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19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

作者： Köster, Raphael Hadfield-Menell, Dylan Hadfield, Gillian K. Leibo, Joel Z. DeepMind United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Center for Human-Compatible AI United States Schwartz Reisman Institute for Technology and Society University of Toronto Vector Institute Center for Human-Compatible AI OpenAI Canada

ISBN: (纸本)9781450375184

How can societies learn to enforce and comply with social norms? Many if not most human norms are functional. Rules that punish non-cooperative behavior, for example, support cooperation. An intriguing feature of human normativity is that many social norms concern behaviors that have no direct impact on material well-being. Examples include rules about what color clothing one wears to a funeral [7] or whether one uses one's left or right hand in particular tasks [2]. Such apparently pointless rules are ubiquitous, often acquiring great social meaning despite the absence of functionality. Hadfield-Menell et al. (2019) call these norms "silly rules" and distinguish them from "important rules," such as rules that govern resource sharing or prohibit harmful conduct, that directly impact welfare [3]. © 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

关键词： Reinforcement learning

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Unmasking Illusions: Understanding human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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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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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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System Identification of User Engagement in mHealth Behavioral Interventions ⁎

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IFAC-PapersOnLine 2024年第15期58卷 508-513页

作者： Mohamed El Mistiri Steven De La Torre Karine Tung Benjamin M. Marlin Misha Pavel Predrag Klasnja Donna Spruijt-Metz Daniel E. Rivera Control Systems Engineering Lab Arizona State University Tempe AZ 85281 USA The Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego CA 92093 USA Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA 01003 USA Khoury College of Computer Sciences & Bouve College of Health Sciences Northeastern University Boston MA 02115 USA Division of Biomedical and Health Informatics School of Information University of Michigan Ann Arbor MU 48109 USA USC Dornsife Center for Economic and Social Research Department of Psychology and Population and Public Health Sciences University of Southern California Los Angeles CA 90089 USA

Digital behavior change interventions (DBCIs) such as “just-in-time” adaptive interventions (JITAIs) have demonstrated efficacy for increasing physical activity behavior. However, the effectiveness of these interventions is heavily dependent upon user engagement. Despite the inherent dynamic nature of engagement, as it varies over time based on an individual’s changing environment, context, and psychological state, the current understanding of engagement primarily comes from static snapshots of the behavior. The availability of intensive longitudinal data from JITAIs provides a unique opportunity to build and test dynamic models of behavior change from a process systems lens, relying on prediction-error methods from system identification. However, data missingness is a significant practical consideration in this process. Therefore, in this work we address missingness using a Bayesian imputation approach, which we evaluate using data from the HeartSteps II JITAI. Ultimately, the methods presented support the discovery of key factors that impact engagement behavior over time and can play an important role in the development of large-scale personalized interventions.

关键词： eHealth Bayesian methods Computational Social sciences Time series modelling

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Designing Survey-Based Mobile Interfaces for Rural Patients With Cancer Using Apple's ResearchKit and CareKit: Usability Study

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JMIR Formative Research 2024年 8卷 e57801页

作者： Donawa, Alyssa Powell, Christian Wang, Rong Chih, Ming-Yuan Patel, Reema Zinner, Ralph Aronoff-Spencer, Eliah Baker, Corey E. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Computer Science University of Kentucky Lexington KY United States Department of Human and Organizational Development Vanderbilt University Nashville TN United States Department of Health and Clinical Sciences University of Kentucky Lexington KY United States Division of Medical Oncology Markey Cancer Center University of Kentucky Lexington KY United States Division of Infectious Diseases & Global Public Health School of Medicine University of California San Diego San Diego CA United States

Background: Despite the increased accessibility and availability of technology in recent years, equality and access to health-related technology remain limited to some demographics. In particular, patients who are older or from rural communities represent a large segment of people who are currently underusing mobile health (mHealth) solutions. System usability continues to hinder mHealth adoption among users with nontraditional digital literacy. Objective: This study aims to investigate if state-of-the-art mobile app interfaces from open-source libraries provide sufficient usability for rural patients with cancer, with minimal design changes and forgoing the co-design process. Methods: We developed Assuage (Network Reconnaissance Lab) as a research platform for any mHealth study. We conducted a pilot study using Assuage to assess the usability of 4 mobile user interfaces (UIs) based on open-source libraries from Apple's ResearchKit and CareKit. These UIs varied in complexity for reporting distress symptoms. Patients with cancer were recruited at the Markey Cancer center, and all research procedures were conducted in person. Participants completed the distress assessment using a randomly selected UI in Assuage with little to no assistance. Data were collected on participant age, location, mobile app use, and familiarity with mHealth apps. Participants rated usability with the System Usability Scale (SUS), and usability issues were documented and compared. A one-way ANOVA was used to compare the effect of the UIs on the SUS scores. Results: We recruited 30 current or postsurgery patients with cancer for this pilot study. Most participants were aged >50 years (24/30, 80%), from rural areas (25/30, 83%), had up to a high school education (19/30, 63%), and were unfamiliar with mHealth apps (21/30, 70%). General mobile app use was split, with 43% (14/30) of the patients not regularly using mobile apps. The mean SUS score across the UIs was 75.8 (SD 22.2), with UI 3 and UI

