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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Runze Cai Nuwan Janaka Hyeongcheol Kim Yang Chen Shengdong Zhao Yun Huang David Hsu Synteraction Lab School of Computing National University of Singapore Singapore Singapore Smart Systems Institute Synteraction lab National University of Singapore Singapore Singapore Synteraction Lab National University of Singapore Singapore Singapore College of Design and Engineering National University of Singapore Singapore Singapore School of Creative Media & Department of Computer Science City University of Hong Kong Hong Kong China School of Information Sciences University of Illinois at Urbana-Champaign Champaign Illinois USA School of Computing Smart Systems Institute National University of Singapore Singapore Singapore

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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Design oligoporous-core based multimode fiber for mode division multiplexing applications

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Optical and Quantum Electronics 2025年第5期57卷 1-24页

作者： Mitu, Sumaiya Akhtar Abdulrazak, Lway Faisal Ibrahim, Sobhy M. Tahhan, Shaymaa R. Hossain, Md Bellal Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh Department of Information and Communication Technology Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail Bangladesh Department of Computer Science Cihan University Sulaimaniya Sulaimaniya Kurdistan Region Iraq Department of Biochemistry College of Science King Saud University Riyadh Saudi Arabia Department of Laser and Optoelectronics Engineering College of Engineering Al-Nahrain University Jadriya Baghdad Iraq School of Electrical and Information Engineering The University of Sydney Sydney Australia Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka Bangladesh

A polarization-maintaining oligoporous-core-based multi-mode fiber is proposed. By tuning the air hole, as well as the core number, shape, size, and position up to 28 distinct linearly polarized (LP) modes are obtained. The Finite Element Method (FEM) is used to perform the numerical investigations. In addition, various materials combinations are used as a doping with silica which is highly helpful to increase or decrease the refractive index of the core material. The multimode fiber is identified by the normalized frequency or V parameter. Besides, the high birefringence value, low loss value, minimum crosstalk with high sensitivity response of $$1.46\times {10}^{-2}$$ , $$2\times {10}^{-11}$$ dB/m, 41.80 dB and 88,280.46 nm/RIU, respectively, are achieved from the numerical investigations over the wavelength range from 1.55 µm to1.65 µm for the different LP modes. Moreover, good responses also obtain for the numerical aperture, V number, coupling length and other parameters. In the end, every value reveals both an easy-to-fabricate structure design as well as adequate performance analysis. The suggested fiber structure can support many modes and might be applicable in the field of optical communications and spatial multiplexing based on the user demand.

关键词： Fiber to the x

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Markerless Sagittal Gait Analysis of Cerebral Palsy in Children Using Pose Estimation Techniques

Markerless Sagittal Gait Analysis of Cerebral Palsy in Child...

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Innovation in Computing and engineering (ICE), International Conference on

作者： Vinay Kumar Vats Chandra Prakash Amandeep Kaur Rajesh Kumar Computer Science and Engineering National Institute of Technology Delhi India Computational Intelligence and Smart Motion Research (CISMR) Lab SVNIT Surat India Malaviya National Institute of Technology Jaipur India Department of Human Anatomy and Physiology Faculty of Health Sciences University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

Cerebral Palsy (CP) is a neurological disorder affecting children’s motor function and posture. Early detection and monitoring of Cerebral palsy is crucial for effective intervention and improved quality of life. Gait analysis is a critical tool for assessing motor function in children with cerebral palsy, often requiring controlled environments and markers for accuracy. Markerless gait analysis has emerged as a promising tool for objectively evaluating gait abnormalities in children with CP. In this study, we propose a markerless methodology utilising pose estimation to analyse gait patterns from sagittal video recordings captured in unconstrained environments. The dataset consists of 49 children, including 10 typically developing children and 39 with cerebral palsy, focusing on side-view videos for the analysis. Given the motor disabilities of these children, conducting gait analysis in a constrained environment can alter their natural walking patterns, making our approach particularly advantageous. Then we identify the key gait events, heel strike and toe-off, to distinguish gait between cycles by tracing knee joint movements. This technique demonstrates the potential for practical, non-invasive gait assessment in natural settings, offering insights into the distinct gait characteristics of children with cerebral palsy compared to their typically developing peers.

关键词： Legged locomotion Neurological diseases Cerebral palsy Technological innovation Pose estimation Motors Motion control Gait recognition Video recording Monitoring

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FediLive: A Framework for Collecting and Preprocessing Snapshots of Decentralized Online Social Networks 25

FediLive: A Framework for Collecting and Preprocessing Snaps...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Shaojie Min Shaobin Wang Yaxiao Luo Min Gao Qingyuan Gong Yu Xiao Yang Chen Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (纸本)9798400713316

Decentralized online social networks such as Mastodon have emerged quickly during the past years, and offer unique opportunities to investigate user behavior, moderation strategies, and community evolution. However, their decentralized nature imposes challenges for data collection and preprocessing, particularly for obtaining a real-time snapshot in a timely manner. This paper introduces FediLive, a framework designed to rapidly collect and preprocess the live feeds from Mastodon, generate a comprehensive snapshot in real-time, including user-generated contents, interaction networks, and users' demographic attributes. Such a snapshot could further be leveraged for data analysis from different angles, leading to a deeper understanding of user activities on Mastodon. Using FediLive, we collected a 13-day snapshot of Mastodon, covering the publicly-visible activities of all Mastodon users. Our study demonstrates the usefulness of FediLive, and reveals its potential in facilitating data-driven analysis for decentralized online social networks.

