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arXiv 2024年

作者： Ganesan, Adithya V. Varadarajan, Vasudha Lal, Yash Kumar Eijsbroek, Veerle C. Kjell, Katarina Kjell, Oscar N.E. Dhanasekaran, Tanuja Stade, Elizabeth C. Eichstaedt, Johannes C. Boyd, Ryan L. Schwartz, H. Andrew Flek, Lucie Department of Computer Science Stony Brook University United States Department of Psychology Lund University Sweden United States Department of Psychology Stanford University United States Institute for Human-Centered AI Stanford University United States Department of Psychology School of Behavioral and Brain Sciences The University of Texas Dallas United States Bonn-Aachen International Center for Information Technology University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interprets symptoms. In this work, we leveraged contemporary measurement theory to decode how GPT-4 interrelates depressive symptoms to inform both clinical utility and theoretical understanding. We found GPT-4's assessment of depression: (a) had high overall convergent validity (r = 0.71 with self-report on 955 samples, and r = 0.81 with experts judgments on 209 samples);(b) had moderately high internal consistency (symptom inter-correlates r = .23 to .78) that largely aligned with literature and self-report except that GPT-4;(c) underemphasized suicidality's - and overemphasized psychomotor's - relationship with other symptoms, and (d) had symptom inference patterns that suggest nuanced hypotheses (e.g. sleep and fatigue are influenced by most other symptoms while feelings of worthlessness/guilt is mostly influenced by depressed mood). © 2024, CC BY-SA.

关键词： Measurement theory

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GroundTruth: Augmenting expert image geolocation with crowdsourcing and shared representations

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Proceedings of the ACM on human-computer interaction 2019年第CSCW期3卷 1-30页

作者： Venkatagiri, Sukrit Thebault-Spieker, Jacob Kohler, Rachel Purviance, John Mansur, Rifat Sabbir Luther, Kurt Department of Computer Science and Center for Human-Computer Interaction Virginia Tech ArlingtonVA United States BNSF Railway Company Fort WorthTX United States Bloomberg L.P New YorkNY United States

Expert investigators bring advanced skills and deep experience to analyze visual evidence, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise. In this paper, we introduce the concept of shared representations for crowd–augmented expert work, focusing on the complex sensemaking task of image geolocation performed by professional journalists and human rights investigators. We built GroundTruth, an online system that uses three shared representations—a diagram, grid, and heatmap—to allow experts to work with crowds in real time to geolocate images. Our mixed-methods evaluation with 11 experts and 567 crowd workers found that GroundTruth helped experts geolocate images, and revealed challenges and success strategies for expert–crowd interaction. We also discuss designing shared representations for visual search, sensemaking, and beyond. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： crowd crowd-augmented expert work crowdsourcing expert geolocation investigation journalism misinformation real-time crowdsourcing sensemaking shared representations verification visual search

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Harnessing the power of AI in qualitative research: Exploring, using and redesigning ChatGPT

Computers in Human Behavior: Artificial Humans

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computers in human Behavior: Artificial humans 2025年 4卷

作者： He Zhang Chuhao Wu Jingyi Xie Yao Lyu Jie Cai John M. Carroll College of Information Sciences and Technology The Pennsylvania State University Westgate Building University Park PA 16803 USA The School of Information University of Michigan MI 48109 USA The Pervasive Human Computer Interaction Laboratory Department of Computer Science and Technology Tsinghua University Future Internet Technology Research Center (FIT) Haidian District Beijing 100084 China

AI tools, particularly large-scale language model (LLM) based applications such as ChatGPT, have the potential to mitigate qualitative research workload. In this study, we conducted semi-structured interviews with 17 participants and held a co-design session with 13 qualitative researchers to develop a framework for designing prompts specifically crafted to support junior researchers and stakeholders interested in leveraging AI for qualitative research. Our findings indicate that improving transparency, providing guidance on prompts, and strengthening users' understanding of LLMs' capabilities significantly enhance their ability to interact with ChatGPT. By comparing researchers' attitudes toward LLM-supported qualitative analysis before and after the co-design process, we reveal that the shift from an initially negative to a positive perception is driven by increased familiarity with the LLM's capabilities and the implementation of prompt engineering techniques that enhance response transparency and, in turn, foster greater trust. This research not only highlights the importance of well-designed prompts in LLM applications but also offers reflections for qualitative researchers on the perception of AI's role. Finally, we emphasize the potential ethical risks and the impact of constructing AI ethical expectations by researchers, particularly those who are novices, on future research and AI development.

