III: Medium: Collaborative Research: MUDL: Multidimensional Uncertainty-Aware Deep Learning Framework

III：媒介：协作研究：MUDL：多维不确定性感知深度学习框架

• 批准号: 2107449
• 负责人: Feng Chen
• 金额: $ 50万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107449&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; MUDL

People encounter serious hurdles in finding effective decision-making solutions to real world problems because of uncertainty from a lack of information, conflicting information, and/or unsure observations. Critical safety concerns have been consistently highlighted because how to interpret this uncertainty has not been carefully investigated. If the uncertainty is misinterpreted, this can result in unnecessary risk. For example, a self-driving autonomous car can misdetect a human in the road. An artificial intelligence-based medical assistant may misdiagnose cancer as a benign tumor. Further, a phishing email can be detected as a normal email. The consequences of all these misdetections or misclassifications caused by different types of uncertainty adds risk and potential adverse events. Artificial intelligence (AI) researchers have actively explored how to solve various decision-making problems under uncertainty. However, no prior research has looked into how different approaches of studying uncertainty in AI can leverage each other. This project studies how to measure different causes of uncertainty and use them to solve diverse decision-making problems more effectively. This project can help develop trustworthy AI algorithms that can be used in many real world decision-making problems. In addition, this project is highly transdisciplinary so that it can encourage broader, newer, and more diverse approaches. To magnify the impact of this project in research and education, this project leverages multicultural, diversity, and STEM programs for students with diverse backgrounds and under-represented populations. This project also includes seminar talks, workshops, short courses, and/or research projects for high school and community college students. This project aims to develop a suite of deep learning (DL) techniques by considering multiple types of uncertainties caused by different root causes and employ them to maximize the effectiveness of decision-making in the presence of highly intelligent, adversarial attacks. This project makes a synergistic but transformative research effort to study: (1) how different types of uncertainties can be quantified based on belief theory; (2) how the estimates of different types of uncertainties can be considered in DL-based approaches; and (3) how multiple types of uncertainties influence the effectiveness and efficiency of decision-making in high-dimensional, complex problems. This project advances the state-of-the-art research by performing the following: (1) Proposing a scalable, robust unified DL-based framework to effectively infer predictive multidimensional uncertainty caused by heterogeneous root causes in adversarial environments. (2) Dealing with multidimensional uncertainty based on neural networks. (3) Enhancing both decision effectiveness and efficiency by considering multidimensional uncertainty-aware designs. (4) Testing proposed approaches to ensure their robustness in the presence of intelligent adversarial attackers with advanced deception tactics based on both simulation models and visualization tools.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.

人们在为现实世界的问题寻找有效的决策解决方案时遇到了严重的障碍，因为缺乏信息、相互冲突的信息和/或不确定的观察带来的不确定性。关键的安全问题一直被强调，因为如何解释这种不确定性没有得到仔细的调查。如果这种不确定性被曲解，可能会导致不必要的风险。例如，自动驾驶汽车可能会误判道路上的人类。基于人工智能的医疗助理可能会将癌症误诊为良性肿瘤。此外，钓鱼电子邮件可以被检测为正常电子邮件。由不同类型的不确定性造成的所有这些误判或错误分类的后果增加了风险和潜在的不利事件。人工智能(AI)研究人员积极探索如何解决不确定条件下的各种决策问题。然而，之前没有研究过研究人工智能不确定性的不同方法如何相互影响。这个项目研究如何衡量不确定性的不同原因，并使用它们来更有效地解决各种决策问题。这个项目可以帮助开发可靠的人工智能算法，可以用于许多现实世界的决策问题。此外，这个项目是高度跨学科的，因此它可以鼓励更广泛、更新和更多样化的方法。为了扩大该项目在研究和教育方面的影响，该项目利用多元文化、多样性和STEM项目，面向不同背景和代表性不足的学生。该项目还包括为高中生和社区大学学生举办的研讨会讲座、研讨会、短期课程和/或研究项目。该项目旨在开发一套深度学习(DL)技术，通过考虑由不同根本原因造成的多种类型的不确定性，并使用它们来最大限度地提高存在高度智能的对抗性攻击时的决策效率。该项目进行了一项协同性但变革性的研究工作，以研究：(1)如何基于信念理论对不同类型的不确定性进行量化；(2)如何在基于DL的方法中考虑不同类型的不确定性的估计；以及(3)在高维、复杂问题中，多种类型的不确定性如何影响决策的有效性和效率。(1)提出了一个可扩展的、健壮的、统一的基于DL的框架，以有效地推断由对抗环境中的异质根源引起的预测性多维不确定性。(2)基于神经网络的多维不确定性处理。(3)考虑多维不确定性感知设计，提高决策的有效性和效率。(4)测试提出的方法，以确保其在智能对手攻击者面前的稳健性，基于模拟模型和可视化工具的先进欺骗策略。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Interactive Web-Based Visual Analysis on Network Traffic Data

基于交互式网络的网络流量数据可视化分析

• DOI: 10.3390/info14010016
• 发表时间: 2023
• 期刊: Information
• 影响因子: 3.1
• 作者: Jeong, Dong Hyun;Cho, Jin-Hee;Chen, Feng;Kaplan, Lance;Jøsang, Audun;Ji, Soo-Yeon
• 通讯作者: Ji, Soo-Yeon

A survey on uncertainty reasoning and quantification in belief theory and its application to deep learning

• DOI: 10.1016/j.inffus.2023.101987
• 发表时间: 2023-08
• 期刊: Inf. Fusion
• 作者: Zhen Guo;Zelin Wan;Qisheng Zhang;Xujiang Zhao;Qi Zhang;L. Kaplan;A. Jøsang;Dong-Ho Jeong

Multi-Label Temporal Evidential Neural Networks for Early Event Detection

• DOI: 10.1109/icassp49357.2023.10096305
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Xujiang Zhao;Xuchao Zhang;Chengli Zhao;Jinny Cho;L. Kaplan;D. Jeong;A. Jøsang;Haifeng Chen;F. Chen

How Out-of-Distribution Data Hurts Semi-Supervised Learning

• DOI: 10.1109/icdm54844.2022.00087
• 发表时间: 2020-10
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Xujiang Zhao;Killamsetty Krishnateja;Rishabh K. Iyer;Feng Chen

Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation

• DOI: 10.1109/isec57711.2023.10402169
• 发表时间: 2023-03
• 期刊: 2023 IEEE Integrated STEM Education Conference (ISEC)
• 作者: Dong H. Jeong;Jin-Hee Cho;Feng Chen;A. Jøsang;Soo-Yeon Ji