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Raise Your Eyebrows Higher: Facilitating Emotional Communication in Social Virtual Reality Through Region-Specific Facial Expression Exaggeration 25

Raise Your Eyebrows Higher: Facilitating Emotional Communica...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Xueyang Wang Sheng Zhao Yihe Wang Howard Ziyu Han Xinge Liu Xin Yi Xin Tong Hewu Li Institute for Network Sciences and Cyberspace Tsinghua University Beijing China and Zhongguancun Laboratory Beijing China Weiyang College Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University pittsburgh Pennsylvania USA Department of Computer Science and Technology Tsinghua University Beijing China and Beijing National Research Center for Information Science and Technology Beijing China Thrust of Computational Media and Arts Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (纸本)9798400713941

While exaggerated facial expressions in cartoon avatars can enhance emotional communication in social virtual reality (VR), they risk triggering the uncanny valley effect. Our research reveals that this effect varies significantly across different emotions. In Study 1 (N=30), participants evaluated scaled facial expressions during simulated VR conversations. We found that expression exaggeration had opposing effects: it decreased facial realism for joy, surprise, and disgust due to overly dramatic mouth movements, while enhancing realism for fear, sadness, and anger - emotions that rely on upper facial expressions typically constrained by HMD pressure. Based on these findings, we developed a region-specific facial expression exaggeration strategy that enhances under-expressed upper facial features while maintaining natural lower facial movements. Study 2 (N=20) validated this approach, demonstrating enhanced emotional intensity and contagion for negative emotions while mitigating the uncanny valley effect. Our research provides practical guidelines for optimizing avatar-mediated emotional communication in social VR environments. © 2025 Copyright held by the owner/author(s).

关键词： Social Virtual Reality

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SemanticAdapt: Optimization-based Adaptation of Mixed Reality Layouts Leveraging Virtual-Physical Semantic Connections 21

SemanticAdapt: Optimization-based Adaptation of Mixed Realit...

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34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021

作者： Cheng, Yifei Yan, Yukang Yi, Xin Shi, Yuanchun Lindlbauer, David Department of Computer Science and Technology Tsinghua University China Swarthmore College United States Institute for Network Sciences and Cyberspace Tsinghua University China Human-Computer Interaction Institute Carnegie Mellon University United States

ISBN: (纸本)9781450386357

We present an optimization-based approach that automatically adapts Mixed Reality (MR) interfaces to different physical environments. Current MR layouts, including the position and scale of virtual interface elements, need to be manually adapted by users whenever they move between environments, and whenever they switch tasks. This process is tedious and time consuming, and arguably needs to be automated for MR systems to be beneficial for end users. We contribute an approach that formulates this challenge as a combinatorial optimization problem and automatically decides the placement of virtual interface elements in new environments. To achieve this, we exploit the semantic association between the virtual interface elements and physical objects in an environment. Our optimization furthermore considers the utility of elements for users' current task, layout factors, and spatio-temporal consistency to previous layouts. All those factors are combined in a single linear program, which is used to adapt the layout of MR interfaces in real time. We demonstrate a set of application scenarios, showcasing the versatility and applicability of our approach. Finally, we show that compared to a naive adaptive baseline approach that does not take semantic associations into account, our approach decreased the number of manual interface adaptations by 33%. © 2021 ACM.

关键词： Mixed reality

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Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm

arXiv

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arXiv 2024年

作者： Beber, Luca Lamon, Edoardo Moretti, Giacomo Fontanelli, Daniele Saveriano, Matteo Palopoli, Luigi Department of Information Engineering and Computer Science Università di Trento Trento Italy DRIM Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy Department of Industrial Engineering Università di Trento Trento Italy

This paper introduces a new method for estimating the penetration of the end effector and the parameters of a soft body using a collaborative robotic arm. This is possible using the dimensionality reduction method that simplifies the Hunt-Crossley model. The parameters can be found without a force sensor thanks to the information of the robotic arm controller. To achieve an online estimation, an extended Kalman filter is employed, which embeds the contact dynamic model. The algorithm is tested with various types of silicone, including samples with hard intrusions to simulate cancerous cells within a soft tissue. The results indicate that this technique can accurately determine the parameters and estimate the penetration of the end effector into the soft body. These promising preliminary results demonstrate the potential for robots to serve as an effective tool for early-stage cancer diagnostics. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic arms

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Virtual Reality Pōwhiri—Practicing an indigenous welcoming ceremony 23

Virtual Reality Pōwhiri—Practicing an indigenous welcoming...