关键词： ChatGPT Qualitative analysis Prompt design Large language models AI-Assisted research Ethical considerations

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Supporting child–group interactions with hands-off museum exhibit

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International Journal of Child-computer interaction 2021年 27卷

作者： Kotut, Lindah Hoang, Ann Panda, Havisha Shenk, Jennifer Newbill, Phyllis Kellogg, Derek Scott McCrickard, D. Department of Computer Science Virginia Tech Blacksburg VA United States Center for Human Computer Interaction Virginia Tech Blacksburg VA United States Institute of Creativity Arts and Technology (ICAT) Virginia Tech Blacksburg VA United States Science Museum of Western Virginia Roanoke VA United States

Technology can be used to augment visitors’ experience with a museum exhibit and provide museum visitors with means of interacting with otherwise hands-off exhibits. However, introducing technology to support interactivity has the likelihood of usurping users’ attention from the exhibit or diminishing interactions with other visitors. Our approach to this problem involves supporting hands-on interactions for groups visiting a pollinator garden—a science museum exhibit containing fragile species where touching is discouraged. Through interviews with museum experts and docents, we elicited design goals that we then enfolded into an Android application that leverages visual recognition to support interactions with the exhibit. We tested our application in-situ with 65 children in three groups and subsequently propose and describe design approaches that uses the ethos of scaling to support different number of users and leveling to varied cognitive levels. We conclude by recommending reusable designs guidelines to support interactions within other evolving and hands-off contexts. © 2020 Elsevier B.V.

关键词： Child–group interaction Child–mobile interactions Hands-off exhibits Museum

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The Golden Age of Silver Workers?: The Role of Age in the Perception of Increasing Digital Work Environments 5th

The Golden Age of Silver Workers?: The Role of Age in the Pe...

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5th International Conference on human Aspects of IT for the Aged Population, ITAP 2019, held as part of the 21st International Conference on human-computer interaction, HCI International 2019

作者： Kluge, Johanna Hildebrandt, Julian Ziefle, Martina Chair of Communication Science Human Computer Interaction Center RWTH Aachen Campus-Boulevard 57 Aachen52074 Germany

ISBN: (纸本)9783030220143

Digitization is progressing intensively, in particular in the world of work. Thereby, intelligent systems, mobile devices, cloud computing, and social media change the development of work and employment by (in parts) replacing employees or changing ways of production. In line with demographic change and in order to face economic, societal, and demographic challenges of a future labor market, it is necessary to consider the integration of aging employees. Therefore, this paper investigates the perception of motivational factors and stress factors triggered by technology in the working context. In addition, factors influencing the individual perception of strains are analyzed. For this purpose, an online survey was conducted (N = 507) asking for agreement to motivational and stressing factors in the context of increasing digital working environments. The results show that the overall motivational factors received a similar evaluation pattern for younger and older participants. However, the younger employees showed stronger agreements to all motivational aspects. Additionally, the results show that the perception of techno-stress factors was significant lower in the older aged group compared to the younger aged group. The study’s findings highlight the importance of user diversity for a successful and healthy transformation to a digital work environment putting employees needs and requirements into the center. © 2019, Springer Nature Switzerland AG.

关键词： Personnel

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Skeleton-Based Intake Gesture Detection With Spatial-Temporal Graph Convolutional Networks

arXiv

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arXiv 2025年

作者： Wang, Chunzhuo Xue, Zhewen Kumar, T. Sunil Camps, Guido Hallez, Hans Vanrumste, Bart e-Media Research Lab the ESAT-STADIUS Division KU Leuven Leuven3000 Belgium University of Gävle Gävle801 76 Sweden Division of Human Nutrition and Health Department of Agrotechnology and Food Sciences Wageningen University and Research OnePlanet Research Center 6700EA Wageningen Wageningen6708WE Netherlands M-Group DistriNet Department of Computer Science KU Leuven Sint-Michiels8200 Belgium

Overweight and obesity have emerged as widespread societal challenges, frequently linked to unhealthy eating patterns. A promising approach to enhance dietary monitoring in everyday life involves automated detection of food intake gestures. This study introduces a skeleton based approach using a model that combines a dilated spatial-temporal graph convolutional network (ST-GCN) with a bidirectional long-short-term memory (BiLSTM) framework, as called ST-GCN-BiLSTM, to detect intake gestures. The skeleton-based method provides key benefits, including environmental robustness, reduced data dependency, and enhanced privacy preservation. Two datasets were employed for model validation. The OREBA dataset, which consists of laboratory-recorded videos, achieved segmental F1-scores of 86.18% and 74.84% for identifying eating and drinking gestures. Additionally, a self-collected dataset using smartphone recordings in more adaptable experimental conditions was evaluated with the model trained on OREBA, yielding F1-scores of 85.40% and 67.80% for detecting eating and drinking gestures. The results not only confirm the feasibility of utilizing skeleton data for intake gesture detection but also highlight the robustness of the proposed approach in cross-dataset validation. Copyright © 2025, The Authors. All rights reserved.

