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Improving the Interpretability Through Maximizing Mutual Information for EEG Emotion Recognition

IEEE Transactions on Affective Computing 2025年第2期16卷 744-757页

作者： Yang, Hua Chen, C. L. Philip Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science RaleighNC27695 United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou510335 China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategies 25

How Users Who are Blind or Low Vision Play Mobile Games: Per...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Ran, Zihe Li, Xiyu Xiao, Qing Fan, Xianzhe Li, Franklin Mingzhe Wang, Yanyun Lu, Zhicong Communication University of China Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Tsinghua University Beijing China College of Media Communication and Information University of Colorado Boulder BoulderCO United States Department of Computer Science George Mason University FairfaxVA United States

ISBN: (纸本)9798400713941

As blind and low-vision (BLV) players engage more deeply with games, accessibility features have become essential. While some research has explored tools and strategies to enhance game accessibility, the specific experiences of these players with mobile games remain underexamined. This study addresses this gap by investigating how BLV users experience mobile games with varying accessibility levels. Through interviews with 32 experienced BLV mobile players, we explore their perceptions, challenges, and strategies for engaging with mobile games. Our findings reveal that BLV players turn to mobile games to alleviate boredom, achieve a sense of accomplishment, and build social connections, but face barriers depending on the game's accessibility level. We also compare mobile games to other forms of gaming, highlighting the relative advantages of mobile games, such as the inherent accessibility of smartphones. This study contributes to understanding BLV mobile gaming experiences and provides insights for enhancing accessible mobile game design. © 2025 Copyright held by the owner/author(s).

关键词： computer games

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Exploring Digital Misinformation as a Sociotechnical Phenomenon: Insights from a Small-scale Study 23

Exploring Digital Misinformation as a Sociotechnical Phenome...

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Proceedings of the XXII Brazilian Symposium on human Factors in Computing Systems

作者： Alisson Andery Puska Roberto Pereira Human-Computer Interaction Laboratory Universidade Federal do Paraná Brasil Department of Computer Science Universidade Federal do Paraná Brasil

ISBN: (纸本)9798400717154

This article addresses the challenge of comprehending digital misinformation as a sociotechnical phenomenon within the field of human-computer interaction, particularly in the context of COVID-19-related misinformation in Brazil. The study employed an exploratory approach to analyze misinformation cases using various artifacts and conducted a focus group with eight experts in the field. The findings revealed 137 sociotechnical aspects and 29 stakeholders, shedding light on the complex interplay of factors influencing digital misinformation’s creation, dissemination, and consumption. The study emphasizes the necessity of adopting a sociotechnical approach and highlights the challenges in comprehensively analyzing stakeholders, communication channels, and the stages of the misinformation lifecycle. Overall, the research contributes to a deeper understanding of digital misinformation in sociotechnical contexts and its implications for human-computer interaction.

关键词： misinformation misinformation life cycle organizational semiotics sociotechnical analysis

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Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions 25

Let's Influence Algorithms Together: How Millions of Fans Bu...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Xiao, Qing Zheng, Yuhang Fan, Xianzhe Zhang, Bingbing Lu, Zhicong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States University of Amsterdam Amsterdam Amsterdam Netherlands Tsinghua University Beijing China School of Journalism and Mass Communication University of Iowa iowa CityIA United States Department of Computer Science George Mason University FairfaxVA United States

ISBN: (纸本)9798400713941

Previous research pays attention to how users strategically understand and consciously interact with algorithms but mainly focuses on an individual level, making it difficult to explore how users within communities could develop a collective understanding of algorithms and organize collective algorithmic actions. Through a two-year ethnography of online fan activities, this study investigates 43 core fans who always organize large-scale fans collective actions and their corresponding general fan groups. This study aims to reveal how these core fans mobilize millions of general fans through collective algorithmic actions. These core fans reported the rhetorical strategies used to persuade general fans, the steps taken to build a collective understanding of algorithms, and the collaborative processes that adapt collective actions across platforms and cultures. Our findings highlight the key factors that enable computer-supported collective algorithmic actions and extend collective action research into the large-scale domain targeting algorithms. © 2025 Copyright held by the owner/author(s).

