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

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

ISBN: (纸本)9798350360721

ESG reporting, covering environmental, social, and governance aspects, is a crucial resource for investors, companies, and governments to understand a company's value. However, the sheer volume of data and information in the reports makes it difficult to retrieve specific information on ESG. ESG-BERT models are developed to categorize the content of ESG reports based on the relevant field. However, the accurate prediction of multiple classifications is difficult due to unevenly distributed labels. To address this problem, we used a self-made assistant based on GPT to label the data and then resampling technology to adjust the balance of the data set. After the data set was balanced, we adjusted to ESG-BERT to improve its multi-label classification accuracy. We evaluated multiple resampling methods and determined the most suitable strategy for classifying ESG report content. Thus, the accuracy of ESG content analysis was improved through more refined model adjustment and preprocessing methods. The results indicated that balancing the data for improved classification ensures fairness and objectivity in distributing content for ESG reports with multiple labels. © 2024 IEEE.

关键词： Classification (of information)

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Tian-Fang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (纸本)9798350386226

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods. © 2024 IEEE.

关键词： Emotion Recognition

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks 12

Real-Time Facial Expression Recognition using Facial Landmar...

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12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

作者： Haghpanah, Mohammad A. Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Lab School of Electrical and Computer Engineering Tehran Iran University of Nevada Department of Computer Science and Engineering Reno United States

ISBN: (纸本)9781665412162

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. © 2022 IEEE.

关键词： Emotion Recognition

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Situational Adaptive Autonomy in human-AI Cooperation

Situational Adaptive Autonomy in Human-AI Cooperation

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2023 Workshop on Intervening, Teaming, Delegating, AutomationXP 2023

作者： Salikutluk, Vildan Frodl, Eric Herbert, Franziska Balfanz, Dirk Koert, Dorothea Interactive AI Algorithms & Cognitive Models for Human-AI Interaction Computer Science Department Technical University Darmstadt Germany Models of Higher Cognition Human Sciences Department Technical University Darmstadt Germany Centre for Cognitive Science Technical University Darmstadt Germany

human-AI teams have the potential to produce improved outcomes in various tasks as opposed to each team member working alone. However, there are various factors that influence human-AI team performance which potentially differ from classical human-computer interaction settings. Specifically, there is existing work indicating that it is beneficial for AI systems to automatically adapt their autonomy within the team and task in order to work towards achieving a shared goal more effectively. Thus, in this paper, we describe a concept of situational adaptive autonomy for human-AI cooperation in a shared workspace setting. We discuss that task complexity and models for the AI system’s understanding of their human teammate, i.e. Theory of Mind models (ToMMs) might be helpful to implement situational adaptation of AI autonomy such that interaction and team performance can be improved. We present an experimental setup for a cooperative real-world robotic task and a corresponding approximation in a grid-world in which we plan to investigate situational adaptive autonomy within a shared workspace in an interactive human-AI team. © 2023 Copyright for this paper by its authors.

关键词： human computer interaction

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science 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 The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou 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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Conversational Product Recommendation using LLM 4

Conversational Product Recommendation using LLM

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

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

ISBN: (纸本)9798350360721

It is expected to deploy chatbots as sales assistants on e-commerce platforms soon. In recent years, the capabilities demonstrated by large language models (LLMs) indicate their suitability for this role. However, a dialogue process is not fully established. Most existing works concentrate on searching for ideal products, such as Retrieval-Augmented Generation (RAG) and product search. Many proposed methods rely on product reviews. However, in the case of some e-commerce platform products, valuable reviews are often lacking, rendering these approaches impractical. Thus, we studied how to assist users and select a set of candidate products. Accordingly, we simulated sales conversations based on the product features provided by the seller, using LLM and reliable content while addressing the cold-start problem. Specifically, we used product features to generate customer persona for user simulation. LLM was used to play the role of sales assistant in giving recommendations. The experimental results showed that practical conversation was created between customers and chatbots in Traditional Chinese. The code and results are available at: https://***/terryobe-ncu/CPR_LLM © 2024 IEEE.

关键词： Sales

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[DC] Context-Aware Inference and Adaptation in Augmented Reality

[DC] Context-Aware Inference and Adaptation in Augmented Rea...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Shakiba Davari Center for Human-Computer Interaction and Department of Computer Science Virginia TechUSA

ISBN: (纸本)9781665484039

Augmented Reality(AR) offers the potential for easy and efficient information access, reinforcing the wide belief that AR Glasses are the next-generation of personal computing devices. However, to realize this all-day AR vision, the AR interface must be able to address the challenges that constant and pervasive presence of virtual content can cause for the user. The optimal interface, that is the most efficient yet least intrusive, in one context may be the worst interface for another context. Throughout the day, as the user switches context, an optimal all-day interface must adapts its virtual content display and interactions as well. This work aims to propose a research agenda to design and validate different adaptation techniques and context-aware AR interfaces and introduce a framework for the design of such intelligent interfaces.

关键词： human computer interaction Three-dimensional displays Conferences Glass Next generation networking Augmented reality

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Daily Pain Prediction in Workplace Using Gaussian Processes

Daily Pain Prediction in Workplace Using Gaussian Processes

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16th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Puri, Chetanya Keyaerts, Stijn Szymanski, Maxwell Godderis, Lode Verbert, Katrien Luca, Stijn Vanrumste, Bart KU Leuven Belgium Leuven Belgium Department ofPublic Health and Primary Care KU Leuven Leuven Belgium Department of Computer Science KU Leuven Belgium Human-computer Interaction research group Department of Computer Science KU Leuven Belgium Department of Data Analysis and Mathematical Modelling Ghent University Ghent Belgium

Work-related Musculoskeletal disorders (MSDs) account for 60% of sickness-related absences and even permanent inability to work in the Europe, Long term impacts of MSDs include "Pain chronification'' which is the transition of temporary pain into persistent pain. Preventive pain management can lower the risk of chronic pain. It is therefore important to appropriately assess pain in advance, which can assist a person in improving their fear of returning to work. In this study, we analysed pain data acquired over time by a smartphone application from a number of participants. We attempt to forecast a person's future pain levels based on his or her prior pain data. Due to the self-reported nature of the data, modelling daily pain is challenging due to the large number of missing values. For pain prediction modelling of a test subject, we employ a subset selection strategy that dynamically selects a closest subset of individuals from the training data. The similarity between the test subject and the training subjects is determined via dynamic time warping-based dissimilarity measure based on the time limited historical data until a given point in time. The pain trends of these selected subset subjects is more similar to that of the individual of interest. Then, we employ a Gaussian processes regression model for modelling the pain. We empirically test our model using a leave-one-subject-out cross validation to attain 20% improvement over state-of-the-art results in early prediction of pain. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Prediction models

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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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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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