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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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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger Robot Hand

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Arash Akbari Arman Akbari Mehdi Tale Masouleh Department of Computer Science University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

Robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point,

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TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRESENTATION 11

TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wu, Hongqiu Liu, Yongxiang Shi, Hanwen Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective Contextualized representation-Adversarial Training (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pretraining where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g. AdvGLUE (59.1 → 61.1), HellaSWAG (93.0 → 94.9), ANLI (68.1 → 69.3). © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Global optimization

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Clustering Approaches for Gait Analysis within Neurological Disorders: A Narrative Review

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Digital Biomarkers 2024年第1期8卷 93-101页

作者： Hummel, Jonas Schwenk, Michael Seebacher, Daniel Barzyk, Philipp Liepert, Joachim Stein, Manuel Department of Computer and Information Science University of Konstanz Konstanz Germany Human Performance Research Centre University of Konstanz Konstanz Germany Research and Development Subsequent GmbH Konstanz Germany Neurologische Rehabilitation Kliniken Schmieder Allensbach Germany

Background: The prevalence of neurological disorders is increasing, underscoring the importance of objective gait analysis to help clinicians identify specific deficits. Nevertheless, existing technological solutions for gait analysis often suffer from impracticality in daily clinical use, including excessive cost, time constraints, and limited processing capabilities. Summary: This review aims to evaluate existing techniques for clustering patients with the same neurological disorder to assist clinicians in optimizing treatment options. A narrative review of thirteen relevant studies was conducted, characterizing their methods, and evaluating them against seven criteria. Additionally, the results are summarized in two comprehensive tables. Recent approaches show promise;however, our results indicate that, overall, only three approaches display medium or high process maturity, and only two show high clinical applicability. Key Messages: Our findings highlight the necessity for advancements, specifically regarding the use of markerless optical tracking systems, the optimization of experimental plans, and the external validation of results. This narrative review provides a comprehensive overview of existing clustering techniques, bridging the gap between instrumented gait analysis and its real-world clinical utility. We encourage researchers to use our findings and those from other medical fields to enhance clustering techniques for patients with neurological disorders, facilitating the identification of disparities within groups and their extent, ultimately improving patient outcomes. © 2024 The Author(s). Published by S. Karger AG, Basel.

关键词： Gait analysis

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Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

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computers, Materials & Continua 2023年第11期77卷 2179-2188页

作者： V.Dhilip Kumar J.Praveenchandar Muhammad Arif Adrian Brezulianu Oana Geman Atif Ikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreTamil Nadu641114India Department of Computer Science Superior UniversityLahorePakistan Faculty of Electronics Telecommunications and Information Technology Gheorghe Asachi Technical University of IasiIa¸si700050Romania Department of Health and Human Development Stefan cel Mare University of SuceavaSuceava720229Romania Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia

Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system *** is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the *** a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase *** is also known as a server pool or server *** a single node is overwhelmed,balancing the workload is needed to manage unpredictable *** load balancer sends the load to another free node in this *** focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling *** of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at *** on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.

关键词： Load balancing throttled algorithm efficient resource allocation

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How Students in Creative Educations Appropriate Technology: A phenomenological analysis. 25

How Students in Creative Educations Appropriate Technology: ...

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

作者： Horvath, Anca-Simona Raptis, Dimitrios Department of Communication and Psychology Research Laboratory for Art and Technology Aalborg University Aalborg Denmark Department of Computer Science Human-Centred Computing Group Aalborg University Aalborg Denmark

ISBN: (纸本)9798400713941

Building technological literacy is an important topic in education today while at the same time, creativity is seen as a desirable skill for professional practice and education alike as it is considered a catalyst for innovation. In this paper, we present a case study where we aim to understand how students in a creative education appropriate technology. We analyze qualitative data collected from students in a STEAM higher education undergraduate program called Art&Technology: a program where students are introduced to an assortment of technological tools including software, programming, digital fabrication and physical prototyping which they employ in creating a variety of artifacts. We analyze how students learn and interact with these technologies by analyzing the collected data through a phenomenological lens of technology appropriation. We contribute with understandings on how technology is appropriated as it transforms from an object, to a tool until it finally becomes equipment. © 2025 Copyright held by the owner/author(s).

