DELMIA人机教程（Human Task Simulation），学习DELMIA的入门资料，难得的教程！！

The Human-Computer Interaction Handbook 2ed[2008].pdf

人机交互经典书籍，系统阐述人机交互内涵、方法、实践、案例等。.epub书籍，可在ibook中打开。

[2005 CVPR] Histograms of Oriented Gradients for Human Detection 用于人体检测的方向梯度直方图 Navneet Dalal，Bill Triggs

Device Class Definition for Human Interface Devices(HID)

MAX30102与Arduino + BPM测量项目与OLED +蜂鸣器的接口。

自行车仿真流程开发和虚拟现实系统，包括运动学、动力学和交互式仿真。

行人属性识别纸张列表[ ] 人物属性识别纸质清单 从2014年到2020年在PETA和RAP数据集上的性能比较。我们发现，在两个大型基准数据集上，基线方法CNN-SVM的性能远胜于最近基于深度学习的PAR方法。 有趣的是，我们还可以发现，当前基于深度学习的方法的准确性是可比的，并且与几年前提出的深度PAR算法相比，当前方法（在2020年）没有显着改善。 那么，如果基于深度学习的PAR算法达到瓶颈，那么下一步该怎么办？ 笔记： 欢迎来到我们的微信群进行进一步讨论，请扫描此 或扫描此内容以添加我的[注意：姓名+学校/公司] 如果您找到有关人的属性识别的更多相关论文，请给我发送电子邮件： [ ] [] [] 如果您发现此调查对您的研究有用，请考虑引用以下文章： @article{wang2019parsurvey, title={Pedestrian attribute re

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Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Summary Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. About the book Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows. What's inside Identifying the right training and evaluation data Finding and managing people to annotate data Selecting annotation quality control strategies Designing interfaces to improve accuracy and efficiency About the author Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as