关键词： decentralized online social networks

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Counting on AR: EEG responses to incongruent information with real-world context

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computers in Biology and Medicine 2025年 185卷 109483-109483页

作者： Wimmer, Michael Pepicelli, Alex Volmer, Ben ElSayed, Neven Cunningham, Andrew Thomas, Bruce H. Müller-Putz, Gernot R. Veas, Eduardo E. Know Center Research GmbH Graz Austria Institute of Neural Engineering Graz University of Technology Graz Austria Wearable Computer Lab University of South Australia AdelaideSA Australia BioTechMed Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions. © 2024

关键词： Interfaces (computer)

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Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

Heart Rhythm O2

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Heart Rhythm O2 2025年第5期6卷 678.0-686.0页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

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Leveraging Artificial Intelligence for Advancements in Mental Disorders: A Short Review

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Procedia computer Science 2025年 257卷 676-683页

作者： Selain K. Kasereka Kenny N. Tshibangu Myriam M. Nyembo Lionel K. Tshitenge Marina K. Muzindusi Godwill W.K. Ilunga Soufian Ben Amor Christian D. Bope Tasho Tashev Kyandoghere Kyamakya Mathematics Statistics and Computer Science Department University of Kinshasa Kinshasa DR. Congo Faculty of English Engineering Studies Technical University of Sofia Sofia Bulgaria ABIL Research Center Kinshasa DR. Congo Faculty of Computer Science Université Nouveaux Horizons Lubumbashi DR Congo LI-PaRAD Université de Versailles-Saint-Quentin-en-Yvelines France Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria Polytechnic Faculty University of Kinshasa Kinshasa Democratic Republic of the Congo

This study explores the expanding influence of artificial intelligence (AI) on the comprehension and management of mental disorders conditions. By analyzing recent advancements, this article highlights how AI technologies are being applied to diagnose, model, predict, and treat conditions, such as depression, anxiety, and bipolar disorder. It examines innovative approaches, including AI-powered mental wellness apps, that enable users to monitor their symptoms and access personalized support. Additionally, it discusses research on advanced algorithms designed to analyze online social interactions to identify early signs of psychological distress and to facilitate timely interventions. The paper also addresses the challenges involved in integrating AI into mental healthcare and highlights ethical and legal concerns that should be addressed when developing AI mental health models.

关键词： Mental Health Predictive Analytics Mental Health Interventions Machine Learning AI in Healthcare

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Cellulose-Based Nanocomposite Insulation Material with Excellent Thermal Aging Resistance: KH550-hBN-Modified Cellulose Insulation Paper

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Wang, Zuhao Peng, Xiao Chronis, Ioannis Wu, Yingrui Psomopoulos, Constantinos S. Tang, Chao Southwest University College of Engineering and Technology International R & D Center for New Technologies of Smart Grid and Equipment Chongqing400715 China Southwest University College of Engineering and Technology Chongqing400715 China Chongqing University State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing400044 China University of West Attica High Voltage and Energy Systems Research Lab Dept. of Electrical and Electronics Engineering EgaleoGR-122 44 Greece

As the global power industry evolves, transformer insulation systems face increasing challenges. The aging of cellulose insulation paper (cellulose-IP) at elevated temperatures significantly impacts its insulation properties and overall lifespan. To enhance the thermal aging resistance and industrial usability of cellulose-IP, this study employs nano-boron nitride (h-BN) along with three silane coupling agents - 3-ammonopropyltriethoxysilane (KH550), 3-glycidyloxypropyltrimethoxysilane (KH560), and 3-methacryloyloxypropyltrimethoxysilane (KH570) - to modified cellulose-IP. Upon evaluating the properties and simulation results of the modified cellulose-IP, KH550-hBN demonstrated superior properties and was selected for an accelerated thermal aging test lasting 60 days. The results indicated that KH550-hBN effectively mitigates the degradation of cellulose-IP. In the late aging stage, the mechanical properties of the modified cellulose-IP deteriorated more slowly than those of pure cellulose-IP. Additionally, thermal conductivity improved by 26.9%, while breakdown field strength increased by 12.1%. The introduction of KH550-hBN promotes the formation of a complete thermal conductivity network within the cellulose matrix, increases the number of intermolecular hydrogen bonds and introduces nano-barrier regions. These improvements enhance the interactions between the cellulose systems and effectively inhibit the generation and migration of polar derivatives during the aging process, resulting in a significant improvement in the longevity and stability of transformer internal insulation systems. © 1994-2012 IEEE.

关键词： Boron nitride

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