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8th International Conference on Design, User Experience, and Usability, DUXU 2019, held as part of the 21st International Conference on human-computer interaction, HCI International 2019

作者： Aguirre, Andrés F. Villareal-Freire, Angela Diaz, Jaime Gil, Rosa Collazos, César A. IDIS Research Group University of Cauca Popayán Colombia Department of Computer and Information Sciences University of La Frontera Temuco Chile Research Group on Human Computer Interaction and Data Integration University of Lleida Lleida Spain

ISBN: (纸本)9783030235697

Current trends in the development of applications bring new challenges that re-quire both a rapprochement and an understanding of the elements implicit in the interaction of this type of system and the users who use them. One of the most relevant aspects in this interaction is user satisfaction;as a result, it is necessary to establish a broader and more precise definition of user satisfaction in interactive systems from customer experience (CX), at the same time giving thought to the different constructs that characterize the software. This article presents a proposal that extends the concept of the user satisfaction to establish the characteristics that derive from the CX, along with traditional approaches that support user satisfaction. From this characterization, it is intended to propose a construct through which the different factors that influence user satisfaction are understood, how these factors condition it and how they converge to impact it. © 2019, Springer Nature Switzerland AG.

关键词： Sales

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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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A SURVEY ON BRAIN-INSPIRED DEEP LEARNING VIA PREDICTIVE CODING

arXiv

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arXiv 2023年

作者： Salvatori, Tommaso Mali, Ankur Buckley, Christopher L. Lukasiewicz, Thomas Rao, Rajesh P.N. Friston, Karl Ororbia, Alexander VERSES AI Research Lab Los AngelesCA United States Institute of Logic and Computation Vienna University of Technology Austria University of South Florida TampaFL33620 United States Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science University of Oxford United Kingdom Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States The Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London United Kingdom The Neural Adaptive Computing Laboratory Rochester Institute of Technology RochesterNY14623 United States

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, such an algorithm has always been considered biologically implausible. To this end, recent works have studied learning algorithms for deep neural networks inspired by the neurosciences. One such theory, called predictive coding (PC), has shown promising properties that make it potentially valuable for the machine learning community: it can model information processing in different areas of the brain, can be used in cognitive control and robotics, has a solid mathematical foundation in variational inference, and performs its computations asynchronously. Inspired by such properties, works that propose novel PC-like algorithms are starting to be present in multiple sub-fields of machine learning and artificial intelligence at large. Here, we survey such efforts by first providing a broad overview of the history of PC to provide a common ground for the understanding of the recent developments, then by surveying current efforts and results, and concluding with a large discussion of possible implications and ways forward. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

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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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Precision health monitoring in spaceflight with integration of lower body negative pressure and advanced large language model artificial intelligence

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Life sciences in Space Research 2025年 47卷 57-60页

作者： Rahul Kumar Ethan Waisberg Joshua Ong Phani Paladugu Kyle Sporn Karsten Chima Dylan Amiri Nasif Zaman Alireza Tavakkoli Department of Biochemistry and Molecular Biology University of Miami Miller School of Medicine 1011 NW 15th Street Gautier Building MC R629 Miami FL 33136-1019 United States Department of Clinical Neurosciences University of Cambridge Cambridge United Kingdom Department of Ophthalmology and Visual Sciences University of Michigan Kellogg Eye Center Ann Arbor MI United States Brigham and Women’s Hospital Boston MA United States Upstate Medical University Syracuse NY United States Mecklenburg Neurology Group Charlotte NC United States Human-Machine Perception Laboratory Department of Computer Science and Engineering University of Nevada Reno NV United States

Long-term exposure to microgravity influences musculoskeletal health and enhances the likelihood of sustaining orthopedic injuries while on a microgravity mission and upon return to Earth. Although countermeasures are being investigated to alleviate some risks of injury, such as resistive (or weight) exercise and Lower Body Negative Pressure (LBNP), evidence is accumulating that current paradigms do not ensure the safety or health of astronauts because of a lack of in-flight diagnostic methods, in which load/diagnostic metrics can be assessed over time. Here, we suggest the integration of a new vision-language large language model (DeepSeek-VL) as a potential autonomous diagnostic agent for monitoring musculoskeletal health in a microgravity environment. DeepSeek-VL will autonomously analyze radiographic data and biomechanical data streamed from a LBNP device. Determinations will be made based on lost or compromised density in bone, lost joint-centered stability, or ineffective loading patterns - providing personalized and specific feedback regarding musculoskeletal health with the astronaut as the primary user. Unlike conventional reporting approaches that rely on cross-institutional analysis by household experts, DeepSeek-VL allows for real-time, and autonomous interpretation of musculoskeletal imaging metrics (and physiological metrics) for on-time personalized countermeasure development. Here, we review architectural adaptations including microgravity specific samplings of data, training protocols and implications of deployment in the ISS. We anticipate DeepSeek's timely development of flight-ready diagnostic reporting will facilitate in-flight/systematic monitoring of musculoskeletal health and safety, especially for astronauts undergoing load management training (e.g., LBNP) and ensure effectiveness of countermeasures, their outputs. We will address methods to circumvent limitations and barriers to risk, and establish the importance of a federated, adaptive, a

关键词： Artificial intelligence Bone density DeepSeek AI Lower body negative pressure Musculoskeletal health Space medicine

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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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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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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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Building Expertise on FAIR Through Evolving Bring Your Own Data(BYOD) Workshops: Describing the Data, Software, and Management-focused Approaches and Their Evolution

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Data Intelligence 2024年第2期6卷 429-456页

作者： César H.Bernabé Lieze Thielemans Rajaram Kaliyaperumal Claudio Carta Shuxin Zhang Celia W.G.van Gelder Nirupama Benis Luiz Olavo Bonino da Silva Santos Ronald Cornet Bruna dos Santos Vieira Nawel Lalout Ines Henriques Alberto Camara Ballesteros Kees Burger Martijn G.Kersloot Friederike Ehrhart Esther van Enckevort Chris T.Evelo Alasdair J.G.Gray Marc Hanauer Kristina Hettne Joep de Ligt Arnaldo Pereira Nuria Queralt-Rosinach Erik Schultes Domenica Taruscio Andra Waagmeester Mark D.Wilkinson Egon L.Willighagen Mascha Jansen Barend Mons Marco Roos Annika Jacobsen Biosemantics Group Department of Human GeneticsLeiden University Medical CentreLeidenThe Netherlands NIHR Academic Fellow in General Practice University College London and The Royal Free NHS Foundation Trust National Centre for Rare Diseases Istituto Superiore di SanitaRomeItaly Department of Medical Informatics Amsterdam UMC location University of AmsterdamMeibergdreef 9AmsterdamThe Netherlands Amsterdam Public Health Methodology&Digital HealthAmsterdamThe Netherlands Health-Rl UtrechtThe Netherlands Semantics Services and Cybersecurity groupUniversity of TwenteEnschedeThe Netherlands Department of Medical Imaging Radboud Institute for Health SciencesRadboud University Medical CenterNijmegenThe Netherlands Department of Medical BioSciences Radboud University Medical CenterNijmegenThe Netherlands The Duchenne Parent Project VeneendaalThe Netherlands Departamento de Biotecnologia-Biologia Vegetal Escuela Tecnica Superior de Ingenieria AgronomicaAlimentaria y de BiosistemasCentro de Biotecnologia y Genomica de PlantasUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria-CSIC(INIA-CSIC).Campus MontegancedoPozuelo de Alarcon(Madrid)Spain Castor AmsterdamThe Netherlands Department of Bioinformatics-BiGCaT Maastricht UniversityMaastrichtThe Netherlands University Medical Center Groningen University of GroningenGroningenThe Netherlands Maastricht Centre for Systems Biology(MaCSBio) Maastricht UniversityMaastrichtThe Netherlands Department of Computer Science Heriot-Watt UniversityEdinburghUK INSERM US14-OrphanetPlateforme Maladies RaresParisFrance Centre for Digital Scholarship Leiden University LibrariesThe Netherlands Center for Molecular Medicine Cancer Genomics NetherlandsDepartment of GeneticsUniversity Medical Center UtrechtUniversity UtrechtThe Netherlands University of Aveiro DETI/IEETAAveiroPortugal GO FAIR Foundation LeidenThe Netherlands The Leiden Academic Center for Drug Research LeidenThe Netherlands Micel

Since 2014,"Bring Your Own Data"workshops(BYODs)have been organised to inform people about the process and benefits of making resources Findable,Accessible,Interoperable,and Reusable(FAIR,and the FAIRification process).The BYOD workshops'content and format differ depending on their goal,context,and the background and needs of ***-focused BYODs educate domain experts on how to make their data FAIR to find new answers to research ***-focused BYODs promote the benefits of making data FAIR and instruct project managers and policy-makers on the characteristics of FAIRification ***-focused BYODs gather software developers and experts on FAIR to implement or improve software resources that are used to support ***,these BYODs intend to foster collaboration between different types of stakeholders involved in data management,curation,and reuse(*** experts,trainers,developers,data owners,data analysts,FAIR experts).The BYODs also serve as an opportunity to learn what kind of support for FAIRification is needed from different communities and to develop teaching materials based on practical examples and *** this paper,we detail the three different structures of the BYODs and describe examples of early BYODs related to plant breeding data,and rare disease registries and biobanks,which have shaped the structure of the *** discuss the latest insights into making BYODs more productive by leveraging our almost ten years of training experience in these workshops,including successes and encountered ***,we examine how the participants'feedback has motivated the research on FAIR,including the development of workflows and software.

关键词： FAIR FAIRification FAIR expertise Bring Your Own Data Workshop BYOD

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AlloHaptic: Robot-Mediated Haptic Collaboration for Learning Linear Functions*

AlloHaptic: Robot-Mediated Haptic Collaboration for Learning...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Hala Khodr Soheil Kianzad Wafa Johal Aditi Kothiyal Barbara Bruno Pierre Dillenbourg Computer Human Interaction in Learning and Instruction Laboratory (CHILI) Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland Department of Computer Science University of British Columbia University of New South Wales Sydney Australia MOBOTS Group in BIOROB EPFL Lausanne

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Collaborative learning appears in a joint intellectual efforts of individuals to understand an object of knowledge collectively. In their search for understanding the problems, meanings, and solutions, learners employ different multi-modal strategies. In this work, we explore the role of force feedback in learners interaction with tangible hand-held robots. We designed a collaborative learning environment to provide embodied intuitions on linear mathematical functions combined with graphical representations and ran a first study involving 24 participants. Our analysis shows a positive learning gain for our learning activity. Moreover, to explore the link between different types of force feedback and learners' collaboration, we designed a focus group study with 12 participants. Our results suggest that the haptic communication channel affects the collaboration dynamic differently according to the nature of the learning task. We finish by proposing design insights for future exploration of haptic in collaborative learning.

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