IEEE Standard for Environmental and Social Responsibility Assessment of Computers and Displays-2020

From AI to Robotics: Mobile, Social, and Sentient Robots By 作者: Arkapravo Bhaumik ISBN-10 书号: 1482251477 ISBN-13 书号: 9781482251470 Edition 版本: 1 出版日期: 2018-03-01 pages 页数: 430 From AI to Robotics: Mobile, Social, and Sentient Robots is a journey into the world of agent-based robotics and it covers a number of interesting topics, both in the theory and practice of the discipline. The book traces the earliest ideas for autonomous machines to the mythical lore of ancient Greece and ends the last chapter with a debate on a prophecy set in the apparent future, where human beings and robots/technology may merge to create superior beings – the era of transhumanism. Throughout the text, the work of leading researchers is presented in depth, which helps to paint the socio-economic picture of how robots are transforming our world and will continue to do so. This work is presented along with the influences and ideas from futurists, such as Asimov, Moravec, Lem, Vinge, and of course Kurzweil. The book furthers the discussion with concepts of Artificial Intelligence and how it manifests in robotic agents. Discussions across various topics are presented in the book, including control paradigm, navigation, software, multi-robot systems, swarm robotics, robots in social roles, and artificial consciousness in robots. These discussions help to provide an overall picture of current day agent- based robotics and its prospects for the future. Examples of software and implementation in hardware are covered in Chapter 5 to encourage the imagination and creativity of budding robot enthusiasts. The book addresses several broad themes, such as AI in theory versus applied AI for robots, concepts of anthropomorphism, embodiment and situatedness, extending theory of psychology and animal behavior to robots, and the proposal that in the future, AI may be the new definition of science. Behavior-based robotics is covered in Chapter 2 and retells the debate between deliberative and reactive approaches. The text reiterates that the effort of modern day robotics is to replicate human-like intelligence and behavior, and the tools that a roboticist has at his or her disposal are open source software, which is often powered by crowd-sourcing. Open source meta-projects, such as Robot Operating System (ROS), etc. are briefly discussed in Chapter 5. The ideas and themes presented in the book are supplemented with cartoons, images, schematics and a number of special sections to make the material engaging for the reader. Designed for robot enthusiasts – researchers, students, or the hobbyist, this comprehensive book will entertain and inspire anyone interested in the exciting world of robots.

老师推荐的，不错，做视觉和听觉的可以来看看。

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

跨社交媒体的用户链接- 从用户个人资料和用户生成的内容中提取特征，并判断两个帐户是否属于社交媒体上的同一用户

社交媒体为许多人提供了一个在线表达情感的机会。 对用户情绪进行自动分类可以帮助我们理解公众的偏爱，公众有很多有用的应用程序，包括情感检索和意见汇总。 短文本在Web上很普遍，尤其是在推文，问题和新闻标题中。 现有的大多数社会情感分类模型都集中在长文档传达的用户情感的检测上。 在本文中，我们介绍了一种用于对短文本进行用户情感分类的多标签最大熵（MME）模型。 MME通过对多个用户共同评分的多个情感标签和价进行建模，从而生成丰富的功能。 为了提高该方法在变尺度语料库上的鲁棒性，我们进一步开发了一种针对MME的协同训练算法，并将L-BFGS算法用于广义MME模型。 在现实世界中的短文本集合上进行的实验验证了这些方法对稀疏特征进行社会情感分类的有效性。 我们还演示了生成的词典在识别传达不同社会情感的实体和行为中的应用。 （C）2016 Elsevier BV保留所有权利。

Social Ski-Driver (SSD) 优化算法有关该算法的更多详细信息，请参见[请引用原始论文（下）]。 Alaa Tharwat、Thomas Gabel，“使用社交滑雪驱动程序算法对不平衡数据进行支持向量机的参数优化”-神经计算和应用，第 1-14 页，2019 Tharwat, A. & Gabel, T. Neural Comput & Applic (2019)。 https://doi.org/10.1007/s00521-019-04159-z

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks. Table of Contents Chapter 1 - Graphs in Social and Digital Media Chapter 2 - Mathematical Preliminaries: Graphs and Matrices Chapter 3 - Algebraic Graph Analysis Chapter 4 - Web Search Based on Ranking Chapter 5 - Label Propagation and Information Diffusion in Graphs Chapter 6 - Graph-Based Pattern Classification and Dimensionality Reduction Chapter 7 - Matrix and Tensor Factorization with Recommender System Applications Chapter 8 - Multimedia Social Search Based on Hypergraph Learning Chapter 9 - Graph Signal Processing in Social Media Chapter 10 - Big Data Analytics for Social Networks Chapter 11 - Semantic Model Adaptation for Evolving Big Social Data Chapter 12 - Big Graph Storage, Processing and Visualization

在在线社交网络中的虚拟社区检测领域，大多数现有方法经常从单一角度检测社区，而忽略了网络相关特性对社区检测的影响。 所有这些降低了社区划分结果的可解释性和准确性。 为了解决这个问题，提出了一种在线社交网络的虚拟社区检测模型框架。 该模型框架考虑了影响社区检测结果的三个关键因素：结构特征，属性信息和节点对网络的影响程度。 提出的模型不仅是现有社区检测模型的映射，而且是为社区检测方法设计更多未来模型的参考。

如今，网络欺凌已成为一项重要的社会挑战。 网络欺凌会影响一个人的心理和情感方式。 因此，需要设计一种方法来检测和防止社交网络中的网络欺凌。 大多数现有的网络欺凌方法仅涉及文本检测，很少有方法可用于分析视觉检测。 在这项拟议的工作中，将检测多模型网络欺凌，例如音频、视频、图像以及社交网络中的文本。 网络欺凌图像将使用计算机视觉算法进行检测，该算法包括图像相似性和光学字符识别 (OCR) 两种方法。 网络欺凌视频将使用镜头边界检测算法进行检测，其中视频将被分成帧并使用其中的各种方法进行分析。 提议的框架还支持识别社交网络中的网络欺凌音频。 最后，使用分类器将网络欺凌数据分为身体欺凌、社交欺凌和言语欺凌。