本系统实现了在线答题系统，包括答题系统的后台管理系统，可以自在添加题目选项信息，客户端菜品H5页面，Ajax提交和获取数据，具体说明请看：https://www.jianshu.com/p/1b2ca978c50b重新开放下载

该数据是kaggle2013年一个比赛的数据集，比赛网址为：https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data数据包含三个文件：fer2013.bib，fer2013.csv，README

colResizable-1.6。
http://www.bacubacu.com/colresizable/#samples这里是这个文件的官网去这里下载别在这里下载。
之前下载的花了我四块钱。
外面有demo

概况可见https://www.cnblogs.com/l2017/p/9898512.html

https://www.bilibili.com/video/BV1rW411o7ex?from=search&seid=3883136608712101086（爱笑集会室）

codeproject上的共享工程，源地址：https://www.codeproject.com/Articles/2463/CSHA-A-C-Class-Implementation-of-the-SHA-Hash-A，紧缩包里包含SHA1.h和SHA1.cpp文件，不能设置免费所以设置了最低分，欢迎交流暗号271888395。

AG'sNewsTopicClassificationDatasetVersion3,Updated09/09/2015ORIGINAGisacollectionofmorethan1millionnewsarticles.Newsarticleshavebeengatheredfrommorethan2000newssourcesbyComeToMyHeadinmorethan1yearofactivity.ComeToMyHeadisanacademicnewssearchenginewhichhasbeenrunningsinceJuly,2004.Thedatasetisprovidedbytheacademiccomunityforresearchpurposesindatamining(clustering,classification,etc),informationretrieval(ranking,search,etc),xml,datacompression,datastreaming,andanyothernon-commercialactivity.Formoreinformation,pleaserefertothelinkhttp://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html.TheAG'snewstopicclassificationdatasetisconstructedbyXiangZhang(xiang.zhang@nyu.edu)fromthedatasetabove.Itisusedasatextclassificationbenchmarkinthefollowingpaper:XiangZhang,JunboZhao,YannLeCun.Character-levelConvolutionalNetworksforTextClassification.AdvancesinNeuralInformationProcessingSystems28(NIPS2015).DESCRIPTIONTheAG'snewstopicclassificationdatasetisconstructedbychoosing4largestclassesfromtheoriginalcorpus.Eachclasscontains30,000trainingsamplesand1,900testingsamples.Thetotalnumberoftrainingsamplesis120,000andtesting7,600.Thefileclasses.txtcontainsalistofclassescorrespondingtoeachlabel.Thefilestrain.csvandtest.csvcontainallthetrainingsamplesascomma-sparatedvalues.Thereare3columnsinthem,correspondingtoclassindex(1to4),titleanddescription.Thetitleanddescriptionareescapedusingdoublequotes("),andanyinternaldoublequoteisescapedby2doublequotes("").Newlinesareescapedbyabackslashfollowedwithan"n"character,thatis"\n".

预览网址：https://www.cnblogs.com/luxiaoguogege/p/9848078.html

1.Java环境gerrit依赖，用于安装gerrit环境。
下载：jdk-8u181-linux-x64.tar.gzhttp://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html安装：sudotarzxvf./jdk-8u181-linux-x64.tar.gz-C/opt配置：vim~/.bashrc(针对当前用户)orvim/etc/profile(针对所有用户，引荐)

Mateda2.0一个极度好用的分布估计算法matlab工具箱，ForapreliminaryexplanationofMateda2.0seethefileMateda2.0-UserGuide.pdfinthisdirectory.Generaldocumentationabouttheprogramsisavailableinthe/docdirectoryorfrom:http://www.sc.ehu.es/ccwbayes/members/rsantana/software/matlab/MATEDA.htmlMATEDA-2.0employstheMatlabBayesNet(BNT)toolbox(Murphy:2001)andtheBNTstructurelearningpackage(Leray_and_Francois:2004).Theseprograms,whicharefreelyavailablefromtheauthorswebsite(theycanberespectivelydownloadedfromhttp://people.cs.ubc.ca/~murphyk/Software/BNT/bnt.htmlandhttp://banquiseasi.insa-rouen.fr/projects/bnt-slp/),shouldbeinstalledpreviouslytotheMATEDA-2.0installation.SomeoftheMATEDA-2.0routinesalsoemploystheMATLABstatisticaltoolboxandtheaffinitypropagationclusteringalgorithm(Frey_and_Dueck:2007)(theMatlabimplementationofaffinitypropagationisavailablefromhttp://www.psi.toronto.edu/affinitypropagation/).AfterinstallingtheBNTandBNTstructurelearningtools:1)SetthepathtothecurrentBNTstructurelearningtooldirectory.Thisisdonebymodifyingfileadd_SLP.m.2)UnpackthefileIntEDA.zipandcopythefilestoadirectorynamedMATEDA.3)EditfileInitEnvironment.mupdatingthepathspath_MATEDA,path_FullBNTandpath_BNT_SLP.4)SetthecurrentMatlabdirectorytotheMATEDAdirectory.5)ExecuteprogramInitEnvironments.m.Severalwarningsbutnoerrorshouldappear.ThefolderScriptsMatedacontainsseveralexamplesofEDAsimplementations.ThefileMateda2.0-UserGuide.pdfcontainsadetailedexplanationofhowtousetheprograms.Thislibraryisdistributedinthehopethatitwillbeuseful,butWITHOUTANYWARRANTY;withouteventheimpliedwarrantyofMERCHANTABILITYorFITNESSFORAPARTICULARPURPOSE.Lastversion2/04/2009.RobertoSantana(roberto.santana@ehu.es)

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。