EAGER: SaTC-EDU: Improving Cybersecurity Education for Adolescents with Autism Through Automated Augmented Self-Monitoring Applications

EAGER：SaTC-EDU：通过自动增强自我监控应用程序改善自闭症青少年的网络安全教育

• 批准号: 2114808
• 负责人: Charles Hughes
• 金额: $ 30万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114808&HistoricalAwards=false
• 关键词: EAGER; SaTC; EDU; Improving; Cybersecurity

Social media and online gaming sites are ubiquitous platforms that provide peer interaction and social development for adolescents but also pose potential threats to health and safety. For example, cyberbullying has been connected to negative mental health outcomes for adolescents. Addressing cyberbullying and cybersecurity issues in these spaces is therefore essential for healthy social development. While cyberbullying is of concern for all adolescents, adolescents with Autism Spectrum Disorder (ASD) are especially vulnerable and are at a higher risk of becoming victims of bullying and theft of personal data. This research addresses these issues by creating and employing personalized virtual companions, whose interactions with the adolescent are guided by artificial intelligence (AI). The personalized virtual companion will guide adolescents with ASD to improve their self-protection strategies and increase their cybertechnology knowledge. The goal of the project is to help prepare these individuals to become productive and satisfied members of a modern, inclusive, and technology-savvy workforce.This project will address the problem of cyberbullying and cybersecurity for adolescents with ASD by developing tools based on deep learning algorithms that employ multimodal data, e.g., facial expression, body posture, breathing rate, skin temperature, heart rate, heart rate variability, eye gaze, verbalization, and vocalization. Fusing data from these diverse sources is expected to enable creation of a virtual learning environment that is inhabited by autonomous agents, some of which have bullying or privacy-invading tendencies, and one of which is a personalized companion that seeks to help its human friend navigate the Internet. The AI companion must adapt its interactions based on understanding the external (verbal and nonverbal) behaviors and internal stress indicators (e.g., neurosensory) of its human counterpart. Development of an AI companion is especially challenging when working with members of a vulnerable population whose facial expressions and vocalizations do not always match those of neurotypical adolescents. A current issue with this type of research is that training datasets are populated by neurotypical subjects and thus are potentially not representative of the population this project is intended to serve, adolescents with ASD. The desired outcomes from the project are novel deep learning algorithms to address the shortcomings of the state-of-the-art multimodal Facial Expression Recognition algorithms currently designed for neurotypical adolescents. The project may also aid self-regulation of ASD adolescents when faced with cyberbullying and attempts to invade privacy as well as other social situations. It should also lead to an understanding of how this AI intervention strengthens the individual’s knowledge of technology and their affinity for cybersecurity careers.This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case specifically cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

社交媒体和在线游戏网站是为青少年提供同伴互动和社交发展的普遍平台，但也对健康和安全构成潜在威胁，例如，网络欺凌与青少年的负面心理健康结果有关。因此，虽然网络欺凌是所有青少年都关心的问题，但患有自闭症谱系障碍 (ASD) 的青少年尤其容易受到伤害，并且更有可能成为欺凌和个人数据盗窃的受害者。研究通过创建和使用个性化虚拟伴侣来解决这些问题，其与青少年的互动由人工智能（AI）引导，个性化虚拟伴侣将指导患有自闭症谱系障碍的青少年改善他们的自我保护策略并增加他们的网络技术知识。该项目的目的是帮助这些人成为现代化、包容性和精通技术的劳动力队伍中富有成效和满意的成员。该项目将通过开发基于深度学习算法的工具来解决患有自闭症谱系障碍（ASD）青少年的网络欺凌和网络安全问题采用多模态数据，例如，面部表情、身体姿势、呼吸频率、皮肤温度、心率、心率变异性、眼睛注视、言语和发声数据预计将能够创建一个自主的虚拟学习环境。代理，其中一些有欺凌或侵犯隐私的倾向，其中之一是寻求帮助其人类朋友浏览互联网的个性化伴侣人工智能伴侣必须基于对外部（语言和非语言）的理解来调整其交互。 ) 行为和内部压力指标当与面部表情和发声并不总是与神经正常青少年相匹配的弱势群体成员合作时，开发人工智能伴侣尤其具有挑战性。训练数据集由神经典型受试者填充，因此可能无法代表该项目旨在服务的人群，即患有自闭症谱系障碍的青少年。该项目的预期结果是新颖的深度学习算法，以解决现有技术的缺点。艺术目前为神经质青少年设计的多模态面部表情识别算法还可以帮助自闭症青少年在面临网络欺凌和试图侵犯隐私以及其他社交情况时进行自我调节。增强个人的技术知识及其对网络安全职业的亲和力。该项目得到安全可信网络空间 (SaTC) 计划的支持，该计划提议为解决网络安全和隐私问题提供资金，在这种情况下，SaTC 计划特别关注网络安全教育。与联邦网络安全研究与发展战略计划和国家隐私研究战略相结合，在确保安全和隐私的同时，保护和维护网络系统不断增长的社会和经济效益。该奖项授予 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

The Past, the Present, and the Future of the Evolution of Mixed Reality in Teacher Education

混合现实在教师教育中的发展的过去、现在和未来

• DOI: 10.3390/educsci13111070
• 发表时间: 2023-10-24
• 期刊: Education Sciences
• 影响因子: 3
• 作者: Lisa Dieker;Charles Hughes;Michael Hynes
• 通讯作者: Michael Hynes

RAISE: Robotics & AI to improve STEM and social skills for elementary school students

提高：机器人技术

• DOI: 10.3389/frvir.2022.968312
• 发表时间: 2022-10
• 期刊: Frontiers in Virtual Reality
• 作者: Hughes, Charles E.;Dieker, Lisa A.;Glavey, Eileen M.;Hines, Rebecca A.;Wilkins, Ilene;Ingraham, Kathleen;Bukaty, Caitlyn A.;Ali, Kamran;Shah, Sachin;Murphy, John;et al
• 通讯作者: et al

WAVE: A Web-Based Platform for Delivering Knowledge-Driven Virtual Experiences

WAVE：基于 Web 的平台，用于提供知识驱动的虚拟体验

• DOI: 10.1109/mcg.2023.3260599
• 发表时间: 2023-05
• 期刊: IEEE Computer Graphics and Applications
• 影响因子: 1.8
• 作者: Shah, Sachin;Ali, Kamran;Dieker, Lisa;Hughes, Charles
• 通讯作者: Hughes, Charles

Semi-supervised Drifted Stream Learning with Short Lookback

具有短回溯的半监督漂移流学习

• DOI: 10.1145/3534678.3539297
• 发表时间: 2022-05-25
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Wei;Pengyang Wang;Xiaolin Li;C. Hughes;Yanjie Fu
• 通讯作者: Yanjie Fu

Law enforcement training using simulation for locally customized encounters

使用模拟进行本地定制的执法培训

• DOI: 10.3389/frvir.2022.960146
• 发表时间: 2022-09
• 期刊: Frontiers in Virtual Reality
• 作者: Kent, Julie A;Hughes, Charles E
• 通讯作者: Hughes, Charles E