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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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Unlocking the Emotional World of Visual Media: An Overview of the science, Research, and Impact of Understanding Emotion

arXiv

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arXiv 2023年

作者： Wang, James Z. Zhao, Sicheng Wu, Chenyan Adams, Reginald B. Newman, Michelle G. Shafir, Tal Tsachor, Rachelle The Data Science and Artificial Intelligence Area the Human-Computer Interaction Area the College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The Department of Psychology The Pennsylvania State University University ParkPA16802 United States BNRist Tsinghua University Beijing100084 China The Emily Sagol Creative Arts Therapies Research Center University of Haifa 3498838 Israel The School of Theatre and Music University of Illinois ChicagoIL60607 United States

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. Whereas recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances. In this paper, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent robots

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Difficulty-aware meta-learning for rare disease diagnosis 23rd

Difficulty-aware meta-learning for rare disease diagnosis

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Li, Xiaomeng Yu, Lequan Jin, Yueming Fu, Chi-Wing Xing, Lei Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Radiation Oncology Stanford University Stanford United States Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597092

Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples is very challenging, and so far, catches very little attention. In this paper, we present a difficulty-aware meta-learning method to address rare disease classifications and demonstrate its capability to classify dermoscopy images. Our key approach is to first train and construct a meta-learning model from data of common diseases, then adapt the model to perform rare disease classification. To achieve this, we develop the difficulty-aware meta-learning method that dynamically monitors the importance of learning tasks during the meta-optimization stage. To evaluate our method, we use the recent ISIC 2018 skin lesion classification dataset, and show that with only five samples per class, our model can quickly adapt to classify unseen classes by a high AUC of 83.3%. Also, we evaluated several rare disease classification results in the public Dermofit Image Library to demonstrate the potential of our method for real clinical practice. © Springer Nature Switzerland AG 2020.

关键词： Learning systems

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Explaining autonomous drones: An XAI journey

Applied AI Letters

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Applied AI Letters 2021年第4期2卷

作者： Stefik, Mark Youngblood, Michael Pirolli, Peter Lebiere, Christian Thomson, Robert Price, Robert Nelson, Lester D. Krivacic, Robert Le, Jacob Mitsopoulos, Konstantinos Somers, Sterling Schooler, Joel Intelligent Systems Laboratory PARC A Xerox Company Palo Alto CA United States The Institute for Human and Machine Cognition Pensacola FL United States Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University Pittsburgh PA United States Army Cyber Institute United States Military Academy New York NY United States

COGLE (COmmon Ground Learning and Explanation) is an explainable artificial intelligence (XAI) system where autonomous drones deliver supplies to field units in mountainous areas. The mission risks vary with topography, flight decisions, and mission goals. The missions engage a human plus AI team where users determine which of two AI-controlled drones is better for each mission. This article reports on the technical approach and findings of the project and reflects on challenges that complex combinatorial problems present for users, machine learning, user studies, and the context of use for XAI systems. COGLE creates explanations in multiple modalities. Narrative “What” explanations compare what each drone does on a mission and “Why” based on drone competencies determined from experiments using counterfactuals. Visual “Where” explanations highlight risks on maps to help users to interpret flight plans. One branch of the research studied whether the explanations helped users to predict drone performance. In this branch, a model induction user study showed that post-decision explanations had only a small effect in teaching users to determine by themselves which drone is better for a mission. Subsequent reflection suggests that supporting human plus AI decision making with pre-decision explanations is a better context for benefiting from explanations on combinatorial tasks. © 2021 PARC, a Xerox Company. Applied AI Letters published by John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

关键词： autonomous systems explainable artificial intelligence hierarchical multifunction modeling

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A mobile visual analytics approach for instant trend analysis in mobile contexts 5

A mobile visual analytics approach for instant trend analysi...

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5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019

作者： Burkhardt, Dirk Nazemi, Kawa Kuijper, Arjan Ginters, Egils Human-Computer Interaction and Visual Analytics Research Group Darmstadt University of Applied Sciences Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Riga Technical University Riga Latvia

ISBN: (纸本)9788885741423

The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design. © 5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019. All rights reserved.