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Proceedings of the 35th Australian computer-human interaction Conference

作者： Laurence Lloyd-Jones Claudia Ott Holger Regenbrecht Hēmi Whaanga Human Computer Interaction (HCI) Lab University of Otago New Zealand Information Science University of Otago New Zealand Department of Information Science University of Otago New Zealand Te P?tahi-a-Toi Massey University New Zealand

ISBN: (纸本)9798400717079

Pōwhiri is a traditional welcoming ceremony in Māori culture in Aotearoa New Zealand. Certain protocols (tikanga) have to be followed and must be learned. There is a general lack of understanding regarding this due to the scarcity of pōwhiri. The immersive and experiential nature of Virtual Reality (VR) can be used as a tool to increase understanding and confidence. We have implemented a VR learning tool in the reconstructed context of Te Rau Aroha marae in Bluff which allows for safe practice of pōwhiri before applying it in a real welcoming ceremony. A cultural evaluation study was conducted first followed by a user study to determine understanding and confidence gains, highlighting the complexity of the topic. The user study clearly showed that the system was useful to increase participants’ understanding in regards to pōwhiri as well as their confidence surrounding pōwhiri. We are confident that our experiences and key findings from the studies can be used to drive further development of VR tools in the context of visualising cultural ceremonies.

关键词： 3D Reconstruction Indigenous User Interfaces VR Learning Volumetric Video

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A Graph-based Approach to Variant Extraction

arXiv

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arXiv 2025年

作者： Santcroos, Mark A. Kosters, Walter A. Lefter, Mihai Laros, Jeroen F.J. Vis, Jonathan K. Department of Human Genetics Leiden University Medical Center Netherlands Department of Clinical Genetics Leiden University Medical Center Netherlands Leiden Institute of Advanced Computer Science Leiden University Netherlands Netherlands

Motivation: Accurate variant descriptions are of paramount importance in the field of genetics. The domain is confronted with increasingly complex variants, making it more challenging to generate proper variant descriptions. Results: We present a graph based on all minimal alignments that is a complete representation of a variant and we provide three complementary extraction methods to derive variant descriptions from this graph. Our experiments show that our method in comparison with dbSNP results in identical HGVS descriptions for simple variants and more meaningful descriptions for complex variants. © 2025, CC BY.

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Enhancing ESG Reporting Analysis: Leveraging GPT and Resampling Methods for Improved Multi-Label Classification

Enhancing ESG Reporting Analysis: Leveraging GPT and Resampl...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

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

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

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.

关键词： Analytical models Accuracy Government Training data Companies Big Data Data models

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CL-MIL: Multi-Instance Learning on Whole-Slide Images with Self-Supervised Contrastive Learning

CL-MIL: Multi-Instance Learning on Whole-Slide Images with S...

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International Conference on computer and Communications (ICCC)

作者： Pengfei Xiu Dongying Liu Sheng Wang Hongliang Wang Wei Wang Shun Li Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Department of Gastroenterology Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Histopathological examination of tissue slides is considered the gold standard for clinical cancer diagnosis. However, it heavily relies on pathologists' subjective judgment and involves substantial workload. Existing convolutional neural network (CNN)-based whole slide image (WSI) diagnostic methods suffer from high annotation costs and complex data acquisition processes. Therefore, this study proposes a weakly supervised multiple instance learning method based on self-supervised contrastive learning (CL-MIL) for WSI classification. By integrating self-supervised contrastive learning strategies, leveraging data augmentation through H&E stain separation, and introducing causal intervention via backdoor adjustment to mitigate dataset bias, our approach significantly enhances feature representation and classification performance in pathological images. Experimental results on the Camelyon16 and TCGA lung cancer datasets demonstrate that our method outperforms other comparable WSI classification models across various evaluation metrics, achieving higher diagnostic efficiency and accuracy. Additionally, our method exhibits good interpretability, enabling precise localization of pathological regions and providing robust technical support for clinical applications.