关键词： Algorithm Folk Theory of Algorithm Collective Understanding of Algorithm Collective Algorithmic Action Fandom Studies

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GastroConcerto: Towards Designing Auditory Dining System to Enrich Chefs’ Culinary Practices

GastroConcerto: Towards Designing Auditory Dining System to ...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Wang, Hongyue Deng, Jialin Kong, Dehui Fang, Ziqi Luo, Hong Pasumarthy, Nandini Patibanda, Rakesh Abewickrema, Sasindu Fang, Xiao Zoe Elvitigala, Don Samitha Mueller, Florian Exertion Games Lab. Department of Human-Centred Computing Monash University MelbourneVIC Australia Diverse-ability Interaction Lab. School of Computer Science Bristol United Kingdom Karlsruhe Institute of Technology Karlsruhe Germany Imperial College London London United Kingdom Exertion Games Lab. Department of Human-Centred Computing Monash University Melbourne Australia College of Computer Science Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9798400713958

Sound plays a crucial role in shaping diners’ perceptions and enhancing the dining experience. Recognizing this, designers have started to integrate auditory elements into interactive interfaces that connect diners with food. However, how chefs incorporate sound into their culinary practices remains underexplored. To bridge this gap, we introduced GastroConcerto, an auditory dining system that allows chefs to link specific sounds with ingredients, enabling the creation of unique sonic dishes. Our ultimate goal is to enrich chefs’ creative repertoire through novel auditory interactions. © 2025 Copyright held by the owner/author(s).

关键词： auditory interface chefs’ culinary practices human-food interaction

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing 35

Auto-PIP: Real-time Identification of Critical Performance I...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Xiong, Xiao Li, Mengyao Sun, Yongqian Zhao, Yongxin Chen, Xia Deng, Bowen Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area"(SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research. © 2024 IEEE.

关键词： Software testing

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Tiles to Move: Investigating Tile-Based Locomotion for Virtual Reality

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Proceedings of the ACM on human-computer interaction 2023年第CHI PLAY期7卷 1044–1067页

作者： Funke, Jana Franceska Schikorr, Anja Karaosmanoglu, Sukran Hirzle, Teresa Steinicke, Frank Rukzio, Enrico Institute of Media Informatics Ulm University James-Franck Ring Ulm Ulm89081 Germany Human-Computer Interaction Universität Hamburg Hamburg Vogt-Kölln-Straße 30 Hamburg22527 Germany Department of Computer Science University of Copenhagen Copenhagen Universitetsparken 1 Copenhagen2100 Denmark Institute of Media Informatics Ulm University Ulm Germany

Tile-based locomotion (TBL) is a popular locomotion technique for computer, console, and board games. However, despite its simplicity and unconventional movement, the transfer of TBL to virtual reality (VR) as a game platform remains unexplored. To fill this gap, we introduce TBL for VR on the example of two techniques: a controller and a feet-based one. In a first user study, we evaluated the usability and acceptance of the techniques compared to teleportation and touchpad locomotion. In a second exploratory user study, we evaluated the user experience of both TBL techniques in a maze and a museum scenario. The findings show that both techniques provide enjoyment and acceptable usability by creating either a relaxing (controller-based) or a physically active (feet-based) solution. Finally, our results highlight that TBL techniques work particularly well for small, constrained spaces that allow users to focus on exploring details in the nearby environment (important for games) in contrast to large open spaces that require faster locomotion, like teleportation. © 2023 Owner/Author.

关键词： Virtual reality

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Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism 35

Multivariate Time Series Anomaly Detection based on Pre-trai...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Sun, Yongqian Guo, Yang Liang, Minghan Wen, Xidao Kuang, Junhua Zhang, Shenglin Li, Hongbo Xia, Kaixu Pei, Dan Nankai University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China BizSeer Haihe Laboratory of Information Technology Application Innovation China Tencent Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%. © 2024 IEEE.

关键词： Time series

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GPT4ESG: Streamlining Environment, Society, and Governance Analysis with Custom AI Models 4

GPT4ESG: Streamlining Environment, Society, and Governance A...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Lin, Lydia Hsiao-Mei Ting, Fang-Kai Chang, Ting-Jui Wu, Jun-Wei Tsai, Richard Tzong-Han National Taipei University of Technology Department of Interaction Design Taipei City Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City Taiwan Center for Gis Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan

ISBN: (纸本)9798350360721

Many companies now prioritize Environment, Society, and Governance (ESG) and financial performance due to concerns about climate change. We created GPT4ESG, a quick method to analyze a company's ESG investment and influence. GPT4ESG is a model system architecture based on BERT and GPT with five parts:(1) Data cleaning: The data is cleaned and preprocessed to ensure it is in a suitable format for the model. (2) Tokenizer adaptation: A customized GPT assistant is used to assist in scoring, and results are given after expert review. (3) Model adjustment: A classification layer is added to the output. (4) Fine-tuning: The pre-trained model is fine-tuned according to the ESG field know-how. (5) Evaluation and Testing: The model was tested on a validation set during training and then on a testing set. We collected ESG reports of well-known listed companies in the technology industries in the United States as a data set. The experimental results showed that the customized GPT4ESG model was more effective than ESG-BERT in classifying ESG data. This model simplifies ESG reporting for stakeholders to make responsible decisions. © 2024 IEEE.

关键词： Climate change

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Decentralized Federated Graph Learning With Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku 102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users’ efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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