenderer if it s reasonable replace changelistener and promiserenderer instances with component state in code you work on it s more verbose but it s more readable and it ll get us closer to rendering on the server in the future css conventions include any css required for a component s functionality inline in with component otherwise keep it in a separate file one per component for a given component pick a unique top level class name for that component and nest child classes under it keep common base styles and variables in common styl stylus formatting yes colons no semicolons no braces extends up top then properties alphabetically then descendant selectors prefer use of display flex and flex wrap wrap to explicit media queries wherever possible our css has gotten really huge so we re trying out bem http getbem com introduction for organization styl special button styl special button background red color white special button icon width 1em special container styl special container margin 1em 1vw special container button border 1px solid writing components in es6 es2015 we re migrating from coffeescript to es6 this can be done incrementally by writing a new component or rewriting an existing component in es6 a few gotchas should be mentioned the existential operator does not exist in es6 either compare explicitly to null or use thing if it just needs to be truthy native es6 classes are preferred since react createclass is deprecated however if the existing component is relying on mixins then consider using createreactclass mixins are being deprecated and not supported with native classes so do not use them in new components use backticks to import es6 components into coffeescript components import newcomponent from new component an eslint configuration file is setup in the root of the repository for you to use with your text editor to lint both es6 and use airbnb s react style guide a guide https reactjs org docs react without es6 html on writing native classes versus using createreactclass custom projects see the panoptes client library https www npmjs com package panoptes client format of annotation values the format of an annotation s value depends on the task used to generate it single the index of the chosen answer multiple an array of the indices of the chosen answers in the order they were chosen drawing an array of drawing tool marks descriptions of which follow below survey an array of identifications as objects each identification a choice the id of the identified animal and answers an object each key in answers is the id of a question if that question allows multiple answers the value will be an array of answer ids otherwise just a single answer id crop an object containing the x y width and height of the cropped region text a string combo a sub array of annotations dropdown an array of objects where the string value refers to the answer to the corresponding question and the boolean option indicates that the answer was in the list of options drawing tool marks all coordinates are relative to the top left of the image all marks have a tool which is the index of the tool e g workflow tasks t0 tools 0 used to make the mark all marks contain a frame which is the index of the subject frame e g subject locations 0 the mark was made on if details tasks are defined for a tool its marks will have a details array of sub classifications each with a value following the descriptions above drawing annotation value are as follows point the x and y coordinates line the start x1 y1 and end x2 y2 coordinates polygon an array of objects each containing the x and y coordinate of a vertex if the mark was not explicitly closed by the user auto closed is true rectangle the x y coordinate of the top left point of the rectangle along with its width and height circle the x and y coordinate of the center of the circle and its radius r ellipse the x and y coordinate of the center of the ellipse its radii rx and ry and the angle of rx relative to the x axis in degrees counterclockwise from 3 00 bezier the same as polygon but every odd indexed point is the coordinate of the control point of a quadratic bezier curve column the left most x pixel and the width of the column selection acknowledgements thanks to browserstack https www browserstack com for supporting open source and allowing us to test this project on multiple platforms browserstack logo https static zooniverse org browserstack logo 300x158 png https www browserstack com pullreminders https pullreminders com badge svg https pullreminders com ref badge | panoptes-platform hacktoberfest zooniverse | front_end |
bingo-lottery | task image https user images githubusercontent com 47791892 233772151 ec8d2971 16da 4055 b0b1 2f707b2c6f04 png prou ite pravila loto 6 39 https www lutrijabih ba igre loto 639 title loto 639 lutrija bosne i hercegovine kreirajte program u c koji simulira loto 6 39 lutriju prema tekstu molimo formatirajte sav svoj kod prema ovom vodi u za stil c coding conventions https learn microsoft com en us dotnet csharp fundamentals coding style coding conventions dio 1 generisanje listi a 50 program treba da zatra i unos od korisnika o tome koliko listi a da generi e program bi tada trebao generirati taj broj lutrijskih listi a ove sre ke treba sa uvati u txt datoteci itljivoj za ljude i brojeve sortirani od najmanje do najve e numeri ke vrijednosti dio 2 provesti izvla enje 50 jedan set dobitnih brojeva i loto 6 39 treba generisati nasumi no i od tampati u txt u igri loto 6 39 postoji osam vrsta dobitaka koje se utvr uju na temelju izvu ene dobitne kombinacije pojedinog kola privremeni izvje taj https www lutrijabih ba igre loto 639 privremeni izvjestaj assignments organizacija edin ahbaz organizacija formiranje zadataka kreiranje inicijalnog koda klase metode i pozivi metoda kodiranje lan tima issue zadatak edin canovi 1 generisanje kombinacije https github com edinsahbaz bingo lottery issues 1 azra daut 2 generisanje dopunskog broja https github com edinsahbaz bingo lottery issues 2 faris muminovi 3 generisanje listica https github com edinsahbaz bingo lottery issues 3 din ahovi 4 spremanje listica u txt fajl https github com edinsahbaz bingo lottery issues 4 belmin hadrovi 5 citanje kombinacija iz txt fajla https github com edinsahbaz bingo lottery issues 5 edin ahbaz 6 generisanje izvjestaja https github com edinsahbaz bingo lottery issues 6 | front_end |
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Project | lr 3 information system and technologies natoloka mykola 3 github | server |
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