关键词： apps CareKit digital divide digital literacy distress ehealth adoption ehealth literacy HealthKit mHealth mobile health patients with cancer rural health usability usability testing

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Heterogeneous Oblique Double Random Forest

arXiv

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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges

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Informatics in Medicine Unlocked 2024年 51卷

作者： Mienye, Ibomoiye Domor Obaido, George Jere, Nobert Mienye, Ebikella Aruleba, Kehinde Emmanuel, Ikiomoye Douglas Ogbuokiri, Blessing Institute for Intelligent Systems University of Johannesburg Auckland Park Johannesburg 2006 South Africa Center for Human-Compatible Artificial Intelligence (CHAI) Berkeley Institute for Data Science (BIDS) University of California Berkeley 94720 CA United States Department of Computer Science University of Fort Hare Alice Campus Eastern Cape Alice South Africa School of Computing and Mathematical Sciences University of Leicester Leicester LE1 7RH United Kingdom School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Computer Science Brock University Niagara Region St. Catharines L2S 3A1 ON Canada

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, reliable, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including the need to balance interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends, offering valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, fostering the development of transparent, trustworthy, and effective AI-driven solutions. © 2024 The Authors

关键词： AI Bias Ethics Fairness Healthcare Machine learning

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A New Physical Reservoir Using the Complex Dynamics of Electric Fields in Type-Ii Superconductors Containing Pinning centers Interacting with Quantized Flux Lines

SSRN

引用

SSRN 2024年

作者： Ueda, Tenma Otabe, Edmund Soji Arita, Ken Usami, Yuki Tanaka, Hirofumi Matsuno, Tetsuya Department of Physics and Computer Science Kyushu Institute of Technology 680–4 Kawazu Iizuka Fukuoka820–8502 Japan Department of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology 2–4 Hibikino Wakamatsu Kitakyushu808–019 Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology 2–4 Hibikino Wakamatsu Kitakyushu808–019 Japan Department of Creative Engineering National Institute of Technology Ariake College 150 Higashihagio-Machi Omuta Fukuoka836-8585 Japan

Reservoir computing, which takes advantage of physical phenomena with nonlinearities, is attracting a lot of ***, it is expected to focus on superconductivity, a physical phenomenon with nonlinearities between the output electric field and the input current *** to other physical phenomena used in reservoirs, it is considered that with superconductivity, the nonlinearity can easily be adjusted spatially by pin placement to produce dynamics suitable for *** nonlinearity between the electric field generated by the motion of the quantized magnetic flux lines and the input current density was used to perform a reservoir computing *** waveform generation tasks, a NARMA2 task and a nonlinear-memory task were performed, and all tasks were generally *** was found that the electromagnetic phenomenon in superconductors can be used as a physical reservoir. © 2024, The Authors. All rights reserved.

关键词： Electric lines

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Correction: Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

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International journal of obesity (2005) 2025年第2期49卷 364-365页

作者： Arthur Leroy Varsha Gupta Mya Thway Tint Delicia Shu Qin Ooi Fabian Yap Ngee Lek Keith M Godfrey Yap Seng Chong Yung Seng Lee Johan G Eriksson Mauricio A Álvarez Navin Michael Dennis Wang Department of Computer Science The University of Manchester Manchester UK. Institute for Human Development and Potential Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. Bioinformatics Institute Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. Department of Paediatrics Yong Loo Lin School of Medicine National University of Singapore Singapore Republic of Singapore. Department of Paediatrics KK Women's and Children's Hospital Singapore Republic of Singapore. Duke-NUS Medical School Singapore Republic of Singapore. MRC Lifecourse Epidemiology Centre and NIHR Southampton Biomedical Research Centre University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK. Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Republic of Singapore. Division of Paediatric Endocrinology Department of Paediatrics Khoo Teck Puat-National University Children's Medical Institute National University Hospital National University Health System Singapore Republic of Singapore. Department of General Practice and Primary Health Care University of Helsinki Helsinki Finland. Folkhälsan Research Center Helsinki Finland. Department of Computer Science University of Sheffield Sheffield UK. Institute for Human Development and Potential Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. dennis_wang@sics.a-star.edu.sg. Bioinformatics Institute Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. dennis_wang@sics.a-star.edu.sg. Department of Computer Science University of Sheffield Sheffield UK. dennis_wang@sics.a-star.edu.sg. National Heart and Lung Institute Imperial College London London UK. dennis_wang@sics.a-star.edu.sg.

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