关键词： Decision support systems

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Do I enjoy my friends? Friendship and enjoyment during recess in autistic and non-autistic children

Research in Autism

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Research in Autism 2025年 124卷

作者： Jiayin Zhao Boya Li Yung-Ting Tsou Mitra Baratchi Els Blijd-Hoogewys Özgür Gülen Carolien Rieffe Unit of Developmental and Educational Psychology Institute of Psychology Leiden University Leiden the Netherlands Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Amsterdam the Netherlands Leiden Institute of Advanced Computer Science (LIACS) Leiden University Leiden the Netherlands Developmental Psychology Faculty of Behavioral and Social Sciences. University of Groningen Groningen the Netherlands Psychiatrie Noord Groningen the Netherlands Department of Psychology Faculty of Economics Administrative and Social Sciences Bahçeşehir University İstanbul Turkey Department of Human Media Interaction Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands Department of Psychology and Human Development Institute of Education University College London London UK

Friendships play a crucial role in children’s well-being and school experiences. This study aimed to gain a better understanding of how autistic and non-autistic children's friendships are related to their enjoyment of the school time spent with peers, i.e. at recess time (school break time). A multi-method approach, including self-reports, peer nominations, and objective measures based on sensor data was used. Forty-five autistic children and 45 non-autistic children from two special education schools participated, aged between 8 to 14 years. Outcomes showed that autistic and non-autistic children did not differ regarding the number of reciprocal and non-reciprocated friends. Yet, autistic children spent less time in contact with their reciprocal friends during recess at the schoolyard compared to their non-autistic peers. Also, while non-autistic pupils spent more time with reciprocal friends than with non-reciprocated ones, this difference was not found among autistic pupils. Notably, spending more time with non-reciprocated friends during recess was related to lower levels of enjoyment in both autistic and non-autistic children. Our findings suggest that autistic children may approach friendships with different priorities. Furthermore, this study underscores the need to consider broader factors beyond reciprocity when assessing children’s social experience at school.

关键词： Peer contact Social Functioning School environment RFID data Schoolyard

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MACHINE-LEARNING-BASED DIFFERENTIATION BETWEEN SUICIDE ATTEMPT AND SUICIDE IDEATION IN PATIENTS WITH MAJOR DEPRESSIVE DISORDER USING CORTICAL FUNCTIONAL NETWORK

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IBRO Neuroscience Reports 2023年 15卷 S784-S784页

作者： Kuk-In Jang Sungkean Kim Jihoon Kim Chany Lee Se-Hoon Shim Ji Sun Kim Korea Brain Research Institute Cognitive Science Research Group Daegu Korea Republic of Hanyang University Human-computer Interaction Ansan Korea Republic of Soonchunhyang University Cheonan Hospital Department Of Psychiatry Cheonan Korea Republic of

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Queer in HCI: Strengthening the Community of LGBTQIA+ Researchers and Research

Queer in HCI: Strengthening the Community of LGBTQIA+ Resear...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021

作者： Devito, Michael Ann Lustig, Caitlin Simpson, Ellen Allison, Kimberley Chuanromanee, Tya Spiel, Katta Ko, Amy Rode, Jennifer Dym, Brianna Muller, Michael Klaus Scheuerman, Morgan Marie Walker, Ashley Brubaker, Jed Ahmed, Alex Department of Information Science University of Colorado United States Human Centered Design and Engineering University of Washington United States School of Information Studies Syracuse University United States Department of Psychology Macquarie University Australia University of Notre Dame United States Human-Computer Interaction Group Tu Wien Austria The Information School University of Washington United States Knowledge Lab University College London United Kingdom Department of Information Science University of Colorado Boulder United States Ai Interactions Ibm Research United States Information Science University of Colorado Boulder United States Pitch Lab Northwestern University United States University of Colorado Boulder United States Carnegie Mellon University United States

ISBN: (纸本)9781450380959

As Queer human-computer interaction (HCI) becomes an established part of the larger field, both in terms of research on and with queer populations and in terms of employing queering theories and methods, the role of queer researchers has become a timely topic of discussion. However, these discussions have largely centered around member-researcher status and positionality when working with queer populations. Based on insights gathered at multiple ACM events over the past two years, we identified two pressing issues: (1) we need to better support queer people doing HCI research not specific to queer populations, and (2) we need to identify how to best support member-researchers in leading Queer HCI while including collaborators beyond the queer community. This Special Interest Group (SIG) aims to directly address these challenges by convening a broad community of queer researchers and allies, working not only on explicitly-queer topics but across a broad range of HCI topics. © 2021 Owner/Author.

