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arXiv 2105.09489cs.AI
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基于深度神经网络的社会行为理解:社会智能系统的开发

Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems

University of Glasgow(格拉斯哥大学);Ngee Ann Polytechnic(义安理工学院);National College for Education(国家教育学院);National University Health System(国立大学健保系统);National University of Singapore(新加坡国立大学)

Ethan Lim Ding Feng, Zhi-Wei Neo, Aaron William De Silva, Kellie Sim, Hong-Ray Tan, Thi-Thanh Nguyen, Karen Wei Ling Koh, Wenru Wang, Hoang D. Nguyen

AI总结:

本研究顺应社会计算向社会智能系统演进的趋势,提出基于深度神经网络的社会行为理解框架,整合多类理解模块,开发三类应用系统,推动相关领域发展并提供实践启示。

摘要:

随着人工智能的快速发展,社会计算已从社会信息学演进,催生了社会智能系统。为此,本文率先提出一种利用深度神经网络开展社会与行为分析的社会行为理解框架。该框架整合信息融合、人员与物体检测、社会信号理解、行为理解及语境理解,各模块协同作用以识别社会行为。研究开发了抑郁检测、活动识别和认知障碍筛查三类系统,清晰印证了社会智能的重要性。本研究显著推动了社会计算与健康信息学的积累式发展,也为学术机构、医疗从业者及社会智能体开发者带来诸多启示。

英文摘要:

With the rapid development in artificial intelligence, social computing has evolved beyond social informatics toward the birth of social intelligence systems. This paper, therefore, takes initiatives to propose a social behaviour understanding framework with the use of deep neural networks for social and behavioural analysis. The integration of information fusion, person and object detection, social signal understanding, behaviour understanding, and context understanding plays a harmonious role to elicit social behaviours. Three systems, including depression detection, activity recognition and cognitive impairment screening, are developed to evidently demonstrate the importance of social intelligence. The study considerably contributes to the cumulative development of social computing and health informatics. It also provides a number of implications for academic bodies, healthcare practitioners, and developers of socially intelligent agents.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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