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Name: 华东师范大学智能教育技术小组
Type: Organization
Bio: 华东师范大学智能教育技术小组
Name: 华东师范大学智能教育技术小组
Type: Organization
Bio: 华东师范大学智能教育技术小组
学术研究相关的问题
Easily create a beautiful website using Academic and Hugo
Roadmap to becoming an Artificial Intelligence Expert in 2022
教育期刊词汇分析
Active learning for systematic reviews
books papers notes
运维之道
学习资源分类
课堂情感分析
Deep Learning Specialization by Andrew Ng on Coursera.
This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
Deep learning tutorials (2nd ed.)
Customizable admin dashboard template based on Angular 10+
教育文本情感分析
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.
教育资源分类
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
A curated collection of free Deep Learning related eBooks
USC CS621 Course Project
A Free, Offline, Real-Time, Open-source web-app to assist organisers of any event in allowing only authorised/invited people using Face-Recognition Technology or QR Code.
google training camp 2018.5.27
The website builder for Hugo. Build and deploy a beautiful website in minutes :rocket:
JavaScript/WebGL lib: detect if the user is looking at the screen or not from the webcam video feed. Lightweight and robust to all lighting conditions. Great for play/pause videos !
Oxford Deep NLP 2017 course
This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Machine learning application in education
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.