关键词： Students

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Bridging Borders, Breaking Biases: Envisioning Technologies to Support North Korean Defectors in South Korea 25

Bridging Borders, Breaking Biases: Envisioning Technologies ...

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

作者： Noh, Hayoun Jo, Hyunah Wang, Ge Van Kleek, Max Kang, Younah Department of Computer Science University of Oxford Oxford United Kingdom Yonsei University Seoul Korea Republic of Stanford University StanfordCA United States Information and Interaction Design Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400713941

North Korean defectors (NKDs) face significant challenges when transitioning to South Korean society. Leaving their homes permanently and adapting to a new, digitally connected environment for the first time presents difficulties, compounded by the pervasive stigma associated with their identities. Although technology alone cannot solve these issues, it can play a role in easing their transition. In this study, we conducted eight speculative co-creation sessions with 22 NKDs to identify their main challenges and envision potential technological interventions. We propose conceptualization of thirteen technologies aimed at addressing key issues NKDs face related to identity stigma, disconnection from their past, and challenges of adapting to a highly digital society. Through this empirical research on underrepresented populations undergoing significant life transitions, we provide insights into how future technologies can support other marginalized individuals as they navigate pervasive stigma and establish new lives in a digital society. © 2025 Copyright held by the owner/author(s).

关键词： Envisioning Technologies

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Visualizing Exercise Data from Combat Exergame for Exploring the Insight from Personal Informatics with Large Language Models

Visualizing Exercise Data from Combat Exergame for Exploring...

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

作者： Li, Zong-Ying Lai, Yen-Hua Lin, Chi-Yu Chou, Chien-Hsing Han, Ping-Hsuan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Interaction Design National Taipei University of Technology Taipei Taiwan Department of Mathematics and Information Education National Taipei University of Education Taipei City Taiwan

ISBN: (纸本)9798400713958

Previous research has explored the potential of personal informatics and data visualization to enhance reflection. However, these studies primarily focus on improving user performance in traditional sports. In this work, we present a visualization system for innovative sports integrating sports data collected from a VR combat exergame with personalized narratives generated by Large Language Models (LLMs). We conducted a user study with Data Visualization and LLM Suggestions to evaluate their impact on self-reflection and reflection. The results show that our system facilitated data exploration and comparison with reflection. In addition, this study reveals the potential of combining LLMs and VR-based data visualization to provide personalized feedback for users. © 2025 Copyright held by the owner/author(s).

关键词： Data visualization

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What Lies Beneath? Exploring the Impact of Underlying AI Model Updates in AI-Infused Systems

arXiv

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arXiv 2023年

作者： Mohanty, Vikram Lim, Jude Luther, Kurt Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Independent Researcher ArlingtonVA United States Department of Computer Science Center for Human-Computer Interaction Virginia Tech AlexandriaVA United States

AI models are constantly evolving, with new versions released frequently. human-AI interaction guidelines encourage notifying users about changes in model capabilities, ideally supported by thorough benchmarking. However, as AI systems integrate into domain-specific workflows, exhaustive benchmarking can become impractical, often resulting in silent or minimally communicated updates. This raises critical questions: Can users notice these updates? What cues do they rely on to distinguish between models? How do such changes affect their behavior and task performance? We address these questions through two studies in the context of facial recognition for historical photo identification: an online experiment examining users’ ability to detect model updates, followed by a diary study exploring perceptions in a real-world deployment. Our findings highlight challenges in noticing AI model updates, their impact on downstream user behavior and performance, and how they lead users to develop divergent folk theories. Drawing on these insights, we discuss strategies for effectively communicating model updates in AI-infused systems. © 2023, CC BY.

关键词： Face recognition

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Improving the Interpretability Through Maximizing Mutual Information for EEG Emotion Recognition

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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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