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

作者： Wang, Xueyang Zhao, Sheng Wang, Yihe Han, Howard Ziyu Liu, Xinge Yi, Xin Tong, Xin Li, Hewu Institute for Network Sciences and Cyberspace Tsinghua University Beijing China 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 PA United States 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： Cartoon Avatars Facial Expression Exaggeration Social Virtual Reality Uncanny Valley Effect User Empathy

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Discrepancies in online postoperative care protocols for Mohs surgery

JAAD Reviews

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JAAD Reviews 2025年 3卷 128-130页

作者： Afua Ofori-Darko Ashley M. Perez Devin Barzallo Jill Lehman Bryan T. Carroll Case Western Reserve University School of Medicine Cleveland Ohio Carnegie Mellon University Human-Computer Interaction Institute Pittsburgh Pennsylvania Department of Dermatology University Hospitals Cleveland Medical Center Cleveland Ohio

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Leveraging Part-of-Speech Tagging for Enhanced Stylometry of Latin Literature 1

Leveraging Part-of-Speech Tagging for Enhanced Stylometry of...

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1st Workshop on Machine Learning for Ancient Languages, ML4AL 2024

作者： Chen, Sarah Li Burns, Patrick J. Bolt, Thomas J. Chaudhuri, Pramit Dexter, Joseph P. Phillips Academy Andover United States Research Science Institute Center for Excellence in Education United States Department of Computer Science Stanford University United States Institute for the Study of the Ancient World New York University United States Department of Languages & Literary Studies Lafayette College United States Department of Classics University of Texas Austin United States Institute of Collaborative Innovation Department of Computer and Information Science University of Macau China Department of Human Evolutionary Biology Harvard University United States

ISBN: (纸本)9798891761445

In literary critical applications, stylometry can benefit from hand-curated feature sets capturing various syntactic and rhetorical functions. For premodern languages, calculation of such features is hampered by a lack of computational resources for accurate part-of-speech tagging and semantic disambiguation. This paper reports an evaluation of POS taggers for Latin and their use in augmenting a hand-curated stylometric feature set. Our analyses show that POS-augmented features not only provide more accurate counts but also perform well on tasks such as genre classification. In the course of this work, we introduce POS n-grams as a feature for Latin stylometry. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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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 South Korea Institute for Human-Centered Artificial Intelligence (HAI) Stanford University Stanford CA United States Information and Interaction Design Yonsei University Seoul South Korea

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： Co-Creation Workshops Envisioning Technologies Future Technologies Marginalized Individuals North Korean Defector

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The IoT Codex: A Book of Programmable Stickers for Authoring and Composing Embedded Computing Applications

The IoT Codex: A Book of Programmable Stickers for Authoring...

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IEEE Symposium on Visual Languages and human Centric Computing (VL/HCC)

作者： Kristin Williams Jessica Hammer Scott Hudson Computer Science Emory University Atlanta USA Human Computer Interaction Institute Carnegie Mellon University Pittsburgh USA Entertainment Technology Center Carnegie Mellon University Pittsburgh USA

The Internet-of-Things (IoT) promises to enhance everyday objects with computing, but rarely enables directly authoring or composing that behavior. Lightweight IoT approaches attach identifiers (e.g., RFID tags) to objects to enable networked services. Typically these tags are passive, and so, depend on activity recognition and predefined context. This limits interaction to invoking predetermined behavior. Instead, this work presents The IoT Codex: a lightweight approach to customizing everyday objects with IoT by enabling interactive attachable IDs (aIDs) to compose software-supported behavior in situ. This work contributes 1) paper engineering techniques to construct aIDs that embody state, and 2) a tangible, end user programming (EUP) language for customizing IoT within symbolic and idiosyncratic contexts. Here, we provide preliminary validation of our approach with an empirically informed design space, sample applications, and a small co-design workshop. In doing so, we offer preliminary evidence for tangible, end user programming to enable meaningful control over IoT services.

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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How can we can create an equitable CHI

How can we can create an equitable CHI

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Grady, Siobahn Day Bauer, Christine Wehbe, Rina Spiel, Katta Muller, Michael Harrington, Christina School of Library and Information Sciences North Carolina Central University DurhamNC United States Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Computer Science Dalhousie University HalifaxNS Canada Faculty of Informatics Tu Wien Vienna Austria Human Centered Ai Ibm Research CambridgeMA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450394222

This panel aims to generate conversation toward creating a more equitable CHI. In recognizing our community's hard work thus far, this panel seeks to engage panelists and participants with thought-provoking questions to garner and promote actionable items for the community. We intend to have an open dialogue on allyship, diversity, equity, and inclusion to achieve a CHI for all. © 2023 Owner/Author.

关键词： allyship diversity equality equity inclusion

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Investigating fairness in machine learning-based audio sentiment analysis

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AI and Ethics 2024年第2期5卷 1099-1108页

作者： Luitel, Sophina Liu, Yang Anwar, Mohd Human-Centered AI (HCAI) Lab Department of Computer Science North Carolina Agricultural and Technical State University Greensboro USA Quantum Computing Research Center <QC|RC> Department of Computer Science North Carolina Agricultural and Technical State University Greensboro USA

Audio sentiment analysis is a growing area of research, however little attention has been paid to the fairness of machine learning models in this field. Whilst the current literature covers research on machine learning models’ reliability and fairness in various demographic groups, fairness in audio sentiment analysis with respect to gender is still an uninvestigated field. To fill this knowledge gap, we conducted experiments aimed at assessing the fairness of machine learning algorithms concerning gender within the context of audio sentiment analysis. In this research, we used 442 audio files of happiness and sadness—representing equal samples of male and female subjects—and generated spectrograms for each file. Then we performed feature extraction using bag-of-visual-words method followed by building classifiers using Random Forest, Support Vector Machines, and K-nearest Neighbors algorithms. We investigated whether the machine learning models for audio sentiment analysis are fair across female and male genders. We found the need for gender-specific models for audio sentiment analysis instead of a gender-agnostic-model. Our results provided three pieces of evidence to back up our claim that gender-specific models demonstrate bias in terms of overall accuracy equality when tested using audio samples representing the other gender, as well as combination of both genders. Furthermore, gender-agnostic-model performs poorly in comparison to gender-specific models in classifying sentiments of both male and female audio samples. These findings emphasize the importance of employing an appropriate gender-specific model for an audio sentiment analysis task to ensure fairness and accuracy. The best performance is achieved when using a female-model (78% accuracy) and a male-model (74% accuracy), significantly outperforming the 66% accuracy of the gender-agnostic model.

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AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

AI Literacy: Finding Common Threads between Education, Desig...

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

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Correction: Prompt4LJP: prompt learning for legal judgment prediction

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The Journal of Supercomputing 2025年第5期81卷 1-2页

作者： Huang, Qiongyan Xia, Yuhan Long, Yunfei Fang, Hui Liang, Ruiwei Guan, Yin Xu, Ge College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China School of Computer Science and Electronic Engineering University of Essex Colchester UK School of Computer and Big Data Minjiang University Fuzhou China Fujian Mental Health Human-Computer Interaction Technology Research Center Minjiang University Fuzhou China Fuzhou Institute of Oceanography Fuzhou China

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