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elasticsearch-head

A web front end for an elastic search cluster

elasticsearch-hq

Monitoring and Management Web Application for ElasticSearch instances and clusters.

elasticsearch-service-broker

service broker for elastic search

elasticsearch-sql

Use SQL to query Elasticsearch

electron-vue

An Electron & Vue.js quick start boilerplate with vue-cli scaffolding, common Vue plugins, electron-packager/electron-builder, unit/e2e testing, vue-devtools, and webpack.

electron-vue-admin

vue electron admin template web: http://panjiachen.github.io/vue-admin-template

electron.net

:electron: Build cross platform desktop apps with ASP.NET Core (Razor Pages, MVC, Blazor).

electronic-wechat

:speech_balloon: A better WeChat on macOS and Linux. Built with Electron by Zhongyi Tong.

element

A Vue.js 2.0 UI Toolkit for Web

element-cooking-starter

A starter kit for Element UI generated by cooking

element-theme

Theme generator cli tool for Element.

elementplugin

idea plugin for develop vue with element ui

elevate.metabase.tools

Tools for managing Metabase

elinq

ELinq 是一个轻量简单易用的开源Linq ORM数据访问组件，支持Nullable类型和枚举类型，支持根据实体类自动建库建表建关系，支持根据数据库通过T4模版自动生成实体代码，对Linq的谓词提供了完美的支持，旨在让绝大部份的主流数据库都使用 Linq 来进行程序开发，让开发人员访问数据库从SQL中解放出来，易学易用上手快，配置简单，并且提供了源代码下载，方便定制。支持多数据库，目前支持Access、SQLServer、SqlCE、SQLite、MySQL、ORACLE，未来还会支持更多的数据库。

eliot

Eliot: the logging system that tells you *why* it happened

elsa

A .NET Standard 2.0 Workflows Library

embedded-spring-boot-keycloak-server

Keycloak JAX-RS application embedded in a Spring-Boot App.

emby-theater-windows

emgucv

Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library.

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

emqx

EMQ X Broker - Scalable Distributed MQTT Message Broker for IoT in the 5G Era

encryption-algorithm

DES、AES、Present、Extended Euclidean Algorithm、Miller-Rabin（ 常用密码学算法）推荐书籍《现代密码学趣味之旅》---彭长根

end-to-end-for-chinese-plate-recognition

多标签分类,端到端的中文车牌识别基于mxnet, End-to-End Chinese plate recognition base on mxnet

enduro

Minimalistic, lean & mean, node.js cms

engineercms

工程师知识管理系统：基于golang go语言（beego框架）。每个行业都有自己的知识管理系统，engineercms旨在为土木工程师们打造一款适用的基于web的知识管理系统。它既可以用于管理个人的项目资料，也可以用于管理项目团队资料；它既可以运行于个人电脑，也可以放到服务器上。支持提取码分享文件，onlyoffice实时文档协作，直接在线编辑dwg文件、office文档，在线利用mindoc创作你的书籍，阅览PDF文件。通用的业务流程设置。手机端配套小程序，微信搜索“珠三角设代”或“青少儿书画”即可呼出小程序。

enode

ENode is a framework aims to help us developing ddd, cqrs, eda, and event sourcing style applications.

ensemble-methods-for-image-classification

In this project, I implemented several ensemble methods (including bagging, AdaBoost, SAMME, stacking, snapshot ensemble) for a normal CNN model and Residual Neural Network.

ent

An entity framework for Go

entity-relation-extraction

Entity and Relation Extraction Based on TensorFlow and BERT. 基于TensorFlow和BERT的管道式实体及关系抽取，2019语言与智能技术竞赛信息抽取任务解决方案。Schema based Knowledge Extraction, SKE 2019

entityframework-effort