关键词： Musculoskeletal system

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Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults

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Journal of Sport and Health science 2024年第5期13卷 611-620页

作者： Benjamin T.Schumacher Michael J.LaMonte Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoLa JollaCA 92093USA Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at BuffaloState University of New YorkBuffaloNY 14214USA Translational Gerontology Branch Intramural Research ProgramNational Institute on AgingNational Institutes of HealthBaltimoreMD 21225USA College of Health and Human Services San Diego State UniversitySan DiegoCA 92182USA Division of Epidemiology and Biostatistics School of Public HealthSan Diego State UniversitySan DiegoCA 92182USA University of California San Diego Moores Cancer Center La JollaCA 92093USA Computer Science and Engineering and Halicioglu Data Science Institute University of California San DiegoLa JollaCA 92093USA

Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older *** direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction ***:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)*** developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging ***,we quantified the associations between measured and predicted VO_(2max)and ***:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/*** absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,*** quartiles of measured VO_(2max)showed an inverse gradient in mortality *** VO_(2max)variables yielded similar effect estimates but were not robust to ***:Measured VO_(2max)is a strong predictor of *** ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to *** studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关键词： Cardiorespiratory fitness Prediction algorithms Epidemiology Mortality

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EEG Source Localization of μ-Rhythm Event-Related Desynchronization during Tactile Imagery

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Moscow University Biological sciences Bulletin 2024年第Suppl 1期79卷 S100-S106页

作者： Yakovlev, L. Syrov, N. Miroshnikov, A. Morozova, M. Berkmush-Antipova, A. Petrova, D. Kaplan, A. Laboratory for Neurophysiology and Neuro-Computer Interfaces Department of Human and Animal Physiology Faculty of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation Vladimir Zelman Center for Neurobiology and Brain Rehabilitation Skolkovo Institute of Science and Technology Moscow 121205 Russian Federation Baltic Center for Neurotechnology and Artificial Intelligence Immanuel Kant Baltic Federal University Kaliningrad 236041 Russian Federation

Abstract: Tactile imagery remains a relatively understudied phenomenon of mental imagery research. However, this technique holds promise as an effective tool for sensorimotor rehabilitation following stroke and spinal cord injuries alongside motor imagery. In this study, conducted with 22 healthy volunteers, the authors investigated the source localization of μ-rhythm event-related desynchronization (ERD) using multichannel electroencephalogram recordings and subsequent inverse problem solution with the sLORETA. All participants exhibited ERD during tactile imagery task and under vibrotactile stimulation. It was demonstrated that μ-rhythm ERD during both vibrotactile stimulation and tactile imagery was localized in the sensorimotor areas of the contralateral hemisphere. Within the source space, ERD in the postcentral gyrus was significantly stronger compared to the precentral gyrus. These findings indicate that tactile imagery, akin to the perception of real tactile stimuli, leads to prominent activation of sensorimotor cortical areas, consistent with the general understanding of the shared neural substrate during mental imagery and perception. © Allerton Press, Inc. 2024. ISSN 0096-3925, Moscow University Biological sciences Bulletin, 2024, Vol. 79, Suppl. 1, pp. S100–S106. Allerton Press, Inc., 2024. Russian Text The Author(s), 2024, published in Vestnik Moskovskogo Universiteta, Seriya 16: Biologiya, 2024, Vol. 79, No. 2 (Suppl.), pp. 105–112.

关键词： event-related desynchronization inverse task sensorimotor rhythm source localization tactile imagery μ-rhythm

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DEMONSTRATING DATA-TO-KNOWLEDGE PIPELINES FOR CONNECTING PRODUCTION SITES IN THE WORLD WIDE LAB A PREPRINT

arXiv

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arXiv 2024年

作者： Gorisen, Leon Schneider, Jan-Niklas Behery, Mohamed Brauner, Philipp Lennartz, Moritz Kotter, David Kaster, Thomas Petrovic, Oliver Hinke, Christian Gries, Thomas Lakemeyer, Gerhard Ziefle, Martina Brecher, Christian Hafner, Constantin Chair for Laser Technology RWTH Aachen University Aachen52074 Germany Knowledge Based Systems Group RWTH Aachen University Aachen52074 Germany Communication Science RWTH Aachen University Aachen52074 Germany Chair of Textile Technology RWTH Aachen University Aachen52074 Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Aachen52074 Germany Human Computer Interaction Center RWTH Aachen University Aachen52074 Germany Fraunhofer Institute for Laser Technology Aachen52074 Germany

The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamics foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook. Copyright © 2024, The Authors. All rights reserved.

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Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks

arXiv

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arXiv 2021年

作者： Naik, Aakanksha Lehman, Jill Rosé, Carolyn Language Technologies Institute Carnegie Mellon University United States Human-Computer Interaction Institute Carnegie Mellon University United States Rehabilitation Medicine Department Clinical Center National Institutes of Health United States

Natural language understanding (NLU) has made massive progress driven by large benchmarks, but benchmarks often leave a long tail of infrequent phenomena underrepresented. We reflect on the question: have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail? We conceptualize the long tail using macro-level dimensions (e.g., underrepresented genres, topics, etc.), and perform a qualitative meta-analysis of 100 representative papers on transfer learning research for NLU. Our analysis asks three questions: (i) Which long tail dimensions do transfer learning studies target? (ii) Which properties of adaptation methods help improve performance on the long tail? (iii) Which methodological gaps have greatest negative impact on long tail performance? Our answers highlight major avenues for future research in transfer learning for the long tail. Lastly, using our meta-analysis framework, we perform a case study comparing the performance of various adaptation methods on clinical narratives, which provides interesting insights that may enable us to make progress along these future avenues. © 2021, CC BY-NC-SA.

关键词： Benchmarking

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