社会距离估计 该存储库包含使用YOLOv4对象检测器和OpenPose人类姿势估计器根据单个RGB图像自动进行社交距离估计的代码和教程。 内容 入门 该代码要求安装以下库： python 3.8 张量流2.3.1 的opencv 4.4.0.44 numpy的1.18.5 该代码要求安装YOLOv4和OpenPose模型。 有关安装说明，请参阅和 。 安装后，从此页面下载3个脚本automatic_evaluation_API.py，valuate_labeled_images.py和valuate_unlabeled_images.py。 最后，项目文件夹应如下所示： ${project_dir} / ├── labels │ ├── body_pixel_locations.csv │ ├── camera_locations_photoshoot_identifi

社会认同网络 BrightID允许您向应用程序证明您仅使用帐户。 。 经过信任的密切私人联络人小组和。 。 去中心化和非介入式 该网络由的志愿者。 节点托管用于验证的社交图，但是那里没有存储个人信息。 移动应用 通过BrightID移动应用程序管理ID创建以及与人和应用程序的连接。 有助于 许多问题有，以支付。 通过欢迎对翻译做出贡献。 有关贡献的更多信息，请参见我们的 。 以与其他贡献者交谈。 在项目中集成BrightID 集成BrightID的开发人员的。 另请参见。

第1篇 研究概论 第1章 科学与社会研究 第2章 社会研究的伦理与政治 第3章 研究、理论与范式 第2篇 研究的建构：定量与定性 第4章 研究项目的目的与研究设计 第5章 抽样逻辑 第6章 从概念到测量 第7章 指标、量表和分类法 第3篇 观察的方式 第8章 问卷调查 第9章 实验方法 第10章 非介入性测量 第11章 定型实地研究的范式、方法与伦理 第12章 评估研究：类型、方法与议题

UCINET Version 6.708 | 28 June 2020 非破解版 Fixes ◾In the CLI, triadcensus was giving results only for the first matrix in a dataset ◾In the menu, Network|Whole networks|density|density by groups was printing the within group densities multiple times, with only the last one being complete

现有的寻找社会群体的研究主要集中在社交网络中的密集子图。然而，寻找社会脆弱的群体也有许多重要的应用。在本文中，我们引入了K三角的概念来度量群的最小值。然后，我们制定了一个新的研究问题，最小K三角形断开组（MKTG），以找到一个社会脆弱的群体从在线社交网络。我们证明了MKTG是任意图中任意比率内的NPHard和不可逼近的，但在阈值图中是多项式时间可跟踪的。设计了两种算法，即TARA和TRA-ADV，利用图论方法有效地解决了一般图上的MKTG问题。在七个真实数据集上的实验结果表明，所提出的算法在效率和解决方案质量方面优于现有方法。

Will your next doctor be a human being―or a machine? Will you have a choice? If you do, what should you know before making it? This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to “reach off the Web” into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species. AI experts and researchers James Hendler―co-originator of the Semantic Web (Web 3.0)―and Alice Mulvehill―developer of AI-based operational systems for DARPA, the Air Force, and NASA―explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines. What Readers Will Learn What the concept of a social machine is and how the activities of non-programmers are contributing to machine intelligence How modern artificial intelligence technologies, such as Watson, are evolving and how they process knowledge from both carefully produced information (such as Wikipedia and journal articles) and from big data collections The fundamentals of neuromorphic computing, knowledge graph search, and linked data, as well as the basic technology concepts that underlie networking applications such as Facebook and Twitter How the change in attitudes towards cooperative work on the Web, especially in the younger demographic, is critical to the future of Web applications

信息安全_数据安全_Putin_is_Posting：Social_Media,_V 安全运营 安全防御 安全测试 web安全 安全人才

信息安全_数据安全_D2T1 - Social Media Mining for T 信息安全 安全加固 安全现状 内网安全 基础设施

five-steps-to-defend-against-social-media-weaponization.pdf

This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies?