II-New: Infrastructure to Support Integrated Research and Education in Socially Intelligent Computing at Missouri S&T

II-新：支持密苏里州社会智能计算综合研究和教育的基础设施

• 批准号: 1205695
• 负责人: Sriram Chellappan
• 金额: $ 28.17万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1205695&HistoricalAwards=false
• 关键词: II; New; Infrastructure; Support; Integrated

The PI's efforts to conduct original and practical research in socially intelligent computing - an emerging and important paradigm centered on integrating people and computers to create new forms of collaboration, communication, and intelligence previously unachievable by humans or computers alone - have been hindered, in scope, scale and quality, by the lack of a dedicated and realistic infrastructure. This proposal requests funds to set up such an infrastructure at the PI's institution, which will support integrated research and education. The infrastructure requested includes high-end computational and storage servers, desktop machines, laptops, smart phones, sensors, cameras, software and accessories for collecting, processing and extracting knowledge from large scale data arising from the daily interactions of society with the Internet and with mobile phones. The overall goal is to utilize the knowledge gained from social computing data to create a spectrum of practical services and applications benefitting society. In particular, three research projects that emphasize the close integrations of society with technology are identified in the proposal: a) Detection of depressive disorders in college settings by mining Internet usage data; b) Human "fingerprinting" by mining Internet and smart phone usage; and c) Tracking humans in the social world by fusing heterogeneous sensor data.Intellectual MeritThe planned research activities are well described and will likely significantly advance the state of the art in socially intelligent computing. The PI has pioneered the mining of real Internet data to detect depressive behavior in college students. His prior research has identified critical Internet usage features that show strong statistical differences between students with and without depressive symptoms. He next plans to design, using computational intelligence techniques, classifiers which can proactively detect depressive behavior in college students with high accuracy while being transparent and preserving privacy. He is also exploring the feasibility of mining Internet usage patterns to fingerprint humans, with applications to Internet forensics and mitigation of insider attacks. Similar techniques will be applied to mine sensor data from smart phones in order to fingerprint mobility patterns and to lay the foundation for a variety of pervasive services. While conventional tracking algorithms leverage either a network of cameras or physical sensory data or electronic signals, the PI plans to pursue an integrated approach that fuses multiple orthogonal data source and which incorporates novel feature extraction and pattern recognition techniques for human tracking in both outdoor and indoor environments.Broader ImpactThis project has applications in diverse areas including mental health screening, insider attack and fraud prevention, phone and vehicle theft detection, participatory sensing etc. Research outcomes will be shared periodically with diverse stakeholders in psychology, law enforcement, forensics, business, etc. The courses taught by the PI and his team in networking, security and computer vision will be enhanced with content deriving from this project, and the infrastructure will help students learn by practical experience. Research findings, learning materials and team experiences will be disseminated periodically to a wide audience (including educators and students in HBCUs and K-12) via conferences and the Web.

社会智能计算是一种新兴的重要范式，其核心是将人与计算机相结合，以创造新形式的协作、通信和智能，而这在以前是人类或计算机无法实现的，PI在社会智能计算领域进行原创性和实用性研究的努力在范围、规模和质量上都受到了阻碍，因为缺乏专用和现实的基础设施。 该提案要求提供资金，在PI的机构建立这样一个基础设施，以支持综合研究和教育。 所要求的基础设施包括高端计算和存储服务器、台式机、笔记本电脑、智能手机、传感器、摄像头、软件和配件，用于从社会与互联网和移动的手机的日常互动中产生的大规模数据中收集、处理和提取知识。 总体目标是利用从社会计算数据中获得的知识，创造一系列有益于社会的实用服务和应用。 特别是，在提案中确定了三个强调社会与技术密切结合的研究项目：a）通过挖掘互联网使用数据来检测大学环境中的抑郁症; B）通过挖掘互联网和智能手机使用情况来识别人类“指纹”;（c）通过融合异构传感器数据来跟踪社会世界中的人类。智力优点计划中的研究活动得到了很好的描述，并将可能会大大推进社会智能计算领域的技术发展。 PI率先挖掘真实的互联网数据来检测大学生的抑郁行为。 他之前的研究已经确定了关键的互联网使用特征，这些特征显示出有抑郁症状和没有抑郁症状的学生之间存在很大的统计差异。 他接下来计划使用计算智能技术设计分类器，这些分类器可以高精度地主动检测大学生的抑郁行为，同时保持透明并保护隐私。 他还在探索挖掘互联网使用模式以识别人类的可行性，并将其应用于互联网取证和缓解内部攻击。 类似的技术将被应用于从智能手机中挖掘传感器数据，以识别移动模式，并为各种普及服务奠定基础。 传统的跟踪算法利用摄像机网络或物理传感数据或电子信号，PI计划采用一种综合方法，融合多个正交数据源，并结合新颖的特征提取和模式识别技术，用于室外和室内环境中的人体跟踪。内部攻击和欺诈预防，电话和车辆盗窃检测，参与式传感等研究成果将定期与心理学，执法，法医学，商业等不同的利益相关者分享PI及其团队在网络，安全和计算机视觉方面教授的课程将通过该项目的内容得到加强，基础设施将帮助学生通过实践经验学习。 研究结果、学习材料和团队经验将通过会议和网络定期向广大受众（包括HBCU和K-12的教育工作者和学生）传播。