Collaborative Research: SAI-R: Integrative Cyberinfrastructure for Enhancing and Accelerating Online Abuse Research

合作研究：SAI-R：用于加强和加速在线滥用研究的综合网络基础设施

• 批准号: 2228617
• 负责人: Hongxin Hu
• 金额: $ 37.5万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228617&HistoricalAwards=false
• 关键词: Collaborative; Research; SAI; Integrative; Cyberinfrastructure

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.Online abuse is a pressing and growing societal challenge. Online hate and harassment, cyberbullying, and extremism threaten the safety and psychological well-being of targeted groups. Understanding the problem and developing ways to address it is the active focus of many fields of research in the social and behavioral sciences and in computer science. Machine learning and the use of artificial intelligence (AI) offers great potential to support research in this area. Still, researchers face fundamental challenges in leveraging emerging machine learning techniques for innovative studies and scientific discoveries in online abuse. This SAI research project strengthens and transforms the current disperse machine learning software infrastructure. It develops a scalable, customizable, extendable, and user-friendly Integrative Cyberinfrastructure for Online Abuse Research (ICOAR). The new infrastructure advances the research capability for scholars in different fields of science to leverage advanced machine learning methods for online abuse research. The ICOAR software infrastructure can be utilized by a large and growing number of researchers on online abuse detection and is a stimulus to research and innovation in AI for social good.This project enables easy access to state-of-the-art machine learning techniques and datasets for rapid online abuse analysis. It supports and advances future investigations of new concepts and phenomena, assessments of prevalence, measures of causal effects, predictions, and evaluation of online abuse detection algorithms. ICOAR offers a modular and user-centered approach, ensuring future enhancements and long-term sustainability. The open software infrastructure consists of three major layers: a data layer, a capability layer, and an application layer. The data layer includes tools for automatic data collection and preparation of online social media data from different sources, and access to public benchmark datasets. The capability layer is composed of modularized machine learning-based capabilities and algorithms for the study of online abuse. The application layer allows researchers to easily develop different applications based on their research priorities. The ICOAR resources are open-source and provide an easy-to-use learning platform for curriculum development and workforce training.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences.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.

加强美国基础设施（SAI）是一项NSF计划，旨在刺激以人为本的基本和潜在的变革性研究，从而增强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善奠定了坚实的基础。强大，可靠和有效的基础设施刺激了私营部门的创新，发展经济，创造就业机会，使公共部门服务提供效率更高，增强社区，促进平等的机会，保护自然环境，增强国家安全，并为美国的领导燃料。为了实现这些目标，需要从整个科学和工程学科的专业知识。 SAI专注于对人类推理和决策，治理以及社会和文化过程的了解如何使建立和维护有效的基础设施，从而改善生活和社会，并在技术和工程的进步上建立滥用，这是一项紧迫的和日益增长的社会挑战。在线仇恨和骚扰，网络欺凌和极端威胁到目标群体的安全和心理健康。了解问题并开发解决方法的方法是许多研究领域在社会和行为科学以及计算机科学领域的积极重点。机器学习和人工智能（AI）的使用提供了支持该领域研究的巨大潜力。尽管如此，研究人员仍在利用新兴的机器学习技术来进行创新研究和在线滥用方面的科学发现方面面临着根本的挑战。 SAI研究项目的优势并改变了当前的分散机器学习软件基础架构。它为在线滥用研究（ICOAR）开发了可扩展，可定制，可扩展和用户友好的集成网络架构。新的基础设施提高了不同科学领域的学者的研究能力，以利用先进的机器学习方法进行在线滥用研究。 ICOAR软件基础架构可以通过大量且越来越多的在线滥用检测的研究人员来利用，这是针对社会产品进行研究和创新的刺激。该项目使该项目可以轻松访问先进的机器学习技术和数据集，以进行快速在线滥用分析。它支持并推进未来对新概念和现象的投资，评估患病率，因果效应的衡量标准，预测和在线滥用检测算法的评估。 ICOAR提供了一种模块化和以用户为中心的方法，可确保未来的增强和长期可持续性。开放软件基础架构由三个主要层组成：数据层，功能层和应用程序层。数据层包括用于自动数据收集的工具和从不同来源的在线社交媒体数据制备，以及访问公共基准数据集。功能层由基于机器学习的模块化功能和用于在线滥用的算法组成。该应用层允许研究人员根据研究的重点轻松开发不同的应用程序。 ICOAR资源是开源的，为课程开发和劳动力培训提供了易于使用的学习平台。该奖项得到了社会，行为和经济和经济（SBE）科学局的支持。该奖项反映了NSF的法定任务，并通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务。

项目成果

Return-to-Non-Secure Vulnerabilities on ARM Cortex-M TrustZone: Attack and Defense

• DOI: 10.1109/dac56929.2023.10247972
• 发表时间: 2023-07
• 期刊: 2023 60th ACM/IEEE Design Automation Conference (DAC)
• 作者: Zheyuan Ma;Xi Tan;Lukasz Ziarek;Ning Zhang;Hongxin Hu;Ziming Zhao

Understanding and Measuring Robustness of Vision and Language Multimodal Models

理解和测量视觉和语言多模态模型的鲁棒性

• 发表时间: 2023
• 期刊: Proceedings of the International Conference on Secure Knowledge Management (SKM 2023
• 作者: Vishwamitra, Nishant;Guo, Keyan;Hu, Hongxin;Zhao, Ziming;Cheng, Long;Luo, Feng
• 通讯作者: Luo, Feng

xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses

• 发表时间: 2023
• 期刊: Proceedings of the 36th Annual Computer Security Applications Conference
• 作者: Feng Wei;Hongda Li;Ziming Zhao;Hongxin Hu