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allali2018's Projects

amundsen

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

awesome-deeplearning

深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

brms

业务规则管理系统,集成Drools,以数据库的形式实现决策表,提供动态规则配置功能

cp-all-in-one

docker-compose.yml files for cp-all-in-one , cp-all-in-one-community, cp-all-in-one-cloud

cube-studio

云原生一站式机器学习平台，多租户，数据资产，notebook在线开发，拖拉拽任务流编排，多机多卡分布式训练，超参搜索，推理服务，多集群调度，多项目组资源组，边缘计算，大模型实时训练, ai应用商店

datahub

The Metadata Platform for the Modern Data Stack

datart

Datart is a next generation Data Visualization Open Platform

DataSphereStudio is a one stop data application development& management portal, covering scenarios including data exchange, desensitization/cleansing, analysis/mining, quality measurement, visualization, and task scheduling.

datax

DataX是阿里云DataWorks数据集成的开源版本。

datax-cloud

智数通提供了元数据管理、数据标准管理、数据质量管理、主数据管理、数据集市管理、可视化图表看板、流程管理等微服务，是为数字化建设而生的企业级一站式数据治理平台。

datax-web

DataX集成可视化页面，选择数据源即可一键生成数据同步任务，支持RDBMS、Hive、HBase、ClickHouse、MongoDB等数据源，批量创建RDBMS数据同步任务，集成开源调度系统，支持分布式、增量同步数据、实时查看运行日志、监控执行器资源、KILL运行进程、数据源信息加密等。

davinci

Davinci is a DVsaaS (Data Visualization as a Service) Platform

deequ

Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.

dlink

Dinky is an out of the box one-stop real-time computing platform dedicated to the construction and practice of Unified Batch & Streaming and Unified Data Lake & Data Warehouse. Based on Apache Flink, Dinky provides the ability to connect many big data frameworks including OLAP and Data Lake.

dootask

DooTask是一款轻量级的开源在线项目任务管理工具，提供各类文档协作工具、在线思维导图、在线流程图、项目管理、任务分发、即时IM，文件管理等工具。

drawio-desktop

Official electron build of diagrams.net

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.

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