关键词： Measurement Location awareness Pathology Accuracy Weak supervision Lung cancer Contrastive learning Feature extraction Standards Image classification

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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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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Qing Xiao Yuhang Zheng Xianzhe Fan Bingbing Zhang Zhicong Lu Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA University of Amsterdam Amsterdam Amsterdam Netherlands Tsinghua University Beijing China School of Journalism and Mass Communication University of Iowa iowa City Iowa USA Department of Computer Science George Mason University Fairfax Virginia USA

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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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

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

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian-Fang Ma Xuan-Hao Liu Wei-Long Zheng Bao-Liang Lu 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 Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

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.

关键词： Heating systems Emotion recognition Adaptation models Accuracy Psychology Gaze tracking Biomedical measurement Data models Physiology Trajectory

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Dietary carbohydrate intake and risk of type 2 diabetes:a 16-year prospective cohort study

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science China(Life sciences) 2025年第4期68卷 1149-1157页

作者： Lu Gan Yi Yang Bin Zhao Kai Yu Kehua Guo Fang Fang Zhiguang Zhou Demetrius Albanes Jiaqi Huang National Clinical Research Center for Metabolic Diseases Metabolic Syndrome Research CenterKey Laboratory of Diabetes ImmunologyMinistry of Educationand Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityCSU-Sinocare Research Center for Nutrition and Metabolic HealthXiangya School of Public HealthCentral South UniversityFurong LaboratoryChangsha410011China Division of Cancer Epidemiology and Genetics National Cancer InstituteNIHDepartment of Health and Human ServicesBethesdaMD20892USA School of Computer Science and Engineering Central South UniversityChangsha410083China Institute of Environmental Medicine Karolinska InstituteStockholm17177Sweden National Clinical Research Center for Metabolic Diseases Key Laboratory of Diabetes ImmunologyMinistry of Educationand Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangsha410011China

Despite considerable research underscoring the importance of carbohydrate intake in relation to the risk of type 2 diabetes(T2D),a comprehensive assessment of this relationship is currently *** aimed to examine the associations of various types and food sources of dietary carbohydrate intake with the risk of T2D,to evaluate potential effect modification by other factors,including genetic susceptibility,and to explore the potential mediators for such *** present study included 161,872 participants of the UK Biobank who were free of prevalent cancer,cardiovascular disease,or diabetes,and had at least one validated 24-h dietary recall *** age-stratified Cox proportional hazard regression models were applied to estimate hazard ratios(HRs)and 95%confidence intervals(CI)for the associations of various types and food sources of dietary carbohydrate intake with risk of *** a median follow-up of 13.6 years,4,176 incident cases of T2D were *** the multivariable-adjusted models,a greater intake of fiber,carbohydrates from whole grains,and carbohydrates from non-starchy vegetables was significantly associated with a lower risk of T2D(highest *** quantile,HR[95%CI]=0.70[0.62–0.79],0.74[0.67–0.82],and 0.83[0.75–0.92],respectively,all P for trend<0.005).In contrast,a higher intake of starch and carbohydrate from starchy vegetables was associated with an increased risk of T2D(highest *** quantile,HR[95%CI]=1.31[1.16–1.48]and 1.19[1.09–1.31],respectively,both P for trend<0.005).Replacing one serving of refined grains or starchy vegetables with an equal amount of whole grains or non-starchy vegetables was associated with 4%to 10%lower risk of T2D(all P values<0.001).The observed associations were generally similar across population subgroups,including individuals with different genetic susceptibility to *** analyses of the inverse association between T2D risk and isocaloric substitution of carbohy

关键词： dietary carbohydrate type 2 diabetes prospective cohort study UK Biobank

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