关键词： Computation theory

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A Simulation Study of Transcranial Magnetoacoustic Stimulation of the Basal Ganglia Thalamic Neural Network to Improve Parkinson's Disease Status

SSRN

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SSRN 2023年

作者： Zhang, Yanqiu Zhang, Hao Xu, Tianya Liu, Jiahe Mu, Jiayang Chen, Rongjie Yang, Jiumin Wang, Peiguo Jian, Xiqi School of Biomedical Engineering and Technology Tianjin Medical University Tianjin300070 China Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin300392 China Neurology Department Tianjin Huanhu Hospital Tianjin300350 China Department of Oncology Radiotherapy Cancer Institute Hospital of Tianjin Medical University Tianjin300202 China

Background: Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively. Objective: To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage. Methods: The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network. Results: When the stimulating effect of ultrasound is ignored, TMAS-STN suppresses the beta band amplitude of each nucleus, whereas TMAS-GPi affects only some nuclei. TMAS-STN&GPi can reduce the beta band amplitude. TMAS-STN&GPi&GPe is more effective. When considering the stimulatory effect of ultrasound, the stimulatory effect of lower sound pressures is negligible, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents. Conclusions: TMAS stimulation of multinuclear with appropriate ultrasound intensity is effective in PD and may be clinically useful. © 202

关键词： Ultrasonics

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Experience with the use of a digital sleep diary in symptom management by individuals with insomnia -a pilot mixed method study

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Sleep Medicine: X 2023年 6卷 100093页

作者： Thorshov, Thea Christine Øverby, Caroline Tonje Hansen, Diana Dobran Bong, Way Kiat Skifjeld, Knut Hurlen, Petter Dammen, Toril Moen, Anne Hrubos-Strøm, Harald Division of Surgery Department of Otorhinolaryngology Akershus University Hospital Lørenskog Norway Faculty of Technology Art and Design Department of Computer Science Human-Computer Interaction and Universal Design of ICT Oslo Metropolitan University Oslo Norway Norsk e-helse Oslo Norway Division of Clinical Informatics Department of Diagnostics and Technology Akershus University Hospital Lørenskog Norway Faculty of Medicine Institute of Clinical Medicine Campus Ahus University of Oslo Norway Faculty of Medicine Institute of Health and Society Department of Nursing Science University of Oslo Norway Faculty of Medicine Institute of Clinical Medicine University of Oslo Norway Department of Research and Innovation Division of Mental Health and Addiction Oslo University Hospital Oslo Norway

Background: Insomnia is the most common sleep disorder. The recommended treatment is cognitive behavioural therapy for insomnia (CBTi). A sleep diary is a core tool in CBTi. We have developed a digital sleep diary with a standardised feedback function. Aim: To study feasibility of the digital sleep diary in participants of the Akershus Sleep Apnea (ASAP) cohorts with difficulties falling asleep or maintaining sleep. To describe sleep diary engagement and explore experiences with the digital sleep diary with potential influences in insomnia symptom management. Material and methods: Twenty participants were recruited from the ASAP. All filled out a digital sleep diary up to 12 weeks. Treatment options provided were a self-help book (N = 11) or electroencephalography neurofeedback (N = 9) in addition to the sleep diary standardised feedback function. We collected quantitative data from the sleep diary reports and we sub-divided insomnia by sleep onset insomnia and non-sleep onset insomnia. Finally, we performed qualitative interviews. Results: The median number of entries to the sleep diary was 81 (25th quartile: 26, 75th quartile 84). In the qualitative analysis, we identified two main themes;“structure and overview” and “usability and digital features”. Conclusion: The sleep diary was found to be feasible when distributed in combination with a self-help book or electroencephalography neurofeedback. The qualitative results emphasised the importance of a timely graphical overview and visualisations of self-recorded sleep. © 2023 The Authors

关键词： Digital sleep diary Engagement Feasibility Insomnia Motivation Patient-reported outcomes Self-management

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